diff --git a/README.md b/README.md index 9b738c8..05b7dd0 100644 --- a/README.md +++ b/README.md @@ -120,9 +120,6 @@ ichimoku_cloud = companies.technicals.get_ichimoku_cloud() # a Fixed Income example corporate_bond_yields = companies.fixed_income.get_ice_bofa_effective_yield() -# a Fixed Income example -corporate_bond_yields = companies.fixed_income.get_ice_bofa_effective_yield() - # an Economics example unemployment_rates = companies.economics.get_unemployment_rate() ```` @@ -2685,223 +2682,6 @@ fixedincome.get_european_central_bank_rates() The main refinancing operations (MRO) rate is the interest rate banks pay when they borrow money from the ECB for one week. When they do this, they have to provide collateral to guarantee that the money will be paid back. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_european_central_bank_rates). -> **Marginal Lending Facility** - -The marginal lending facility rate is the interest rate banks pay when they borrow from the ECB overnight. When they do this, they have to provide collateral, for example securities, to guarantee that the money will be paid back. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_european_central_bank_rates). - -> **Deposit Facility** - -The deposit facility rate is one of the three interest rates the ECB sets every six weeks as part of its monetary policy. The rate defines the interest banks receive for depositing money with the central bank overnight. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_european_central_bank_rates). - -> **Euribor Rates** - -The Euro Interbank Offered Rate (Euribor) is a daily reference rate based on the averaged interest rates at which Eurozone banks offer to lend unsecured funds to other banks in the euro wholesale money market. It is widely used as the base rate for a variety of financial products, including mortgages, savings accounts, and derivatives. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_euribor_rates). - -> **Effective Federal Funds Rate** - -The effective federal funds rate (EFFR) is calculated as a volume-weighted median of overnight federal funds transactions reported in the FR 2420 Report of Selected Money Market Rates. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_federal_reserve_rates). - -> **Overnight Bank Funding Rate** - -The overnight bank funding rate (OBFR) is calculated as a volume-weighted median of overnight federal funds transactions, Eurodollar transactions, and the domestic deposits reported as “Selected Deposits” in the FR 2420 Report. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_federal_reserve_rates). - -> **Tri-Party General Collateral Rate** - -The TGCR is calculated as a volume-weighted median of transaction-level tri-party repo data collected from the Bank of New York Mellon. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_federal_reserve_rates). - -> **Broad General Collateral Rate** - -The BGCR is calculated as a volume-weighted median of transaction-level tri-party repo data collected from the Bank of New York Mellon as well as GCF Repo transaction data obtained from the U.S. Department of the Treasury’s Office of Financial Research (OFR). Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_federal_reserve_rates). - -> **Secured Overnight Financing Rate (SOFR)** - -The SOFR is calculated as a volume-weighted median of transaction-level tri-party repo data collected from the Bank of New York Mellon as well as GCF Repo transaction data and data on bilateral Treasury repo transactions cleared through FICC’s DVP service, which are obtained from the U.S. Department of the Treasury’s Office of Financial Research (OFR). Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_federal_reserve_rates). - - - -
- Government Bonds - -It is possible to view both short-term (3-month) and long-term (10-year) interest rates for each of the available countries. These rates relate to the interest rates at which countries issue government bonds and are used as a benchmark for other interest rates in the economy. For example, the German government bond yield is an overall indicator of the European economy. - -These interest rates can be obtained with `get_government_bond_yield`. As an example: - -```python -from financetoolkit import FixedIncome - -fixedincome = FixedIncome() - -fixedincome.get_government_bond_yield() -``` - -> **Long Term Interest Rates (10 year)** - -Long-term interest rates refer to government bonds maturing in ten years. Rates are mainly determined by the price charged by the lender, the risk from the borrower and the fall in the capital value. Long-term interest rates are generally averages of daily rates, measured as a percentage. These interest rates are implied by the prices at which the government bonds are traded on financial markets, not the interest rates at which the loans were issued. - -In all cases, they refer to bonds whose capital repayment is guaranteed by governments. Long-term interest rates are one of the determinants of business investment. Low long term interest rates encourage investment in new equipment and high interest rates discourage it. Investment is, in turn, a major source of economic growth. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_government_bond_yield). - -> **Short Term Interest Rates (3 month)** - -Short-term interest rates are the rates at which short-term borrowings are effected between financial institutions or the rate at which short-term government paper is issued or traded in the market. Short-term interest rates are generally averages of daily rates, measured as a percentage. - -Short-term interest rates are based on three-month money market rates where available. Typical standardised names are “money market rate” and “treasury bill rate”. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_government_bond_yield). - -
- -
- Corporate Bonds - -The Corporate Bonds section features the widely used ICE BofA benchmarks which include option-adjusted spreads, effective yields and the total returns. It is possible to view both the indices of the ratings (AAA, AA, A, BBB, BB, B and CCC) and the maturities (1-3 years, 3-5 years, 5-7 years, 7-10 years, 10-15 years, 15-30 years and 30+ years). - -All corporate bond metrics can be called by using `get_` to get a single metric. E.g. `get_ice_bofa_option_adjusted_spread` or `get_ice_bofa_yield_to_worst`. As an example: - -```python -from financetoolkit import FixedIncome - -fixedincome = FixedIncome() - -fixedincome.get_ice_bofa_option_adjusted_spread() -``` - -> **Option-Adjusted Spread (OAS)** - -The Option-Adjusted Spread (OAS) is the spread relative to a risk-free interest rate, usually measured in basis points (bp), that equates the theoretical present value of a series of uncertain cash flows to the market price of a fixed-income investment. The spread is added to the risk-free rate to compensate for the uncertainty of the cash flows. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_ice_bofa_option_adjusted_spread). - -> **Effective Yield** - -The Effective Yield is the yield of a bond, calculated by dividing the bond's coupon payments by its market price. The effective yield is not the same as the stated yield, which is the yield on the bond's coupon payments divided by the bond's principal value. The effective yield is a more accurate measure of a bond's return, as it takes into account the fact that the investor will not hold the bond to maturity and will likely sell it before it matures. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_ice_bofa_effective_yield). - -> **Total Return** - -The total return is the actual rate of return of an investment or a pool of investments over a given evaluation period. Total return includes interest, capital gains, dividends and distributions realized over a given period of time. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_ice_bofa_total_return). - -> **Yield to Worst** - -Yield to worst is the lowest potential yield that a bond can generate without the issuer defaulting. The standard US convention for this series is to use semi-annual coupon payments, whereas the standard in the foreign markets is to use coupon payments with frequencies of annual, semi-annual, quarterly, and monthly. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_ice_bofa_yield_to_worst). - -
- -## Fixed Income - -The Fixed Income module contains a wide variety of fixed income related calculations such as the Effective Yield, the Macaulay Duration, the Modified Duration Convexity, the Yield to Maturity and models such as Black and Bachelier to valuate derivative instruments such as Swaptions. This module can be called directly via the Toolkit but also separately if desired through `from financetoolkit import FixedIncome`. **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/fixedincome-module) and the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome) which includes an explanation about each indicator, the parameters and an example.** - -
- Bond Valuations -The Bond Valuations section contains a variety of metrics to evaluate the performance of bonds. These metrics include Present Value calculations, the Effective Yield, the Macaulay and Modified Duration and convexity. - -All bond valuations can be called by using `get_` to get a single valuation. E.g. `get_present_value` or `get_duration`. As an example: - -```python -from financetoolkit import FixedIncome - -fixedincome = FixedIncome() - -fixedincome.get_present_value() -``` - -> **Bond Statistics** - -The bond statistics contains a variety of different metrics to evaluate a bond. These include: - -- **Par Value:** The face value of the bond. -- **Coupon Rate:** The annual coupon rate (in decimal). -- **Years to Maturity:** The number of years until the bond matures. -- **Yield to Maturity:** The yield to maturity of the bond (in decimal). -- **Frequency:** The number of coupon payments per year. -- **Present Value:** The present value of the bond. -- **Current Yield:** The annual coupon payment divided by the bond price. -- **Effective Yield:** The return on a bond that has its interest payments (or coupons) reinvested at the same rate by the bondholder. -- **Macaulay's Duration:** The weighted average time to receive the bond's cash flows. -- **Modified Duration:** The Macaulay's duration divided by 1 plus the yield to maturity. -- **Effective Duration:** The percentage change in the bond price for a 1% change in the yield to maturity. -- **Dollar Duration:** The modified duration multiplied by the bond price. -- **DV01:** The dollar value of a 0.01% change in yield to maturity. -- **Convexity:** The second derivative of the bond price with respect to the yield to maturity. - -It gives a complete overview of the bond's performance. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_bond_statistics) and an example below which shows the bond statistics for six different bonds using this functionality. - -| | Bond 1 | Bond 2 | Bond 3 | Bond 4 | Bond 5 | Bond 6 | -|:--------------------|---------:|---------:|---------:|----------:|---------:|---------:| -| Par Value | 100 | 250 | 50 | 1000 | 85 | 320 | -| Coupon Rate | 0.05 | 0.02 | 0.075 | 0 | 0.15 | 0.015 | -| Years to Maturity | 5 | 10 | 2 | 10 | 3 | 1 | -| Yield to Maturity | 0.08 | 0.021 | 0.03 | 0 | 0.16 | 0.04 | -| Frequency | 1 | 1 | 4 | 1 | 2 | 12 | -| Present Value | 88.0219 | 247.766 | 54.3518 | 1000 | 83.0353 | 312.171 | -| Current Yield | 0.0568 | 0.0202 | 0.069 | 0 | 0.1535 | 0.0154 | -| Effective Yield | 0.05 | 0.02 | 0.0771 | 0 | 0.1556 | 0.0151 | -| Macaulay's Duration | 4.5116 | 9.1576 | 1.8849 | 10 | 2.5667 | 0.9932 | -| Modified Duration | 4.1774 | 8.9693 | 1.8709 | 10 | 2.3766 | 0.9899 | -| Effective Duration | 4.0677 | 8.5181 | 1.8477 | 9.4713 | 2.2952 | 0.9844 | -| Dollar Duration | 3.677 | 22.2228 | 1.0168 | 100 | 1.9734 | 3.0902 | -| DV01 | 0.0004 | 0.0022 | 0 | 0.01 | 0.0001 | 0 | -| Convexity | 22.4017 | 93.7509 | 4.0849 | 110 | 7.0923 | 1.0662 | - -> **Present Value** - -The bond price is the present value of the bond's future cash flows. It is calculated by discounting the bond's coupon payments and principal repayment to the present value using the bond's yield to maturity. The present value is depicted over a variety of coupon rates and years of maturities. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_bond_price). - -> **Duration** - -The bond duration is a measure of the bond's sensitivity to changes in interest rates. It is the weighted average of the bond's cash flows, where the weights are the present value of each cash flow divided by the bond's price. It is possible to calculate the following durations: - -- **Macaulay's Duration:** The weighted average time to receive the bond's cash flows. -- **Modified Duration:** The Macaulay's duration divided by 1 plus the yield to maturity. -- **Effective Duration:** The percentage change in the bond price for a 1% change in the yield to maturity. -- **Dollar Duration:** The modified duration multiplied by the bond price. - -The duration values are depicted over a variety of coupon rates and years of maturities. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_bond_duration). - -> **Yield to Maturity** - -The Yield to Maturity (YTM) is the total return anticipated on a bond if it is held until it matures. It is the internal rate of return of an investment in a bond if the investor holds the bond until maturity and receives all payments as scheduled. The yield to maturity is depicted over a variety of coupon rates and years of maturities. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_yield_to_maturity). - -
- -
- Derivative Valuations -The Derivative Valuations section contains a variety of models that can be used to value derivative instruments such as Swaptions. These models include the Black Model and the Bachelier Model. - -All derivative valuations can be called by using `get_` to get a single valuation. E.g. `get_derivative_price`. As an example: - -```python -from financetoolkit import FixedIncome - -fixedincome = FixedIncome() - -fixedincome.get_derivative_price(model_type="black") -``` - -> **Black Model** - -The Black Model is a mathematical model used to calculate the price of European-style options. It is based on the Black-Scholes model but is used for interest rate options. The Black Model is used to value interest rate options, such as caps, floors, and swaptions. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_derivative_price). - -> **Bachelier Model** - -The Bachelier Model is a mathematical model used to calculate the price of European-style options. It is based on the normal distribution and is used for interest rate options as opposed to the Black model which uses a log-normal distribution. The Bachelier Model is used to value interest rate options, such as caps, floors, and swaptions. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_derivative_price). - -
- -
- Central Banks - -The central bank metrics revolve around the interest rates of the European Central Bank (ECB) and the Federal Reserve (FED). This includes the main refinancing operations, marginal lending facility, deposit facility, effective federal funds rate, overnight bank funding rate, tri-party general collateral rate, broad general collateral rate and secured overnight financing rate. - -All central bank metrics can be called by using `get_` to get a single metric. E.g. `get_european_central_bank_rates` or `get_federal_reserve_rates`. As an example: - -```python -from financetoolkit import FixedIncome - -fixedincome = FixedIncome() - -fixedincome.get_european_central_bank_rates() -``` - -> **Main Refinancing Operations** - -The main refinancing operations (MRO) rate is the interest rate banks pay when they borrow money from the ECB for one week. When they do this, they have to provide collateral to guarantee that the money will be paid back. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_european_central_bank_rates). - - > **Marginal Lending Facility** The marginal lending facility rate is the interest rate banks pay when they borrow from the ECB overnight. When they do this, they have to provide collateral, for example securities, to guarantee that the money will be paid back. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome#get_european_central_bank_rates). @@ -3015,7 +2795,7 @@ from financetoolkit import Toolkit toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY") # Get Economy Results -toolkit.economics.get_consumer_price_index() +toolkit.economics.get_consumer_confidence_index() ``` > **Gross Domestic Product (GDP)** @@ -3042,20 +2822,6 @@ Numbers above 100 suggest an increased confidence in near future business perfor The composite leading indicator (CLI) is designed to provide early signals of turning points in business cycles showing fluctuation of the economic activity around its long term potential level. CLIs show short-term economic movements in qualitative rather than quantitative terms. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_composite_leading_indicator). -> **Consumer Price Index (CPI)** - -Inflation measured by consumer price index (CPI) is defined as the change in the prices of a basket of goods and services that are typically purchased by specific groups of households. Inflation is measured in terms an index, 2015 base year with a breakdown for food, energy and total. - -Inflation measures the erosion of living standards. A consumer price index is estimated as a series of summary measures of the period-to-period proportional change in the prices of a fixed set of consumer goods and services of constant quantity and characteristics, acquired, used or paid for by the reference population. - -Each summary measure is constructed as a weighted average of a large number of elementary aggregate indices. Each of the elementary aggregate indices is estimated using a sample of prices for a defined set of goods and services obtained in, or by residents of, a specific region from a given set of outlets or other sources of consumption goods and services. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_consumer_price_index). - -> **Producer Price Index (PPI)** - -Producer price indices in manufacturing measure the rate of change in prices of products sold as they leave the producer. They exclude any taxes, transport and trade margins that the purchaser may have to pay. PPIs provide measures of average movements of prices received by the producers of various commodities. hey are often seen as advanced indicators of price changes throughout the economy, including changes in the prices of consumer goods and services. - -Manufacturing covers the production of semi-processed goods and other intermediate goods as well as final products such as consumer goods and capital equipment. A variety of price indices may be used to measure inflation in an economy. These include consumer price indices (CPI), price indices relating to specific goods and/or services, GDP deflators and producer price indices (PPI). Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_producer_price_index). - > **House Prices** In most cases, the nominal house price index covers the sales of newly-built and existing dwellings, following the recommendations from the RPPI (Residential Property Prices Indices) manual. @@ -3104,24 +2870,14 @@ Short-term interest rates are the rates at which short-term borrowings are effec Short-term interest rates are based on three-month money market rates where available. Typical standardised names are “money market rate” and “treasury bill rate”. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_short_term_interest_rate). -> ***Narrow Money (M1)** - -M1 includes currency i.e. banknotes and coins, plus overnight deposits. M1 is expressed as a seasonally adjusted index based on 2015=100. - -Broad money (M3) includes currency, deposits with an agreed maturity of up to two years, deposits redeemable at notice of up to three months and repurchase agreements, money market fund shares/units and debt securities up to two years. M3 is measured as a seasonally adjusted index based on 2015=100. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_narrow_and_broad_money). - -> **Broad Money (M3)** - -Broad money (M3) includes currency, deposits with an agreed maturity of up to two years, deposits redeemable at notice of up to three months and repurchase agreements, money market fund shares/units and debt securities up to two years. M3 is measured as a seasonally adjusted index based on 2015=100. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_narrow_and_broad_money). - -> **Purchasing Power Parity (PPP)** - -Purchasing power parities (PPPs) are the rates of currency conversion that try to equalise the purchasing power of different currencies, by eliminating the differences in price levels between countries. The basket of goods and services priced is a sample of all those that are part of final expenditures: final consumption of households and government, fixed capital formation, and net exports. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_purchasing_power_parity). - > **Exchange Rates** Exchange rates are defined as the price of one country’s’ currency in relation to another country’s currency. This indicator is measured in terms of national currency per US dollar. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_exchange_rates). +> **Trust in Government** + +Trust in government refers to the share of people who report having confidence in the national government. The data shown reflect the share of respondents answering “yes” (the other response categories being “no”, and “dont know”) to the survey question: “In this country, do you have confidence in… national government? The sample is ex ante designed to be nationally representative of the population aged 15 and over. This indicator is measured as a percentage of all survey respondents. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_trust_in_government). +
@@ -3148,109 +2904,22 @@ Energy derived from solid biofuels, biogasoline, biodiesels, other liquid biofue This includes wood, vegetal waste (including wood waste and crops used for energy production), ethanol, animal materials/wastes and sulphite lyes. Municipal waste comprises wastes produced by the residential, commercial and public service sectors that are collected by local authorities for disposal in a central location for the production of heat and/or power. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_renewable_energy). -> **Environmental Tax as % of GDP** - -Environmentally related taxes are an important instrument for governments to shape relative prices of goods and services. - -The characteristics of such taxes included in the database (e.g. revenue, tax base, tax rates, exemptions, etc.) are used to construct the environmentally related tax revenues with a breakdown by environmental domain: - -- Energy products (including vehicle fuels); -- Motor vehicles and transport services; -- Measured or estimated emissions to air and water, ozone depleting substances, certain non-point sources of water pollution, waste management and noise, as well as management of water, land, soil, forests, biodiversity, wildlife and fish stocks. - -The data have been cross-validated and complemented with Revenue statistics from the OECD Tax statistics database and official national sources. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_environmental_tax). - -> **Greenhouse Gas Emissions** - -Greenhouse gases refer to the sum of seven gases that have direct effects on climate change: - -- Carbon Dioxide (CO2) -- Methane (CH4) -- Nitrous Oxide (N2O) -- Chlorofluorocarbons (CFCs) -- Hydrofluorocarbons (HFCs) -- Perfluorocarbons (PFCs) -- Sulphur Hexafluoride (SF6) -- Nitrogen Trifluoride (NF3). - -The data are expressed in CO2 equivalents and refer to gross direct emissions from human activities. CO2 refers to gross direct emissions from fuel combustion only and data are provided by the International Energy Agency. Other air emissions include emissions of sulphur oxides (SOx) and nitrogen oxides (NOx) given as quantities of SO2 and NO2, emissions of carbon monoxide (CO), and emissions of volatile organic compounds (VOC), excluding methane. - -Air and greenhouse gas emissions are measured in tonnes per capita and kilogram per capita in which all metrics are converted to tonnes (1000kg) per capita. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_greenhouse_emissions). - -> **Crude Oil Production** - -Crude oil production is defined as the quantities of oil extracted from the ground after the removal of inert matter or impurities. It includes crude oil, natural gas liquids (NGLs) and additives. This indicator is measured in thousand tonne of oil equivalent (toe). - -Crude oil is a mineral oil consisting of a mixture of hydrocarbons of natural origin, yellow to black in colour, and of variable density and viscosity. NGLs are the liquid or liquefied hydrocarbons produced in the manufacture, purification and stabilisation of natural gas. - -Additives are non-hydrocarbon substances added to or blended with a product to modify its properties, for example, to improve its combustion characteristics (e.g. MTBE and tetraethyl lead). Refinery production refers to the output of secondary oil products from an oil refinery. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_crude_oil_production). +> **Carbon Footprint** -> **Crude Oil Prices** +The carbon footprint is a measure of the total amount of greenhouse gases produced to directly and indirectly support human activities, usually expressed in equivalent tons of carbon dioxide (CO2). -Crude oil import prices come from the IEA’s Crude Oil Import Register and are influenced not only by traditional movements of supply and demand, but also by other factors such as geopolitics. +The carbon footprint is a subset of the ecological footprint and of the more comprehensive Life Cycle Assessment (LCA). An individual, nation, or organization's carbon footprint can be measured by undertaking a GHG emissions assessment or other calculative activities denoted as carbon accounting. -Information is collected from national agencies according to the type of crude oil, by geographic origin and by quality of crude. Average prices are obtained by dividing value by volume as recorded by customs administrations for each tariff position. - -Values are recorded at the time of import and include cost, insurance and freight, but exclude import duties. The nominal crude oil spot price from 2003 to 2011 is for Dubai and from 1970 to 2002 for Arabian Light. This indicator is measured in USD per barrel of oil. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_crude_oil_prices). +The data have been cross-validated and complemented with Revenue statistics from the OECD Tax statistics database and official national sources. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_carbon_footprint).
- Government 🏛️ + Jobs & Society 🗂️ -The government metrics revolve around the deficit, revenue, spending, debt, financial wealth and production costs of countries. This includes the deficit as a percentage of GDP, revenue as a percentage of GDP, spending as a percentage of GDP, debt as a percentage of GDP, financial wealth as a percentage of GDP and production costs as a percentage of GDP. +The jobs metrics revolve around the unemployment rates, labour productivity and income inequality of countries. Society metrics include the population and poverty rates of countries. -All government metrics can be called by using `get_` to get a single metric. E.g. `get_government_statistics` or `get_trust_in_government`. As an example: - -```python -from financetoolkit import Toolkit - -toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY") - -# Get Government Results -toolkit.economics.get_government_statistics() -``` - -> **Deficit as % of GDP** - -General government deficit is defined as the balance of income and expenditure of government, including capital income and capital expenditures. “Net lending” means that government has a surplus, and is providing financial resources to other sectors, while “net borrowing” means that government has a deficit, and requires financial resources from other sectors. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_government_statistics). - -> **Revenue as % of GDP** - -General government revenue is defined as the revenue required to finance the goods and services they provide to citizens and businesses, and to fulfil their redistributive role. Comparing levels of government revenues across countries provides an indication of the importance of the government sector in the economy in terms of available financial resources. The total amount of revenues collected by governments is determined by past and current political decisions. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_government_statistics). - -> **Spending as % of GDP** - -General government spending is defined as an indicator of the size of government across countries. The large variation in this indicator highlights the variety of countries’ approaches to delivering public goods and services and providing social protection, not necessarily differences in resources spent. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_government_statistics). - -> **Debt as % of GDP** - -General government debt-to-GDP ratio measures the gross debt of the general government as a percentage of GDP. It is a key indicator for the sustainability of government finance. Debt is calculated as the sum of the following liability categories (as applicable): currency and deposits; debt securities, loans; insurance, pensions and standardised guarantee schemes, and other accounts payable. Changes in government debt over time primarily reflect the impact of past government deficits. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_government_statistics). - -> **Financial Wealth as % of GDP** - -The net financial worth of the general government sector is the total value of its financial assets minus the total value of its outstanding liabilities. The general government sector consists of central, state and local governments as well as social security funds. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_government_statistics). - -> **Production Costs as % of GDP** - -General government production costs are decisions about the amount and type of goods and services governments produce, as well as on how best to produce them. They are often political in nature and based on a country’s social and cultural context. Governments use a mix of their own employees, capital, and outside contractors (non-profit institutions or private sector entities) to produce goods and services. Government production costs include: compensation costs of general government employees; goods and services used and financed by general government (including intermediate consumption and social transfer in kind via market producers paid for by government); and other costs, including depreciation of capital and other taxes on production less other subsidies on production. The data include government employment and intermediate consumption for output produced by the government for its own use, such as roads and other capital investment projects built by government employees. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_government_statistics). - -> **Central Government Spending** - -Central government expenditure is defined as the central government budget expenditure as reported in the final central government accounts. Data are based on the System of National accounts (SNA), a set of internationally agreed concepts, definitions, classifications and rules for national accounting. Central government spending by function is the breakdown of expenditures on the basis of the activities governments support. The classification system used to provide this breakdown on an internationally comparable basis is known as Classification of Functions of Government (COFOG). Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_central_government_spending). - -> **Trust in Government** - -Trust in government refers to the share of people who report having confidence in the national government. The data shown reflect the share of respondents answering “yes” (the other response categories being “no”, and “dont know”) to the survey question: “In this country, do you have confidence in… national government? The sample is ex ante designed to be nationally representative of the population aged 15 and over. This indicator is measured as a percentage of all survey respondents. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_trust_in_government). - -
- -
- Jobs 🗂️ - -The jobs metrics revolve around the unemployment rates, labour productivity and income inequality of countries. - -All jobs metrics can be called by using `get_` to get a single metric. E.g. `get_unemployment_rate` or `get_income_inequality`. As an example: +All job and society metrics can be called by using `get_` to get a single metric. E.g. `get_unemployment_rate` or `get_income_inequality`. As an example: ```python from financetoolkit import Toolkit @@ -3279,58 +2948,14 @@ The ratio between the output measure and the labour input depends to a large deg Income is defined as household disposable income in a particular year. It consists of earnings, self-employment and capital income and public cash transfers; income taxes and social security contributions paid by households are deducted. The income of the household is attributed to each of its members, with an adjustment to reflect differences in needs for households of different sizes. Income inequality among individuals is measured here by five indicators. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_income_inequality). -
- -
- Society 👪 - -The society metrics revolve around the population, young population, working age population, elderly population, fertility rates, old-age dependency ratio and poverty rate of countries. - -All society metrics can be called by using `get_` to get a single metric. E.g. `get_population_statistics` or `get_poverty_rate`. As an example: - -```python -from financetoolkit import Toolkit - -toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY") - -# Get Society Results -toolkit.economics.get_poverty_rate() -``` - > **Population** Population is defined as all nationals present in, or temporarily absent from a country, and aliens permanently settled in a country. This indicator shows the number of people that usually live in an area. Growth rates are the annual changes in population resulting from births, deaths and net migration during the year. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_population_statistics). -> **Young Population** - -The youth population is defined as those people aged less than 15 as a percentage of the total population. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_population_statistics). - -> **Working Age Population** - -The working age population is defined as those aged 15 to 64 as a percentage of the total population. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_population_statistics). - -> **Elderly Population** - -The elderly population is defined as those aged 65 and over as a percentage of the total population. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_population_statistics). - -> **Fertility Rates** - -The total fertility rate in a specific year is defined as the total number of children that would be born to each woman if she were to live to the end of her child-bearing years and give birth to children in alignment with the prevailing age-specific fertility rates. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_population_statistics). - -> **Old-Age Dependency Ratio** - -The old-age to working-age demographic ratio is defined as the number of individuals aged 65 and over per 100 people of working age defined as those at ages 20 to 64. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_population_statistics). - > **Poverty Rate** The poverty rate is the ratio of the number of people (in a given age group) whose income falls below the poverty line; taken as half the median household income of the total population. -It is also available by broad age group: - -- child poverty (0 to 17 year-olds); -- working-age poverty (18 to 65 year-olds); -- and elderly poverty (66 year-olds or more). - However, two countries with the same poverty rates may differ in terms of the relative income-level of the poor. Find the documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/economics#get_poverty_rate).
diff --git a/examples/Finance Toolkit - 9. Economics Module.ipynb b/examples/Finance Toolkit - 9. Economics Module.ipynb index c27943c..3bfd951 100644 --- a/examples/Finance Toolkit - 9. Economics Module.ipynb +++ b/examples/Finance Toolkit - 9. Economics Module.ipynb @@ -115,7 +115,7 @@ "id": "e925d767", "metadata": {}, "source": [ - "For example, it is possible to show the Consumer Price Index (CPI) and the Producer Price Index (PPI) over time for 60+ countries." + "For example, it is possible to show the Consumer Confidence Index (CCI) and the Producer Price Index (PPI) over time for 60+ countries." ] }, { @@ -145,11 +145,11 @@ " \n", " \n", " \n", + " A5M\n", " Australia\n", " Austria\n", " Belgium\n", " Brazil\n", - " Canada\n", " Chile\n", " China\n", " Colombia\n", @@ -170,1000 +170,328 @@ " \n", " \n", " \n", - " 2015Q1\n", - " 99.1874\n", - " 99.1584\n", - " 99.2476\n", - " 96.5619\n", - " 99.0255\n", - " 98.0161\n", - " 99.9000\n", - " 97.9415\n", - " 100.3688\n", - " 99.6667\n", - " ...\n", - " 99.8624\n", - " 99.3667\n", - " 97.5357\n", - " 99.7341\n", - " 98.9856\n", - " 99.6630\n", - " 100.2071\n", - " 96.9511\n", - " 99.4\n", - " 99.0854\n", - " \n", - " \n", - " 2015Q2\n", - " 99.8375\n", - " 100.3583\n", - " 99.9546\n", - " 99.2407\n", - " 100.1580\n", - " 99.3926\n", - " 99.5667\n", - " 99.4344\n", - " 100.2806\n", - " 100.3333\n", - " ...\n", - " 100.3518\n", - " 100.5933\n", - " 99.7862\n", - " 99.9442\n", - " 100.7976\n", - " 100.0737\n", - " 100.3367\n", - " 99.7435\n", - " 100.0\n", - " 100.2800\n", - " \n", - " \n", - " 2015Q3\n", - " 100.3018\n", - " 100.0917\n", - " 100.1759\n", - " 100.9553\n", - " 100.5267\n", - " 100.8126\n", - " 100.2667\n", - " 100.3399\n", - " 99.8908\n", - " 100.2000\n", - " ...\n", - " 100.0459\n", - " 100.0433\n", - " 101.0915\n", - " 100.2376\n", - " 99.8741\n", - " 100.0279\n", - " 99.7726\n", - " 100.2398\n", - " 100.2\n", - " 100.5434\n", - " \n", - " \n", - " 2015Q4\n", - " 100.6733\n", - " 100.3916\n", - " 100.6219\n", - " 103.2421\n", - " 100.2897\n", - " 101.7787\n", - " 100.2667\n", - " 102.2842\n", - " 99.4599\n", - " 99.7000\n", - " ...\n", - " 99.7400\n", - " 100.0000\n", - " 101.5866\n", - " 100.0841\n", - " 100.3427\n", - " 100.2354\n", - " 99.6836\n", - " 103.0655\n", - " 100.4\n", - " 100.0911\n", + " 2015-01\n", + " 99.9069\n", + " 99.4035\n", + " 99.5728\n", + " 100.2037\n", + " 98.9693\n", + " 100.7247\n", + " 99.2430\n", + " 101.7736\n", + " 99.9042\n", + " 102.0716\n", + " ...\n", + " 101.7614\n", + " 100.4395\n", + " 100.1280\n", + " 100.3108\n", + " 101.0099\n", + " 100.0964\n", + " 100.3682\n", + " 100.5026\n", + " 101.9486\n", + " 101.1545\n", " \n", " \n", - " 2016Q1\n", - " 100.4876\n", - " 100.1250\n", - " 101.0249\n", - " 106.3569\n", - " 100.5531\n", - " 102.5741\n", - " 102.0000\n", - " 105.4604\n", - " 99.9367\n", - " 100.1333\n", - " ...\n", - " 99.3322\n", - " 98.6500\n", - " 103.8821\n", - " 100.5911\n", - " 98.3343\n", - " 100.3120\n", - " 99.2197\n", - " 105.2875\n", - " 100.1\n", - " 100.1558\n", + " 2015-02\n", + " 100.3544\n", + " 99.6770\n", + " 99.5821\n", + " 100.5416\n", + " 98.4184\n", + " 100.4826\n", + " 99.5538\n", + " 101.0862\n", + " 99.8915\n", + " 101.9239\n", + " ...\n", + " 101.9955\n", + " 100.6631\n", + " 99.9006\n", + " 100.3609\n", + " 101.5843\n", + " 100.1213\n", + " 100.3988\n", + " 100.2183\n", + " 102.2155\n", + " 101.1571\n", " \n", " \n", - " 2016Q2\n", - " 100.8591\n", - " 100.9249\n", - " 102.0854\n", - " 108.3177\n", - " 101.7119\n", - " 103.5765\n", - " 101.6667\n", - " 107.6331\n", - " 99.5348\n", - " 100.6000\n", - " ...\n", - " 99.6584\n", - " 100.3500\n", - " 106.2676\n", - " 100.7840\n", - " 99.8449\n", - " 100.8885\n", - " 99.9467\n", - " 106.6536\n", - " 100.8\n", - " 101.3300\n", + " 2015-03\n", + " 100.6180\n", + " 99.7776\n", + " 99.7218\n", + " 100.7137\n", + " 98.1051\n", + " 99.8292\n", + " 99.6062\n", + " 100.5230\n", + " 99.8419\n", + " 101.9677\n", + " ...\n", + " 102.1121\n", + " 101.0000\n", + " 99.5509\n", + " 100.4005\n", + " 102.0184\n", + " 100.1768\n", + " 100.5459\n", + " 99.9413\n", + " 102.4027\n", + " 101.0608\n", " \n", " \n", - " 2016Q3\n", - " 101.6020\n", - " 100.7916\n", - " 102.2803\n", - " 109.7670\n", - " 101.7645\n", - " 104.3355\n", - " 101.9000\n", - " 108.4734\n", - " 100.3759\n", - " 100.7333\n", - " ...\n", - " 99.2914\n", - " 100.2000\n", - " 107.5729\n", - " 100.9741\n", - " 99.6904\n", - " 101.0619\n", - " 99.5971\n", - " 108.2961\n", - " 101.2\n", - " 101.6671\n", + " 2015-04\n", + " 100.7268\n", + " 99.7613\n", + " 99.8351\n", + " 100.8188\n", + " 98.0114\n", + " 99.1378\n", + " 99.6178\n", + " 100.5682\n", + " 99.6859\n", + " 102.0907\n", + " ...\n", + " 102.0548\n", + " 101.4116\n", + " 99.0745\n", + " 100.4670\n", + " 102.1910\n", + " 100.2013\n", + " 100.7093\n", + " 99.8651\n", + " 102.3960\n", + " 101.0062\n", " \n", " \n", - " 2016Q4\n", - " 102.1593\n", - " 101.7249\n", - " 102.5049\n", - " 110.5150\n", - " 101.6855\n", - " 104.6587\n", - " 102.4333\n", - " 108.4870\n", - " 100.0826\n", - " 101.1667\n", - " ...\n", - " 99.6380\n", - " 100.5833\n", - " 108.5631\n", - " 101.5374\n", - " 101.3201\n", - " 101.6747\n", - " 99.4980\n", - " 110.8634\n", - " 101.9\n", - " 101.8934\n", + " 2015-05\n", + " 100.6796\n", + " 99.7306\n", + " 99.8178\n", + " 100.8799\n", + " 97.9134\n", + " 98.6489\n", + " 99.5286\n", + " 100.7959\n", + " 99.3073\n", + " 102.1982\n", + " ...\n", + " 101.9069\n", + " 101.6278\n", + " 98.6212\n", + " 100.3898\n", + " 102.0865\n", + " 100.2551\n", + " 100.7530\n", + " 99.8793\n", + " 102.2672\n", + " 100.9260\n", " \n", " \n", - " 2017Q1\n", - " 102.6236\n", - " 102.1581\n", - " 103.6842\n", - " 111.5608\n", - " 102.4756\n", - " 105.3996\n", - " 103.4667\n", - " 110.8528\n", - " 101.0425\n", - " 102.5667\n", - " ...\n", - " 100.2498\n", - " 100.4300\n", - " 110.6335\n", - " 102.8042\n", - " 101.0321\n", - " 101.7949\n", - " 99.7367\n", - " 116.0402\n", - " 102.3\n", - " 102.6991\n", - " \n", - " \n", - " 2017Q2\n", - " 102.8094\n", - " 102.8914\n", - " 104.0377\n", - " 112.1711\n", - " 103.0550\n", - " 105.9626\n", - " 103.2000\n", - " 112.3022\n", - " 101.2235\n", - " 102.8333\n", - " ...\n", - " 100.6169\n", - " 101.7533\n", - " 111.8038\n", - " 102.7209\n", - " 101.8393\n", - " 102.6821\n", - " 100.3179\n", - " 118.9145\n", - " 103.4\n", - " 103.2573\n", - " \n", - " \n", - " 2017Q3\n", - " 103.4595\n", - " 102.9581\n", - " 104.2194\n", - " 112.5860\n", - " 103.1604\n", - " 106.0715\n", - " 103.5333\n", - " 112.5360\n", - " 101.6183\n", - " 103.3000\n", - " ...\n", - " 100.8208\n", - " 101.3967\n", - " 112.4789\n", - " 103.2139\n", - " 101.3390\n", - " 103.2395\n", - " 100.0765\n", - " 119.7268\n", - " 103.9\n", - " 103.6668\n", - " \n", - " \n", - " 2017Q4\n", - " 104.1096\n", - " 103.9580\n", - " 104.6258\n", - " 113.6290\n", - " 103.5028\n", - " 106.7725\n", - " 104.3000\n", - " 112.9167\n", - " 102.5483\n", - " 103.8000\n", - " ...\n", - " 101.4530\n", - " 101.9167\n", - " 113.4691\n", - " 103.0006\n", - " 102.7878\n", - " 103.4693\n", - " 100.2563\n", - " 124.4623\n", - " 104.7\n", - " 104.0510\n", - " \n", - " \n", - " 2018Q1\n", - " 104.5740\n", - " 103.9913\n", - " 105.2666\n", - " 114.6772\n", - " 104.5826\n", - " 107.5061\n", - " 105.6667\n", - " 114.6136\n", - " 103.4784\n", - " 104.4667\n", - " ...\n", - " 102.5950\n", - " 101.7367\n", - " 114.9994\n", - " 103.9107\n", - " 101.9956\n", - " 103.5214\n", - " 100.4561\n", - " 127.9685\n", - " 104.8\n", - " 104.9731\n", - " \n", - " \n", - " 2018Q2\n", - " 104.9454\n", - " 104.8913\n", - " 105.9174\n", - " 115.9132\n", - " 105.4253\n", - " 108.2470\n", - " 104.9667\n", - " 115.8536\n", - " 103.4318\n", - " 105.1667\n", - " ...\n", - " 103.4310\n", - " 103.6667\n", - " 116.6198\n", - " 104.2540\n", - " 103.6741\n", - " 104.6320\n", - " 101.3041\n", - " 134.1290\n", - " 105.8\n", - " 106.0575\n", - " \n", - " \n", - " 2018Q3\n", - " 105.4098\n", - " 105.1579\n", - " 106.5682\n", - " 117.5412\n", - " 105.9257\n", - " 109.0569\n", - " 105.9000\n", - " 116.0793\n", - " 103.8319\n", - " 105.7667\n", - " ...\n", - " 103.5738\n", - " 103.3167\n", - " 118.0151\n", - " 104.8106\n", - " 103.6003\n", - " 105.4320\n", - " 101.2108\n", - " 143.0000\n", - " 106.3\n", - " 106.4046\n", - " \n", - " \n", - " 2018Q4\n", - " 105.9670\n", - " 106.1578\n", - " 107.3677\n", - " 118.3051\n", - " 105.6097\n", - " 109.7252\n", - " 106.5667\n", - " 116.5987\n", - " 104.7179\n", - " 105.9667\n", - " ...\n", - " 103.6758\n", - " 103.8267\n", - " 119.0053\n", - " 104.8409\n", - " 104.5452\n", - " 105.6330\n", - " 101.1654\n", - " 152.3022\n", - " 106.9\n", - " 106.3434\n", - " \n", - " \n", - " 2019Q1\n", - " 105.9670\n", - " 105.7912\n", - " 107.5428\n", - " 119.3573\n", - " 106.2681\n", - " 109.9758\n", - " 107.6000\n", - " 118.2001\n", - " 105.0721\n", - " 107.3000\n", - " ...\n", - " 105.0421\n", - " 103.0867\n", - " 119.7705\n", - " 104.4744\n", - " 103.1542\n", - " 105.4607\n", - " 101.0817\n", - " 153.4483\n", - " 106.7\n", - " 106.6998\n", - " \n", - " \n", - " 2019Q2\n", - " 106.6171\n", - " 106.6578\n", - " 107.9293\n", - " 120.9170\n", - " 107.6903\n", - " 111.0944\n", - " 107.7667\n", - " 119.7138\n", - " 105.7806\n", - " 108.1333\n", - " ...\n", - " 106.0413\n", - " 105.3767\n", - " 121.7959\n", - " 104.9340\n", - " 104.6255\n", - " 106.7542\n", - " 101.9599\n", - " 158.2056\n", - " 107.8\n", - " 107.9786\n", - " \n", - " \n", - " 2019Q3\n", - " 107.1744\n", - " 106.6245\n", - " 107.8070\n", - " 121.2801\n", - " 107.9800\n", - " 111.7445\n", - " 108.9333\n", - " 120.4749\n", - " 106.6956\n", - " 108.7667\n", - " ...\n", - " 106.5307\n", - " 105.3733\n", - " 122.9211\n", - " 104.8574\n", - " 103.9239\n", - " 107.0319\n", - " 101.4733\n", - " 162.3424\n", - " 108.2\n", - " 108.2747\n", - " \n", - " \n", - " 2019Q4\n", - " 107.9173\n", - " 107.5577\n", - " 107.9491\n", - " 122.2942\n", - " 107.8220\n", - " 112.8340\n", - " 111.0667\n", - " 121.0731\n", - " 106.6199\n", - " 109.1667\n", - " ...\n", - " 106.6735\n", - " 105.4367\n", - " 123.4612\n", - " 105.1508\n", - " 105.0062\n", - " 107.4510\n", - " 101.0880\n", - " 167.9989\n", - " 108.4\n", - " 108.5053\n", - " \n", - " \n", - " 2020Q1\n", - " 108.2888\n", - " 107.7713\n", - " 108.6627\n", - " 123.9305\n", - " 108.1907\n", - " 114.0434\n", - " 112.9333\n", - " 122.6125\n", - " 106.9214\n", - " 111.1667\n", - " ...\n", - " 107.9378\n", - " 104.6367\n", - " 124.9465\n", - " 105.4843\n", - " 103.7755\n", - " 106.5064\n", - " 100.9542\n", - " 172.0552\n", - " 108.5\n", - " 108.9604\n", - " \n", - " \n", - " 2020Q2\n", - " 106.2456\n", - " 107.7713\n", - " 108.5206\n", - " 123.5002\n", - " 107.7166\n", - " 114.3521\n", - " 110.7000\n", - " 123.1237\n", - " 106.4188\n", - " 111.5000\n", - " ...\n", - " 108.1009\n", - " 104.4067\n", - " 124.6315\n", - " 104.9431\n", - " 103.9368\n", - " 106.8734\n", - " 100.7151\n", - " 176.6397\n", - " 108.7\n", - " 108.3720\n", - " \n", - " \n", - " 2020Q3\n", - " 107.9173\n", - " 108.1317\n", - " 108.6891\n", - " 124.4655\n", - " 108.2697\n", - " 114.7371\n", - " 111.0667\n", - " 122.8103\n", - " 106.7182\n", - " 112.3667\n", - " ...\n", - " 108.1621\n", - " 105.3033\n", - " 126.6569\n", - " 105.6143\n", - " 103.4012\n", - " 107.6457\n", - " 100.5986\n", - " 181.4354\n", - " 109.1\n", - " 109.5982\n", - " \n", - " \n", - " 2020Q4\n", - " 108.8461\n", - " 108.8526\n", - " 108.5503\n", - " 127.4925\n", - " 108.6647\n", - " 116.0881\n", - " 111.2000\n", - " 123.0296\n", - " 107.1846\n", - " 112.0333\n", - " ...\n", - " 108.3049\n", - " 104.6967\n", - " 127.3771\n", - " 105.6283\n", - " 104.2515\n", - " 107.7946\n", - " 100.3909\n", - " 190.6953\n", - " 109.3\n", - 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" 107.5867\n", - " 132.9132\n", - " 108.2919\n", - " 106.9139\n", - " 109.7786\n", - " 101.4379\n", - " 216.3875\n", - " 112.0\n", - " 115.4460\n", - " \n", - " \n", - " 2021Q4\n", - " 112.6538\n", - " 113.2860\n", - " 114.1597\n", - " 140.8642\n", - " 113.8004\n", - " 123.7876\n", - " 113.1821\n", - " 129.3722\n", - " 110.4538\n", - " 118.9000\n", - " ...\n", - " 114.2595\n", - " 109.0533\n", - " 134.3085\n", - " 109.3742\n", - " 110.3104\n", - " 111.3722\n", - " 101.8316\n", - " 239.9870\n", - " 114.1\n", - " 117.1982\n", - " \n", - " \n", - " 2022Q1\n", - " 115.0685\n", - " 115.5928\n", - " 117.9621\n", - " 144.4992\n", - " 116.1443\n", - " 127.1616\n", - " 114.1116\n", - " 134.2682\n", - " NaN\n", - " 126.3667\n", - " ...\n", - " 119.1048\n", - " 110.3267\n", - " 136.3340\n", - " 111.0573\n", - " 112.5970\n", - " 113.1785\n", - " 102.5922\n", - " 307.8714\n", - " 115.5\n", - " 119.8744\n", - " \n", - " \n", - " 2022Q2\n", - " 117.1117\n", - " 119.1611\n", - " 119.9640\n", - " 148.8869\n", - 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" 2022Q4\n", - " 121.4767\n", - " 125.2886\n", - " 126.8056\n", - " 149.3866\n", - " 121.3853\n", - " 139.8549\n", - " 115.2643\n", - " 145.7082\n", - " NaN\n", - " 137.5667\n", - " ...\n", - " 131.6340\n", - " 120.0067\n", - " 144.6157\n", - " 115.1124\n", - " 117.5796\n", - " 124.2429\n", - " 104.8175\n", - " 425.6717\n", - " 124.8\n", - " 125.5212\n", + " 2024-05\n", + " 97.1659\n", + " 98.0426\n", + " 98.8480\n", + " 99.9111\n", + " 100.6574\n", + " 96.8854\n", + " 93.7667\n", + " 97.4475\n", + " 101.4923\n", + " 100.3474\n", + " ...\n", + " 101.4337\n", + " 98.8211\n", + " 98.3088\n", + " 99.9166\n", + " 99.5472\n", + " 99.7972\n", + " 96.9121\n", + " 97.4463\n", + " 99.5466\n", + " 98.3388\n", " \n", " \n", - " 2023Q1\n", - " 123.1484\n", - " 127.6314\n", - " 126.3464\n", - " 152.2088\n", - " 122.1227\n", - " 142.1502\n", - " 115.5246\n", - " 152.1153\n", - " NaN\n", - " 147.0667\n", - " ...\n", - " 137.0992\n", - " 121.3033\n", - " 146.2361\n", - " 116.3247\n", - " 118.2873\n", - " 126.1013\n", - " 105.8744\n", - " 475.1565\n", - " 125.9\n", - " 126.8328\n", + " 2024-06\n", + " 97.0254\n", + " 98.0907\n", + " 98.9278\n", + " 100.0943\n", + " 100.7319\n", + " 96.9271\n", + " 93.5654\n", + " 97.5653\n", + " NaN\n", + " 100.2980\n", + " ...\n", + " 101.5624\n", + " 99.0805\n", + " 98.4659\n", + " 100.0310\n", + " 99.6151\n", + " 100.0921\n", + " 97.1891\n", + " 97.0393\n", + " 99.9870\n", + " 98.0036\n", " \n", " \n", - " 2023Q2\n", - " 124.1700\n", - " 129.6859\n", - " 125.9401\n", - " 154.4831\n", - " 123.9399\n", - " 143.6284\n", - " 114.7437\n", - " 155.8206\n", - " NaN\n", - " 147.6333\n", - " ...\n", - " 139.4851\n", - " 124.8800\n", - " 148.5316\n", - " 117.0310\n", - " 119.8445\n", - " 128.3577\n", - " 106.4415\n", - " 505.5587\n", - " 128.9\n", - " 128.3451\n", + " 2024-07\n", + " 96.9198\n", + " 98.1492\n", + " 98.9477\n", + " 100.1920\n", + " 100.8917\n", + " 96.7050\n", + " 93.4216\n", + " 97.8347\n", + " NaN\n", + " 100.1191\n", + " ...\n", + " 101.5873\n", + " 99.2105\n", + " NaN\n", + " 100.1550\n", + " 99.5869\n", + " 100.2558\n", + " 97.5011\n", + " 96.5904\n", + " 100.4009\n", + " 97.8282\n", " \n", " \n", - " 2023Q3\n", - " 125.6559\n", - " 130.7673\n", - " 127.3474\n", - " 155.0760\n", - " 125.1778\n", - " 144.4710\n", - " 115.0784\n", - " 158.1563\n", - " NaN\n", - " 148.6333\n", - " ...\n", - " 139.4443\n", - " 126.8733\n", - " 150.6470\n", - " 118.2503\n", - " 120.9846\n", - " 129.7491\n", - " 106.5485\n", - " 612.1086\n", - " 129.5\n", - " 129.4571\n", + " 2024-08\n", + " NaN\n", + " 98.2503\n", + " 99.0323\n", + " 100.3270\n", + " 101.0453\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 99.9795\n", + " ...\n", + " 101.5408\n", + " 99.3303\n", + " NaN\n", + " 100.1475\n", + " 99.4364\n", + " 100.4116\n", + " 97.8254\n", + " 96.2822\n", + " 100.7519\n", + " 97.7806\n", " \n", " \n", "\n", - "

35 rows × 50 columns

\n", + "

116 rows × 49 columns

\n", "" ], "text/plain": [ - " Australia Austria Belgium Brazil Canada Chile China \\\n", - "2015Q1 99.1874 99.1584 99.2476 96.5619 99.0255 98.0161 99.9000 \n", - "2015Q2 99.8375 100.3583 99.9546 99.2407 100.1580 99.3926 99.5667 \n", - "2015Q3 100.3018 100.0917 100.1759 100.9553 100.5267 100.8126 100.2667 \n", - "2015Q4 100.6733 100.3916 100.6219 103.2421 100.2897 101.7787 100.2667 \n", - "2016Q1 100.4876 100.1250 101.0249 106.3569 100.5531 102.5741 102.0000 \n", - "2016Q2 100.8591 100.9249 102.0854 108.3177 101.7119 103.5765 101.6667 \n", - "2016Q3 101.6020 100.7916 102.2803 109.7670 101.7645 104.3355 101.9000 \n", - "2016Q4 102.1593 101.7249 102.5049 110.5150 101.6855 104.6587 102.4333 \n", - "2017Q1 102.6236 102.1581 103.6842 111.5608 102.4756 105.3996 103.4667 \n", - "2017Q2 102.8094 102.8914 104.0377 112.1711 103.0550 105.9626 103.2000 \n", - "2017Q3 103.4595 102.9581 104.2194 112.5860 103.1604 106.0715 103.5333 \n", - "2017Q4 104.1096 103.9580 104.6258 113.6290 103.5028 106.7725 104.3000 \n", - "2018Q1 104.5740 103.9913 105.2666 114.6772 104.5826 107.5061 105.6667 \n", - "2018Q2 104.9454 104.8913 105.9174 115.9132 105.4253 108.2470 104.9667 \n", - "2018Q3 105.4098 105.1579 106.5682 117.5412 105.9257 109.0569 105.9000 \n", - "2018Q4 105.9670 106.1578 107.3677 118.3051 105.6097 109.7252 106.5667 \n", - "2019Q1 105.9670 105.7912 107.5428 119.3573 106.2681 109.9758 107.6000 \n", - "2019Q2 106.6171 106.6578 107.9293 120.9170 107.6903 111.0944 107.7667 \n", - "2019Q3 107.1744 106.6245 107.8070 121.2801 107.9800 111.7445 108.9333 \n", - "2019Q4 107.9173 107.5577 107.9491 122.2942 107.8220 112.8340 111.0667 \n", - "2020Q1 108.2888 107.7713 108.6627 123.9305 108.1907 114.0434 112.9333 \n", - "2020Q2 106.2456 107.7713 108.5206 123.5002 107.7166 114.3521 110.7000 \n", - "2020Q3 107.9173 108.1317 108.6891 124.4655 108.2697 114.7371 111.0667 \n", - "2020Q4 108.8461 108.8526 108.5503 127.4925 108.6647 116.0881 111.2000 \n", - "2021Q1 109.4962 109.2130 109.2441 130.4809 109.7445 117.4065 112.8103 \n", - "2021Q2 110.3320 110.4745 110.0832 133.0346 111.3247 118.4597 112.1038 \n", - "2021Q3 111.1679 111.5198 111.5367 136.4667 112.6679 120.3447 112.1782 \n", - "2021Q4 112.6538 113.2860 114.1597 140.8642 113.8004 123.7876 113.1821 \n", - "2022Q1 115.0685 115.5928 117.9621 144.4992 116.1443 127.1616 114.1116 \n", - "2022Q2 117.1117 119.1611 119.9640 148.8869 119.7261 132.1046 114.6322 \n", - "2022Q3 119.2477 122.4411 123.0032 148.2647 120.7269 136.7678 115.1527 \n", - "2022Q4 121.4767 125.2886 126.8056 149.3866 121.3853 139.8549 115.2643 \n", - "2023Q1 123.1484 127.6314 126.3464 152.2088 122.1227 142.1502 115.5246 \n", - "2023Q2 124.1700 129.6859 125.9401 154.4831 123.9399 143.6284 114.7437 \n", - "2023Q3 125.6559 130.7673 127.3474 155.0760 125.1778 144.4710 115.0784 \n", + " A5M Australia Austria Belgium Brazil Chile China \\\n", + "2015-01 99.9069 99.4035 99.5728 100.2037 98.9693 100.7247 99.2430 \n", + "2015-02 100.3544 99.6770 99.5821 100.5416 98.4184 100.4826 99.5538 \n", + "2015-03 100.6180 99.7776 99.7218 100.7137 98.1051 99.8292 99.6062 \n", + "2015-04 100.7268 99.7613 99.8351 100.8188 98.0114 99.1378 99.6178 \n", + "2015-05 100.6796 99.7306 99.8178 100.8799 97.9134 98.6489 99.5286 \n", + "... ... ... ... ... ... ... ... \n", + "2024-04 97.3675 98.0656 98.7699 99.7894 100.7182 96.9378 94.0522 \n", + "2024-05 97.1659 98.0426 98.8480 99.9111 100.6574 96.8854 93.7667 \n", + "2024-06 97.0254 98.0907 98.9278 100.0943 100.7319 96.9271 93.5654 \n", + "2024-07 96.9198 98.1492 98.9477 100.1920 100.8917 96.7050 93.4216 \n", + "2024-08 NaN 98.2503 99.0323 100.3270 101.0453 NaN NaN \n", "\n", - " Colombia Costa Rica Czech Republic ... Slovakia Slovenia \\\n", - "2015Q1 97.9415 100.3688 99.6667 ... 99.8624 99.3667 \n", - "2015Q2 99.4344 100.2806 100.3333 ... 100.3518 100.5933 \n", - "2015Q3 100.3399 99.8908 100.2000 ... 100.0459 100.0433 \n", - "2015Q4 102.2842 99.4599 99.7000 ... 99.7400 100.0000 \n", - "2016Q1 105.4604 99.9367 100.1333 ... 99.3322 98.6500 \n", - "2016Q2 107.6331 99.5348 100.6000 ... 99.6584 100.3500 \n", - "2016Q3 108.4734 100.3759 100.7333 ... 99.2914 100.2000 \n", - "2016Q4 108.4870 100.0826 101.1667 ... 99.6380 100.5833 \n", - "2017Q1 110.8528 101.0425 102.5667 ... 100.2498 100.4300 \n", - "2017Q2 112.3022 101.2235 102.8333 ... 100.6169 101.7533 \n", - "2017Q3 112.5360 101.6183 103.3000 ... 100.8208 101.3967 \n", - "2017Q4 112.9167 102.5483 103.8000 ... 101.4530 101.9167 \n", - "2018Q1 114.6136 103.4784 104.4667 ... 102.5950 101.7367 \n", - "2018Q2 115.8536 103.4318 105.1667 ... 103.4310 103.6667 \n", - "2018Q3 116.0793 103.8319 105.7667 ... 103.5738 103.3167 \n", - "2018Q4 116.5987 104.7179 105.9667 ... 103.6758 103.8267 \n", - "2019Q1 118.2001 105.0721 107.3000 ... 105.0421 103.0867 \n", - "2019Q2 119.7138 105.7806 108.1333 ... 106.0413 105.3767 \n", - "2019Q3 120.4749 106.6956 108.7667 ... 106.5307 105.3733 \n", - "2019Q4 121.0731 106.6199 109.1667 ... 106.6735 105.4367 \n", - "2020Q1 122.6125 106.9214 111.1667 ... 107.9378 104.6367 \n", - "2020Q2 123.1237 106.4188 111.5000 ... 108.1009 104.4067 \n", - "2020Q3 122.8103 106.7182 112.3667 ... 108.1621 105.3033 \n", - "2020Q4 123.0296 107.1846 112.0333 ... 108.3049 104.6967 \n", - "2021Q1 124.5196 107.5790 113.6000 ... 108.9982 104.0767 \n", - "2021Q2 126.7650 108.0007 114.7333 ... 110.5073 106.3600 \n", - "2021Q3 128.1001 108.5858 117.0000 ... 112.3630 107.5867 \n", - "2021Q4 129.3722 110.4538 118.9000 ... 114.2595 109.0533 \n", - "2022Q1 134.2682 NaN 126.3667 ... 119.1048 110.3267 \n", - "2022Q2 138.5860 NaN 132.8333 ... 124.3334 115.4067 \n", - "2022Q3 141.9720 NaN 137.5667 ... 128.0449 119.0633 \n", - "2022Q4 145.7082 NaN 137.5667 ... 131.6340 120.0067 \n", - "2023Q1 152.1153 NaN 147.0667 ... 137.0992 121.3033 \n", - "2023Q2 155.8206 NaN 147.6333 ... 139.4851 124.8800 \n", - "2023Q3 158.1563 NaN 148.6333 ... 139.4443 126.8733 \n", + " Colombia Costa Rica Czech Republic ... Slovakia Slovenia \\\n", + "2015-01 101.7736 99.9042 102.0716 ... 101.7614 100.4395 \n", + "2015-02 101.0862 99.8915 101.9239 ... 101.9955 100.6631 \n", + "2015-03 100.5230 99.8419 101.9677 ... 102.1121 101.0000 \n", + "2015-04 100.5682 99.6859 102.0907 ... 102.0548 101.4116 \n", + "2015-05 100.7959 99.3073 102.1982 ... 101.9069 101.6278 \n", + "... ... ... ... ... ... ... \n", + "2024-04 97.5212 102.0232 100.2295 ... 101.2322 98.5529 \n", + "2024-05 97.4475 101.4923 100.3474 ... 101.4337 98.8211 \n", + "2024-06 97.5653 NaN 100.2980 ... 101.5624 99.0805 \n", + "2024-07 97.8347 NaN 100.1191 ... 101.5873 99.2105 \n", + "2024-08 NaN NaN 99.9795 ... 101.5408 99.3303 \n", "\n", - " South Africa South Korea Spain Sweden Switzerland Turkey \\\n", - "2015Q1 97.5357 99.7341 98.9856 99.6630 100.2071 96.9511 \n", - "2015Q2 99.7862 99.9442 100.7976 100.0737 100.3367 99.7435 \n", - "2015Q3 101.0915 100.2376 99.8741 100.0279 99.7726 100.2398 \n", - "2015Q4 101.5866 100.0841 100.3427 100.2354 99.6836 103.0655 \n", - "2016Q1 103.8821 100.5911 98.3343 100.3120 99.2197 105.2875 \n", - "2016Q2 106.2676 100.7840 99.8449 100.8885 99.9467 106.6536 \n", - "2016Q3 107.5729 100.9741 99.6904 101.0619 99.5971 108.2961 \n", - "2016Q4 108.5631 101.5374 101.3201 101.6747 99.4980 110.8634 \n", - "2017Q1 110.6335 102.8042 101.0321 101.7949 99.7367 116.0402 \n", - "2017Q2 111.8038 102.7209 101.8393 102.6821 100.3179 118.9145 \n", - "2017Q3 112.4789 103.2139 101.3390 103.2395 100.0765 119.7268 \n", - "2017Q4 113.4691 103.0006 102.7878 103.4693 100.2563 124.4623 \n", - "2018Q1 114.9994 103.9107 101.9956 103.5214 100.4561 127.9685 \n", - "2018Q2 116.6198 104.2540 103.6741 104.6320 101.3041 134.1290 \n", - "2018Q3 118.0151 104.8106 103.6003 105.4320 101.2108 143.0000 \n", - "2018Q4 119.0053 104.8409 104.5452 105.6330 101.1654 152.3022 \n", - "2019Q1 119.7705 104.4744 103.1542 105.4607 101.0817 153.4483 \n", - "2019Q2 121.7959 104.9340 104.6255 106.7542 101.9599 158.2056 \n", - "2019Q3 122.9211 104.8574 103.9239 107.0319 101.4733 162.3424 \n", - "2019Q4 123.4612 105.1508 105.0062 107.4510 101.0880 167.9989 \n", - "2020Q1 124.9465 105.4843 103.7755 106.5064 100.9542 172.0552 \n", - "2020Q2 124.6315 104.9431 103.9368 106.8734 100.7151 176.6397 \n", - "2020Q3 126.6569 105.6143 103.4012 107.6457 100.5986 181.4354 \n", - "2020Q4 127.3771 105.6283 104.2515 107.7946 100.3909 190.6953 \n", - "2021Q1 128.8174 106.9847 104.3911 108.1489 100.5190 198.8846 \n", - "2021Q2 130.7978 107.5540 106.5974 108.7967 101.2131 206.8231 \n", - "2021Q3 132.9132 108.2919 106.9139 109.7786 101.4379 216.3875 \n", - "2021Q4 134.3085 109.3742 110.3104 111.3722 101.8316 239.9870 \n", - "2022Q1 136.3340 111.0573 112.5970 113.1785 102.5922 307.8714 \n", - "2022Q2 139.5296 113.3695 116.2968 116.8879 104.2079 360.0169 \n", - "2022Q3 143.4455 114.6556 117.6688 120.4526 104.8658 391.8836 \n", - "2022Q4 144.6157 115.1124 117.5796 124.2429 104.8175 425.6717 \n", - "2023Q1 146.2361 116.3247 118.2873 126.1013 105.8744 475.1565 \n", - "2023Q2 148.5316 117.0310 119.8445 128.3577 106.4415 505.5587 \n", - "2023Q3 150.6470 118.2503 120.9846 129.7491 106.5485 612.1086 \n", + " South Africa South Korea Spain Sweden Switzerland Turkey \\\n", + "2015-01 100.1280 100.3108 101.0099 100.0964 100.3682 100.5026 \n", + "2015-02 99.9006 100.3609 101.5843 100.1213 100.3988 100.2183 \n", + "2015-03 99.5509 100.4005 102.0184 100.1768 100.5459 99.9413 \n", + "2015-04 99.0745 100.4670 102.1910 100.2013 100.7093 99.8651 \n", + "2015-05 98.6212 100.3898 102.0865 100.2551 100.7530 99.8793 \n", + "... ... ... ... ... ... ... \n", + "2024-04 98.1546 99.9530 99.3896 99.3756 96.6885 97.6054 \n", + "2024-05 98.3088 99.9166 99.5472 99.7972 96.9121 97.4463 \n", + "2024-06 98.4659 100.0310 99.6151 100.0921 97.1891 97.0393 \n", + "2024-07 NaN 100.1550 99.5869 100.2558 97.5011 96.5904 \n", + "2024-08 NaN 100.1475 99.4364 100.4116 97.8254 96.2822 \n", "\n", - " United Kingdom United States \n", - "2015Q1 99.4 99.0854 \n", - "2015Q2 100.0 100.2800 \n", - "2015Q3 100.2 100.5434 \n", - "2015Q4 100.4 100.0911 \n", - "2016Q1 100.1 100.1558 \n", - "2016Q2 100.8 101.3300 \n", - "2016Q3 101.2 101.6671 \n", - "2016Q4 101.9 101.8934 \n", - "2017Q1 102.3 102.6991 \n", - "2017Q2 103.4 103.2573 \n", - "2017Q3 103.9 103.6668 \n", - "2017Q4 104.7 104.0510 \n", - "2018Q1 104.8 104.9731 \n", - "2018Q2 105.8 106.0575 \n", - "2018Q3 106.3 106.4046 \n", - "2018Q4 106.9 106.3434 \n", - "2019Q1 106.7 106.6998 \n", - "2019Q2 107.8 107.9786 \n", - "2019Q3 108.2 108.2747 \n", - "2019Q4 108.4 108.5053 \n", - "2020Q1 108.5 108.9604 \n", - "2020Q2 108.7 108.3720 \n", - "2020Q3 109.1 109.5982 \n", - "2020Q4 109.3 109.8502 \n", - "2021Q1 109.5 111.0290 \n", - "2021Q2 110.9 113.6269 \n", - "2021Q3 112.0 115.4460 \n", - "2021Q4 114.1 117.1982 \n", - "2022Q1 115.5 119.8744 \n", - "2022Q2 119.7 123.4392 \n", - "2022Q3 121.8 125.0621 \n", - "2022Q4 124.8 125.5212 \n", - "2023Q1 125.9 126.8328 \n", - "2023Q2 128.9 128.3451 \n", - "2023Q3 129.5 129.4571 \n", + " United Kingdom United States \n", + "2015-01 101.9486 101.1545 \n", + "2015-02 102.2155 101.1571 \n", + "2015-03 102.4027 101.0608 \n", + "2015-04 102.3960 101.0062 \n", + "2015-05 102.2672 100.9260 \n", + "... ... ... \n", + "2024-04 99.1257 98.8058 \n", + "2024-05 99.5466 98.3388 \n", + "2024-06 99.9870 98.0036 \n", + "2024-07 100.4009 97.8282 \n", + "2024-08 100.7519 97.7806 \n", "\n", - "[35 rows x 50 columns]" + "[116 rows x 49 columns]" ] }, "metadata": {}, @@ -1172,7 +500,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -1181,7 +509,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1191,14 +519,12 @@ } ], "source": [ - "quarterly_cpi = economics.get_consumer_price_index(period=\"quarterly\")\n", + "cci = economics.get_consumer_confidence_index()\n", "\n", - "display(quarterly_cpi)\n", + "display(cci)\n", "\n", - "quarterly_cpi.loc[\n", - " :, [\"United States\", \"South Africa\", \"China\", \"Canada\", \"Germany\"]\n", - "].plot(\n", - " title=\"Consumer Price Index (CPI) for USA, South Africa, China, Canada and Germany\",\n", + "cci.loc[:, [\"United States\", \"South Africa\", \"China\", \"Spain\", \"Germany\"]].plot(\n", + " title=\"Consumer Confidence Index (CCI) for USA, South Africa, China, Spain and Germany\",\n", " figsize=(15, 3),\n", " xlabel=\"Date\",\n", " ylabel=\"CPI (Base 100 = 2015)\",\n", @@ -1232,7 +558,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -1286,7 +612,7 @@ "id": "61379421", "metadata": {}, "source": [ - "It is also possible to see how a country spends its money. This is done on the basis of the System of National accounts (SNA). For example for the Netherlands this results in the following." + "It is also possible to see how a housing prices have changed over time in each country. This can reflect the economic situation of a country and policies in place by the government." ] }, { @@ -1308,219 +634,305 @@ " vertical-align: top;\n", " }\n", "\n", - " .dataframe thead tr th {\n", - " text-align: left;\n", + " .dataframe thead th {\n", + " text-align: right;\n", " }\n", "\n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Australia...United States
General Public ServicesDefencePublic Order and SafetyEconomic AffairsEnvironmental ProtectionHousing and Community AmenitiesHealthRecreation, Culture and ReligionEducationSocial ProtectionAustraliaAustriaBelgiumBrazilBulgariaCanadaChileChinaColombiaCroatia...General Public ServicesDefencePublic Order and SafetyEconomic AffairsEnvironmental ProtectionHousing and Community AmenitiesHealthRecreation, Culture and ReligionEducationSocial ProtectionSlovakiaSloveniaSouth AfricaSouth KoreaSpainSwedenSwitzerlandTurkeyUnited KingdomUnited States
20150.22420.07910.01190.05960.00890.00430.15010.00760.11740.3370...0.12940.14610.01410.04730.00.01610.29190.00120.02600.3279100.0000100.0000100.0000100.0000100.0000100.0000100.0000100.0000100.0000100.0000...100.0000100.0000100.0000100.0000100.0000100.0000100.0000100.0000100.0000100.0000
20160.22550.08170.01190.05760.00960.00390.15620.00710.11820.3282...0.13210.14100.01410.05260.00.01560.29520.00130.02440.3238104.8446105.1797100.796592.3242105.2426109.0845102.8239109.8776103.0774102.0184...106.9119103.630699.3950100.2929104.4502107.2807101.8145106.7692105.6583104.5145
20170.21810.08350.01240.06050.01160.00500.15890.00730.11970.3229...0.13250.14080.01370.05260.00.01710.29610.00130.02330.3226112.1958108.6338102.398890.0256109.3574120.4339108.5273122.4895106.1411105.0047...111.7375110.482799.172599.5477109.2462112.4682102.9525105.8945108.4799109.0228
20180.21550.08520.01230.05650.01070.00530.15700.00740.12070.3294...0.14070.14270.01350.05190.00.01400.29670.00120.02260.3165108.7818112.8524103.420387.2313113.7827122.8673117.3976127.0654108.3883109.8439...117.3166117.807599.038299.5207114.9443108.7094104.780099.8783109.7420113.3595
2019NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN102.4878117.5655105.799384.8474118.2791122.6793125.0414128.3162109.9562118.3661...124.6078124.066098.817098.5304119.6313109.1689108.891591.2173109.1387117.3719
2020106.6616124.9628109.399181.8410124.7011129.7021126.6900129.7462110.2729126.7650...0.14170.14610.01300.05200.00.01330.29710.00120.02100.3147133.4759130.370198.3639100.8826122.2324112.7499114.068399.0582111.5703125.2234
2021122.7863136.4052113.905773.8956127.5348144.6103135.6571133.5644109.3527132.9443...137.6935140.903898.5217107.0372123.8953121.4920121.6597112.3221118.0359140.1543
2022124.4794141.8097109.479767.4327125.3224152.5612130.4363132.2332103.7005137.2105...139.6026147.021095.2591104.6816124.8736118.0426127.6582155.4028119.7156149.7066
2023118.9331127.4048106.225164.8087128.3018144.0986131.4020131.7186102.5343142.2987...126.3703146.855591.027194.1689124.6446104.9650127.2650194.6252112.4491151.4623
\n", - "

5 rows × 360 columns

\n", + "

9 rows × 50 columns

\n", "" ], "text/plain": [ - " Australia \\\n", - " General Public Services Defence Public Order and Safety Economic Affairs \n", - "2015 0.2242 0.0791 0.0119 0.0596 \n", - "2016 0.2255 0.0817 0.0119 0.0576 \n", - "2017 0.2181 0.0835 0.0124 0.0605 \n", - "2018 0.2155 0.0852 0.0123 0.0565 \n", - "2019 NaN NaN NaN NaN \n", - "\n", - " \\\n", - " Environmental Protection Housing and Community Amenities Health \n", - "2015 0.0089 0.0043 0.1501 \n", - "2016 0.0096 0.0039 0.1562 \n", - "2017 0.0116 0.0050 0.1589 \n", - "2018 0.0107 0.0053 0.1570 \n", - "2019 NaN NaN NaN \n", - "\n", - " ... \\\n", - " Recreation, Culture and Religion Education Social Protection ... \n", - "2015 0.0076 0.1174 0.3370 ... \n", - "2016 0.0071 0.1182 0.3282 ... \n", - "2017 0.0073 0.1197 0.3229 ... \n", - "2018 0.0074 0.1207 0.3294 ... \n", - "2019 NaN NaN NaN ... \n", + " Australia Austria Belgium Brazil Bulgaria Canada Chile \\\n", + "2015 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 \n", + "2016 104.8446 105.1797 100.7965 92.3242 105.2426 109.0845 102.8239 \n", + "2017 112.1958 108.6338 102.3988 90.0256 109.3574 120.4339 108.5273 \n", + "2018 108.7818 112.8524 103.4203 87.2313 113.7827 122.8673 117.3976 \n", + "2019 102.4878 117.5655 105.7993 84.8474 118.2791 122.6793 125.0414 \n", + "2020 106.6616 124.9628 109.3991 81.8410 124.7011 129.7021 126.6900 \n", + "2021 122.7863 136.4052 113.9057 73.8956 127.5348 144.6103 135.6571 \n", + "2022 124.4794 141.8097 109.4797 67.4327 125.3224 152.5612 130.4363 \n", + "2023 118.9331 127.4048 106.2251 64.8087 128.3018 144.0986 131.4020 \n", "\n", - " United States \\\n", - " General Public Services Defence Public Order and Safety Economic Affairs \n", - "2015 0.1294 0.1461 0.0141 0.0473 \n", - "2016 0.1321 0.1410 0.0141 0.0526 \n", - "2017 0.1325 0.1408 0.0137 0.0526 \n", - "2018 0.1407 0.1427 0.0135 0.0519 \n", - "2019 0.1417 0.1461 0.0130 0.0520 \n", + " China Colombia Croatia ... Slovakia Slovenia South Africa \\\n", + "2015 100.0000 100.0000 100.0000 ... 100.0000 100.0000 100.0000 \n", + "2016 109.8776 103.0774 102.0184 ... 106.9119 103.6306 99.3950 \n", + "2017 122.4895 106.1411 105.0047 ... 111.7375 110.4827 99.1725 \n", + "2018 127.0654 108.3883 109.8439 ... 117.3166 117.8075 99.0382 \n", + "2019 128.3162 109.9562 118.3661 ... 124.6078 124.0660 98.8170 \n", + "2020 129.7462 110.2729 126.7650 ... 133.4759 130.3701 98.3639 \n", + "2021 133.5644 109.3527 132.9443 ... 137.6935 140.9038 98.5217 \n", + "2022 132.2332 103.7005 137.2105 ... 139.6026 147.0210 95.2591 \n", + "2023 131.7186 102.5343 142.2987 ... 126.3703 146.8555 91.0271 \n", "\n", - " \\\n", - " Environmental Protection Housing and Community Amenities Health \n", - "2015 0.0 0.0161 0.2919 \n", - "2016 0.0 0.0156 0.2952 \n", - "2017 0.0 0.0171 0.2961 \n", - "2018 0.0 0.0140 0.2967 \n", - "2019 0.0 0.0133 0.2971 \n", + " South Korea Spain Sweden Switzerland Turkey United Kingdom \\\n", + "2015 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 \n", + "2016 100.2929 104.4502 107.2807 101.8145 106.7692 105.6583 \n", + "2017 99.5477 109.2462 112.4682 102.9525 105.8945 108.4799 \n", + "2018 99.5207 114.9443 108.7094 104.7800 99.8783 109.7420 \n", + "2019 98.5304 119.6313 109.1689 108.8915 91.2173 109.1387 \n", + "2020 100.8826 122.2324 112.7499 114.0683 99.0582 111.5703 \n", + "2021 107.0372 123.8953 121.4920 121.6597 112.3221 118.0359 \n", + "2022 104.6816 124.8736 118.0426 127.6582 155.4028 119.7156 \n", + "2023 94.1689 124.6446 104.9650 127.2650 194.6252 112.4491 \n", "\n", - " \n", - " Recreation, Culture and Religion Education Social Protection \n", - "2015 0.0012 0.0260 0.3279 \n", - "2016 0.0013 0.0244 0.3238 \n", - "2017 0.0013 0.0233 0.3226 \n", - "2018 0.0012 0.0226 0.3165 \n", - "2019 0.0012 0.0210 0.3147 \n", + " United States \n", + "2015 100.0000 \n", + "2016 104.5145 \n", + "2017 109.0228 \n", + "2018 113.3595 \n", + "2019 117.3719 \n", + "2020 125.2234 \n", + "2021 140.1543 \n", + "2022 149.7066 \n", + "2023 151.4623 \n", "\n", - "[5 rows x 360 columns]" + "[9 rows x 50 columns]" ] }, "metadata": {}, @@ -1529,7 +941,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -1538,7 +950,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1548,24 +960,19 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", + "house_prices = economics.get_house_prices()\n", "\n", - "central_government_spending = economics.get_central_government_spending()\n", + "display(house_prices)\n", "\n", - "display(central_government_spending)\n", - "\n", - "(central_government_spending[\"Netherlands\"] * 100).plot.bar(\n", + "house_prices[\n", + " [\"Australia\", \"Turkey\", \"United States\", \"United Kingdom\", \"Spain\", \"Brazil\"]\n", + "].plot(\n", " figsize=(15, 5),\n", - " colormap=\"tab20\",\n", - " title=\"Central Government Spending (Netherlands)\",\n", - " rot=0,\n", - " xlabel=\"Date\",\n", - " ylabel=\"Spending (% of GDP)\",\n", + " title=\"House Prices Index\",\n", " grid=True,\n", - " stacked=True,\n", - ")\n", - "\n", - "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncols=5)" + " xlabel=\"Date\",\n", + " ylabel=\"House Prices (Base 100 = 2015)\",\n", + ")" ] }, { @@ -1606,13 +1013,13 @@ " Australia\n", " Austria\n", " Belgium\n", + " Bulgaria\n", " Canada\n", " Chile\n", " Colombia\n", " Costa Rica\n", + " Croatia\n", " Czech Republic\n", - " Denmark\n", - " Estonia\n", " ...\n", " Portugal\n", " Slovakia\n", @@ -1632,8 +1039,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.048000\n", " NaN\n", + " 0.048000\n", " NaN\n", " NaN\n", " NaN\n", @@ -1656,8 +1063,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.044333\n", " NaN\n", + " 0.044333\n", " NaN\n", " NaN\n", " NaN\n", @@ -1680,8 +1087,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.040667\n", " NaN\n", + " 0.040667\n", " NaN\n", " NaN\n", " NaN\n", @@ -1704,8 +1111,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.038333\n", " NaN\n", + " 0.038333\n", " NaN\n", " NaN\n", " NaN\n", @@ -1728,8 +1135,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.037000\n", " NaN\n", + " 0.037000\n", " NaN\n", " NaN\n", " NaN\n", @@ -1772,184 +1179,184 @@ " ...\n", " \n", " \n", - " 2022Q3\n", - " 0.035135\n", - " 0.050333\n", - " 0.054333\n", - " 0.051333\n", - " 0.078221\n", - " 0.109695\n", - " 0.119954\n", - " 0.023333\n", - " 0.046000\n", - " 0.055667\n", + " 2023Q2\n", + " 0.036150\n", + " 0.050000\n", + " 0.056333\n", + " 0.044000\n", + " 0.052667\n", + " 0.083022\n", + " 0.099753\n", + " 0.095574\n", + " 0.061333\n", + " 0.026000\n", " ...\n", - " 0.062000\n", - " 0.060333\n", - " 0.040000\n", - " 0.027667\n", - " 0.127667\n", - " 0.070667\n", - " 0.042026\n", - " 0.100000\n", - " 0.036\n", + " 0.064333\n", + " 0.058333\n", + " 0.037000\n", + " 0.026000\n", + " 0.120000\n", + " 0.074667\n", + " 0.039052\n", + " 0.097000\n", + " 0.042\n", " 0.035667\n", " \n", " \n", - " 2022Q4\n", - " 0.034544\n", - " 0.050000\n", - " 0.056667\n", - " 0.051000\n", - " 0.084019\n", - " 0.110022\n", - " 0.117134\n", - " 0.024000\n", - " 0.047667\n", + " 2023Q3\n", + " 0.036631\n", + " 0.054333\n", " 0.053667\n", - " ...\n", - " 0.064333\n", - " 0.060000\n", - " 0.036000\n", - " 0.029000\n", - " 0.129333\n", - " 0.074667\n", - " 0.042514\n", - " 0.102333\n", - " 0.037\n", - " 0.036000\n", + " 0.045333\n", + " 0.055333\n", + " 0.086772\n", + " 0.096295\n", + " 0.081069\n", + " 0.060667\n", + " 0.027000\n", + " ...\n", + " 0.062667\n", + " 0.058667\n", + " 0.037667\n", + " 0.026333\n", + " 0.120000\n", + " 0.078000\n", + " 0.041078\n", + " 0.091333\n", + " 0.041\n", + " 0.037000\n", " \n", " \n", - " 2023Q1\n", - " 0.035771\n", - " 0.048333\n", - " 0.056000\n", - " 0.050000\n", - " 0.087286\n", - " 0.106300\n", - " 0.105677\n", - " 0.025000\n", - " 0.047667\n", - " 0.054667\n", - " ...\n", - " 0.069000\n", - " 0.060000\n", - " 0.035667\n", + " 2023Q4\n", + " 0.038831\n", + " 0.051667\n", + " 0.055333\n", + " 0.044333\n", + " 0.057667\n", + " 0.090260\n", + " 0.104768\n", + " 0.072802\n", + " 0.057000\n", " 0.027333\n", - " 0.127667\n", - " 0.074000\n", - " 0.041123\n", - " 0.099667\n", - " 0.039\n", + " ...\n", + " 0.065333\n", + " 0.055333\n", " 0.035000\n", + " 0.028333\n", + " 0.119000\n", + " 0.080000\n", + " 0.040571\n", + " 0.087667\n", + " 0.038\n", + " 0.037333\n", " \n", " \n", - " 2023Q2\n", - " 0.035814\n", - " 0.049667\n", - " 0.056000\n", - " 0.052000\n", - " 0.082661\n", - " 0.099333\n", - " 0.095389\n", - " 0.026667\n", - " 0.047667\n", - " 0.066667\n", - " ...\n", - " 0.064000\n", - " 0.059000\n", - " 0.037667\n", - " 0.025667\n", - " 0.119667\n", - " 0.074667\n", - " 0.039262\n", - " 0.096333\n", - " 0.042\n", - " 0.035667\n", + " 2024Q1\n", + " 0.038960\n", + " 0.049000\n", + " 0.057000\n", + " 0.045333\n", + " 0.058667\n", + " 0.085982\n", + " 0.107621\n", + " 0.078269\n", + " 0.053000\n", + " 0.028667\n", + " ...\n", + " 0.065667\n", + " 0.055333\n", + " 0.033333\n", + " 0.028000\n", + " 0.118000\n", + " 0.083000\n", + " 0.040504\n", + " 0.088000\n", + " 0.043\n", + " 0.038000\n", " \n", " \n", - " 2023Q3\n", - " 0.036645\n", - " 0.055333\n", - " 0.053333\n", - " 0.055000\n", - " 0.086774\n", - " 0.095356\n", - " 0.080978\n", - " 0.026333\n", - " 0.048333\n", - " 0.071000\n", + " 2024Q2\n", + " 0.040628\n", + " 0.051000\n", + " 0.055667\n", + " 0.040667\n", + " 0.062333\n", + " 0.081366\n", + " 0.102048\n", + " 0.085235\n", + " 0.050667\n", + " 0.027333\n", " ...\n", " 0.064000\n", - " 0.058000\n", - " 0.038333\n", - " 0.026000\n", - " 0.119667\n", - " 0.078000\n", - " 0.041242\n", - " 0.091333\n", + " 0.053667\n", + " 0.035000\n", + " 0.028000\n", + " 0.115667\n", + " 0.083000\n", " NaN\n", - " 0.037000\n", + " 0.088000\n", + " 0.042\n", + " 0.040000\n", " \n", " \n", "\n", - "

275 rows × 42 columns

\n", + "

278 rows × 44 columns

\n", "" ], "text/plain": [ - " Australia Austria Belgium Canada Chile Colombia \\\n", - "1955Q1 NaN NaN NaN 0.048000 NaN NaN \n", - "1955Q2 NaN NaN NaN 0.044333 NaN NaN \n", - "1955Q3 NaN NaN NaN 0.040667 NaN NaN \n", - "1955Q4 NaN NaN NaN 0.038333 NaN NaN \n", - "1956Q1 NaN NaN NaN 0.037000 NaN NaN \n", - "... ... ... ... ... ... ... \n", - "2022Q3 0.035135 0.050333 0.054333 0.051333 0.078221 0.109695 \n", - "2022Q4 0.034544 0.050000 0.056667 0.051000 0.084019 0.110022 \n", - "2023Q1 0.035771 0.048333 0.056000 0.050000 0.087286 0.106300 \n", - "2023Q2 0.035814 0.049667 0.056000 0.052000 0.082661 0.099333 \n", - "2023Q3 0.036645 0.055333 0.053333 0.055000 0.086774 0.095356 \n", + " Australia Austria Belgium Bulgaria Canada Chile Colombia \\\n", + "1955Q1 NaN NaN NaN NaN 0.048000 NaN NaN \n", + "1955Q2 NaN NaN NaN NaN 0.044333 NaN NaN \n", + "1955Q3 NaN NaN NaN NaN 0.040667 NaN NaN \n", + "1955Q4 NaN NaN NaN NaN 0.038333 NaN NaN \n", + "1956Q1 NaN NaN NaN NaN 0.037000 NaN NaN \n", + "... ... ... ... ... ... ... ... \n", + "2023Q2 0.036150 0.050000 0.056333 0.044000 0.052667 0.083022 0.099753 \n", + "2023Q3 0.036631 0.054333 0.053667 0.045333 0.055333 0.086772 0.096295 \n", + "2023Q4 0.038831 0.051667 0.055333 0.044333 0.057667 0.090260 0.104768 \n", + "2024Q1 0.038960 0.049000 0.057000 0.045333 0.058667 0.085982 0.107621 \n", + "2024Q2 0.040628 0.051000 0.055667 0.040667 0.062333 0.081366 0.102048 \n", "\n", - " Costa Rica Czech Republic Denmark Estonia ... Portugal \\\n", - "1955Q1 NaN NaN NaN NaN ... NaN \n", - "1955Q2 NaN NaN NaN NaN ... NaN \n", - "1955Q3 NaN NaN NaN NaN ... NaN \n", - "1955Q4 NaN NaN NaN NaN ... NaN \n", - "1956Q1 NaN NaN NaN NaN ... NaN \n", - "... ... ... ... ... ... ... \n", - "2022Q3 0.119954 0.023333 0.046000 0.055667 ... 0.062000 \n", - "2022Q4 0.117134 0.024000 0.047667 0.053667 ... 0.064333 \n", - "2023Q1 0.105677 0.025000 0.047667 0.054667 ... 0.069000 \n", - "2023Q2 0.095389 0.026667 0.047667 0.066667 ... 0.064000 \n", - "2023Q3 0.080978 0.026333 0.048333 0.071000 ... 0.064000 \n", + " Costa Rica Croatia Czech Republic ... Portugal Slovakia \\\n", + "1955Q1 NaN NaN NaN ... NaN NaN \n", + "1955Q2 NaN NaN NaN ... NaN NaN \n", + "1955Q3 NaN NaN NaN ... NaN NaN \n", + "1955Q4 NaN NaN NaN ... NaN NaN \n", + "1956Q1 NaN NaN NaN ... NaN NaN \n", + "... ... ... ... ... ... ... \n", + "2023Q2 0.095574 0.061333 0.026000 ... 0.064333 0.058333 \n", + "2023Q3 0.081069 0.060667 0.027000 ... 0.062667 0.058667 \n", + "2023Q4 0.072802 0.057000 0.027333 ... 0.065333 0.055333 \n", + "2024Q1 0.078269 0.053000 0.028667 ... 0.065667 0.055333 \n", + "2024Q2 0.085235 0.050667 0.027333 ... 0.064000 0.053667 \n", "\n", - " Slovakia Slovenia South Korea Spain Sweden Switzerland \\\n", - "1955Q1 NaN NaN NaN NaN NaN NaN \n", - "1955Q2 NaN NaN NaN NaN NaN NaN \n", - "1955Q3 NaN NaN NaN NaN NaN NaN \n", - "1955Q4 NaN NaN NaN NaN NaN NaN \n", - "1956Q1 NaN NaN NaN NaN NaN NaN \n", - "... ... ... ... ... ... ... \n", - "2022Q3 0.060333 0.040000 0.027667 0.127667 0.070667 0.042026 \n", - "2022Q4 0.060000 0.036000 0.029000 0.129333 0.074667 0.042514 \n", - "2023Q1 0.060000 0.035667 0.027333 0.127667 0.074000 0.041123 \n", - "2023Q2 0.059000 0.037667 0.025667 0.119667 0.074667 0.039262 \n", - "2023Q3 0.058000 0.038333 0.026000 0.119667 0.078000 0.041242 \n", + " Slovenia South Korea Spain Sweden Switzerland Turkey \\\n", + "1955Q1 NaN NaN NaN NaN NaN NaN \n", + "1955Q2 NaN NaN NaN NaN NaN NaN \n", + "1955Q3 NaN NaN NaN NaN NaN NaN \n", + "1955Q4 NaN NaN NaN NaN NaN NaN \n", + "1956Q1 NaN NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "2023Q2 0.037000 0.026000 0.120000 0.074667 0.039052 0.097000 \n", + "2023Q3 0.037667 0.026333 0.120000 0.078000 0.041078 0.091333 \n", + "2023Q4 0.035000 0.028333 0.119000 0.080000 0.040571 0.087667 \n", + "2024Q1 0.033333 0.028000 0.118000 0.083000 0.040504 0.088000 \n", + "2024Q2 0.035000 0.028000 0.115667 0.083000 NaN 0.088000 \n", "\n", - " Turkey United Kingdom United States \n", - "1955Q1 NaN NaN 0.047333 \n", - "1955Q2 NaN NaN 0.044000 \n", - "1955Q3 NaN NaN 0.041000 \n", - "1955Q4 NaN NaN 0.042333 \n", - "1956Q1 NaN NaN 0.040333 \n", - "... ... ... ... \n", - "2022Q3 0.100000 0.036 0.035667 \n", - "2022Q4 0.102333 0.037 0.036000 \n", - "2023Q1 0.099667 0.039 0.035000 \n", - "2023Q2 0.096333 0.042 0.035667 \n", - "2023Q3 0.091333 NaN 0.037000 \n", + " United Kingdom United States \n", + "1955Q1 NaN 0.047333 \n", + "1955Q2 NaN 0.044000 \n", + "1955Q3 NaN 0.041000 \n", + "1955Q4 NaN 0.042333 \n", + "1956Q1 NaN 0.040333 \n", + "... ... ... \n", + "2023Q2 0.042 0.035667 \n", + "2023Q3 0.041 0.037000 \n", + "2023Q4 0.038 0.037333 \n", + "2024Q1 0.043 0.038000 \n", + "2024Q2 0.042 0.040000 \n", "\n", - "[275 rows x 42 columns]" + "[278 rows x 44 columns]" ] }, "execution_count": 6, diff --git a/financetoolkit/economics/economics_controller.py b/financetoolkit/economics/economics_controller.py index 4636a4b..9682f89 100644 --- a/financetoolkit/economics/economics_controller.py +++ b/financetoolkit/economics/economics_controller.py @@ -90,8 +90,6 @@ def __init__( @handle_errors def get_gross_domestic_product( self, - inflation_adjusted: bool = False, - per_capita: bool = False, rounding: int | None = None, ): """ @@ -107,8 +105,6 @@ def get_gross_domestic_product( See definition: https://data.oecd.org/gdp/gross-domestic-product-gdp.htm Args: - inflation_adjusted (bool, optional): Whether to return the inflation adjusted data or the nominal data. - per_capita (bool, optional): Whether to return the per capita data or the total data. rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. Returns: @@ -138,14 +134,7 @@ def get_gross_domestic_product( | 2020 | 897261 | 3.94717e+06 | 2.35091e+07 | | 2021 | 921282 | 4.07756e+06 | 2.55147e+07 | """ - if inflation_adjusted: - gross_domestic_product = ( - oecd_model.get_gross_domestic_product_forecast_long_term() - ) - else: - gross_domestic_product = oecd_model.get_annual_gross_domestic_product( - per_capita=per_capita - ) + gross_domestic_product = oecd_model.get_annual_gross_domestic_product() gross_domestic_product = gross_domestic_product.loc[ self._start_date : self._end_date @@ -204,7 +193,7 @@ def get_gross_domestic_product_growth( ) else: growth_gdp = calculate_growth( - oecd_model.get_annual_gross_domestic_product(per_capita=False), + oecd_model.get_annual_gross_domestic_product(), axis="rows", ) @@ -214,9 +203,7 @@ def get_gross_domestic_product_growth( def get_gross_domestic_product_forecast( self, - quarterly: bool = False, - inflation_adjusted: bool = True, - long_term: bool = False, + quarterly: bool | None = None, rounding: int | None = None, ): """ @@ -232,8 +219,6 @@ def get_gross_domestic_product_forecast( Args: quarterly (bool, optional): Whether to return the quarterly data or the annual data. - inflation_adjusted (bool, optional): Whether to return the inflation adjusted data or the nominal data. - long_term (bool, optional): Whether to return the long term forecast or the short term forecast. rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. Returns: @@ -246,7 +231,7 @@ def get_gross_domestic_product_forecast( economics = Economics(start_date='2021-01-01') - gdp_growth_forecast = economics.get_gross_domestic_product_forecast(long_term=False) + gdp_growth_forecast = economics.get_gross_domestic_product_forecast() gdp_growth_forecast.loc[:, ['Indonesia', 'China', 'India']] ``` @@ -263,21 +248,9 @@ def get_gross_domestic_product_forecast( """ quarterly = quarterly if quarterly is not None else self._quarterly - if long_term: - if quarterly: - print("Only annual long term forecasts are available.") - if not inflation_adjusted: - print("Only inflation adjusted long term forecasts are available.") - - forecast_gdp = oecd_model.get_gross_domestic_product_forecast_long_term() - - forecast_gdp_growth = calculate_growth(forecast_gdp, axis="rows") - else: - forecast_gdp_growth = ( - oecd_model.get_gross_domestic_product_forecast_short_term( - quarterly=quarterly, inflation_adjusted=inflation_adjusted - ) - ) + forecast_gdp_growth = oecd_model.get_gross_domestic_product_forecast_short_term( + quarterly=quarterly + ) forecast_gdp_growth = forecast_gdp_growth.loc[self._start_date :] @@ -471,191 +444,6 @@ def get_composite_leading_indicator( rounding if rounding else self._rounding ) - def get_consumer_price_index( - self, - period: str | None = None, - inflation_type: str = "total", - forecast: bool = False, - growth: bool = False, - lag: int = 1, - rounding: int | None = None, - ): - """ - Inflation measured by consumer price index (CPI) is defined as the change in the - prices of a basket of goods and services that are typically purchased by specific - groups of households. Inflation is measured in terms an index, 2015 base year - with a breakdown for food, energy and total. - - Inflation measures the erosion of living standards. A consumer price index is - estimated as a series of summary measures of the period-to-period proportional - change in the prices of a fixed set of consumer goods and services of constant - quantity and characteristics, acquired, used or paid for by the reference - population. - - Each summary measure is constructed as a weighted average of a large number of - elementary aggregate indices. Each of the elementary aggregate indices is estimated - using a sample of prices for a defined set of goods and services obtained in, or - by residents of, a specific region from a given set of outlets or other sources of - consumption goods and services. - - This indicator uses index 2015 = 100. - - See definition: https://data.oecd.org/price/inflation-cpi.htm - - Args: - growth (bool, optional): Whether to return the growth data or the actual data. - lag (int, optional): The number of periods to lag the data by. - rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. - - Returns: - pd.DataFrame: A DataFrame containing the Consumer Price Index. - - As an example: - - ```python - from financetoolkit import Economics - - economics = Economics(start_date='2015-01-01', end_date='2023-12-31') - - consumer_price_index = economics.get_consumer_price_index() - - consumer_price_index.loc[:, ['United States', 'South Africa', 'China']] - ``` - - Which returns: - - | | United States | South Africa | China | - |:-----|----------------:|---------------:|--------:| - | 2015 | 100 | 100 | 100 | - | 2016 | 101.262 | 106.571 | 102 | - | 2017 | 103.419 | 112.096 | 103.625 | - | 2018 | 105.945 | 117.16 | 105.775 | - | 2019 | 107.865 | 121.987 | 108.842 | - | 2020 | 109.195 | 125.903 | 111.475 | - | 2021 | 114.325 | 131.709 | 112.569 | - | 2022 | 123.474 | 140.981 | 114.79 | - """ - period = ( - period - if period is not None - else "quarterly" - if self._quarterly - else "yearly" - ) - - if forecast: - consumer_price_index = oecd_model.get_consumer_price_index_forecast( - period=period - ) - - consumer_price_index = consumer_price_index.loc[self._start_date :] - - return consumer_price_index.round(rounding if rounding else self._rounding) - - consumer_price_index = oecd_model.get_consumer_price_index( - period=period, inflation_type=inflation_type - ) - - if growth: - consumer_price_index = calculate_growth( - consumer_price_index, - lag=lag, - rounding=rounding if rounding else self._rounding, - axis="rows", - ) - - consumer_price_index = consumer_price_index.loc[ - self._start_date : self._end_date - ] - - return consumer_price_index.round(rounding if rounding else self._rounding) - - def get_producer_price_index( - self, - period: str | None = None, - growth: bool = False, - lag: int = 1, - rounding: int | None = None, - ): - """ - Producer price indices in manufacturing measure the rate of change in prices - of products sold as they leave the producer. They exclude any taxes, transport - and trade margins that the purchaser may have to pay. PPIs provide measures of - average movements of prices received by the producers of various commodities. - hey are often seen as advanced indicators of price changes throughout the economy, - including changes in the prices of consumer goods and services. - - Manufacturing covers the production of semi-processed goods and other intermediate - goods as well as final products such as consumer goods and capital equipment. - A variety of price indices may be used to measure inflation in an economy. These - include consumer price indices (CPI), price indices relating to specific goods and/or - services, GDP deflators and producer price indices (PPI). - - This indicator is presented for total industry and uses index 2015 = 100. - - See definition: https://data.oecd.org/price/producer-price-indices-ppi.htm - - Args: - growth (bool, optional): Whether to return the growth data or the actual data. - lag (int, optional): The number of periods to lag the data by. - rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. - - Returns: - pd.DataFrame: A DataFrame containing the Consumer Price Index. - - As an example: - - ```python - from financetoolkit import Economics - - economics = Economics(start_date='2020-01-01', end_date='2023-12-31') - - producer_price_index = economics.get_producer_price_index(period='quarterly') - - producer_price_index.loc[:, ['Germany', 'Poland', 'Belgium']] - ``` - - Which returns: - - | | Germany | Poland | Belgium | - |:-------|----------:|---------:|----------:| - | 2020Q1 | 103.9 | 105.193 | 110.167 | - | 2020Q2 | 102.9 | 103.926 | 105.1 | - | 2020Q3 | 102.833 | 104.026 | 107.9 | - | 2020Q4 | 102.967 | 104.693 | 108.233 | - | 2021Q1 | 104.567 | 107.26 | 112.9 | - | 2021Q2 | 106.9 | 110.594 | 118.233 | - | 2021Q3 | 109.567 | 114.362 | 123.033 | - | 2021Q4 | 111.933 | 118.663 | 130.3 | - | 2022Q1 | 117 | 124.298 | 138.433 | - | 2022Q2 | 123 | 135 | 148.533 | - | 2022Q3 | 125.2 | 137.534 | 150.167 | - | 2022Q4 | 125.8 | 138.601 | 149.933 | - """ - period = ( - period - if period is not None - else "quarterly" - if self._quarterly - else "yearly" - ) - - producer_price_index = oecd_model.get_producer_price_index(period=period) - - if growth: - producer_price_index = calculate_growth( - producer_price_index, - lag=lag, - rounding=rounding if rounding else self._rounding, - axis="rows", - ) - - producer_price_index = producer_price_index.loc[ - self._start_date : self._end_date - ] - - return producer_price_index.round(rounding if rounding else self._rounding) - def get_house_prices( self, quarterly: bool | None = None, @@ -885,7 +673,6 @@ def get_share_prices( def get_long_term_interest_rate( self, period: str | None = None, - forecast: bool = False, growth: bool = False, lag: int = 1, rounding: int | None = None, @@ -947,7 +734,7 @@ def get_long_term_interest_rate( ) long_term_interest_rate = oecd_model.get_long_term_interest_rate( - period=period, forecast=forecast + period=period, ) if growth: @@ -959,7 +746,7 @@ def get_long_term_interest_rate( ) long_term_interest_rate = long_term_interest_rate.loc[ - self._start_date : None if forecast else self._end_date + self._start_date : self._end_date ] return long_term_interest_rate.round(rounding if rounding else self._rounding) @@ -967,7 +754,6 @@ def get_long_term_interest_rate( def get_short_term_interest_rate( self, period: str | None = None, - forecast: bool = False, growth: bool = False, lag: int = 1, rounding: int | None = None, @@ -1029,7 +815,7 @@ def get_short_term_interest_rate( ) short_term_interest_rate = oecd_model.get_short_term_interest_rate( - period=period, forecast=forecast + period=period, ) if growth: @@ -1041,167 +827,15 @@ def get_short_term_interest_rate( ) short_term_interest_rate = short_term_interest_rate.loc[ - self._start_date : None if forecast else self._end_date - ] - - return short_term_interest_rate.round(rounding if rounding else self._rounding) - - def get_narrow_and_broad_money( - self, - period: str | None = None, - growth: bool = False, - lag: int = 1, - rounding: int | None = None, - ): - """ - M1 includes currency i.e. banknotes and coins, plus overnight deposits. - M1 is expressed as a seasonally adjusted index based on 2015=100. - - Broad money (M3) includes currency, deposits with an agreed maturity of up - to two years, deposits redeemable at notice of up to three months and - repurchase agreements, money market fund shares/units and debt securities up - to two years. M3 is measured as a seasonally adjusted index based on 2015=100. - - See definition: https://data.oecd.org/money/broad-money-m3.htm - - Args: - growth (bool, optional): Whether to return the growth data or the actual data. - lag (int, optional): The number of periods to lag the data by. - rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. - - Returns: - pd.DataFrame: A DataFrame containing the Narrow and Broad Money. - - As an example: - - ```python - from financetoolkit import Economics - - economics = Economics(start_date='2012-01-01', end_date='2020-01-01') - - narrow_and_broad_money = economics.get_narrow_and_broad_money(growth=True) - - narrow_and_broad_money.loc[:, 'Australia'] - ``` - - Which returns: - - | | Narrow Money (M1) | Broad Money (M3) | - |:-----|--------------------:|-------------------:| - | 2012 | 0.0462 | 0.0786 | - | 2013 | 0.1402 | 0.0667 | - | 2014 | 0.1279 | 0.0722 | - | 2015 | 0.1466 | 0.068 | - | 2016 | 0.0983 | 0.0594 | - | 2017 | 0.0941 | 0.0684 | - | 2018 | 0.0565 | 0.027 | - | 2019 | 0.1066 | 0.0301 | - | 2020 | 0.294 | 0.0835 | - """ - period = ( - period - if period is not None - else "quarterly" - if self._quarterly - else "yearly" - ) - - narrow_and_broad_money = {} - - narrow_and_broad_money["Narrow Money (M1)"] = oecd_model.get_narrow_money( - period=period - ) - narrow_and_broad_money["Broad Money (M3)"] = oecd_model.get_broad_money( - period=period - ) - - narrow_and_broad_money_df = pd.concat(narrow_and_broad_money, axis=0).unstack( - level=0 - ) - - if growth: - narrow_and_broad_money_df = calculate_growth( - narrow_and_broad_money_df, - lag=lag, - rounding=rounding if rounding else self._rounding, - axis="rows", - ) - - narrow_and_broad_money_df = narrow_and_broad_money_df.loc[ self._start_date : self._end_date ] - return narrow_and_broad_money_df.round(rounding if rounding else self._rounding) - - def get_purchasing_power_parity( - self, - growth: bool = False, - lag: int = 1, - rounding: int | None = None, - ): - """ - Purchasing power parities (PPPs) are the rates of currency conversion that - try to equalise the purchasing power of different currencies, by eliminating - the differences in price levels between countries. The basket of goods and services - priced is a sample of all those that are part of final expenditures: final consumption - of households and government, fixed capital formation, and net exports. - - This indicator is measured in terms of national currency per US dollar. - - See definition: https://data.oecd.org/conversion/purchasing-power-parities-ppp.htm - - Args: - growth (bool, optional): Whether to return the growth data or the actual data. - lag (int, optional): The number of periods to lag the data by. - rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. - - Returns: - pd.DataFrame: A DataFrame containing the Purchasing Power Parity. - - As an example: - - ```python - from financetoolkit import Economics - - economics = Economics(start_date='2015-01-01', end_date='2023-01-01') - - purchasing_power_parity = economics.get_purchasing_power_parity() - - purchasing_power_parity.loc[:, ['China', 'Chile', 'France']] - ``` - - Which returns: - - | | China | Chile | France | - |:-----|--------:|--------:|---------:| - | 2015 | 3.8707 | 391.179 | 0.8087 | - | 2016 | 3.9888 | 397.251 | 0.78 | - | 2017 | 4.1838 | 397.689 | 0.7701 | - | 2018 | 4.2292 | 396.229 | 0.7562 | - | 2019 | 4.2083 | 401.613 | 0.7163 | - | 2020 | 4.1787 | 418.446 | 0.7104 | - | 2021 | 4.1873 | 435.156 | 0.7187 | - | 2022 | 4.0219 | 443.416 | 0.701 | - """ - purchasing_power_parity = oecd_model.get_purchasing_power_parity() - - if growth: - purchasing_power_parity = calculate_growth( - purchasing_power_parity, - lag=lag, - rounding=rounding if rounding else self._rounding, - axis="rows", - ) - - purchasing_power_parity = purchasing_power_parity.loc[ - self._start_date : self._end_date - ] - - return purchasing_power_parity.round(rounding if rounding else self._rounding) + return short_term_interest_rate.round(rounding if rounding else self._rounding) def get_exchange_rates( self, growth: bool = False, + period: str | None = None, lag: int = 1, rounding: int | None = None, ): @@ -1247,7 +881,15 @@ def get_exchange_rates( | 2021 | 0.8455 | 109.754 | 14308.1 | | 2022 | 0.9496 | 131.498 | 14849.9 | """ - exchange_rates = oecd_model.get_exchange_rates() + period = ( + period + if period is not None + else "quarterly" + if self._quarterly + else "yearly" + ) + + exchange_rates = oecd_model.get_exchange_rates(period=period) if growth: exchange_rates = calculate_growth( @@ -1335,28 +977,21 @@ def get_renewable_energy( return renewable_energy.round(rounding if rounding else self._rounding) - def get_environmental_tax( + def get_carbon_footprint( self, growth: bool = False, lag: int = 1, rounding: int | None = None, ): """ - Environmentally related taxes are an important instrument for governments to shape - relative prices of goods and services. - - The characteristics of such taxes included in the database (e.g. revenue, tax base, tax - rates, exemptions, etc.) are used to construct the environmentally related tax revenues with a breakdown - by environmental domain: + The carbon footprint is a measure of the total amount of greenhouse gases produced + to directly and indirectly support human activities, usually expressed in equivalent + tons of carbon dioxide (CO2). - - Energy products (including vehicle fuels); - - Motor vehicles and transport services; - - Measured or estimated emissions to air and water, ozone depleting substances, certain non-point - sources of water pollution, waste management and noise, as well as management of water, land, soil, - forests, biodiversity, wildlife and fish stocks. - - The data have been cross-validated and complemented with Revenue statistics from the OECD Tax statistics - database and official national sources. + The carbon footprint is a subset of the ecological footprint and of the more comprehensive + Life Cycle Assessment (LCA). An individual, nation, or organization's carbon footprint can + be measured by undertaking a GHG emissions assessment or other calculative activities + denoted as carbon accounting. See definition: https://data.oecd.org/envpolicy/environmental-tax.htm @@ -1396,543 +1031,19 @@ def get_environmental_tax( | 2019 | 3.42 | 1.94 | 1.04 | 0.25 | 0.19 | | 2020 | 3.21 | 1.8 | 0.96 | 0.26 | 0.2 | """ - environmental_tax = {} - - environmental_tax["Total"] = oecd_model.get_environmental_tax(parameter="TOT") - environmental_tax["Energy"] = oecd_model.get_environmental_tax(parameter="ENRG") - environmental_tax["Transport"] = oecd_model.get_environmental_tax( - parameter="TRANSPORT" - ) - environmental_tax["Resource"] = oecd_model.get_environmental_tax( - parameter="RES" - ) - environmental_tax["Pollution"] = oecd_model.get_environmental_tax( - parameter="POL" - ) - - environmental_tax_df = pd.concat(environmental_tax, axis=0).unstack(level=0) + carbon_footprint_df = oecd_model.get_carbon_footprint() if growth: - environmental_tax_df = calculate_growth( - environmental_tax_df, + carbon_footprint_df = calculate_growth( + carbon_footprint_df, lag=lag, rounding=rounding if rounding else self._rounding, axis="rows", ) - environmental_tax_df = environmental_tax_df.loc[ - self._start_date : self._end_date - ] - - return environmental_tax_df.round(rounding if rounding else self._rounding) - - def get_greenhouse_emissions( - self, - growth: bool = False, - lag: int = 1, - rounding: int | None = None, - ): - """ - Greenhouse gases refer to the sum of seven gases that have direct effects on climate change: - - - Carbon Dioxide (CO2) - - Methane (CH4) - - Nitrous Oxide (N2O) - - Chlorofluorocarbons (CFCs) - - Hydrofluorocarbons (HFCs) - - Perfluorocarbons (PFCs) - - Sulphur Hexafluoride (SF6) - - Nitrogen Trifluoride (NF3). - - The data are expressed in CO2 equivalents and refer to gross direct emissions from human - activities. CO2 refers to gross direct emissions from fuel combustion only and data are - provided by the International Energy Agency. Other air emissions include emissions of - sulphur oxides (SOx) and nitrogen oxides (NOx) given as quantities of SO2 and NO2, emissions of - carbon monoxide (CO), and emissions of volatile organic compounds (VOC), excluding methane. - - Air and greenhouse gas emissions are measured in tonnes per capita and kilogram per capita - in which all metrics are converted to tonnes (1000kg) per capita. - - See definition: https://data.oecd.org/air/air-and-ghg-emissions.htm - - Args: - growth (bool, optional): Whether to return the growth data or the actual data. - lag (int, optional): The number of periods to lag the data by. - rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. - - Returns: - pd.DataFrame: A DataFrame containing the Environmental Tax. - - As an example: - - ```python - from financetoolkit import Economics - - economics = Economics(start_date="2010-01-01", end_date="2020-01-01") - - greenhouse_emissions = economics.get_greenhouse_emissions() - - greenhouse_emissions.loc[:, 'United States'] - ``` - - Which returns: - - | | Carbon Dioxide (CO2) | Carbon Monoxide (CO) | Greenhouse Gases (GHG) | Nitrogen Oxides (NOX) | Sulphur Oxides (SOX) | Volatile Organic Compounds (VOC) | - |:-----|-----------------------:|-----------------------:|-------------------------:|------------------------:|-----------------------:|-----------------------------------:| - | 2010 | 17.28 | 0.1719 | 22.818 | 0.0449 | 0.0203 | 0.0388 | - | 2011 | 16.44 | 0.1658 | 22.168 | 0.0423 | 0.0186 | 0.0389 | - | 2012 | 15.6 | 0.1538 | 21.252 | 0.0395 | 0.0147 | 0.0382 | - | 2013 | 15.93 | 0.1504 | 21.647 | 0.0368 | 0.0139 | 0.0364 | - | 2014 | 15.84 | 0.1443 | 21.667 | 0.0345 | 0.013 | 0.036 | - | 2015 | 15.36 | 0.1345 | 21.006 | 0.031 | 0.0097 | 0.0343 | - | 2016 | 14.97 | 0.1259 | 20.362 | 0.0278 | 0.0074 | 0.0332 | - | 2017 | 14.64 | 0.133 | 20.183 | 0.026 | 0.0067 | 0.0348 | - | 2018 | 15.02 | 0.133 | 20.667 | 0.0248 | 0.0064 | 0.0351 | - | 2019 | 14.44 | 0.1264 | 20.156 | 0.0236 | 0.0054 | 0.0332 | - | 2020 | 12.9 | 0.1172 | 18.178 | 0.0207 | 0.0047 | 0.0329 | - """ - greenhouse_gases = {} - - greenhouse_gases["Carbon Dioxide (CO2)"] = oecd_model.get_greenhouse_emissions( - parameter="CO2" - ) - greenhouse_gases["Carbon Monoxide (CO)"] = oecd_model.get_greenhouse_emissions( - parameter="CO" - ) - greenhouse_gases[ - "Greenhouse Gases (GHG)" - ] = oecd_model.get_greenhouse_emissions(parameter="GHG") - greenhouse_gases["Nitrogen Oxides (NOX)"] = oecd_model.get_greenhouse_emissions( - parameter="NOX" - ) - greenhouse_gases["Sulphur Oxides (SOX)"] = oecd_model.get_greenhouse_emissions( - parameter="SOX" - ) - greenhouse_gases[ - "Volatile Organic Compounds (VOC)" - ] = oecd_model.get_greenhouse_emissions(parameter="VOC") - - greenhouse_gases_df = pd.concat(greenhouse_gases, axis=0).unstack(level=0) - - if growth: - greenhouse_gases_df = calculate_growth( - greenhouse_gases_df, - lag=lag, - rounding=rounding if rounding else self._rounding, - axis="rows", - ) - - greenhouse_gases_df = greenhouse_gases_df.loc[self._start_date : self._end_date] - - return greenhouse_gases_df.round(rounding if rounding else self._rounding) - - def get_crude_oil_production( - self, - growth: bool = False, - lag: int = 1, - rounding: int | None = None, - ): - """ - Crude oil production is defined as the quantities of oil extracted from the ground after - the removal of inert matter or impurities. It includes crude oil, natural gas liquids (NGLs) - and additives. This indicator is measured in thousand tonne of oil equivalent (toe). - - Crude oil is a mineral oil consisting of a mixture of hydrocarbons of natural origin, yellow - to black in colour, and of variable density and viscosity. NGLs are the liquid or liquefied - hydrocarbons produced in the manufacture, purification and stabilisation of natural gas. - - Additives are non-hydrocarbon substances added to or blended with a product to modify its - properties, for example, to improve its combustion characteristics (e.g. MTBE and tetraethyl lead). - Refinery production refers to the output of secondary oil products from an oil refinery. + carbon_footprint_df = carbon_footprint_df.loc[self._start_date : self._end_date] - This indicator is measured in thousand tonne of oil equivalent (toe). - - See definition: https://data.oecd.org/energy/crude-oil-production.htm - - Args: - growth (bool, optional): Whether to return the growth data or the actual data. - lag (int, optional): The number of periods to lag the data by. - rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. - - Returns: - pd.DataFrame: A DataFrame containing the Crude Oil Production. - - As an example: - - ```python - from financetoolkit import Economics - - economics = Economics(start_date='2007-01-01') - - crude_oil_production = economics.get_crude_oil_production() - - crude_oil_production.loc[:, ['China', 'Saudi Arabia', 'Russia', 'Netherlands']] - ``` - - Which returns: - - | | China | Saudi Arabia | Russia | Netherlands | - |:-----|--------:|---------------:|---------:|--------------:| - | 2007 | 186318 | 446438 | 470511 | 2109.1 | - | 2008 | 190440 | 467050 | 471814 | 1765.4 | - | 2009 | 189490 | 414458 | 479089 | 1338.07 | - | 2010 | 203014 | 413505 | 487106 | 1040.27 | - | 2011 | 202876 | 471515 | 494393 | 1102.48 | - | 2012 | 207478 | 495778 | 499908 | 1133.08 | - | 2013 | 209919 | 488039 | 499966 | 1144.3 | - | 2014 | 211429 | 491857 | 505603 | 1555.3 | - | 2015 | 214556 | 516157 | 512777 | 1423.99 | - | 2016 | 199685 | 531161 | 524319 | 975.589 | - | 2017 | 191506 | 504365 | 517105 | 970.892 | - | 2018 | 189324 | 522375 | 525934 | 918.789 | - | 2019 | 191014 | 496688 | 530219 | 761.583 | - | 2020 | 194769 | 467840 | 484621 | 751.952 | - | 2021 | 199264 | 463618 | 495677 | 763.855 | - """ - crude_oil_production = oecd_model.get_crude_oil_production() - - if growth: - crude_oil_production = calculate_growth( - crude_oil_production, - lag=lag, - rounding=rounding if rounding else self._rounding, - axis="rows", - ) - - crude_oil_production = crude_oil_production.loc[ - self._start_date : self._end_date - ] - - return crude_oil_production.round(rounding if rounding else self._rounding) - - def get_crude_oil_prices( - self, - growth: bool = False, - lag: int = 1, - rounding: int | None = None, - ): - """ - Crude oil import prices come from the IEA's Crude Oil Import Register and are influenced - not only by traditional movements of supply and demand, but also by other factors such as - geopolitics. - - Information is collected from national agencies according to the type of crude oil, by - geographic origin and by quality of crude. Average prices are obtained by dividing value - by volume as recorded by customs administrations for each tariff position. - - Values are recorded at the time of import and include cost, insurance and freight, but exclude - import duties. The nominal crude oil spot price from 2003 to 2011 is for Dubai and from 1970 to - 2002 for Arabian Light. This indicator is measured in USD per barrel of oil. - - The real price was calculated using the deflator for GDP at market prices and rebased with - reference year 1970 = 100. - - See definition: https://data.oecd.org/energy/crude-oil-import-prices.htm - - Args: - growth (bool, optional): Whether to return the growth data or the actual data. - lag (int, optional): The number of periods to lag the data by. - rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. - - Returns: - pd.DataFrame: A DataFrame containing the Crude Oil Prices. - - As an example: - - ```python - from financetoolkit import Economics - - economics = Economics(start_date='2015-01-01') - - crude_oil_prices = economics.get_crude_oil_prices() - - crude_oil_prices.loc[:, ['United Kingdom', 'Japan', 'Canada']] - ``` - - Which returns: - - | | United Kingdom | Japan | Canada | - |:-----|-----------------:|--------:|---------:| - | 2015 | 53.81 | 54.2 | 53.48 | - | 2016 | 44.62 | 41.79 | 43.6 | - | 2017 | 54.69 | 54.42 | 54.3 | - | 2018 | 72.65 | 72.85 | 70.88 | - | 2019 | 65.58 | 66.78 | 63.12 | - | 2020 | 44.62 | 46.85 | 45.79 | - | 2021 | 71.2 | 70.25 | 69.44 | - | 2022 | 104.66 | 102.11 | 104.16 | - """ - crude_oil_prices = oecd_model.get_crude_oil_prices() - - if growth: - crude_oil_prices = calculate_growth( - crude_oil_prices, - lag=lag, - rounding=rounding if rounding else self._rounding, - axis="rows", - ) - - crude_oil_prices = crude_oil_prices.loc[self._start_date : self._end_date] - - return crude_oil_prices.round(rounding if rounding else self._rounding) - - def get_government_statistics( - self, - growth: bool = False, - lag: int = 1, - rounding: int | None = None, - ): - """ - This function contains a variety of general government statistics. The following statistics are provided: - - - General government deficit is defined as the balance of income and expenditure of government, - including capital income and capital expenditures. "Net lending" means that government has a - surplus, and is providing financial resources to other sectors, while "net borrowing" means - that government has a deficit, and requires financial resources from other sectors. - - General government revenue is defined as the revenue required to finance the goods - and services they provide to citizens and businesses, and to fulfil their redistributive role. - Comparing levels of government revenues across countries provides an indication of the - importance of the government sector in the economy in terms of available financial resources. - The total amount of revenues collected by governments is determined by past - and current political decisions. - - General government spending is defined as an indicator of the size of government - across countries. The large variation in this indicator highlights the variety of - countries' approaches to delivering public goods and services and providing - social protection, not necessarily differences in resources spent - - General government final consumption can be broken down into two distinct groups. - The first reflects expenditures for collective consumption (defence, justice, etc.) - that benefit the society as a whole, or large parts of society, and are often known - as public goods and services. The second, referred to as "individual", relates to - expenditures for individual consumption (health care, housing, education, etc.), - incurred by government for the benefit of individual households. - - General government debt-to-GDP ratio measures the gross debt of the general - government as a percentage of GDP. It is a key indicator for the sustainability - of government finance. Debt is calculated as the sum of the following liability - categories (as applicable): currency and deposits; debt securities, loans; - insurance, pensions and standardised guarantee schemes, and other accounts payable. - Changes in government debt over time primarily reflect the impact of past government deficits. - - The net financial worth of the general government sector is the total value of its - financial assets minus the total value of its outstanding liabilities. The general - government sector consists of central, state and local governments as well as social - security funds. - - General government production costs are decisions about the amount and type of goods - and services governments produce, as well as on how best to produce them. They are - often political in nature and based on a country's social and cultural context. - Governments use a mix of their own employees, capital, and outside contractors - (non-profit institutions or private sector entities) to produce goods and services. - Government production costs include: compensation costs of general government employees; - goods and services used and financed by general government (including intermediate consumption - and social transfer in kind via market producers paid for by government); and other costs, - including depreciation of capital and other taxes on production less other subsidies on - production. The data include government employment and intermediate consumption for output - produced by the government for its own use, such as roads and other capital investment - projects built by government employees. - - All of these metrics are reported as a percentage of GDP which makes comparison between countries - relatively straight forward. - - See definition: https://data.oecd.org/gga/general-government-deficit.htm - - Args: - growth (bool, optional): Whether to return the growth data or the actual data. - lag (int, optional): The number of periods to lag the data by. - rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. - - Returns: - pd.DataFrame: A DataFrame containing the Government Statistics. - - As an example: - - ```python - from financetoolkit import Economics - - economics = Economics(start_date='2012-01-01') - - goverment_statistics = economics.get_government_statistics() - - goverment_statistics.loc[:, 'Switzerland'] - ``` - - Which returns: - - | | Deficit | Revenue | Spending | Debt | Financial Wealth | Production Costs | - |:-----|----------:|----------:|-----------:|-------:|-------------------:|-------------------:| - | 2012 | 0.0024 | 0.3292 | 0.3269 | 0.4373 | -0.0309 | 0.1559 | - | 2013 | -0.0043 | 0.332 | 0.3363 | 0.4299 | -0.0393 | 0.1579 | - | 2014 | -0.0025 | 0.3302 | 0.3327 | 0.4293 | 0.0158 | 0.1589 | - | 2015 | 0.0055 | 0.3416 | 0.3361 | 0.4287 | -0.0218 | 0.1607 | - | 2016 | 0.0024 | 0.3391 | 0.3367 | 0.4165 | 0.0056 | 0.1612 | - | 2017 | 0.0113 | 0.3484 | 0.3371 | 0.4246 | 0.1066 | 0.1618 | - | 2018 | 0.0129 | 0.3428 | 0.3299 | 0.4028 | 0.0927 | 0.1584 | - | 2019 | 0.0134 | 0.346 | 0.3326 | 0.4007 | 0.1769 | 0.1596 | - | 2020 | -0.0306 | 0.3527 | 0.3834 | 0.4379 | 0.1763 | 0.1687 | - | 2021 | -0.0031 | 0.3545 | 0.3589 | 0.4137 | 0.2029 | 0.1683 | - | 2022 | 0.012 | 0.3417 | nan | 0.3754 | 0.0436 | nan | - """ - government_statistics = {} - - government_statistics["Deficit"] = oecd_model.get_general_government_deficit() - government_statistics["Revenue"] = oecd_model.get_general_government_revenue() - government_statistics["Spending"] = oecd_model.get_general_government_spending( - parameter="TOT" - ) - government_statistics[ - "Spending (Society)" - ] = oecd_model.get_general_government_spending(parameter="SOCIETY") - government_statistics[ - "Spending (Individual)" - ] = oecd_model.get_general_government_spending(parameter="INDIVIDUAL") - government_statistics["Debt"] = oecd_model.get_general_government_debt() - government_statistics[ - "Financial Wealth" - ] = oecd_model.get_general_government_financial_wealth() - government_statistics[ - "Production Costs" - ] = oecd_model.get_general_government_production_costs() - - government_statistics_df = pd.concat(government_statistics, axis=0).unstack( - level=0 - ) - - if growth: - government_statistics_df = calculate_growth( - government_statistics_df, - lag=lag, - rounding=rounding if rounding else self._rounding, - axis="rows", - ) - - government_statistics_df = government_statistics_df.loc[ - self._start_date : self._end_date - ] - - return government_statistics_df.round(rounding if rounding else self._rounding) - - def get_central_government_spending( - self, - growth: bool = False, - lag: int = 1, - rounding: int | None = None, - ): - """ - Central government expenditure is defined as the central government budget expenditure - as reported in the final central government accounts. Data are based on the System of - National accounts (SNA), a set of internationally agreed concepts, definitions, classifications - and rules for national accounting. - - Central government spending by function is the breakdown of expenditures on the basis of the - activities governments support. The classification system used to provide this breakdown on an - internationally comparable basis is known as Classification of Functions of Government (COFOG). - - COFOG expenditures are divided into in the following ten functions: - - - General Public Services; - - Defence; - - Public Order and Safety; - - Economic Affairs; - - Environmental Protection; - - Housing and Community Amenities; - - Health; - - Recreation, Culture and Religion; - - Education; - - Social Protection. - - Data on central government expenditures by function include transfers between the different levels - of government. The general government sector consists of central, state and local governments, - and the social security funds controlled by these units. The political authority of central - government extends over the entire territory of the country; central government has the authority - to impose taxes on all resident and non-resident units engaged in economic activities within the - country. - - The responsibility for the provision of public goods and services and redistribution of income is - divided between different levels of government. Data on the distribution of government spending - by both level and function can provide an indication of the extent to which key government activities - are decentralised to sub-national governments. This indicator of central government spending by - function is measured as a percentage of total expenditures. - - See definition: https://data.oecd.org/gga/central-government-spending.htm - - Args: - growth (bool, optional): Whether to return the growth data or the actual data. - lag (int, optional): The number of periods to lag the data by. - rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. - - Returns: - pd.DataFrame: A DataFrame containing the Central Government Spending. - - As an example: - - ```python - from financetoolkit import Economics - - economics = Economics(start_date='2012-01-01') - - goverment_spending = economics.get_central_government_spending() - - goverment_spending.loc[:, 'OECD - Total'] - ``` - - Which returns: - - | | General Public Services | Defence | Public Order and Safety | Economic Affairs | Environmental Protection | Housing and Community Amenities | Health | Recreation, Culture and Religion | Education | Social Protection | - |:-----|--------------------------:|----------:|--------------------------:|-------------------:|---------------------------:|----------------------------------:|---------:|-----------------------------------:|------------:|--------------------:| - | 2012 | 0.2408 | 0.1005 | 0.0298 | 0.0932 | 0.0054 | 0.0182 | 0.1478 | 0.0085 | 0.0731 | 0.2827 | - | 2013 | 0.2415 | 0.0957 | 0.0302 | 0.0912 | 0.0059 | 0.0159 | 0.1517 | 0.0083 | 0.074 | 0.2856 | - | 2014 | 0.2371 | 0.0924 | 0.0302 | 0.087 | 0.0055 | 0.016 | 0.1615 | 0.0084 | 0.0748 | 0.287 | - | 2015 | 0.2297 | 0.0911 | 0.0306 | 0.0877 | 0.0055 | 0.0152 | 0.1677 | 0.0084 | 0.0748 | 0.2893 | - | 2016 | 0.2263 | 0.0901 | 0.031 | 0.0862 | 0.0056 | 0.014 | 0.1719 | 0.0092 | 0.0735 | 0.2923 | - | 2017 | 0.2236 | 0.0903 | 0.0308 | 0.0892 | 0.0053 | 0.0147 | 0.1726 | 0.0094 | 0.0736 | 0.2906 | - | 2018 | 0.2247 | 0.0922 | 0.0311 | 0.0883 | 0.0051 | 0.0142 | 0.1753 | 0.0093 | 0.074 | 0.286 | - | 2019 | 0.2205 | 0.0945 | 0.0309 | 0.0888 | 0.005 | 0.014 | 0.1774 | 0.0092 | 0.0729 | 0.2866 | - """ - government_spending = {} - - government_spending[ - "General Public Services" - ] = oecd_model.get_central_government_spending(parameter="GRALPUBSER") - government_spending["Defence"] = oecd_model.get_central_government_spending( - parameter="DEF" - ) - government_spending[ - "Public Order and Safety" - ] = oecd_model.get_central_government_spending(parameter="PUBORD") - government_spending[ - "Economic Affairs" - ] = oecd_model.get_central_government_spending(parameter="ECOAFF") - government_spending[ - "Environmental Protection" - ] = oecd_model.get_central_government_spending(parameter="ENVPROT") - government_spending[ - "Housing and Community Amenities" - ] = oecd_model.get_central_government_spending(parameter="HOUCOMM") - government_spending["Health"] = oecd_model.get_central_government_spending( - parameter="HEALTH" - ) - government_spending[ - "Recreation, Culture and Religion" - ] = oecd_model.get_central_government_spending(parameter="RECULTREL") - government_spending["Education"] = oecd_model.get_central_government_spending( - parameter="EDU" - ) - government_spending[ - "Social Protection" - ] = oecd_model.get_central_government_spending(parameter="SOCPROT") - - government_spending_df = pd.concat(government_spending, axis=0).unstack(level=0) - - if growth: - government_spending_df = calculate_growth( - government_spending_df, - lag=lag, - rounding=rounding if rounding else self._rounding, - axis="rows", - ) - - government_spending_df = government_spending_df.loc[ - self._start_date : self._end_date - ] - - return government_spending_df.round(rounding if rounding else self._rounding) + return carbon_footprint_df.round(rounding if rounding else self._rounding) def get_trust_in_government( self, @@ -2174,19 +1285,11 @@ def get_income_inequality( self-employment and capital income and public cash transfers; income taxes and social security contributions paid by households are deducted. The income of the household is attributed to each of its members, with an adjustment to reflect differences in needs for - households of different sizes. Income inequality among individuals is measured here by five indicators. - - - The Gini coefficient is based on the comparison of cumulative proportions of the population against - cumulative proportions of income they receive, and it ranges between 0 in the case of perfect equality - and 1 in the case of perfect inequality. - - P90/P10 is the ratio of the upper bound value of the ninth decile (i.e. the 10% of people - with highest income) to that of the first decile; - - P90/P50 of the upper bound value of the ninth decile to the median income; - - and P50/P10 of median income to the upper bound value of the first decile. - - The Palma ratio is the share of all income received by the 10% people with highest - disposable income divided by the share of all income received by the 40% - people with the lowest disposable income. - - S80/S20 is the ratio of the average income of the 20% richest to the 20% poorest; + households of different sizes. + + The Gini coefficient is based on the comparison of cumulative proportions of the population against + cumulative proportions of income they receive, and it ranges between 0 in the case of perfect equality + and 1 in the case of perfect inequality. See definition: https://data.oecd.org/inequality/income-inequality.htm @@ -2224,42 +1327,19 @@ def get_income_inequality( | 2020 | 0.377 | 5.8 | 2.2 | 2.6 | 1.64 | 7.5 | | 2021 | 0.375 | 5.4 | 2.2 | 2.4 | 1.63 | 7.1 | """ - income_inequality = {} - - income_inequality["Gini Coefficient"] = oecd_model.get_income_inequality( - parameter="GINI" - ) - income_inequality["P90/P10"] = oecd_model.get_income_inequality( - parameter="P90P10" - ) - income_inequality["P90/P50"] = oecd_model.get_income_inequality( - parameter="P90P50" - ) - income_inequality["P50/P10"] = oecd_model.get_income_inequality( - parameter="P50P10" - ) - income_inequality["Palma Ratio"] = oecd_model.get_income_inequality( - parameter="PALMA" - ) - income_inequality["S80/S20"] = oecd_model.get_income_inequality( - parameter="S80S20" - ) - - income_inequality_df = pd.concat(income_inequality, axis=0).unstack(level=0) + income_inequality = oecd_model.get_income_inequality() if growth: - income_inequality_df = calculate_growth( - income_inequality_df, + income_inequality = calculate_growth( + income_inequality, lag=lag, rounding=rounding if rounding else self._rounding, axis="rows", ) - income_inequality_df = income_inequality_df.loc[ - self._start_date : self._end_date - ] + income_inequality = income_inequality.loc[self._start_date : self._end_date] - return income_inequality_df.round(rounding if rounding else self._rounding) + return income_inequality.round(rounding if rounding else self._rounding) def get_population_statistics( self, @@ -2298,13 +1378,6 @@ def get_population_statistics( the total population. - The elderly population is defined as those aged 65 and over as a percentage of the total population. - - The total fertility rate in a specific year is defined as the total number - of children that would be born to each woman if she were to live to the end of - her child-bearing years and give birth to children in alignment with the - prevailing age-specific fertility rates. - - The old-age to working-age demographic ratio is defined as the number of - individuals aged 65 and over per 100 people of working age defined as - those at ages 20 to 64. See definition: https://data.oecd.org/pop/population.htm @@ -2347,35 +1420,8 @@ def get_population_statistics( population_statistics = {} population_statistics["Population"] = oecd_model.get_population() - population_statistics["Men Ratio"] = ( - oecd_model.get_population(gender="men") - / population_statistics["Population"] - ) - population_statistics["Women Ratio"] = ( - oecd_model.get_population(gender="women") - / population_statistics["Population"] - ) - - population_statistics[ - "Young Population" - ] = oecd_model.get_young_population_percentage() - population_statistics[ - "Working Age Population" - ] = oecd_model.get_working_age_population() - population_statistics[ - "Elderly Population" - ] = oecd_model.get_elderly_population_percentage() - - population_statistics["Fertility Rate"] = oecd_model.get_fertility_rate() - - # This is done to align the Old Age Dependency Ratio with the other statistics - latest_year = population_statistics["Population"].index[-1] - population_statistics[ - "Old Age Dependency Ratio" - ] = oecd_model.get_old_age_dependency_ratio() - population_statistics["Old Age Dependency Ratio"] = population_statistics[ - "Old Age Dependency Ratio" - ].loc[:latest_year] + population_statistics["Men"] = oecd_model.get_population(gender="men") + population_statistics["Women"] = oecd_model.get_population(gender="women") population_statistics_df = pd.concat(population_statistics, axis=0).unstack( level=0 @@ -2405,12 +1451,6 @@ def get_poverty_rate( The poverty rate is the ratio of the number of people (in a given age group) whose income falls below the poverty line; taken as half the median household income of the total population. - It is also available by broad age group: - - - child poverty (0 to 17 year-olds); - - working-age poverty (18 to 65 year-olds); - - and elderly poverty (66 year-olds or more). - However, two countries with the same poverty rates may differ in terms of the relative income-level of the poor. See definition: https://data.oecd.org/inequality/poverty-rate.htm @@ -2449,23 +1489,16 @@ def get_poverty_rate( | 2019 | 0.106 | 0.131 | 0.098 | 0.107 | | 2020 | 0.128 | 0.152 | 0.118 | 0.138 | """ - poverty_rate = {} - - poverty_rate["Total"] = oecd_model.get_poverty_rate(parameter="TOT") - poverty_rate["0-17 Year"] = oecd_model.get_poverty_rate(parameter="0_17") - poverty_rate["18-65 Year"] = oecd_model.get_poverty_rate(parameter="18_65") - poverty_rate["66 or More"] = oecd_model.get_poverty_rate(parameter="66MORE") - - poverty_rate_df = pd.concat(poverty_rate, axis=0).unstack(level=0) + poverty_rate = oecd_model.get_poverty_rate() if growth: - poverty_rate_df = calculate_growth( - poverty_rate_df, + poverty_rate = calculate_growth( + poverty_rate, lag=lag, rounding=rounding if rounding else self._rounding, axis="rows", ) - poverty_rate_df = poverty_rate_df.loc[self._start_date : self._end_date] + poverty_rate = poverty_rate.loc[self._start_date : self._end_date] - return poverty_rate_df.round(rounding if rounding else self._rounding) + return poverty_rate.round(rounding if rounding else self._rounding) diff --git a/financetoolkit/economics/oecd_model.py b/financetoolkit/economics/oecd_model.py index e2b57a4..fcad99e 100644 --- a/financetoolkit/economics/oecd_model.py +++ b/financetoolkit/economics/oecd_model.py @@ -5,8 +5,9 @@ # pylint: disable=too-many-lines -BASE_URL = "https://stats.oecd.org/sdmx-json/data/DP_LIVE/" -EXTENSIONS = "/OECD?contentType=csv&detail=code&separator=comma&csv-lang=en" + +BASE_URL = "https://sdmx.oecd.org/public/rest/data/" +EXTENSIONS = "?dimensionAtObservation=AllDimensions&format=csvfilewithlabels" CODE_TO_COUNTRY = { "AGO": "Angola", @@ -199,46 +200,43 @@ def collect_oecd_data(oecd_data_string: str, period_code: str) -> pd.DataFrame: """ oecd_data = pd.read_csv(f"{BASE_URL}{oecd_data_string}{EXTENSIONS}") - oecd_data["LOCATION"] = oecd_data["LOCATION"].replace(CODE_TO_COUNTRY) + oecd_data["REF_AREA"] = oecd_data["REF_AREA"].replace(CODE_TO_COUNTRY) - oecd_data = oecd_data.drop( - columns=["INDICATOR", "SUBJECT", "MEASURE", "FREQUENCY", "Flag Codes"] - ) + oecd_data = oecd_data[["TIME_PERIOD", "REF_AREA", "OBS_VALUE"]] - oecd_data = oecd_data.drop_duplicates(subset=["TIME", "LOCATION"]) + oecd_data = oecd_data.drop_duplicates(subset=["TIME_PERIOD", "REF_AREA"]) - oecd_data = oecd_data.pivot(index="TIME", columns="LOCATION", values="Value") + oecd_data = oecd_data.pivot( + index="TIME_PERIOD", columns="REF_AREA", values="OBS_VALUE" + ) oecd_data.index.name = None oecd_data.columns.name = None oecd_data.index = pd.PeriodIndex(oecd_data.index, freq=period_code) - # Only remove data if all of it is NaN + # Only remove data if all of it is NaN or it is NaT oecd_data = oecd_data.dropna(axis=1, how="all") + oecd_data = oecd_data[~oecd_data.index.isna()] return oecd_data -def get_annual_gross_domestic_product(per_capita: bool = False) -> pd.DataFrame: +def get_annual_gross_domestic_product() -> pd.DataFrame: """ Get the Gross Domestic Product for a variety of countries over time from the OECD. The Gross Domestic Product is the total value of goods produced and services provided in a country during one year. - The data is available in two forms: per capita and total. The per capita - data is the GDP divided by the population of the country. The total data - is the GDP in millions of dollars. - - Args: - per_capita (bool): Whether to return the per capita data or the total data. + The data is displayed as per capita which is the GDP divided by the + population of the country. Returns: pd.DataFrame: A DataFrame containing the Gross Domestic Product for a variety of countries over time. """ oecd_data_string = ( - f".GDP.TOT.{'USD_CAP' if per_capita else 'MLN_USD'}.A{EXTENSIONS}" + "OECD.SDD.NAD,DSD_NAMAIN10@DF_TABLE1_EXPENDITURE_HCPC,/A....B1GQ_POP......." ) gross_domestic_product = collect_oecd_data(oecd_data_string, "Y") @@ -258,13 +256,17 @@ def get_quarterly_gross_domestic_product(year_on_year: bool = True) -> pd.DataFr compared to the previous quarter. Args: - per_capita (bool): Whether to return the per capita data or the total data. + year_on_year (bool): Whether to return the year on year data or the quarter + on quarter data. Returns: pd.DataFrame: A DataFrame containing the Quarterly Gross Domestic Product for a variety of countries over time. """ - oecd_data_string = f".QGDP.TOT.{'PC_CHGPY' if year_on_year else 'PC_CHGPP'}.Q" + if year_on_year: + oecd_data_string = "OECD.SDD.NAD,DSD_NAMAIN1@DF_QNA_EXPENDITURE_GROWTH_OECD,1.0/Q.....B1GQ......GY+G1." + else: + oecd_data_string = "OECD.SDD.NAD,DSD_NAMAIN1@DF_QNA_EXPENDITURE_GROWTH_OECD,1.0/Q.....B1GQ......G1." gross_domestic_product = collect_oecd_data(oecd_data_string, "Q") @@ -275,7 +277,7 @@ def get_quarterly_gross_domestic_product(year_on_year: bool = True) -> pd.DataFr def get_gross_domestic_product_forecast_short_term( - inflation_adjusted: bool = False, quarterly: bool = False + quarterly: bool | None = False, ) -> pd.DataFrame: """ Get the Gross Domestic Product Forecast for a variety of countries over @@ -288,16 +290,13 @@ def get_gross_domestic_product_forecast_short_term( previous year. Args: - per_capita (bool): Whether to return the per capita data or the total data. + quarterly (bool): Whether to return the quarterly data or the yearly data. Returns: pd.DataFrame: A DataFrame containing the Gross Domestic Product for a variety of countries over time. """ - oecd_data_string = ( - f".{'REALGDPFORECAST' if inflation_adjusted else 'NOMGDPFORECAST'}.TOT." - f"AGRWTH.{'Q' if quarterly else 'A'}" - ) + oecd_data_string = f"OECD.ECO.MAD,DSD_EO@DF_EO,/.CBGDPR.{'Q' if quarterly else 'A'}" gross_domestic_product = collect_oecd_data( oecd_data_string, "Q" if quarterly else "Y" @@ -309,27 +308,6 @@ def get_gross_domestic_product_forecast_short_term( return gross_domestic_product -def get_gross_domestic_product_forecast_long_term(): - """ - Get the Gross Domestic Product Forecast for a variety of countries over - time from the OECD. The Gross Domestic Product is the total value - of goods produced and services provided in a country during one year. - - The data represents the millions of US dollars in GDP over a long - period of time (> 40 years) which resembles an estimation of the - total GDP over time. - - Returns: - pd.DataFrame: A DataFrame containing the Gross Domestic Product for a variety - of countries over time. - """ - oecd_data_string = ".GDPLTFORECAST.TOT.MLN_USD.A" - - gross_domestic_product = collect_oecd_data(oecd_data_string, "Y") - - return gross_domestic_product - - def get_consumer_confidence_index(): """ Get the Consumer Confidence Index for a variety of countries over @@ -342,7 +320,7 @@ def get_consumer_confidence_index(): pd.DataFrame: A DataFrame containing the Consumer Confidence Index for a variety of countries over time. """ - oecd_data_string = ".CCI.AMPLITUD.LTRENDIDX.M" + oecd_data_string = "OECD.SDD.STES,DSD_STES@DF_CLI,4.1/.M.CCICP...AA...H" consumer_confidence_index = collect_oecd_data(oecd_data_string, "M") @@ -361,7 +339,7 @@ def get_business_confidence_index(): pd.DataFrame: A DataFrame containing the Business Confidence Index for a variety of countries over time. """ - oecd_data_string = ".BCI.AMPLITUD.LTRENDIDX.M" + oecd_data_string = "OECD.SDD.STES,DSD_STES@DF_CLI,4.1/.M.BCICP...AA...H" business_confidence_index = collect_oecd_data(oecd_data_string, "M") @@ -379,151 +357,44 @@ def get_composite_leading_indicator(): pd.DataFrame: A DataFrame containing the Composite Leading Indicator for a variety of countries over time. """ - oecd_data_string = ".CLI.AMPLITUD.LTRENDIDX.M" + oecd_data_string = "OECD.SDD.STES,DSD_STES@DF_CLI,4.1/.M.LI...AA...H" composite_leading_indicator = collect_oecd_data(oecd_data_string, "M") return composite_leading_indicator -def get_consumer_price_index(period: str, inflation_type: str): - """ - Get the Consumer Price Index for a variety of countries over - time from the OECD. The Consumer Price Index is an indicator - that measures the average change over time in the prices paid - by urban consumers for a market basket of consumer goods and - services. - - Args: - period (str): The period of the data. Can be 'monthly', 'quarterly' or 'yearly'. - inflation_type (str): The type of inflation. Can be 'total', 'food' or 'energy'. - - Returns: - pd.DataFrame: A DataFrame containing the Consumer Price Index for a variety - of countries over time. - """ - inflation_type = inflation_type.lower() - period = period.lower() - - if inflation_type not in ["total", "total_excl", "food", "energy"]: - raise ValueError( - "Inflation type must be one of 'total', 'total_excl', 'food' or 'energy'" - ) - if period not in ["monthly", "quarterly", "yearly"]: - raise ValueError("Period must be one of 'monthly', 'quarterly' or 'yearly'") - - inflation_data = ( - "TOT" - if inflation_type == "total" - else "FOOD" - if inflation_type == "food" - else "TOT_FOODENRG" - if inflation_type == "total_excl" - else "ENRG" - ) - period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" - - oecd_data_string = f".CPI.{inflation_data}.IDX2015.{period_data}" - - consumer_price_index = collect_oecd_data( - oecd_data_string, "Y" if period_data == "A" else period_data - ) - - return consumer_price_index - - -def get_consumer_price_index_forecast(period: str): +def get_house_prices(quarterly: bool | None = False, inflation_adjusted: bool = True): """ - Get the Consumer Price Index Forecast which is a growth rate - for a variety of countries over time from the OECD. The - Consumer Price Index is an indicator that measures the average - change over time in the prices paid by urban consumers for a - market basket of consumer goods and services. - - Args: - period (str): The period of the data. Can be 'monthly', 'quarterly' or 'yearly'. - inflation_type (str): The type of inflation. Can be 'total', 'food' or 'energy'. - - Returns: - pd.DataFrame: A DataFrame containing the Consumer Price Index for a variety - of countries over time. - """ - period = period.lower() - - if period not in ["quarterly", "yearly"]: - raise ValueError("Period must be 'quarterly' or 'yearly'") - - period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" - - oecd_data_string = f".CPIFORECAST.TOT.AGRWTH.{period_data}" - - consumer_price_index = collect_oecd_data( - oecd_data_string, "Y" if period_data == "A" else period_data - ) - - # Division by 100 due to being a growth rate - consumer_price_index = consumer_price_index / 100 - - return consumer_price_index - - -def get_producer_price_index(period: str): - """ - Get the Producer Price Index for a variety of countries over - time from the OECD. The Producer Price Index is an indicator - that measures the average change over time in the selling - prices received by domestic producers for their output. - - Args: - period (str): The period of the data. Can be 'monthly', 'quarterly' or 'yearly'. - - Returns: - pd.DataFrame: A DataFrame containing the Producer Price Index for a variety - of countries over time. - """ - period = period.lower() - - if period not in ["monthly", "quarterly", "yearly"]: - raise ValueError("Period must be one of 'monthly', 'quarterly' or 'yearly'") - - period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" - - oecd_data_string = f".PPI.TOT_MKT.IDX2015.{period_data}" - - producer_price_index = collect_oecd_data( - oecd_data_string, "Y" if period_data == "A" else period_data - ) - - return producer_price_index - - -def get_house_prices(quarterly: bool | None = False, inflation_adjusted: bool = False): - """ - Get the house prices for a variety of countries over time from the OECD. - The house prices are the prices of residential real estate in the country. + Residential Property Prices Indices (RPPIs), also named House price indices (HPIs), + are index numbers that measure the prices of residential properties over time. RPPIs + are key statistics not only for citizens and households across the world, but also + for economic and monetary policy makers. They can help, for example, to monitor + potential macroeconomic imbalances and the risk exposure of the household + and financial sectors. Args: quarterly (bool): Whether to return the quarterly data or the yearly data. - inflation_adjusted (bool): Whether to return the inflation adjusted data or the - nominal data. Returns: pd.DataFrame: A DataFrame containing the house prices for a variety of countries over time. """ - oecd_data_string = f".HOUSECOST.{'REAL' if inflation_adjusted else 'NOMINAL'}.IDX2015.{'Q' if quarterly else 'A'}" + if inflation_adjusted: + oecd_data_string = f"OECD.ECO.MPD,DSD_AN_HOUSE_PRICES@DF_HOUSE_PRICES,1.0/.{'Q' if quarterly else 'A'}.RHP." + else: + oecd_data_string = f"OECD.ECO.MPD,DSD_AN_HOUSE_PRICES@DF_HOUSE_PRICES,1.0/.{'Q' if quarterly else 'A'}.RHP." house_prices = collect_oecd_data(oecd_data_string, "Q" if quarterly else "Y") return house_prices -def get_rent_prices( - quarterly: bool | None = False, -): +def get_rent_prices(quarterly: bool | None = False): """ - Get the rent prices for a variety of countries over time from the OECD. - The rent prices are the prices of residential real estate in the country. + Covers the Rent Prices as an index number that measures the prices of residential + properties over time. Rent Prices are key statistics not only for citizens and + households across the world, but also for economic and monetary policy makers. Args: quarterly (bool): Whether to return the quarterly data or the yearly data. @@ -532,11 +403,11 @@ def get_rent_prices( pd.DataFrame: A DataFrame containing the rent prices for a variety of countries over time. """ - oecd_data_string = f".HOUSECOST.RENT.IDX2015.{'Q' if quarterly else 'A'}" + oecd_data_string = f"OECD.ECO.MPD,DSD_AN_HOUSE_PRICES@DF_HOUSE_PRICES,1.0/.{'Q' if quarterly else 'A'}.RPI." - house_prices = collect_oecd_data(oecd_data_string, "Q" if quarterly else "Y") + rent_prices = collect_oecd_data(oecd_data_string, "Q" if quarterly else "Y") - return house_prices + return rent_prices def get_unemployment_rate(period: str): @@ -555,7 +426,9 @@ def get_unemployment_rate(period: str): period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" - oecd_data_string = f".HUR.TOT.PC_LF.{period_data}" + oecd_data_string = ( + f"OECD.SDD.TPS,DSD_LFS@DF_IALFS_UNE_M,/..._Z.Y._T.Y_GE15..{period_data}" + ) unemployment_rate = collect_oecd_data( oecd_data_string, "Y" if period_data == "A" else period_data @@ -567,7 +440,7 @@ def get_unemployment_rate(period: str): return unemployment_rate -def get_long_term_interest_rate(period: str, forecast: bool = False): +def get_long_term_interest_rate(period: str): """ Get the long term interest rate for a variety of countries over time from the OECD. The long term interest rate is defined as the yield on government bonds with a @@ -575,7 +448,6 @@ def get_long_term_interest_rate(period: str, forecast: bool = False): Args: period (str): The period of the data. Can be 'monthly', 'quarterly' or 'yearly'. - forecast (bool): Whether to return the forecast data or the historical data. Returns: pd.DataFrame: A DataFrame containing the long term interest rate for a variety @@ -589,11 +461,12 @@ def get_long_term_interest_rate(period: str, forecast: bool = False): period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" oecd_data_string = ( - f".{'LTINTFORECAST' if forecast else 'LTINT'}.TOT.PC_PA.{period_data}" + f"OECD.SDD.STES,DSD_STES@DF_FINMARK,4.0/.{period_data}.IRLT.PA....." ) long_term_interest_rate = collect_oecd_data( - oecd_data_string, "Y" if period_data == "A" else period_data + oecd_data_string, + "Y" if period_data == "A" else period_data if period_data == "Q" else "M", ) # Divide by 100 to get percentage @@ -602,7 +475,7 @@ def get_long_term_interest_rate(period: str, forecast: bool = False): return long_term_interest_rate -def get_short_term_interest_rate(period: str, forecast: bool = False): +def get_short_term_interest_rate(period: str): """ Get the short term interest rate for a variety of countries over time from the OECD. The short term interest rate is defined as the yield on government bonds with a @@ -610,7 +483,6 @@ def get_short_term_interest_rate(period: str, forecast: bool = False): Args: period (str): The period of the data. Can be 'monthly', 'quarterly' or 'yearly'. - forecast (bool): Whether to return the forecast data or the historical data. Returns: pd.DataFrame: A DataFrame containing the short term interest rate for a variety @@ -624,11 +496,12 @@ def get_short_term_interest_rate(period: str, forecast: bool = False): period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" oecd_data_string = ( - f".{'STINTFORECAST' if forecast else 'STINT'}.TOT.PC_PA.{period_data}" + f"OECD.SDD.STES,DSD_STES@DF_FINMARK,4.0/.{period_data}.IR3TIB.PA....." ) short_term_interest_rate = collect_oecd_data( - oecd_data_string, "Y" if period_data == "A" else period_data + oecd_data_string, + "Y" if period_data == "A" else period_data if period_data == "Q" else "M", ) # Divide by 100 to get percentage @@ -637,37 +510,30 @@ def get_short_term_interest_rate(period: str, forecast: bool = False): return short_term_interest_rate -def get_purchasing_power_parity(): - """ - Get the purchasing power parity for a variety of countries over time from the OECD. - Purchasing power parity (PPP) is a measure of the price of specific goods in - different countries and is used to compare the absolute purchasing power of - the countries' currencies. - - Returns: - pd.DataFrame: A DataFrame containing the purchasing power parity for a variety - of countries over time. - """ - oecd_data_string = ".PPP.TOT.NATUSD.A" - - purchasing_power_parity = collect_oecd_data(oecd_data_string, "Y") - - return purchasing_power_parity - - -def get_exchange_rates(): +def get_exchange_rates(period: str): """ Get the exchange rates for a variety of countries over time from the OECD. The exchange rate is defined as the number of units of the national currency that are needed to purchase one unit of US dollars. + Args: + period (str): The period of the data. Can be 'monthly', 'quarterly' or 'yearly'. + Returns: pd.DataFrame: A DataFrame containing the exchange rates for a variety of countries over time. """ - oecd_data_string = ".EXCH.TOT.NATUSD.A" + if period not in ["monthly", "quarterly", "yearly"]: + raise ValueError("Period must be one of 'monthly', 'quarterly' or 'yearly'") + + period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" - exchange_rates = collect_oecd_data(oecd_data_string, "Y") + oecd_data_string = f"OECD.SDD.STES,DSD_STES@DF_FINMARK,4.0/.{period_data}.CC......" + + exchange_rates = collect_oecd_data( + oecd_data_string, + "Y" if period_data == "A" else period_data if period_data == "Q" else "M", + ) return exchange_rates @@ -694,7 +560,9 @@ def get_share_prices(period: str): period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" - oecd_data_string = f".SHPRICE.TOT.IDX2015.{period_data}" + oecd_data_string = ( + f"OECD.SDD.STES,DSD_STES@DF_FINMARK,4.0/.{period_data}.SHARE......" + ) share_prices = collect_oecd_data( oecd_data_string, "Y" if period_data == "A" else period_data @@ -705,16 +573,18 @@ def get_share_prices(period: str): def get_labour_productivity(): """ - Labour productivity is defined as the ratio of a volume measure of GDP - to a volume measure of labour input. This ratio is particularly useful - when comparing international productivity levels. Labour productivity - is also known as just productivity. + GDP per hour worked is a measure of labour productivity. It measures + how efficiently labour input is combined with other factors of production + and used in the production process. Labour input is defined as total hours + worked of all persons engaged in production. Labour productivity only partially + reflects the productivity of labour in terms of the personal capacities of + workers or the intensity of their effort. Returns: pd.DataFrame: A DataFrame containing the labour productivity for a variety of countries over time. """ - oecd_data_string = ".GDPHRWKD.TOT.IDX2015.A" + oecd_data_string = "OECD.SDD.TPS,DSD_PDB@DF_PDB_GR,/.A.GDPHRS......" labour_productivity = collect_oecd_data(oecd_data_string, "Y") @@ -738,434 +608,54 @@ def get_population(gender: str | None = None): """ if gender is not None and gender not in ["men", "women"]: raise ValueError("Please choose either 'men' or 'women'.") - gender_parameter = ( - "MEN" if gender == "men" else "WOMEN" if gender == "women" else "TOT" + gender_parameter = "M" if gender == "men" else "F" if gender == "women" else "_T" + oecd_data_string = ( + f"OECD.ELS.SAE,DSD_POPULATION@DF_POP_HIST,/..PS.{gender_parameter}.." ) - oecd_data_string = f".POP.{gender_parameter}.MLN_PER.A" population = collect_oecd_data(oecd_data_string, "Y") return population -def get_young_population_percentage(): - """ - The youth population is defined as those people aged less than 15. - The share of the dependent population is calculated as total - elderly and youth population expressed as a ratio of the total population. - - The number is presented in a % of the total population. - - Returns: - pd.DataFrame: A DataFrame containing the young - population for a variety of countries over time. - """ - oecd_data_string = ".YNGPOP.TOT.PC_POP.A" - - young_population_percentage = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - young_population_percentage = young_population_percentage / 100 - - return young_population_percentage - - -def get_working_age_population(): - """ - The working age population is defined as those aged 15 to 64. - This indicator measures the share of the working age - population in total population. - - Returns: - pd.DataFrame: A DataFrame containing the working age - population for a variety of countries over time. - """ - oecd_data_string = ".WKGPOP.TOT.PC_POP.A" - - working_age_population = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - working_age_population = working_age_population / 100 - - return working_age_population - - -def get_elderly_population_percentage(): - """ - The elderly population is defined as people aged 65 and over. - The share of the dependent population is calculated as total - elderly and youth population expressed as a ratio of - the total population. - - The number is presented in a % of the total population. - - Returns: - pd.DataFrame: A DataFrame containing the elderly - population for a variety of countries over time. - """ - oecd_data_string = ".ELDLYPOP.TOT.PC_POP.A" - - elderly_population_percentage = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - elderly_population_percentage = elderly_population_percentage / 100 - - return elderly_population_percentage - - -def get_fertility_rate(): - """ - The total fertility rate in a specific year is defined as the - total number of children that would be born to each woman if - she were to live to the end of her child-bearing years and - give birth to children in alignment with the prevailing - age-specific fertility rates. - - Returns: - pd.DataFrame: A DataFrame containing the fertility - rate for a variety of countries over time. - """ - oecd_data_string = ".FERTILITY.TOT.CHD_WOMAN.A" - - fertility_rate = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - fertility_rate = fertility_rate / 100 - - return fertility_rate - - -def get_old_age_dependency_ratio(): - """ - The old-age to working-age demographic ratio is defined as - the number of individuals aged 65 and over per 100 people - of working age defined as those at ages 20 to 64. - - Returns: - pd.DataFrame: A DataFrame containing the old-age - dependency ratio for a variety of countries over time - """ - oecd_data_string = ".OLDAGEDEP.TOT.PC.A" - - old_age_dependency = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - old_age_dependency = old_age_dependency / 100 - - return old_age_dependency - - -def get_income_inequality(parameter: str = "GINI"): +def get_income_inequality(): """ Income is defined as household disposable income in a particular year. It consists of earnings, self-employment and capital income and public cash transfers; income taxes and social security contributions paid by households are deducted. - Args: - parameter (str): The parameter of the data. Can be 'GINI', 'P50P10', - 'P90P10', 'P90P50', 'S80S20' or 'PALMA'. + This uses the GINI coefficient which is a measure of statistical dispersion + intended to represent the income inequality or wealth inequality within + a nation or any other group of people. Returns: pd.DataFrame: A DataFrame containing the income inequality for a variety of countries over time """ - parameter = parameter.upper() - - if parameter not in ["GINI", "P50P10", "P90P10", "P90P50", "S80S20", "PALMA"]: - raise ValueError( - "Please choose one of the following parameters: 'GINI', 'P50P10', " - "'P90P10', 'P90P50', 'S80S20', 'PALMA'." - ) - - oecd_data_string = ( - f".INCOMEINEQ.{parameter}.{'INEQ' if parameter == 'GINI' else 'RT'}.A" - ) + oecd_data_string = "OECD.WISE.INE,DSD_WISE_IDD@DF_IDD,/.A.INC_DISP_GINI..._T..." income_inequality = collect_oecd_data(oecd_data_string, "Y") return income_inequality -def get_poverty_rate(parameter: str = "TOT"): +def get_poverty_rate(): """ - Income is defined as household disposable income in a particular year. - It consists of earnings, self-employment and capital income and public - cash transfers; income taxes and social security contributions paid by - households are deducted. - - Args: - parameter (str): The parameter of the data. Can be 'TOT', '0_17', - '18_65' or '66MORE'. + The poverty rate is the ratio of the number of people whose income falls + below the poverty line. Returns: pd.DataFrame: A DataFrame containing the income inequality for a variety of countries over time """ - parameter = parameter.upper() - - if parameter not in ["TOT", "0_17", "18_65", "66MORE"]: - raise ValueError( - "Please choose one of the following parameters: 'TOT', '0_17', " - "'18_65', '66MORE'." - ) - - oecd_data_string = f".POVERTY.{parameter}.RT.A" + oecd_data_string = "OECD.WISE.INE,DSD_WISE_IDD@DF_IDD,/.A.PR_INC_DISP..._T..." poverty_rate = collect_oecd_data(oecd_data_string, "Y") return poverty_rate -def get_narrow_money(period: str): - """ - M1 includes currency i.e. banknotes and coins, plus overnight deposits. - M1 is expressed as a seasonally adjusted index based on 2015=100. - - Args: - period (str): The period of the data. Can be 'monthly', 'quarterly' or 'yearly'. - - Returns: - pd.DataFrame: A DataFrame containing the narrow money for a variety - of countries over time. - """ - period = period.lower() - - if period not in ["monthly", "quarterly", "yearly"]: - raise ValueError("Period must be one of 'monthly', 'quarterly' or 'yearly'") - - period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" - - oecd_data_string = f".M1.TOT.IDX2015.{period_data}" - - narrow_money = collect_oecd_data( - oecd_data_string, "Y" if period_data == "A" else period_data - ) - - return narrow_money - - -def get_broad_money(period: str): - """ - Broad money (M3) includes currency, deposits with an agreed maturity of up - to two years, deposits redeemable at notice of up to three months and - repurchase agreements, money market fund shares/units and debt securities - up to two years. M3 is measured as a seasonally adjusted index based on 2015=100. - - Args: - period (str): The period of the data. Can be 'monthly', 'quarterly' or 'yearly'. - - Returns: - pd.DataFrame: A DataFrame containing the broad money for a variety - of countries over time. - """ - period = period.lower() - - if period not in ["monthly", "quarterly", "yearly"]: - raise ValueError("Period must be one of 'monthly', 'quarterly' or 'yearly'") - - period_data = "M" if period == "monthly" else "Q" if period == "quarterly" else "A" - - oecd_data_string = f".M3.TOT.IDX2015.{period_data}" - - broad_money = collect_oecd_data( - oecd_data_string, "Y" if period_data == "A" else period_data - ) - - return broad_money - - -def get_general_government_deficit(): - """ - General government deficit is defined as the balance of income - and expenditure of government, including capital income and - capital expenditures. - - These metrics are expressed as a percentage of GDP. - - Returns: - pd.DataFrame: A DataFrame containing the general government - deficit for a variety of countries over time - """ - oecd_data_string = ".GGNLEND.TOT.PC_GDP.A" - - general_government_deficit = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - general_government_deficit = general_government_deficit / 100 - - return general_government_deficit - - -def get_general_government_revenue(): - """ - Governments collect revenues mainly for two purposes: to finance the - goods and services they provide to citizens and businesses, and to - fulfil their redistributive role. - - These metrics are expressed as a percentage of GDP. - - Returns: - pd.DataFrame: A DataFrame containing the general government - revenue for a variety of countries over time - """ - oecd_data_string = ".GGREV.TOT.PC_GDP.A" - - general_government_revenue = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - general_government_revenue = general_government_revenue / 100 - - return general_government_revenue - - -def get_general_government_spending(parameter: str = "TOT"): - """ - General government spending provides an indication of the size of - government across countries. - - General government final consumption can be broken down into two - distinct groups. The first reflects expenditures for collective - consumption (defence, justice, etc.) that benefit the society as - a whole, or large parts of society, and are often known as - public goods and services. - - These metrics are expressed as a percentage of GDP. - - Args: - parameter (str): The parameter of the data. Can be 'TOT', - 'SOCIETY' or 'INDIVIDUAL'. - - Returns: - pd.DataFrame: A DataFrame containing the general government - spending for a variety of countries over time - """ - parameter = parameter.upper() - - if parameter not in ["TOT", "SOCIETY", "INDIVIDUAL"]: - raise ValueError( - "Please choose one of the following parameters: 'TOT', 'SOCIETY', 'INDIVIDUAL'." - ) - - oecd_data_string = f".GGEXP.{parameter}.PC_GDP.A" - - general_government_spending = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - general_government_spending = general_government_spending / 100 - - return general_government_spending - - -def get_general_government_debt(): - """ - General government debt-to-GDP ratio measures the - gross debt of the general government as a percentage of GDP. - - These metrics are expressed as a percentage of GDP. - - Returns: - pd.DataFrame: A DataFrame containing the general government - debt for a variety of countries over time - """ - oecd_data_string = ".GGDEBT.TOT.PC_GDP.A" - - general_government_debt = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - general_government_debt = general_government_debt / 100 - - return general_government_debt - - -def get_general_government_financial_wealth(): - """ - The net financial worth of the general government sector is - he total value of its financial assets minus the total value - of its outstanding liabilities. - - These metrics are expressed as a percentage of GDP. - - Returns: - pd.DataFrame: A DataFrame containing the general government - financial wealth for a variety of countries over time - """ - oecd_data_string = ".GGWEALTH.TOT.PC_GDP.A" - - general_government_financial_wealth = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - general_government_financial_wealth = general_government_financial_wealth / 100 - - return general_government_financial_wealth - - -def get_general_government_production_costs(): - """ - General government production costs are decisions about - the amount and type of goods and services governments produce, - as well as on how best to produce them. - - These metrics are expressed as a percentage of GDP. - - Returns: - pd.DataFrame: A DataFrame containing the general government - financial wealth for a variety of countries over time - """ - oecd_data_string = ".GGCOST.TOT.PC_GDP.A" - - general_government_production_costs = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - general_government_production_costs = general_government_production_costs / 100 - - return general_government_production_costs - - -def get_central_government_spending(parameter: str): - """ - Central government expenditure is defined as the central - government budget expenditure as reported in the final - central government accounts. - - This indicator of central government spending by function is - measured as a percentage of total expenditures. - - Args: - parameter (str): The parameter of the data. Can be 'GRALPUBSER', 'DEF', 'PUBORD', - 'ECOAFF', 'ENVPROT', 'HOUCOMM', 'HEALTH', 'RECULTREL', 'EDU', 'SOCPROT'. - - Returns: - pd.DataFrame: A DataFrame containing the central government - spending for a variety of countries over time - """ - if parameter not in [ - "GRALPUBSER", - "DEF", - "PUBORD", - "ECOAFF", - "ENVPROT", - "HOUCOMM", - "HEALTH", - "RECULTREL", - "EDU", - "SOCPROT", - ]: - raise ValueError( - "Please choose one of the following parameters: 'GRALPUBSER', 'DEF', 'PUBORD', " - "'ECOAFF', 'ENVPROT', 'HOUCOMM', 'HEALTH', 'RECULTREL', 'EDU', 'SOCPROT'." - ) - - oecd_data_string = f".CGEXP.{parameter}.PC.A" - - central_government_spending = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - central_government_spending = central_government_spending / 100 - - return central_government_spending - - def get_trust_in_goverment(): """ Trust in government refers to the share of people who report @@ -1175,7 +665,7 @@ def get_trust_in_goverment(): pd.DataFrame: A DataFrame containing the trust in government for a variety of countries over time """ - oecd_data_string = ".TRUSTGOV.TOT.PC.A" + oecd_data_string = "OECD.WISE.WDP,DSD_HSL@DF_HSL_FWB,/.14_3.._T..." trust_in_government = collect_oecd_data(oecd_data_string, "Y") @@ -1194,7 +684,7 @@ def get_renewable_energy(): pd.DataFrame: A DataFrame containing the renewable energy for a variety of countries over time """ - oecd_data_string = ".RENEWABLE.TOT.PC_PRYENRGSUPPLY.A" + oecd_data_string = "OECD.WISE.WDP,DSD_HSL@DF_HSL_FWB,/.12_10.._T..." renewable_energy = collect_oecd_data(oecd_data_string, "Y") @@ -1204,44 +694,7 @@ def get_renewable_energy(): return renewable_energy -def get_crude_oil_production(): - """ - Crude oil production is defined as the quantities of oil extracted - from the ground after the removal of inert matter or impurities. - - The number is presented in thousand tonnes of oil equivalent. - - Returns: - pd.DataFrame: A DataFrame containing the crude oil production - for a variety of countries over time - """ - oecd_data_string = ".OILPROD.TOT.KTOE.A" - - crude_oil_production = collect_oecd_data(oecd_data_string, "Y") - - return crude_oil_production - - -def get_crude_oil_prices(): - """ - Crude oil import prices come from the IEA's Crude Oil Import Register - and are influenced not only by traditional movements of supply and - demand, but also by other factors such as geopolitics. - - The number is presented in US dollars per barrel. - - Returns: - pd.DataFrame: A DataFrame containing the crude oil prices - for a variety of countries over time - """ - oecd_data_string = ".OILIMPPRICE.TOT.USD_BAR.A" - - crude_oil_prices = collect_oecd_data(oecd_data_string, "Y") - - return crude_oil_prices - - -def get_environmental_tax(parameter: str = "TOT"): +def get_carbon_footprint(): """ Environmentally related taxes are an important instrument for governments to shape relative prices of goods and services. @@ -1250,46 +703,8 @@ def get_environmental_tax(parameter: str = "TOT"): pd.DataFrame: A DataFrame containing the environmental tax for a variety of countries over time """ - if parameter not in ["TOT", "ENRG", "TRANSPORT", "RES", "POL"]: - raise ValueError( - "Please choose one of the following parameters: 'TOT', 'ENRG', 'TRANSPORT', 'RES', 'POL'." - ) - oecd_data_string = f".TAXENV.{parameter}.PC_GDP.A" - - environmental_tax = collect_oecd_data(oecd_data_string, "Y") - - # Divide by 100 to get percentage - environmental_tax = environmental_tax / 100 - - return environmental_tax - - -def get_greenhouse_emissions(parameter: str = "CO2"): - """ - Greenhouse gases refer to the sum of seven gases that have direct effects on climate change: - carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), chlorofluorocarbons (CFCs), - hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and - nitrogen trifluoride (NF3). - - Returns: - pd.DataFrame: A DataFrame containing the greenhouse emissions - for a variety of countries over time - """ - if parameter not in ["CO2", "CO", "GHG", "NOX", "SOX", "VOC"]: - raise ValueError( - "Please choose one of the following parameters: 'CO2', 'CO', 'GHG', 'NOX', 'SOX', 'VOC'." - ) - - kilo_or_tonnes = ( - "KG_CAP" if parameter in ["SOX", "CO", "NOX", "SOX", "VOC"] else "TONNE_CAP" - ) - - oecd_data_string = f".AIREMISSION.{parameter}.{kilo_or_tonnes}.A" - - greenhouse_emission = collect_oecd_data(oecd_data_string, "Y") + oecd_data_string = "OECD.WISE.WDP,DSD_HSL@DF_HSL_FWB,/.12_9.._T..." - # Divide by 1000 to get tonnes - if kilo_or_tonnes == "KG_CAP": - greenhouse_emission = greenhouse_emission / 1000 + carbon_footprint = collect_oecd_data(oecd_data_string, "Y") - return greenhouse_emission + return carbon_footprint diff --git a/financetoolkit/fixedincome/fixedincome_controller.py b/financetoolkit/fixedincome/fixedincome_controller.py index 6cbbd92..c954b7f 100644 --- a/financetoolkit/fixedincome/fixedincome_controller.py +++ b/financetoolkit/fixedincome/fixedincome_controller.py @@ -770,7 +770,6 @@ def get_government_bond_yield( self, short_term: bool = False, period: str | None = None, - forecast: bool = False, growth: bool = False, lag: int = 1, rounding: int | None = None, @@ -804,7 +803,6 @@ def get_government_bond_yield( short_term (bool, optional): Whether to return the short-term interest rate. Defaults to False. This means that the long-term interest rate will be returned. period (str | None, optional): Whether to return the monthly, quarterly or the annual data. - forecast (bool, optional): Whether to return the forecasted data. Defaults to False. growth (bool, optional): Whether to return the growth data or the actual data. lag (int, optional): The number of periods to lag the data by. rounding (int | None, optional): The number of decimals to round the results to. Defaults to None. @@ -846,18 +844,15 @@ def get_government_bond_yield( if short_term: government_bond_yield = oecd_model.get_short_term_interest_rate( - period=period, forecast=forecast + period=period, ) else: government_bond_yield = oecd_model.get_long_term_interest_rate( - period=period, forecast=forecast + period=period, ) if government_bond_yield.empty: - raise ValueError( - "No data available for the selected period " - f"{'and forecast' if forecast else ''}." - ) + raise ValueError("No data available for the selected period ") if growth: government_bond_yield = calculate_growth( @@ -868,7 +863,7 @@ def get_government_bond_yield( ) government_bond_yield = government_bond_yield.loc[ - self._start_date : None if forecast else self._end_date + self._start_date : self._end_date ] return government_bond_yield.round(rounding if rounding else self._rounding) diff --git a/pyproject.toml b/pyproject.toml index 4c8e9e2..3b5ef03 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "financetoolkit" -version = "1.9.5" +version = "1.9.6" description = "Transparent and Efficient Financial Analysis" license = "MIT" authors = ["Jeroen Bouma"] diff --git a/tests/csv/test_toolkit_controller/test_toolkit_historical.csv b/tests/csv/test_toolkit_controller/test_toolkit_historical.csv index 426e714..1234be9 100644 --- a/tests/csv/test_toolkit_controller/test_toolkit_historical.csv +++ b/tests/csv/test_toolkit_controller/test_toolkit_historical.csv @@ -1,14 +1,6 @@ ,Open,Open,Open,High,High,High,Low,Low,Low,Close,Close,Close,Adj Close,Adj Close,Adj Close,Volume,Volume,Volume,Dividends,Dividends,Dividends,Return,Return,Return,Volatility,Volatility,Volatility,Excess Return,Excess Return,Excess Return,Excess Volatility,Excess Volatility,Excess Volatility,Cumulative Return,Cumulative Return,Cumulative Return ,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark,AAPL,MSFT,Benchmark Date,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -2010,12.0,28.0,126.0,12.0,28.0,126.0,11.0,28.0,125.0,12.0,28.0,126.0,10.0,22.0,98.0,193508000,24752000,91218900,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0 -2011,14.0,26.0,126.0,15.0,26.0,126.0,14.0,26.0,126.0,14.0,26.0,126.0,12.0,21.0,100.0,179662000,27395700,95599000,0.0,1.0,3.0,0.0,-0.0,0.0,0.0,0.0,0.0,0.0,-0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0 -2012,18.0,27.0,140.0,19.0,27.0,143.0,18.0,26.0,140.0,19.0,27.0,142.0,16.0,22.0,116.0,659492400,42749500,243935200,0.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0 -2013,20.0,37.0,184.0,20.0,38.0,185.0,20.0,37.0,184.0,20.0,37.0,185.0,18.0,31.0,153.0,223084400,17503500,86119900,0.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0 -2014,28.0,47.0,208.0,28.0,47.0,208.0,28.0,46.0,205.0,28.0,46.0,206.0,25.0,40.0,174.0,165613600,21552500,130333800,0.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,2.0 -2015,27.0,56.0,205.0,27.0,56.0,206.0,26.0,55.0,204.0,26.0,55.0,204.0,24.0,49.0,176.0,163649200,27334100,114877900,1.0,1.0,4.0,-0.0,0.0,0.0,0.0,0.0,0.0,-0.0,0.0,-0.0,0.0,0.0,0.0,2.0,2.0,2.0 -2016,29.0,63.0,225.0,29.0,63.0,225.0,29.0,62.0,223.0,29.0,62.0,224.0,27.0,57.0,197.0,122345200,25579900,108998300,1.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,2.0 -2017,43.0,86.0,269.0,43.0,86.0,269.0,42.0,86.0,267.0,42.0,86.0,267.0,40.0,80.0,240.0,103999600,18717400,96007400,1.0,2.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,4.0,2.0 -2018,40.0,101.0,250.0,40.0,102.0,250.0,39.0,100.0,247.0,39.0,102.0,250.0,38.0,96.0,229.0,140014000,33173800,144299400,1.0,2.0,5.0,-0.0,0.0,-0.0,0.0,0.0,0.0,-0.0,0.0,-0.0,0.0,0.0,0.0,4.0,4.0,2.0 -2019,72.0,157.0,321.0,73.0,158.0,322.0,72.0,156.0,320.0,73.0,158.0,322.0,71.0,151.0,300.0,100805600,18369400,57077300,1.0,2.0,6.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,7.0,7.0,3.0 -2020,136.0,225.0,372.0,136.0,226.0,373.0,133.0,221.0,372.0,134.0,222.0,372.0,131.0,215.0,354.0,96452100,20272300,49455300,1.0,2.0,6.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,13.0,10.0,4.0 +2020,134.0,222.0,372.0,135.0,223.0,375.0,132.0,220.0,371.0,133.0,222.0,374.0,131.0,217.0,359.0,99089086,20942100,78520700,3.0,2.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0 +2021,178.0,339.0,476.0,179.0,339.0,477.0,177.0,336.0,475.0,178.0,336.0,475.0,176.0,331.0,462.0,64062261,18000800,65237400,1.0,2.0,6.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,2.0,1.0 +2022,128.0,238.0,381.0,130.0,240.0,383.0,127.0,237.0,378.0,130.0,240.0,382.0,129.0,238.0,378.0,76204209,21938500,84022200,1.0,3.0,6.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,1.0,1.0,1.0 diff --git a/tests/models/csv/test_models_controller/test_get_present_value_of_growth_opportunities.csv b/tests/models/csv/test_models_controller/test_get_present_value_of_growth_opportunities.csv index 3fbca21..a18be55 100644 --- a/tests/models/csv/test_models_controller/test_get_present_value_of_growth_opportunities.csv +++ b/tests/models/csv/test_models_controller/test_get_present_value_of_growth_opportunities.csv @@ -1,4 +1,4 @@ ,AAPL,MSFT -2020,8318.9299,57852.0784 -2021,160.2573,306.022 -2022,155.4936,279.3459 +2020,-3144.788,-4585.8799 +2021,160.457,306.2057 +2022,156.7935,281.276 diff --git a/tests/models/csv/test_models_controller/test_get_present_value_of_growth_opportunities_1.csv b/tests/models/csv/test_models_controller/test_get_present_value_of_growth_opportunities_1.csv index fa12858..e2d527e 100644 --- a/tests/models/csv/test_models_controller/test_get_present_value_of_growth_opportunities_1.csv +++ b/tests/models/csv/test_models_controller/test_get_present_value_of_growth_opportunities_1.csv @@ -1,505 +1,505 @@ Date,AAPL,MSFT -2020-12-31,8318.9299,57852.0784 -2021-01-04,8315.6999,57847.4684 -2021-01-05,8317.2799,57847.6684 -2021-01-06,8312.9399,57842.1584 -2021-01-07,8317.1899,57848.0484 -2021-01-08,8318.2999,57849.3484 -2021-01-11,8315.2799,57847.2684 -2021-01-12,8315.0999,57844.7684 -2021-01-13,8317.1599,57846.1484 -2021-01-14,8315.2099,57842.9084 -2021-01-15,8313.4699,57842.5484 -2021-01-19,8314.1499,57846.2484 -2021-01-20,8318.2799,57853.9584 -2021-01-21,8323.0399,57854.5684 -2021-01-22,8325.2099,57855.5284 -2021-01-25,8328.9899,57859.0184 -2021-01-26,8329.2299,57861.7484 -2021-01-27,8328.1499,57862.3084 -2021-01-28,8323.2599,57868.1884 -2021-01-29,8318.2099,57861.3884 -2021-02-01,8320.3599,57868.8984 -2021-02-02,8321.1899,57868.7584 -2021-02-03,8320.1599,57872.1584 -2021-02-04,8323.5499,57871.1984 -2021-02-05,8323.1299,57871.3784 -2021-02-08,8323.2799,57871.6484 -2021-02-09,8322.3999,57872.9184 -2021-02-10,8321.7799,57871.9884 -2021-02-11,8321.5299,57873.6184 -2021-02-12,8321.7599,57874.1084 -2021-02-16,8319.6199,57872.8484 -2021-02-17,8317.2999,57873.8784 -2021-02-18,8316.1899,57873.4784 -2021-02-19,8316.3499,57870.7284 -2021-02-22,8312.5299,57864.4084 -2021-02-23,8312.3899,57863.1884 -2021-02-24,8311.8899,57864.4484 -2021-02-25,8307.5899,57859.0084 -2021-02-26,8307.8599,57862.3184 -2021-03-01,8314.2999,57866.7784 -2021-03-02,8311.6599,57863.7784 -2021-03-03,8308.6499,57857.6084 -2021-03-04,8306.7499,57856.7984 -2021-03-05,8308.0199,57861.5584 -2021-03-08,8303.0299,57857.4384 -2021-03-09,8307.6899,57863.6884 -2021-03-10,8306.5999,57862.3584 -2021-03-11,8308.5499,57866.9684 -2021-03-12,8307.6299,57865.6184 -2021-03-15,8310.5499,57864.6984 -2021-03-16,8312.1099,57867.5384 -2021-03-17,8311.3099,57866.8784 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b/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital.csv index bbfdb2a..5c430b3 100644 --- a/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital.csv +++ b/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital.csv @@ -1,13 +1,13 @@ ,,2020,2021,2022,2023 AAPL,Market Value Equity,2288483619840.0,2964852760200.0,2112234462220.0, AAPL,Market Value Debt,112436000000.0,124719000000.0,120069000000.0,101266000000.0 -AAPL,Cost of Equity,-0.0,0.38,-0.25, +AAPL,Cost of Equity,0.0,0.38,-0.24, AAPL,Cost of Debt,0.03,0.02,0.02,0.0 AAPL,Corporate Tax Rate,0.14,0.13,0.16,0.15 -AAPL,Weighted Average Cost of Capital,-0.0,0.36,-0.23, +AAPL,Weighted Average Cost of Capital,0.0,0.37,-0.22, MSFT,Market Value Equity,1667518320000.0,2517791520000.0,1795952600000.0, MSFT,Market Value Debt,70998000000.0,67775000000.0,61270000000.0,59965000000.0 -MSFT,Cost of Equity,-0.0,0.33,-0.24, +MSFT,Cost of Equity,0.0,0.33,-0.23, MSFT,Cost of Debt,0.04,0.03,0.03,0.03 MSFT,Corporate Tax Rate,0.17,0.14,0.13,0.19 -MSFT,Weighted Average Cost of Capital,-0.0,0.32,-0.23, +MSFT,Weighted Average Cost of Capital,0.0,0.33,-0.22, diff --git a/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital_1.csv b/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital_1.csv index a9ee4aa..1cebf2c 100644 --- a/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital_1.csv +++ b/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital_1.csv @@ -1,13 +1,13 @@ ,,2020,2021,2022,2023 AAPL,Market Value Equity,,0.3,-0.29,0.0 AAPL,Market Value Debt,,0.11,-0.04,-0.16 -AAPL,Cost of Equity,,-261.18,-1.66,-407414129117.82 +AAPL,Cost of Equity,,inf,-1.62,-427614226073.07 AAPL,Cost of Debt,,-0.17,0.15,-1.0 AAPL,Corporate Tax Rate,,-0.08,0.22,-0.09 -AAPL,Weighted Average Cost of Capital,,-1026.52,-1.65,-1.63 +AAPL,Weighted Average Cost of Capital,,356.37,-1.61,-1.66 MSFT,Market Value Equity,,0.51,-0.29,-1.0 MSFT,Market Value Debt,,-0.05,-0.1,-0.02 -MSFT,Cost of Equity,,-243.23,-1.73,-246418250303.84 +MSFT,Cost of Equity,,inf,-1.7,-257890246384.27 MSFT,Cost of Debt,,-0.05,-0.03,-0.03 MSFT,Corporate Tax Rate,,-0.16,-0.05,0.45 -MSFT,Weighted Average Cost of Capital,,-4843.05,-1.73,-1.81 +MSFT,Weighted Average Cost of Capital,,260.65,-1.69,-1.85 diff --git a/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital_2.csv b/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital_2.csv index dc4edab..e963c66 100644 --- a/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital_2.csv +++ b/tests/models/csv/test_models_controller/test_get_weighted_average_cost_of_capital_2.csv @@ -5,33 +5,33 @@ AAPL,Market Value Equity,Lag 3,,,,-0.077 AAPL,Market Value Debt,Lag 1,,0.1092,-0.0373,-0.1566 AAPL,Market Value Debt,Lag 2,,,0.0679,-0.188 AAPL,Market Value Debt,Lag 3,,,,-0.0993 -AAPL,Cost of Equity,Lag 1,,-261.1799,-1.6617,0.0 -AAPL,Cost of Equity,Lag 2,,,171.1513,-1.6617 -AAPL,Cost of Equity,Lag 3,,,,171.1513 +AAPL,Cost of Equity,Lag 1,,inf,-1.6223,0.0 +AAPL,Cost of Equity,Lag 2,,,-inf,-1.6223 +AAPL,Cost of Equity,Lag 3,,,,-inf AAPL,Cost of Debt,Lag 1,,-0.17,0.151,-1.0 AAPL,Cost of Debt,Lag 2,,,-0.0447,-1.0 AAPL,Cost of Debt,Lag 3,,,,-1.0 AAPL,Corporate Tax Rate,Lag 1,,-0.078,0.2182,-0.0917 AAPL,Corporate Tax Rate,Lag 2,,,0.1231,0.1065 AAPL,Corporate Tax Rate,Lag 3,,,,0.0202 -AAPL,Weighted Average Cost of Capital,Lag 1,,-1026.5214,-1.648,0.0 -AAPL,Weighted Average Cost of Capital,Lag 2,,,663.5625,-1.648 -AAPL,Weighted Average Cost of Capital,Lag 3,,,,663.5625 +AAPL,Weighted Average Cost of Capital,Lag 1,,356.3704,-1.6093,0.0 +AAPL,Weighted Average Cost of Capital,Lag 2,,,-218.7579,-1.6093 +AAPL,Weighted Average Cost of Capital,Lag 3,,,,-218.7579 MSFT,Market Value Equity,Lag 1,,0.5099,-0.2867,0.0 MSFT,Market Value Equity,Lag 2,,,0.077,-0.2867 MSFT,Market Value Equity,Lag 3,,,,0.077 MSFT,Market Value Debt,Lag 1,,-0.0454,-0.096,-0.0213 MSFT,Market Value Debt,Lag 2,,,-0.137,-0.1152 MSFT,Market Value Debt,Lag 3,,,,-0.1554 -MSFT,Cost of Equity,Lag 1,,-243.2339,-1.7349,0.0 -MSFT,Cost of Equity,Lag 2,,,177.0277,-1.7349 -MSFT,Cost of Equity,Lag 3,,,,177.0277 +MSFT,Cost of Equity,Lag 1,,inf,-1.6971,0.0 +MSFT,Cost of Equity,Lag 2,,,-inf,-1.6971 +MSFT,Cost of Equity,Lag 3,,,,-inf MSFT,Cost of Debt,Lag 1,,-0.0515,-0.0273,-0.0253 MSFT,Cost of Debt,Lag 2,,,-0.0774,-0.0519 MSFT,Cost of Debt,Lag 3,,,,-0.1007 MSFT,Corporate Tax Rate,Lag 1,,-0.1624,-0.0516,0.4473 MSFT,Corporate Tax Rate,Lag 2,,,-0.2056,0.3726 MSFT,Corporate Tax Rate,Lag 3,,,,0.1497 -MSFT,Weighted Average Cost of Capital,Lag 1,,-4843.0494,-1.7251,0.0 -MSFT,Weighted Average Cost of Capital,Lag 2,,,3509.847,-1.7251 -MSFT,Weighted Average Cost of Capital,Lag 3,,,,3509.847 +MSFT,Weighted Average Cost of Capital,Lag 1,,260.6504,-1.6877,0.0 +MSFT,Weighted Average Cost of Capital,Lag 2,,,-180.9245,-1.6877 +MSFT,Weighted Average Cost of Capital,Lag 3,,,,-180.9245 diff --git a/tests/options/csv/test_options_controller/test_collect_all_greeks.csv b/tests/options/csv/test_options_controller/test_collect_all_greeks.csv index e6e005d..c2aea60 100644 --- a/tests/options/csv/test_options_controller/test_collect_all_greeks.csv +++ b/tests/options/csv/test_options_controller/test_collect_all_greeks.csv @@ -1,39 +1,39 @@ 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,,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Delta,Dual Delta,Vega,Theta,Rho,Epsilon,Lambda,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima,Speed,Zomma,Color,Ultima Ticker,Strike Price,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 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diff --git a/tests/options/csv/test_options_controller/test_collect_all_greeks_1.csv b/tests/options/csv/test_options_controller/test_collect_all_greeks_1.csv index 21b9b55..0ea6e57 100644 --- a/tests/options/csv/test_options_controller/test_collect_all_greeks_1.csv +++ b/tests/options/csv/test_options_controller/test_collect_all_greeks_1.csv @@ -1,39 +1,39 @@ 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diff --git a/tests/options/csv/test_options_controller/test_collect_first_order_greeks.csv b/tests/options/csv/test_options_controller/test_collect_first_order_greeks.csv index 641be17..36eeab8 100644 --- a/tests/options/csv/test_options_controller/test_collect_first_order_greeks.csv +++ b/tests/options/csv/test_options_controller/test_collect_first_order_greeks.csv @@ -1,39 +1,39 @@ 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diff --git a/tests/options/csv/test_options_controller/test_collect_first_order_greeks_1.csv b/tests/options/csv/test_options_controller/test_collect_first_order_greeks_1.csv index aaf573a..d1ec737 100644 --- a/tests/options/csv/test_options_controller/test_collect_first_order_greeks_1.csv +++ b/tests/options/csv/test_options_controller/test_collect_first_order_greeks_1.csv @@ -1,39 +1,39 @@ 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diff --git a/tests/options/csv/test_options_controller/test_collect_second_order_greeks.csv b/tests/options/csv/test_options_controller/test_collect_second_order_greeks.csv index d2b1ae5..6997c11 100644 --- a/tests/options/csv/test_options_controller/test_collect_second_order_greeks.csv +++ b/tests/options/csv/test_options_controller/test_collect_second_order_greeks.csv @@ -1,39 +1,39 @@ 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Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD,Gamma,Dual Gamma,Vanna,Charm,Vomma,Vera,Veta,PD Ticker,Strike Price,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 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diff --git a/tests/options/csv/test_options_controller/test_collect_second_order_greeks_1.csv b/tests/options/csv/test_options_controller/test_collect_second_order_greeks_1.csv index 537ea3e..8381067 100644 --- a/tests/options/csv/test_options_controller/test_collect_second_order_greeks_1.csv +++ b/tests/options/csv/test_options_controller/test_collect_second_order_greeks_1.csv @@ -1,39 +1,39 @@ 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-MSFT,250,0.0028,0.0025,0.0923,5.861,3.0433,0.0598,806.9749,0.0025,0.0116,0.0104,0.3829,12.2128,12.567,0.4929,1878.4865,0.0104,0.017,0.0154,0.5645,12.0431,18.4363,1.0827,2046.1486,0.0154,0.0198,0.0179,0.6576,10.5525,21.3649,1.6698,1961.1315,0.0179,0.0211,0.0191,0.7031,9.0532,22.7269,2.2163,1825.426,0.0191,0.0217,0.0196,0.7233,7.7845,23.2614,2.7172,1691.3114,0.0196,0.0218,0.0197,0.7297,6.751,23.3458,3.1758,1571.2858,0.0197,0.0217,0.0197,0.7283,5.9127,23.1796,3.5972,1466.8157,0.0197,0.0215,0.0195,0.7225,5.2282,22.8726,3.9861,1376.3914,0.0195,0.0212,0.0192,0.7141,4.6637,22.4876,4.3466,1297.9202,0.0192,0.0209,0.0189,0.7042,4.1932,22.0616,4.6823,1229.4309,0.0189,0.0206,0.0186,0.6937,3.7969,21.6168,4.996,1169.2464,0.0186,0.0202,0.0183,0.6829,3.4598,21.1669,5.2902,1115.991,0.0183,0.0198,0.0179,0.6721,3.1705,20.72,5.5669,1068.5493,0.0179,0.0195,0.0176,0.6615,2.9201,20.2813,5.8279,1026.0188,0.0176,0.0192,0.0173,0.651,2.7017,19.8536,6.0745,987.6665,0.0173,0.0188,0.017,0.6409,2.51,19.4387,6.3081,952.8944,0.017,0.0185,0.0167,0.6311,2.3405,19.0372,6.5296,921.2109,0.0167,0.0182,0.0165,0.6217,2.19,18.6496,6.7401,892.2091,0.0165,0.0179,0.0162,0.6127,2.0555,18.2758,6.9404,865.5503,0.0162,0.0176,0.0159,0.6039,1.9348,17.9153,7.1311,840.9501,0.0159,0.0173,0.0157,0.5956,1.8259,17.5678,7.3129,818.1683,0.0157,0.0171,0.0154,0.5875,1.7273,17.2329,7.4864,797.0009,0.0154,0.0168,0.0152,0.5798,1.6377,16.9098,7.652,777.2733,0.0152,0.0166,0.015,0.5724,1.556,16.5982,7.8103,758.8356,0.015,0.0163,0.0148,0.5652,1.4812,16.2973,7.9615,741.558,0.0148,0.0161,0.0146,0.5584,1.4125,16.0068,8.1062,725.3279,0.0146,0.0159,0.0144,0.5518,1.3493,15.726,8.2446,710.0469,0.0144,0.0157,0.0142,0.5454,1.2909,15.4544,8.377,695.6289,0.0142 -MSFT,255,0.0001,0.0001,0.004,0.246,0.1879,0.0026,46.7233,0.0001,0.0021,0.0018,0.0957,3.0386,4.4391,0.1237,589.8389,0.0018,0.0054,0.0047,0.2521,5.3577,11.6543,0.4864,1099.1666,0.0047,0.0084,0.0073,0.3925,6.2708,18.0762,1.0047,1356.6639,0.0073,0.0107,0.0093,0.4993,6.3965,22.9128,1.5898,1455.2029,0.0093,0.0123,0.0107,0.5767,6.1699,26.368,2.1926,1472.0893,0.0107,0.0135,0.0117,0.6318,5.8063,28.7834,2.7888,1449.3192,0.0117,0.0143,0.0124,0.6707,5.4049,30.4456,3.3669,1408.2733,0.0124,0.0149,0.0129,0.6979,5.0097,31.5659,3.9221,1359.7794,0.0129,0.0152,0.0132,0.7167,4.6393,32.2947,4.4528,1309.33,0.0132,0.0155,0.0134,0.7292,4.3001,32.7385,4.959,1259.6827,0.0134,0.0156,0.0136,0.7372,3.9928,32.9732,5.4417,1212.1686,0.0136,0.0157,0.0136,0.7417,3.7158,33.0534,5.9019,1167.3661,0.0136,0.0157,0.0136,0.7437,3.4665,33.0186,6.3412,1125.4552,0.0136,0.0157,0.0136,0.7438,3.2421,32.8982,6.7607,1086.4079,0.0136,0.0157,0.0136,0.7424,3.0397,32.7138,7.1619,1050.0918,0.0136,0.0156,0.0135,0.74,2.8569,32.4816,7.5459,1016.3268,0.0135,0.0155,0.0134,0.7366,2.6913,32.2139,7.9139,984.9168,0.0134,0.0154,0.0134,0.7327,2.5409,31.9202,8.2669,955.6655,0.0134,0.0153,0.0133,0.7283,2.4039,31.6075,8.6059,928.386,0.0133,0.0152,0.0132,0.7236,2.2789,31.2815,8.9317,902.904,0.0132,0.015,0.013,0.7186,2.1644,30.9465,9.2451,879.0603,0.013,0.0149,0.0129,0.7134,2.0593,30.6057,9.5468,856.7098,0.0129,0.0148,0.0128,0.7082,1.9625,30.2618,9.8376,835.7219,0.0128,0.0146,0.0127,0.7028,1.8733,29.9168,10.1179,815.9788,0.0127,0.0145,0.0126,0.6974,1.7908,29.5723,10.3884,797.3748,0.0126,0.0144,0.0125,0.6921,1.7143,29.2295,10.6495,779.8145,0.0125,0.0143,0.0124,0.6867,1.6433,28.8893,10.9018,763.2128,0.0124,0.0141,0.0123,0.6814,1.5772,28.5526,11.1456,747.4925,0.0123 -MSFT,260,0.0,0.0,0.0001,-0.0072,0.0033,0.0,0.7902,0.0,0.0002,0.0002,0.0127,0.3934,0.7577,0.0164,95.6418,0.0002,0.0012,0.001,0.0716,1.5115,4.262,0.1386,373.524,0.001,0.0027,0.0023,0.163,2.5936,9.6782,0.4192,661.5999,0.0023,0.0044,0.0036,0.2606,3.3248,15.424,0.8342,876.0495,0.0036,0.0058,0.0049,0.3503,3.7332,20.6806,1.3408,1015.3282,0.0049,0.0071,0.0059,0.4278,3.9148,25.1827,1.9029,1097.9443,0.0059,0.0082,0.0068,0.4926,3.9515,28.9169,2.4948,1141.6747,0.0068,0.0091,0.0076,0.5461,3.9,31.9625,3.0991,1159.6786,0.0076,0.0098,0.0082,0.5898,3.7972,34.4223,3.7048,1160.967,0.0082,0.0104,0.0086,0.6254,3.6663,36.3957,4.3045,1151.5488,0.0086,0.0108,0.009,0.6543,3.5218,37.9691,4.894,1135.4014,0.009,0.0112,0.0094,0.6777,3.3725,39.215,5.4703,1115.1563,0.0094,0.0115,0.0096,0.6966,3.224,40.193,6.032,1092.5574,0.0096,0.0118,0.0098,0.7118,3.0795,40.9512,6.5783,1068.7615,0.0098,0.012,0.01,0.7239,2.9408,41.5289,7.1087,1044.5345,0.01,0.0121,0.0101,0.7335,2.8089,41.9578,7.6235,1020.3803,0.0101,0.0122,0.0102,0.741,2.684,42.2636,8.1227,996.6262,0.0102,0.0123,0.0102,0.7468,2.5664,42.4674,8.6067,973.4797,0.0102,0.0124,0.0103,0.7511,2.4558,42.5865,9.0761,951.0669,0.0103,0.0124,0.0103,0.7541,2.3519,42.635,9.5313,929.4583,0.0103,0.0124,0.0103,0.7562,2.2544,42.6245,9.9729,908.6868,0.0103,0.0124,0.0103,0.7573,2.163,42.5647,10.4013,888.7598,0.0103,0.0124,0.0103,0.7578,2.0771,42.4636,10.8171,869.6678,0.0103,0.0124,0.0103,0.7576,1.9965,42.3281,11.2208,851.3901,0.0103,0.0124,0.0103,0.7569,1.9208,42.1637,11.6129,833.899,0.0103,0.0123,0.0103,0.7558,1.8496,41.9751,11.9938,817.1627,0.0103,0.0123,0.0103,0.7543,1.7825,41.7664,12.364,801.147,0.0103,0.0123,0.0102,0.7525,1.7194,41.5408,12.7239,785.8172,0.0102 -MSFT,265,0.0,0.0,0.0,-0.0106,0.0,0.0,0.0044,0.0,0.0,0.0,0.001,0.0198,0.0697,0.0012,8.5673,0.0,0.0002,0.0002,0.0137,0.2807,0.9955,0.0266,83.917,0.0002,0.0007,0.0005,0.0497,0.783,3.6025,0.1283,234.1843,0.0005,0.0014,0.0012,0.1051,1.3328,7.5942,0.3377,405.8111,0.0012,0.0023,0.0019,0.1702,1.8052,12.2711,0.6542,561.1208,0.0019,0.0033,0.0026,0.2374,2.1631,17.0744,1.0611,686.7792,0.0026,0.0041,0.0033,0.302,2.4125,21.6708,1.5379,782.2484,0.0033,0.005,0.004,0.3618,2.5728,25.8951,2.0657,851.6884,0.004,0.0057,0.0046,0.4157,2.6647,29.6845,2.629,900.2259,0.0046,0.0063,0.0051,0.4636,2.7061,33.0332,3.2155,932.5541,0.0051,0.0069,0.0055,0.5059,2.7108,35.9639,3.8158,952.5443,0.0055,0.0074,0.0059,0.5431,2.6896,38.5121,4.4231,963.2436,0.0059,0.0079,0.0063,0.5755,2.6504,40.7177,5.032,966.9973,0.0063,0.0082,0.0066,0.6038,2.5988,42.6197,5.6387,965.5906,0.0066,0.0086,0.0069,0.6285,2.5392,44.255,6.2402,960.3763,0.0069,0.0088,0.0071,0.6499,2.4746,45.6566,6.8347,952.3775,0.0071,0.0091,0.0073,0.6685,2.4072,46.8541,7.4204,942.3686,0.0073,0.0093,0.0075,0.6847,2.3386,47.8734,7.9965,930.9356,0.0075,0.0095,0.0076,0.6987,2.27,48.7369,8.5622,918.5227,0.0076,0.0096,0.0077,0.7108,2.2022,49.4645,9.1171,905.4666,0.0077,0.0098,0.0078,0.7213,2.1357,50.073,9.6609,892.0226,0.0078,0.0099,0.0079,0.7303,2.071,50.5774,10.1935,878.3837,0.0079,0.01,0.008,0.7381,2.0082,50.9904,10.7148,864.6957,0.008,0.0101,0.0081,0.7447,1.9475,51.3232,11.225,851.068,0.0081,0.0101,0.0081,0.7503,1.889,51.5855,11.7241,837.5822,0.0081,0.0102,0.0082,0.755,1.8328,51.7857,12.2123,824.2984,0.0082,0.0102,0.0082,0.759,1.7788,51.9312,12.6899,811.2602,0.0082,0.0103,0.0082,0.7622,1.7269,52.0284,13.1571,798.4983,0.0082 -MSFT,270,0.0,0.0,0.0,-0.0107,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0093,0.0037,0.0001,0.4471,0.0,0.0,0.0,0.0018,0.0284,0.1572,0.0036,12.9516,0.0,0.0001,0.0001,0.0115,0.173,0.9822,0.0298,61.9568,0.0001,0.0004,0.0003,0.0338,0.4206,2.8732,0.1088,147.9937,0.0003,0.0008,0.0006,0.068,0.7142,5.7765,0.2622,252.9806,0.0006,0.0013,0.001,0.1109,1.0032,9.3959,0.4973,359.7401,0.001,0.0019,0.0014,0.1586,1.2593,13.4077,0.8104,457.9697,0.0014,0.0024,0.0019,0.208,1.4717,17.5507,1.1926,543.1361,0.0019,0.003,0.0023,0.257,1.6398,21.6425,1.6329,614.201,0.0023,0.0036,0.0027,0.3042,1.7675,25.5672,2.1205,671.9055,0.0027,0.0041,0.0031,0.3488,1.8606,29.2582,2.6453,717.7441,0.0031,0.0046,0.0035,0.3904,1.925,32.6823,3.1989,753.4175,0.0035,0.005,0.0038,0.4288,1.9661,35.8284,3.774,780.5676,0.0038,0.0054,0.0042,0.4641,1.9885,38.6993,4.3645,800.666,0.0042,0.0058,0.0044,0.4963,1.9962,41.3056,4.9656,814.9802,0.0044,0.0061,0.0047,0.5257,1.9924,43.6629,5.5731,824.5775,0.0047,0.0064,0.0049,0.5524,1.9796,45.7886,6.1839,830.3445,0.0049,0.0067,0.0052,0.5766,1.9599,47.7009,6.7953,833.0116,0.0052,0.0069,0.0053,0.5986,1.935,49.4177,7.4053,833.1781,0.0053,0.0072,0.0055,0.6185,1.9062,50.9563,8.012,831.3344,0.0055,0.0074,0.0057,0.6364,1.8746,52.3328,8.6143,827.8811,0.0057,0.0076,0.0058,0.6527,1.8409,53.5621,9.211,823.1454,0.0058,0.0077,0.0059,0.6674,1.8059,54.658,9.8013,817.3949,0.0059,0.0079,0.0061,0.6807,1.7701,55.6328,10.3846,810.848,0.0061,0.008,0.0062,0.6927,1.7338,56.498,10.9603,803.6835,0.0062,0.0081,0.0063,0.7035,1.6975,57.2638,11.5282,796.0477,0.0063,0.0082,0.0063,0.7133,1.6613,57.9395,12.0879,788.0601,0.0063,0.0083,0.0064,0.7221,1.6255,58.5335,12.6392,779.8188,0.0064 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diff --git a/tests/options/csv/test_options_controller/test_collect_third_order_greeks.csv b/tests/options/csv/test_options_controller/test_collect_third_order_greeks.csv index 5c9468c..92d294c 100644 --- a/tests/options/csv/test_options_controller/test_collect_third_order_greeks.csv +++ b/tests/options/csv/test_options_controller/test_collect_third_order_greeks.csv @@ -1,39 +1,39 @@ 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MSFT,255,UUUUUDDDDD,,,,,,0.0,0.0,0.0,0.0,0.0,0.0 MSFT,255,UUUUDDDDDD,,,,,,,0.0,0.0,0.0,0.0,0.0 MSFT,255,UUUDDDDDDD,,,,,,,,0.0,0.0,0.0,0.0 MSFT,255,UUDDDDDDDD,,,,,,,,,0.0,0.0,0.0 MSFT,255,UDDDDDDDDD,,,,,,,,,,0.0,0.0 MSFT,255,DDDDDDDDDD,,,,,,,,,,,0.0 -MSFT,260,UUUUUUUUUU,35.7138,51.0293,71.3822,97.5743,130.1224,169.1262,214.2882,265.2061,321.8792,384.9452,455.1126 -MSFT,260,UUUUUUUUUD,,19.7905,29.9004,44.2424,63.9274,89.9194,122.7399,162.2002,207.4683,257.8665,313.9634 -MSFT,260,UUUUUUUUDD,,,9.2457,14.9576,23.7614,36.9333,55.9157,81.9869,115.6399,155.8706,200.6744 -MSFT,260,UUUUUUUDDD,,,,3.2709,5.7549,10.0048,17.1324,28.7663,47.0297,74.0067,109.7463 -MSFT,260,UUUUUUDDDD,,,,,0.6658,1.2993,2.5355,4.9477,9.655,18.8407,36.7657 +MSFT,260,UUUUUUUUUU,43.3169,60.1385,81.8702,109.1193,142.2082,181.0917,225.4151,274.7842,329.1744,389.0963,455.1126 +MSFT,260,UUUUUUUUUD,,24.1722,35.4058,50.858,71.461,97.955,130.6474,169.2284,212.883,260.9775,313.9634 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-MSFT,270,UUUUUUUUDD,,,7.6552,12.5191,20.1399,31.7709,48.9452,73.2439,105.7257,145.9137,190.6744 -MSFT,270,UUUUUUUDDD,,,,2.5655,4.5491,7.9851,13.8414,23.6134,39.4508,64.0498,99.7463 -MSFT,270,UUUUUUDDDD,,,,,0.4847,0.9459,1.8459,3.602,7.0289,13.7162,26.7657 +MSFT,270,UUUUUUUUUU,38.7316,54.3482,74.8222,100.89,133.0277,171.3155,215.4151,264.7842,319.1744,379.0963,445.1126 +MSFT,270,UUUUUUUUUD,,20.9583,31.0468,45.1545,64.3142,89.4524,121.1259,159.2284,202.883,250.9775,303.9634 +MSFT,270,UUUUUUUUDD,,,9.4767,14.9909,23.349,35.7045,53.4048,77.7616,109.5452,148.1468,190.6744 +MSFT,270,UUUUUUUDDD,,,,3.2009,5.4785,9.2873,15.5597,25.6845,41.5889,65.6129,99.7463 +MSFT,270,UUUUUUDDDD,,,,,0.6088,1.1438,2.1487,4.0368,7.5837,14.2472,26.7657 MSFT,270,UUUUUDDDDD,,,,,,0.0,0.0,0.0,0.0,0.0,0.0 MSFT,270,UUUUDDDDDD,,,,,,,0.0,0.0,0.0,0.0,0.0 MSFT,270,UUUDDDDDDD,,,,,,,,0.0,0.0,0.0,0.0 MSFT,270,UUDDDDDDDD,,,,,,,,,0.0,0.0,0.0 MSFT,270,UDDDDDDDDD,,,,,,,,,,0.0,0.0 MSFT,270,DDDDDDDDDD,,,,,,,,,,,0.0 -MSFT,275,UUUUUUUUUU,29.7725,43.2944,61.6974,85.9777,116.9119,154.8413,199.5447,250.3989,307.008,370.0098,440.1126 -MSFT,275,UUUUUUUUUD,,15.6982,24.1645,36.4988,53.9391,77.7306,108.8143,147.393,192.5971,242.931,298.9634 -MSFT,275,UUUUUUUUDD,,,6.8599,11.2998,18.3291,29.1897,45.4599,68.8724,100.7686,140.9352,185.6744 -MSFT,275,UUUUUUUDDD,,,,2.2127,3.9462,6.9752,12.196,21.037,35.6614,59.0713,94.7463 -MSFT,275,UUUUUUDDDD,,,,,0.3942,0.7692,1.501,2.9291,5.7159,11.1539,21.7657 +MSFT,275,UUUUUUUUUU,36.4389,51.453,71.2982,96.7753,128.4374,166.4274,210.4151,259.7842,314.1744,374.0963,440.1126 +MSFT,275,UUUUUUUUUD,,19.3514,28.8673,42.3028,60.7407,85.201,116.3652,154.2284,197.883,245.9775,298.9634 +MSFT,275,UUUUUUUUDD,,,8.5213,13.5765,21.3186,32.9025,49.7333,73.2732,104.5452,143.1468,185.6744 +MSFT,275,UUUUUUUDDD,,,,2.7681,4.7652,8.135,13.7475,22.9426,37.6826,60.6129,94.7463 +MSFT,275,UUUUUUDDDD,,,,,0.4951,0.9301,1.7473,3.2827,6.167,11.5858,21.7657 MSFT,275,UUUUUDDDDD,,,,,,0.0,0.0,0.0,0.0,0.0,0.0 MSFT,275,UUUUDDDDDD,,,,,,,0.0,0.0,0.0,0.0,0.0 MSFT,275,UUUDDDDDDD,,,,,,,,0.0,0.0,0.0,0.0 MSFT,275,UUDDDDDDDD,,,,,,,,,0.0,0.0,0.0 MSFT,275,UDDDDDDDDD,,,,,,,,,,0.0,0.0 MSFT,275,DDDDDDDDDD,,,,,,,,,,,0.0 -MSFT,280,UUUUUUUUUU,27.792,40.7162,58.4692,82.1122,112.5084,150.0797,194.6302,245.4631,302.0509,365.0313,435.1126 -MSFT,280,UUUUUUUUUD,,14.3341,22.2525,33.9175,50.6097,73.6676,104.1725,142.4572,187.64,237.9526,293.9634 -MSFT,280,UUUUUUUUDD,,,6.0647,10.0805,16.5184,26.6086,41.9747,64.5009,95.8116,135.9567,180.6744 -MSFT,280,UUUUUUUDDD,,,,1.86,3.3433,5.9654,10.5505,18.4605,31.8719,54.0928,89.7463 -MSFT,280,UUUUUUDDDD,,,,,0.3036,0.5925,1.1562,2.2562,4.4028,8.5916,16.7657 +MSFT,280,UUUUUUUUUU,34.1462,48.5578,67.7742,92.6606,123.8471,161.5393,205.4151,254.7842,309.1744,369.0963,435.1126 +MSFT,280,UUUUUUUUUD,,17.7444,26.6878,39.451,57.1673,80.9497,111.6044,149.2284,192.883,240.9775,293.9634 +MSFT,280,UUUUUUUUDD,,,7.566,12.1621,19.2882,30.1006,46.0617,68.7847,99.5452,138.1468,180.6744 +MSFT,280,UUUUUUUDDD,,,,2.3352,4.0519,6.9826,11.9354,20.2007,33.7763,55.6129,89.7463 +MSFT,280,UUUUUUDDDD,,,,,0.3814,0.7164,1.3459,2.5286,4.7503,8.9243,16.7657 MSFT,280,UUUUUDDDDD,,,,,,0.0,0.0,0.0,0.0,0.0,0.0 MSFT,280,UUUUDDDDDD,,,,,,,0.0,0.0,0.0,0.0,0.0 MSFT,280,UUUDDDDDDD,,,,,,,,0.0,0.0,0.0,0.0 MSFT,280,UUDDDDDDDD,,,,,,,,,0.0,0.0,0.0 MSFT,280,UDDDDDDDDD,,,,,,,,,,0.0,0.0 MSFT,280,DDDDDDDDDD,,,,,,,,,,,0.0 -MSFT,285,UUUUUUUUUU,25.8116,38.1379,55.2409,78.2467,108.1049,145.3181,189.7156,240.5274,297.0938,360.0528,430.1126 -MSFT,285,UUUUUUUUUD,,12.97,20.3406,31.3363,47.2803,69.6047,99.5306,137.5215,182.6829,232.9741,288.9634 -MSFT,285,UUUUUUUUDD,,,5.2694,8.8613,14.7076,24.0274,38.4895,60.1294,90.8545,130.9782,175.6744 -MSFT,285,UUUUUUUDDD,,,,1.5073,2.7404,4.9555,8.905,15.8841,28.0825,49.1143,84.7463 -MSFT,285,UUUUUUDDDD,,,,,0.2131,0.4158,0.8114,1.5834,3.0898,6.0294,11.7657 +MSFT,285,UUUUUUUUUU,31.8536,45.6627,64.2502,88.5459,119.2568,156.6512,200.4151,249.7842,304.1744,364.0963,430.1126 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-MSFT,290,UUUUUUUDDD,,,,1.1546,2.1375,3.9457,7.2595,13.3076,24.293,44.1358,79.7463 -MSFT,290,UUUUUUDDDD,,,,,0.1225,0.2391,0.4666,0.9105,1.7767,3.4671,6.7657 +MSFT,290,UUUUUUUUUU,29.5609,42.7675,60.7261,84.4312,114.6665,151.7631,195.4151,244.7842,299.1744,359.0963,425.1126 +MSFT,290,UUUUUUUUUD,,14.5305,22.3289,33.7475,50.0204,72.4471,102.0829,139.2284,182.883,230.9775,283.9634 +MSFT,290,UUUUUUUUDD,,,5.6553,9.3333,15.2274,24.4968,38.7186,59.8078,89.5452,128.1468,170.6744 +MSFT,290,UUUUUUUDDD,,,,1.4694,2.6253,4.6779,8.311,14.717,25.9638,45.6129,79.7463 +MSFT,290,UUUUUUDDDD,,,,,0.1539,0.2891,0.5431,1.0204,1.917,3.6013,6.7657 MSFT,290,UUUUUDDDDD,,,,,,0.0,0.0,0.0,0.0,0.0,0.0 MSFT,290,UUUUDDDDDD,,,,,,,0.0,0.0,0.0,0.0,0.0 MSFT,290,UUUDDDDDDD,,,,,,,,0.0,0.0,0.0,0.0 diff --git a/tests/options/csv/test_options_controller/test_get_binomial_model_1.csv b/tests/options/csv/test_options_controller/test_get_binomial_model_1.csv index 293e278..3c089de 100644 --- 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MSFT,190,DDDDDDDDDD,,,,,,,,,,,110.6636 -MSFT,195,UUUUUUUUUU,8.1489,4.092,1.6203,0.4304,0.0487,0.0,0.0,0.0,0.0,0.0,0.0 -MSFT,195,UUUUUUUUUD,,12.5235,6.7496,2.8964,0.8391,0.1008,0.0,0.0,0.0,0.0,0.0 -MSFT,195,UUUUUUUUDD,,,18.7579,10.8958,5.1038,1.6295,0.2085,0.0,0.0,0.0,0.0 -MSFT,195,UUUUUUUDDD,,,,27.2621,17.1349,8.8336,3.1509,0.4315,0.0,0.0,0.0 -MSFT,195,UUUUUUDDDD,,,,,38.2441,26.0906,14.9384,6.0627,0.893,0.0,0.0 -MSFT,195,UUUUUDDDDD,,,,,,51.4727,38.1491,24.4835,11.5989,1.8479,0.0 -MSFT,195,UUUUDDDDDD,,,,,,,66.0597,52.9804,38.3648,22.0425,3.824 -MSFT,195,UUUDDDDDDD,,,,,,,,80.5177,68.9511,56.0153,41.5584 -MSFT,195,UUDDDDDDDD,,,,,,,,,93.5002,83.2826,71.8447 -MSFT,195,UDDDDDDDDD,,,,,,,,,,105.1678,96.1532 +MSFT,195,UUUUUUUUUU,7.0605,3.4734,1.3484,0.3526,0.04,0.0,0.0,0.0,0.0,0.0,0.0 +MSFT,195,UUUUUUUUUD,,11.1429,5.8918,2.4818,0.7083,0.0856,0.0,0.0,0.0,0.0,0.0 +MSFT,195,UUUUUUUUDD,,,17.1191,9.7728,4.5002,1.4171,0.183,0.0,0.0,0.0,0.0 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MSFT,235,DDDDDDDDDD,,,,,,,,,,,155.6636 -MSFT,240,UUUUUUUUUU,23.2896,14.2848,7.4628,3.0262,0.7684,0.0477,0.0,0.0,0.0,0.0,0.0 -MSFT,240,UUUUUUUUUD,,33.046,21.6463,12.2339,5.4473,1.5396,0.0987,0.0,0.0,0.0,0.0 -MSFT,240,UUUUUUUUDD,,,45.429,31.8203,19.5397,9.6397,3.0813,0.2043,0.0,0.0,0.0 -MSFT,240,UUUUUUUDDD,,,,60.2646,45.1264,30.2121,16.6804,6.1596,0.4227,0.0,0.0 -MSFT,240,UUUUUUDDDD,,,,,76.8544,61.3438,44.8306,27.9856,12.2981,0.8747,0.0 -MSFT,240,UUUUUDDDDD,,,,,,93.9867,79.4013,63.0929,44.8705,24.5215,1.81 -MSFT,240,UUUUDDDDDD,,,,,,,110.2904,97.4022,82.9785,66.8489,48.824 -MSFT,240,UUUDDDDDDD,,,,,,,,124.9395,113.5648,100.8217,86.5584 -MSFT,240,UUDDDDDDDD,,,,,,,,,138.1139,128.089,116.8447 -MSFT,240,UDDDDDDDDD,,,,,,,,,,149.9742,141.1532 +MSFT,240,UUUUUUUUUU,21.0176,12.6395,6.4686,2.569,0.6404,0.0405,0.0,0.0,0.0,0.0,0.0 +MSFT,240,UUUUUUUUUD,,30.5527,19.6625,10.9068,4.7639,1.3232,0.0866,0.0,0.0,0.0,0.0 +MSFT,240,UUUUUUUUDD,,,42.9469,29.6274,17.8979,8.6798,2.7306,0.1852,0.0,0.0,0.0 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-MSFT,265,UUUUDDDDDD,,,,,,,134.863,122.0809,107.7639,91.7414,73.824 -MSFT,265,UUUDDDDDDD,,,,,,,,149.6182,138.3501,125.7142,111.5584 -MSFT,265,UUDDDDDDDD,,,,,,,,,162.8993,152.9814,141.8447 -MSFT,265,UDDDDDDDDD,,,,,,,,,,174.8667,166.1532 +MSFT,265,UUUUUUUUUU,34.5543,23.1636,13.8486,6.9956,2.689,0.6,0.0,0.0,0.0,0.0,0.0 +MSFT,265,UUUUUUUUUD,,47.518,33.765,21.648,11.8968,5.0666,1.2829,0.0,0.0,0.0,0.0 +MSFT,265,UUUUUUUUDD,,,63.1702,47.5554,32.7459,19.6702,9.3727,2.7429,0.0,0.0,0.0 +MSFT,265,UUUUUUUDDD,,,,80.9413,64.4101,47.6272,31.3898,16.9181,5.8647,0.0,0.0 +MSFT,265,UUUUUUDDDD,,,,,99.7554,83.5106,66.1069,47.8599,29.498,12.5392,0.0 +MSFT,265,UUUUUDDDDD,,,,,,118.2436,103.3177,86.8738,68.7575,48.7987,26.81 +MSFT,265,UUUUDDDDDD,,,,,,,135.2306,122.0324,107.4919,91.4726,73.824 +MSFT,265,UUUDDDDDDD,,,,,,,,150.2514,138.5809,125.7235,111.5584 +MSFT,265,UUDDDDDDDD,,,,,,,,,163.5336,153.2139,141.8447 +MSFT,265,UDDDDDDDDD,,,,,,,,,,175.2783,166.1532 MSFT,265,DDDDDDDDDD,,,,,,,,,,,185.6636 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-MSFT,275,UUUUDDDDDD,,,,,,,144.692,131.9524,117.678,101.6983,83.824 -MSFT,275,UUUDDDDDDD,,,,,,,,159.4897,148.2643,135.6712,121.5584 -MSFT,275,UUDDDDDDDD,,,,,,,,,172.8134,162.9384,151.8447 -MSFT,275,UDDDDDDDDD,,,,,,,,,,184.8237,176.1532 +MSFT,275,UUUUUUUUUU,39.969,27.3733,16.8006,8.7662,3.5085,0.8238,0.0,0.0,0.0,0.0,0.0 +MSFT,275,UUUUUUUUUD,,54.3041,39.4061,25.9445,14.7499,6.5639,1.7614,0.0,0.0,0.0,0.0 +MSFT,275,UUUUUUUUDD,,,71.2595,54.7266,38.685,24.0664,12.0296,3.766,0.0,0.0,0.0 +MSFT,275,UUUUUUUDDD,,,,90.0755,72.9834,55.3225,37.7654,21.4344,8.0521,0.0,0.0 +MSFT,275,UUUUUUDDDD,,,,,109.528,93.0833,75.3041,56.3517,36.6646,17.2162,0.0 +MSFT,275,UUUUUDDDDD,,,,,,128.2436,113.3177,96.8738,78.7575,58.7987,36.81 +MSFT,275,UUUUDDDDDD,,,,,,,145.2306,132.0324,117.4919,101.4726,83.824 +MSFT,275,UUUDDDDDDD,,,,,,,,160.2514,148.5809,135.7235,121.5584 +MSFT,275,UUDDDDDDDD,,,,,,,,,173.5336,163.2139,151.8447 +MSFT,275,UDDDDDDDDD,,,,,,,,,,185.2783,176.1532 MSFT,275,DDDDDDDDDD,,,,,,,,,,,195.6636 -MSFT,280,UUUUUUUUUU,45.7583,32.1363,20.2807,10.9129,4.519,1.1018,0.0,0.0,0.0,0.0,0.0 -MSFT,280,UUUUUUUUUD,,60.6109,44.9946,30.3952,17.7905,8.1829,2.2801,0.0,0.0,0.0,0.0 -MSFT,280,UUUUUUUUDD,,,77.7108,60.877,44.0323,28.1372,14.5155,4.7182,0.0,0.0,0.0 -MSFT,280,UUUUUUUDDD,,,,96.2538,79.2818,61.2802,42.8328,25.0341,9.7637,0.0,0.0 -MSFT,280,UUUUUUDDDD,,,,,115.1086,99.0772,81.3882,62.0886,41.4503,20.2044,0.0 -MSFT,280,UUUUUDDDDD,,,,,,133.1337,118.7174,102.5789,84.5271,64.3494,41.81 -MSFT,280,UUUUDDDDDD,,,,,,,149.6065,136.8882,122.6351,106.6768,88.824 -MSFT,280,UUUDDDDDDD,,,,,,,,164.4254,153.2214,140.6497,126.5584 -MSFT,280,UUDDDDDDDD,,,,,,,,,177.7705,167.9169,156.8447 -MSFT,280,UDDDDDDDDD,,,,,,,,,,189.8022,181.1532 +MSFT,280,UUUUUUUUUU,42.6763,29.4781,18.2766,9.6515,3.9182,0.9357,0.0,0.0,0.0,0.0,0.0 +MSFT,280,UUUUUUUUUD,,57.6972,42.2266,28.0928,16.1765,7.3126,2.0006,0.0,0.0,0.0,0.0 +MSFT,280,UUUUUUUUDD,,,75.3042,58.3122,41.6546,26.2645,13.358,4.2776,0.0,0.0,0.0 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-MSFT,285,UUUUDDDDDD,,,,,,,154.5211,141.8239,127.5922,111.6553,93.824 -MSFT,285,UUUDDDDDDD,,,,,,,,169.3612,158.1784,145.6281,131.5584 -MSFT,285,UUDDDDDDDD,,,,,,,,,182.7276,172.8954,161.8447 -MSFT,285,UDDDDDDDDD,,,,,,,,,,194.7807,186.1532 +MSFT,285,UUUUUUUUUU,45.3837,31.583,19.7526,10.5368,4.3279,1.0476,0.0,0.0,0.0,0.0,0.0 +MSFT,285,UUUUUUUUUD,,61.0902,45.0471,30.241,17.6031,8.0612,2.2399,0.0,0.0,0.0,0.0 +MSFT,285,UUUUUUUUDD,,,79.3488,61.8978,44.6242,28.4626,14.6865,4.7891,0.0,0.0,0.0 +MSFT,285,UUUUUUUDDD,,,,99.2097,81.5568,63.0178,44.1411,25.9506,10.2396,0.0,0.0 +MSFT,285,UUUUUUDDDD,,,,,119.3005,102.656,84.5013,64.8435,43.8312,21.8933,0.0 +MSFT,285,UUUUUDDDDD,,,,,,138.2436,123.3177,106.8738,88.7575,68.7987,46.81 +MSFT,285,UUUUDDDDDD,,,,,,,155.2306,142.0324,127.4919,111.4726,93.824 +MSFT,285,UUUDDDDDDD,,,,,,,,170.2514,158.5809,145.7235,131.5584 +MSFT,285,UUDDDDDDDD,,,,,,,,,183.5336,173.2139,161.8447 +MSFT,285,UDDDDDDDDD,,,,,,,,,,195.2783,186.1532 MSFT,285,DDDDDDDDDD,,,,,,,,,,,205.6636 -MSFT,290,UUUUUUUUUU,51.3754,36.5992,23.4852,12.8846,5.4566,1.3654,0.0,0.0,0.0,0.0,0.0 -MSFT,290,UUUUUUUUUD,,67.5022,50.8317,34.9355,20.8763,9.8438,2.8254,0.0,0.0,0.0,0.0 -MSFT,290,UUUUUUUUDD,,,85.7812,68.1412,50.1555,32.7616,17.374,5.8467,0.0,0.0,0.0 -MSFT,290,UUUUUUUDDD,,,,105.2511,87.8206,69.0473,49.3709,29.7527,12.0989,0.0,0.0 -MSFT,290,UUUUUUDDDD,,,,,124.6722,108.5106,90.5276,70.6143,48.7384,25.0369,0.0 -MSFT,290,UUUUUDDDDD,,,,,,142.9204,128.5464,112.4504,94.4412,74.3064,51.81 -MSFT,290,UUUUDDDDDD,,,,,,,159.4356,146.7597,132.5492,116.6338,98.824 -MSFT,290,UUUDDDDDDD,,,,,,,,174.2969,163.1355,150.6066,136.5584 -MSFT,290,UUDDDDDDDD,,,,,,,,,187.6846,177.8739,166.8447 -MSFT,290,UDDDDDDDDD,,,,,,,,,,199.7591,191.1532 +MSFT,290,UUUUUUUUUU,48.091,33.6878,21.2286,11.4221,4.7377,1.1595,0.0,0.0,0.0,0.0,0.0 +MSFT,290,UUUUUUUUUD,,64.4833,47.8676,32.3893,19.0296,8.8099,2.4792,0.0,0.0,0.0,0.0 +MSFT,290,UUUUUUUUDD,,,83.3935,65.4834,47.5938,30.6606,16.0149,5.3007,0.0,0.0,0.0 +MSFT,290,UUUUUUUDDD,,,,103.7769,85.8435,66.8654,47.3289,28.2087,11.3334,0.0,0.0 +MSFT,290,UUUUUUDDDD,,,,,124.1868,107.4423,89.0999,69.0894,47.4146,24.2318,0.0 +MSFT,290,UUUUUDDDDD,,,,,,143.2436,128.3177,111.8738,93.7575,73.7987,51.81 +MSFT,290,UUUUDDDDDD,,,,,,,160.2306,147.0324,132.4919,116.4726,98.824 +MSFT,290,UUUDDDDDDD,,,,,,,,175.2514,163.5809,150.7235,136.5584 +MSFT,290,UUDDDDDDDD,,,,,,,,,188.5336,178.2139,166.8447 +MSFT,290,UDDDDDDDDD,,,,,,,,,,200.2783,191.1532 MSFT,290,DDDDDDDDDD,,,,,,,,,,,210.6636 diff --git a/tests/options/csv/test_options_controller/test_get_binomial_model_2.csv b/tests/options/csv/test_options_controller/test_get_binomial_model_2.csv index 5f1b2e7..687ce51 100644 --- a/tests/options/csv/test_options_controller/test_get_binomial_model_2.csv +++ b/tests/options/csv/test_options_controller/test_get_binomial_model_2.csv @@ -1,394 +1,394 @@ Ticker,Strike Price,Movement,2022-12-30,2023-02-04,2023-03-13,2023-04-18,2023-05-25,2023-06-30,2023-08-06,2023-09-11,2023-10-18,2023-11-23,2023-12-30 -AAPL,95,UUUUUUUUUU,59.368,70.0553,83.8909,101.2009,122.097,146.4706,174.1294,205.0749,239.6923,278.4123,321.7158 -AAPL,95,UUUUUUUUUD,,48.5202,55.9511,66.2114,79.8582,97.2387,118.3324,142.739,170.0513,200.6101,234.796 -AAPL,95,UUUUUUUUDD,,,41.0422,45.5274,52.2741,62.0678,75.6502,93.4053,114.9363,139.0362,166.0062 -AAPL,95,UUUUUUUDDD,,,,36.6336,38.7511,42.3143,48.1619,57.4234,71.3173,90.3055,111.5648 -AAPL,95,UUUUUUDDDD,,,,,34.7062,35.3034,36.4665,38.732,43.1446,51.7392,68.4789 +AAPL,95,UUUUUUUUUU,65.7169,77.2023,91.6894,109.3963,130.3491,154.4013,181.3675,211.2494,244.3622,281.0553,321.7158 +AAPL,95,UUUUUUUUUD,,52.6499,60.7203,71.5444,85.5584,102.985,123.722,147.371,173.5771,202.6166,234.796 +AAPL,95,UUUUUUUUDD,,,43.4683,48.4059,55.6007,65.7321,79.3926,96.8166,117.5565,140.5389,166.0062 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AAPL,125,UUUUUDDDDD,,,,,,4.38,4.38,4.38,4.38,4.38,4.38 AAPL,125,UUUUDDDDDD,,,,,,,4.38,4.38,4.38,4.38,0.0 AAPL,125,UUUDDDDDDD,,,,,,,,4.38,4.38,4.38,0.0 AAPL,125,UUDDDDDDDD,,,,,,,,,4.38,4.38,0.0 AAPL,125,UDDDDDDDDD,,,,,,,,,,4.38,0.0 AAPL,125,DDDDDDDDDD,,,,,,,,,,,0.0 -AAPL,130,UUUUUUUUUU,26.0323,36.5689,50.2283,67.3451,88.0445,112.2332,139.7278,170.5246,204.9928,243.5629,286.7158 -AAPL,130,UUUUUUUUUD,,15.0476,22.3536,32.4543,45.9098,63.0793,83.9643,108.1888,135.3518,165.7607,199.796 -AAPL,130,UUUUUUUUDD,,,7.404,11.8001,18.4195,28.0411,41.4084,58.9246,80.2368,104.1867,131.0062 -AAPL,130,UUUUUUUDDD,,,,2.7902,4.8584,8.341,14.0608,23.1304,36.762,55.456,76.5648 -AAPL,130,UUUUUUDDDD,,,,,0.6132,1.1944,2.3263,4.5309,8.825,17.1887,33.4789 +AAPL,130,UUUUUUUUUU,31.0527,42.4634,56.8726,74.5062,95.4,119.4152,146.3675,176.2494,209.3622,246.0553,286.7158 +AAPL,130,UUUUUUUUUD,,18.0708,26.0702,36.8109,50.7353,68.0779,88.7517,112.371,138.5771,167.6166,199.796 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MSFT,215,UUUDDDDDDD,,,,,,,,23.19,23.19,23.19,0.0 MSFT,215,UUDDDDDDDD,,,,,,,,,23.19,23.19,0.0 MSFT,215,UDDDDDDDDD,,,,,,,,,,23.19,0.0 MSFT,215,DDDDDDDDDD,,,,,,,,,,,0.0 -MSFT,220,UUUUUUUUUU,61.8598,79.8233,103.062,132.0943,167.0581,207.6984,253.6044,304.6921,361.5358,424.7732,495.1126 -MSFT,220,UUUUUUUUUD,,43.3611,55.8906,73.1923,96.1931,125.4484,160.8666,201.6862,247.1249,297.6944,353.9634 -MSFT,220,UUUUUUUUDD,,,30.4602,38.0407,49.4528,66.0258,89.0061,119.0117,155.2964,195.6986,240.6744 -MSFT,220,UUUUUUUDDD,,,,22.6926,26.2774,32.3183,42.2441,57.9792,81.5931,113.8347,149.7463 -MSFT,220,UUUUUUDDDD,,,,,19.0932,20.1053,22.0802,25.9339,33.4542,48.1291,76.7657 +MSFT,220,UUUUUUUUUU,71.5075,90.9572,115.4319,145.2576,180.421,220.6036,265.4151,314.7842,369.1744,429.0963,495.1126 +MSFT,220,UUUUUUUUUD,,49.3717,63.1027,81.4873,105.2383,134.6893,169.6038,209.2284,252.883,300.9775,353.9634 +MSFT,220,UUUUUUUUDD,,,33.7445,42.1793,54.4564,71.7203,94.9533,124.5071,159.5452,198.1468,240.6744 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MSFT,270,UUUUUDDDDD,,,,,,0.0,0.0,0.0,0.0,0.0,0.0 MSFT,270,UUUUDDDDDD,,,,,,,0.0,0.0,0.0,0.0,0.0 MSFT,270,UUUDDDDDDD,,,,,,,,0.0,0.0,0.0,0.0 MSFT,270,UUDDDDDDDD,,,,,,,,,0.0,0.0,0.0 MSFT,270,UDDDDDDDDD,,,,,,,,,,0.0,0.0 MSFT,270,DDDDDDDDDD,,,,,,,,,,,0.0 -MSFT,275,UUUUUUUUUU,29.7725,43.2944,61.6974,85.9777,116.9119,154.8413,199.5447,250.3989,307.008,370.0098,440.1126 -MSFT,275,UUUUUUUUUD,,15.6982,24.1645,36.4988,53.9391,77.7306,108.8143,147.393,192.5971,242.931,298.9634 -MSFT,275,UUUUUUUUDD,,,6.8599,11.2998,18.3291,29.1897,45.4599,68.8724,100.7686,140.9352,185.6744 -MSFT,275,UUUUUUUDDD,,,,2.2127,3.9462,6.9752,12.196,21.037,35.6614,59.0713,94.7463 -MSFT,275,UUUUUUDDDD,,,,,0.3942,0.7692,1.501,2.9291,5.7159,11.1539,21.7657 +MSFT,275,UUUUUUUUUU,36.4389,51.453,71.2982,96.7753,128.4374,166.4274,210.4151,259.7842,314.1744,374.0963,440.1126 +MSFT,275,UUUUUUUUUD,,19.3514,28.8673,42.3028,60.7407,85.201,116.3652,154.2284,197.883,245.9775,298.9634 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MSFT,290,UUUUDDDDDD,,,,,,,0.0,0.0,0.0,0.0,0.0 MSFT,290,UUUDDDDDDD,,,,,,,,0.0,0.0,0.0,0.0 diff --git a/tests/options/csv/test_options_controller/test_get_black_scholes_model.csv b/tests/options/csv/test_options_controller/test_get_black_scholes_model.csv index 7d2ff84..1a5f612 100644 --- a/tests/options/csv/test_options_controller/test_get_black_scholes_model.csv +++ b/tests/options/csv/test_options_controller/test_get_black_scholes_model.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,34.3876,34.3952,34.4028,34.4104,34.418,34.4256,34.4332,34.4408,34.4484,34.456,34.4636,34.4712,34.4788,34.4864,34.494,34.5016,34.5092,34.5169,34.5246,34.5323,34.5401,34.5479,34.5557,34.5637,34.5717,34.5798,34.588,34.5963,34.6047 -AAPL,100,29.3881,29.3963,29.4044,29.4125,29.4207,29.4288,29.4369,29.4451,29.4532,29.4613,29.4695,29.4777,29.4859,29.4942,29.5025,29.511,29.5196,29.5283,29.5372,29.5463,29.5557,29.5652,29.575,29.585,29.5954,29.606,29.6169,29.6281,29.6396 -AAPL,105,24.3887,24.3973,24.406,24.4147,24.4233,24.432,24.4407,24.4494,24.4582,24.4672,24.4764,24.4858,24.4956,24.5058,24.5165,24.5277,24.5394,24.5517,24.5646,24.578,24.5921,24.6068,24.6222,24.6381,24.6547,24.6718,24.6895,24.7078,24.7266 -AAPL,110,19.3892,19.3984,19.4076,19.4168,19.4261,19.4356,19.4456,19.4563,19.4679,19.4806,19.4946,19.5099,19.5265,19.5445,19.5639,19.5845,19.6065,19.6296,19.654,19.6794,19.7059,19.7333,19.7617,19.7909,19.8209,19.8516,19.8831,19.9152,19.9479 -AAPL,115,14.3897,14.3995,14.4094,14.4204,14.4337,14.4501,14.4702,14.4939,14.5212,14.5518,14.5854,14.6217,14.6604,14.7012,14.7438,14.788,14.8337,14.8806,14.9285,14.9774,15.0271,15.0775,15.1284,15.1799,15.2318,15.2841,15.3366,15.3894,15.4425 -AAPL,120,9.3903,9.4035,9.4284,9.4675,9.5188,9.5794,9.6469,9.7192,9.795,9.8733,9.9533,10.0343,10.116,10.1981,10.2803,10.3624,10.4443,10.5259,10.607,10.6877,10.7679,10.8475,10.9265,11.005,11.0828,11.16,11.2367,11.3127,11.388 -AAPL,125,4.4274,4.5725,4.7446,4.9189,5.0889,5.2529,5.4107,5.5626,5.7089,5.8502,5.9869,6.1194,6.248,6.373,6.4947,6.6133,6.7292,6.8423,6.9531,7.0614,7.1677,7.2719,7.3741,7.4745,7.5732,7.6703,7.7658,7.8598,7.9524 -AAPL,130,0.7265,1.1377,1.4573,1.7285,1.9685,2.1863,2.3871,2.5745,2.7509,2.9181,3.0774,3.2299,3.3764,3.5175,3.6539,3.786,3.9142,4.0388,4.1601,4.2784,4.3939,4.5069,4.6173,4.7256,4.8317,4.9357,5.0379,5.1383,5.237 -AAPL,135,0.0128,0.0952,0.2194,0.3586,0.5022,0.6458,0.7874,0.9261,1.0617,1.194,1.3231,1.4491,1.5722,1.6925,1.8101,1.9252,2.0379,2.1485,2.2569,2.3633,2.4677,2.5704,2.6714,2.7707,2.8685,2.9648,3.0597,3.1532,3.2454 -AAPL,140,0.0,0.0022,0.0145,0.041,0.0806,0.1308,0.1893,0.2538,0.323,0.3957,0.4709,0.5479,0.6263,0.7056,0.7856,0.8659,0.9463,1.0268,1.1072,1.1874,1.2673,1.3469,1.4261,1.5049,1.5833,1.6612,1.7386,1.8155,1.8919 -AAPL,145,0.0,0.0,0.0004,0.0025,0.0081,0.0181,0.033,0.0528,0.0773,0.106,0.1385,0.1745,0.2134,0.2549,0.2987,0.3445,0.3921,0.4411,0.4915,0.5431,0.5956,0.649,0.7031,0.7579,0.8132,0.869,0.9252,0.9817,1.0385 -AAPL,150,0.0,0.0,0.0,0.0001,0.0005,0.0017,0.0042,0.0084,0.0146,0.0231,0.0339,0.047,0.0624,0.0801,0.0999,0.1217,0.1454,0.1709,0.198,0.2267,0.2568,0.2883,0.3209,0.3547,0.3896,0.4254,0.4621,0.4996,0.5379 -AAPL,155,0.0,0.0,0.0,0.0,0.0,0.0001,0.0004,0.001,0.0022,0.0041,0.0069,0.0108,0.0158,0.022,0.0295,0.0384,0.0485,0.06,0.0727,0.0867,0.102,0.1185,0.1361,0.1548,0.1746,0.1954,0.2172,0.2399,0.2635 -MSFT,180,58.2022,58.2143,58.2265,58.2387,58.2509,58.263,58.2752,58.2873,58.2995,58.3117,58.3238,58.336,58.3482,58.3603,58.3725,58.3848,58.397,58.4094,58.4218,58.4343,58.4469,58.4596,58.4725,58.4856,58.4988,58.5123,58.5259,58.5398,58.554 -MSFT,185,53.2027,53.2154,53.2281,53.2408,53.2535,53.2662,53.2789,53.2916,53.3043,53.317,53.3297,53.3424,53.3552,53.368,53.381,53.394,53.4072,53.4205,53.4341,53.4479,53.462,53.4764,53.4911,53.5062,53.5216,53.5375,53.5537,53.5704,53.5876 -MSFT,190,48.2032,48.2165,48.2297,48.2429,48.2562,48.2694,48.2826,48.2959,48.3091,48.3224,48.3358,48.3492,48.3629,48.3768,48.3909,48.4054,48.4202,48.4355,48.4513,48.4677,48.4846,48.5021,48.5202,48.5389,48.5584,48.5785,48.5992,48.6207,48.6428 -MSFT,195,43.2038,43.2175,43.2313,43.2451,43.2588,43.2726,43.2864,43.3002,43.3142,43.3284,43.3428,43.3577,43.3731,43.3892,43.4059,43.4234,43.4418,43.4611,43.4814,43.5026,43.5248,43.5481,43.5723,43.5976,43.6239,43.6511,43.6793,43.7085,43.7386 -MSFT,200,38.2043,38.2186,38.2329,38.2472,38.2615,38.2759,38.2904,38.3053,38.3207,38.3369,38.3541,38.3724,38.392,38.413,38.4355,38.4594,38.485,38.5121,38.5407,38.5709,38.6025,38.6356,38.6701,38.706,38.7431,38.7815,38.8211,38.8618,38.9036 -MSFT,205,33.2048,33.2197,33.2345,33.2493,33.2644,33.2799,33.2963,33.3142,33.3338,33.3556,33.3797,33.4062,33.4353,33.4668,33.5008,33.5372,33.5759,33.6168,33.6597,33.7045,33.7512,33.7997,33.8497,33.9012,33.9541,34.0083,34.0638,34.1203,34.1779 -MSFT,210,28.2054,28.2207,28.2361,28.252,28.2692,28.2889,28.3119,28.339,28.3703,28.4059,28.4458,28.4897,28.5373,28.5885,28.6428,28.7001,28.76,28.8223,28.8867,28.9532,29.0214,29.0912,29.1624,29.2349,29.3085,29.3832,29.4588,29.5352,29.6123 -MSFT,215,23.2059,23.2218,23.2387,23.2594,23.2864,23.3213,23.3644,23.4153,23.4733,23.5376,23.6074,23.682,23.7606,23.8428,23.9279,24.0156,24.1055,24.1971,24.2903,24.3848,24.4804,24.5768,24.674,24.7718,24.87,24.9686,25.0674,25.1664,25.2656 -MSFT,220,18.2064,18.2245,18.2525,18.2974,18.3596,18.4368,18.5261,18.625,18.7313,18.8432,18.9594,19.0789,19.2008,19.3245,19.4495,19.5754,19.7017,19.8283,19.955,20.0816,20.2078,20.3338,20.4592,20.5842,20.7085,20.8323,20.9553,21.0777,21.1993 -MSFT,225,13.2079,13.2514,13.3412,13.466,13.6128,13.7729,13.9406,14.1126,14.2866,14.4611,14.6353,14.8085,14.9802,15.1503,15.3186,15.4849,15.6492,15.8115,15.9718,16.1301,16.2864,16.4408,16.5933,16.744,16.8928,17.0399,17.1853,17.3291,17.4712 -MSFT,230,8.2514,8.4631,8.7313,9.011,9.2883,9.5586,9.8204,10.0738,10.3189,10.5564,10.7866,11.0101,11.2275,11.4391,11.6454,11.8468,12.0435,12.2358,12.4242,12.6086,12.7895,12.967,13.1413,13.3126,13.481,13.6466,13.8096,13.9702,14.1283 -MSFT,235,3.7774,4.3748,4.8734,5.3087,5.6999,6.0582,6.3909,6.7029,6.9975,7.2776,7.5451,7.8015,8.0483,8.2864,8.5167,8.7399,8.9567,9.1677,9.3732,9.5738,9.7696,9.9612,10.1487,10.3324,10.5126,10.6894,10.863,11.0336,11.2014 -MSFT,240,0.9849,1.669,2.2095,2.6712,3.0813,3.454,3.7984,4.12,4.4229,4.7102,4.9841,5.2463,5.4982,5.7411,5.9758,6.2032,6.4238,6.6384,6.8473,7.051,7.2499,7.4444,7.6347,7.8211,8.0038,8.183,8.359,8.5319,8.702 -MSFT,245,0.1162,0.4355,0.7827,1.121,1.4438,1.751,2.0436,2.3233,2.5914,2.8492,3.0976,3.3377,3.5702,3.7958,4.015,4.2284,4.4364,4.6395,4.8379,5.0319,5.2219,5.4081,5.5907,5.77,5.946,6.1191,6.2892,6.4567,6.6215 -MSFT,250,0.0054,0.0741,0.2113,0.3863,0.5802,0.7831,0.9895,1.1965,1.4023,1.6059,1.8069,2.0049,2.1997,2.3914,2.5798,2.7652,2.9476,3.127,3.3036,3.4774,3.6486,3.8173,3.9835,4.1473,4.3089,4.4683,4.6256,4.7809,4.9342 -MSFT,255,0.0001,0.0081,0.0429,0.1084,0.1988,0.3077,0.4298,0.5613,0.6994,0.8421,0.9879,1.1357,1.2848,1.4345,1.5843,1.734,1.8832,2.0318,2.1796,2.3266,2.4725,2.6174,2.7613,2.904,3.0457,3.1862,3.3255,3.4637,3.6008 -MSFT,260,0.0,0.0006,0.0065,0.0247,0.058,0.106,0.1673,0.2396,0.3213,0.4106,0.5061,0.6066,0.7112,0.8192,0.9298,1.0425,1.1569,1.2727,1.3895,1.507,1.6252,1.7436,1.8624,1.9812,2.1,2.2187,2.3373,2.4556,2.5737 -MSFT,265,0.0,0.0,0.0008,0.0046,0.0144,0.0321,0.0584,0.0932,0.136,0.1862,0.243,0.3056,0.3733,0.4455,0.5216,0.6012,0.6837,0.7688,0.8561,0.9454,1.0364,1.1288,1.2225,1.3172,1.4129,1.5093,1.6064,1.7041,1.8022 -MSFT,270,0.0,0.0,0.0001,0.0007,0.0031,0.0085,0.0183,0.033,0.0531,0.0787,0.1095,0.1453,0.1859,0.2309,0.28,0.3327,0.3889,0.4481,0.5101,0.5747,0.6416,0.7105,0.7814,0.8539,0.928,1.0034,1.0801,1.158,1.2368 -MSFT,275,0.0,0.0,0.0,0.0001,0.0006,0.002,0.0052,0.0107,0.0192,0.031,0.0463,0.0653,0.088,0.1142,0.1439,0.1769,0.2131,0.2522,0.2942,0.3387,0.3858,0.4351,0.4866,0.54,0.5953,0.6522,0.7108,0.7709,0.8323 -MSFT,280,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0013,0.0032,0.0064,0.0114,0.0185,0.0278,0.0396,0.0539,0.0709,0.0904,0.1126,0.1372,0.1643,0.1938,0.2255,0.2594,0.2954,0.3333,0.3732,0.4148,0.4581,0.503,0.5494 -MSFT,285,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0009,0.002,0.0039,0.0069,0.0112,0.017,0.0244,0.0335,0.0445,0.0574,0.0722,0.089,0.1076,0.1282,0.1507,0.175,0.201,0.2288,0.2582,0.2893,0.3219,0.356 -MSFT,290,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0006,0.0013,0.0025,0.0043,0.007,0.0106,0.0152,0.0211,0.0283,0.0368,0.0468,0.0581,0.071,0.0853,0.1012,0.1185,0.1373,0.1575,0.1791,0.2022,0.2265 +AAPL,95,34.3775,34.375,34.3725,34.37,34.3675,34.365,34.3625,34.3601,34.3576,34.3551,34.3526,34.3501,34.3476,34.3451,34.3426,34.3402,34.3377,34.3353,34.3329,34.3306,34.3283,34.326,34.3239,34.3218,34.3198,34.3179,34.3161,34.3144,34.3128 +AAPL,100,29.3775,29.375,29.3725,29.37,29.3675,29.365,29.3625,29.3601,29.3576,29.3551,29.3526,29.3502,29.3478,29.3455,29.3433,29.3411,29.3392,29.3374,29.3357,29.3343,29.3331,29.3322,29.3316,29.3312,29.3311,29.3314,29.332,29.3329,29.3341 +AAPL,105,24.3775,24.375,24.3725,24.37,24.3675,24.365,24.3626,24.3602,24.3578,24.3557,24.3537,24.3521,24.3508,24.35,24.3497,24.3499,24.3507,24.3522,24.3543,24.357,24.3604,24.3645,24.3692,24.3747,24.3808,24.3875,24.3949,24.4029,24.4115 +AAPL,110,19.3775,19.375,19.3725,19.37,19.3676,19.3655,19.3638,19.3629,19.363,19.3643,19.3668,19.3708,19.3762,19.3831,19.3915,19.4012,19.4124,19.4248,19.4386,19.4535,19.4695,19.4866,19.5048,19.5238,19.5438,19.5646,19.5861,19.6084,19.6314 +AAPL,115,14.3775,14.375,14.3727,14.3716,14.3728,14.3773,14.3856,14.3978,14.4137,14.4331,14.4557,14.4812,14.5093,14.5396,14.5719,14.6059,14.6415,14.6785,14.7166,14.7558,14.7958,14.8367,14.8783,14.9204,14.9631,15.0062,15.0496,15.0934,15.1375 +AAPL,120,9.3775,9.3781,9.3906,9.4178,9.4578,9.5074,9.5642,9.6262,9.692,9.7604,9.8307,9.9023,9.9747,10.0476,10.1207,10.1939,10.2669,10.3398,10.4123,10.4844,10.5561,10.6272,10.6979,10.7681,10.8377,10.9067,10.9752,11.0431,11.1105 +AAPL,125,4.4147,4.5488,4.711,4.8759,5.037,5.1923,5.3417,5.4852,5.6234,5.7567,5.8854,6.01,6.1308,6.248,6.3621,6.4731,6.5814,6.687,6.7902,6.8912,6.99,7.0868,7.1817,7.2748,7.3662,7.456,7.5443,7.6311,7.7165 +AAPL,130,0.721,1.1259,1.4391,1.7039,1.9375,2.1488,2.3432,2.5241,2.694,2.8547,3.0076,3.1535,3.2935,3.4282,3.5581,3.6837,3.8055,3.9236,4.0385,4.1504,4.2594,4.3659,4.4699,4.5717,4.6714,4.769,4.8648,4.9588,5.0511 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+MSFT,215,23.183,23.1761,23.1702,23.1682,23.1729,23.1858,23.2073,23.2371,23.2743,23.3183,23.3681,23.423,23.4823,23.5455,23.6119,23.6811,23.7527,23.8264,23.9018,23.9787,24.0568,24.136,24.2161,24.297,24.3784,24.4604,24.5427,24.6254,24.7083 +MSFT,220,18.183,18.1777,18.1828,18.2053,18.2458,18.302,18.3709,18.45,18.5369,18.6299,18.7276,18.8289,18.933,19.0391,19.1468,19.2555,19.365,19.4749,19.5851,19.6953,19.8054,19.9152,20.0248,20.134,20.2427,20.3508,20.4584,20.5655,20.6719 +MSFT,225,13.1841,13.2043,13.272,13.3761,13.5032,13.6443,13.7937,13.9479,14.1045,14.262,14.4195,14.5762,14.7318,14.8859,15.0383,15.189,15.3379,15.4848,15.6299,15.7731,15.9145,16.054,16.1917,16.3277,16.462,16.5946,16.7256,16.855,16.9829 +MSFT,230,8.2276,8.4185,8.6679,8.9301,9.1905,9.4444,9.6904,9.9282,10.158,10.3804,10.5957,10.8045,11.0073,11.2045,11.3964,11.5835,11.7661,11.9444,12.1188,12.2894,12.4564,12.6202,12.7808,12.9384,13.0932,13.2453,13.3949,13.542,13.6868 +MSFT,235,3.7582,4.3401,4.8239,5.2449,5.6221,5.9666,6.2856,6.584,6.8653,7.132,7.3862,7.6295,7.8632,8.0882,8.3055,8.5158,8.7197,8.9178,9.1105,9.2982,9.4814,9.6602,9.835,10.0061,10.1736,10.3378,10.4988,10.6569,10.8122 +MSFT,240,0.9764,1.6496,2.1789,2.6292,3.0278,3.389,3.7217,4.0316,4.3229,4.5985,4.8607,5.1112,5.3514,5.5826,5.8055,6.0212,6.2301,6.4329,6.6301,6.8221,7.0093,7.192,7.3706,7.5452,7.7162,7.8838,8.0481,8.2093,8.3676 +MSFT,245,0.1146,0.4285,0.7685,1.0987,1.4127,1.7108,1.994,2.2641,2.5224,2.7702,3.0086,3.2385,3.4607,3.676,3.8848,4.0878,4.2853,4.4778,4.6656,4.849,5.0283,5.2039,5.3758,5.5443,5.7096,5.8718,6.0312,6.1878,6.3419 +MSFT,250,0.0053,0.0726,0.2065,0.3768,0.5651,0.7616,0.9611,1.1608,1.3589,1.5546,1.7473,1.9369,2.1231,2.306,2.4855,2.6619,2.8351,3.0053,3.1725,3.337,3.4987,3.6578,3.8144,3.9686,4.1204,4.2701,4.4176,4.563,4.7064 +MSFT,255,0.0001,0.0079,0.0417,0.1052,0.1927,0.2978,0.4155,0.542,0.6746,0.8114,0.951,1.0922,1.2345,1.3771,1.5196,1.6618,1.8034,1.9442,2.0841,2.2229,2.3607,2.4973,2.6327,2.767,2.9,3.0318,3.1624,3.2918,3.42 +MSFT,260,0.0,0.0005,0.0063,0.0239,0.0559,0.1021,0.1609,0.2303,0.3084,0.3937,0.4849,0.5807,0.6802,0.7828,0.8877,0.9946,1.1029,1.2124,1.3226,1.4335,1.5448,1.6563,1.7678,1.8794,1.9908,2.1021,2.213,2.3237,2.4339 +MSFT,265,0.0,0.0,0.0007,0.0044,0.0138,0.0307,0.0559,0.0891,0.1299,0.1777,0.2317,0.2911,0.3553,0.4237,0.4957,0.5709,0.6488,0.729,0.8112,0.8952,0.9807,1.0674,1.1552,1.244,1.3335,1.4236,1.5143,1.6054,1.6968 +MSFT,270,0.0,0.0,0.0001,0.0007,0.0029,0.0081,0.0174,0.0314,0.0505,0.0747,0.1039,0.1378,0.1761,0.2186,0.2648,0.3145,0.3673,0.4229,0.4811,0.5417,0.6043,0.6688,0.735,0.8028,0.8719,0.9422,1.0136,1.086,1.1593 +MSFT,275,0.0,0.0,0.0,0.0001,0.0005,0.0019,0.0049,0.0101,0.0181,0.0293,0.0437,0.0616,0.0829,0.1076,0.1354,0.1664,0.2003,0.2369,0.2761,0.3178,0.3617,0.4077,0.4556,0.5053,0.5567,0.6097,0.664,0.7197,0.7766 +MSFT,280,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0012,0.003,0.006,0.0107,0.0173,0.0261,0.0371,0.0506,0.0664,0.0847,0.1053,0.1283,0.1535,0.1809,0.2104,0.2419,0.2753,0.3105,0.3474,0.386,0.426,0.4675,0.5104 +MSFT,285,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0008,0.0019,0.0037,0.0065,0.0105,0.0159,0.0227,0.0313,0.0415,0.0534,0.0672,0.0827,0.1,0.1191,0.1399,0.1623,0.1864,0.212,0.2392,0.2678,0.2978,0.3292 +MSFT,290,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0005,0.0012,0.0023,0.004,0.0065,0.0098,0.0141,0.0196,0.0262,0.0341,0.0433,0.0538,0.0656,0.0789,0.0934,0.1094,0.1266,0.1452,0.1651,0.1862,0.2085 diff --git a/tests/options/csv/test_options_controller/test_get_black_scholes_model_1.csv b/tests/options/csv/test_options_controller/test_get_black_scholes_model_1.csv index 8e2a1dc..397a576 100644 --- a/tests/options/csv/test_options_controller/test_get_black_scholes_model_1.csv +++ b/tests/options/csv/test_options_controller/test_get_black_scholes_model_1.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0003,0.0004,0.0006,0.0008,0.0011,0.0014,0.0019,0.0024,0.003,0.0038,0.0046 -AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0006,0.0009,0.0014,0.002,0.0028,0.0038,0.005,0.0065,0.0081,0.0101,0.0123,0.0148,0.0176,0.0207,0.0241 -AAPL,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0006,0.0011,0.0019,0.003,0.0046,0.0066,0.0091,0.0122,0.0158,0.0201,0.0249,0.0303,0.0364,0.0431,0.0504,0.0583,0.0668,0.0759,0.0855,0.0957 -AAPL,110,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0012,0.0027,0.0051,0.0087,0.0134,0.0195,0.027,0.0358,0.046,0.0575,0.0702,0.0842,0.0994,0.1156,0.1329,0.1512,0.1704,0.1904,0.2113,0.2328,0.2551,0.2781,0.3016 -AAPL,115,0.0,0.0,0.0002,0.0015,0.005,0.0117,0.0221,0.0361,0.0537,0.0745,0.0984,0.125,0.154,0.185,0.218,0.2525,0.2884,0.3256,0.3639,0.403,0.443,0.4837,0.5249,0.5667,0.6089,0.6515,0.6943,0.7375,0.7808 -AAPL,120,0.0,0.003,0.0176,0.0464,0.0875,0.1379,0.1951,0.2572,0.3227,0.3908,0.4605,0.5313,0.6027,0.6746,0.7465,0.8184,0.89,0.9613,1.0323,1.1027,1.1726,1.242,1.3108,1.379,1.4466,1.5136,1.58,1.6458,1.711 -AAPL,125,0.0366,0.1709,0.3322,0.4958,0.655,0.8082,0.9552,1.0963,1.2318,1.3624,1.4883,1.61,1.7278,1.842,1.9529,2.0608,2.1659,2.2683,2.3682,2.4658,2.5613,2.6547,2.7462,2.8359,2.9238,3.0101,3.0948,3.1781,3.26 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-AAPL,150,20.6065,20.5931,20.5797,20.5663,20.5533,20.541,20.5301,20.5208,20.5136,20.5087,20.506,20.5057,20.5077,20.5119,20.5183,20.5267,20.5369,20.549,20.5627,20.578,20.5947,20.6127,20.632,20.6524,20.6738,20.6962,20.7195,20.7436,20.7684 -AAPL,155,25.606,25.592,25.5781,25.5641,25.5501,25.5362,25.5226,25.5092,25.4964,25.4844,25.4732,25.4631,25.4541,25.4464,25.44,25.4348,25.431,25.4285,25.4273,25.4274,25.4287,25.4313,25.4349,25.4397,25.4456,25.4524,25.4603,25.4691,25.4787 -MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0003,0.0005,0.0008,0.0011,0.0016,0.0022,0.003,0.0039,0.005,0.0063,0.0079,0.0097,0.0117 -MSFT,185,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0006,0.0009,0.0014,0.0021,0.003,0.0042,0.0056,0.0073,0.0094,0.0118,0.0146,0.0177,0.0213,0.0254,0.0299 -MSFT,190,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0005,0.001,0.0016,0.0025,0.0038,0.0055,0.0076,0.0102,0.0133,0.017,0.0213,0.0262,0.0318,0.038,0.0449,0.0525,0.0608,0.0698 -MSFT,195,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0008,0.0015,0.0026,0.0043,0.0066,0.0096,0.0134,0.018,0.0236,0.0301,0.0376,0.0461,0.0556,0.0661,0.0777,0.0902,0.1038,0.1183,0.1337,0.1501 -MSFT,200,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0009,0.0021,0.004,0.0069,0.0109,0.0162,0.023,0.0312,0.0409,0.0522,0.065,0.0794,0.0953,0.1127,0.1315,0.1518,0.1733,0.1962,0.2204,0.2457,0.2722,0.2997 -MSFT,205,0.0,0.0,0.0,0.0,0.0002,0.0009,0.0026,0.0056,0.0104,0.0174,0.0267,0.0384,0.0526,0.0694,0.0886,0.1102,0.1341,0.1601,0.1882,0.2183,0.2502,0.2838,0.3191,0.3558,0.394,0.4334,0.474,0.5158,0.5586 -MSFT,210,0.0,0.0,0.0,0.0005,0.0024,0.0067,0.0144,0.0261,0.0421,0.0624,0.0869,0.1155,0.1478,0.1836,0.2226,0.2645,0.3091,0.3561,0.4052,0.4563,0.5092,0.5637,0.6196,0.6768,0.7351,0.7945,0.8547,0.9158,0.9776 -MSFT,215,0.0,0.0,0.0011,0.0058,0.017,0.036,0.0632,0.0982,0.1404,0.1888,0.2427,0.3014,0.3642,0.4305,0.4998,0.5716,0.6456,0.7214,0.7987,0.8773,0.9571,1.0377,1.119,1.2009,1.2833,1.366,1.449,1.5322,1.6155 -MSFT,220,0.0,0.0016,0.0133,0.0417,0.0875,0.1483,0.2212,0.3037,0.3935,0.489,0.5888,0.6919,0.7975,0.9048,1.0134,1.1228,1.2328,1.343,1.4533,1.5635,1.6734,1.7829,1.892,2.0006,2.1086,2.2159,2.3226,2.4286,2.5339 -MSFT,225,0.001,0.0275,0.1003,0.2082,0.338,0.4811,0.632,0.787,0.944,1.1016,1.2589,1.4151,1.57,1.7231,1.8745,2.0239,2.1713,2.3166,2.46,2.6014,2.7408,2.8783,3.0139,3.1476,3.2796,3.4098,3.5383,3.6652,3.7904 -MSFT,230,0.0439,0.2381,0.4888,0.7511,1.0109,1.2637,1.5081,1.7439,1.9716,2.1916,2.4043,2.6104,2.8103,3.0045,3.1934,3.3772,3.5565,3.7314,3.9023,4.0693,4.2328,4.3928,4.5497,4.7035,4.8544,5.0027,5.1483,5.2914,5.4321 -MSFT,235,0.5694,1.1488,1.6293,2.0466,2.4198,2.7602,3.0748,3.3688,3.6454,3.9075,4.157,4.3954,4.6242,4.8443,5.0566,5.2619,5.4607,5.6537,5.8413,6.0238,6.2017,6.3753,6.5448,6.7106,6.8728,7.0316,7.1873,7.34,7.4898 -MSFT,240,2.7764,3.4419,3.9639,4.407,4.7985,5.1528,5.4785,5.7816,6.066,6.3348,6.5901,6.8338,7.0673,7.2916,7.5078,7.7166,7.9188,8.1148,8.3052,8.4904,8.6709,8.8468,9.0186,9.1865,9.3507,9.5115,9.669,9.8234,9.975 -MSFT,245,6.9071,7.2073,7.5355,7.8547,8.1584,8.4465,8.7201,8.9807,9.2298,9.4685,9.6979,9.9189,10.1323,10.3389,10.539,10.7334,10.9224,11.1064,11.2857,11.4607,11.6317,11.7989,11.9624,12.1227,12.2797,12.4337,12.5849,12.7333,12.8791 -MSFT,250,11.7958,11.8449,11.9625,12.1178,12.2922,12.4754,12.6622,12.8496,13.0358,13.2199,13.4013,13.5797,13.7549,13.927,14.0959,14.2617,14.4245,14.5843,14.7413,14.8956,15.0472,15.1963,15.343,15.4873,15.6293,15.7692,15.9069,16.0427,16.1764 -MSFT,255,16.79,16.7778,16.7925,16.8378,16.9081,16.9968,17.0988,17.2102,17.3282,17.4507,17.5764,17.7041,17.8331,17.9626,18.0924,18.2219,18.3511,18.4796,18.6073,18.7341,18.86,18.9848,19.1086,19.2313,19.3528,19.4732,19.5925,19.7106,19.8276 -MSFT,260,21.7893,21.7692,21.7545,21.752,21.7646,21.792,21.8326,21.8843,21.9453,22.0139,22.0888,22.1686,22.2526,22.3399,22.4299,22.522,22.6158,22.7109,22.8071,22.904,23.0015,23.0994,23.1975,23.2957,23.3939,23.492,23.59,23.6877,23.7851 -MSFT,265,26.7888,26.7676,26.7471,26.7298,26.7184,26.7149,26.72,26.7336,26.7552,26.7842,26.8198,26.8612,26.9078,26.9588,27.0138,27.0722,27.1335,27.1975,27.2636,27.3318,27.4016,27.4728,27.5454,27.619,27.6935,27.7688,27.8448,27.9213,27.9983 -MSFT,270,31.7883,31.7665,31.7449,31.7237,31.7044,31.6881,31.6762,31.6692,31.6676,31.6714,31.6805,31.6946,31.7135,31.7368,31.7641,31.7952,31.8297,31.8672,31.9075,31.9504,31.9956,32.0429,32.092,32.1429,32.1953,32.2491,32.3041,32.3603,32.4175 -MSFT,275,36.7877,36.7655,36.7432,36.721,36.6992,36.6784,36.6593,36.6426,36.6288,36.6184,36.6115,36.6082,36.6087,36.6126,36.6201,36.6309,36.6448,36.6618,36.6815,36.7038,36.7287,36.7558,36.785,36.8162,36.8493,36.8841,36.9205,36.9583,36.9975 -MSFT,280,41.7872,41.7644,41.7416,41.7188,41.6961,41.6736,41.6518,41.6308,41.6113,41.5935,41.5778,41.5643,41.5534,41.545,41.5391,41.5359,41.5353,41.5372,41.5415,41.5482,41.5572,41.5684,41.5816,41.5969,41.614,41.6329,41.6534,41.6756,41.6993 -MSFT,285,46.7867,46.7633,46.74,46.7167,46.6934,46.6701,46.647,46.6243,46.6021,46.5807,46.5604,46.5414,46.5239,46.508,46.4938,46.4815,46.4711,46.4626,46.4561,46.4515,46.4488,46.448,46.449,46.4518,46.4563,46.4625,46.4703,46.4796,46.4905 -MSFT,290,51.7861,51.7623,51.7384,51.7146,51.6907,51.6669,51.6431,51.6194,51.5959,51.5727,51.5501,51.5281,51.5069,51.4867,51.4676,51.4496,51.433,51.4177,51.4038,51.3914,51.3804,51.371,51.363,51.3565,51.3515,51.348,51.3458,51.345,51.3456 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+MSFT,285,46.817,46.8239,46.8309,46.8378,46.8448,46.8518,46.859,46.8665,46.8745,46.8833,46.893,46.904,46.9163,46.9301,46.9456,46.9628,46.9817,47.0024,47.0249,47.0492,47.0752,47.1029,47.1323,47.1633,47.1959,47.23,47.2656,47.3026,47.3409 +MSFT,290,51.817,51.8239,51.8309,51.8378,51.8448,51.8518,51.8588,51.8659,51.8732,51.8808,51.8888,51.8975,51.9069,51.9172,51.9285,51.9409,51.9545,51.9693,51.9855,52.0029,52.0217,52.0419,52.0634,52.0863,52.1105,52.1361,52.1629,52.191,52.2203 diff --git a/tests/options/csv/test_options_controller/test_get_charm.csv b/tests/options/csv/test_options_controller/test_get_charm.csv index 2ecafc1..93a4dd6 100644 --- a/tests/options/csv/test_options_controller/test_get_charm.csv +++ b/tests/options/csv/test_options_controller/test_get_charm.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.007,0.007,0.007,0.007,0.007,0.007,0.007,0.007,0.0071,0.0071,0.0073,0.0076,0.0081,0.009,0.0103,0.012,0.0143,0.0172,0.0206,0.0245,0.029,0.034,0.0395,0.0453,0.0515,0.058,0.0647,0.0715,0.0785 -AAPL,100,0.007,0.007,0.007,0.007,0.007,0.0071,0.0072,0.0076,0.0086,0.0106,0.014,0.0189,0.0256,0.0341,0.0442,0.0557,0.0685,0.0823,0.0968,0.1118,0.1271,0.1425,0.1578,0.1729,0.1876,0.2019,0.2157,0.2289,0.2414 -AAPL,105,0.007,0.007,0.007,0.0071,0.0076,0.0098,0.0159,0.0275,0.0455,0.0696,0.099,0.1322,0.168,0.205,0.2421,0.2785,0.3136,0.3468,0.3779,0.4066,0.4329,0.4568,0.4783,0.4976,0.5146,0.5297,0.5427,0.554,0.5637 -AAPL,110,0.007,0.007,0.0079,0.0174,0.0497,0.1116,0.1982,0.2994,0.4054,0.5087,0.6043,0.6898,0.7641,0.8271,0.8795,0.9221,0.956,0.9821,1.0015,1.0152,1.0238,1.0283,1.0291,1.027,1.0223,1.0155,1.007,0.9971,0.986 -AAPL,115,0.007,0.0207,0.169,0.498,0.8916,1.2514,1.5379,1.7467,1.8879,1.9749,2.0204,2.035,2.0271,2.0031,1.9678,1.9247,1.8766,1.8253,1.7724,1.7189,1.6655,1.6128,1.5611,1.5108,1.462,1.4148,1.3693,1.3255,1.2833 -AAPL,120,0.1466,2.1562,4.113,4.9937,5.1933,5.0629,4.7914,4.4712,4.146,3.8354,3.5474,3.2842,3.0456,2.8298,2.635,2.4589,2.2995,2.155,2.0237,1.9041,1.7948,1.6948,1.603,1.5186,1.4409,1.369,1.3025,1.2408,1.1834 -AAPL,125,25.7511,19.9206,14.001,10.2911,7.9045,6.2865,5.1372,4.289,3.6433,3.1389,2.7366,2.4097,2.1401,1.9148,1.7242,1.5615,1.4212,1.2993,1.1927,1.0988,1.0156,0.9415,0.8752,0.8156,0.7618,0.7131,0.6688,0.6284,0.5914 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-MSFT,190,0.0107,0.0107,0.0107,0.0107,0.0107,0.0108,0.0113,0.0128,0.016,0.0213,0.0293,0.04,0.0532,0.0688,0.0863,0.1052,0.1252,0.1457,0.1665,0.1873,0.2076,0.2275,0.2466,0.2649,0.2823,0.2987,0.3141,0.3285,0.3418 -MSFT,195,0.0107,0.0107,0.0107,0.0107,0.011,0.0126,0.0172,0.0265,0.0413,0.0618,0.0874,0.1169,0.1493,0.1834,0.2181,0.2525,0.2861,0.3182,0.3486,0.377,0.4033,0.4275,0.4494,0.4693,0.4871,0.503,0.517,0.5293,0.54 -MSFT,200,0.0107,0.0107,0.0107,0.0114,0.0161,0.0297,0.0559,0.095,0.1445,0.2011,0.2613,0.3221,0.3813,0.4373,0.4891,0.5361,0.5782,0.6153,0.6477,0.6756,0.6993,0.7192,0.7356,0.7488,0.7593,0.7672,0.7729,0.7766,0.7785 -MSFT,205,0.0107,0.0107,0.0119,0.0242,0.0638,0.1359,0.2336,0.345,0.4591,0.5684,0.668,0.7557,0.8309,0.8937,0.9452,0.9863,1.0183,1.0424,1.0595,1.0708,1.0772,1.0793,1.0779,1.0736,1.0668,1.058,1.0475,1.0358,1.0229 -MSFT,210,0.0107,0.0117,0.0406,0.1534,0.3483,0.5803,0.8087,1.0105,1.1769,1.3074,1.4052,1.475,1.5217,1.5497,1.5628,1.5642,1.5564,1.5417,1.5216,1.4976,1.4706,1.4416,1.4111,1.3798,1.348,1.3161,1.2843,1.2528,1.2217 -MSFT,215,0.0107,0.0592,0.3801,0.9069,1.4232,1.8282,2.1085,2.2834,2.3781,2.4148,2.4112,2.38,2.3305,2.2694,2.2012,2.1292,2.0557,1.9822,1.9098,1.8392,1.7708,1.7049,1.6416,1.5811,1.5233,1.4681,1.4155,1.3654,1.3177 -MSFT,220,0.0324,0.9088,2.3961,3.4277,3.9353,4.0992,4.0683,3.935,3.7525,3.5506,3.3456,3.1462,2.9567,2.7792,2.6141,2.4613,2.3202,2.19,2.07,1.9592,1.8569,1.7622,1.6746,1.5933,1.5179,1.4477,1.3823,1.3213,1.2643 -MSFT,225,1.6306,6.6203,8.1169,7.896,7.1872,6.4098,5.6913,5.0622,4.5215,4.0589,3.6624,3.3213,3.0262,2.7695,2.545,2.3474,2.1726,2.0173,1.8786,1.7542,1.6421,1.5408,1.4489,1.3652,1.2888,1.2188,1.1545,1.0952,1.0406 -MSFT,230,21.3437,18.8828,13.8895,10.4502,8.1439,6.5424,5.3868,4.5245,3.8624,3.3419,2.9244,2.5838,2.3017,2.0652,1.8647,1.693,1.5446,1.4155,1.3023,1.2025,1.1139,1.0349,0.9641,0.9003,0.8427,0.7905,0.743,0.6995,0.6598 -MSFT,235,39.8666,15.8747,8.8787,5.7897,4.1199,3.1016,2.4286,1.9574,1.613,1.3526,1.1503,0.9897,0.8598,0.7531,0.6643,0.5894,0.5257,0.471,0.4237,0.3824,0.3462,0.3143,0.2859,0.2607,0.2381,0.2177,0.1994,0.1827,0.1676 -MSFT,240,-28.8209,-10.9667,-6.2359,-4.1968,-3.1001,-2.4291,-1.9823,-1.6666,-1.4331,-1.2545,-1.1139,-1.0008,-0.908,-0.8307,-0.7654,-0.7096,-0.6614,-0.6194,-0.5825,-0.5499,-0.5208,-0.4948,-0.4714,-0.4501,-0.4308,-0.4132,-0.3971,-0.3823,-0.3686 -MSFT,245,-34.9769,-22.7202,-15.2303,-11.0221,-8.4423,-6.738,-5.5456,-4.6738,-4.0137,-3.4998,-3.0902,-2.7576,-2.483,-2.2531,-2.0583,-1.8915,-1.7473,-1.6216,-1.5112,-1.4136,-1.3268,-1.2492,-1.1795,-1.1165,-1.0595,-1.0075,-0.96,-0.9165,-0.8766 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031d2eb..7608c32 100644 --- a/tests/options/csv/test_options_controller/test_get_color.csv +++ b/tests/options/csv/test_options_controller/test_get_color.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0003,0.0005,0.0009,0.0013,0.0019,0.0026,0.0034,0.0043,0.0052,0.0062,0.0073,0.0083,0.0094,0.0104,0.0115,0.0124,0.0134,0.0143 -AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0009,0.0018,0.0032,0.005,0.0071,0.0096,0.0122,0.0149,0.0177,0.0203,0.0229,0.0252,0.0274,0.0293,0.031,0.0325,0.0338,0.0349,0.0358,0.0365,0.037 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-AAPL,145,0.0,0.0315,0.1988,0.3974,0.5233,0.5711,0.5662,0.5325,0.4855,0.4346,0.3845,0.3377,0.2951,0.2571,0.2235,0.1939,0.168,0.1452,0.1253,0.1079,0.0926,0.0792,0.0674,0.0571,0.0479,0.0399,0.0328,0.0265,0.0209 -AAPL,150,0.0,0.0001,0.0069,0.0374,0.0903,0.1486,0.1985,0.2346,0.2569,0.2674,0.2689,0.2641,0.2549,0.2431,0.2297,0.2156,0.2013,0.1872,0.1736,0.1607,0.1484,0.1369,0.1261,0.1161,0.1068,0.0982,0.0902,0.0828,0.076 -AAPL,155,0.0,0.0,0.0001,0.0016,0.008,0.0215,0.0407,0.0626,0.0843,0.1038,0.1199,0.1324,0.1413,0.1471,0.1502,0.1511,0.1502,0.1479,0.1446,0.1405,0.1359,0.1309,0.1256,0.1203,0.1149,0.1095,0.1043,0.0991,0.0942 -MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0006,0.0009,0.0013,0.0018,0.0024,0.003,0.0037,0.0044,0.0052,0.0059,0.0067,0.0074,0.0081,0.0088,0.0094,0.01,0.0105 -MSFT,185,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0005,0.001,0.0016,0.0025,0.0035,0.0046,0.0058,0.0071,0.0083,0.0096,0.0108,0.012,0.0131,0.0141,0.015,0.0158,0.0165,0.0171,0.0176,0.0181 -MSFT,190,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0007,0.0016,0.0029,0.0046,0.0065,0.0087,0.011,0.0133,0.0154,0.0175,0.0194,0.021,0.0225,0.0237,0.0248,0.0256,0.0263,0.0268,0.0271,0.0273,0.0274,0.0274 -MSFT,195,0.0,0.0,0.0,0.0,0.0002,0.0008,0.0023,0.0047,0.0081,0.012,0.0163,0.0205,0.0245,0.0281,0.0313,0.0339,0.036,0.0377,0.0389,0.0397,0.0402,0.0404,0.0403,0.04,0.0395,0.0389,0.0381,0.0373,0.0364 -MSFT,200,0.0,0.0,0.0,0.0004,0.0023,0.0066,0.0134,0.0216,0.0301,0.0381,0.0451,0.0509,0.0552,0.0584,0.0604,0.0615,0.0617,0.0614,0.0605,0.0593,0.0577,0.056,0.0541,0.0521,0.05,0.0479,0.0459,0.0438,0.0418 -MSFT,205,0.0,0.0,0.0008,0.0061,0.0189,0.0366,0.055,0.0711,0.0834,0.092,0.097,0.0992,0.0991,0.0974,0.0946,0.091,0.0869,0.0825,0.078,0.0734,0.069,0.0647,0.0605,0.0566,0.0528,0.0492,0.0459,0.0427,0.0398 -MSFT,210,0.0,0.0008,0.015,0.0526,0.0975,0.1342,0.1576,0.1688,0.171,0.167,0.1593,0.1494,0.1386,0.1276,0.1167,0.1064,0.0967,0.0877,0.0793,0.0717,0.0648,0.0584,0.0527,0.0474,0.0427,0.0384,0.0344,0.0309,0.0276 -MSFT,215,0.0001,0.0297,0.1458,0.2567,0.3127,0.3234,0.3082,0.281,0.2497,0.2187,0.1897,0.1637,0.1407,0.1207,0.1033,0.0882,0.0751,0.0638,0.0541,0.0456,0.0383,0.0319,0.0263,0.0215,0.0172,0.0135,0.0103,0.0074,0.0049 -MSFT,220,0.0208,0.4073,0.681,0.6881,0.592,0.4788,0.3772,0.2935,0.2269,0.1744,0.1332,0.1007,0.0751,0.0547,0.0385,0.0255,0.0151,0.0067,-0.0002,-0.0057,-0.0102,-0.0138,-0.0167,-0.0191,-0.021,-0.0226,-0.0238,-0.0247,-0.0255 -MSFT,225,1.0552,1.9161,1.3715,0.8569,0.5183,0.3059,0.172,0.0861,0.03,-0.0069,-0.0315,-0.0479,-0.0587,-0.0656,-0.0699,-0.0724,-0.0735,-0.0737,-0.0733,-0.0724,-0.0712,-0.0698,-0.0682,-0.0665,-0.0648,-0.0631,-0.0614,-0.0597,-0.0581 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Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,1.0,1.0,0.9999,0.9999,0.9999,0.9999,0.9999,0.9998,0.9998,0.9998,0.9998,0.9998,0.9997,0.9997,0.9997,0.9997,0.9996,0.9996,0.9995,0.9995,0.9994,0.9993,0.9992,0.9991,0.999,0.9988,0.9986,0.9985,0.9983 -AAPL,100,1.0,1.0,0.9999,0.9999,0.9999,0.9999,0.9999,0.9998,0.9998,0.9998,0.9998,0.9997,0.9997,0.9996,0.9995,0.9993,0.9992,0.999,0.9987,0.9984,0.9981,0.9977,0.9973,0.9969,0.9964,0.9958,0.9953,0.9947,0.994 -AAPL,105,1.0,1.0,0.9999,0.9999,0.9999,0.9999,0.9998,0.9998,0.9997,0.9995,0.9993,0.999,0.9986,0.9981,0.9975,0.9967,0.9959,0.995,0.994,0.993,0.9918,0.9906,0.9893,0.988,0.9866,0.9851,0.9837,0.9822,0.9806 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-AAPL,130,0.408,0.4386,0.453,0.4621,0.4686,0.4736,0.4777,0.4811,0.484,0.4866,0.489,0.491,0.4929,0.4947,0.4963,0.4978,0.4993,0.5006,0.5019,0.5031,0.5042,0.5053,0.5064,0.5074,0.5083,0.5093,0.5102,0.511,0.5119 -AAPL,135,0.0145,0.0626,0.1068,0.1423,0.1709,0.1943,0.214,0.2307,0.2452,0.2579,0.2692,0.2792,0.2883,0.2966,0.3042,0.3111,0.3176,0.3235,0.3291,0.3343,0.3392,0.3438,0.3481,0.3522,0.3561,0.3598,0.3633,0.3667,0.3699 -AAPL,140,0.0,0.0021,0.01,0.0223,0.0367,0.0517,0.0665,0.0807,0.0941,0.1068,0.1187,0.1298,0.1403,0.1501,0.1593,0.168,0.1762,0.1839,0.1913,0.1982,0.2048,0.2111,0.2171,0.2228,0.2283,0.2335,0.2386,0.2434,0.248 -AAPL,145,0.0,0.0,0.0004,0.0018,0.0046,0.0089,0.0143,0.0206,0.0274,0.0346,0.042,0.0494,0.0568,0.0642,0.0714,0.0785,0.0854,0.0921,0.0987,0.1051,0.1112,0.1172,0.123,0.1287,0.1342,0.1395,0.1446,0.1496,0.1545 -AAPL,150,0.0,0.0,0.0,0.0001,0.0004,0.001,0.0022,0.0039,0.0061,0.0089,0.012,0.0155,0.0193,0.0234,0.0276,0.0319,0.0364,0.0409,0.0454,0.05,0.0546,0.0591,0.0636,0.0681,0.0725,0.0769,0.0813,0.0855,0.0897 -AAPL,155,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0006,0.0011,0.0018,0.0028,0.0041,0.0056,0.0073,0.0093,0.0114,0.0137,0.0162,0.0188,0.0215,0.0243,0.0272,0.0302,0.0332,0.0363,0.0394,0.0425,0.0456,0.0488 -MSFT,180,1.0,0.9999,0.9999,0.9999,0.9999,0.9998,0.9998,0.9998,0.9997,0.9997,0.9997,0.9996,0.9996,0.9996,0.9995,0.9995,0.9994,0.9994,0.9993,0.9992,0.9991,0.9989,0.9988,0.9986,0.9984,0.9982,0.998,0.9977,0.9975 -MSFT,185,1.0,0.9999,0.9999,0.9999,0.9999,0.9998,0.9998,0.9998,0.9997,0.9997,0.9997,0.9996,0.9996,0.9995,0.9994,0.9993,0.9992,0.999,0.9989,0.9986,0.9984,0.9981,0.9978,0.9974,0.997,0.9966,0.9962,0.9957,0.9951 -MSFT,190,1.0,0.9999,0.9999,0.9999,0.9999,0.9998,0.9998,0.9998,0.9997,0.9997,0.9996,0.9995,0.9994,0.9992,0.999,0.9987,0.9984,0.9981,0.9976,0.9971,0.9966,0.996,0.9954,0.9947,0.9939,0.9931,0.9923,0.9914,0.9905 -MSFT,195,1.0,0.9999,0.9999,0.9999,0.9999,0.9998,0.9998,0.9997,0.9996,0.9995,0.9993,0.999,0.9986,0.9982,0.9976,0.997,0.9963,0.9954,0.9945,0.9935,0.9925,0.9913,0.9901,0.9889,0.9875,0.9862,0.9848,0.9834,0.9819 -MSFT,200,1.0,0.9999,0.9999,0.9999,0.9998,0.9998,0.9997,0.9995,0.9991,0.9987,0.998,0.9972,0.9963,0.9952,0.9939,0.9925,0.9909,0.9893,0.9876,0.9858,0.9839,0.9819,0.9799,0.9779,0.9758,0.9738,0.9716,0.9695,0.9674 -MSFT,205,1.0,0.9999,0.9999,0.9999,0.9998,0.9995,0.999,0.9982,0.9971,0.9957,0.994,0.992,0.9899,0.9875,0.985,0.9823,0.9796,0.9768,0.9739,0.971,0.968,0.9651,0.9621,0.9591,0.9562,0.9533,0.9504,0.9476,0.9447 -MSFT,210,1.0,0.9999,0.9999,0.9996,0.999,0.9977,0.9958,0.9933,0.9903,0.9869,0.9832,0.9792,0.9751,0.9709,0.9666,0.9623,0.9581,0.9538,0.9496,0.9455,0.9414,0.9374,0.9335,0.9297,0.9259,0.9223,0.9187,0.9153,0.9119 -MSFT,215,1.0,0.9999,0.9994,0.9976,0.9944,0.9899,0.9845,0.9785,0.9721,0.9655,0.9589,0.9523,0.9459,0.9396,0.9334,0.9275,0.9218,0.9162,0.9109,0.9058,0.9008,0.8961,0.8915,0.8871,0.8828,0.8787,0.8748,0.871,0.8673 -MSFT,220,1.0,0.999,0.9944,0.9863,0.9761,0.9651,0.9538,0.9429,0.9323,0.9223,0.9129,0.904,0.8956,0.8878,0.8804,0.8734,0.8669,0.8607,0.8549,0.8494,0.8441,0.8392,0.8345,0.83,0.8257,0.8217,0.8178,0.8141,0.8106 -MSFT,225,0.9991,0.9872,0.9663,0.9442,0.9235,0.9049,0.8883,0.8736,0.8605,0.8488,0.8382,0.8286,0.82,0.812,0.8047,0.7981,0.7919,0.7861,0.7808,0.7758,0.7712,0.7668,0.7627,0.7589,0.7552,0.7518,0.7485,0.7455,0.7425 -MSFT,230,0.9735,0.9159,0.8714,0.8383,0.8131,0.7931,0.7768,0.7633,0.7518,0.742,0.7334,0.7259,0.7192,0.7133,0.7079,0.703,0.6986,0.6945,0.6908,0.6874,0.6842,0.6813,0.6785,0.676,0.6736,0.6714,0.6693,0.6673,0.6654 -MSFT,235,0.7746,0.7063,0.6739,0.6543,0.6409,0.6311,0.6236,0.6176,0.6128,0.6087,0.6053,0.6024,0.5999,0.5977,0.5957,0.594,0.5925,0.5911,0.5899,0.5888,0.5878,0.5869,0.5861,0.5853,0.5846,0.584,0.5834,0.5829,0.5824 -MSFT,240,0.3436,0.3915,0.414,0.428,0.4379,0.4454,0.4514,0.4563,0.4606,0.4643,0.4675,0.4704,0.473,0.4754,0.4776,0.4796,0.4814,0.4832,0.4848,0.4864,0.4879,0.4893,0.4906,0.4918,0.493,0.4942,0.4953,0.4964,0.4974 -MSFT,245,0.0623,0.1408,0.1918,0.2272,0.2536,0.2743,0.291,0.305,0.3168,0.3271,0.3361,0.3441,0.3513,0.3577,0.3636,0.369,0.374,0.3786,0.3829,0.3869,0.3907,0.3942,0.3975,0.4007,0.4036,0.4065,0.4092,0.4117,0.4142 -MSFT,250,0.0041,0.0313,0.0652,0.0963,0.1231,0.1461,0.166,0.1833,0.1986,0.2121,0.2243,0.2352,0.2452,0.2542,0.2626,0.2703,0.2775,0.2841,0.2903,0.2961,0.3016,0.3068,0.3117,0.3163,0.3207,0.3248,0.3288,0.3326,0.3362 -MSFT,255,0.0001,0.0043,0.0162,0.0324,0.0499,0.0671,0.0836,0.099,0.1133,0.1265,0.1388,0.1501,0.1607,0.1706,0.1798,0.1884,0.1965,0.2041,0.2113,0.2181,0.2245,0.2306,0.2364,0.2419,0.2472,0.2522,0.257,0.2616,0.2661 -MSFT,260,0.0,0.0004,0.0029,0.0086,0.0169,0.0266,0.0372,0.048,0.0588,0.0694,0.0796,0.0895,0.099,0.108,0.1167,0.1249,0.1328,0.1404,0.1476,0.1545,0.1611,0.1674,0.1735,0.1793,0.1849,0.1903,0.1955,0.2005,0.2053 -MSFT,265,0.0,0.0,0.0004,0.0018,0.0048,0.0091,0.0146,0.0209,0.0278,0.035,0.0424,0.0499,0.0573,0.0647,0.0719,0.0789,0.0858,0.0925,0.0991,0.1054,0.1115,0.1175,0.1233,0.1289,0.1343,0.1396,0.1447,0.1497,0.1545 -MSFT,270,0.0,0.0,0.0,0.0003,0.0011,0.0027,0.0051,0.0082,0.012,0.0163,0.021,0.026,0.0313,0.0366,0.0421,0.0476,0.053,0.0585,0.0639,0.0693,0.0746,0.0798,0.0849,0.0899,0.0948,0.0997,0.1044,0.109,0.1135 -MSFT,275,0.0,0.0,0.0,0.0,0.0002,0.0007,0.0016,0.0029,0.0048,0.007,0.0097,0.0128,0.0161,0.0197,0.0234,0.0274,0.0314,0.0355,0.0397,0.044,0.0482,0.0525,0.0567,0.0609,0.0651,0.0693,0.0734,0.0775,0.0815 -MSFT,280,0.0,0.0,0.0,0.0,0.0,0.0002,0.0004,0.001,0.0017,0.0028,0.0042,0.0059,0.0078,0.01,0.0124,0.0151,0.0178,0.0208,0.0238,0.0269,0.0302,0.0334,0.0368,0.0401,0.0435,0.0469,0.0504,0.0538,0.0572 -MSFT,285,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0006,0.001,0.0017,0.0026,0.0036,0.0049,0.0063,0.0079,0.0097,0.0117,0.0138,0.016,0.0183,0.0207,0.0232,0.0257,0.0283,0.031,0.0337,0.0365,0.0392 -MSFT,290,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0006,0.001,0.0016,0.0022,0.0031,0.004,0.0051,0.0063,0.0077,0.0092,0.0107,0.0124,0.0142,0.0161,0.018,0.02,0.0221,0.0242,0.0264 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a/tests/options/csv/test_options_controller/test_get_delta_1.csv +++ b/tests/options/csv/test_options_controller/test_get_delta_1.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0001,-0.0001,-0.0001,-0.0002,-0.0003,-0.0003,-0.0004,-0.0006,-0.0007,-0.0008,-0.001,-0.0012 -AAPL,100,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0001,-0.0001,-0.0002,-0.0002,-0.0004,-0.0005,-0.0007,-0.0009,-0.0012,-0.0015,-0.0018,-0.0022,-0.0027,-0.0031,-0.0037,-0.0042,-0.0048,-0.0054 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+MSFT,275,1.0,1.0,1.0,1.0,0.9998,0.9994,0.9987,0.9976,0.9961,0.9943,0.9922,0.9897,0.9871,0.9842,0.9813,0.9782,0.975,0.9717,0.9684,0.9651,0.9618,0.9585,0.9552,0.952,0.9488,0.9456,0.9424,0.9393,0.9363 +MSFT,280,1.0,1.0,1.0,1.0,1.0,0.9999,0.9996,0.9992,0.9986,0.9978,0.9967,0.9954,0.9939,0.9921,0.9903,0.9882,0.9861,0.9838,0.9815,0.9791,0.9766,0.9741,0.9716,0.969,0.9664,0.9639,0.9613,0.9587,0.9562 +MSFT,285,1.0,1.0,1.0,1.0,1.0,1.0,0.9999,0.9998,0.9995,0.9992,0.9987,0.998,0.9972,0.9963,0.9952,0.9939,0.9926,0.9911,0.9895,0.9879,0.9861,0.9843,0.9825,0.9805,0.9786,0.9766,0.9746,0.9726,0.9705 +MSFT,290,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9999,0.9999,0.9997,0.9995,0.9992,0.9988,0.9983,0.9977,0.997,0.9962,0.9953,0.9943,0.9932,0.992,0.9908,0.9895,0.9881,0.9867,0.9852,0.9837,0.9822,0.9806 diff --git a/tests/options/csv/test_options_controller/test_get_dual_gamma.csv b/tests/options/csv/test_options_controller/test_get_dual_gamma.csv index 23d3c64..58bcc7d 100644 --- a/tests/options/csv/test_options_controller/test_get_dual_gamma.csv +++ b/tests/options/csv/test_options_controller/test_get_dual_gamma.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0002,0.0002,0.0002,0.0003,0.0003,0.0004,0.0005,0.0005 -AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0002,0.0003,0.0004,0.0005,0.0006,0.0007,0.0008,0.001,0.0011,0.0013,0.0014,0.0016,0.0018,0.0019 -AAPL,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0003,0.0005,0.0007,0.0009,0.0011,0.0014,0.0016,0.0019,0.0022,0.0025,0.0028,0.0031,0.0034,0.0037,0.004,0.0043,0.0046,0.0049,0.0052 -AAPL,110,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0005,0.0009,0.0013,0.0018,0.0024,0.003,0.0036,0.0042,0.0048,0.0054,0.006,0.0065,0.007,0.0075,0.008,0.0084,0.0088,0.0092,0.0096,0.0099,0.0102,0.0105,0.0108 -AAPL,115,0.0,0.0,0.0002,0.0009,0.002,0.0034,0.005,0.0065,0.0079,0.0092,0.0103,0.0114,0.0123,0.0131,0.0139,0.0145,0.0151,0.0155,0.016,0.0163,0.0167,0.0169,0.0172,0.0174,0.0176,0.0177,0.0179,0.018,0.0181 -AAPL,120,0.0001,0.0028,0.0081,0.0132,0.0173,0.0203,0.0225,0.0241,0.0252,0.026,0.0265,0.0269,0.0271,0.0273,0.0273,0.0273,0.0272,0.0271,0.0269,0.0268,0.0266,0.0264,0.0262,0.026,0.0258,0.0256,0.0254,0.0252,0.025 -AAPL,125,0.0342,0.0533,0.0567,0.056,0.0542,0.0521,0.0501,0.0482,0.0465,0.0449,0.0434,0.042,0.0408,0.0396,0.0386,0.0376,0.0367,0.0358,0.035,0.0343,0.0336,0.0329,0.0323,0.0317,0.0311,0.0306,0.0301,0.0296,0.0292 -AAPL,130,0.1535,0.1102,0.0904,0.0785,0.0703,0.0643,0.0595,0.0557,0.0525,0.0498,0.0475,0.0455,0.0437,0.0421,0.0407,0.0394,0.0382,0.0372,0.0362,0.0353,0.0344,0.0336,0.0329,0.0322,0.0315,0.0309,0.0303,0.0298,0.0293 -AAPL,135,0.0135,0.0319,0.039,0.0413,0.0416,0.0412,0.0404,0.0394,0.0384,0.0375,0.0365,0.0356,0.0347,0.0339,0.0331,0.0324,0.0317,0.031,0.0304,0.0298,0.0293,0.0288,0.0283,0.0278,0.0273,0.0269,0.0265,0.0261,0.0257 -AAPL,140,0.0,0.0016,0.0052,0.009,0.0122,0.0147,0.0166,0.018,0.0191,0.0198,0.0204,0.0208,0.0211,0.0213,0.0214,0.0214,0.0214,0.0214,0.0213,0.0212,0.0211,0.021,0.0209,0.0208,0.0206,0.0205,0.0203,0.0202,0.02 -AAPL,145,0.0,0.0,0.0002,0.0009,0.0019,0.0031,0.0044,0.0056,0.0067,0.0077,0.0086,0.0094,0.0101,0.0107,0.0112,0.0116,0.012,0.0124,0.0127,0.0129,0.0131,0.0133,0.0135,0.0136,0.0137,0.0138,0.0139,0.014,0.014 -AAPL,150,0.0,0.0,0.0,0.0,0.0002,0.0004,0.0008,0.0012,0.0017,0.0023,0.0028,0.0033,0.0039,0.0044,0.0049,0.0053,0.0057,0.0061,0.0065,0.0068,0.0072,0.0075,0.0077,0.008,0.0082,0.0084,0.0086,0.0088,0.0089 -AAPL,155,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0005,0.0007,0.001,0.0012,0.0015,0.0018,0.0021,0.0024,0.0027,0.0029,0.0032,0.0035,0.0037,0.004,0.0042,0.0044,0.0046,0.0048,0.005,0.0052 -MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0001,0.0002,0.0002,0.0002,0.0003,0.0003,0.0004,0.0004 -MSFT,185,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0001,0.0002,0.0002,0.0003,0.0003,0.0004,0.0005,0.0005,0.0006,0.0007,0.0008,0.0008 -MSFT,190,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0002,0.0002,0.0003,0.0004,0.0005,0.0006,0.0007,0.0008,0.0009,0.001,0.0011,0.0012,0.0013,0.0015,0.0016 -MSFT,195,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0003,0.0004,0.0005,0.0007,0.0008,0.001,0.0011,0.0013,0.0015,0.0016,0.0018,0.0019,0.0021,0.0023,0.0024,0.0026,0.0027 -MSFT,200,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0004,0.0005,0.0007,0.0009,0.0011,0.0014,0.0016,0.0018,0.0021,0.0023,0.0026,0.0028,0.003,0.0032,0.0034,0.0036,0.0038,0.004,0.0042,0.0043 -MSFT,205,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0006,0.0008,0.0012,0.0015,0.0019,0.0023,0.0026,0.003,0.0033,0.0036,0.004,0.0043,0.0045,0.0048,0.005,0.0053,0.0055,0.0057,0.0059,0.0061,0.0062,0.0064 -MSFT,210,0.0,0.0,0.0,0.0001,0.0004,0.0008,0.0013,0.0019,0.0025,0.0031,0.0037,0.0042,0.0047,0.0052,0.0056,0.006,0.0064,0.0067,0.007,0.0073,0.0075,0.0077,0.0079,0.0081,0.0083,0.0084,0.0085,0.0086,0.0087 -MSFT,215,0.0,0.0,0.0003,0.001,0.002,0.003,0.0041,0.0051,0.006,0.0068,0.0075,0.0081,0.0086,0.009,0.0094,0.0097,0.01,0.0102,0.0104,0.0106,0.0107,0.0108,0.0109,0.011,0.0111,0.0111,0.0112,0.0112,0.0112 -MSFT,220,0.0,0.0006,0.0025,0.0047,0.0068,0.0085,0.0099,0.0109,0.0118,0.0124,0.0129,0.0133,0.0135,0.0138,0.0139,0.014,0.0141,0.0141,0.0141,0.0141,0.0141,0.014,0.014,0.0139,0.0139,0.0138,0.0137,0.0136,0.0136 -MSFT,225,0.0007,0.006,0.0111,0.0145,0.0166,0.0178,0.0186,0.0189,0.0191,0.0191,0.0191,0.019,0.0188,0.0186,0.0184,0.0182,0.0179,0.0177,0.0175,0.0173,0.0171,0.0168,0.0166,0.0164,0.0162,0.016,0.0158,0.0156,0.0155 -MSFT,230,0.0151,0.027,0.03,0.0303,0.0297,0.0288,0.0279,0.027,0.0261,0.0253,0.0245,0.0238,0.0231,0.0225,0.0219,0.0214,0.0209,0.0204,0.02,0.0196,0.0192,0.0188,0.0185,0.0181,0.0178,0.0175,0.0172,0.017,0.0167 -MSFT,235,0.0711,0.0577,0.0493,0.0437,0.0396,0.0365,0.034,0.032,0.0302,0.0288,0.0275,0.0264,0.0254,0.0245,0.0237,0.023,0.0223,0.0217,0.0211,0.0206,0.0201,0.0197,0.0192,0.0188,0.0185,0.0181,0.0178,0.0175,0.0172 -MSFT,240,0.0836,0.0617,0.0511,0.0446,0.04,0.0367,0.034,0.0319,0.0301,0.0285,0.0272,0.0261,0.0251,0.0242,0.0234,0.0226,0.022,0.0213,0.0208,0.0202,0.0198,0.0193,0.0189,0.0185,0.0181,0.0178,0.0174,0.0171,0.0168 -MSFT,245,0.0267,0.0345,0.0344,0.0329,0.0312,0.0297,0.0283,0.027,0.0259,0.0249,0.024,0.0232,0.0224,0.0218,0.0211,0.0206,0.02,0.0195,0.0191,0.0187,0.0183,0.0179,0.0175,0.0172,0.0169,0.0166,0.0163,0.016,0.0158 -MSFT,250,0.0025,0.0104,0.0154,0.0179,0.0191,0.0196,0.0197,0.0197,0.0195,0.0192,0.0189,0.0186,0.0183,0.0179,0.0176,0.0173,0.017,0.0167,0.0165,0.0162,0.0159,0.0157,0.0154,0.0152,0.015,0.0148,0.0146,0.0144,0.0142 -MSFT,255,0.0001,0.0018,0.0047,0.0073,0.0093,0.0107,0.0117,0.0124,0.0129,0.0132,0.0134,0.0136,0.0136,0.0136,0.0136,0.0136,0.0135,0.0134,0.0134,0.0133,0.0132,0.013,0.0129,0.0128,0.0127,0.0126,0.0125,0.0124,0.0123 -MSFT,260,0.0,0.0002,0.001,0.0023,0.0036,0.0049,0.0059,0.0068,0.0076,0.0082,0.0086,0.009,0.0094,0.0096,0.0098,0.01,0.0101,0.0102,0.0102,0.0103,0.0103,0.0103,0.0103,0.0103,0.0103,0.0103,0.0103,0.0103,0.0102 -MSFT,265,0.0,0.0,0.0002,0.0005,0.0012,0.0019,0.0026,0.0033,0.004,0.0046,0.0051,0.0055,0.0059,0.0063,0.0066,0.0069,0.0071,0.0073,0.0075,0.0076,0.0077,0.0078,0.0079,0.008,0.0081,0.0081,0.0082,0.0082,0.0082 -MSFT,270,0.0,0.0,0.0,0.0001,0.0003,0.0006,0.001,0.0014,0.0019,0.0023,0.0027,0.0031,0.0035,0.0038,0.0042,0.0044,0.0047,0.0049,0.0052,0.0053,0.0055,0.0057,0.0058,0.0059,0.0061,0.0062,0.0063,0.0063,0.0064 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a/tests/options/csv/test_options_controller/test_get_epsilon.csv +++ b/tests/options/csv/test_options_controller/test_get_epsilon.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,-0.3545,-0.7089,-1.0633,-1.4178,-1.7722,-2.1265,-2.4809,-2.8353,-3.1896,-3.544,-3.8983,-4.2526,-4.6069,-4.9611,-5.3154,-5.6696,-6.0237,-6.3777,-6.7317,-7.0856,-7.4393,-7.7929,-8.1463,-8.4995,-8.8525,-9.2052,-9.5576,-9.9098,-10.2616 -AAPL,100,-0.3545,-0.7089,-1.0633,-1.4178,-1.7722,-2.1265,-2.4809,-2.8353,-3.1896,-3.5439,-3.8982,-4.2524,-4.6065,-4.9604,-5.3142,-5.6677,-6.0209,-6.3737,-6.7262,-7.0782,-7.4296,-7.7805,-8.1308,-8.4805,-8.8295,-9.1777,-9.5253,-9.872,-10.2179 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b/tests/options/csv/test_options_controller/test_get_epsilon_1.csv index 6f36558..3f68ae4 100644 --- a/tests/options/csv/test_options_controller/test_get_epsilon_1.csv +++ b/tests/options/csv/test_options_controller/test_get_epsilon_1.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0003,0.0004,0.0007,0.001,0.0015,0.002,0.0028,0.0037,0.0049,0.0063,0.008,0.0099,0.0122 -AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0008,0.0013,0.002,0.0031,0.0044,0.0062,0.0084,0.0111,0.0144,0.0183,0.0227,0.0279,0.0338,0.0403,0.0477,0.0558 -AAPL,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0002,0.0004,0.001,0.0019,0.0033,0.0054,0.0083,0.012,0.0167,0.0226,0.0295,0.0377,0.0472,0.058,0.0701,0.0836,0.0984,0.1147,0.1323,0.1512,0.1716,0.1932 -AAPL,110,0.0,0.0,0.0,0.0,0.0001,0.0006,0.0017,0.0038,0.0073,0.0125,0.0196,0.0288,0.0403,0.0542,0.0704,0.089,0.1099,0.1332,0.1588,0.1866,0.2166,0.2487,0.2828,0.3189,0.3569,0.3968,0.4384,0.4817,0.5266 -AAPL,115,0.0,0.0,0.0002,0.0015,0.0052,0.0125,0.0241,0.0403,0.0612,0.0867,0.1166,0.1508,0.189,0.2308,0.2761,0.3247,0.3762,0.4306,0.4876,0.547,0.6087,0.6726,0.7385,0.8062,0.8757,0.9469,1.0196,1.0938,1.1694 -AAPL,120,0.0,0.002,0.0124,0.0345,0.068,0.1115,0.1636,0.2229,0.2883,0.359,0.4343,0.5134,0.5961,0.6817,0.7701,0.8609,0.9538,1.0487,1.1454,1.2437,1.3434,1.4445,1.5468,1.6503,1.7548,1.8603,1.9666,2.0739,2.1819 -AAPL,125,0.0129,0.0713,0.1558,0.2543,0.3616,0.4749,0.5927,0.7137,0.8374,0.9633,1.0908,1.2198,1.3501,1.4813,1.6135,1.7465,1.8801,2.0143,2.1491,2.2843,2.4199,2.5559,2.6922,2.8287,2.9656,3.1026,3.2399,3.3773,3.5149 -AAPL,130,0.2098,0.398,0.5816,0.7626,0.9417,1.1193,1.2957,1.471,1.6454,1.819,1.9918,2.1639,2.3354,2.5062,2.6765,2.8462,3.0154,3.1842,3.3524,3.5202,3.6875,3.8544,4.0209,4.187,4.3528,4.5181,4.6831,4.8477,5.012 -AAPL,135,0.3493,0.6645,0.9497,1.2159,1.4693,1.7132,1.95,2.1811,2.4074,2.6298,2.8488,3.0648,3.2782,3.4893,3.6982,3.9051,4.1103,4.3139,4.516,4.7166,4.916,5.114,5.311,5.5068,5.7016,5.8953,6.0882,6.2801,6.4711 -AAPL,140,0.3545,0.7074,1.0527,1.3862,1.7072,2.0167,2.316,2.6066,2.8894,3.1655,3.4356,3.7005,3.9606,4.2165,4.4685,4.717,4.9624,5.2047,5.4443,5.6814,5.9161,6.1486,6.3791,6.6076,6.8342,7.0591,7.2824,7.5041,7.7244 -AAPL,145,0.3545,0.7089,1.0629,1.4152,1.7639,2.1076,2.4453,2.7769,3.1021,3.4213,3.7346,4.0424,4.345,4.6427,4.9358,5.2245,5.5093,5.7903,6.0677,6.3418,6.6128,6.8808,7.1459,7.4084,7.6684,7.926,8.1813,8.4344,8.6855 -AAPL,150,0.3545,0.7089,1.0633,1.4176,1.7715,2.1244,2.4755,2.8242,3.17,3.5125,3.8514,4.1865,4.5178,4.8452,5.1688,5.4886,5.8047,6.1173,6.4263,6.7321,7.0346,7.3339,7.6303,7.9238,8.2145,8.5025,8.7879,9.0708,9.3513 -AAPL,155,0.3545,0.7089,1.0633,1.4178,1.7721,2.1264,2.4803,2.8337,3.1862,3.5375,3.8873,4.2352,4.5812,4.9249,5.2662,5.605,5.9412,6.2748,6.6058,6.9341,7.2597,7.5827,7.903,8.2208,8.536,8.8487,9.159,9.4669,9.7724 -MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0003,0.0005,0.0008,0.0013,0.0021,0.003,0.0043,0.0059,0.008,0.0105,0.0135,0.0171,0.0214,0.0263,0.032 -MSFT,185,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0008,0.0013,0.0022,0.0034,0.0051,0.0073,0.0101,0.0136,0.0179,0.0231,0.0292,0.0363,0.0445,0.0538,0.0642,0.0759 -MSFT,190,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0005,0.0011,0.002,0.0034,0.0055,0.0083,0.012,0.0167,0.0225,0.0297,0.0382,0.0481,0.0596,0.0727,0.0875,0.104,0.1223,0.1424,0.1643 -MSFT,195,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0006,0.0014,0.0028,0.005,0.0083,0.0128,0.0188,0.0265,0.036,0.0475,0.0611,0.0769,0.0951,0.1155,0.1384,0.1636,0.1914,0.2215,0.2542,0.2892,0.3267 -MSFT,200,0.0,0.0,0.0,0.0,0.0,0.0002,0.0006,0.0016,0.0035,0.0068,0.0118,0.0189,0.0284,0.0406,0.0556,0.0737,0.0949,0.1194,0.1471,0.1781,0.2125,0.2501,0.2909,0.335,0.3822,0.4324,0.4857,0.542,0.6011 -MSFT,205,0.0,0.0,0.0,0.0,0.0003,0.0013,0.0037,0.0082,0.0155,0.0263,0.0409,0.0596,0.0828,0.1105,0.1428,0.1797,0.2211,0.2669,0.3171,0.3715,0.43,0.4925,0.5588,0.6288,0.7024,0.7794,0.8597,0.9431,1.0296 -MSFT,210,0.0,0.0,0.0001,0.0006,0.0029,0.0083,0.0183,0.0338,0.0555,0.0837,0.1186,0.1601,0.2081,0.2623,0.3225,0.3885,0.4599,0.5364,0.6179,0.7041,0.7946,0.8893,0.9879,1.0903,1.1962,1.3055,1.418,1.5334,1.6518 -MSFT,215,0.0,0.0,0.001,0.0059,0.0177,0.0387,0.0698,0.1111,0.1624,0.2232,0.2928,0.3706,0.456,0.5484,0.6473,0.7521,0.8624,0.9777,1.0978,1.2222,1.3507,1.4829,1.6187,1.7577,1.8998,2.0449,2.1926,2.3429,2.4955 -MSFT,220,0.0,0.0012,0.0107,0.0354,0.0774,0.1361,0.2099,0.2971,0.3959,0.505,0.6231,0.7492,0.8822,1.0216,1.1666,1.3166,1.4712,1.63,1.7925,1.9584,2.1276,2.2996,2.4744,2.6516,2.8311,3.0127,3.1964,3.3819,3.5691 -MSFT,225,0.0005,0.0167,0.0657,0.1454,0.2492,0.3718,0.5093,0.6587,0.8178,0.9851,1.1592,1.3392,1.5242,1.7137,1.907,2.1037,2.3035,2.506,2.711,2.9183,3.1275,3.3386,3.5513,3.7656,3.9813,4.1983,4.4166,4.6359,4.8563 -MSFT,230,0.0172,0.1097,0.2517,0.4217,0.6094,0.8095,1.0185,1.2345,1.4559,1.6817,1.9111,2.1436,2.3787,2.6159,2.8551,3.096,3.3383,3.5819,3.8267,4.0725,4.3191,4.5666,4.8148,5.0637,5.3131,5.5631,5.8135,6.0644,6.3157 -MSFT,235,0.147,0.3832,0.6382,0.9021,1.1711,1.4436,1.7184,1.9949,2.2727,2.5514,2.8309,3.1109,3.3913,3.6721,3.9531,4.2342,4.5154,4.7967,5.078,5.3593,5.6405,5.9217,6.2027,6.4837,6.7645,7.0452,7.3257,7.606,7.8862 -MSFT,240,0.4283,0.7941,1.147,1.4928,1.8337,2.1709,2.5052,2.837,3.1666,3.4943,3.8202,4.1447,4.4677,4.7893,5.1098,5.429,5.7472,6.0643,6.3805,6.6957,7.01,7.3234,7.636,7.9477,8.2587,8.569,8.8785,9.1873,9.4954 -MSFT,245,0.6119,1.1213,1.5821,2.0168,2.4348,2.8408,3.2377,3.6273,4.0109,4.3894,4.7634,5.1336,5.5004,5.864,6.2249,6.5831,6.939,7.2927,7.6443,7.994,8.3419,8.688,9.0326,9.3756,9.7172,10.0574,10.3962,10.7338,11.0701 -MSFT,250,0.6499,1.2642,1.8299,2.3587,2.8607,3.3426,3.8087,4.2622,4.7052,5.1395,5.5661,5.9862,6.4004,6.8095,7.2139,7.614,8.0103,8.403,8.7924,9.1787,9.5622,9.9431,10.3214,10.6974,11.0711,11.4427,11.8123,12.18,12.5458 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+AAPL,140,0.3545,0.7074,1.053,1.387,1.7089,2.0196,2.3206,2.6131,2.8981,3.1767,3.4496,3.7174,3.9807,4.24,4.4957,4.748,4.9973,5.2438,5.4878,5.7294,5.9688,6.2061,6.4415,6.6751,6.907,7.1373,7.3661,7.5935,7.8195 +AAPL,145,0.3545,0.7089,1.063,1.4153,1.7642,2.1083,2.4466,2.779,3.1054,3.4259,3.7409,4.0505,4.3552,4.6553,4.9509,5.2425,5.5303,5.8145,6.0953,6.373,6.6477,6.9197,7.189,7.4558,7.7202,7.9824,8.2425,8.5005,8.7566 +AAPL,150,0.3545,0.7089,1.0633,1.4177,1.7716,2.1245,2.4757,2.8247,3.1709,3.514,3.8536,4.1896,4.522,4.8507,5.1758,5.4973,5.8154,6.13,6.4414,6.7497,7.0549,7.3571,7.6566,7.9533,8.2474,8.539,8.8281,9.115,9.3996 +AAPL,155,0.3545,0.7089,1.0633,1.4178,1.7721,2.1264,2.4804,2.8338,3.1864,3.5378,3.8879,4.2362,4.5826,4.9269,5.2689,5.6086,5.9459,6.2807,6.613,6.9428,7.2701,7.595,7.9173,8.2373,8.5549,8.8701,9.1831,9.4938,9.8024 +MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0005,0.0009,0.0015,0.0023,0.0033,0.0047,0.0065,0.0088,0.0116,0.0149,0.0189,0.0237,0.0291,0.0354 +MSFT,185,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0008,0.0015,0.0024,0.0037,0.0056,0.008,0.0111,0.0149,0.0196,0.0253,0.032,0.0398,0.0487,0.0589,0.0705,0.0833 +MSFT,190,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0006,0.0012,0.0022,0.0037,0.0059,0.009,0.013,0.0181,0.0244,0.0322,0.0414,0.0523,0.0648,0.079,0.0951,0.1131,0.1331,0.155,0.1789 +MSFT,195,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0007,0.0015,0.003,0.0054,0.0089,0.0138,0.0202,0.0285,0.0387,0.0511,0.0658,0.0828,0.1024,0.1245,0.1491,0.1765,0.2064,0.2391,0.2744,0.3124,0.353 +MSFT,200,0.0,0.0,0.0,0.0,0.0,0.0002,0.0006,0.0017,0.0037,0.0072,0.0125,0.0201,0.0303,0.0432,0.0593,0.0786,0.1013,0.1275,0.1572,0.1904,0.2272,0.2675,0.3113,0.3586,0.4093,0.4633,0.5207,0.5812,0.6448 +MSFT,205,0.0,0.0,0.0,0.0,0.0003,0.0014,0.0039,0.0087,0.0164,0.0277,0.0432,0.063,0.0876,0.117,0.1512,0.1903,0.2343,0.283,0.3364,0.3943,0.4566,0.5232,0.5939,0.6686,0.7471,0.8293,0.9151,1.0044,1.097 +MSFT,210,0.0,0.0,0.0001,0.0007,0.003,0.0087,0.0191,0.0354,0.0582,0.0878,0.1245,0.1681,0.2186,0.2757,0.3391,0.4087,0.4841,0.565,0.6511,0.7422,0.838,0.9383,1.0429,1.1515,1.2639,1.38,1.4995,1.6223,1.7483 +MSFT,215,0.0,0.0,0.0011,0.0061,0.0184,0.0402,0.0725,0.1156,0.1691,0.2325,0.3052,0.3865,0.4759,0.5726,0.6762,0.7861,0.9019,1.0231,1.1493,1.2802,1.4155,1.5548,1.698,1.8447,1.9948,2.148,2.3042,2.4633,2.6249 +MSFT,220,0.0,0.0013,0.0111,0.0365,0.0798,0.1405,0.2168,0.3071,0.4095,0.5227,0.6454,0.7764,0.9149,1.0601,1.2113,1.3678,1.5293,1.6952,1.8652,2.039,2.2163,2.3967,2.5801,2.7662,2.9549,3.146,3.3393,3.5347,3.7321 +MSFT,225,0.0006,0.017,0.0672,0.1488,0.2553,0.3813,0.5228,0.6767,0.8408,1.0136,1.1936,1.3798,1.5715,1.7679,1.9686,2.173,2.3807,2.5915,2.8051,3.0211,3.2395,3.4599,3.6823,3.9064,4.1323,4.3596,4.5884,4.8186,5.0499 +MSFT,230,0.0175,0.1113,0.2558,0.4292,0.6209,0.8256,1.0397,1.2613,1.4886,1.7208,1.9571,2.1967,2.4392,2.6843,2.9316,3.1809,3.4319,3.6845,3.9385,4.1938,4.4503,4.7078,4.9663,5.2256,5.4858,5.7468,6.0084,6.2707,6.5336 +MSFT,235,0.1482,0.3869,0.6454,0.9134,1.1871,1.4648,1.7452,2.0279,2.3122,2.5978,2.8845,3.1721,3.4605,3.7495,4.039,4.329,4.6194,4.91,5.201,5.4922,5.7836,6.0752,6.3669,6.6587,6.9507,7.2427,7.5348,7.8269,8.1191 +MSFT,240,0.4297,0.7982,1.1547,1.5047,1.8505,2.1931,2.5331,2.8711,3.2074,3.5421,3.8755,4.2076,4.5387,4.8687,5.1979,5.5261,5.8536,6.1802,6.5062,6.8315,7.1561,7.4801,7.8035,8.1263,8.4486,8.7704,9.0916,9.4124,9.7326 +MSFT,245,0.6124,1.1237,1.5875,2.026,2.4484,2.8594,3.2618,3.6574,4.0474,4.4327,4.814,5.1917,5.5664,5.9383,6.3077,6.6748,7.0398,7.4029,7.7643,8.124,8.4821,8.8388,9.1941,9.5481,9.901,10.2526,10.6031,10.9526,11.3011 +MSFT,250,0.6499,1.2649,1.8324,2.3639,2.8693,3.3553,3.8262,4.2849,4.7337,5.1741,5.6074,6.0345,6.4562,6.873,7.2855,7.6941,8.0991,8.5009,8.8997,9.2957,9.6892,10.0802,10.469,10.8557,11.2404,11.6232,12.0043,12.3837,12.7614 +MSFT,255,0.6525,1.2997,1.9267,2.5277,3.1041,3.6591,4.196,4.7175,5.226,5.7231,6.2104,6.6891,7.1601,7.6242,8.0822,8.5346,8.9819,9.4246,9.8629,10.2974,10.7281,11.1554,11.5795,12.0007,12.419,12.8346,13.2478,13.6585,14.067 +MSFT,260,0.6526,1.3046,1.952,2.5882,3.2092,3.8139,4.403,4.9773,5.5383,6.087,6.6248,7.1526,7.6714,8.182,8.6851,9.1813,9.6712,10.1552,10.6339,11.1075,11.5764,12.041,12.5015,12.9581,13.4111,13.8607,14.307,14.7503,15.1906 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+MSFT,285,0.6526,1.3051,1.9576,2.61,3.2624,3.9146,4.5666,5.218,5.8684,6.5174,7.1645,7.8094,8.4515,9.0906,9.7264,10.3586,10.9871,11.6117,12.2323,12.8488,13.4612,14.0696,14.6738,15.2739,15.8701,16.4622,17.0503,17.6346,18.2151 +MSFT,290,0.6526,1.3051,1.9576,2.61,3.2624,3.9147,4.567,5.219,5.8706,6.5216,7.1717,7.8205,8.4677,9.1131,9.7564,10.3972,11.0354,11.6708,12.3032,12.9325,13.5585,14.1813,14.8007,15.4166,16.0292,16.6382,17.2439,17.8461,18.4448 diff --git a/tests/options/csv/test_options_controller/test_get_gamma.csv b/tests/options/csv/test_options_controller/test_get_gamma.csv index 156f4a4..ff35464 100644 --- a/tests/options/csv/test_options_controller/test_get_gamma.csv +++ b/tests/options/csv/test_options_controller/test_get_gamma.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0001,0.0002,0.0002,0.0002,0.0002,0.0003 -AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0002,0.0002,0.0003,0.0003,0.0004,0.0005,0.0006,0.0007,0.0008,0.0008,0.0009,0.001,0.0011 -AAPL,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0003,0.0004,0.0006,0.0007,0.0009,0.0011,0.0013,0.0014,0.0016,0.0018,0.002,0.0022,0.0024,0.0026,0.0028,0.003,0.0032,0.0034 -AAPL,110,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0006,0.0009,0.0013,0.0017,0.0022,0.0026,0.003,0.0035,0.0039,0.0043,0.0047,0.0051,0.0054,0.0057,0.006,0.0063,0.0066,0.0069,0.0071,0.0073,0.0075,0.0077 -AAPL,115,0.0,0.0,0.0002,0.0007,0.0016,0.0027,0.0039,0.0051,0.0062,0.0072,0.0081,0.0089,0.0097,0.0103,0.0109,0.0114,0.0118,0.0122,0.0125,0.0128,0.0131,0.0133,0.0135,0.0137,0.0138,0.0139,0.014,0.0141,0.0142 -AAPL,120,0.0001,0.0024,0.007,0.0113,0.0148,0.0174,0.0193,0.0206,0.0216,0.0223,0.0227,0.0231,0.0232,0.0233,0.0234,0.0234,0.0233,0.0232,0.0231,0.0229,0.0228,0.0226,0.0225,0.0223,0.0221,0.0219,0.0218,0.0216,0.0214 -AAPL,125,0.0319,0.0497,0.0528,0.0522,0.0505,0.0486,0.0467,0.0449,0.0433,0.0418,0.0404,0.0392,0.038,0.0369,0.0359,0.035,0.0342,0.0334,0.0326,0.0319,0.0313,0.0307,0.0301,0.0295,0.029,0.0285,0.028,0.0276,0.0272 -AAPL,130,0.155,0.1113,0.0913,0.0793,0.071,0.0649,0.0601,0.0562,0.053,0.0503,0.048,0.046,0.0442,0.0426,0.0411,0.0398,0.0386,0.0375,0.0365,0.0356,0.0347,0.0339,0.0332,0.0325,0.0318,0.0312,0.0306,0.0301,0.0295 -AAPL,135,0.0148,0.0349,0.0425,0.045,0.0454,0.0449,0.0441,0.043,0.0419,0.0409,0.0398,0.0388,0.0379,0.037,0.0361,0.0353,0.0346,0.0339,0.0332,0.0325,0.0319,0.0314,0.0308,0.0303,0.0298,0.0293,0.0289,0.0284,0.028 -AAPL,140,0.0,0.0019,0.0061,0.0106,0.0144,0.0173,0.0195,0.0212,0.0224,0.0233,0.024,0.0244,0.0248,0.025,0.0251,0.0252,0.0252,0.0251,0.025,0.0249,0.0248,0.0247,0.0245,0.0244,0.0242,0.0241,0.0239,0.0237,0.0235 -AAPL,145,0.0,0.0,0.0003,0.0011,0.0024,0.004,0.0055,0.007,0.0085,0.0097,0.0108,0.0118,0.0127,0.0135,0.0141,0.0147,0.0152,0.0156,0.016,0.0163,0.0166,0.0168,0.017,0.0172,0.0174,0.0175,0.0176,0.0177,0.0177 -AAPL,150,0.0,0.0,0.0,0.0001,0.0002,0.0006,0.001,0.0016,0.0023,0.0031,0.0038,0.0045,0.0052,0.0059,0.0066,0.0072,0.0078,0.0083,0.0088,0.0093,0.0097,0.0101,0.0105,0.0108,0.0111,0.0114,0.0116,0.0119,0.0121 -AAPL,155,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0003,0.0005,0.0007,0.0011,0.0014,0.0018,0.0022,0.0026,0.003,0.0034,0.0038,0.0042,0.0046,0.005,0.0054,0.0057,0.0061,0.0064,0.0067,0.007,0.0073,0.0076 -MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0001,0.0001,0.0001,0.0002,0.0002,0.0002 -MSFT,185,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0001,0.0002,0.0002,0.0002,0.0003,0.0003,0.0004,0.0004,0.0004,0.0005 -MSFT,190,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0002,0.0002,0.0002,0.0003,0.0004,0.0004,0.0005,0.0006,0.0006,0.0007,0.0008,0.0008,0.0009,0.001 -MSFT,195,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0002,0.0003,0.0004,0.0004,0.0005,0.0006,0.0007,0.0008,0.001,0.0011,0.0012,0.0013,0.0014,0.0015,0.0016,0.0017,0.0018 -MSFT,200,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0004,0.0005,0.0006,0.0008,0.0009,0.0011,0.0013,0.0014,0.0016,0.0018,0.0019,0.0021,0.0022,0.0024,0.0025,0.0026,0.0028,0.0029,0.003 -MSFT,205,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0006,0.0009,0.0011,0.0014,0.0016,0.0019,0.0022,0.0024,0.0027,0.0029,0.0031,0.0033,0.0035,0.0037,0.0039,0.004,0.0042,0.0043,0.0044,0.0045,0.0047 -MSFT,210,0.0,0.0,0.0,0.0001,0.0003,0.0006,0.001,0.0015,0.0019,0.0024,0.0028,0.0032,0.0036,0.004,0.0043,0.0046,0.0049,0.0052,0.0054,0.0056,0.0058,0.0059,0.0061,0.0062,0.0063,0.0065,0.0066,0.0066,0.0067 -MSFT,215,0.0,0.0,0.0002,0.0008,0.0016,0.0025,0.0033,0.0041,0.0048,0.0055,0.006,0.0065,0.0069,0.0073,0.0076,0.0078,0.0081,0.0083,0.0084,0.0085,0.0087,0.0088,0.0088,0.0089,0.0089,0.009,0.009,0.009,0.0091 -MSFT,220,0.0,0.0005,0.0021,0.004,0.0057,0.0072,0.0083,0.0093,0.01,0.0105,0.0109,0.0112,0.0115,0.0116,0.0118,0.0119,0.0119,0.0119,0.0119,0.0119,0.0119,0.0119,0.0118,0.0118,0.0117,0.0117,0.0116,0.0115,0.0115 -MSFT,225,0.0007,0.0054,0.0099,0.0129,0.0147,0.0158,0.0165,0.0168,0.017,0.017,0.0169,0.0168,0.0167,0.0165,0.0163,0.0161,0.0159,0.0157,0.0155,0.0153,0.0151,0.0149,0.0148,0.0146,0.0144,0.0142,0.014,0.0139,0.0137 -MSFT,230,0.0141,0.0251,0.0279,0.0282,0.0276,0.0268,0.0259,0.0251,0.0242,0.0235,0.0228,0.0221,0.0215,0.0209,0.0204,0.0199,0.0194,0.019,0.0186,0.0182,0.0178,0.0175,0.0172,0.0168,0.0166,0.0163,0.016,0.0158,0.0155 -MSFT,235,0.0692,0.0561,0.0479,0.0425,0.0385,0.0355,0.0331,0.0311,0.0294,0.028,0.0267,0.0256,0.0247,0.0238,0.023,0.0223,0.0217,0.0211,0.0205,0.02,0.0195,0.0191,0.0187,0.0183,0.0179,0.0176,0.0173,0.017,0.0167 -MSFT,240,0.0849,0.0627,0.0519,0.0453,0.0407,0.0372,0.0345,0.0324,0.0305,0.029,0.0277,0.0265,0.0255,0.0246,0.0237,0.023,0.0223,0.0217,0.0211,0.0206,0.0201,0.0196,0.0192,0.0188,0.0184,0.018,0.0177,0.0174,0.0171 -MSFT,245,0.0284,0.0365,0.0364,0.0349,0.0331,0.0315,0.03,0.0286,0.0274,0.0264,0.0254,0.0246,0.0238,0.0231,0.0224,0.0218,0.0212,0.0207,0.0202,0.0198,0.0194,0.019,0.0186,0.0182,0.0179,0.0176,0.0173,0.017,0.0167 -MSFT,250,0.0028,0.0116,0.017,0.0198,0.0211,0.0217,0.0218,0.0217,0.0215,0.0212,0.0209,0.0206,0.0202,0.0198,0.0195,0.0192,0.0188,0.0185,0.0182,0.0179,0.0176,0.0173,0.0171,0.0168,0.0166,0.0163,0.0161,0.0159,0.0157 -MSFT,255,0.0001,0.0021,0.0054,0.0084,0.0107,0.0123,0.0135,0.0143,0.0149,0.0152,0.0155,0.0156,0.0157,0.0157,0.0157,0.0157,0.0156,0.0155,0.0154,0.0153,0.0152,0.015,0.0149,0.0148,0.0146,0.0145,0.0144,0.0143,0.0141 -MSFT,260,0.0,0.0002,0.0012,0.0027,0.0044,0.0058,0.0071,0.0082,0.0091,0.0098,0.0104,0.0108,0.0112,0.0115,0.0118,0.012,0.0121,0.0122,0.0123,0.0124,0.0124,0.0124,0.0124,0.0124,0.0124,0.0124,0.0123,0.0123,0.0123 -MSFT,265,0.0,0.0,0.0002,0.0007,0.0014,0.0023,0.0033,0.0041,0.005,0.0057,0.0063,0.0069,0.0074,0.0079,0.0082,0.0086,0.0088,0.0091,0.0093,0.0095,0.0096,0.0098,0.0099,0.01,0.0101,0.0101,0.0102,0.0102,0.0103 -MSFT,270,0.0,0.0,0.0,0.0001,0.0004,0.0008,0.0013,0.0019,0.0024,0.003,0.0036,0.0041,0.0046,0.005,0.0054,0.0058,0.0061,0.0064,0.0067,0.0069,0.0072,0.0074,0.0076,0.0077,0.0079,0.008,0.0081,0.0082,0.0083 -MSFT,275,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0005,0.0007,0.0011,0.0014,0.0018,0.0022,0.0026,0.003,0.0033,0.0037,0.004,0.0043,0.0046,0.0049,0.0051,0.0053,0.0055,0.0057,0.0059,0.0061,0.0063,0.0064,0.0065 -MSFT,280,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0003,0.0004,0.0006,0.0009,0.0011,0.0014,0.0017,0.0019,0.0022,0.0025,0.0028,0.003,0.0033,0.0035,0.0037,0.0039,0.0041,0.0043,0.0045,0.0047,0.0048,0.005 -MSFT,285,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0004,0.0005,0.0007,0.0009,0.0011,0.0013,0.0015,0.0017,0.0019,0.0021,0.0023,0.0025,0.0027,0.0029,0.003,0.0032,0.0034,0.0035,0.0037 -MSFT,290,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0002,0.0003,0.0004,0.0006,0.0007,0.0008,0.001,0.0011,0.0013,0.0015,0.0016,0.0018,0.0019,0.0021,0.0022,0.0024,0.0025,0.0027 +AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0001,0.0001,0.0002,0.0002,0.0002,0.0003,0.0003 +AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0002,0.0002,0.0003,0.0004,0.0005,0.0005,0.0006,0.0007,0.0008,0.0009,0.001,0.0011,0.0012 +AAPL,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0003,0.0005,0.0006,0.0008,0.0009,0.0011,0.0013,0.0015,0.0017,0.002,0.0022,0.0024,0.0026,0.0028,0.003,0.0032,0.0034,0.0036 +AAPL,110,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0006,0.001,0.0014,0.0018,0.0023,0.0027,0.0032,0.0036,0.0041,0.0045,0.0049,0.0053,0.0057,0.006,0.0063,0.0066,0.0069,0.0072,0.0074,0.0077,0.0079,0.0081 +AAPL,115,0.0,0.0,0.0002,0.0007,0.0017,0.0028,0.004,0.0052,0.0064,0.0074,0.0084,0.0093,0.01,0.0107,0.0113,0.0118,0.0122,0.0126,0.013,0.0133,0.0135,0.0138,0.014,0.0141,0.0143,0.0144,0.0145,0.0146,0.0147 +AAPL,120,0.0001,0.0025,0.0071,0.0116,0.0151,0.0178,0.0197,0.0211,0.0221,0.0228,0.0233,0.0236,0.0238,0.0239,0.0239,0.0239,0.0238,0.0237,0.0236,0.0235,0.0233,0.0232,0.023,0.0228,0.0226,0.0225,0.0223,0.0221,0.0219 +AAPL,125,0.0322,0.0502,0.0533,0.0527,0.051,0.0491,0.0472,0.0454,0.0437,0.0422,0.0408,0.0396,0.0384,0.0373,0.0363,0.0354,0.0346,0.0338,0.033,0.0323,0.0316,0.031,0.0304,0.0299,0.0293,0.0288,0.0284,0.0279,0.0275 +AAPL,130,0.1548,0.1112,0.0912,0.0792,0.0709,0.0648,0.0601,0.0562,0.053,0.0503,0.048,0.0459,0.0441,0.0425,0.0411,0.0398,0.0386,0.0375,0.0365,0.0356,0.0347,0.0339,0.0332,0.0325,0.0318,0.0312,0.0306,0.0301,0.0296 +AAPL,135,0.0146,0.0344,0.042,0.0445,0.0449,0.0444,0.0435,0.0425,0.0415,0.0404,0.0394,0.0384,0.0374,0.0366,0.0357,0.0349,0.0342,0.0335,0.0328,0.0322,0.0316,0.031,0.0305,0.03,0.0295,0.029,0.0286,0.0282,0.0277 +AAPL,140,0.0,0.0018,0.006,0.0104,0.0141,0.0169,0.0191,0.0207,0.0219,0.0228,0.0235,0.0239,0.0242,0.0244,0.0246,0.0246,0.0246,0.0246,0.0245,0.0244,0.0243,0.0242,0.024,0.0239,0.0237,0.0236,0.0234,0.0232,0.0231 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+MSFT,190,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0002,0.0002,0.0003,0.0003,0.0004,0.0005,0.0005,0.0006,0.0007,0.0007,0.0008,0.0009,0.001,0.0011 +MSFT,195,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0002,0.0003,0.0004,0.0005,0.0006,0.0007,0.0008,0.0009,0.001,0.0011,0.0013,0.0014,0.0015,0.0016,0.0017,0.0018,0.0019 +MSFT,200,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0004,0.0005,0.0007,0.0008,0.001,0.0012,0.0014,0.0015,0.0017,0.0019,0.002,0.0022,0.0024,0.0025,0.0027,0.0028,0.0029,0.0031,0.0032 +MSFT,205,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0004,0.0007,0.0009,0.0012,0.0014,0.0017,0.002,0.0023,0.0025,0.0028,0.003,0.0033,0.0035,0.0037,0.0039,0.0041,0.0042,0.0044,0.0045,0.0047,0.0048,0.0049 +MSFT,210,0.0,0.0,0.0,0.0001,0.0003,0.0007,0.0011,0.0015,0.002,0.0025,0.0029,0.0034,0.0038,0.0042,0.0045,0.0048,0.0051,0.0054,0.0056,0.0058,0.006,0.0062,0.0063,0.0065,0.0066,0.0067,0.0068,0.0069,0.007 +MSFT,215,0.0,0.0,0.0003,0.0008,0.0016,0.0025,0.0034,0.0043,0.005,0.0057,0.0062,0.0067,0.0072,0.0075,0.0078,0.0081,0.0083,0.0085,0.0087,0.0088,0.009,0.0091,0.0091,0.0092,0.0093,0.0093,0.0093,0.0094,0.0094 +MSFT,220,0.0,0.0005,0.0021,0.0041,0.0059,0.0074,0.0086,0.0095,0.0102,0.0108,0.0112,0.0115,0.0118,0.012,0.0121,0.0122,0.0122,0.0123,0.0123,0.0123,0.0122,0.0122,0.0122,0.0121,0.0121,0.012,0.0119,0.0119,0.0118 +MSFT,225,0.0007,0.0055,0.0101,0.0131,0.015,0.0161,0.0168,0.0171,0.0173,0.0173,0.0173,0.0172,0.017,0.0168,0.0166,0.0164,0.0162,0.016,0.0158,0.0156,0.0154,0.0152,0.015,0.0149,0.0147,0.0145,0.0143,0.0142,0.014 +MSFT,230,0.0142,0.0254,0.0282,0.0285,0.0279,0.0271,0.0262,0.0254,0.0245,0.0238,0.023,0.0224,0.0217,0.0212,0.0206,0.0201,0.0197,0.0192,0.0188,0.0184,0.018,0.0177,0.0174,0.0171,0.0168,0.0165,0.0162,0.016,0.0157 +MSFT,235,0.0695,0.0563,0.0482,0.0427,0.0387,0.0356,0.0332,0.0312,0.0295,0.0281,0.0269,0.0258,0.0248,0.0239,0.0232,0.0224,0.0218,0.0212,0.0206,0.0201,0.0197,0.0192,0.0188,0.0184,0.0181,0.0177,0.0174,0.0171,0.0168 +MSFT,240,0.0847,0.0626,0.0518,0.0452,0.0406,0.0372,0.0345,0.0323,0.0305,0.0289,0.0276,0.0265,0.0254,0.0245,0.0237,0.0229,0.0223,0.0216,0.0211,0.0205,0.02,0.0196,0.0192,0.0188,0.0184,0.018,0.0177,0.0174,0.0171 +MSFT,245,0.0281,0.0362,0.0361,0.0346,0.0328,0.0312,0.0297,0.0284,0.0272,0.0262,0.0252,0.0244,0.0236,0.0229,0.0222,0.0216,0.0211,0.0206,0.0201,0.0196,0.0192,0.0188,0.0184,0.0181,0.0178,0.0174,0.0172,0.0169,0.0166 +MSFT,250,0.0027,0.0114,0.0167,0.0195,0.0208,0.0213,0.0215,0.0214,0.0212,0.0209,0.0206,0.0203,0.0199,0.0196,0.0192,0.0189,0.0186,0.0182,0.0179,0.0176,0.0174,0.0171,0.0168,0.0166,0.0163,0.0161,0.0159,0.0157,0.0155 +MSFT,255,0.0001,0.002,0.0053,0.0082,0.0105,0.0121,0.0132,0.014,0.0145,0.0149,0.0152,0.0153,0.0154,0.0154,0.0154,0.0153,0.0153,0.0152,0.0151,0.015,0.0149,0.0147,0.0146,0.0145,0.0144,0.0142,0.0141,0.014,0.0139 +MSFT,260,0.0,0.0002,0.0012,0.0027,0.0042,0.0057,0.0069,0.008,0.0088,0.0095,0.0101,0.0106,0.0109,0.0112,0.0115,0.0116,0.0118,0.0119,0.012,0.012,0.0121,0.0121,0.0121,0.0121,0.0121,0.0121,0.012,0.012,0.0119 +MSFT,265,0.0,0.0,0.0002,0.0007,0.0014,0.0023,0.0032,0.004,0.0048,0.0055,0.0061,0.0067,0.0072,0.0076,0.008,0.0083,0.0086,0.0088,0.009,0.0092,0.0093,0.0095,0.0096,0.0097,0.0097,0.0098,0.0099,0.0099,0.0099 +MSFT,270,0.0,0.0,0.0,0.0001,0.0004,0.0008,0.0012,0.0018,0.0023,0.0029,0.0034,0.0039,0.0044,0.0048,0.0052,0.0056,0.0059,0.0062,0.0064,0.0067,0.0069,0.0071,0.0073,0.0074,0.0076,0.0077,0.0078,0.0079,0.008 +MSFT,275,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0007,0.001,0.0014,0.0018,0.0021,0.0025,0.0028,0.0032,0.0035,0.0038,0.0041,0.0044,0.0046,0.0049,0.0051,0.0053,0.0055,0.0057,0.0058,0.006,0.0061,0.0063 +MSFT,280,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0003,0.0004,0.0006,0.0008,0.0011,0.0013,0.0016,0.0019,0.0021,0.0024,0.0026,0.0029,0.0031,0.0033,0.0035,0.0037,0.0039,0.0041,0.0043,0.0044,0.0046,0.0047 +MSFT,285,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0002,0.0004,0.0005,0.0007,0.0008,0.001,0.0012,0.0014,0.0016,0.0018,0.002,0.0022,0.0024,0.0025,0.0027,0.0029,0.003,0.0032,0.0033,0.0035 +MSFT,290,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0002,0.0003,0.0004,0.0005,0.0007,0.0008,0.0009,0.0011,0.0012,0.0014,0.0015,0.0017,0.0018,0.002,0.0021,0.0022,0.0024,0.0025 diff --git a/tests/options/csv/test_options_controller/test_get_lambda.csv b/tests/options/csv/test_options_controller/test_get_lambda.csv index e7aa506..a07e75a 100644 --- a/tests/options/csv/test_options_controller/test_get_lambda.csv +++ b/tests/options/csv/test_options_controller/test_get_lambda.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,3.7623,3.7614,3.7605,3.7596,3.7587,3.7578,3.7569,3.756,3.7551,3.7542,3.7533,3.7524,3.7515,3.7506,3.7497,3.7487,3.7478,3.7468,3.7457,3.7447,3.7435,3.7424,3.7412,3.7399,3.7385,3.7371,3.7355,3.7339,3.7323 -AAPL,100,4.4024,4.4011,4.3998,4.3985,4.3972,4.3959,4.3946,4.3933,4.392,4.3906,4.3893,4.3879,4.3864,4.3848,4.3831,4.3812,4.3792,4.377,4.3746,4.372,4.3692,4.3662,4.3629,4.3594,4.3558,4.3519,4.3478,4.3435,4.339 -AAPL,105,5.3048,5.3028,5.3008,5.2989,5.2969,5.2949,5.2928,5.2906,5.2882,5.2854,5.2822,5.2785,5.2742,5.2693,5.2638,5.2577,5.2509,5.2435,5.2355,5.227,5.2179,5.2084,5.1984,5.188,5.1773,5.1662,5.1547,5.1431,5.1311 -AAPL,110,6.6727,6.6694,6.6661,6.6627,6.659,6.6543,6.6481,6.6399,6.6295,6.6169,6.602,6.585,6.5662,6.5457,6.5238,6.5006,6.4763,6.4511,6.4253,6.3988,6.3718,6.3446,6.317,6.2893,6.2614,6.2335,6.2056,6.1778,6.15 -AAPL,115,8.991,8.9846,8.9765,8.9617,8.9365,8.8999,8.8532,8.7984,8.7374,8.6719,8.6033,8.5327,8.461,8.389,8.317,8.2454,8.1746,8.1047,8.036,7.9685,7.9022,7.8373,7.7737,7.7115,7.6506,7.5911,7.5329,7.476,7.4204 -AAPL,120,13.7771,13.7194,13.562,13.3326,13.0696,12.7965,12.5257,12.2635,12.0127,11.7742,11.5483,11.3344,11.132,10.9404,10.7588,10.5864,10.4227,10.267,10.1187,9.9772,9.842,9.7128,9.5891,9.4705,9.3566,9.2473,9.1421,9.0408,8.9433 -AAPL,125,28.1558,25.4466,23.2728,21.5832,20.2342,19.127,18.197,17.4012,16.7097,16.1012,15.5601,15.0745,14.6355,14.2358,13.8699,13.5331,13.2217,12.9326,12.6632,12.4114,12.1752,11.9531,11.7437,11.5458,11.3585,11.1807,11.0117,10.8507,10.6972 -AAPL,130,72.6489,49.875,40.2191,34.5875,30.7973,28.0269,25.8897,24.1771,22.7655,21.5761,20.5564,19.6696,18.8893,18.1958,17.5742,17.0129,16.5027,16.0365,15.6081,15.2129,14.8466,14.5061,14.1883,13.8909,13.6118,13.3492,13.1015,12.8675,12.6457 -AAPL,135,147.0978,85.0881,63.0126,51.3564,44.0264,38.9331,35.1573,32.2284,29.8788,27.9445,26.3193,24.9308,23.7282,22.6743,21.7416,20.9091,20.1606,19.483,18.8662,18.3018,17.7829,17.3039,16.86,16.4473,16.0622,15.702,15.3642,15.0465,14.7471 -AAPL,140,232.7833,125.5082,88.9694,70.3031,58.8688,51.0937,45.4344,41.1132,37.6943,34.9143,32.6039,30.6497,28.9722,27.5143,26.234,25.0993,24.0856,23.1737,22.3483,21.597,20.9099,20.2786,19.6962,19.157,18.656,18.1892,17.753,17.3442,16.9602 -AAPL,145,320.649,167.6521,116.2139,90.2464,74.5086,63.9093,56.261,50.4669,45.9155,42.239,39.2022,36.648,34.467,32.5809,30.9321,29.4772,28.1827,27.0227,25.9766,25.0278,24.1628,23.3706,22.642,21.9693,21.3461,20.7669,20.227,19.7223,19.2494 -AAPL,150,407.7308,209.945,143.7533,110.4975,90.4374,76.9882,67.3254,60.035,54.3305,49.7393,45.9603,42.7922,40.0955,37.7705,35.7437,33.96,32.3772,30.9623,29.6894,28.5375,27.4896,26.5319,25.653,24.843,24.0941,23.3993,22.7527,22.1494,21.585 -AAPL,155,493.036,251.7089,171.0983,130.6857,106.3638,90.0948,78.4326,69.6534,62.799,57.294,52.7721,48.9887,45.7746,43.0084,40.6015,38.4869,36.6136,34.9419,33.4401,32.0832,30.8507,29.7259,28.6949,27.7463,26.8702,26.0584,25.304,24.6009,23.9438 -MSFT,180,4.0923,4.0914,4.0904,4.0894,4.0884,4.0875,4.0865,4.0855,4.0846,4.0836,4.0826,4.0816,4.0806,4.0796,4.0786,4.0776,4.0765,4.0753,4.0741,4.0729,4.0715,4.0701,4.0686,4.067,4.0654,4.0636,4.0617,4.0597,4.0575 -MSFT,185,4.4769,4.4757,4.4745,4.4733,4.4721,4.4709,4.4697,4.4685,4.4673,4.4661,4.4649,4.4636,4.4623,4.461,4.4595,4.458,4.4563,4.4545,4.4525,4.4504,4.4481,4.4457,4.443,4.4402,4.4372,4.434,4.4306,4.427,4.4233 -MSFT,190,4.9412,4.9397,4.9382,4.9367,4.9352,4.9337,4.9322,4.9307,4.9292,4.9276,4.9259,4.924,4.922,4.9198,4.9173,4.9145,4.9115,4.9081,4.9044,4.9004,4.896,4.8913,4.8863,4.881,4.8753,4.8694,4.8632,4.8568,4.85 -MSFT,195,5.513,5.5111,5.5092,5.5073,5.5054,5.5034,5.5015,5.4994,5.4971,5.4945,5.4916,5.4882,5.4842,5.4797,5.4745,5.4688,5.4624,5.4555,5.4479,5.4398,5.4312,5.4221,5.4125,5.4025,5.3921,5.3813,5.3702,5.3588,5.3471 -MSFT,200,6.2345,6.2319,6.2294,6.2269,6.2244,6.2217,6.2186,6.2149,6.2104,6.2048,6.1981,6.1902,6.181,6.1707,6.1592,6.1467,6.1331,6.1187,6.1035,6.0875,6.0709,6.0537,6.036,6.0179,5.9994,5.9806,5.9616,5.9423,5.9229 -MSFT,205,7.1731,7.1697,7.1663,7.1628,7.1587,7.1535,7.1464,7.1369,7.1248,7.1101,7.0929,7.0733,7.0517,7.0282,7.0031,6.9767,6.9492,6.9207,6.8915,6.8617,6.8314,6.8008,6.77,6.739,6.7079,6.6768,6.6458,6.6149,6.5841 -MSFT,210,8.4446,8.4398,8.4347,8.4279,8.4171,8.4005,8.3777,8.3486,8.3142,8.2752,8.2324,8.1867,8.1387,8.0891,8.0382,7.9866,7.9346,7.8823,7.8301,7.7781,7.7265,7.6753,7.6246,7.5746,7.5252,7.4764,7.4285,7.3812,7.3347 -MSFT,215,10.2639,10.2562,10.2433,10.2164,10.1717,10.1107,10.0368,9.9535,9.864,9.7705,9.6748,9.5783,9.4819,9.3862,9.2919,9.1991,9.1082,9.0192,8.9323,8.8475,8.7649,8.6844,8.6059,8.5295,8.4551,8.3826,8.3121,8.2433,8.1763 -MSFT,220,13.0823,13.0566,12.977,12.8395,12.6639,12.4679,12.2635,12.058,11.8556,11.6587,11.4686,11.2857,11.1104,10.9424,10.7817,10.6278,10.4805,10.3394,10.2041,10.0744,9.9498,9.8302,9.7151,9.6043,9.4976,9.3948,9.2956,9.1998,9.1072 -MSFT,225,18.0183,17.7438,17.2527,16.7012,16.1588,15.649,15.1777,14.7445,14.3464,13.9799,13.6416,13.3284,13.0375,12.7664,12.5131,12.2757,12.0526,11.8425,11.6441,11.4564,11.2784,11.1094,10.9485,10.7952,10.6488,10.5089,10.3749,10.2465,10.1233 -MSFT,230,28.1032,25.7783,23.7709,22.1602,20.8506,19.7629,18.8415,18.0479,17.3548,16.7424,16.1959,15.7042,15.2584,14.8517,14.4787,14.1348,13.8163,13.5202,13.244,12.9854,12.7427,12.5142,12.2987,12.0948,11.9016,11.7181,11.5435,11.3772,11.2184 -MSFT,235,48.8466,38.456,32.9377,29.357,26.7834,24.8142,23.2423,21.9484,20.8585,19.9233,19.1092,18.3917,17.7531,17.1796,16.6608,16.1885,15.756,15.3581,14.9902,14.6488,14.3308,14.0336,13.755,13.4932,13.2465,13.0135,12.793,12.5838,12.3851 -MSFT,240,83.0884,55.8742,44.6331,38.1643,33.848,30.7125,28.305,26.3832,24.8039,23.4767,22.3414,21.356,20.4905,19.7223,19.0348,18.4147,17.8517,17.3377,16.8659,16.431,16.0283,15.6541,15.3052,14.9789,14.6729,14.3851,14.1139,13.8576,13.615 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b/tests/options/csv/test_options_controller/test_get_partial_derivative.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0002,0.0002,0.0002,0.0003,0.0003,0.0004,0.0005,0.0005 -AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0002,0.0003,0.0004,0.0005,0.0006,0.0007,0.0008,0.001,0.0011,0.0013,0.0014,0.0016,0.0018,0.0019 -AAPL,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0003,0.0005,0.0007,0.0009,0.0011,0.0014,0.0016,0.0019,0.0022,0.0025,0.0028,0.0031,0.0034,0.0037,0.004,0.0043,0.0046,0.0049,0.0052 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+MSFT,235,0.0715,0.058,0.0495,0.0439,0.0398,0.0367,0.0342,0.0321,0.0304,0.0289,0.0276,0.0265,0.0255,0.0246,0.0238,0.0231,0.0224,0.0218,0.0212,0.0207,0.0202,0.0198,0.0193,0.019,0.0186,0.0182,0.0179,0.0176,0.0173 +MSFT,240,0.0834,0.0616,0.051,0.0445,0.04,0.0366,0.0339,0.0318,0.03,0.0285,0.0272,0.026,0.025,0.0241,0.0233,0.0226,0.0219,0.0213,0.0207,0.0202,0.0197,0.0193,0.0189,0.0185,0.0181,0.0177,0.0174,0.0171,0.0168 +MSFT,245,0.0265,0.0342,0.0341,0.0326,0.031,0.0294,0.028,0.0268,0.0257,0.0247,0.0238,0.023,0.0222,0.0216,0.021,0.0204,0.0199,0.0194,0.0189,0.0185,0.0181,0.0177,0.0174,0.0171,0.0168,0.0165,0.0162,0.0159,0.0157 +MSFT,250,0.0025,0.0103,0.0151,0.0176,0.0188,0.0193,0.0194,0.0194,0.0192,0.0189,0.0186,0.0183,0.018,0.0177,0.0174,0.0171,0.0168,0.0165,0.0162,0.016,0.0157,0.0155,0.0152,0.015,0.0148,0.0146,0.0144,0.0142,0.014 +MSFT,255,0.0001,0.0017,0.0046,0.0071,0.0091,0.0105,0.0114,0.0121,0.0126,0.0129,0.0131,0.0133,0.0133,0.0134,0.0133,0.0133,0.0132,0.0132,0.0131,0.013,0.0129,0.0128,0.0127,0.0126,0.0125,0.0123,0.0122,0.0121,0.012 +MSFT,260,0.0,0.0002,0.001,0.0022,0.0035,0.0047,0.0058,0.0066,0.0074,0.0079,0.0084,0.0088,0.0091,0.0093,0.0095,0.0097,0.0098,0.0099,0.01,0.01,0.0101,0.0101,0.0101,0.0101,0.0101,0.01,0.01,0.01,0.01 +MSFT,265,0.0,0.0,0.0001,0.0005,0.0011,0.0018,0.0025,0.0032,0.0038,0.0044,0.0049,0.0054,0.0057,0.0061,0.0064,0.0066,0.0069,0.007,0.0072,0.0074,0.0075,0.0076,0.0077,0.0077,0.0078,0.0079,0.0079,0.0079,0.008 +MSFT,270,0.0,0.0,0.0,0.0001,0.0003,0.0006,0.001,0.0014,0.0018,0.0022,0.0026,0.003,0.0034,0.0037,0.004,0.0043,0.0045,0.0048,0.005,0.0051,0.0053,0.0055,0.0056,0.0057,0.0058,0.0059,0.006,0.0061,0.0062 +MSFT,275,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0005,0.0008,0.001,0.0013,0.0016,0.0018,0.0021,0.0024,0.0026,0.0028,0.0031,0.0033,0.0034,0.0036,0.0038,0.0039,0.0041,0.0042,0.0043,0.0044,0.0045,0.0046 +MSFT,280,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0004,0.0006,0.0008,0.0009,0.0011,0.0013,0.0015,0.0017,0.0019,0.002,0.0022,0.0024,0.0025,0.0027,0.0028,0.0029,0.0031,0.0032,0.0033,0.0034 +MSFT,285,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0003,0.0003,0.0005,0.0006,0.0007,0.0008,0.001,0.0011,0.0012,0.0014,0.0015,0.0016,0.0017,0.0019,0.002,0.0021,0.0022,0.0023,0.0024 +MSFT,290,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0001,0.0002,0.0003,0.0004,0.0004,0.0005,0.0006,0.0007,0.0008,0.0009,0.001,0.0011,0.0012,0.0013,0.0014,0.0015,0.0016,0.0017 diff --git a/tests/options/csv/test_options_controller/test_get_rho.csv b/tests/options/csv/test_options_controller/test_get_rho.csv index 922606c..0c6f4f9 100644 --- a/tests/options/csv/test_options_controller/test_get_rho.csv +++ b/tests/options/csv/test_options_controller/test_get_rho.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.2602,0.5204,0.7806,1.0407,1.3007,1.5606,1.8206,2.0804,2.3402,2.6,2.8597,3.1193,3.3789,3.6384,3.8978,4.1572,4.4164,4.6755,4.9345,5.1934,5.4521,5.7106,5.9688,6.2268,6.4845,6.742,6.9991,7.2558,7.5122 -AAPL,100,0.2739,0.5478,0.8217,1.0954,1.3691,1.6428,1.9164,2.1899,2.4634,2.7368,3.0101,3.2833,3.5563,3.8291,4.1017,4.374,4.646,4.9175,5.1886,5.4592,5.7292,5.9985,6.2672,6.5352,6.8024,7.0688,7.3344,7.5992,7.863 -AAPL,105,0.2876,0.5752,0.8627,1.1502,1.4376,1.7249,2.0122,2.2992,2.5861,2.8727,3.1588,3.4443,3.729,4.013,4.2959,4.5778,4.8584,5.1379,5.4159,5.6926,5.9679,6.2417,6.514,6.7848,7.054,7.3218,7.588,7.8527,8.1159 -AAPL,110,0.3013,0.6026,0.9038,1.2049,1.5059,1.8065,2.1063,2.4051,2.7023,2.9978,3.2912,3.5824,3.8711,4.1574,4.4411,4.7222,5.0008,5.2769,5.5505,5.8216,6.0904,6.3568,6.621,6.883,7.1428,7.4006,7.6564,7.9103,8.1623 -AAPL,115,0.315,0.63,0.9447,1.2582,1.5692,1.8765,2.1793,2.4774,2.7704,3.0586,3.3421,3.6211,3.8958,4.1665,4.4334,4.6968,4.9568,5.2137,5.4677,5.7189,5.9674,6.2136,6.4573,6.6989,6.9384,7.1759,7.4115,7.6453,7.8774 -AAPL,120,0.3287,0.6554,0.9735,1.2795,1.5738,1.8576,2.1324,2.3994,2.6598,2.9145,3.1641,3.4093,3.6505,3.8883,4.1229,4.3546,4.5837,4.8103,5.0348,5.2573,5.4778,5.6966,5.9137,6.1293,6.3435,6.5563,6.7677,6.978,7.1871 -AAPL,125,0.3294,0.6125,0.8686,1.1096,1.3408,1.5653,1.7845,1.9996,2.2114,2.4204,2.627,2.8316,3.0343,3.2354,3.435,3.6333,3.8304,4.0264,4.2214,4.4154,4.6085,4.8008,4.9923,5.183,5.3731,5.5625,5.7513,5.9394,6.127 -AAPL,130,0.1426,0.3047,0.4697,0.6362,0.8035,0.9713,1.1395,1.3078,1.4764,1.645,1.8137,1.9825,2.1513,2.32,2.4888,2.6575,2.8262,2.9948,3.1634,3.332,3.5005,3.6689,3.8372,4.0055,4.1737,4.3418,4.5099,4.6778,4.8457 -AAPL,135,0.0051,0.0439,0.1118,0.1979,0.296,0.4027,0.5158,0.6339,0.756,0.8814,1.0096,1.1401,1.2727,1.407,1.5429,1.6802,1.8187,1.9583,2.0989,2.2405,2.3828,2.526,2.6698,2.8143,2.9593,3.105,3.2511,3.3977,3.5448 -AAPL,140,0.0,0.0015,0.0105,0.0311,0.0639,0.1077,0.1613,0.2232,0.2923,0.3677,0.4485,0.5341,0.624,0.7176,0.8146,0.9147,1.0175,1.1228,1.2304,1.3401,1.4517,1.5651,1.6802,1.7967,1.9147,2.034,2.1546,2.2763,2.3991 -AAPL,145,0.0,0.0,0.0004,0.0025,0.0081,0.0187,0.035,0.0573,0.0856,0.1198,0.1595,0.2045,0.2543,0.3087,0.3674,0.4301,0.4964,0.5661,0.6391,0.715,0.7937,0.8751,0.9589,1.045,1.1333,1.2236,1.3159,1.41,1.5058 -AAPL,150,0.0,0.0,0.0,0.0001,0.0006,0.0021,0.0054,0.0109,0.0192,0.0308,0.0459,0.0646,0.0869,0.1129,0.1426,0.1758,0.2125,0.2525,0.2957,0.3421,0.3914,0.4436,0.4986,0.5561,0.6162,0.6787,0.7435,0.8106,0.8797 -AAPL,155,0.0,0.0,0.0,0.0,0.0,0.0002,0.0006,0.0016,0.0034,0.0064,0.0108,0.017,0.0252,0.0355,0.0481,0.063,0.0804,0.1004,0.1228,0.1477,0.1752,0.2051,0.2375,0.2723,0.3094,0.3489,0.3905,0.4344,0.4804 -MSFT,180,0.4931,0.9861,1.479,1.9718,2.4644,2.957,3.4495,3.9419,4.4341,4.9263,5.4183,5.9102,6.402,6.8937,7.3852,7.8765,8.3676,8.8584,9.3489,9.839,10.3287,10.8179,11.3066,11.7947,12.2822,12.7689,13.2549,13.7401,14.2244 -MSFT,185,0.5068,1.0135,1.5201,2.0265,2.5329,3.0392,3.5453,4.0513,4.5573,5.0631,5.5687,6.0742,6.5795,7.0846,7.5893,8.0936,8.5974,9.1006,9.6033,10.1051,10.6061,11.1063,11.6054,12.1034,12.6003,13.096,13.5904,14.0834,14.5751 -MSFT,190,0.5205,1.0409,1.5611,2.0813,2.6014,3.1213,3.6411,4.1608,4.6804,5.1997,5.7188,6.2375,6.7557,7.2733,7.7902,8.3062,8.8211,9.3349,9.8474,10.3584,10.868,11.3759,11.8821,12.3865,12.889,13.3896,13.8883,14.3849,14.8796 -MSFT,195,0.5342,1.0683,1.6022,2.1361,2.6698,3.2034,3.7369,4.2701,4.803,5.3354,5.867,6.3977,6.9272,7.4553,7.9817,8.5063,9.0289,9.5494,10.0675,10.5832,11.0965,11.6071,12.1151,12.6205,13.1232,13.6231,14.1204,14.6149,15.1068 -MSFT,200,0.5479,1.0957,1.6433,2.1908,2.7382,3.2854,3.8322,4.3783,4.9232,5.4667,6.0084,6.5477,7.0845,7.6184,8.1492,8.6767,9.2009,9.7216,10.2387,10.7523,11.2622,11.7686,12.2715,12.7708,13.2667,13.7591,14.2482,14.734,15.2166 -MSFT,205,0.5616,1.123,1.6844,2.2456,2.8064,3.3664,3.9248,4.481,5.0342,5.5837,6.1292,6.6702,7.2066,7.7381,8.2648,8.7865,9.3034,9.8154,10.3228,10.8255,11.3238,11.8177,12.3073,12.7929,13.2744,13.7522,14.2262,14.6966,15.1635 -MSFT,210,0.5753,1.1504,1.7254,2.2997,2.8723,3.4414,4.0058,4.5645,5.1166,5.6619,6.2001,6.7314,7.2558,7.7735,8.2847,8.7898,9.2889,9.7824,10.2704,10.7533,11.2314,11.7047,12.1736,12.6382,13.0988,13.5555,14.0085,14.458,14.9041 -MSFT,215,0.589,1.1778,1.7655,2.3492,2.9257,3.4927,4.0492,4.5951,5.1304,5.6558,6.1718,6.6789,7.1779,7.6694,8.1537,8.6315,9.1032,9.5692,10.0298,10.4855,10.9366,11.3832,11.8257,12.2643,12.6992,13.1306,13.5587,13.9837,14.4057 -MSFT,220,0.6027,1.204,1.7968,2.3741,2.9335,3.4756,4.0019,4.5141,5.0138,5.5026,5.9815,6.4517,6.9141,7.3695,7.8185,8.2616,8.6994,9.1324,9.5608,9.9851,10.4054,10.8222,11.2356,11.6458,12.053,12.4574,12.8591,13.2583,13.6552 -MSFT,225,0.6158,1.2158,1.7822,2.3171,2.8268,3.3166,3.7905,4.2514,4.7016,5.1426,5.5758,6.0021,6.4225,6.8375,7.2478,7.6538,8.0559,8.4544,8.8496,9.2418,9.6312,10.0179,10.4022,10.7842,11.1641,11.5419,11.9178,12.2919,12.6642 -MSFT,230,0.6127,1.149,1.6341,2.0896,2.5257,2.9481,3.3602,3.7641,4.1613,4.5529,4.9398,5.3226,5.7017,6.0776,6.4506,6.821,7.189,7.5548,7.9186,8.2805,8.6407,8.9992,9.3562,9.7118,10.066,10.4189,10.7705,11.121,11.4704 -MSFT,235,0.4952,0.8979,1.2793,1.6497,2.0132,2.3716,2.7261,3.0776,3.4264,3.773,4.1178,4.4608,4.8023,5.1424,5.4813,5.819,6.1557,6.4914,6.8261,7.16,7.4931,7.8253,8.1569,8.4877,8.8179,9.1475,9.4764,9.8047,10.1325 -MSFT,240,0.2215,0.5018,0.7924,1.0879,1.3865,1.687,1.989,2.2921,2.596,2.9005,3.2056,3.511,3.8168,4.1228,4.429,4.7354,5.0419,5.3485,5.6551,5.9619,6.2686,6.5753,6.8821,7.1888,7.4955,7.8022,8.1088,8.4154,8.7219 -MSFT,245,0.0403,0.1814,0.369,0.5809,0.8078,1.0452,1.2902,1.5411,1.7968,2.0564,2.3192,2.5848,2.8527,3.1227,3.3945,3.6678,3.9426,4.2187,4.4959,4.7742,5.0534,5.3334,5.6142,5.8958,6.178,6.4608,6.7442,7.0281,7.3124 -MSFT,250,0.0026,0.0405,0.126,0.2471,0.3937,0.5593,0.7394,0.931,1.1318,1.3404,1.5554,1.7761,2.0015,2.2311,2.4645,2.7011,2.9407,3.183,3.4277,3.6746,3.9235,4.1743,4.4267,4.6807,4.9362,5.1931,5.4511,5.7104,5.9708 -MSFT,255,0.0001,0.0055,0.0313,0.0833,0.16,0.2579,0.3736,0.5044,0.648,0.8024,0.9663,1.1383,1.3176,1.5032,1.6945,1.8909,2.0919,2.297,2.506,2.7184,2.934,3.1526,3.3739,3.5977,3.8238,4.0521,4.2825,4.5148,4.7489 -MSFT,260,0.0,0.0005,0.0057,0.0223,0.0542,0.1025,0.1666,0.2453,0.3373,0.4414,0.5563,0.6809,0.8142,0.9554,1.1039,1.2589,1.4198,1.5862,1.7577,1.9338,2.1142,2.2986,2.4867,2.6782,2.873,3.0708,3.2714,3.4747,3.6806 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-MSFT,290,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0011,0.0023,0.0046,0.0081,0.0132,0.0201,0.0293,0.041,0.0554,0.0726,0.0929,0.1164,0.1431,0.1733,0.2068,0.2439,0.2844,0.3284,0.3758,0.4268,0.4811 +AAPL,95,0.2603,0.5205,0.7808,1.0411,1.3014,1.5616,1.8219,2.0822,2.3425,2.6027,2.863,3.1233,3.3835,3.6438,3.904,4.1642,4.4244,4.6845,4.9445,5.2044,5.4641,5.7237,5.9831,6.2423,6.5013,6.76,7.0184,7.2764,7.5341 +AAPL,100,0.274,0.5479,0.8219,1.0959,1.3699,1.6438,1.9178,2.1918,2.4657,2.7397,3.0136,3.2874,3.5612,3.8348,4.1082,4.3813,4.6541,4.9266,5.1986,5.4701,5.741,6.0113,6.281,6.5499,6.8181,7.0855,7.352,7.6176,7.8824 +AAPL,105,0.2877,0.5753,0.863,1.1507,1.4384,1.726,2.0136,2.3012,2.5886,2.8757,3.1623,3.4484,3.7338,4.0184,4.3019,4.5844,4.8657,5.1456,5.4242,5.7014,5.9771,6.2512,6.5239,6.7949,7.0644,7.3323,7.5987,7.8634,8.1266 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--git a/tests/options/csv/test_options_controller/test_get_rho_1.csv b/tests/options/csv/test_options_controller/test_get_rho_1.csv index 131a5c5..6736d44 100644 --- a/tests/options/csv/test_options_controller/test_get_rho_1.csv +++ b/tests/options/csv/test_options_controller/test_get_rho_1.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0001,-0.0002,-0.0003,-0.0004,-0.0007,-0.001,-0.0015,-0.0021,-0.0029,-0.0038,-0.005,-0.0065,-0.0082,-0.0102,-0.0126 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+MSFT,290,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0012,0.0032,0.0068,0.0124,0.0201,0.03,0.042,0.0559,0.0714,0.0883,0.1063,0.1251,0.1447,0.1646,0.1848,0.2051,0.2254,0.2455,0.2654,0.2851,0.3043,0.3232,0.3416 diff --git a/tests/options/csv/test_options_controller/test_get_vega.csv b/tests/options/csv/test_options_controller/test_get_vega.csv index c44db0a..36e89a3 100644 --- a/tests/options/csv/test_options_controller/test_get_vega.csv +++ b/tests/options/csv/test_options_controller/test_get_vega.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 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-AAPL,140,0.0,0.0006,0.0031,0.0072,0.0122,0.0176,0.0231,0.0287,0.0341,0.0394,0.0446,0.0496,0.0544,0.0591,0.0636,0.068,0.0723,0.0764,0.0804,0.0843,0.0881,0.0918,0.0954,0.0989,0.1024,0.1057,0.109,0.1122,0.1154 -AAPL,145,0.0,0.0,0.0002,0.0008,0.0021,0.004,0.0065,0.0095,0.0128,0.0164,0.0201,0.024,0.0279,0.0318,0.0358,0.0397,0.0436,0.0475,0.0513,0.0551,0.0588,0.0625,0.0661,0.0697,0.0732,0.0767,0.0801,0.0834,0.0867 -AAPL,150,0.0,0.0,0.0,0.0,0.0002,0.0006,0.0012,0.0022,0.0035,0.0051,0.007,0.0092,0.0115,0.014,0.0166,0.0194,0.0223,0.0252,0.0282,0.0312,0.0343,0.0374,0.0405,0.0436,0.0467,0.0498,0.0529,0.056,0.0591 -AAPL,155,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0004,0.0007,0.0013,0.0019,0.0028,0.0039,0.0051,0.0065,0.0081,0.0098,0.0116,0.0136,0.0156,0.0177,0.0199,0.0222,0.0245,0.0269,0.0294,0.0318,0.0343,0.0369 -MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0002,0.0003,0.0005,0.0007,0.0009,0.0011,0.0014,0.0018,0.0022,0.0026,0.0031,0.0037 -MSFT,185,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0004,0.0006,0.0008,0.0011,0.0015,0.0019,0.0024,0.003,0.0036,0.0044,0.0051,0.006,0.007,0.008 -MSFT,190,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0004,0.0006,0.001,0.0014,0.0019,0.0025,0.0032,0.004,0.0049,0.0059,0.007,0.0083,0.0096,0.011,0.0125,0.0141,0.0158 -MSFT,195,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0006,0.001,0.0015,0.0021,0.003,0.0039,0.005,0.0063,0.0078,0.0093,0.011,0.0129,0.0148,0.0169,0.0191,0.0214,0.0237,0.0262,0.0287 -MSFT,200,0.0,0.0,0.0,0.0,0.0,0.0,0.0002,0.0004,0.0008,0.0014,0.0022,0.0032,0.0045,0.0061,0.0078,0.0098,0.012,0.0143,0.0168,0.0195,0.0223,0.0252,0.0282,0.0313,0.0345,0.0378,0.0411,0.0445,0.0479 -MSFT,205,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0009,0.0018,0.0031,0.0047,0.0067,0.0091,0.0117,0.0147,0.0179,0.0212,0.0248,0.0285,0.0324,0.0363,0.0403,0.0444,0.0486,0.0528,0.057,0.0613,0.0655,0.0698,0.074 -MSFT,210,0.0,0.0,0.0,0.0002,0.0008,0.002,0.0039,0.0064,0.0095,0.0131,0.017,0.0213,0.0259,0.0306,0.0355,0.0405,0.0456,0.0507,0.0559,0.0611,0.0662,0.0714,0.0765,0.0816,0.0867,0.0917,0.0967,0.1017,0.1065 -MSFT,215,0.0,0.0,0.0004,0.0018,0.0043,0.0081,0.0127,0.018,0.0238,0.0299,0.0362,0.0426,0.0491,0.0556,0.062,0.0684,0.0748,0.081,0.0872,0.0932,0.0992,0.1051,0.1108,0.1165,0.1221,0.1275,0.1329,0.1382,0.1434 -MSFT,220,0.0,0.0006,0.0034,0.0087,0.0156,0.0235,0.0318,0.0403,0.0488,0.0572,0.0654,0.0734,0.0812,0.0887,0.0961,0.1033,0.1102,0.117,0.1236,0.13,0.1363,0.1424,0.1483,0.1541,0.1598,0.1653,0.1708,0.1761,0.1813 -MSFT,225,0.0004,0.0058,0.0161,0.028,0.04,0.0516,0.0626,0.0731,0.0829,0.0923,0.1012,0.1097,0.1178,0.1256,0.133,0.1402,0.1471,0.1538,0.1603,0.1666,0.1727,0.1786,0.1844,0.19,0.1955,0.2008,0.2061,0.2112,0.2162 -MSFT,230,0.0076,0.0272,0.0453,0.061,0.0749,0.0872,0.0984,0.1087,0.1183,0.1273,0.1357,0.1438,0.1514,0.1587,0.1657,0.1724,0.1789,0.1852,0.1913,0.1972,0.2029,0.2085,0.214,0.2193,0.2245,0.2295,0.2345,0.2393,0.2441 -MSFT,235,0.0374,0.0607,0.0778,0.0919,0.1042,0.1152,0.1252,0.1345,0.1432,0.1513,0.1591,0.1665,0.1736,0.1804,0.1869,0.1933,0.1994,0.2053,0.2111,0.2167,0.2222,0.2275,0.2328,0.2378,0.2428,0.2477,0.2525,0.2572,0.2618 -MSFT,240,0.0459,0.0677,0.0841,0.0978,0.1099,0.1207,0.1306,0.1398,0.1484,0.1566,0.1644,0.1718,0.1789,0.1857,0.1923,0.1986,0.2048,0.2107,0.2165,0.2222,0.2277,0.2331,0.2383,0.2434,0.2485,0.2534,0.2582,0.263,0.2676 -MSFT,245,0.0153,0.0394,0.0589,0.0752,0.0893,0.1018,0.1131,0.1235,0.1332,0.1422,0.1508,0.1589,0.1667,0.1741,0.1812,0.1881,0.1947,0.2011,0.2073,0.2133,0.2192,0.2249,0.2305,0.2359,0.2413,0.2465,0.2516,0.2566,0.2615 -MSFT,250,0.0015,0.0124,0.0275,0.0426,0.0568,0.07,0.0822,0.0936,0.1043,0.1143,0.1238,0.1328,0.1413,0.1495,0.1574,0.1649,0.1722,0.1793,0.1861,0.1927,0.1991,0.2053,0.2113,0.2173,0.223,0.2287,0.2342,0.2395,0.2448 -MSFT,255,0.0,0.0022,0.0087,0.0181,0.0287,0.0397,0.0507,0.0614,0.0718,0.0818,0.0915,0.1007,0.1097,0.1183,0.1266,0.1346,0.1423,0.1499,0.1571,0.1642,0.1711,0.1778,0.1843,0.1906,0.1968,0.2029,0.2088,0.2146,0.2203 -MSFT,260,0.0,0.0002,0.0019,0.0058,0.0117,0.0188,0.0268,0.0352,0.0438,0.0526,0.0612,0.0698,0.0783,0.0866,0.0947,0.1026,0.1104,0.1179,0.1253,0.1325,0.1396,0.1465,0.1532,0.1598,0.1663,0.1726,0.1788,0.1849,0.1909 -MSFT,265,0.0,0.0,0.0003,0.0015,0.0039,0.0075,0.0122,0.0177,0.0239,0.0305,0.0374,0.0445,0.0516,0.0589,0.0662,0.0734,0.0806,0.0877,0.0947,0.1016,0.1085,0.1152,0.1219,0.1284,0.1349,0.1412,0.1474,0.1536,0.1596 -MSFT,270,0.0,0.0,0.0,0.0003,0.0011,0.0026,0.0049,0.0079,0.0117,0.0161,0.0209,0.0261,0.0316,0.0374,0.0433,0.0494,0.0556,0.0618,0.068,0.0743,0.0805,0.0868,0.0929,0.0991,0.1052,0.1113,0.1173,0.1232,0.1291 -MSFT,275,0.0,0.0,0.0,0.0,0.0002,0.0007,0.0017,0.0032,0.0052,0.0077,0.0108,0.0142,0.0181,0.0223,0.0267,0.0314,0.0363,0.0414,0.0466,0.0519,0.0572,0.0627,0.0681,0.0736,0.0791,0.0847,0.0902,0.0957,0.1012 -MSFT,280,0.0,0.0,0.0,0.0,0.0,0.0002,0.0005,0.0011,0.0021,0.0034,0.0051,0.0072,0.0097,0.0125,0.0156,0.0189,0.0226,0.0264,0.0305,0.0347,0.039,0.0435,0.0481,0.0528,0.0575,0.0623,0.0672,0.0721,0.077 -MSFT,285,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0008,0.0014,0.0023,0.0034,0.0049,0.0066,0.0086,0.0109,0.0134,0.0161,0.0191,0.0223,0.0256,0.0291,0.0328,0.0366,0.0405,0.0445,0.0486,0.0527,0.057 -MSFT,290,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0005,0.0009,0.0015,0.0023,0.0033,0.0045,0.0059,0.0076,0.0094,0.0115,0.0138,0.0162,0.0188,0.0216,0.0245,0.0276,0.0308,0.0341,0.0375,0.041 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a/tests/options/csv/test_options_controller/test_get_vera.csv b/tests/options/csv/test_options_controller/test_get_vera.csv index 941d469..dbda47f 100644 --- a/tests/options/csv/test_options_controller/test_get_vera.csv +++ b/tests/options/csv/test_options_controller/test_get_vera.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0002,-0.0004,-0.001,-0.0021,-0.0039,-0.0069,-0.0114,-0.0178,-0.0265,-0.0381,-0.0529,-0.0713,-0.0938,-0.1208,-0.1526,-0.1894,-0.2315,-0.2792,-0.3326 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+MSFT,280,0.0,0.0,0.0,0.0007,0.0059,0.024,0.0663,0.1434,0.263,0.4296,0.6446,0.9072,1.2152,1.5652,1.9536,2.3763,2.8296,3.3098,3.8132,4.3367,4.8775,5.4328,6.0003,6.5779,7.1637,7.7561,8.3535,8.9548,9.5586 +MSFT,285,0.0,0.0,0.0,0.0001,0.0011,0.0059,0.0202,0.0513,0.1068,0.1931,0.3148,0.4747,0.6742,0.9131,1.1903,1.5041,1.8523,2.2323,2.6415,3.0774,3.5374,4.019,4.5199,5.038,5.5712,6.1176,6.6755,7.2434,7.8199 +MSFT,290,0.0,0.0,0.0,0.0,0.0002,0.0013,0.0054,0.0165,0.0394,0.0795,0.1417,0.2304,0.3486,0.4986,0.6813,0.8971,1.1457,1.4262,1.7373,2.0774,2.4449,2.838,3.2549,3.6938,4.1528,4.6302,5.1245,5.634,6.1573 diff --git a/tests/options/csv/test_options_controller/test_get_veta.csv b/tests/options/csv/test_options_controller/test_get_veta.csv index 6c2935c..87dbbeb 100644 --- a/tests/options/csv/test_options_controller/test_get_veta.csv +++ b/tests/options/csv/test_options_controller/test_get_veta.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0003,0.0021,0.0101,0.0356,0.0985,0.2276,0.4581,0.8279,1.3733,2.1261,3.1105,4.3426,5.83,7.5723,9.5622,11.7867,14.2288,16.8682,19.6825,22.6488,25.7437,28.944,32.2278 -AAPL,100,0.0,0.0,0.0,0.0,0.0005,0.0073,0.0477,0.1908,0.5495,1.2609,2.4564,4.237,6.6611,9.7429,13.4581,17.7538,22.5576,27.7868,33.3551,39.1778,45.1756,51.276,57.415,63.5371,69.595,75.5497,81.3692,87.0281,92.5065 -AAPL,105,0.0,0.0,0.0001,0.0113,0.1488,0.7931,2.5334,5.9042,11.1848,18.3655,27.2189,37.3983,48.5186,60.2108,72.1512,84.0727,95.7655,107.0726,117.8823,128.1197,137.7398,146.7205,155.0574,162.7585,169.8413,176.3297,182.2517,187.6376,192.519 -AAPL,110,0.0,0.001,0.1851,2.2755,9.5553,23.7499,44.0615,68.3924,94.5375,120.7382,145.7954,168.9923,189.9691,208.6078,224.9423,239.0955,251.2367,261.5545,270.2402,277.4778,283.4385,288.2788,292.1395,295.1459,297.409,299.0265,300.0842,300.6571,300.8108 -AAPL,115,0.0006,2.1694,26.5167,82.5953,152.7892,220.5635,278.2878,324.1814,359.0952,384.7476,402.9424,415.279,423.0777,427.3914,429.0451,428.679,426.7883,423.755,419.8744,415.374,410.4302,405.1789,399.7257,394.1518,388.5201,382.879,377.2653,371.7074,366.2263 -AAPL,120,14.3464,235.5513,478.0459,614.8666,675.3842,693.6489,689.9955,675.3955,655.676,633.9373,611.8273,590.2084,569.5124,549.9333,531.533,514.3016,498.1912,483.1355,469.0607,455.8922,443.5575,431.9883,421.1215,410.899,401.2678,392.1797,383.5912,375.4628,367.7587 -AAPL,125,1501.6043,1451.9505,1227.6903,1057.061,932.607,839.2074,766.73,708.8129,661.3833,621.7454,588.0533,559.0043,533.6531,511.2978,491.4062,473.567,457.4574,442.8203,429.4483,417.1721,405.852,395.3718,385.6339,376.5556,368.0666,360.1063,352.6226,345.5702,338.9094 -AAPL,130,1857.8955,1295.1148,1051.9841,908.4874,811.08,739.41,683.8273,639.0895,602.0713,570.7786,543.8713,520.4108,499.7179,481.2869,464.7331,449.7572,436.1229,423.6407,412.1565,401.5438,391.6971,382.5284,373.9628,365.937,358.3961,351.2932,344.5872,338.2422,332.227 -AAPL,135,969.1108,1340.565,1250.2881,1119.4589,1005.8019,913.3206,838.2683,776.6396,725.2825,681.8633,644.6681,612.4279,584.1905,559.2304,536.987,517.0205,498.9819,482.5904,467.6177,453.8766,441.2118,429.4934,418.6121,408.4752,399.0032,390.128,381.7908,373.9402,366.5315 -AAPL,140,8.3241,197.4525,451.7526,615.845,699.6352,734.2374,741.2407,733.2778,717.4763,697.8426,676.6338,655.119,634.0019,613.6592,594.2769,575.9293,558.6263,542.341,527.0269,512.6275,499.0827,486.3326,474.3188,462.9864,452.284,442.1639,432.5823,423.4989,414.8771 -AAPL,145,0.0021,4.3725,43.9169,124.0912,216.6286,300.9971,369.6693,422.1263,460.4917,487.479,505.6054,516.9623,523.2047,525.6113,525.1584,522.588,518.4629,513.2096,507.1521,500.5362,493.5488,486.332,478.9936,471.615,464.2578,456.968,449.7797,442.7179,435.8002 -AAPL,150,0.0,0.0198,1.4156,10.6242,33.2089,67.8477,109.4959,153.17,195.3583,234.0405,268.2916,297.8794,322.9664,343.9149,361.1687,375.1864,386.4051,395.2236,401.9966,407.0335,410.6019,412.9315,414.218,414.6285,414.3046,413.3666,411.9167,410.0414,407.8142 -AAPL,155,0.0,0.0,0.0177,0.4376,2.792,9.1649,20.7274,37.3057,57.8326,80.925,105.2661,129.7779,153.6618,176.3741,197.5775,217.0912,234.8467,250.8523,265.167,277.8807,289.1008,298.9423,307.5216,314.9525,321.3434,326.796,331.4044,335.2551,338.427 -MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0001,0.0016,0.0108,0.0471,0.1506,0.3847,0.8316,1.5823,2.7252,4.3365,6.4736,9.1722,12.446,16.289,20.6782,25.5775,30.9413,36.7176,42.8511,49.2853,55.9645,62.8349,69.846,76.9507 -MSFT,185,0.0,0.0,0.0,0.0,0.0002,0.0039,0.0309,0.1421,0.4567,1.1439,2.394,4.3831,7.2469,11.067,15.8688,21.6283,28.282,35.7385,43.8893,52.6177,61.8057,71.3394,81.1119,91.0259,100.9945,110.9416,120.8017,130.5193,140.0482 -MSFT,190,0.0,0.0,0.0,0.0002,0.0079,0.0796,0.3991,1.3034,3.2088,6.4954,11.421,18.0894,26.4613,36.3885,47.6525,59.9994,73.1672,86.9041,100.9801,115.1929,129.3697,143.3675,157.0705,170.3876,183.2494,195.6051,207.4193,218.67,229.3453 -MSFT,195,0.0,0.0,0.0001,0.0122,0.1811,1.0412,3.511,8.5212,16.6586,28.0496,42.4333,59.3055,78.0555,98.0651,118.7688,139.6827,160.4135,180.6552,200.1799,218.826,236.4864,253.0977,268.63,283.0796,296.4617,308.8055,320.15,330.5403,340.0258 -MSFT,200,0.0,0.0,0.0113,0.3452,2.5028,8.9448,21.4931,40.4772,64.9932,93.5288,124.4714,156.3921,188.1559,218.9309,248.1517,275.467,300.6883,323.7449,344.6491,363.4682,380.3045,395.2807,408.5294,420.1861,430.3843,439.2524,446.9119,453.4763,459.0509 -MSFT,205,0.0,0.003,0.485,5.4308,21.5654,51.6474,93.3201,142.0229,193.3416,243.9446,291.6752,335.3272,374.367,408.6974,438.4843,464.0381,485.7371,503.9798,519.1571,531.6367,541.7556,549.8172,556.0916,560.8172,564.2033,566.433,567.6658,568.0407,567.6783 -MSFT,210,0.0,0.3345,10.0671,49.084,118.5851,204.2408,291.9856,373.1923,443.9486,503.2532,551.615,590.2004,620.3726,643.4646,660.6798,673.0607,681.4886,686.6982,689.2966,689.783,688.5668,685.9833,682.3069,677.7622,672.5331,666.7704,660.5975,654.1157,647.408 -MSFT,215,0.0132,13.2799,104.338,261.0237,423.9024,561.6211,667.0135,743.0346,795.3531,829.5175,850.1303,860.7671,864.1242,862.2082,856.504,848.1087,837.8333,826.2776,813.8861,800.9876,787.8256,774.5796,761.3814,748.327,735.4855,722.9055,710.6202,698.6515,687.0123 -MSFT,220,4.5567,198.3567,553.5344,831.3629,999.319,1087.6233,1125.8818,1134.1246,1124.7804,1105.3908,1080.5199,1052.9352,1024.3156,995.6739,967.6116,940.4742,914.4468,889.6142,865.9987,843.5841,822.3315,802.1889,783.0977,764.997,747.8257,731.5246,716.037,701.3092,687.291 -MSFT,225,255.421,1143.456,1527.6676,1608.9563,1577.1283,1507.7136,1429.2403,1352.4158,1281.0104,1216.0247,1157.3593,1104.507,1056.8449,1013.7559,974.6748,939.1026,906.6063,876.813,849.4026,824.1007,800.671,778.9103,758.6427,739.7156,721.9964,705.3689,689.7314,674.9943,661.0787 -MSFT,230,2387.9937,2580.0616,2246.0254,1955.3417,1733.4579,1563.2638,1429.5869,1321.9896,1233.4775,1159.2931,1096.1208,1041.591,993.9697,951.9602,914.5745,881.0462,850.772,823.2703,798.1517,775.0981,753.8466,734.1784,715.9094,698.8839,682.969,668.0505,654.0299,640.8218,628.3513 -MSFT,235,3860.7629,2575.7716,2042.7953,1739.438,1538.6878,1393.6684,1282.74,1194.4054,1121.9346,1061.0979,1009.0888,963.9631,924.3268,889.1507,857.6566,829.2437,803.4401,779.8689,758.2254,738.26,719.766,702.5708,686.5288,671.5163,657.4273,644.1706,631.6671,619.8479,608.6524 -MSFT,240,3583.0739,2449.6194,1974.5936,1698.3363,1512.4344,1376.4369,1271.3959,1187.1077,1117.5288,1058.8228,1008.4219,964.5341,925.8658,891.4567,860.5768,832.6604,807.2608,784.0204,762.6488,742.9077,724.5992,707.5575,691.6423,676.7345,662.7316,649.5453,637.0989,625.3254,614.166 -MSFT,245,3463.1693,2886.3635,2354.4093,2002.4041,1758.5484,1580.0007,1443.2701,1334.8228,1246.3948,1172.6733,1110.0906,1056.1618,1009.1016,967.593,930.6426,897.4858,867.5242,840.282,815.3761,792.4941,771.3787,751.8155,733.6248,716.6543,700.7744,685.8736,671.8558,658.6376,646.1458 -MSFT,250,806.9749,1878.4865,2046.1486,1961.1315,1825.426,1691.3114,1571.2858,1466.8157,1376.3914,1297.9202,1229.4309,1169.2464,1115.991,1068.5493,1026.0188,987.6665,952.8944,921.2109,892.2091,865.5503,840.9501,818.1683,797.0009,777.2733,758.8356,741.558,725.3279,710.0469,695.6289 -MSFT,255,46.7233,589.8389,1099.1666,1356.6639,1455.2029,1472.0893,1449.3192,1408.2733,1359.7794,1309.33,1259.6827,1212.1686,1167.3661,1125.4552,1086.4079,1050.0918,1016.3268,984.9168,955.6655,928.386,902.904,879.0603,856.7098,835.7219,815.9788,797.3748,779.8145,763.2128,747.4925 -MSFT,260,0.7902,95.6418,373.524,661.5999,876.0495,1015.3282,1097.9443,1141.6747,1159.6786,1160.967,1151.5488,1135.4014,1115.1563,1092.5574,1068.7615,1044.5345,1020.3803,996.6262,973.4797,951.0669,929.4583,908.6868,888.7598,869.6678,851.3901,833.899,817.1627,801.147,785.8172 -MSFT,265,0.0044,8.5673,83.917,234.1843,405.8111,561.1208,686.7792,782.2484,851.6884,900.2259,932.5541,952.5443,963.2436,966.9973,965.5906,960.3763,952.3775,942.3686,930.9356,918.5227,905.4666,892.0226,878.3837,864.6957,851.068,837.5822,824.2984,811.2602,798.4983 -MSFT,270,0.0,0.4471,12.9516,61.9568,147.9937,252.9806,359.7401,457.9697,543.1361,614.201,671.9055,717.7441,753.4175,780.5676,800.666,814.9802,824.5775,830.3445,833.0116,833.1781,831.3344,827.8811,823.1454,817.3949,810.848,803.6835,796.0477,788.0601,779.8188 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b/tests/options/csv/test_options_controller/test_get_vomma.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0014,0.0042,0.0107,0.0232,0.0449,0.0796,0.1311,0.2031,0.2994,0.4229,0.5765,0.762,0.981,1.2344,1.5223,1.8448,2.2012,2.5904,3.0114,3.4626 -AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0002,0.0013,0.006,0.0192,0.0488,0.104,0.1949,0.3303,0.518,0.7631,1.069,1.4367,1.8656,2.3534,2.8969,3.4922,4.1345,4.8191,5.541,6.2954,7.0773,7.8824,8.7062,9.545 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-AAPL,145,0.0,0.0335,0.4899,1.7952,3.813,6.1919,8.6461,11.0024,13.1736,15.1256,16.8536,18.3682,19.6871,20.83,21.8167,22.666,23.3947,24.0179,24.5488,24.9992,25.379,25.6972,25.9612,26.1777,26.3526,26.4907,26.5965,26.6738,26.7259 -AAPL,150,0.0,0.0002,0.0163,0.1607,0.6171,1.4877,2.7551,4.3332,6.1181,8.0155,9.9502,11.8669,13.7273,15.5067,17.1902,18.7701,20.2437,21.6116,22.8769,24.0438,25.1174,26.1033,27.0072,27.8346,28.591,29.2817,29.9116,30.4852,31.0069 -AAPL,155,0.0,0.0,0.0002,0.0068,0.0533,0.2076,0.5414,1.101,1.8983,2.9183,4.1292,5.4923,6.9681,8.52,10.1164,11.7305,13.341,14.9309,16.4873,18.0005,19.4635,20.8716,22.2218,23.5125,24.7431,25.9138,27.0254,28.0794,29.0772 -MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0004,0.0018,0.0063,0.0176,0.0413,0.0848,0.1568,0.2664,0.4227,0.6342,0.9082,1.2505,1.6654,2.1558,2.7231,3.3673,4.0873,4.8809,5.7455,6.6774,7.6727,8.7271 -MSFT,185,0.0,0.0,0.0,0.0,0.0,0.0001,0.0009,0.0047,0.0171,0.0473,0.1083,0.2154,0.3842,0.6292,0.9626,1.3936,1.9282,2.5693,3.3169,4.1688,5.1208,6.1673,7.3015,8.5161,9.8035,11.1558,12.5651,14.0239,15.5248 -MSFT,190,0.0,0.0,0.0,0.0,0.0002,0.002,0.0116,0.043,0.1186,0.2652,0.5103,0.877,1.3824,2.0365,2.8425,3.7979,4.8956,6.1253,7.4748,8.9305,10.4784,12.1047,13.796,15.5399,17.3245,19.1392,20.9745,22.8216,24.673 -MSFT,195,0.0,0.0,0.0,0.0002,0.0038,0.0258,0.1009,0.2778,0.6068,1.1275,1.8634,2.8219,3.9967,5.3717,6.9247,8.6304,10.4627,12.3961,14.4068,16.4732,18.5759,20.6982,22.8257,24.9461,27.0491,29.1263,31.1707,33.1768,35.1403 -MSFT,200,0.0,0.0,0.0001,0.0057,0.0515,0.2188,0.6076,1.2959,2.3199,3.6764,5.3346,7.2484,9.3661,11.6364,14.0126,16.4537,18.9252,21.3988,23.8518,26.2662,28.6284,30.9281,33.1581,35.3132,37.3903,39.3877,41.3048,43.1419,44.9002 -MSFT,205,0.0,0.0,0.006,0.089,0.436,1.2373,2.5762,4.4265,6.6982,9.2798,12.0633,14.9561,17.8844,20.792,23.6378,26.3934,29.0399,31.5655,33.9642,36.2337,38.3748,40.3901,42.2836,44.0602,45.7254,47.2849,48.7443,50.1095,51.3861 -MSFT,210,0.0,0.0028,0.1232,0.7864,2.3328,4.7379,7.768,11.1578,14.6885,18.2041,21.6035,24.8264,27.8412,30.6351,33.2077,35.5656,37.7198,39.6835,41.4705,43.0951,44.5708,45.9105,47.1263,48.2291,49.2293,50.136,50.9577,51.7021,52.376 -MSFT,215,0.0001,0.1082,1.2404,4.0276,7.9654,12.3467,16.6911,20.7461,24.4062,27.647,30.4843,32.9517,35.089,36.936,38.5297,39.9034,41.0866,42.1046,42.9794,43.7299,44.3725,44.9211,45.3878,45.7828,46.1151,46.3924,46.6214,46.808,46.9571 -MSFT,220,0.0187,1.5564,6.232,11.9623,17.2607,21.6864,25.2356,28.0335,30.2228,31.9293,33.2553,34.2815,35.0706,35.6716,36.1223,36.4526,36.6858,36.8405,36.9314,36.9705,36.9672,36.9292,36.863,36.7736,36.6653,36.5415,36.4053,36.2589,36.1045 -MSFT,225,1.0036,8.2465,15.2769,19.9532,22.8589,24.6311,25.6892,26.2916,26.5981,26.7092,26.6899,26.5828,26.4167,26.2114,25.9803,25.733,25.4762,25.2147,24.952,24.6905,24.4318,24.1772,23.9275,23.6832,23.4448,23.2123,22.986,22.7657,22.5515 -MSFT,230,8.1525,14.589,16.2776,16.5122,16.2649,15.8547,15.3998,14.9468,14.5142,14.1086,13.7313,13.3816,13.0575,12.7569,12.4776,12.2176,11.9751,11.7483,11.5358,11.3363,11.1485,10.9713,10.804,10.6455,10.4952,10.3525,10.2167,10.0872,9.9637 -MSFT,235,5.9766,4.9004,4.2324,3.7882,3.4688,3.2261,3.0339,2.8772,2.7464,2.6351,2.5389,2.4547,2.3801,2.3135,2.2535,2.199,2.1492,2.1035,2.0612,2.0219,1.9852,1.9509,1.9186,1.8881,1.8593,1.8318,1.8057,1.7808,1.7569 -MSFT,240,2.2358,1.6145,1.3062,1.1112,0.9721,0.8654,0.7793,0.7074,0.6457,0.5917,0.5435,0.5,0.4602,0.4235,0.3894,0.3573,0.3271,0.2984,0.2711,0.2449,0.2197,0.1955,0.172,0.1492,0.127,0.1054,0.0843,0.0636,0.0433 -MSFT,245,10.4986,13.4491,13.3347,12.6877,11.9781,11.3118,10.71,10.1715,9.6892,9.2555,8.8634,8.5068,8.1809,7.8813,7.6046,7.348,7.109,6.8856,6.6759,6.4787,6.2925,6.1163,5.9491,5.7901,5.6385,5.4938,5.3553,5.2225,5.0951 -MSFT,250,3.0433,12.567,18.4363,21.3649,22.7269,23.2614,23.3458,23.1796,22.8726,22.4876,22.0616,21.6168,21.1669,20.72,20.2813,19.8536,19.4387,19.0372,18.6496,18.2758,17.9153,17.5678,17.2329,16.9098,16.5982,16.2973,16.0068,15.726,15.4544 -MSFT,255,0.1879,4.4391,11.6543,18.0762,22.9128,26.368,28.7834,30.4456,31.5659,32.2947,32.7385,32.9732,33.0534,33.0186,32.8982,32.7138,32.4816,32.2139,31.9202,31.6075,31.2815,30.9465,30.6057,30.2618,29.9168,29.5723,29.2295,28.8893,28.5526 -MSFT,260,0.0033,0.7577,4.262,9.6782,15.424,20.6806,25.1827,28.9169,31.9625,34.4223,36.3957,37.9691,39.215,40.193,40.9512,41.5289,41.9578,42.2636,42.4674,42.5865,42.635,42.6245,42.5647,42.4636,42.3281,42.1637,41.9751,41.7664,41.5408 -MSFT,265,0.0,0.0697,0.9955,3.6025,7.5942,12.2711,17.0744,21.6708,25.8951,29.6845,33.0332,35.9639,38.5121,40.7177,42.6197,44.255,45.6566,46.8541,47.8734,48.7369,49.4645,50.073,50.5774,50.9904,51.3232,51.5855,51.7857,51.9312,52.0284 -MSFT,270,0.0,0.0037,0.1572,0.9822,2.8732,5.7765,9.3959,13.4077,17.5507,21.6425,25.5672,29.2582,32.6823,35.8284,38.6993,41.3056,43.6629,45.7886,47.7009,49.4177,50.9563,52.3328,53.5621,54.658,55.6328,56.498,57.2638,57.9395,58.5335 -MSFT,275,0.0,0.0001,0.0175,0.203,0.8624,2.2232,4.3193,7.0423,10.2257,13.701,17.3235,20.9797,24.5862,28.0847,31.4366,34.6185,37.6175,40.4288,43.053,45.4943,47.7593,49.8563,51.7942,53.5822,55.2298,56.7462,58.1402,59.4203,60.5945 -MSFT,280,0.0,0.0,0.0014,0.0326,0.2101,0.7147,1.6926,3.2016,5.2182,7.6689,10.4587,13.4908,16.6771,19.9427,23.2269,26.4824,29.6732,32.773,35.7634,38.632,41.3714,43.9776,46.4498,48.7889,50.9974,53.079,55.0382,56.8796,58.6085 -MSFT,285,0.0,0.0,0.0001,0.0042,0.0423,0.1951,0.5742,1.2782,2.3647,3.8462,5.6992,7.878,10.3258,12.9834,15.7942,18.707,21.6777,24.6689,27.65,30.5963,33.4884,36.3114,39.054,41.7083,44.2685,46.7313,49.0949,51.3586,53.523 -MSFT,290,0.0,0.0,0.0,0.0004,0.0072,0.0458,0.1707,0.4533,0.9621,1.7465,2.8313,4.2183,5.8909,7.821,9.9733,12.3101,14.7938,17.389,20.0633,22.7879,25.5378,28.2915,31.031,33.7413,36.4102,39.0279,41.5865,44.0802,46.5042 +AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0015,0.0046,0.0116,0.0251,0.0486,0.0861,0.1417,0.2194,0.3231,0.4562,0.6214,0.8209,1.0562,1.3282,1.637,1.9825,2.364,2.7804,3.2302,3.7119 +AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0002,0.0014,0.0064,0.0205,0.0521,0.111,0.2077,0.3519,0.5513,0.8117,1.1361,1.5257,1.9796,2.4954,3.0694,3.6973,4.3741,5.0945,5.8532,6.645,7.4647,8.3075,9.1689,10.0446 +AAPL,105,0.0,0.0,0.0,0.0002,0.0031,0.0195,0.072,0.1901,0.4019,0.7275,1.1765,1.7495,2.4397,3.2351,4.1216,5.0836,6.1058,7.1738,8.2744,9.396,10.5285,11.663,12.7923,13.9104,15.0123,16.094,17.1523,18.1848,19.1896 +AAPL,110,0.0,0.0,0.0023,0.0366,0.1894,0.5574,1.1907,2.0849,3.2007,4.4847,5.8828,7.3473,8.8388,10.3271,11.7896,13.21,14.5773,15.8845,17.1273,18.304,19.4144,20.4592,21.4403,22.36,23.2207,24.0254,24.777,25.4783,26.1322 +AAPL,115,0.0,0.0173,0.3086,1.2501,2.8212,4.7724,6.8638,8.9331,10.8878,12.6833,14.304,15.7507,17.0325,18.1626,19.1555,20.0257,20.7868,21.4512,22.0302,22.5338,22.9707,23.3489,23.675,23.9551,24.1944,24.3976,24.5687,24.7112,24.8283 +AAPL,120,0.0558,1.7235,4.955,8.0497,10.4987,12.3212,13.6468,14.5998,15.2776,15.7521,16.0757,16.2865,16.4122,16.4734,16.4854,16.4599,16.4055,16.3289,16.2354,16.1289,16.0127,15.8891,15.7601,15.6273,15.4919,15.355,15.2171,15.0791,14.9414 +AAPL,125,4.6718,7.2778,7.7252,7.6225,7.3684,7.08,6.7958,6.5284,6.2811,6.0539,5.8451,5.6531,5.4761,5.3123,5.1603,5.0187,4.8865,4.7627,4.6463,4.5367,4.4331,4.3351,4.242,4.1536,4.0693,3.9888,3.9118,3.8381,3.7673 +AAPL,130,0.4357,0.3139,0.2577,0.2232,0.1988,0.1801,0.1649,0.152,0.1407,0.1306,0.1213,0.1127,0.1046,0.0969,0.0895,0.0823,0.0753,0.0685,0.0617,0.055,0.0484,0.0419,0.0354,0.0289,0.0224,0.0159,0.0094,0.0028,-0.0037 +AAPL,135,3.2221,7.6155,9.3016,9.8557,9.9517,9.8514,9.6655,9.4444,9.2124,8.9813,8.7571,8.5423,8.3381,8.1447,7.9617,7.7887,7.6251,7.4702,7.3234,7.1841,7.0518,6.9258,6.8059,6.6914,6.582,6.4773,6.377,6.2808,6.1885 +AAPL,140,0.0312,1.4004,4.5636,7.897,10.7015,12.8884,14.5454,15.7857,16.7074,17.3873,17.8833,18.2389,18.4866,18.6507,18.7499,18.7983,18.8069,18.784,18.7365,18.6694,18.5869,18.4923,18.3883,18.277,18.16,18.0389,17.9147,17.7883,17.6605 +AAPL,145,0.0,0.0325,0.4771,1.7516,3.7275,6.0648,8.4851,10.8184,12.9785,14.9304,16.6685,18.2019,19.5467,20.7217,21.7457,22.6362,23.4096,24.0801,24.6606,25.1622,25.5946,25.9663,26.2845,26.5559,26.7858,26.9792,27.1404,27.2729,27.3802 +AAPL,150,0.0,0.0001,0.0157,0.155,0.5962,1.4395,2.6699,4.2054,5.9467,7.8028,9.7007,11.5868,13.4236,15.1865,16.8607,18.4382,19.9159,21.2939,22.5747,23.7622,24.8609,25.876,26.8127,27.6762,28.4716,29.2039,29.8775,30.4969,31.0662 +AAPL,155,0.0,0.0,0.0002,0.0065,0.051,0.1987,0.5189,1.0564,1.8238,2.8072,3.9769,5.2963,6.7278,8.2365,9.7919,11.3685,12.9454,14.5063,16.0384,17.5323,18.981,20.3797,21.7253,23.016,24.2509,25.4301,26.5543,27.6245,28.6421 +MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0019,0.0068,0.0191,0.0448,0.092,0.1699,0.2884,0.4574,0.6857,0.9813,1.3501,1.7969,2.3244,2.934,3.6255,4.3977,5.248,6.1732,7.1695,8.2325,9.3574 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+MSFT,225,1.0183,8.3373,15.3886,20.0257,22.8582,24.5404,25.5012,26.0041,26.2113,26.2249,26.1104,25.911,25.6554,25.3633,25.0482,24.7195,24.3837,24.0457,23.7085,23.3746,23.0455,22.7223,22.4057,22.0962,21.7939,21.4992,21.2118,20.9318,20.6591 +MSFT,230,8.1952,14.5776,16.1676,16.3027,15.9626,15.4673,14.9339,14.4082,13.9078,13.4387,13.0014,12.5948,12.2165,11.8641,11.5351,11.2274,10.9388,10.6675,10.4118,10.1704,9.9419,9.7251,9.5191,9.3229,9.1358,8.957,8.7858,8.6218,8.4643 +MSFT,235,5.9092,4.7699,4.0559,3.574,3.222,2.9501,2.7313,2.5499,2.3959,2.2627,2.1457,2.0415,1.9479,1.8628,1.7849,1.7131,1.6465,1.5844,1.5261,1.4711,1.4192,1.3699,1.3229,1.278,1.2349,1.1935,1.1537,1.1152,1.0779 +MSFT,240,2.2943,1.7046,1.4195,1.2436,1.1209,1.0288,0.9559,0.8962,0.8458,0.8022,0.764,0.7298,0.6989,0.6706,0.6444,0.62,0.5971,0.5755,0.5549,0.5352,0.5163,0.4981,0.4804,0.4633,0.4466,0.4302,0.4142,0.3984,0.3829 +MSFT,245,10.4835,13.5326,13.5203,12.9632,12.3325,11.7365,11.1982,10.7179,10.2894,9.9058,9.5607,9.2486,8.9647,8.7052,8.4669,8.247,8.0433,7.8538,7.677,7.5115,7.356,7.2096,7.0714,6.9406,6.8164,6.6984,6.586,6.4787,6.3761 +MSFT,250,3.0098,12.4842,18.3967,21.4145,22.8819,23.5253,23.7169,23.6541,23.4462,23.1557,22.8199,22.4613,22.0937,21.7258,21.3628,21.008,20.6631,20.3294,20.0071,19.6965,19.3974,19.1094,18.8322,18.5653,18.3084,18.0608,17.8222,17.5921,17.3701 +MSFT,255,0.1844,4.3707,11.5113,17.9114,22.7764,26.2948,28.7954,30.5558,31.7818,32.6199,33.1745,33.5198,33.7094,33.7826,33.7681,33.6873,33.5564,33.3877,33.1906,32.9723,32.7384,32.4933,32.2403,31.9822,31.7211,31.4587,31.1961,30.9346,30.6748 +MSFT,260,0.0032,0.7404,4.1748,9.504,15.1842,20.4099,24.9152,28.6813,31.7815,34.3132,36.3712,38.0388,39.3857,40.4694,41.3366,42.0253,42.5662,42.9846,43.301,43.5322,43.6919,43.7918,43.8412,43.8481,43.8192,43.76,43.6752,43.5687,43.444 +MSFT,265,0.0,0.0676,0.9679,3.5099,7.4144,12.0053,16.739,21.289,25.4913,29.282,32.6527,35.6231,38.2262,40.4991,42.4788,44.2001,45.6947,46.9905,48.1125,49.0823,49.9186,50.638,51.2547,51.781,52.2278,52.6043,52.9189,53.1786,53.3897 +MSFT,270,0.0,0.0036,0.1518,0.9501,2.7842,5.6075,9.1371,13.0614,17.1276,21.1581,25.0391,28.7044,32.1204,35.2748,38.1687,40.8114,43.2169,45.4012,47.3813,49.1737,50.7948,52.2596,53.5823,54.7758,55.852,56.8216,57.6944,58.4793,59.1843 +MSFT,275,0.0,0.0001,0.0168,0.1951,0.8299,2.1427,4.1694,6.8083,9.9012,13.2868,16.8257,20.4084,23.9537,27.4046,30.7229,33.885,36.8775,39.6951,42.3373,44.8075,47.1115,49.2565,51.2506,53.1024,54.8205,56.4134,57.8894,59.2562,60.5215 +MSFT,280,0.0,0.0,0.0013,0.0312,0.2009,0.6842,1.6226,3.0733,5.016,7.3819,10.0812,13.0218,16.1196,19.3027,22.5126,25.7035,28.8403,31.8972,34.8559,37.7039,40.4334,43.0402,45.5227,47.8815,50.1184,52.2367,54.24,56.1326,57.9193 +MSFT,285,0.0,0.0,0.0001,0.004,0.0402,0.1856,0.5468,1.2187,2.2576,3.6765,5.4546,7.5493,9.9073,12.4728,15.192,18.0163,20.9035,23.8177,26.7293,29.6146,32.4546,35.2346,37.9435,40.5731,43.1178,45.5738,47.9389,50.2124,52.3944 +MSFT,290,0.0,0.0,0.0,0.0004,0.0068,0.0433,0.1615,0.4294,0.9125,1.6584,2.6916,4.0147,5.613,7.4606,9.5246,11.7697,14.1606,16.6639,19.2487,21.8878,24.5572,27.2364,29.9081,32.5577,35.1734,37.7455,40.2663,42.7299,45.1316 diff --git a/tests/options/csv/test_options_controller/test_get_zomma.csv b/tests/options/csv/test_options_controller/test_get_zomma.csv index 6a01b54..ada41ce 100644 --- a/tests/options/csv/test_options_controller/test_get_zomma.csv +++ b/tests/options/csv/test_options_controller/test_get_zomma.csv @@ -1,37 +1,37 @@ Ticker,Strike Price,2022-12-31,2023-01-01,2023-01-02,2023-01-03,2023-01-04,2023-01-05,2023-01-06,2023-01-07,2023-01-08,2023-01-09,2023-01-10,2023-01-11,2023-01-12,2023-01-13,2023-01-14,2023-01-15,2023-01-16,2023-01-17,2023-01-18,2023-01-19,2023-01-20,2023-01-21,2023-01-22,2023-01-23,2023-01-24,2023-01-25,2023-01-26,2023-01-27,2023-01-28 -AAPL,95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0004,0.0006,0.0009,0.0012,0.0015,0.0019,0.0024,0.0028,0.0033,0.0039,0.0044,0.005,0.0056,0.0062 -AAPL,100,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0003,0.0005,0.0009,0.0014,0.002,0.0027,0.0035,0.0045,0.0054,0.0065,0.0075,0.0086,0.0096,0.0107,0.0117,0.0127,0.0136,0.0145,0.0153,0.0161 -AAPL,105,0.0,0.0,0.0,0.0,0.0,0.0002,0.0005,0.0012,0.0023,0.0037,0.0054,0.0073,0.0093,0.0114,0.0134,0.0154,0.0172,0.019,0.0205,0.022,0.0232,0.0243,0.0253,0.0261,0.0268,0.0274,0.0279,0.0282,0.0285 -AAPL,110,0.0,0.0,0.0,0.0005,0.002,0.0048,0.0086,0.0131,0.0176,0.0218,0.0257,0.0289,0.0317,0.0338,0.0355,0.0367,0.0375,0.0379,0.0381,0.038,0.0377,0.0373,0.0367,0.036,0.0352,0.0344,0.0335,0.0326,0.0316 -AAPL,115,0.0,0.0005,0.0054,0.0159,0.028,0.0383,0.0458,0.0506,0.0531,0.0539,0.0534,0.0521,0.0501,0.0478,0.0452,0.0425,0.0398,0.0371,0.0344,0.0318,0.0293,0.0269,0.0247,0.0225,0.0204,0.0184,0.0166,0.0148,0.0131 -AAPL,120,0.003,0.0433,0.077,0.0865,0.0827,0.0734,0.0626,0.0519,0.0421,0.0333,0.0256,0.0188,0.0128,0.0077,0.0031,-0.0009,-0.0044,-0.0075,-0.0102,-0.0126,-0.0147,-0.0166,-0.0183,-0.0198,-0.0212,-0.0224,-0.0235,-0.0245,-0.0253 -AAPL,125,0.1881,0.0808,0.0106,-0.0267,-0.0475,-0.0596,-0.0668,-0.0711,-0.0737,-0.0751,-0.0758,-0.076,-0.0758,-0.0754,-0.0749,-0.0742,-0.0735,-0.0727,-0.0719,-0.0711,-0.0703,-0.0695,-0.0687,-0.0679,-0.0671,-0.0663,-0.0656,-0.0648,-0.0641 -AAPL,130,-0.3948,-0.2926,-0.2426,-0.2117,-0.1902,-0.1742,-0.1616,-0.1514,-0.1429,-0.1357,-0.1295,-0.1241,-0.1192,-0.115,-0.1111,-0.1076,-0.1044,-0.1015,-0.0988,-0.0963,-0.094,-0.0919,-0.0899,-0.088,-0.0862,-0.0845,-0.083,-0.0815,-0.0801 -AAPL,135,0.1516,0.1308,0.0671,0.0221,-0.0073,-0.0268,-0.04,-0.049,-0.0554,-0.0599,-0.0631,-0.0654,-0.0669,-0.068,-0.0687,-0.0691,-0.0693,-0.0693,-0.0692,-0.069,-0.0687,-0.0684,-0.068,-0.0676,-0.0671,-0.0666,-0.0661,-0.0656,-0.0651 -AAPL,140,0.0018,0.037,0.0746,0.0892,0.0885,0.0807,0.0701,0.059,0.0485,0.0388,0.0301,0.0224,0.0155,0.0095,0.0042,-0.0005,-0.0047,-0.0084,-0.0117,-0.0146,-0.0171,-0.0195,-0.0215,-0.0234,-0.025,-0.0265,-0.0279,-0.0291,-0.0302 -AAPL,145,0.0,0.0009,0.0088,0.0235,0.0386,0.0504,0.0581,0.0623,0.0637,0.0631,0.0612,0.0584,0.055,0.0514,0.0476,0.0438,0.04,0.0363,0.0328,0.0294,0.0262,0.0232,0.0203,0.0176,0.015,0.0126,0.0103,0.0082,0.0062 -AAPL,150,0.0,0.0,0.0003,0.0022,0.0067,0.0132,0.0205,0.0277,0.034,0.0392,0.0433,0.0463,0.0483,0.0495,0.05,0.0499,0.0494,0.0485,0.0473,0.0459,0.0444,0.0428,0.041,0.0393,0.0375,0.0356,0.0338,0.0321,0.0303 -AAPL,155,0.0,0.0,0.0,0.0001,0.0006,0.0019,0.0042,0.0074,0.0112,0.0153,0.0194,0.0234,0.027,0.0302,0.033,0.0353,0.0372,0.0388,0.0399,0.0408,0.0413,0.0416,0.0417,0.0415,0.0412,0.0408,0.0402,0.0396,0.0388 -MSFT,180,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0001,0.0002,0.0003,0.0004,0.0006,0.0008,0.0011,0.0014,0.0017,0.002,0.0024,0.0028,0.0032,0.0036,0.004,0.0044,0.0048 -MSFT,185,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0003,0.0005,0.0008,0.0011,0.0015,0.0019,0.0024,0.0029,0.0034,0.004,0.0045,0.0051,0.0057,0.0062,0.0068,0.0073,0.0078,0.0083 -MSFT,190,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0005,0.0008,0.0012,0.0018,0.0024,0.0031,0.0039,0.0047,0.0055,0.0063,0.0071,0.0079,0.0086,0.0093,0.01,0.0106,0.0112,0.0118,0.0123,0.0127 -MSFT,195,0.0,0.0,0.0,0.0,0.0,0.0001,0.0002,0.0006,0.0011,0.0019,0.0028,0.0039,0.005,0.0062,0.0074,0.0086,0.0097,0.0107,0.0117,0.0126,0.0134,0.0141,0.0148,0.0153,0.0158,0.0162,0.0165,0.0168,0.017 -MSFT,200,0.0,0.0,0.0,0.0,0.0002,0.0006,0.0015,0.0027,0.0043,0.006,0.0078,0.0096,0.0114,0.0129,0.0144,0.0156,0.0167,0.0176,0.0184,0.019,0.0195,0.0198,0.02,0.0202,0.0202,0.0202,0.0201,0.02,0.0198 -MSFT,205,0.0,0.0,0.0,0.0004,0.0015,0.0034,0.0061,0.009,0.0119,0.0145,0.0169,0.0189,0.0205,0.0218,0.0227,0.0233,0.0237,0.0239,0.0239,0.0238,0.0235,0.0232,0.0228,0.0223,0.0217,0.0212,0.0206,0.0199,0.0193 -MSFT,210,0.0,0.0,0.0007,0.0033,0.0077,0.0127,0.0174,0.0214,0.0244,0.0266,0.028,0.0287,0.029,0.0288,0.0283,0.0276,0.0268,0.0258,0.0248,0.0237,0.0226,0.0215,0.0204,0.0193,0.0182,0.0171,0.0161,0.0151,0.0141 -MSFT,215,0.0,0.0009,0.0069,0.0162,0.0247,0.0308,0.0343,0.0359,0.036,0.0352,0.0338,0.032,0.0299,0.0278,0.0257,0.0236,0.0215,0.0196,0.0177,0.016,0.0143,0.0127,0.0112,0.0098,0.0085,0.0073,0.0062,0.0051,0.0041 -MSFT,220,0.0003,0.0128,0.0323,0.0437,0.0472,0.046,0.0426,0.0381,0.0335,0.0289,0.0246,0.0206,0.017,0.0138,0.0109,0.0082,0.0059,0.0037,0.0018,0.0001,-0.0015,-0.0029,-0.0041,-0.0053,-0.0063,-0.0073,-0.0082,-0.009,-0.0097 -MSFT,225,0.0166,0.0607,0.0656,0.0551,0.0422,0.0305,0.0207,0.0126,0.0061,0.0008,-0.0036,-0.0071,-0.0101,-0.0125,-0.0146,-0.0163,-0.0178,-0.019,-0.02,-0.0209,-0.0217,-0.0223,-0.0228,-0.0233,-0.0237,-0.024,-0.0243,-0.0245,-0.0247 -MSFT,230,0.1101,0.0629,0.0204,-0.0043,-0.0189,-0.0279,-0.0335,-0.0372,-0.0396,-0.0411,-0.042,-0.0426,-0.0429,-0.0429,-0.0429,-0.0427,-0.0425,-0.0422,-0.0419,-0.0415,-0.0411,-0.0407,-0.0404,-0.04,-0.0396,-0.0392,-0.0388,-0.0384,-0.038 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+MSFT,285,0.0,0.0,0.0,0.0,0.0001,0.0005,0.0014,0.0026,0.0043,0.0062,0.0082,0.0103,0.0124,0.0143,0.016,0.0176,0.019,0.0201,0.0211,0.022,0.0226,0.0231,0.0235,0.0237,0.0239,0.0239,0.0239,0.0238,0.0236 +MSFT,290,0.0,0.0,0.0,0.0,0.0,0.0001,0.0004,0.0009,0.0018,0.0028,0.0042,0.0056,0.0072,0.0088,0.0104,0.0119,0.0133,0.0147,0.0159,0.017,0.0179,0.0188,0.0195,0.0202,0.0207,0.0211,0.0214,0.0217,0.0218 diff --git a/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model.csv b/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model.csv index 4a9f79b..e7effea 100644 --- a/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model.csv +++ b/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT -2020,-0.0014,-0.0014 -2021,0.3757,0.3311 -2022,-0.2486,-0.2433 +2020,0.0,0.0 +2021,0.3806,0.3335 +2022,-0.2368,-0.2325 diff --git a/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_1.csv b/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_1.csv index 470ad3d..ebc1d14 100644 --- a/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_1.csv +++ b/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_1.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT 2020,, -2021,-269.3571,-237.5 -2022,-1.6617,-1.7348 +2021,inf,inf +2022,-1.6222,-1.6972 diff --git a/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_2.csv b/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_2.csv index be1607e..f5efd8a 100644 --- a/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_2.csv +++ b/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_2.csv @@ -2,5 +2,5 @@ ,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3 Date,,,,,, 2020,,,,,, -2021,-269.3571,,,-237.5,, -2022,-1.6617,176.5714,,-1.7348,172.7857, +2021,inf,,,inf,, +2022,-1.6222,-inf,,-1.6972,-inf, diff --git a/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_3.csv b/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_3.csv index a1f7e96..f1733e4 100644 --- a/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_3.csv +++ b/tests/performance/csv/test_performance_controller/test_get_capital_asset_pricing_model_3.csv @@ -1,12 +1,12 @@ Date,Risk Free Rate,Beta AAPL,Beta MSFT,Benchmark Returns,CAPM AAPL,CAPM MSFT -2013,0.03,,,,, -2014,0.02,,,,, -2015,0.02,,,,, -2016,0.02,,,,, -2017,0.02,,,,, -2018,0.03,,,,, -2019,0.02,,,,, -2020,0.01,1.16,1.15,0.0,-0.0,-0.0 -2021,0.02,1.32,1.16,0.29,0.38,0.33 -2022,0.04,1.3,1.28,-0.18,-0.25,-0.24 -2023,0.04,,,,, +2013,0,,,,, +2014,0,,,,, +2015,0,,,,, +2016,0,,,,, +2017,0,,,,, +2018,0,,,,, +2019,0,,,,, +2020,0,1.16,1.15,0.0,0.0,0.0 +2021,0,1.32,1.16,0.29,0.38,0.33 +2022,0,1.3,1.28,-0.18,-0.24,-0.23 +2023,0,,,,, diff --git a/tests/performance/csv/test_performance_controller/test_get_factor_asset_correlations_1.csv b/tests/performance/csv/test_performance_controller/test_get_factor_asset_correlations_1.csv index 324e4f2..27f933a 100644 --- a/tests/performance/csv/test_performance_controller/test_get_factor_asset_correlations_1.csv +++ b/tests/performance/csv/test_performance_controller/test_get_factor_asset_correlations_1.csv @@ -1,12 +1,12 @@ ,AAPL,AAPL,AAPL,AAPL,AAPL,MSFT,MSFT,MSFT,MSFT,MSFT ,Mkt-RF,SMB,HML,RMW,CMA,Mkt-RF,SMB,HML,RMW,CMA -2020-01,0.8,-0.1,-0.1,0.6,0.6,0.8,-0.1,0.1,0.3,0.2 -2020-02,0.9,-0.2,-0.2,0.5,-0.2,0.8,-0.4,-0.5,0.2,-0.6 +2020-01,0.8,-0.1,-0.1,0.7,0.6,0.8,-0.1,0.1,0.3,0.3 +2020-02,0.9,-0.3,-0.2,0.5,-0.1,0.8,-0.4,-0.5,0.2,-0.6 2020-03,1.0,-0.1,0.5,0.3,-0.0,1.0,-0.0,0.5,0.2,-0.2 2020-04,0.8,0.1,-0.1,0.3,-0.5,0.9,0.1,-0.2,0.3,-0.5 2020-05,0.8,0.5,0.2,0.2,-0.0,0.7,0.4,-0.0,-0.0,-0.4 2020-06,0.8,0.4,0.2,0.2,-0.3,0.8,0.3,0.1,0.0,-0.3 2020-07,0.5,-0.4,-0.3,0.1,0.1,0.7,-0.3,-0.5,-0.2,-0.5 -2020-08,0.4,-0.5,-0.5,-0.1,0.3,0.7,-0.2,-0.4,-0.1,-0.3 +2020-08,0.4,-0.5,-0.5,-0.0,0.2,0.7,-0.2,-0.4,-0.2,-0.3 2020-09,0.8,-0.4,-0.5,0.2,0.1,0.9,-0.4,-0.6,0.3,-0.0 -2020-10,0.8,-0.3,-0.4,0.3,0.3,0.9,-0.3,-0.4,0.1,-0.1 +2020-10,0.8,-0.3,-0.4,0.3,0.3,0.9,-0.3,-0.5,0.1,-0.1 diff --git a/tests/performance/csv/test_performance_controller/test_get_factor_correlations.csv b/tests/performance/csv/test_performance_controller/test_get_factor_correlations.csv index 2decd44..24e58d3 100644 --- a/tests/performance/csv/test_performance_controller/test_get_factor_correlations.csv +++ b/tests/performance/csv/test_performance_controller/test_get_factor_correlations.csv @@ -195,25 +195,25 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2001,RMW,-0.63,-0.05,0.66,1.0,0.79 2001,CMA,-0.75,0.25,0.82,0.79,1.0 2002,Mkt-RF,1.0,-0.45,-0.36,-0.46,-0.15 -2002,SMB,-0.45,1.0,0.19,-0.05,0.17 -2002,HML,-0.36,0.19,1.0,0.0,0.32 +2002,SMB,-0.45,1.0,0.18,-0.05,0.17 +2002,HML,-0.36,0.18,1.0,0.0,0.32 2002,RMW,-0.46,-0.05,0.0,1.0,0.07 2002,CMA,-0.15,0.17,0.32,0.07,1.0 -2003,Mkt-RF,1.0,-0.16,-0.14,-0.58,0.38 +2003,Mkt-RF,1.0,-0.16,-0.14,-0.58,0.37 2003,SMB,-0.16,1.0,0.13,-0.27,0.32 2003,HML,-0.14,0.13,1.0,-0.04,0.22 2003,RMW,-0.58,-0.27,-0.04,1.0,-0.71 -2003,CMA,0.38,0.32,0.22,-0.71,1.0 -2004,Mkt-RF,1.0,0.53,-0.16,-0.55,0.46 -2004,SMB,0.53,1.0,-0.02,-0.62,0.57 -2004,HML,-0.16,-0.02,1.0,0.1,0.13 +2003,CMA,0.37,0.32,0.22,-0.71,1.0 +2004,Mkt-RF,1.0,0.53,-0.15,-0.55,0.46 +2004,SMB,0.53,1.0,-0.01,-0.62,0.57 +2004,HML,-0.15,-0.01,1.0,0.1,0.14 2004,RMW,-0.55,-0.62,0.1,1.0,-0.82 -2004,CMA,0.46,0.57,0.13,-0.82,1.0 -2005,Mkt-RF,1.0,0.54,-0.26,-0.0,-0.13 +2004,CMA,0.46,0.57,0.14,-0.82,1.0 +2005,Mkt-RF,1.0,0.54,-0.26,0.0,-0.14 2005,SMB,0.54,1.0,-0.16,-0.1,-0.02 2005,HML,-0.26,-0.16,1.0,0.32,-0.0 -2005,RMW,-0.0,-0.1,0.32,1.0,-0.44 -2005,CMA,-0.13,-0.02,-0.0,-0.44,1.0 +2005,RMW,0.0,-0.1,0.32,1.0,-0.44 +2005,CMA,-0.14,-0.02,-0.0,-0.44,1.0 2006,Mkt-RF,1.0,0.64,-0.4,-0.07,-0.21 2006,SMB,0.64,1.0,-0.4,-0.11,-0.24 2006,HML,-0.4,-0.4,1.0,0.16,0.19 @@ -225,10 +225,10 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2007,RMW,0.16,-0.1,0.13,1.0,0.11 2007,CMA,-0.16,-0.1,0.36,0.11,1.0 2008,Mkt-RF,1.0,-0.05,0.44,-0.26,-0.32 -2008,SMB,-0.05,1.0,0.14,-0.17,-0.03 -2008,HML,0.44,0.14,1.0,-0.69,-0.08 -2008,RMW,-0.26,-0.17,-0.69,1.0,0.14 -2008,CMA,-0.32,-0.03,-0.08,0.14,1.0 +2008,SMB,-0.05,1.0,0.13,-0.17,-0.03 +2008,HML,0.44,0.13,1.0,-0.69,-0.08 +2008,RMW,-0.26,-0.17,-0.69,1.0,0.13 +2008,CMA,-0.32,-0.03,-0.08,0.13,1.0 2009,Mkt-RF,1.0,0.43,0.76,-0.49,-0.08 2009,SMB,0.43,1.0,0.28,-0.17,0.15 2009,HML,0.76,0.28,1.0,-0.64,-0.02 @@ -242,8 +242,8 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2011,Mkt-RF,1.0,0.64,0.38,-0.79,0.05 2011,SMB,0.64,1.0,0.15,-0.62,0.05 2011,HML,0.38,0.15,1.0,-0.61,0.52 -2011,RMW,-0.79,-0.62,-0.61,1.0,-0.22 -2011,CMA,0.05,0.05,0.52,-0.22,1.0 +2011,RMW,-0.79,-0.62,-0.61,1.0,-0.23 +2011,CMA,0.05,0.05,0.52,-0.23,1.0 2012,Mkt-RF,1.0,0.38,0.16,-0.43,-0.37 2012,SMB,0.38,1.0,-0.03,-0.34,-0.25 2012,HML,0.16,-0.03,1.0,-0.53,0.44 @@ -251,13 +251,13 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2012,CMA,-0.37,-0.25,0.44,-0.08,1.0 2013,Mkt-RF,1.0,0.31,0.21,-0.53,-0.01 2013,SMB,0.31,1.0,0.04,-0.31,-0.21 -2013,HML,0.21,0.04,1.0,-0.37,0.38 -2013,RMW,-0.53,-0.31,-0.37,1.0,0.02 +2013,HML,0.21,0.04,1.0,-0.38,0.38 +2013,RMW,-0.53,-0.31,-0.38,1.0,0.02 2013,CMA,-0.01,-0.21,0.38,0.02,1.0 -2014,Mkt-RF,1.0,0.29,-0.24,-0.44,-0.39 +2014,Mkt-RF,1.0,0.29,-0.24,-0.45,-0.39 2014,SMB,0.29,1.0,-0.36,-0.55,-0.45 2014,HML,-0.24,-0.36,1.0,0.32,0.72 -2014,RMW,-0.44,-0.55,0.32,1.0,0.51 +2014,RMW,-0.45,-0.55,0.32,1.0,0.51 2014,CMA,-0.39,-0.45,0.72,0.51,1.0 2015,Mkt-RF,1.0,-0.06,-0.04,-0.16,-0.26 2015,SMB,-0.06,1.0,-0.11,-0.38,-0.0 @@ -280,32 +280,32 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2018,RMW,-0.24,-0.13,0.02,1.0,0.07 2018,CMA,-0.43,-0.06,0.65,0.07,1.0 2019,Mkt-RF,1.0,0.26,-0.1,-0.01,-0.42 -2019,SMB,0.26,1.0,0.3,-0.05,-0.01 +2019,SMB,0.26,1.0,0.3,-0.06,-0.01 2019,HML,-0.1,0.3,1.0,0.31,0.51 -2019,RMW,-0.01,-0.05,0.31,1.0,0.24 -2019,CMA,-0.42,-0.01,0.51,0.24,1.0 +2019,RMW,-0.01,-0.06,0.31,1.0,0.25 +2019,CMA,-0.42,-0.01,0.51,0.25,1.0 2020,Mkt-RF,1.0,0.19,0.29,0.19,-0.06 -2020,SMB,0.19,1.0,0.6,0.08,0.1 -2020,HML,0.29,0.6,1.0,0.49,0.47 +2020,SMB,0.19,1.0,0.6,0.08,0.11 +2020,HML,0.29,0.6,1.0,0.49,0.48 2020,RMW,0.19,0.08,0.49,1.0,0.42 -2020,CMA,-0.06,0.1,0.47,0.42,1.0 +2020,CMA,-0.06,0.11,0.48,0.42,1.0 2021,Mkt-RF,1.0,0.29,-0.21,-0.35,-0.19 2021,SMB,0.29,1.0,0.3,-0.4,0.28 2021,HML,-0.21,0.3,1.0,0.44,0.7 2021,RMW,-0.35,-0.4,0.44,1.0,0.41 2021,CMA,-0.19,0.28,0.7,0.41,1.0 -2022,Mkt-RF,1.0,0.2,-0.56,-0.39,-0.57 +2022,Mkt-RF,1.0,0.2,-0.55,-0.38,-0.57 2022,SMB,0.2,1.0,-0.29,-0.48,-0.36 -2022,HML,-0.56,-0.29,1.0,0.49,0.84 -2022,RMW,-0.39,-0.48,0.49,1.0,0.43 +2022,HML,-0.55,-0.29,1.0,0.49,0.84 +2022,RMW,-0.38,-0.48,0.49,1.0,0.43 2022,CMA,-0.57,-0.36,0.84,0.43,1.0 2023,Mkt-RF,1.0,0.3,-0.05,-0.34,-0.45 -2023,SMB,0.3,1.0,0.41,-0.41,0.08 +2023,SMB,0.3,1.0,0.41,-0.4,0.08 2023,HML,-0.05,0.41,1.0,0.21,0.62 -2023,RMW,-0.34,-0.41,0.21,1.0,0.31 +2023,RMW,-0.34,-0.4,0.21,1.0,0.31 2023,CMA,-0.45,0.08,0.62,0.31,1.0 -2024,Mkt-RF,1.0,0.24,-0.17,-0.21,-0.48 -2024,SMB,0.24,1.0,0.39,-0.5,0.31 -2024,HML,-0.17,0.39,1.0,0.06,0.59 -2024,RMW,-0.21,-0.5,0.06,1.0,-0.06 -2024,CMA,-0.48,0.31,0.59,-0.06,1.0 +2024,Mkt-RF,1.0,0.15,-0.28,-0.2,-0.31 +2024,SMB,0.15,1.0,0.45,-0.53,0.2 +2024,HML,-0.28,0.45,1.0,0.04,0.38 +2024,RMW,-0.2,-0.53,0.04,1.0,-0.07 +2024,CMA,-0.31,0.2,0.38,-0.07,1.0 diff --git a/tests/performance/csv/test_performance_controller/test_get_factor_correlations_1.csv b/tests/performance/csv/test_performance_controller/test_get_factor_correlations_1.csv index 65f0440..d07405e 100644 --- a/tests/performance/csv/test_performance_controller/test_get_factor_correlations_1.csv +++ b/tests/performance/csv/test_performance_controller/test_get_factor_correlations_1.csv @@ -2286,9 +2286,9 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2001-07,CMA,-0.84,-0.06,0.6,0.79,1.0 2001-08,Mkt-RF,1.0,-0.56,-0.73,-0.57,-0.85 2001-08,SMB,-0.56,1.0,0.24,0.31,0.44 -2001-08,HML,-0.73,0.24,1.0,0.44,0.68 +2001-08,HML,-0.73,0.24,1.0,0.44,0.67 2001-08,RMW,-0.57,0.31,0.44,1.0,0.83 -2001-08,CMA,-0.85,0.44,0.68,0.83,1.0 +2001-08,CMA,-0.85,0.44,0.67,0.83,1.0 2001-09,Mkt-RF,1.0,0.07,0.37,-0.32,-0.52 2001-09,SMB,0.07,1.0,-0.04,-0.26,-0.04 2001-09,HML,0.37,-0.04,1.0,0.14,0.13 @@ -2305,8 +2305,8 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2001-11,RMW,-0.53,-0.03,0.2,1.0,0.83 2001-11,CMA,-0.75,0.21,0.49,0.83,1.0 2001-12,Mkt-RF,1.0,-0.06,-0.45,-0.68,-0.88 -2001-12,SMB,-0.06,1.0,-0.08,-0.26,0.03 -2001-12,HML,-0.45,-0.08,1.0,0.2,0.56 +2001-12,SMB,-0.06,1.0,-0.09,-0.26,0.03 +2001-12,HML,-0.45,-0.09,1.0,0.2,0.56 2001-12,RMW,-0.68,-0.26,0.2,1.0,0.81 2001-12,CMA,-0.88,0.03,0.56,0.81,1.0 2002-01,Mkt-RF,1.0,-0.2,-0.45,-0.35,-0.66 @@ -2337,23 +2337,23 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2002-06,Mkt-RF,1.0,-0.33,-0.6,-0.2,-0.76 2002-06,SMB,-0.33,1.0,0.24,-0.04,0.36 2002-06,HML,-0.6,0.24,1.0,-0.46,0.53 -2002-06,RMW,-0.2,-0.04,-0.46,1.0,0.4 -2002-06,CMA,-0.76,0.36,0.53,0.4,1.0 +2002-06,RMW,-0.2,-0.04,-0.46,1.0,0.39 +2002-06,CMA,-0.76,0.36,0.53,0.39,1.0 2002-07,Mkt-RF,1.0,-0.63,-0.3,-0.2,-0.19 2002-07,SMB,-0.63,1.0,0.31,-0.06,-0.2 2002-07,HML,-0.3,0.31,1.0,-0.28,0.15 -2002-07,RMW,-0.2,-0.06,-0.28,1.0,-0.13 -2002-07,CMA,-0.19,-0.2,0.15,-0.13,1.0 +2002-07,RMW,-0.2,-0.06,-0.28,1.0,-0.12 +2002-07,CMA,-0.19,-0.2,0.15,-0.12,1.0 2002-08,Mkt-RF,1.0,-0.49,-0.48,-0.66,0.48 2002-08,SMB,-0.49,1.0,0.32,-0.1,0.04 2002-08,HML,-0.48,0.32,1.0,0.31,-0.26 2002-08,RMW,-0.66,-0.1,0.31,1.0,-0.55 2002-08,CMA,0.48,0.04,-0.26,-0.55,1.0 -2002-09,Mkt-RF,1.0,-0.38,-0.34,-0.28,0.4 -2002-09,SMB,-0.38,1.0,0.14,-0.32,0.12 -2002-09,HML,-0.34,0.14,1.0,0.07,0.1 -2002-09,RMW,-0.28,-0.32,0.07,1.0,-0.66 -2002-09,CMA,0.4,0.12,0.1,-0.66,1.0 +2002-09,Mkt-RF,1.0,-0.38,-0.34,-0.28,0.41 +2002-09,SMB,-0.38,1.0,0.14,-0.33,0.12 +2002-09,HML,-0.34,0.14,1.0,0.07,0.09 +2002-09,RMW,-0.28,-0.33,0.07,1.0,-0.66 +2002-09,CMA,0.41,0.12,0.09,-0.66,1.0 2002-10,Mkt-RF,1.0,-0.58,-0.16,-0.51,0.42 2002-10,SMB,-0.58,1.0,0.17,0.12,-0.19 2002-10,HML,-0.16,0.17,1.0,-0.48,0.26 @@ -2364,105 +2364,105 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2002-11,HML,-0.56,-0.19,1.0,0.44,-0.31 2002-11,RMW,-0.76,-0.28,0.44,1.0,-0.77 2002-11,CMA,0.73,0.44,-0.31,-0.77,1.0 -2002-12,Mkt-RF,1.0,-0.18,-0.28,-0.57,0.53 -2002-12,SMB,-0.18,1.0,0.1,-0.36,0.45 -2002-12,HML,-0.28,0.1,1.0,-0.24,0.14 -2002-12,RMW,-0.57,-0.36,-0.24,1.0,-0.88 -2002-12,CMA,0.53,0.45,0.14,-0.88,1.0 -2003-01,Mkt-RF,1.0,-0.79,-0.43,-0.55,0.8 -2003-01,SMB,-0.79,1.0,0.27,0.38,-0.58 -2003-01,HML,-0.43,0.27,1.0,0.48,-0.56 -2003-01,RMW,-0.55,0.38,0.48,1.0,-0.61 +2002-12,Mkt-RF,1.0,-0.19,-0.29,-0.57,0.53 +2002-12,SMB,-0.19,1.0,0.1,-0.36,0.45 +2002-12,HML,-0.29,0.1,1.0,-0.23,0.13 +2002-12,RMW,-0.57,-0.36,-0.23,1.0,-0.88 +2002-12,CMA,0.53,0.45,0.13,-0.88,1.0 +2003-01,Mkt-RF,1.0,-0.8,-0.44,-0.55,0.8 +2003-01,SMB,-0.8,1.0,0.28,0.39,-0.58 +2003-01,HML,-0.44,0.28,1.0,0.49,-0.56 +2003-01,RMW,-0.55,0.39,0.49,1.0,-0.61 2003-01,CMA,0.8,-0.58,-0.56,-0.61,1.0 -2003-02,Mkt-RF,1.0,-0.62,-0.21,-0.46,0.15 +2003-02,Mkt-RF,1.0,-0.62,-0.21,-0.46,0.14 2003-02,SMB,-0.62,1.0,0.14,0.34,-0.14 2003-02,HML,-0.21,0.14,1.0,-0.05,-0.09 -2003-02,RMW,-0.46,0.34,-0.05,1.0,-0.29 -2003-02,CMA,0.15,-0.14,-0.09,-0.29,1.0 -2003-03,Mkt-RF,1.0,-0.84,-0.26,-0.64,0.23 -2003-03,SMB,-0.84,1.0,0.16,0.4,-0.07 -2003-03,HML,-0.26,0.16,1.0,0.15,-0.23 -2003-03,RMW,-0.64,0.4,0.15,1.0,-0.44 +2003-02,RMW,-0.46,0.34,-0.05,1.0,-0.28 +2003-02,CMA,0.14,-0.14,-0.09,-0.28,1.0 +2003-03,Mkt-RF,1.0,-0.84,-0.27,-0.64,0.23 +2003-03,SMB,-0.84,1.0,0.15,0.39,-0.07 +2003-03,HML,-0.27,0.15,1.0,0.16,-0.23 +2003-03,RMW,-0.64,0.39,0.16,1.0,-0.44 2003-03,CMA,0.23,-0.07,-0.23,-0.44,1.0 -2003-04,Mkt-RF,1.0,-0.73,-0.3,-0.47,0.16 -2003-04,SMB,-0.73,1.0,0.25,0.1,0.11 -2003-04,HML,-0.3,0.25,1.0,0.28,-0.04 -2003-04,RMW,-0.47,0.1,0.28,1.0,-0.62 -2003-04,CMA,0.16,0.11,-0.04,-0.62,1.0 +2003-04,Mkt-RF,1.0,-0.73,-0.29,-0.47,0.15 +2003-04,SMB,-0.73,1.0,0.25,0.1,0.12 +2003-04,HML,-0.29,0.25,1.0,0.28,-0.04 +2003-04,RMW,-0.47,0.1,0.28,1.0,-0.61 +2003-04,CMA,0.15,0.12,-0.04,-0.61,1.0 2003-05,Mkt-RF,1.0,-0.23,-0.42,-0.61,0.34 -2003-05,SMB,-0.23,1.0,-0.48,0.14,-0.15 +2003-05,SMB,-0.23,1.0,-0.48,0.14,-0.14 2003-05,HML,-0.42,-0.48,1.0,0.36,-0.21 -2003-05,RMW,-0.61,0.14,0.36,1.0,-0.6 -2003-05,CMA,0.34,-0.15,-0.21,-0.6,1.0 -2003-06,Mkt-RF,1.0,0.03,-0.34,-0.62,0.01 +2003-05,RMW,-0.61,0.14,0.36,1.0,-0.59 +2003-05,CMA,0.34,-0.14,-0.21,-0.59,1.0 +2003-06,Mkt-RF,1.0,0.03,-0.35,-0.62,0.01 2003-06,SMB,0.03,1.0,-0.17,-0.61,0.23 -2003-06,HML,-0.34,-0.17,1.0,0.28,0.14 +2003-06,HML,-0.35,-0.17,1.0,0.28,0.13 2003-06,RMW,-0.62,-0.61,0.28,1.0,-0.46 -2003-06,CMA,0.01,0.23,0.14,-0.46,1.0 +2003-06,CMA,0.01,0.23,0.13,-0.46,1.0 2003-07,Mkt-RF,1.0,0.06,0.33,-0.62,0.57 -2003-07,SMB,0.06,1.0,0.34,-0.54,0.48 +2003-07,SMB,0.06,1.0,0.34,-0.53,0.48 2003-07,HML,0.33,0.34,1.0,-0.77,0.8 -2003-07,RMW,-0.62,-0.54,-0.77,1.0,-0.89 +2003-07,RMW,-0.62,-0.53,-0.77,1.0,-0.89 2003-07,CMA,0.57,0.48,0.8,-0.89,1.0 -2003-08,Mkt-RF,1.0,0.26,0.22,-0.4,0.32 -2003-08,SMB,0.26,1.0,0.31,-0.7,0.67 +2003-08,Mkt-RF,1.0,0.25,0.22,-0.4,0.32 +2003-08,SMB,0.25,1.0,0.31,-0.7,0.68 2003-08,HML,0.22,0.31,1.0,-0.43,0.45 2003-08,RMW,-0.4,-0.7,-0.43,1.0,-0.92 -2003-08,CMA,0.32,0.67,0.45,-0.92,1.0 -2003-09,Mkt-RF,1.0,0.39,0.27,-0.78,0.63 +2003-08,CMA,0.32,0.68,0.45,-0.92,1.0 +2003-09,Mkt-RF,1.0,0.39,0.27,-0.77,0.63 2003-09,SMB,0.39,1.0,0.26,-0.52,0.56 2003-09,HML,0.27,0.26,1.0,-0.34,0.53 -2003-09,RMW,-0.78,-0.52,-0.34,1.0,-0.86 +2003-09,RMW,-0.77,-0.52,-0.34,1.0,-0.86 2003-09,CMA,0.63,0.56,0.53,-0.86,1.0 2003-10,Mkt-RF,1.0,0.6,0.06,-0.52,0.04 -2003-10,SMB,0.6,1.0,0.4,-0.7,0.56 +2003-10,SMB,0.6,1.0,0.4,-0.7,0.57 2003-10,HML,0.06,0.4,1.0,-0.27,0.54 2003-10,RMW,-0.52,-0.7,-0.27,1.0,-0.74 -2003-10,CMA,0.04,0.56,0.54,-0.74,1.0 +2003-10,CMA,0.04,0.57,0.54,-0.74,1.0 2003-11,Mkt-RF,1.0,0.59,0.1,-0.78,0.59 2003-11,SMB,0.59,1.0,0.32,-0.77,0.78 2003-11,HML,0.1,0.32,1.0,-0.43,0.58 2003-11,RMW,-0.78,-0.77,-0.43,1.0,-0.88 2003-11,CMA,0.59,0.78,0.58,-0.88,1.0 -2003-12,Mkt-RF,1.0,0.61,0.21,-0.71,0.64 -2003-12,SMB,0.61,1.0,0.24,-0.58,0.49 -2003-12,HML,0.21,0.24,1.0,-0.38,0.55 -2003-12,RMW,-0.71,-0.58,-0.38,1.0,-0.85 +2003-12,Mkt-RF,1.0,0.61,0.2,-0.71,0.64 +2003-12,SMB,0.61,1.0,0.23,-0.58,0.49 +2003-12,HML,0.2,0.23,1.0,-0.37,0.55 +2003-12,RMW,-0.71,-0.58,-0.37,1.0,-0.85 2003-12,CMA,0.64,0.49,0.55,-0.85,1.0 -2004-01,Mkt-RF,1.0,0.03,0.18,-0.43,0.3 -2004-01,SMB,0.03,1.0,0.46,-0.53,0.66 -2004-01,HML,0.18,0.46,1.0,-0.5,0.55 +2004-01,Mkt-RF,1.0,0.03,0.19,-0.43,0.3 +2004-01,SMB,0.03,1.0,0.47,-0.53,0.66 +2004-01,HML,0.19,0.47,1.0,-0.5,0.55 2004-01,RMW,-0.43,-0.53,-0.5,1.0,-0.89 2004-01,CMA,0.3,0.66,0.55,-0.89,1.0 2004-02,Mkt-RF,1.0,0.72,0.78,-0.84,0.77 -2004-02,SMB,0.72,1.0,0.79,-0.92,0.91 -2004-02,HML,0.78,0.79,1.0,-0.88,0.85 +2004-02,SMB,0.72,1.0,0.78,-0.92,0.91 +2004-02,HML,0.78,0.78,1.0,-0.88,0.85 2004-02,RMW,-0.84,-0.92,-0.88,1.0,-0.93 2004-02,CMA,0.77,0.91,0.85,-0.93,1.0 -2004-03,Mkt-RF,1.0,0.66,0.35,-0.74,0.82 -2004-03,SMB,0.66,1.0,0.56,-0.78,0.84 -2004-03,HML,0.35,0.56,1.0,-0.39,0.58 -2004-03,RMW,-0.74,-0.78,-0.39,1.0,-0.92 +2004-03,Mkt-RF,1.0,0.66,0.36,-0.74,0.82 +2004-03,SMB,0.66,1.0,0.57,-0.78,0.84 +2004-03,HML,0.36,0.57,1.0,-0.4,0.58 +2004-03,RMW,-0.74,-0.78,-0.4,1.0,-0.92 2004-03,CMA,0.82,0.84,0.58,-0.92,1.0 2004-04,Mkt-RF,1.0,0.62,0.18,-0.53,0.41 2004-04,SMB,0.62,1.0,0.19,-0.54,0.55 -2004-04,HML,0.18,0.19,1.0,-0.13,0.25 -2004-04,RMW,-0.53,-0.54,-0.13,1.0,-0.9 -2004-04,CMA,0.41,0.55,0.25,-0.9,1.0 -2004-05,Mkt-RF,1.0,0.78,0.5,-0.39,0.56 +2004-04,HML,0.18,0.19,1.0,-0.14,0.26 +2004-04,RMW,-0.53,-0.54,-0.14,1.0,-0.9 +2004-04,CMA,0.41,0.55,0.26,-0.9,1.0 +2004-05,Mkt-RF,1.0,0.78,0.51,-0.39,0.56 2004-05,SMB,0.78,1.0,0.73,-0.63,0.63 -2004-05,HML,0.5,0.73,1.0,-0.58,0.6 -2004-05,RMW,-0.39,-0.63,-0.58,1.0,-0.9 +2004-05,HML,0.51,0.73,1.0,-0.59,0.6 +2004-05,RMW,-0.39,-0.63,-0.59,1.0,-0.9 2004-05,CMA,0.56,0.63,0.6,-0.9,1.0 -2004-06,Mkt-RF,1.0,0.51,0.63,-0.67,0.65 +2004-06,Mkt-RF,1.0,0.51,0.63,-0.67,0.66 2004-06,SMB,0.51,1.0,0.36,-0.72,0.66 -2004-06,HML,0.63,0.36,1.0,-0.41,0.37 +2004-06,HML,0.63,0.36,1.0,-0.41,0.38 2004-06,RMW,-0.67,-0.72,-0.41,1.0,-0.89 -2004-06,CMA,0.65,0.66,0.37,-0.89,1.0 -2004-07,Mkt-RF,1.0,0.76,-0.83,-0.86,0.73 +2004-06,CMA,0.66,0.66,0.38,-0.89,1.0 +2004-07,Mkt-RF,1.0,0.76,-0.83,-0.87,0.73 2004-07,SMB,0.76,1.0,-0.66,-0.8,0.66 2004-07,HML,-0.83,-0.66,1.0,0.93,-0.71 -2004-07,RMW,-0.86,-0.8,0.93,1.0,-0.81 +2004-07,RMW,-0.87,-0.8,0.93,1.0,-0.81 2004-07,CMA,0.73,0.66,-0.71,-0.81,1.0 2004-08,Mkt-RF,1.0,0.78,-0.78,-0.69,0.4 2004-08,SMB,0.78,1.0,-0.75,-0.76,0.42 @@ -2520,140 +2520,140 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2005-06,RMW,-0.04,-0.02,0.42,1.0,-0.08 2005-06,CMA,-0.06,-0.07,-0.09,-0.08,1.0 2005-07,Mkt-RF,1.0,0.63,-0.5,0.44,-0.3 -2005-07,SMB,0.63,1.0,-0.11,0.52,-0.16 -2005-07,HML,-0.5,-0.11,1.0,0.24,0.2 -2005-07,RMW,0.44,0.52,0.24,1.0,-0.34 -2005-07,CMA,-0.3,-0.16,0.2,-0.34,1.0 +2005-07,SMB,0.63,1.0,-0.11,0.52,-0.17 +2005-07,HML,-0.5,-0.11,1.0,0.25,0.2 +2005-07,RMW,0.44,0.52,0.25,1.0,-0.34 +2005-07,CMA,-0.3,-0.17,0.2,-0.34,1.0 2005-08,Mkt-RF,1.0,0.57,-0.51,0.1,-0.48 2005-08,SMB,0.57,1.0,-0.21,0.38,-0.35 2005-08,HML,-0.51,-0.21,1.0,0.29,0.22 2005-08,RMW,0.1,0.38,0.29,1.0,-0.47 2005-08,CMA,-0.48,-0.35,0.22,-0.47,1.0 -2005-09,Mkt-RF,1.0,0.55,-0.52,0.05,-0.05 -2005-09,SMB,0.55,1.0,-0.55,-0.28,-0.21 -2005-09,HML,-0.52,-0.55,1.0,0.17,0.04 +2005-09,Mkt-RF,1.0,0.55,-0.52,0.05,-0.06 +2005-09,SMB,0.55,1.0,-0.55,-0.28,-0.2 +2005-09,HML,-0.52,-0.55,1.0,0.17,0.05 2005-09,RMW,0.05,-0.28,0.17,1.0,-0.57 -2005-09,CMA,-0.05,-0.21,0.04,-0.57,1.0 -2005-10,Mkt-RF,1.0,0.65,0.28,0.35,-0.46 +2005-09,CMA,-0.06,-0.2,0.05,-0.57,1.0 +2005-10,Mkt-RF,1.0,0.65,0.28,0.36,-0.46 2005-10,SMB,0.65,1.0,-0.17,-0.21,-0.27 2005-10,HML,0.28,-0.17,1.0,0.56,0.09 -2005-10,RMW,0.35,-0.21,0.56,1.0,-0.43 +2005-10,RMW,0.36,-0.21,0.56,1.0,-0.43 2005-10,CMA,-0.46,-0.27,0.09,-0.43,1.0 2005-11,Mkt-RF,1.0,0.51,-0.45,0.02,-0.2 -2005-11,SMB,0.51,1.0,-0.24,-0.26,0.14 -2005-11,HML,-0.45,-0.24,1.0,0.15,0.28 +2005-11,SMB,0.51,1.0,-0.24,-0.26,0.13 +2005-11,HML,-0.45,-0.24,1.0,0.15,0.29 2005-11,RMW,0.02,-0.26,0.15,1.0,-0.24 -2005-11,CMA,-0.2,0.14,0.28,-0.24,1.0 +2005-11,CMA,-0.2,0.13,0.29,-0.24,1.0 2005-12,Mkt-RF,1.0,0.6,-0.34,0.18,-0.04 2005-12,SMB,0.6,1.0,-0.11,-0.23,0.2 2005-12,HML,-0.34,-0.11,1.0,0.06,0.09 2005-12,RMW,0.18,-0.23,0.06,1.0,-0.71 2005-12,CMA,-0.04,0.2,0.09,-0.71,1.0 2006-01,Mkt-RF,1.0,-0.01,-0.51,-0.13,-0.26 -2006-01,SMB,-0.01,1.0,0.01,-0.08,0.22 +2006-01,SMB,-0.01,1.0,0.01,-0.07,0.22 2006-01,HML,-0.51,0.01,1.0,0.49,-0.03 -2006-01,RMW,-0.13,-0.08,0.49,1.0,-0.53 +2006-01,RMW,-0.13,-0.07,0.49,1.0,-0.53 2006-01,CMA,-0.26,0.22,-0.03,-0.53,1.0 2006-02,Mkt-RF,1.0,0.43,-0.1,-0.25,-0.07 -2006-02,SMB,0.43,1.0,0.12,0.13,-0.4 -2006-02,HML,-0.1,0.12,1.0,0.53,-0.2 -2006-02,RMW,-0.25,0.13,0.53,1.0,-0.62 -2006-02,CMA,-0.07,-0.4,-0.2,-0.62,1.0 +2006-02,SMB,0.43,1.0,0.12,0.14,-0.4 +2006-02,HML,-0.1,0.12,1.0,0.52,-0.21 +2006-02,RMW,-0.25,0.14,0.52,1.0,-0.62 +2006-02,CMA,-0.07,-0.4,-0.21,-0.62,1.0 2006-03,Mkt-RF,1.0,0.51,0.14,-0.13,0.33 2006-03,SMB,0.51,1.0,0.06,-0.22,0.4 2006-03,HML,0.14,0.06,1.0,0.34,0.27 2006-03,RMW,-0.13,-0.22,0.34,1.0,-0.29 2006-03,CMA,0.33,0.4,0.27,-0.29,1.0 2006-04,Mkt-RF,1.0,0.37,0.15,-0.08,0.03 -2006-04,SMB,0.37,1.0,-0.3,-0.02,-0.27 -2006-04,HML,0.15,-0.3,1.0,0.44,-0.05 +2006-04,SMB,0.37,1.0,-0.29,-0.02,-0.27 +2006-04,HML,0.15,-0.29,1.0,0.44,-0.05 2006-04,RMW,-0.08,-0.02,0.44,1.0,-0.51 2006-04,CMA,0.03,-0.27,-0.05,-0.51,1.0 -2006-05,Mkt-RF,1.0,0.58,-0.21,0.24,-0.18 -2006-05,SMB,0.58,1.0,-0.04,-0.12,0.04 +2006-05,Mkt-RF,1.0,0.58,-0.21,0.24,-0.17 +2006-05,SMB,0.58,1.0,-0.04,-0.13,0.05 2006-05,HML,-0.21,-0.04,1.0,0.34,-0.05 -2006-05,RMW,0.24,-0.12,0.34,1.0,-0.37 -2006-05,CMA,-0.18,0.04,-0.05,-0.37,1.0 +2006-05,RMW,0.24,-0.13,0.34,1.0,-0.38 +2006-05,CMA,-0.17,0.05,-0.05,-0.38,1.0 2006-06,Mkt-RF,1.0,0.86,-0.36,-0.19,-0.04 2006-06,SMB,0.86,1.0,-0.39,-0.28,-0.06 -2006-06,HML,-0.36,-0.39,1.0,0.1,-0.23 +2006-06,HML,-0.36,-0.39,1.0,0.1,-0.22 2006-06,RMW,-0.19,-0.28,0.1,1.0,-0.56 -2006-06,CMA,-0.04,-0.06,-0.23,-0.56,1.0 -2006-07,Mkt-RF,1.0,0.9,-0.75,0.15,-0.5 +2006-06,CMA,-0.04,-0.06,-0.22,-0.56,1.0 +2006-07,Mkt-RF,1.0,0.9,-0.75,0.15,-0.49 2006-07,SMB,0.9,1.0,-0.8,0.23,-0.63 2006-07,HML,-0.75,-0.8,1.0,-0.1,0.68 2006-07,RMW,0.15,0.23,-0.1,1.0,0.02 -2006-07,CMA,-0.5,-0.63,0.68,0.02,1.0 -2006-08,Mkt-RF,1.0,0.8,-0.77,-0.34,-0.26 +2006-07,CMA,-0.49,-0.63,0.68,0.02,1.0 +2006-08,Mkt-RF,1.0,0.8,-0.77,-0.33,-0.26 2006-08,SMB,0.8,1.0,-0.81,-0.34,-0.04 -2006-08,HML,-0.77,-0.81,1.0,0.31,-0.06 -2006-08,RMW,-0.34,-0.34,0.31,1.0,-0.45 +2006-08,HML,-0.77,-0.81,1.0,0.3,-0.06 +2006-08,RMW,-0.33,-0.34,0.3,1.0,-0.45 2006-08,CMA,-0.26,-0.04,-0.06,-0.45,1.0 2006-09,Mkt-RF,1.0,0.75,-0.72,-0.06,-0.27 2006-09,SMB,0.75,1.0,-0.63,-0.12,-0.37 2006-09,HML,-0.72,-0.63,1.0,-0.15,0.37 -2006-09,RMW,-0.06,-0.12,-0.15,1.0,-0.53 -2006-09,CMA,-0.27,-0.37,0.37,-0.53,1.0 +2006-09,RMW,-0.06,-0.12,-0.15,1.0,-0.52 +2006-09,CMA,-0.27,-0.37,0.37,-0.52,1.0 2006-10,Mkt-RF,1.0,0.65,-0.57,0.15,-0.52 -2006-10,SMB,0.65,1.0,-0.51,0.04,-0.51 +2006-10,SMB,0.65,1.0,-0.51,0.03,-0.51 2006-10,HML,-0.57,-0.51,1.0,-0.46,0.62 -2006-10,RMW,0.15,0.04,-0.46,1.0,-0.69 +2006-10,RMW,0.15,0.03,-0.46,1.0,-0.69 2006-10,CMA,-0.52,-0.51,0.62,-0.69,1.0 2006-11,Mkt-RF,1.0,0.7,-0.61,-0.26,-0.39 2006-11,SMB,0.7,1.0,-0.47,-0.3,-0.54 -2006-11,HML,-0.61,-0.47,1.0,0.03,0.43 -2006-11,RMW,-0.26,-0.3,0.03,1.0,-0.24 -2006-11,CMA,-0.39,-0.54,0.43,-0.24,1.0 +2006-11,HML,-0.61,-0.47,1.0,0.03,0.42 +2006-11,RMW,-0.26,-0.3,0.03,1.0,-0.23 +2006-11,CMA,-0.39,-0.54,0.42,-0.23,1.0 2006-12,Mkt-RF,1.0,0.5,-0.2,0.42,-0.14 2006-12,SMB,0.5,1.0,-0.34,0.38,-0.3 2006-12,HML,-0.2,-0.34,1.0,-0.41,0.25 2006-12,RMW,0.42,0.38,-0.41,1.0,-0.52 2006-12,CMA,-0.14,-0.3,0.25,-0.52,1.0 2007-01,Mkt-RF,1.0,0.51,-0.26,-0.01,-0.33 -2007-01,SMB,0.51,1.0,-0.16,-0.09,-0.25 +2007-01,SMB,0.51,1.0,-0.16,-0.1,-0.25 2007-01,HML,-0.26,-0.16,1.0,0.28,0.22 -2007-01,RMW,-0.01,-0.09,0.28,1.0,-0.22 -2007-01,CMA,-0.33,-0.25,0.22,-0.22,1.0 -2007-02,Mkt-RF,1.0,0.15,-0.2,-0.06,-0.49 +2007-01,RMW,-0.01,-0.1,0.28,1.0,-0.23 +2007-01,CMA,-0.33,-0.25,0.22,-0.23,1.0 +2007-02,Mkt-RF,1.0,0.15,-0.2,-0.06,-0.48 2007-02,SMB,0.15,1.0,-0.33,-0.03,-0.38 2007-02,HML,-0.2,-0.33,1.0,-0.27,0.35 2007-02,RMW,-0.06,-0.03,-0.27,1.0,-0.02 -2007-02,CMA,-0.49,-0.38,0.35,-0.02,1.0 -2007-03,Mkt-RF,1.0,0.57,0.12,0.5,-0.46 -2007-03,SMB,0.57,1.0,0.14,0.19,-0.28 -2007-03,HML,0.12,0.14,1.0,0.23,-0.02 -2007-03,RMW,0.5,0.19,0.23,1.0,-0.55 +2007-02,CMA,-0.48,-0.38,0.35,-0.02,1.0 +2007-03,Mkt-RF,1.0,0.57,0.12,0.49,-0.46 +2007-03,SMB,0.57,1.0,0.14,0.18,-0.28 +2007-03,HML,0.12,0.14,1.0,0.24,-0.02 +2007-03,RMW,0.49,0.18,0.24,1.0,-0.55 2007-03,CMA,-0.46,-0.28,-0.02,-0.55,1.0 -2007-04,Mkt-RF,1.0,0.55,0.12,0.29,-0.41 +2007-04,Mkt-RF,1.0,0.55,0.12,0.29,-0.42 2007-04,SMB,0.55,1.0,-0.34,-0.04,-0.51 -2007-04,HML,0.12,-0.34,1.0,-0.03,-0.11 -2007-04,RMW,0.29,-0.04,-0.03,1.0,-0.08 -2007-04,CMA,-0.41,-0.51,-0.11,-0.08,1.0 +2007-04,HML,0.12,-0.34,1.0,-0.02,-0.11 +2007-04,RMW,0.29,-0.04,-0.02,1.0,-0.08 +2007-04,CMA,-0.42,-0.51,-0.11,-0.08,1.0 2007-05,Mkt-RF,1.0,0.5,-0.26,-0.18,-0.32 -2007-05,SMB,0.5,1.0,-0.47,-0.61,-0.41 +2007-05,SMB,0.5,1.0,-0.47,-0.61,-0.42 2007-05,HML,-0.26,-0.47,1.0,-0.07,0.37 -2007-05,RMW,-0.18,-0.61,-0.07,1.0,0.24 -2007-05,CMA,-0.32,-0.41,0.37,0.24,1.0 +2007-05,RMW,-0.18,-0.61,-0.07,1.0,0.25 +2007-05,CMA,-0.32,-0.42,0.37,0.25,1.0 2007-06,Mkt-RF,1.0,0.08,-0.2,0.62,0.11 2007-06,SMB,0.08,1.0,-0.08,-0.11,-0.13 2007-06,HML,-0.2,-0.08,1.0,-0.46,-0.08 -2007-06,RMW,0.62,-0.11,-0.46,1.0,0.02 -2007-06,CMA,0.11,-0.13,-0.08,0.02,1.0 -2007-07,Mkt-RF,1.0,0.1,0.12,0.47,0.21 -2007-07,SMB,0.1,1.0,0.02,0.25,0.1 +2007-06,RMW,0.62,-0.11,-0.46,1.0,0.03 +2007-06,CMA,0.11,-0.13,-0.08,0.03,1.0 +2007-07,Mkt-RF,1.0,0.09,0.12,0.48,0.21 +2007-07,SMB,0.09,1.0,0.02,0.25,0.1 2007-07,HML,0.12,0.02,1.0,0.08,-0.26 -2007-07,RMW,0.47,0.25,0.08,1.0,-0.27 +2007-07,RMW,0.48,0.25,0.08,1.0,-0.27 2007-07,CMA,0.21,0.1,-0.26,-0.27,1.0 2007-08,Mkt-RF,1.0,-0.06,0.22,0.41,-0.08 -2007-08,SMB,-0.06,1.0,0.12,-0.43,-0.29 -2007-08,HML,0.22,0.12,1.0,0.36,0.7 +2007-08,SMB,-0.06,1.0,0.12,-0.43,-0.28 +2007-08,HML,0.22,0.12,1.0,0.36,0.71 2007-08,RMW,0.41,-0.43,0.36,1.0,0.57 -2007-08,CMA,-0.08,-0.29,0.7,0.57,1.0 +2007-08,CMA,-0.08,-0.28,0.71,0.57,1.0 2007-09,Mkt-RF,1.0,0.58,0.38,0.31,-0.1 2007-09,SMB,0.58,1.0,0.34,0.46,0.25 2007-09,HML,0.38,0.34,1.0,0.42,0.14 -2007-09,RMW,0.31,0.46,0.42,1.0,-0.14 -2007-09,CMA,-0.1,0.25,0.14,-0.14,1.0 +2007-09,RMW,0.31,0.46,0.42,1.0,-0.13 +2007-09,CMA,-0.1,0.25,0.14,-0.13,1.0 2007-10,Mkt-RF,1.0,0.61,-0.04,-0.11,-0.2 2007-10,SMB,0.61,1.0,0.22,-0.28,0.02 2007-10,HML,-0.04,0.22,1.0,-0.22,0.53 @@ -2669,16 +2669,16 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2007-12,HML,0.03,0.03,1.0,0.03,0.04 2007-12,RMW,0.12,0.04,0.03,1.0,0.34 2007-12,CMA,-0.03,0.12,0.04,0.34,1.0 -2008-01,Mkt-RF,1.0,0.13,0.35,0.38,0.19 +2008-01,Mkt-RF,1.0,0.13,0.35,0.38,0.18 2008-01,SMB,0.13,1.0,0.64,0.02,0.5 2008-01,HML,0.35,0.64,1.0,-0.1,0.49 2008-01,RMW,0.38,0.02,-0.1,1.0,0.14 -2008-01,CMA,0.19,0.5,0.49,0.14,1.0 +2008-01,CMA,0.18,0.5,0.49,0.14,1.0 2008-02,Mkt-RF,1.0,0.39,0.09,0.24,-0.29 2008-02,SMB,0.39,1.0,-0.23,0.08,-0.23 2008-02,HML,0.09,-0.23,1.0,-0.17,0.51 -2008-02,RMW,0.24,0.08,-0.17,1.0,-0.01 -2008-02,CMA,-0.29,-0.23,0.51,-0.01,1.0 +2008-02,RMW,0.24,0.08,-0.17,1.0,-0.0 +2008-02,CMA,-0.29,-0.23,0.51,-0.0,1.0 2008-03,Mkt-RF,1.0,0.49,0.04,0.22,-0.66 2008-03,SMB,0.49,1.0,-0.33,0.05,-0.72 2008-03,HML,0.04,-0.33,1.0,-0.07,0.43 @@ -2706,49 +2706,49 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2008-07,CMA,-0.05,0.31,0.36,-0.49,1.0 2008-08,Mkt-RF,1.0,0.19,0.67,-0.47,0.18 2008-08,SMB,0.19,1.0,0.2,-0.27,0.17 -2008-08,HML,0.67,0.2,1.0,-0.82,0.46 +2008-08,HML,0.67,0.2,1.0,-0.82,0.45 2008-08,RMW,-0.47,-0.27,-0.82,1.0,-0.18 -2008-08,CMA,0.18,0.17,0.46,-0.18,1.0 +2008-08,CMA,0.18,0.17,0.45,-0.18,1.0 2008-09,Mkt-RF,1.0,-0.53,0.9,-0.71,-0.64 -2008-09,SMB,-0.53,1.0,-0.28,0.1,0.49 +2008-09,SMB,-0.53,1.0,-0.28,0.1,0.5 2008-09,HML,0.9,-0.28,1.0,-0.85,-0.48 2008-09,RMW,-0.71,0.1,-0.85,1.0,0.49 -2008-09,CMA,-0.64,0.49,-0.48,0.49,1.0 +2008-09,CMA,-0.64,0.5,-0.48,0.49,1.0 2008-10,Mkt-RF,1.0,-0.26,-0.01,0.16,-0.4 -2008-10,SMB,-0.26,1.0,0.16,-0.29,-0.55 +2008-10,SMB,-0.26,1.0,0.16,-0.29,-0.54 2008-10,HML,-0.01,0.16,1.0,-0.67,-0.02 2008-10,RMW,0.16,-0.29,-0.67,1.0,0.22 -2008-10,CMA,-0.4,-0.55,-0.02,0.22,1.0 -2008-11,Mkt-RF,1.0,-0.09,0.53,-0.41,-0.32 +2008-10,CMA,-0.4,-0.54,-0.02,0.22,1.0 +2008-11,Mkt-RF,1.0,-0.09,0.53,-0.41,-0.33 2008-11,SMB,-0.09,1.0,0.1,-0.03,0.2 2008-11,HML,0.53,0.1,1.0,-0.83,-0.61 2008-11,RMW,-0.41,-0.03,-0.83,1.0,0.53 -2008-11,CMA,-0.32,0.2,-0.61,0.53,1.0 +2008-11,CMA,-0.33,0.2,-0.61,0.53,1.0 2008-12,Mkt-RF,1.0,0.54,0.85,-0.62,-0.34 -2008-12,SMB,0.54,1.0,0.56,-0.5,-0.04 -2008-12,HML,0.85,0.56,1.0,-0.77,0.03 -2008-12,RMW,-0.62,-0.5,-0.77,1.0,0.0 -2008-12,CMA,-0.34,-0.04,0.03,0.0,1.0 +2008-12,SMB,0.54,1.0,0.56,-0.49,-0.04 +2008-12,HML,0.85,0.56,1.0,-0.77,0.02 +2008-12,RMW,-0.62,-0.49,-0.77,1.0,0.01 +2008-12,CMA,-0.34,-0.04,0.02,0.01,1.0 2009-01,Mkt-RF,1.0,0.29,0.82,-0.69,-0.54 2009-01,SMB,0.29,1.0,0.19,-0.19,0.08 2009-01,HML,0.82,0.19,1.0,-0.84,-0.64 2009-01,RMW,-0.69,-0.19,-0.84,1.0,0.59 2009-01,CMA,-0.54,0.08,-0.64,0.59,1.0 2009-02,Mkt-RF,1.0,-0.16,0.61,-0.51,-0.34 -2009-02,SMB,-0.16,1.0,-0.24,-0.01,0.26 -2009-02,HML,0.61,-0.24,1.0,-0.77,-0.28 +2009-02,SMB,-0.16,1.0,-0.24,-0.01,0.27 +2009-02,HML,0.61,-0.24,1.0,-0.77,-0.29 2009-02,RMW,-0.51,-0.01,-0.77,1.0,0.3 -2009-02,CMA,-0.34,0.26,-0.28,0.3,1.0 +2009-02,CMA,-0.34,0.27,-0.29,0.3,1.0 2009-03,Mkt-RF,1.0,0.53,0.8,-0.78,-0.06 2009-03,SMB,0.53,1.0,0.35,-0.25,0.16 2009-03,HML,0.8,0.35,1.0,-0.8,0.26 -2009-03,RMW,-0.78,-0.25,-0.8,1.0,-0.17 -2009-03,CMA,-0.06,0.16,0.26,-0.17,1.0 -2009-04,Mkt-RF,1.0,0.67,0.81,-0.46,-0.19 -2009-04,SMB,0.67,1.0,0.43,-0.22,-0.05 -2009-04,HML,0.81,0.43,1.0,-0.51,-0.22 -2009-04,RMW,-0.46,-0.22,-0.51,1.0,-0.03 -2009-04,CMA,-0.19,-0.05,-0.22,-0.03,1.0 +2009-03,RMW,-0.78,-0.25,-0.8,1.0,-0.18 +2009-03,CMA,-0.06,0.16,0.26,-0.18,1.0 +2009-04,Mkt-RF,1.0,0.67,0.81,-0.46,-0.2 +2009-04,SMB,0.67,1.0,0.43,-0.21,-0.05 +2009-04,HML,0.81,0.43,1.0,-0.51,-0.23 +2009-04,RMW,-0.46,-0.21,-0.51,1.0,-0.02 +2009-04,CMA,-0.2,-0.05,-0.23,-0.02,1.0 2009-05,Mkt-RF,1.0,0.45,0.82,-0.25,-0.35 2009-05,SMB,0.45,1.0,0.24,-0.4,-0.38 2009-05,HML,0.82,0.24,1.0,-0.38,-0.52 @@ -2762,48 +2762,48 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2009-07,Mkt-RF,1.0,0.42,0.68,-0.24,0.42 2009-07,SMB,0.42,1.0,0.41,-0.03,0.63 2009-07,HML,0.68,0.41,1.0,-0.63,0.44 -2009-07,RMW,-0.24,-0.03,-0.63,1.0,-0.13 -2009-07,CMA,0.42,0.63,0.44,-0.13,1.0 +2009-07,RMW,-0.24,-0.03,-0.63,1.0,-0.12 +2009-07,CMA,0.42,0.63,0.44,-0.12,1.0 2009-08,Mkt-RF,1.0,0.65,0.75,-0.35,0.19 -2009-08,SMB,0.65,1.0,0.3,0.09,0.24 -2009-08,HML,0.75,0.3,1.0,-0.74,0.28 -2009-08,RMW,-0.35,0.09,-0.74,1.0,-0.18 -2009-08,CMA,0.19,0.24,0.28,-0.18,1.0 -2009-09,Mkt-RF,1.0,0.7,0.9,-0.81,0.74 +2009-08,SMB,0.65,1.0,0.31,0.09,0.24 +2009-08,HML,0.75,0.31,1.0,-0.74,0.28 +2009-08,RMW,-0.35,0.09,-0.74,1.0,-0.17 +2009-08,CMA,0.19,0.24,0.28,-0.17,1.0 +2009-09,Mkt-RF,1.0,0.7,0.9,-0.81,0.73 2009-09,SMB,0.7,1.0,0.7,-0.58,0.58 -2009-09,HML,0.9,0.7,1.0,-0.85,0.7 +2009-09,HML,0.9,0.7,1.0,-0.85,0.69 2009-09,RMW,-0.81,-0.58,-0.85,1.0,-0.59 -2009-09,CMA,0.74,0.58,0.7,-0.59,1.0 +2009-09,CMA,0.73,0.58,0.69,-0.59,1.0 2009-10,Mkt-RF,1.0,0.71,0.92,-0.6,0.62 2009-10,SMB,0.71,1.0,0.57,-0.23,0.53 2009-10,HML,0.92,0.57,1.0,-0.74,0.69 -2009-10,RMW,-0.6,-0.23,-0.74,1.0,-0.55 -2009-10,CMA,0.62,0.53,0.69,-0.55,1.0 +2009-10,RMW,-0.6,-0.23,-0.74,1.0,-0.54 +2009-10,CMA,0.62,0.53,0.69,-0.54,1.0 2009-11,Mkt-RF,1.0,0.63,0.73,-0.16,0.55 -2009-11,SMB,0.63,1.0,0.5,-0.25,0.64 +2009-11,SMB,0.63,1.0,0.5,-0.25,0.63 2009-11,HML,0.73,0.5,1.0,-0.54,0.55 -2009-11,RMW,-0.16,-0.25,-0.54,1.0,-0.11 -2009-11,CMA,0.55,0.64,0.55,-0.11,1.0 +2009-11,RMW,-0.16,-0.25,-0.54,1.0,-0.12 +2009-11,CMA,0.55,0.63,0.55,-0.12,1.0 2009-12,Mkt-RF,1.0,0.35,0.33,-0.13,0.02 2009-12,SMB,0.35,1.0,0.28,-0.2,0.27 2009-12,HML,0.33,0.28,1.0,-0.68,0.2 2009-12,RMW,-0.13,-0.2,-0.68,1.0,0.01 2009-12,CMA,0.02,0.27,0.2,0.01,1.0 -2010-01,Mkt-RF,1.0,0.43,0.66,-0.43,0.37 +2010-01,Mkt-RF,1.0,0.43,0.66,-0.42,0.37 2010-01,SMB,0.43,1.0,0.07,-0.15,-0.05 2010-01,HML,0.66,0.07,1.0,-0.57,0.43 -2010-01,RMW,-0.43,-0.15,-0.57,1.0,-0.13 +2010-01,RMW,-0.42,-0.15,-0.57,1.0,-0.13 2010-01,CMA,0.37,-0.05,0.43,-0.13,1.0 -2010-02,Mkt-RF,1.0,0.11,0.87,-0.2,0.38 -2010-02,SMB,0.11,1.0,-0.04,-0.09,-0.1 -2010-02,HML,0.87,-0.04,1.0,-0.41,0.42 +2010-02,Mkt-RF,1.0,0.1,0.86,-0.2,0.38 +2010-02,SMB,0.1,1.0,-0.05,-0.09,-0.1 +2010-02,HML,0.86,-0.05,1.0,-0.41,0.42 2010-02,RMW,-0.2,-0.09,-0.41,1.0,0.2 2010-02,CMA,0.38,-0.1,0.42,0.2,1.0 2010-03,Mkt-RF,1.0,0.78,0.41,-0.04,0.67 -2010-03,SMB,0.78,1.0,0.22,0.11,0.56 +2010-03,SMB,0.78,1.0,0.22,0.12,0.57 2010-03,HML,0.41,0.22,1.0,-0.66,0.57 -2010-03,RMW,-0.04,0.11,-0.66,1.0,-0.26 -2010-03,CMA,0.67,0.56,0.57,-0.26,1.0 +2010-03,RMW,-0.04,0.12,-0.66,1.0,-0.26 +2010-03,CMA,0.67,0.57,0.57,-0.26,1.0 2010-04,Mkt-RF,1.0,0.38,0.9,-0.7,0.84 2010-04,SMB,0.38,1.0,0.34,-0.15,0.24 2010-04,HML,0.9,0.34,1.0,-0.83,0.75 @@ -2812,16 +2812,16 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2010-05,Mkt-RF,1.0,0.68,0.87,-0.74,0.78 2010-05,SMB,0.68,1.0,0.5,-0.32,0.61 2010-05,HML,0.87,0.5,1.0,-0.9,0.8 -2010-05,RMW,-0.74,-0.32,-0.9,1.0,-0.66 -2010-05,CMA,0.78,0.61,0.8,-0.66,1.0 -2010-06,Mkt-RF,1.0,0.56,0.8,-0.34,0.57 -2010-06,SMB,0.56,1.0,0.62,-0.34,0.39 +2010-05,RMW,-0.74,-0.32,-0.9,1.0,-0.65 +2010-05,CMA,0.78,0.61,0.8,-0.65,1.0 +2010-06,Mkt-RF,1.0,0.55,0.8,-0.34,0.57 +2010-06,SMB,0.55,1.0,0.62,-0.34,0.39 2010-06,HML,0.8,0.62,1.0,-0.56,0.55 2010-06,RMW,-0.34,-0.34,-0.56,1.0,0.01 2010-06,CMA,0.57,0.39,0.55,0.01,1.0 -2010-07,Mkt-RF,1.0,0.55,0.64,-0.86,0.86 +2010-07,Mkt-RF,1.0,0.55,0.63,-0.86,0.86 2010-07,SMB,0.55,1.0,0.37,-0.67,0.74 -2010-07,HML,0.64,0.37,1.0,-0.75,0.58 +2010-07,HML,0.63,0.37,1.0,-0.75,0.58 2010-07,RMW,-0.86,-0.67,-0.75,1.0,-0.85 2010-07,CMA,0.86,0.74,0.58,-0.85,1.0 2010-08,Mkt-RF,1.0,0.59,0.5,-0.84,0.77 @@ -2845,15 +2845,15 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2010-11,RMW,-0.79,-0.41,-0.74,1.0,-0.8 2010-11,CMA,0.64,0.17,0.85,-0.8,1.0 2010-12,Mkt-RF,1.0,0.02,0.32,-0.6,0.65 -2010-12,SMB,0.02,1.0,-0.26,0.0,-0.0 +2010-12,SMB,0.02,1.0,-0.26,0.0,0.0 2010-12,HML,0.32,-0.26,1.0,-0.7,0.62 2010-12,RMW,-0.6,0.0,-0.7,1.0,-0.75 -2010-12,CMA,0.65,-0.0,0.62,-0.75,1.0 +2010-12,CMA,0.65,0.0,0.62,-0.75,1.0 2011-01,Mkt-RF,1.0,0.66,0.34,-0.63,0.69 2011-01,SMB,0.66,1.0,-0.06,-0.55,0.25 2011-01,HML,0.34,-0.06,1.0,-0.37,0.56 -2011-01,RMW,-0.63,-0.55,-0.37,1.0,-0.41 -2011-01,CMA,0.69,0.25,0.56,-0.41,1.0 +2011-01,RMW,-0.63,-0.55,-0.37,1.0,-0.42 +2011-01,CMA,0.69,0.25,0.56,-0.42,1.0 2011-02,Mkt-RF,1.0,0.72,0.45,-0.76,0.82 2011-02,SMB,0.72,1.0,0.07,-0.6,0.71 2011-02,HML,0.45,0.07,1.0,-0.36,0.44 @@ -2864,15 +2864,15 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2011-03,HML,0.28,-0.15,1.0,-0.13,0.2 2011-03,RMW,-0.79,-0.34,-0.13,1.0,-0.79 2011-03,CMA,0.67,0.45,0.2,-0.79,1.0 -2011-04,Mkt-RF,1.0,0.64,-0.06,-0.71,0.52 +2011-04,Mkt-RF,1.0,0.64,-0.07,-0.72,0.52 2011-04,SMB,0.64,1.0,-0.27,-0.67,0.44 -2011-04,HML,-0.06,-0.27,1.0,0.14,0.19 -2011-04,RMW,-0.71,-0.67,0.14,1.0,-0.39 -2011-04,CMA,0.52,0.44,0.19,-0.39,1.0 +2011-04,HML,-0.07,-0.27,1.0,0.14,0.19 +2011-04,RMW,-0.72,-0.67,0.14,1.0,-0.4 +2011-04,CMA,0.52,0.44,0.19,-0.4,1.0 2011-05,Mkt-RF,1.0,0.72,-0.06,-0.69,0.47 -2011-05,SMB,0.72,1.0,-0.4,-0.75,0.63 +2011-05,SMB,0.72,1.0,-0.4,-0.74,0.63 2011-05,HML,-0.06,-0.4,1.0,0.11,0.03 -2011-05,RMW,-0.69,-0.75,0.11,1.0,-0.65 +2011-05,RMW,-0.69,-0.74,0.11,1.0,-0.65 2011-05,CMA,0.47,0.63,0.03,-0.65,1.0 2011-06,Mkt-RF,1.0,0.54,-0.11,-0.53,0.65 2011-06,SMB,0.54,1.0,-0.57,-0.35,0.37 @@ -2880,60 +2880,60 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2011-06,RMW,-0.53,-0.35,-0.14,1.0,-0.69 2011-06,CMA,0.65,0.37,0.06,-0.69,1.0 2011-07,Mkt-RF,1.0,0.59,-0.01,-0.66,-0.24 -2011-07,SMB,0.59,1.0,-0.38,-0.37,-0.32 +2011-07,SMB,0.59,1.0,-0.38,-0.37,-0.31 2011-07,HML,-0.01,-0.38,1.0,-0.44,0.76 2011-07,RMW,-0.66,-0.37,-0.44,1.0,-0.08 -2011-07,CMA,-0.24,-0.32,0.76,-0.08,1.0 +2011-07,CMA,-0.24,-0.31,0.76,-0.08,1.0 2011-08,Mkt-RF,1.0,0.8,0.36,-0.84,0.02 2011-08,SMB,0.8,1.0,0.22,-0.76,-0.11 2011-08,HML,0.36,0.22,1.0,-0.69,0.72 2011-08,RMW,-0.84,-0.76,-0.69,1.0,-0.26 2011-08,CMA,0.02,-0.11,0.72,-0.26,1.0 2011-09,Mkt-RF,1.0,0.48,0.48,-0.81,0.03 -2011-09,SMB,0.48,1.0,0.26,-0.58,0.19 +2011-09,SMB,0.48,1.0,0.26,-0.58,0.2 2011-09,HML,0.48,0.26,1.0,-0.77,0.79 2011-09,RMW,-0.81,-0.58,-0.77,1.0,-0.38 -2011-09,CMA,0.03,0.19,0.79,-0.38,1.0 +2011-09,CMA,0.03,0.2,0.79,-0.38,1.0 2011-10,Mkt-RF,1.0,0.72,0.59,-0.88,-0.24 -2011-10,SMB,0.72,1.0,0.46,-0.71,-0.2 -2011-10,HML,0.59,0.46,1.0,-0.8,0.5 -2011-10,RMW,-0.88,-0.71,-0.8,1.0,-0.01 -2011-10,CMA,-0.24,-0.2,0.5,-0.01,1.0 +2011-10,SMB,0.72,1.0,0.46,-0.71,-0.19 +2011-10,HML,0.59,0.46,1.0,-0.8,0.51 +2011-10,RMW,-0.88,-0.71,-0.8,1.0,-0.02 +2011-10,CMA,-0.24,-0.19,0.51,-0.02,1.0 2011-11,Mkt-RF,1.0,0.78,0.52,-0.88,-0.34 2011-11,SMB,0.78,1.0,0.24,-0.71,-0.49 2011-11,HML,0.52,0.24,1.0,-0.64,0.46 2011-11,RMW,-0.88,-0.71,-0.64,1.0,0.28 2011-11,CMA,-0.34,-0.49,0.46,0.28,1.0 2011-12,Mkt-RF,1.0,0.7,0.41,-0.75,-0.3 -2011-12,SMB,0.7,1.0,0.28,-0.5,-0.12 +2011-12,SMB,0.7,1.0,0.28,-0.51,-0.12 2011-12,HML,0.41,0.28,1.0,-0.73,0.6 -2011-12,RMW,-0.75,-0.5,-0.73,1.0,0.04 +2011-12,RMW,-0.75,-0.51,-0.73,1.0,0.04 2011-12,CMA,-0.3,-0.12,0.6,0.04,1.0 -2012-01,Mkt-RF,1.0,0.2,0.41,-0.58,-0.21 -2012-01,SMB,0.2,1.0,0.1,-0.58,0.07 +2012-01,Mkt-RF,1.0,0.2,0.41,-0.58,-0.22 +2012-01,SMB,0.2,1.0,0.1,-0.58,0.08 2012-01,HML,0.41,0.1,1.0,-0.67,0.25 2012-01,RMW,-0.58,-0.58,-0.67,1.0,-0.23 -2012-01,CMA,-0.21,0.07,0.25,-0.23,1.0 +2012-01,CMA,-0.22,0.08,0.25,-0.23,1.0 2012-02,Mkt-RF,1.0,0.81,0.39,-0.73,-0.11 2012-02,SMB,0.81,1.0,0.34,-0.83,0.15 -2012-02,HML,0.39,0.34,1.0,-0.56,0.39 +2012-02,HML,0.39,0.34,1.0,-0.56,0.38 2012-02,RMW,-0.73,-0.83,-0.56,1.0,-0.18 -2012-02,CMA,-0.11,0.15,0.39,-0.18,1.0 +2012-02,CMA,-0.11,0.15,0.38,-0.18,1.0 2012-03,Mkt-RF,1.0,0.47,0.44,-0.7,-0.2 2012-03,SMB,0.47,1.0,0.21,-0.4,-0.31 2012-03,HML,0.44,0.21,1.0,-0.8,0.17 2012-03,RMW,-0.7,-0.4,-0.8,1.0,0.08 2012-03,CMA,-0.2,-0.31,0.17,0.08,1.0 2012-04,Mkt-RF,1.0,0.67,-0.01,-0.53,-0.46 -2012-04,SMB,0.67,1.0,0.14,-0.35,-0.02 +2012-04,SMB,0.67,1.0,0.14,-0.35,-0.01 2012-04,HML,-0.01,0.14,1.0,-0.72,0.61 2012-04,RMW,-0.53,-0.35,-0.72,1.0,-0.12 -2012-04,CMA,-0.46,-0.02,0.61,-0.12,1.0 +2012-04,CMA,-0.46,-0.01,0.61,-0.12,1.0 2012-05,Mkt-RF,1.0,0.38,-0.19,-0.57,-0.27 -2012-05,SMB,0.38,1.0,-0.44,-0.11,-0.14 +2012-05,SMB,0.38,1.0,-0.44,-0.11,-0.13 2012-05,HML,-0.19,-0.44,1.0,-0.23,0.57 2012-05,RMW,-0.57,-0.11,-0.23,1.0,0.3 -2012-05,CMA,-0.27,-0.14,0.57,0.3,1.0 +2012-05,CMA,-0.27,-0.13,0.57,0.3,1.0 2012-06,Mkt-RF,1.0,0.65,0.0,-0.76,-0.59 2012-06,SMB,0.65,1.0,0.05,-0.62,-0.41 2012-06,HML,0.0,0.05,1.0,-0.39,0.47 @@ -2941,13 +2941,13 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2012-06,CMA,-0.59,-0.41,0.47,0.42,1.0 2012-07,Mkt-RF,1.0,0.27,0.03,-0.11,-0.42 2012-07,SMB,0.27,1.0,-0.35,-0.02,-0.42 -2012-07,HML,0.03,-0.35,1.0,-0.5,0.62 -2012-07,RMW,-0.11,-0.02,-0.5,1.0,-0.39 +2012-07,HML,0.03,-0.35,1.0,-0.51,0.62 +2012-07,RMW,-0.11,-0.02,-0.51,1.0,-0.39 2012-07,CMA,-0.42,-0.42,0.62,-0.39,1.0 -2012-08,Mkt-RF,1.0,0.47,0.29,-0.25,-0.42 +2012-08,Mkt-RF,1.0,0.47,0.29,-0.24,-0.42 2012-08,SMB,0.47,1.0,-0.13,-0.33,-0.49 -2012-08,HML,0.29,-0.13,1.0,0.06,0.27 -2012-08,RMW,-0.25,-0.33,0.06,1.0,0.36 +2012-08,HML,0.29,-0.13,1.0,0.05,0.27 +2012-08,RMW,-0.24,-0.33,0.05,1.0,0.36 2012-08,CMA,-0.42,-0.49,0.27,0.36,1.0 2012-09,Mkt-RF,1.0,0.12,0.54,-0.55,-0.38 2012-09,SMB,0.12,1.0,0.13,-0.34,-0.27 @@ -2955,39 +2955,39 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2012-09,RMW,-0.55,-0.34,-0.49,1.0,0.12 2012-09,CMA,-0.38,-0.27,0.04,0.12,1.0 2012-10,Mkt-RF,1.0,-0.12,0.15,-0.1,-0.18 -2012-10,SMB,-0.12,1.0,0.13,-0.14,-0.1 +2012-10,SMB,-0.12,1.0,0.13,-0.14,-0.11 2012-10,HML,0.15,0.13,1.0,-0.71,0.55 -2012-10,RMW,-0.1,-0.14,-0.71,1.0,-0.69 -2012-10,CMA,-0.18,-0.1,0.55,-0.69,1.0 -2012-11,Mkt-RF,1.0,0.47,0.41,0.16,-0.28 -2012-11,SMB,0.47,1.0,-0.09,-0.19,-0.52 +2012-10,RMW,-0.1,-0.14,-0.71,1.0,-0.68 +2012-10,CMA,-0.18,-0.11,0.55,-0.68,1.0 +2012-11,Mkt-RF,1.0,0.47,0.41,0.15,-0.29 +2012-11,SMB,0.47,1.0,-0.09,-0.2,-0.53 2012-11,HML,0.41,-0.09,1.0,-0.22,0.34 -2012-11,RMW,0.16,-0.19,-0.22,1.0,-0.19 -2012-11,CMA,-0.28,-0.52,0.34,-0.19,1.0 -2012-12,Mkt-RF,1.0,-0.09,0.14,-0.19,-0.32 +2012-11,RMW,0.15,-0.2,-0.22,1.0,-0.19 +2012-11,CMA,-0.29,-0.53,0.34,-0.19,1.0 +2012-12,Mkt-RF,1.0,-0.09,0.14,-0.18,-0.32 2012-12,SMB,-0.09,1.0,-0.58,0.46,-0.65 2012-12,HML,0.14,-0.58,1.0,-0.82,0.6 -2012-12,RMW,-0.19,0.46,-0.82,1.0,-0.58 +2012-12,RMW,-0.18,0.46,-0.82,1.0,-0.58 2012-12,CMA,-0.32,-0.65,0.6,-0.58,1.0 2013-01,Mkt-RF,1.0,0.08,0.34,-0.61,-0.03 2013-01,SMB,0.08,1.0,-0.13,0.09,0.12 2013-01,HML,0.34,-0.13,1.0,-0.54,0.46 2013-01,RMW,-0.61,0.09,-0.54,1.0,0.06 2013-01,CMA,-0.03,0.12,0.46,0.06,1.0 -2013-02,Mkt-RF,1.0,0.46,0.63,-0.61,0.09 -2013-02,SMB,0.46,1.0,0.59,-0.61,0.06 +2013-02,Mkt-RF,1.0,0.45,0.63,-0.6,0.09 +2013-02,SMB,0.45,1.0,0.59,-0.61,0.06 2013-02,HML,0.63,0.59,1.0,-0.89,0.56 -2013-02,RMW,-0.61,-0.61,-0.89,1.0,-0.44 -2013-02,CMA,0.09,0.06,0.56,-0.44,1.0 -2013-03,Mkt-RF,1.0,-0.22,0.08,-0.44,-0.07 +2013-02,RMW,-0.6,-0.61,-0.89,1.0,-0.45 +2013-02,CMA,0.09,0.06,0.56,-0.45,1.0 +2013-03,Mkt-RF,1.0,-0.22,0.08,-0.45,-0.08 2013-03,SMB,-0.22,1.0,0.26,0.03,-0.07 2013-03,HML,0.08,0.26,1.0,-0.35,0.26 -2013-03,RMW,-0.44,0.03,-0.35,1.0,-0.39 -2013-03,CMA,-0.07,-0.07,0.26,-0.39,1.0 +2013-03,RMW,-0.45,0.03,-0.35,1.0,-0.38 +2013-03,CMA,-0.08,-0.07,0.26,-0.38,1.0 2013-04,Mkt-RF,1.0,0.67,0.34,-0.56,-0.1 2013-04,SMB,0.67,1.0,0.51,-0.29,-0.04 -2013-04,HML,0.34,0.51,1.0,-0.42,-0.04 -2013-04,RMW,-0.56,-0.29,-0.42,1.0,-0.29 +2013-04,HML,0.34,0.51,1.0,-0.41,-0.04 +2013-04,RMW,-0.56,-0.29,-0.41,1.0,-0.29 2013-04,CMA,-0.1,-0.04,-0.04,-0.29,1.0 2013-05,Mkt-RF,1.0,0.51,0.4,-0.4,0.05 2013-05,SMB,0.51,1.0,0.21,-0.21,-0.23 @@ -2995,8 +2995,8 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2013-05,RMW,-0.4,-0.21,-0.46,1.0,0.16 2013-05,CMA,0.05,-0.23,-0.01,0.16,1.0 2013-06,Mkt-RF,1.0,-0.0,0.38,-0.62,0.52 -2013-06,SMB,-0.0,1.0,0.34,-0.34,0.05 -2013-06,HML,0.38,0.34,1.0,-0.72,0.34 +2013-06,SMB,-0.0,1.0,0.33,-0.34,0.05 +2013-06,HML,0.38,0.33,1.0,-0.72,0.34 2013-06,RMW,-0.62,-0.34,-0.72,1.0,-0.42 2013-06,CMA,0.52,0.05,0.34,-0.42,1.0 2013-07,Mkt-RF,1.0,0.31,0.1,-0.53,-0.02 @@ -3011,148 +3011,148 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2013-08,CMA,-0.17,-0.13,0.63,0.09,1.0 2013-09,Mkt-RF,1.0,-0.29,-0.12,-0.32,0.11 2013-09,SMB,-0.29,1.0,-0.01,-0.01,-0.27 -2013-09,HML,-0.12,-0.01,1.0,-0.54,0.59 -2013-09,RMW,-0.32,-0.01,-0.54,1.0,-0.01 +2013-09,HML,-0.12,-0.01,1.0,-0.55,0.59 +2013-09,RMW,-0.32,-0.01,-0.55,1.0,-0.01 2013-09,CMA,0.11,-0.27,0.59,-0.01,1.0 2013-10,Mkt-RF,1.0,0.48,-0.28,-0.74,-0.6 2013-10,SMB,0.48,1.0,-0.52,-0.3,-0.56 2013-10,HML,-0.28,-0.52,1.0,0.27,0.41 2013-10,RMW,-0.74,-0.3,0.27,1.0,0.61 2013-10,CMA,-0.6,-0.56,0.41,0.61,1.0 -2013-11,Mkt-RF,1.0,0.37,0.06,-0.64,-0.54 +2013-11,Mkt-RF,1.0,0.37,0.06,-0.64,-0.53 2013-11,SMB,0.37,1.0,-0.51,-0.5,-0.68 2013-11,HML,0.06,-0.51,1.0,-0.09,0.45 2013-11,RMW,-0.64,-0.5,-0.09,1.0,0.72 -2013-11,CMA,-0.54,-0.68,0.45,0.72,1.0 +2013-11,CMA,-0.53,-0.68,0.45,0.72,1.0 2013-12,Mkt-RF,1.0,0.18,-0.04,-0.32,0.23 2013-12,SMB,0.18,1.0,-0.52,-0.28,-0.56 2013-12,HML,-0.04,-0.52,1.0,-0.25,0.5 2013-12,RMW,-0.32,-0.28,-0.25,1.0,0.23 2013-12,CMA,0.23,-0.56,0.5,0.23,1.0 2014-01,Mkt-RF,1.0,0.62,-0.34,-0.62,-0.58 -2014-01,SMB,0.62,1.0,-0.58,-0.47,-0.63 -2014-01,HML,-0.34,-0.58,1.0,0.09,0.71 -2014-01,RMW,-0.62,-0.47,0.09,1.0,0.28 -2014-01,CMA,-0.58,-0.63,0.71,0.28,1.0 -2014-02,Mkt-RF,1.0,0.34,-0.07,-0.44,-0.29 -2014-02,SMB,0.34,1.0,-0.42,-0.55,-0.5 -2014-02,HML,-0.07,-0.42,1.0,0.59,0.56 -2014-02,RMW,-0.44,-0.55,0.59,1.0,0.72 -2014-02,CMA,-0.29,-0.5,0.56,0.72,1.0 +2014-01,SMB,0.62,1.0,-0.57,-0.47,-0.63 +2014-01,HML,-0.34,-0.57,1.0,0.1,0.72 +2014-01,RMW,-0.62,-0.47,0.1,1.0,0.28 +2014-01,CMA,-0.58,-0.63,0.72,0.28,1.0 +2014-02,Mkt-RF,1.0,0.34,-0.07,-0.44,-0.28 +2014-02,SMB,0.34,1.0,-0.42,-0.55,-0.49 +2014-02,HML,-0.07,-0.42,1.0,0.59,0.57 +2014-02,RMW,-0.44,-0.55,0.59,1.0,0.71 +2014-02,CMA,-0.28,-0.49,0.57,0.71,1.0 2014-03,Mkt-RF,1.0,0.58,-0.25,-0.48,-0.31 2014-03,SMB,0.58,1.0,-0.51,-0.65,-0.59 -2014-03,HML,-0.25,-0.51,1.0,0.33,0.75 -2014-03,RMW,-0.48,-0.65,0.33,1.0,0.69 +2014-03,HML,-0.25,-0.51,1.0,0.32,0.75 +2014-03,RMW,-0.48,-0.65,0.32,1.0,0.69 2014-03,CMA,-0.31,-0.59,0.75,0.69,1.0 2014-04,Mkt-RF,1.0,0.65,-0.67,-0.66,-0.83 2014-04,SMB,0.65,1.0,-0.64,-0.78,-0.64 2014-04,HML,-0.67,-0.64,1.0,0.46,0.8 2014-04,RMW,-0.66,-0.78,0.46,1.0,0.68 2014-04,CMA,-0.83,-0.64,0.8,0.68,1.0 -2014-05,Mkt-RF,1.0,0.56,-0.06,-0.67,-0.27 -2014-05,SMB,0.56,1.0,-0.41,-0.55,-0.43 +2014-05,Mkt-RF,1.0,0.56,-0.06,-0.67,-0.26 +2014-05,SMB,0.56,1.0,-0.41,-0.55,-0.42 2014-05,HML,-0.06,-0.41,1.0,0.26,0.86 -2014-05,RMW,-0.67,-0.55,0.26,1.0,0.48 -2014-05,CMA,-0.27,-0.43,0.86,0.48,1.0 -2014-06,Mkt-RF,1.0,0.5,-0.0,-0.02,-0.37 +2014-05,RMW,-0.67,-0.55,0.26,1.0,0.47 +2014-05,CMA,-0.26,-0.42,0.86,0.47,1.0 +2014-06,Mkt-RF,1.0,0.5,-0.0,-0.02,-0.35 2014-06,SMB,0.5,1.0,-0.26,-0.52,-0.26 2014-06,HML,-0.0,-0.26,1.0,-0.12,0.44 2014-06,RMW,-0.02,-0.52,-0.12,1.0,-0.08 -2014-06,CMA,-0.37,-0.26,0.44,-0.08,1.0 +2014-06,CMA,-0.35,-0.26,0.44,-0.08,1.0 2014-07,Mkt-RF,1.0,0.44,-0.52,-0.5,-0.56 -2014-07,SMB,0.44,1.0,-0.71,-0.56,-0.63 -2014-07,HML,-0.52,-0.71,1.0,0.64,0.69 +2014-07,SMB,0.44,1.0,-0.71,-0.56,-0.64 +2014-07,HML,-0.52,-0.71,1.0,0.64,0.7 2014-07,RMW,-0.5,-0.56,0.64,1.0,0.78 -2014-07,CMA,-0.56,-0.63,0.69,0.78,1.0 -2014-08,Mkt-RF,1.0,-0.02,-0.08,-0.04,-0.19 -2014-08,SMB,-0.02,1.0,-0.46,-0.47,-0.27 -2014-08,HML,-0.08,-0.46,1.0,0.14,0.62 -2014-08,RMW,-0.04,-0.47,0.14,1.0,0.3 -2014-08,CMA,-0.19,-0.27,0.62,0.3,1.0 +2014-07,CMA,-0.56,-0.64,0.7,0.78,1.0 +2014-08,Mkt-RF,1.0,-0.02,-0.08,-0.04,-0.18 +2014-08,SMB,-0.02,1.0,-0.45,-0.48,-0.27 +2014-08,HML,-0.08,-0.45,1.0,0.15,0.62 +2014-08,RMW,-0.04,-0.48,0.15,1.0,0.31 +2014-08,CMA,-0.18,-0.27,0.62,0.31,1.0 2014-09,Mkt-RF,1.0,0.21,-0.44,-0.4,-0.52 -2014-09,SMB,0.21,1.0,-0.51,-0.61,-0.48 -2014-09,HML,-0.44,-0.51,1.0,0.37,0.82 -2014-09,RMW,-0.4,-0.61,0.37,1.0,0.47 +2014-09,SMB,0.21,1.0,-0.51,-0.62,-0.48 +2014-09,HML,-0.44,-0.51,1.0,0.38,0.82 +2014-09,RMW,-0.4,-0.62,0.38,1.0,0.47 2014-09,CMA,-0.52,-0.48,0.82,0.47,1.0 2014-10,Mkt-RF,1.0,-0.03,-0.19,-0.4,-0.19 -2014-10,SMB,-0.03,1.0,-0.24,-0.56,-0.46 -2014-10,HML,-0.19,-0.24,1.0,0.48,0.74 -2014-10,RMW,-0.4,-0.56,0.48,1.0,0.39 -2014-10,CMA,-0.19,-0.46,0.74,0.39,1.0 -2014-11,Mkt-RF,1.0,0.45,0.15,-0.56,-0.07 -2014-11,SMB,0.45,1.0,-0.05,-0.6,-0.31 +2014-10,SMB,-0.03,1.0,-0.23,-0.55,-0.45 +2014-10,HML,-0.19,-0.23,1.0,0.48,0.73 +2014-10,RMW,-0.4,-0.55,0.48,1.0,0.39 +2014-10,CMA,-0.19,-0.45,0.73,0.39,1.0 +2014-11,Mkt-RF,1.0,0.46,0.15,-0.56,-0.07 +2014-11,SMB,0.46,1.0,-0.05,-0.6,-0.3 2014-11,HML,0.15,-0.05,1.0,-0.17,0.65 2014-11,RMW,-0.56,-0.6,-0.17,1.0,0.34 -2014-11,CMA,-0.07,-0.31,0.65,0.34,1.0 +2014-11,CMA,-0.07,-0.3,0.65,0.34,1.0 2014-12,Mkt-RF,1.0,0.12,-0.02,-0.47,-0.36 2014-12,SMB,0.12,1.0,-0.19,-0.4,-0.43 -2014-12,HML,-0.02,-0.19,1.0,0.21,0.8 -2014-12,RMW,-0.47,-0.4,0.21,1.0,0.43 +2014-12,HML,-0.02,-0.19,1.0,0.22,0.8 +2014-12,RMW,-0.47,-0.4,0.22,1.0,0.43 2014-12,CMA,-0.36,-0.43,0.8,0.43,1.0 -2015-01,Mkt-RF,1.0,0.22,0.22,-0.25,-0.23 -2015-01,SMB,0.22,1.0,-0.26,-0.44,-0.02 -2015-01,HML,0.22,-0.26,1.0,-0.07,0.52 +2015-01,Mkt-RF,1.0,0.22,0.23,-0.25,-0.23 +2015-01,SMB,0.22,1.0,-0.27,-0.44,-0.01 +2015-01,HML,0.23,-0.27,1.0,-0.07,0.51 2015-01,RMW,-0.25,-0.44,-0.07,1.0,0.21 -2015-01,CMA,-0.23,-0.02,0.52,0.21,1.0 +2015-01,CMA,-0.23,-0.01,0.51,0.21,1.0 2015-02,Mkt-RF,1.0,0.02,0.28,-0.2,0.12 2015-02,SMB,0.02,1.0,-0.25,-0.36,-0.26 -2015-02,HML,0.28,-0.25,1.0,-0.12,0.76 +2015-02,HML,0.28,-0.25,1.0,-0.12,0.77 2015-02,RMW,-0.2,-0.36,-0.12,1.0,0.11 -2015-02,CMA,0.12,-0.26,0.76,0.11,1.0 -2015-03,Mkt-RF,1.0,-0.17,-0.11,-0.2,0.01 -2015-03,SMB,-0.17,1.0,-0.37,-0.26,-0.44 -2015-03,HML,-0.11,-0.37,1.0,0.22,0.79 -2015-03,RMW,-0.2,-0.26,0.22,1.0,0.37 +2015-02,CMA,0.12,-0.26,0.77,0.11,1.0 +2015-03,Mkt-RF,1.0,-0.17,-0.11,-0.19,0.01 +2015-03,SMB,-0.17,1.0,-0.38,-0.26,-0.44 +2015-03,HML,-0.11,-0.38,1.0,0.22,0.79 +2015-03,RMW,-0.19,-0.26,0.22,1.0,0.37 2015-03,CMA,0.01,-0.44,0.79,0.37,1.0 2015-04,Mkt-RF,1.0,0.38,-0.38,-0.04,-0.3 2015-04,SMB,0.38,1.0,-0.28,-0.13,-0.2 2015-04,HML,-0.38,-0.28,1.0,-0.13,0.68 2015-04,RMW,-0.04,-0.13,-0.13,1.0,-0.35 2015-04,CMA,-0.3,-0.2,0.68,-0.35,1.0 -2015-05,Mkt-RF,1.0,-0.22,-0.66,0.19,-0.74 -2015-05,SMB,-0.22,1.0,0.05,-0.33,0.01 -2015-05,HML,-0.66,0.05,1.0,-0.33,0.39 -2015-05,RMW,0.19,-0.33,-0.33,1.0,-0.04 -2015-05,CMA,-0.74,0.01,0.39,-0.04,1.0 +2015-05,Mkt-RF,1.0,-0.22,-0.67,0.19,-0.74 +2015-05,SMB,-0.22,1.0,0.04,-0.34,0.01 +2015-05,HML,-0.67,0.04,1.0,-0.33,0.4 +2015-05,RMW,0.19,-0.34,-0.33,1.0,-0.04 +2015-05,CMA,-0.74,0.01,0.4,-0.04,1.0 2015-06,Mkt-RF,1.0,0.31,-0.24,-0.42,-0.28 -2015-06,SMB,0.31,1.0,-0.12,-0.72,-0.35 -2015-06,HML,-0.24,-0.12,1.0,0.17,0.65 +2015-06,SMB,0.31,1.0,-0.13,-0.72,-0.34 +2015-06,HML,-0.24,-0.13,1.0,0.17,0.65 2015-06,RMW,-0.42,-0.72,0.17,1.0,0.57 -2015-06,CMA,-0.28,-0.35,0.65,0.57,1.0 -2015-07,Mkt-RF,1.0,0.04,-0.2,0.09,-0.13 -2015-07,SMB,0.04,1.0,-0.12,-0.18,0.1 +2015-06,CMA,-0.28,-0.34,0.65,0.57,1.0 +2015-07,Mkt-RF,1.0,0.04,-0.2,0.09,-0.12 +2015-07,SMB,0.04,1.0,-0.12,-0.17,0.09 2015-07,HML,-0.2,-0.12,1.0,0.13,0.51 -2015-07,RMW,0.09,-0.18,0.13,1.0,0.49 -2015-07,CMA,-0.13,0.1,0.51,0.49,1.0 +2015-07,RMW,0.09,-0.17,0.13,1.0,0.49 +2015-07,CMA,-0.12,0.09,0.51,0.49,1.0 2015-08,Mkt-RF,1.0,-0.54,-0.04,-0.13,-0.58 2015-08,SMB,-0.54,1.0,0.04,-0.55,0.4 -2015-08,HML,-0.04,0.04,1.0,0.29,0.65 -2015-08,RMW,-0.13,-0.55,0.29,1.0,0.16 +2015-08,HML,-0.04,0.04,1.0,0.3,0.65 +2015-08,RMW,-0.13,-0.55,0.3,1.0,0.16 2015-08,CMA,-0.58,0.4,0.65,0.16,1.0 2015-09,Mkt-RF,1.0,-0.34,-0.13,-0.18,-0.29 2015-09,SMB,-0.34,1.0,-0.65,-0.51,-0.39 2015-09,HML,-0.13,-0.65,1.0,0.78,0.82 2015-09,RMW,-0.18,-0.51,0.78,1.0,0.78 2015-09,CMA,-0.29,-0.39,0.82,0.78,1.0 -2015-10,Mkt-RF,1.0,0.62,0.06,-0.4,0.07 +2015-10,Mkt-RF,1.0,0.62,0.06,-0.41,0.07 2015-10,SMB,0.62,1.0,0.23,-0.35,0.11 -2015-10,HML,0.06,0.23,1.0,0.2,0.62 -2015-10,RMW,-0.4,-0.35,0.2,1.0,0.34 -2015-10,CMA,0.07,0.11,0.62,0.34,1.0 -2015-11,Mkt-RF,1.0,0.26,0.12,0.18,-0.15 -2015-11,SMB,0.26,1.0,0.07,-0.25,0.13 +2015-10,HML,0.06,0.23,1.0,0.19,0.62 +2015-10,RMW,-0.41,-0.35,0.19,1.0,0.33 +2015-10,CMA,0.07,0.11,0.62,0.33,1.0 +2015-11,Mkt-RF,1.0,0.26,0.12,0.19,-0.16 +2015-11,SMB,0.26,1.0,0.07,-0.25,0.12 2015-11,HML,0.12,0.07,1.0,-0.16,0.39 -2015-11,RMW,0.18,-0.25,-0.16,1.0,0.1 -2015-11,CMA,-0.15,0.13,0.39,0.1,1.0 +2015-11,RMW,0.19,-0.25,-0.16,1.0,0.09 +2015-11,CMA,-0.16,0.12,0.39,0.09,1.0 2015-12,Mkt-RF,1.0,-0.24,0.17,-0.23,-0.34 2015-12,SMB,-0.24,1.0,0.04,-0.53,0.21 -2015-12,HML,0.17,0.04,1.0,-0.12,0.61 -2015-12,RMW,-0.23,-0.53,-0.12,1.0,0.27 +2015-12,HML,0.17,0.04,1.0,-0.11,0.61 +2015-12,RMW,-0.23,-0.53,-0.11,1.0,0.27 2015-12,CMA,-0.34,0.21,0.61,0.27,1.0 2016-01,Mkt-RF,1.0,0.23,0.04,-0.48,-0.33 2016-01,SMB,0.23,1.0,-0.26,-0.14,-0.23 -2016-01,HML,0.04,-0.26,1.0,0.1,0.58 -2016-01,RMW,-0.48,-0.14,0.1,1.0,0.65 +2016-01,HML,0.04,-0.26,1.0,0.09,0.58 +2016-01,RMW,-0.48,-0.14,0.09,1.0,0.65 2016-01,CMA,-0.33,-0.23,0.58,0.65,1.0 2016-02,Mkt-RF,1.0,0.02,-0.05,-0.62,-0.61 2016-02,SMB,0.02,1.0,-0.27,-0.17,-0.08 @@ -3184,30 +3184,30 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2016-07,HML,0.48,0.35,1.0,-0.4,0.75 2016-07,RMW,-0.4,-0.56,-0.4,1.0,-0.66 2016-07,CMA,0.48,0.46,0.75,-0.66,1.0 -2016-08,Mkt-RF,1.0,0.7,0.3,-0.5,0.11 +2016-08,Mkt-RF,1.0,0.7,0.3,-0.49,0.11 2016-08,SMB,0.7,1.0,0.22,-0.53,0.13 2016-08,HML,0.3,0.22,1.0,-0.56,0.54 -2016-08,RMW,-0.5,-0.53,-0.56,1.0,-0.33 +2016-08,RMW,-0.49,-0.53,-0.56,1.0,-0.33 2016-08,CMA,0.11,0.13,0.54,-0.33,1.0 2016-09,Mkt-RF,1.0,0.63,-0.03,-0.38,0.29 2016-09,SMB,0.63,1.0,0.15,-0.11,0.24 2016-09,HML,-0.03,0.15,1.0,-0.43,0.75 2016-09,RMW,-0.38,-0.11,-0.43,1.0,-0.63 2016-09,CMA,0.29,0.24,0.75,-0.63,1.0 -2016-10,Mkt-RF,1.0,0.62,0.05,-0.26,-0.08 -2016-10,SMB,0.62,1.0,-0.01,-0.27,-0.05 +2016-10,Mkt-RF,1.0,0.63,0.05,-0.26,-0.08 +2016-10,SMB,0.63,1.0,-0.01,-0.27,-0.04 2016-10,HML,0.05,-0.01,1.0,-0.18,0.63 -2016-10,RMW,-0.26,-0.27,-0.18,1.0,-0.13 -2016-10,CMA,-0.08,-0.05,0.63,-0.13,1.0 -2016-11,Mkt-RF,1.0,0.31,-0.08,-0.31,-0.05 +2016-10,RMW,-0.26,-0.27,-0.18,1.0,-0.14 +2016-10,CMA,-0.08,-0.04,0.63,-0.14,1.0 +2016-11,Mkt-RF,1.0,0.31,-0.08,-0.3,-0.06 2016-11,SMB,0.31,1.0,0.22,-0.23,0.01 -2016-11,HML,-0.08,0.22,1.0,-0.53,0.79 -2016-11,RMW,-0.31,-0.23,-0.53,1.0,-0.62 -2016-11,CMA,-0.05,0.01,0.79,-0.62,1.0 +2016-11,HML,-0.08,0.22,1.0,-0.53,0.78 +2016-11,RMW,-0.3,-0.23,-0.53,1.0,-0.62 +2016-11,CMA,-0.06,0.01,0.78,-0.62,1.0 2016-12,Mkt-RF,1.0,0.39,0.08,0.04,0.01 -2016-12,SMB,0.39,1.0,0.05,-0.45,-0.13 +2016-12,SMB,0.39,1.0,0.05,-0.46,-0.13 2016-12,HML,0.08,0.05,1.0,0.23,0.7 -2016-12,RMW,0.04,-0.45,0.23,1.0,0.09 +2016-12,RMW,0.04,-0.46,0.23,1.0,0.09 2016-12,CMA,0.01,-0.13,0.7,0.09,1.0 2017-01,Mkt-RF,1.0,0.63,0.65,-0.68,0.36 2017-01,SMB,0.63,1.0,0.46,-0.47,0.13 @@ -3219,16 +3219,16 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2017-02,HML,0.29,0.05,1.0,-0.47,0.37 2017-02,RMW,-0.37,-0.33,-0.47,1.0,-0.28 2017-02,CMA,0.68,0.47,0.37,-0.28,1.0 -2017-03,Mkt-RF,1.0,0.66,0.56,-0.66,0.45 +2017-03,Mkt-RF,1.0,0.66,0.56,-0.66,0.44 2017-03,SMB,0.66,1.0,0.42,-0.54,0.22 2017-03,HML,0.56,0.42,1.0,-0.46,0.38 2017-03,RMW,-0.66,-0.54,-0.46,1.0,-0.24 -2017-03,CMA,0.45,0.22,0.38,-0.24,1.0 -2017-04,Mkt-RF,1.0,0.55,0.65,-0.31,0.34 -2017-04,SMB,0.55,1.0,0.55,0.12,0.3 -2017-04,HML,0.65,0.55,1.0,-0.21,0.7 -2017-04,RMW,-0.31,0.12,-0.21,1.0,-0.37 -2017-04,CMA,0.34,0.3,0.7,-0.37,1.0 +2017-03,CMA,0.44,0.22,0.38,-0.24,1.0 +2017-04,Mkt-RF,1.0,0.55,0.65,-0.32,0.34 +2017-04,SMB,0.55,1.0,0.55,0.11,0.3 +2017-04,HML,0.65,0.55,1.0,-0.21,0.69 +2017-04,RMW,-0.32,0.11,-0.21,1.0,-0.37 +2017-04,CMA,0.34,0.3,0.69,-0.37,1.0 2017-05,Mkt-RF,1.0,0.64,0.31,-0.41,-0.07 2017-05,SMB,0.64,1.0,0.13,-0.47,-0.13 2017-05,HML,0.31,0.13,1.0,-0.5,0.59 @@ -3239,40 +3239,40 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2017-06,HML,-0.3,0.19,1.0,0.02,0.77 2017-06,RMW,-0.13,-0.42,0.02,1.0,-0.13 2017-06,CMA,-0.19,0.14,0.77,-0.13,1.0 -2017-07,Mkt-RF,1.0,0.53,-0.11,-0.1,0.0 +2017-07,Mkt-RF,1.0,0.53,-0.11,-0.1,-0.0 2017-07,SMB,0.53,1.0,0.33,-0.45,0.37 2017-07,HML,-0.11,0.33,1.0,-0.33,0.74 2017-07,RMW,-0.1,-0.45,-0.33,1.0,-0.47 -2017-07,CMA,0.0,0.37,0.74,-0.47,1.0 -2017-08,Mkt-RF,1.0,0.3,-0.21,-0.06,-0.27 -2017-08,SMB,0.3,1.0,-0.04,-0.29,0.35 -2017-08,HML,-0.21,-0.04,1.0,-0.44,0.22 -2017-08,RMW,-0.06,-0.29,-0.44,1.0,-0.39 -2017-08,CMA,-0.27,0.35,0.22,-0.39,1.0 -2017-09,Mkt-RF,1.0,0.25,0.51,-0.22,-0.48 -2017-09,SMB,0.25,1.0,0.26,0.02,-0.24 -2017-09,HML,0.51,0.26,1.0,-0.08,-0.04 -2017-09,RMW,-0.22,0.02,-0.08,1.0,-0.14 -2017-09,CMA,-0.48,-0.24,-0.04,-0.14,1.0 +2017-07,CMA,-0.0,0.37,0.74,-0.47,1.0 +2017-08,Mkt-RF,1.0,0.3,-0.2,-0.06,-0.27 +2017-08,SMB,0.3,1.0,-0.05,-0.29,0.35 +2017-08,HML,-0.2,-0.05,1.0,-0.43,0.22 +2017-08,RMW,-0.06,-0.29,-0.43,1.0,-0.38 +2017-08,CMA,-0.27,0.35,0.22,-0.38,1.0 +2017-09,Mkt-RF,1.0,0.25,0.51,-0.22,-0.47 +2017-09,SMB,0.25,1.0,0.25,0.02,-0.25 +2017-09,HML,0.51,0.25,1.0,-0.07,-0.04 +2017-09,RMW,-0.22,0.02,-0.07,1.0,-0.14 +2017-09,CMA,-0.47,-0.25,-0.04,-0.14,1.0 2017-10,Mkt-RF,1.0,0.41,-0.07,-0.16,-0.07 -2017-10,SMB,0.41,1.0,0.04,0.03,0.25 -2017-10,HML,-0.07,0.04,1.0,-0.01,0.41 -2017-10,RMW,-0.16,0.03,-0.01,1.0,-0.16 -2017-10,CMA,-0.07,0.25,0.41,-0.16,1.0 -2017-11,Mkt-RF,1.0,0.29,-0.17,0.01,-0.14 -2017-11,SMB,0.29,1.0,0.34,0.03,0.25 -2017-11,HML,-0.17,0.34,1.0,0.09,0.39 -2017-11,RMW,0.01,0.03,0.09,1.0,-0.26 +2017-10,SMB,0.41,1.0,0.03,0.03,0.24 +2017-10,HML,-0.07,0.03,1.0,-0.01,0.41 +2017-10,RMW,-0.16,0.03,-0.01,1.0,-0.15 +2017-10,CMA,-0.07,0.24,0.41,-0.15,1.0 +2017-11,Mkt-RF,1.0,0.29,-0.16,0.0,-0.14 +2017-11,SMB,0.29,1.0,0.33,0.03,0.25 +2017-11,HML,-0.16,0.33,1.0,0.1,0.39 +2017-11,RMW,0.0,0.03,0.1,1.0,-0.26 2017-11,CMA,-0.14,0.25,0.39,-0.26,1.0 -2017-12,Mkt-RF,1.0,0.62,0.1,-0.06,-0.2 +2017-12,Mkt-RF,1.0,0.62,0.1,-0.07,-0.2 2017-12,SMB,0.62,1.0,0.05,-0.02,0.08 2017-12,HML,0.1,0.05,1.0,0.06,0.49 -2017-12,RMW,-0.06,-0.02,0.06,1.0,-0.19 -2017-12,CMA,-0.2,0.08,0.49,-0.19,1.0 -2018-01,Mkt-RF,1.0,0.21,-0.09,-0.45,0.57 +2017-12,RMW,-0.07,-0.02,0.06,1.0,-0.18 +2017-12,CMA,-0.2,0.08,0.49,-0.18,1.0 +2018-01,Mkt-RF,1.0,0.21,-0.09,-0.44,0.57 2018-01,SMB,0.21,1.0,0.06,-0.24,0.44 -2018-01,HML,-0.09,0.06,1.0,-0.2,0.53 -2018-01,RMW,-0.45,-0.24,-0.2,1.0,-0.59 +2018-01,HML,-0.09,0.06,1.0,-0.21,0.53 +2018-01,RMW,-0.44,-0.24,-0.21,1.0,-0.59 2018-01,CMA,0.57,0.44,0.53,-0.59,1.0 2018-02,Mkt-RF,1.0,-0.59,0.02,-0.49,0.03 2018-02,SMB,-0.59,1.0,0.4,0.23,0.28 @@ -3284,16 +3284,16 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2018-03,HML,0.05,-0.13,1.0,0.18,0.42 2018-03,RMW,-0.43,-0.18,0.18,1.0,-0.06 2018-03,CMA,-0.5,0.13,0.42,-0.06,1.0 -2018-04,Mkt-RF,1.0,-0.18,-0.42,-0.1,0.03 -2018-04,SMB,-0.18,1.0,0.14,-0.06,0.42 -2018-04,HML,-0.42,0.14,1.0,0.19,0.49 -2018-04,RMW,-0.1,-0.06,0.19,1.0,-0.03 +2018-04,Mkt-RF,1.0,-0.19,-0.41,-0.09,0.03 +2018-04,SMB,-0.19,1.0,0.14,-0.07,0.42 +2018-04,HML,-0.41,0.14,1.0,0.19,0.49 +2018-04,RMW,-0.09,-0.07,0.19,1.0,-0.03 2018-04,CMA,0.03,0.42,0.49,-0.03,1.0 2018-05,Mkt-RF,1.0,-0.4,0.33,-0.34,0.04 2018-05,SMB,-0.4,1.0,-0.36,0.22,0.21 -2018-05,HML,0.33,-0.36,1.0,-0.43,0.42 -2018-05,RMW,-0.34,0.22,-0.43,1.0,-0.23 -2018-05,CMA,0.04,0.21,0.42,-0.23,1.0 +2018-05,HML,0.33,-0.36,1.0,-0.42,0.42 +2018-05,RMW,-0.34,0.22,-0.42,1.0,-0.22 +2018-05,CMA,0.04,0.21,0.42,-0.22,1.0 2018-06,Mkt-RF,1.0,0.35,-0.49,-0.16,-0.2 2018-06,SMB,0.35,1.0,-0.52,-0.19,-0.22 2018-06,HML,-0.49,-0.52,1.0,0.02,0.7 @@ -3306,12 +3306,12 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2018-07,CMA,-0.6,0.08,0.51,0.38,1.0 2018-08,Mkt-RF,1.0,0.04,0.11,-0.23,0.01 2018-08,SMB,0.04,1.0,-0.2,-0.45,-0.15 -2018-08,HML,0.11,-0.2,1.0,0.43,0.63 -2018-08,RMW,-0.23,-0.45,0.43,1.0,0.44 +2018-08,HML,0.11,-0.2,1.0,0.44,0.63 +2018-08,RMW,-0.23,-0.45,0.44,1.0,0.44 2018-08,CMA,0.01,-0.15,0.63,0.44,1.0 -2018-09,Mkt-RF,1.0,0.27,-0.28,-0.4,-0.61 +2018-09,Mkt-RF,1.0,0.27,-0.29,-0.4,-0.61 2018-09,SMB,0.27,1.0,-0.2,-0.22,-0.17 -2018-09,HML,-0.28,-0.2,1.0,0.09,0.53 +2018-09,HML,-0.29,-0.2,1.0,0.09,0.53 2018-09,RMW,-0.4,-0.22,0.09,1.0,0.61 2018-09,CMA,-0.61,-0.17,0.53,0.61,1.0 2018-10,Mkt-RF,1.0,0.01,-0.51,-0.21,-0.67 @@ -3320,342 +3320,347 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA 2018-10,RMW,-0.21,0.09,-0.03,1.0,0.22 2018-10,CMA,-0.67,-0.16,0.82,0.22,1.0 2018-11,Mkt-RF,1.0,0.05,-0.6,-0.38,-0.76 -2018-11,SMB,0.05,1.0,-0.4,-0.25,-0.25 -2018-11,HML,-0.6,-0.4,1.0,0.17,0.87 -2018-11,RMW,-0.38,-0.25,0.17,1.0,0.38 +2018-11,SMB,0.05,1.0,-0.4,-0.24,-0.25 +2018-11,HML,-0.6,-0.4,1.0,0.18,0.87 +2018-11,RMW,-0.38,-0.24,0.18,1.0,0.38 2018-11,CMA,-0.76,-0.25,0.87,0.38,1.0 -2018-12,Mkt-RF,1.0,0.01,-0.57,-0.02,-0.8 -2018-12,SMB,0.01,1.0,0.08,-0.26,-0.28 +2018-12,Mkt-RF,1.0,0.02,-0.57,-0.02,-0.8 +2018-12,SMB,0.02,1.0,0.08,-0.26,-0.28 2018-12,HML,-0.57,0.08,1.0,-0.09,0.65 2018-12,RMW,-0.02,-0.26,-0.09,1.0,-0.22 2018-12,CMA,-0.8,-0.28,0.65,-0.22,1.0 -2019-01,Mkt-RF,1.0,0.11,-0.69,-0.27,-0.79 -2019-01,SMB,0.11,1.0,0.08,-0.49,-0.01 -2019-01,HML,-0.69,0.08,1.0,0.38,0.65 +2019-01,Mkt-RF,1.0,0.12,-0.69,-0.27,-0.8 +2019-01,SMB,0.12,1.0,0.07,-0.49,-0.01 +2019-01,HML,-0.69,0.07,1.0,0.38,0.65 2019-01,RMW,-0.27,-0.49,0.38,1.0,0.42 -2019-01,CMA,-0.79,-0.01,0.65,0.42,1.0 -2019-02,Mkt-RF,1.0,0.33,-0.13,-0.24,-0.45 -2019-02,SMB,0.33,1.0,0.01,0.13,-0.09 -2019-02,HML,-0.13,0.01,1.0,-0.27,0.63 +2019-01,CMA,-0.8,-0.01,0.65,0.42,1.0 +2019-02,Mkt-RF,1.0,0.33,-0.14,-0.24,-0.45 +2019-02,SMB,0.33,1.0,0.0,0.13,-0.09 +2019-02,HML,-0.14,0.0,1.0,-0.27,0.63 2019-02,RMW,-0.24,0.13,-0.27,1.0,0.06 2019-02,CMA,-0.45,-0.09,0.63,0.06,1.0 2019-03,Mkt-RF,1.0,0.68,-0.23,-0.44,-0.69 2019-03,SMB,0.68,1.0,-0.03,-0.38,-0.55 -2019-03,HML,-0.23,-0.03,1.0,0.14,0.47 +2019-03,HML,-0.23,-0.03,1.0,0.14,0.46 2019-03,RMW,-0.44,-0.38,0.14,1.0,0.4 -2019-03,CMA,-0.69,-0.55,0.47,0.4,1.0 -2019-04,Mkt-RF,1.0,0.51,0.18,-0.2,-0.3 -2019-04,SMB,0.51,1.0,-0.03,-0.01,-0.15 -2019-04,HML,0.18,-0.03,1.0,0.38,0.21 +2019-03,CMA,-0.69,-0.55,0.46,0.4,1.0 +2019-04,Mkt-RF,1.0,0.51,0.18,-0.2,-0.31 +2019-04,SMB,0.51,1.0,-0.04,-0.01,-0.16 +2019-04,HML,0.18,-0.04,1.0,0.38,0.2 2019-04,RMW,-0.2,-0.01,0.38,1.0,0.2 -2019-04,CMA,-0.3,-0.15,0.21,0.2,1.0 +2019-04,CMA,-0.31,-0.16,0.2,0.2,1.0 2019-05,Mkt-RF,1.0,0.53,-0.4,-0.48,-0.77 -2019-05,SMB,0.53,1.0,-0.11,-0.4,-0.58 -2019-05,HML,-0.4,-0.11,1.0,-0.22,0.55 +2019-05,SMB,0.53,1.0,-0.12,-0.4,-0.58 +2019-05,HML,-0.4,-0.12,1.0,-0.22,0.56 2019-05,RMW,-0.48,-0.4,-0.22,1.0,0.25 -2019-05,CMA,-0.77,-0.58,0.55,0.25,1.0 +2019-05,CMA,-0.77,-0.58,0.56,0.25,1.0 2019-06,Mkt-RF,1.0,0.12,-0.31,-0.09,-0.59 2019-06,SMB,0.12,1.0,0.19,-0.47,0.11 2019-06,HML,-0.31,0.19,1.0,0.21,0.7 2019-06,RMW,-0.09,-0.47,0.21,1.0,0.04 2019-06,CMA,-0.59,0.11,0.7,0.04,1.0 -2019-07,Mkt-RF,1.0,0.0,-0.07,-0.22,-0.22 +2019-07,Mkt-RF,1.0,0.0,-0.06,-0.22,-0.22 2019-07,SMB,0.0,1.0,0.71,0.11,0.12 -2019-07,HML,-0.07,0.71,1.0,0.38,0.14 +2019-07,HML,-0.06,0.71,1.0,0.38,0.14 2019-07,RMW,-0.22,0.11,0.38,1.0,0.44 2019-07,CMA,-0.22,0.12,0.14,0.44,1.0 2019-08,Mkt-RF,1.0,0.23,0.23,0.59,-0.29 -2019-08,SMB,0.23,1.0,0.61,0.44,0.29 +2019-08,SMB,0.23,1.0,0.61,0.43,0.3 2019-08,HML,0.23,0.61,1.0,0.46,0.5 -2019-08,RMW,0.59,0.44,0.46,1.0,0.32 -2019-08,CMA,-0.29,0.29,0.5,0.32,1.0 -2019-09,Mkt-RF,1.0,0.43,0.04,0.28,-0.42 -2019-09,SMB,0.43,1.0,0.62,0.08,0.24 -2019-09,HML,0.04,0.62,1.0,0.48,0.49 +2019-08,RMW,0.59,0.43,0.46,1.0,0.34 +2019-08,CMA,-0.29,0.3,0.5,0.34,1.0 +2019-09,Mkt-RF,1.0,0.43,0.04,0.28,-0.41 +2019-09,SMB,0.43,1.0,0.63,0.08,0.24 +2019-09,HML,0.04,0.63,1.0,0.48,0.49 2019-09,RMW,0.28,0.08,0.48,1.0,0.19 -2019-09,CMA,-0.42,0.24,0.49,0.19,1.0 -2019-10,Mkt-RF,1.0,-0.06,-0.01,0.08,-0.08 -2019-10,SMB,-0.06,1.0,0.44,0.29,0.22 -2019-10,HML,-0.01,0.44,1.0,0.5,0.6 -2019-10,RMW,0.08,0.29,0.5,1.0,0.4 -2019-10,CMA,-0.08,0.22,0.6,0.4,1.0 -2019-11,Mkt-RF,1.0,0.6,0.32,0.38,0.34 -2019-11,SMB,0.6,1.0,0.22,0.14,0.11 -2019-11,HML,0.32,0.22,1.0,0.55,0.49 -2019-11,RMW,0.38,0.14,0.55,1.0,0.18 -2019-11,CMA,0.34,0.11,0.49,0.18,1.0 -2019-12,Mkt-RF,1.0,-0.13,0.2,0.21,0.23 -2019-12,SMB,-0.13,1.0,0.22,-0.17,0.01 -2019-12,HML,0.2,0.22,1.0,0.61,0.58 -2019-12,RMW,0.21,-0.17,0.61,1.0,0.43 -2019-12,CMA,0.23,0.01,0.58,0.43,1.0 +2019-09,CMA,-0.41,0.24,0.49,0.19,1.0 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2020-02,SMB,-0.08,1.0,0.66,0.38,0.48 -2020-02,HML,0.0,0.66,1.0,0.57,0.7 -2020-02,RMW,0.52,0.38,0.57,1.0,0.42 -2020-02,CMA,-0.18,0.48,0.7,0.42,1.0 -2020-03,Mkt-RF,1.0,0.05,0.62,0.14,-0.15 -2020-03,SMB,0.05,1.0,0.22,-0.4,-0.63 -2020-03,HML,0.62,0.22,1.0,0.1,0.02 -2020-03,RMW,0.14,-0.4,0.1,1.0,0.4 -2020-03,CMA,-0.15,-0.63,0.02,0.4,1.0 -2020-04,Mkt-RF,1.0,0.47,0.29,0.3,-0.42 -2020-04,SMB,0.47,1.0,0.85,0.21,-0.2 -2020-04,HML,0.29,0.85,1.0,0.28,0.06 -2020-04,RMW,0.3,0.21,0.28,1.0,-0.14 -2020-04,CMA,-0.42,-0.2,0.06,-0.14,1.0 -2020-05,Mkt-RF,1.0,0.71,0.64,0.52,0.14 +2020-02,HML,0.01,0.66,1.0,0.59,0.69 +2020-02,RMW,0.51,0.38,0.59,1.0,0.43 +2020-02,CMA,-0.17,0.48,0.69,0.43,1.0 +2020-03,Mkt-RF,1.0,0.05,0.63,0.13,-0.15 +2020-03,SMB,0.05,1.0,0.21,-0.39,-0.63 +2020-03,HML,0.63,0.21,1.0,0.1,0.02 +2020-03,RMW,0.13,-0.39,0.1,1.0,0.39 +2020-03,CMA,-0.15,-0.63,0.02,0.39,1.0 +2020-04,Mkt-RF,1.0,0.47,0.29,0.31,-0.41 +2020-04,SMB,0.47,1.0,0.85,0.21,-0.19 +2020-04,HML,0.29,0.85,1.0,0.28,0.07 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-2020-11,CMA,0.01,0.72,0.77,0.71,1.0 +2020-09,RMW,0.24,-0.28,0.22,1.0,0.43 +2020-09,CMA,0.03,0.13,0.62,0.43,1.0 +2020-10,Mkt-RF,1.0,0.04,-0.17,0.16,0.13 +2020-10,SMB,0.04,1.0,0.51,-0.18,0.18 +2020-10,HML,-0.17,0.51,1.0,0.48,0.62 +2020-10,RMW,0.16,-0.18,0.48,1.0,0.62 +2020-10,CMA,0.13,0.18,0.62,0.62,1.0 +2020-11,Mkt-RF,1.0,0.11,0.09,0.38,0.01 +2020-11,SMB,0.11,1.0,0.88,0.69,0.73 +2020-11,HML,0.09,0.88,1.0,0.81,0.77 +2020-11,RMW,0.38,0.69,0.81,1.0,0.7 +2020-11,CMA,0.01,0.73,0.77,0.7,1.0 2020-12,Mkt-RF,1.0,0.39,0.09,0.06,0.42 -2020-12,SMB,0.39,1.0,0.09,-0.47,0.38 -2020-12,HML,0.09,0.09,1.0,0.59,0.75 -2020-12,RMW,0.06,-0.47,0.59,1.0,0.43 -2020-12,CMA,0.42,0.38,0.75,0.43,1.0 -2021-01,Mkt-RF,1.0,-0.09,-0.29,-0.22,-0.52 -2021-01,SMB,-0.09,1.0,0.82,-0.1,0.54 -2021-01,HML,-0.29,0.82,1.0,0.06,0.48 -2021-01,RMW,-0.22,-0.1,0.06,1.0,0.46 -2021-01,CMA,-0.52,0.54,0.48,0.46,1.0 -2021-02,Mkt-RF,1.0,0.41,-0.42,-0.71,-0.44 -2021-02,SMB,0.41,1.0,0.09,-0.48,0.44 -2021-02,HML,-0.42,0.09,1.0,0.55,0.65 -2021-02,RMW,-0.71,-0.48,0.55,1.0,0.3 -2021-02,CMA,-0.44,0.44,0.65,0.3,1.0 -2021-03,Mkt-RF,1.0,0.49,-0.49,-0.62,-0.18 -2021-03,SMB,0.49,1.0,0.21,-0.31,0.28 -2021-03,HML,-0.49,0.21,1.0,0.8,0.78 -2021-03,RMW,-0.62,-0.31,0.8,1.0,0.7 -2021-03,CMA,-0.18,0.28,0.78,0.7,1.0 -2021-04,Mkt-RF,1.0,0.24,-0.05,-0.2,-0.04 -2021-04,SMB,0.24,1.0,0.27,-0.54,0.37 -2021-04,HML,-0.05,0.27,1.0,0.43,0.62 -2021-04,RMW,-0.2,-0.54,0.43,1.0,0.25 -2021-04,CMA,-0.04,0.37,0.62,0.25,1.0 -2021-05,Mkt-RF,1.0,0.48,-0.24,-0.02,-0.16 -2021-05,SMB,0.48,1.0,0.1,-0.25,0.13 -2021-05,HML,-0.24,0.1,1.0,0.73,0.87 -2021-05,RMW,-0.02,-0.25,0.73,1.0,0.75 -2021-05,CMA,-0.16,0.13,0.87,0.75,1.0 +2020-12,SMB,0.39,1.0,0.1,-0.47,0.37 +2020-12,HML,0.09,0.1,1.0,0.58,0.77 +2020-12,RMW,0.06,-0.47,0.58,1.0,0.44 +2020-12,CMA,0.42,0.37,0.77,0.44,1.0 +2021-01,Mkt-RF,1.0,-0.1,-0.28,-0.2,-0.53 +2021-01,SMB,-0.1,1.0,0.83,-0.11,0.55 +2021-01,HML,-0.28,0.83,1.0,0.03,0.49 +2021-01,RMW,-0.2,-0.11,0.03,1.0,0.44 +2021-01,CMA,-0.53,0.55,0.49,0.44,1.0 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2023-10,CMA,-0.33,0.53,0.57,-0.19,1.0 -2023-11,Mkt-RF,1.0,0.61,0.46,-0.33,0.07 -2023-11,SMB,0.61,1.0,0.69,-0.63,0.35 -2023-11,HML,0.46,0.69,1.0,-0.26,0.44 -2023-11,RMW,-0.33,-0.63,-0.26,1.0,-0.2 -2023-11,CMA,0.07,0.35,0.44,-0.2,1.0 -2023-12,Mkt-RF,1.0,0.51,0.39,-0.67,-0.04 -2023-12,SMB,0.51,1.0,0.86,-0.64,0.57 -2023-12,HML,0.39,0.86,1.0,-0.37,0.54 -2023-12,RMW,-0.67,-0.64,-0.37,1.0,-0.25 -2023-12,CMA,-0.04,0.57,0.54,-0.25,1.0 -2024-01,Mkt-RF,1.0,0.51,-0.1,-0.36,-0.42 -2024-01,SMB,0.51,1.0,0.04,-0.55,0.05 -2024-01,HML,-0.1,0.04,1.0,0.41,0.53 -2024-01,RMW,-0.36,-0.55,0.41,1.0,0.17 -2024-01,CMA,-0.42,0.05,0.53,0.17,1.0 -2024-02,Mkt-RF,1.0,0.22,-0.26,-0.27,-0.57 -2024-02,SMB,0.22,1.0,0.42,-0.72,0.41 -2024-02,HML,-0.26,0.42,1.0,0.0,0.37 -2024-02,RMW,-0.27,-0.72,0.0,1.0,-0.22 -2024-02,CMA,-0.57,0.41,0.37,-0.22,1.0 -2024-03,Mkt-RF,1.0,0.05,-0.2,-0.18,-0.57 -2024-03,SMB,0.05,1.0,0.7,-0.45,0.52 -2024-03,HML,-0.2,0.7,1.0,-0.21,0.56 -2024-03,RMW,-0.18,-0.45,-0.21,1.0,-0.14 -2024-03,CMA,-0.57,0.52,0.56,-0.14,1.0 -2024-04,Mkt-RF,1.0,0.3,-0.3,-0.32,-0.56 -2024-04,SMB,0.3,1.0,0.44,-0.35,0.36 -2024-04,HML,-0.3,0.44,1.0,-0.01,0.79 -2024-04,RMW,-0.32,-0.35,-0.01,1.0,-0.18 -2024-04,CMA,-0.56,0.36,0.79,-0.18,1.0 -2024-05,Mkt-RF,1.0,0.1,0.17,-0.17,-0.31 -2024-05,SMB,0.1,1.0,0.32,-0.41,0.25 -2024-05,HML,0.17,0.32,1.0,0.05,0.65 -2024-05,RMW,-0.17,-0.41,0.05,1.0,0.07 -2024-05,CMA,-0.31,0.25,0.65,0.07,1.0 -2024-06,Mkt-RF,1.0,0.21,-0.22,0.13,-0.57 -2024-06,SMB,0.21,1.0,0.51,-0.42,0.24 -2024-06,HML,-0.22,0.51,1.0,0.03,0.75 -2024-06,RMW,0.13,-0.42,0.03,1.0,-0.02 -2024-06,CMA,-0.57,0.24,0.75,-0.02,1.0 +2023-11,Mkt-RF,1.0,0.62,0.46,-0.32,0.07 +2023-11,SMB,0.62,1.0,0.69,-0.61,0.34 +2023-11,HML,0.46,0.69,1.0,-0.26,0.43 +2023-11,RMW,-0.32,-0.61,-0.26,1.0,-0.21 +2023-11,CMA,0.07,0.34,0.43,-0.21,1.0 +2023-12,Mkt-RF,1.0,0.51,0.39,-0.65,-0.04 +2023-12,SMB,0.51,1.0,0.86,-0.63,0.56 +2023-12,HML,0.39,0.86,1.0,-0.34,0.54 +2023-12,RMW,-0.65,-0.63,-0.34,1.0,-0.25 +2023-12,CMA,-0.04,0.56,0.54,-0.25,1.0 +2024-01,Mkt-RF,1.0,0.52,-0.11,-0.35,-0.42 +2024-01,SMB,0.52,1.0,0.05,-0.55,0.06 +2024-01,HML,-0.11,0.05,1.0,0.41,0.53 +2024-01,RMW,-0.35,-0.55,0.41,1.0,0.17 +2024-01,CMA,-0.42,0.06,0.53,0.17,1.0 +2024-02,Mkt-RF,1.0,0.23,-0.27,-0.27,-0.58 +2024-02,SMB,0.23,1.0,0.41,-0.72,0.4 +2024-02,HML,-0.27,0.41,1.0,-0.0,0.38 +2024-02,RMW,-0.27,-0.72,-0.0,1.0,-0.23 +2024-02,CMA,-0.58,0.4,0.38,-0.23,1.0 +2024-03,Mkt-RF,1.0,0.05,-0.19,-0.16,-0.57 +2024-03,SMB,0.05,1.0,0.7,-0.43,0.52 +2024-03,HML,-0.19,0.7,1.0,-0.21,0.55 +2024-03,RMW,-0.16,-0.43,-0.21,1.0,-0.14 +2024-03,CMA,-0.57,0.52,0.55,-0.14,1.0 +2024-04,Mkt-RF,1.0,0.3,-0.31,-0.31,-0.56 +2024-04,SMB,0.3,1.0,0.43,-0.35,0.35 +2024-04,HML,-0.31,0.43,1.0,-0.02,0.79 +2024-04,RMW,-0.31,-0.35,-0.02,1.0,-0.2 +2024-04,CMA,-0.56,0.35,0.79,-0.2,1.0 +2024-05,Mkt-RF,1.0,0.05,0.28,-0.11,-0.26 +2024-05,SMB,0.05,1.0,0.19,-0.47,0.2 +2024-05,HML,0.28,0.19,1.0,0.07,0.59 +2024-05,RMW,-0.11,-0.47,0.07,1.0,0.04 +2024-05,CMA,-0.26,0.2,0.59,0.04,1.0 +2024-06,Mkt-RF,1.0,0.22,-0.23,0.12,-0.58 +2024-06,SMB,0.22,1.0,0.47,-0.43,0.21 +2024-06,HML,-0.23,0.47,1.0,0.04,0.75 +2024-06,RMW,0.12,-0.43,0.04,1.0,-0.02 +2024-06,CMA,-0.58,0.21,0.75,-0.02,1.0 +2024-07,Mkt-RF,1.0,-0.08,-0.63,-0.21,0.28 +2024-07,SMB,-0.08,1.0,0.56,-0.68,-0.1 +2024-07,HML,-0.63,0.56,1.0,-0.02,-0.23 +2024-07,RMW,-0.21,-0.68,-0.02,1.0,-0.07 +2024-07,CMA,0.28,-0.1,-0.23,-0.07,1.0 diff --git a/tests/performance/csv/test_performance_controller/test_get_factor_correlations_2.csv b/tests/performance/csv/test_performance_controller/test_get_factor_correlations_2.csv index b7e5147..d05fb95 100644 --- a/tests/performance/csv/test_performance_controller/test_get_factor_correlations_2.csv +++ b/tests/performance/csv/test_performance_controller/test_get_factor_correlations_2.csv @@ -81,8 +81,8 @@ Date,,HML,Mkt-RF 2002,Mkt-RF,-0.36,1.0 2003,HML,1.0,-0.14 2003,Mkt-RF,-0.14,1.0 -2004,HML,1.0,-0.16 -2004,Mkt-RF,-0.16,1.0 +2004,HML,1.0,-0.15 +2004,Mkt-RF,-0.15,1.0 2005,HML,1.0,-0.26 2005,Mkt-RF,-0.26,1.0 2006,HML,1.0,-0.4 @@ -117,9 +117,9 @@ Date,,HML,Mkt-RF 2020,Mkt-RF,0.29,1.0 2021,HML,1.0,-0.21 2021,Mkt-RF,-0.21,1.0 -2022,HML,1.0,-0.56 -2022,Mkt-RF,-0.56,1.0 +2022,HML,1.0,-0.55 +2022,Mkt-RF,-0.55,1.0 2023,HML,1.0,-0.05 2023,Mkt-RF,-0.05,1.0 -2024,HML,1.0,-0.17 -2024,Mkt-RF,-0.17,1.0 +2024,HML,1.0,-0.28 +2024,Mkt-RF,-0.28,1.0 diff --git a/tests/performance/csv/test_performance_controller/test_get_factor_correlations_3.csv b/tests/performance/csv/test_performance_controller/test_get_factor_correlations_3.csv index 4806698..aaa4133 100644 --- a/tests/performance/csv/test_performance_controller/test_get_factor_correlations_3.csv +++ b/tests/performance/csv/test_performance_controller/test_get_factor_correlations_3.csv @@ -234,28 +234,28 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA,RF 2001,CMA,-0.75,0.25,0.82,0.79,1.0,0.0 2001,RF,-0.05,-0.03,0.02,0.02,0.0,1.0 2002,Mkt-RF,1.0,-0.45,-0.36,-0.46,-0.15,-0.03 -2002,SMB,-0.45,1.0,0.19,-0.05,0.17,0.01 -2002,HML,-0.36,0.19,1.0,0.0,0.32,0.03 +2002,SMB,-0.45,1.0,0.18,-0.05,0.17,0.01 +2002,HML,-0.36,0.18,1.0,0.0,0.32,0.03 2002,RMW,-0.46,-0.05,0.0,1.0,0.07,0.05 2002,CMA,-0.15,0.17,0.32,0.07,1.0,0.07 2002,RF,-0.03,0.01,0.03,0.05,0.07,1.0 -2003,Mkt-RF,1.0,-0.16,-0.14,-0.58,0.38,-0.04 +2003,Mkt-RF,1.0,-0.16,-0.14,-0.58,0.37,-0.04 2003,SMB,-0.16,1.0,0.13,-0.27,0.32,-0.09 2003,HML,-0.14,0.13,1.0,-0.04,0.22,-0.08 2003,RMW,-0.58,-0.27,-0.04,1.0,-0.71,0.07 -2003,CMA,0.38,0.32,0.22,-0.71,1.0,-0.11 +2003,CMA,0.37,0.32,0.22,-0.71,1.0,-0.11 2003,RF,-0.04,-0.09,-0.08,0.07,-0.11,1.0 -2004,Mkt-RF,1.0,0.53,-0.16,-0.55,0.46,0.05 -2004,SMB,0.53,1.0,-0.02,-0.62,0.57,0.03 -2004,HML,-0.16,-0.02,1.0,0.1,0.13,0.0 +2004,Mkt-RF,1.0,0.53,-0.15,-0.55,0.46,0.05 +2004,SMB,0.53,1.0,-0.01,-0.62,0.57,0.03 +2004,HML,-0.15,-0.01,1.0,0.1,0.14,0.0 2004,RMW,-0.55,-0.62,0.1,1.0,-0.82,-0.01 -2004,CMA,0.46,0.57,0.13,-0.82,1.0,-0.01 +2004,CMA,0.46,0.57,0.14,-0.82,1.0,-0.01 2004,RF,0.05,0.03,0.0,-0.01,-0.01,1.0 -2005,Mkt-RF,1.0,0.54,-0.26,-0.0,-0.13,0.05 +2005,Mkt-RF,1.0,0.54,-0.26,0.0,-0.14,0.05 2005,SMB,0.54,1.0,-0.16,-0.1,-0.02,0.03 2005,HML,-0.26,-0.16,1.0,0.32,-0.0,-0.14 -2005,RMW,-0.0,-0.1,0.32,1.0,-0.44,-0.14 -2005,CMA,-0.13,-0.02,-0.0,-0.44,1.0,-0.0 +2005,RMW,0.0,-0.1,0.32,1.0,-0.44,-0.14 +2005,CMA,-0.14,-0.02,-0.0,-0.44,1.0,-0.0 2005,RF,0.05,0.03,-0.14,-0.14,-0.0,1.0 2006,Mkt-RF,1.0,0.64,-0.4,-0.07,-0.21,-0.05 2006,SMB,0.64,1.0,-0.4,-0.11,-0.24,-0.18 @@ -270,10 +270,10 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA,RF 2007,CMA,-0.16,-0.1,0.36,0.11,1.0,0.07 2007,RF,0.02,0.01,0.03,-0.0,0.07,1.0 2008,Mkt-RF,1.0,-0.05,0.44,-0.26,-0.32,0.01 -2008,SMB,-0.05,1.0,0.14,-0.17,-0.03,0.02 -2008,HML,0.44,0.14,1.0,-0.69,-0.08,0.05 -2008,RMW,-0.26,-0.17,-0.69,1.0,0.14,-0.02 -2008,CMA,-0.32,-0.03,-0.08,0.14,1.0,-0.01 +2008,SMB,-0.05,1.0,0.13,-0.17,-0.03,0.02 +2008,HML,0.44,0.13,1.0,-0.69,-0.08,0.05 +2008,RMW,-0.26,-0.17,-0.69,1.0,0.13,-0.02 +2008,CMA,-0.32,-0.03,-0.08,0.13,1.0,-0.01 2008,RF,0.01,0.02,0.05,-0.02,-0.01,1.0 2009,Mkt-RF,1.0,0.43,0.76,-0.49,-0.08,0.04 2009,SMB,0.43,1.0,0.28,-0.17,0.15,0.06 @@ -288,27 +288,27 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA,RF 2010,CMA,0.64,0.44,0.62,-0.53,1.0,-0.05 2010,RF,-0.02,-0.01,-0.11,0.04,-0.05,1.0 2011,Mkt-RF,1.0,0.64,0.38,-0.79,0.05,0.03 -2011,SMB,0.64,1.0,0.15,-0.62,0.05,0.04 +2011,SMB,0.64,1.0,0.15,-0.62,0.05,0.05 2011,HML,0.38,0.15,1.0,-0.61,0.52,0.05 -2011,RMW,-0.79,-0.62,-0.61,1.0,-0.22,-0.08 -2011,CMA,0.05,0.05,0.52,-0.22,1.0,0.05 -2011,RF,0.03,0.04,0.05,-0.08,0.05,1.0 +2011,RMW,-0.79,-0.62,-0.61,1.0,-0.23,-0.08 +2011,CMA,0.05,0.05,0.52,-0.23,1.0,0.05 +2011,RF,0.03,0.05,0.05,-0.08,0.05,1.0 2012,Mkt-RF,1.0,0.38,0.16,-0.43,-0.37,-0.0 2012,SMB,0.38,1.0,-0.03,-0.34,-0.25,0.06 2012,HML,0.16,-0.03,1.0,-0.53,0.44,0.11 -2012,RMW,-0.43,-0.34,-0.53,1.0,-0.08,-0.07 +2012,RMW,-0.43,-0.34,-0.53,1.0,-0.08,-0.08 2012,CMA,-0.37,-0.25,0.44,-0.08,1.0,0.01 -2012,RF,-0.0,0.06,0.11,-0.07,0.01,1.0 +2012,RF,-0.0,0.06,0.11,-0.08,0.01,1.0 2013,Mkt-RF,1.0,0.31,0.21,-0.53,-0.01, 2013,SMB,0.31,1.0,0.04,-0.31,-0.21, -2013,HML,0.21,0.04,1.0,-0.37,0.38, -2013,RMW,-0.53,-0.31,-0.37,1.0,0.02, +2013,HML,0.21,0.04,1.0,-0.38,0.38, +2013,RMW,-0.53,-0.31,-0.38,1.0,0.02, 2013,CMA,-0.01,-0.21,0.38,0.02,1.0, 2013,RF,,,,,, -2014,Mkt-RF,1.0,0.29,-0.24,-0.44,-0.39, +2014,Mkt-RF,1.0,0.29,-0.24,-0.45,-0.39, 2014,SMB,0.29,1.0,-0.36,-0.55,-0.45, 2014,HML,-0.24,-0.36,1.0,0.32,0.72, -2014,RMW,-0.44,-0.55,0.32,1.0,0.51, +2014,RMW,-0.45,-0.55,0.32,1.0,0.51, 2014,CMA,-0.39,-0.45,0.72,0.51,1.0, 2014,RF,,,,,, 2015,Mkt-RF,1.0,-0.06,-0.04,-0.16,-0.26, @@ -336,38 +336,38 @@ Date,,Mkt-RF,SMB,HML,RMW,CMA,RF 2018,CMA,-0.43,-0.06,0.65,0.07,1.0,0.06 2018,RF,-0.1,-0.16,0.02,0.03,0.06,1.0 2019,Mkt-RF,1.0,0.26,-0.1,-0.01,-0.42,0.04 -2019,SMB,0.26,1.0,0.3,-0.05,-0.01,0.02 +2019,SMB,0.26,1.0,0.3,-0.06,-0.01,0.02 2019,HML,-0.1,0.3,1.0,0.31,0.51,0.05 -2019,RMW,-0.01,-0.05,0.31,1.0,0.24,0.05 -2019,CMA,-0.42,-0.01,0.51,0.24,1.0,-0.01 +2019,RMW,-0.01,-0.06,0.31,1.0,0.25,0.05 +2019,CMA,-0.42,-0.01,0.51,0.25,1.0,-0.01 2019,RF,0.04,0.02,0.05,0.05,-0.01,1.0 2020,Mkt-RF,1.0,0.19,0.29,0.19,-0.06,-0.11 -2020,SMB,0.19,1.0,0.6,0.08,0.1,-0.14 -2020,HML,0.29,0.6,1.0,0.49,0.47,-0.1 +2020,SMB,0.19,1.0,0.6,0.08,0.11,-0.13 +2020,HML,0.29,0.6,1.0,0.49,0.48,-0.1 2020,RMW,0.19,0.08,0.49,1.0,0.42,-0.08 -2020,CMA,-0.06,0.1,0.47,0.42,1.0,-0.04 -2020,RF,-0.11,-0.14,-0.1,-0.08,-0.04,1.0 +2020,CMA,-0.06,0.11,0.48,0.42,1.0,-0.04 +2020,RF,-0.11,-0.13,-0.1,-0.08,-0.04,1.0 2021,Mkt-RF,1.0,0.29,-0.21,-0.35,-0.19, 2021,SMB,0.29,1.0,0.3,-0.4,0.28, 2021,HML,-0.21,0.3,1.0,0.44,0.7, 2021,RMW,-0.35,-0.4,0.44,1.0,0.41, 2021,CMA,-0.19,0.28,0.7,0.41,1.0, 2021,RF,,,,,, -2022,Mkt-RF,1.0,0.2,-0.56,-0.39,-0.57,0.02 -2022,SMB,0.2,1.0,-0.29,-0.48,-0.36,-0.0 -2022,HML,-0.56,-0.29,1.0,0.49,0.84,-0.04 -2022,RMW,-0.39,-0.48,0.49,1.0,0.43,0.03 +2022,Mkt-RF,1.0,0.2,-0.55,-0.38,-0.57,0.02 +2022,SMB,0.2,1.0,-0.29,-0.48,-0.36,0.0 +2022,HML,-0.55,-0.29,1.0,0.49,0.84,-0.04 +2022,RMW,-0.38,-0.48,0.49,1.0,0.43,0.03 2022,CMA,-0.57,-0.36,0.84,0.43,1.0,-0.01 -2022,RF,0.02,-0.0,-0.04,0.03,-0.01,1.0 +2022,RF,0.02,0.0,-0.04,0.03,-0.01,1.0 2023,Mkt-RF,1.0,0.3,-0.05,-0.34,-0.45,-0.01 -2023,SMB,0.3,1.0,0.41,-0.41,0.08,0.06 +2023,SMB,0.3,1.0,0.41,-0.4,0.08,0.06 2023,HML,-0.05,0.41,1.0,0.21,0.62,0.22 -2023,RMW,-0.34,-0.41,0.21,1.0,0.31,0.0 +2023,RMW,-0.34,-0.4,0.21,1.0,0.31,0.0 2023,CMA,-0.45,0.08,0.62,0.31,1.0,0.13 2023,RF,-0.01,0.06,0.22,0.0,0.13,1.0 -2024,Mkt-RF,1.0,0.24,-0.17,-0.21,-0.48,-0.04 -2024,SMB,0.24,1.0,0.39,-0.5,0.31,-0.13 -2024,HML,-0.17,0.39,1.0,0.06,0.59,-0.06 -2024,RMW,-0.21,-0.5,0.06,1.0,-0.06,-0.07 -2024,CMA,-0.48,0.31,0.59,-0.06,1.0,0.03 -2024,RF,-0.04,-0.13,-0.06,-0.07,0.03,1.0 +2024,Mkt-RF,1.0,0.15,-0.28,-0.2,-0.31,-0.03 +2024,SMB,0.15,1.0,0.45,-0.53,0.2,-0.12 +2024,HML,-0.28,0.45,1.0,0.04,0.38,-0.06 +2024,RMW,-0.2,-0.53,0.04,1.0,-0.07,-0.05 +2024,CMA,-0.31,0.2,0.38,-0.07,1.0,0.02 +2024,RF,-0.03,-0.12,-0.06,-0.05,0.02,1.0 diff --git a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model.csv b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model.csv index a3e1859..c19e2f9 100644 --- a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model.csv +++ b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model.csv @@ -1,5 +1,5 @@ ,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT ,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared -2020,-0.0006,0.0129,-0.0018,-0.0075,0.007,0.0137,0.0002,0.8172,0.0,0.013,-0.0029,-0.0097,0.0031,0.0166,0.0001,0.7648 -2021,-0.0057,0.0124,-0.0032,-0.0056,0.0021,0.0065,0.0001,0.7466,-0.0018,0.0122,-0.0029,-0.004,0.0029,-0.0056,0.0001,0.8859 -2022,0.0008,0.011,-0.0024,-0.0061,0.0041,0.0036,0.0,0.7233,-0.0054,0.0112,-0.0077,-0.0037,0.0016,-0.0006,0.0001,0.7703 +2020,-0.0006,0.0129,-0.0018,-0.0075,0.007,0.0134,0.0002,0.8165,0.0,0.0131,-0.0029,-0.0098,0.0032,0.0165,0.0001,0.765 +2021,-0.0057,0.0125,-0.0032,-0.0057,0.0022,0.0065,0.0001,0.7473,-0.0018,0.0122,-0.0029,-0.004,0.003,-0.0056,0.0001,0.8859 +2022,0.0008,0.011,-0.0024,-0.0061,0.0041,0.0036,0.0,0.7238,-0.0054,0.0112,-0.0077,-0.0038,0.0017,-0.0005,0.0001,0.7706 diff --git a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_1.csv b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_1.csv index 0c3536f..f5923f2 100644 --- a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_1.csv +++ b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_1.csv @@ -1,38 +1,38 @@ ,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT ,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared -2020-01,-0.0035,0.0152,-0.0048,-0.007,0.0077,0.0325,0.0,0.8686,-0.0068,0.0116,-0.0062,-0.0159,0.0213,0.014,0.0,0.9418 -2020-02,-0.0038,0.0109,-0.0048,-0.0013,0.0082,-0.0025,0.0002,0.9602,-0.0004,0.0104,0.0007,-0.0067,0.0061,-0.0034,0.0001,0.8748 -2020-03,0.0018,0.0087,0.0028,-0.004,-0.0013,0.009,0.0,0.774,0.0046,0.0112,0.0003,-0.0053,0.011,-0.0135,0.0001,0.8596 -2020-04,-0.0018,0.0189,-0.0103,-0.0082,0.008,0.028,0.0002,0.7054,0.0068,0.0103,-0.0101,-0.0078,-0.0028,0.0325,0.0001,0.842 -2020-05,-0.001,0.0152,-0.0091,-0.0078,-0.0048,0.0272,0.0002,0.8488,0.0035,0.0123,-0.0011,-0.0158,0.0151,0.0304,0.0001,0.879 -2020-06,-0.0019,0.0129,-0.0088,-0.0023,-0.0098,0.0176,0.0001,0.81,0.0027,0.012,-0.0036,-0.0184,0.0041,0.0396,0.0001,0.6968 -2020-07,-0.0026,0.0159,-0.0023,-0.0127,0.0021,0.0232,0.0001,0.8719,-0.0037,0.0128,0.0,-0.006,0.0057,0.0036,0.0,0.8192 -2020-08,0.0001,0.0123,-0.007,-0.0045,-0.0098,0.0184,0.0001,0.8832,0.0015,0.0155,-0.0125,-0.0035,-0.0076,0.0205,0.0,0.8398 -2020-09,-0.002,0.0142,-0.0031,-0.0088,0.0013,0.016,0.0,0.8337,0.0013,0.0094,0.0017,-0.0081,0.0053,0.01,0.0,0.5204 -2020-10,-0.0007,0.0151,-0.0026,-0.0088,0.0072,0.0145,0.0,0.9116,0.0008,0.0159,-0.0051,-0.0087,0.0032,0.015,0.0,0.8753 -2020-11,-0.0008,0.0114,-0.0038,-0.0078,-0.0037,0.0174,0.0,0.7727,-0.0011,0.0131,-0.0066,-0.0107,0.0,0.0209,0.0,0.7883 -2020-12,-0.001,0.008,0.0043,-0.0196,0.0119,0.0289,0.0001,0.6037,-0.0016,0.0137,-0.0034,-0.0101,0.0018,0.0218,0.0,0.8779 -2021-01,0.0018,0.0146,0.0006,-0.0095,0.0015,0.0215,0.0,0.9085,-0.0016,0.009,0.0053,-0.0022,0.0057,0.0001,0.0,0.9497 -2021-02,-0.0013,0.013,0.0,-0.0069,0.0007,0.0121,0.0,0.878,-0.0019,0.011,-0.0016,-0.01,0.0059,0.0113,0.0,0.9054 -2021-03,-0.0023,0.0137,-0.0076,-0.0084,0.0056,0.0113,0.0001,0.9209,-0.0019,0.0111,-0.0103,-0.0101,0.0032,0.0136,0.0,0.9355 -2021-04,0.0005,0.0087,-0.0032,-0.0063,0.0009,0.0023,0.0001,0.7421,-0.0069,0.0128,-0.0091,-0.001,-0.0017,0.0002,0.0,0.8873 -2021-05,-0.0095,0.0108,-0.0028,-0.0074,0.009,0.0005,0.0002,0.636,-0.0097,0.0135,-0.0043,-0.0187,0.0094,0.0163,0.0001,0.888 -2021-06,-0.0201,0.0124,-0.0101,-0.0102,0.0055,-0.0012,0.0001,0.9319,-0.0184,0.0141,0.0006,-0.0098,0.0059,0.0066,0.0,0.9004 -2021-07,-0.0036,0.0136,-0.0032,0.0007,-0.0056,0.0029,0.0001,0.6177,-0.0081,0.0107,-0.0035,-0.0175,0.0156,-0.0142,0.0001,0.9091 -2021-08,-0.0021,0.0118,-0.0046,-0.0025,0.0033,-0.0145,0.0002,0.9648,-0.0006,0.0101,-0.0022,-0.0049,0.0035,-0.0094,0.0001,0.9319 -2021-09,-0.0027,0.0093,0.002,-0.0038,-0.0015,-0.0043,0.0,0.88,0.0032,0.0141,-0.005,-0.0066,0.0045,0.0007,0.0,0.9327 -2021-10,-0.0046,0.0146,-0.0036,-0.0028,0.0028,-0.0081,0.0001,0.8368,-0.0058,0.0195,0.007,-0.0082,0.0124,-0.0121,0.0002,0.5719 -2021-11,-0.0008,0.0131,-0.008,-0.0036,0.0017,0.0042,0.0,0.9674,0.0011,0.0135,-0.006,-0.002,0.0036,-0.0096,0.0,0.9684 -2021-12,-0.0048,0.0162,-0.0064,-0.0007,-0.0051,-0.0006,0.0,0.9185,0.0023,0.0069,-0.0139,-0.0074,-0.0078,0.0208,0.0,0.6582 -2022-01,0.0033,0.0107,-0.0061,-0.0053,0.0003,0.0085,0.0001,0.7378,0.0022,0.0076,0.0002,-0.0078,0.0042,0.0028,0.0001,0.6511 -2022-02,0.0002,0.0098,-0.0036,-0.0037,0.0019,-0.0013,0.0,0.8345,-0.0,0.0151,-0.0055,-0.0044,-0.0004,0.0087,0.0001,0.6905 -2022-03,0.0013,0.011,0.0009,-0.0065,0.0098,-0.005,0.0,0.9579,0.0006,0.0108,-0.0008,-0.0059,0.0006,0.0041,0.0,0.7984 -2022-04,0.0004,0.0103,-0.0111,-0.0015,-0.0039,0.003,0.0,0.8631,0.0008,0.0123,0.0027,-0.0046,0.0094,-0.0068,0.0,0.7151 -2022-05,0.0012,0.0131,-0.0047,-0.0049,0.0031,0.0013,0.0,0.8567,0.0044,0.0064,-0.0047,-0.0152,0.0148,0.0105,0.0001,0.6854 -2022-06,-0.001,0.0127,-0.0028,-0.0049,0.0034,0.0044,0.0,0.9434,-0.0029,0.013,-0.0003,-0.008,0.0069,0.0087,0.0,0.8587 -2022-07,0.0005,0.0089,-0.0112,-0.0018,0.0015,-0.0065,0.0001,0.7273,0.0018,0.0067,-0.0055,-0.0086,0.0024,-0.0025,0.0,0.9349 -2022-08,-0.0001,0.0105,-0.003,-0.0034,0.0035,-0.0012,0.0,0.9569,-0.0007,0.0108,-0.008,-0.0103,0.0013,0.0096,0.0001,0.7581 -2022-09,-0.0005,0.0097,-0.0089,-0.0037,0.0001,0.0014,0.0001,0.8799,-0.0016,0.0098,-0.0085,-0.0093,0.0023,0.0106,0.0,0.9112 -2022-10,-0.0075,0.0114,-0.0066,-0.005,0.0087,-0.0081,0.0001,0.8251,-0.0088,0.0122,-0.0127,-0.0001,-0.0039,-0.0005,0.0,0.9439 -2022-11,-0.0093,0.0099,-0.0006,-0.0067,0.0068,0.0024,0.0,0.8859,-0.012,0.014,-0.0158,-0.0122,0.0077,0.002,0.0001,0.8411 -2022-12,-0.0159,0.0176,-0.0112,-0.0041,0.0037,0.0058,0.0,0.9361,-0.0149,0.0125,-0.0049,-0.0029,-0.0028,0.0003,0.0,0.9311 +2020-01,-0.0036,0.0151,-0.0046,-0.0071,0.0083,0.0323,0.0,0.8689,-0.0066,0.0119,-0.0063,-0.0157,0.0203,0.0143,0.0,0.9374 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+2022-10,-0.0075,0.0114,-0.0067,-0.005,0.0085,-0.008,0.0001,0.8244,-0.0088,0.0122,-0.0126,-0.0003,-0.0038,-0.0003,0.0,0.9437 +2022-11,-0.0093,0.0099,-0.0005,-0.0067,0.0068,0.0025,0.0,0.8868,-0.0119,0.014,-0.0159,-0.012,0.0076,0.0018,0.0001,0.8408 +2022-12,-0.0158,0.0175,-0.0112,-0.0042,0.0038,0.0058,0.0,0.9356,-0.0148,0.0125,-0.005,-0.003,-0.0027,0.0003,0.0,0.9313 diff --git a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_2.csv b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_2.csv index a3e1859..c19e2f9 100644 --- a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_2.csv +++ b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_2.csv @@ -1,5 +1,5 @@ ,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT ,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared -2020,-0.0006,0.0129,-0.0018,-0.0075,0.007,0.0137,0.0002,0.8172,0.0,0.013,-0.0029,-0.0097,0.0031,0.0166,0.0001,0.7648 -2021,-0.0057,0.0124,-0.0032,-0.0056,0.0021,0.0065,0.0001,0.7466,-0.0018,0.0122,-0.0029,-0.004,0.0029,-0.0056,0.0001,0.8859 -2022,0.0008,0.011,-0.0024,-0.0061,0.0041,0.0036,0.0,0.7233,-0.0054,0.0112,-0.0077,-0.0037,0.0016,-0.0006,0.0001,0.7703 +2020,-0.0006,0.0129,-0.0018,-0.0075,0.007,0.0134,0.0002,0.8165,0.0,0.0131,-0.0029,-0.0098,0.0032,0.0165,0.0001,0.765 +2021,-0.0057,0.0125,-0.0032,-0.0057,0.0022,0.0065,0.0001,0.7473,-0.0018,0.0122,-0.0029,-0.004,0.003,-0.0056,0.0001,0.8859 +2022,0.0008,0.011,-0.0024,-0.0061,0.0041,0.0036,0.0,0.7238,-0.0054,0.0112,-0.0077,-0.0038,0.0017,-0.0005,0.0001,0.7706 diff --git a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_3.csv b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_3.csv index 783de60..a4f7037 100644 --- a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_3.csv +++ b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_3.csv @@ -1,6 +1,6 @@ ,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT ,Mkt-RF,Mkt-RF,Mkt-RF,Mkt-RF,Mkt-RF,Mkt-RF,Mkt-RF,SMB,SMB,SMB,SMB,SMB,SMB,SMB,HML,HML,HML,HML,HML,HML,HML,RMW,RMW,RMW,RMW,RMW,RMW,RMW,CMA,CMA,CMA,CMA,CMA,CMA,CMA,Mkt-RF,Mkt-RF,Mkt-RF,Mkt-RF,Mkt-RF,Mkt-RF,Mkt-RF,SMB,SMB,SMB,SMB,SMB,SMB,SMB,HML,HML,HML,HML,HML,HML,HML,RMW,RMW,RMW,RMW,RMW,RMW,RMW,CMA,CMA,CMA,CMA,CMA,CMA,CMA ,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals,Intercept,Slope,R Squared,P Value,Standard Error,Factor Value,Residuals -2020,0.0377,60.3904,0.6814,0.0,2.6063,0.39,-23.59,0.0267,-3.5785,0.0098,0.1167,2.273,-0.72,-2.6032,-0.1301,-0.2987,0.0,0.9308,3.4374,0.41,0.2526,-0.016,3.9531,0.0409,0.0012,1.2077,0.56,-2.1978,-0.0371,0.1155,0.0001,0.9048,0.965,-0.24,0.0648,0.0968,68.8804,0.7778,0.0,2.3236,0.39,-26.9602,0.023,-2.941,0.0058,0.2276,2.4315,-0.72,-2.1405,-0.1302,-2.0386,0.0012,0.5788,3.6674,0.41,0.966,-0.0117,1.7407,0.007,0.1858,1.312,0.56,-0.9631,-0.0363,-4.0381,0.0612,0.0001,0.9982,-0.24,-0.9329 -2021,0.0387,37.3834,0.4468,0.0,2.6306,-0.31,11.8967,0.0044,-3.2158,0.0034,0.3585,3.4954,0.17,0.8888,0.1245,-31.5644,0.1916,0.0,4.1004,0.53,16.9511,0.0949,-5.4474,0.0133,0.068,2.9717,0.36,2.2127,0.0427,-1.3384,0.0015,0.5415,2.189,0.4,0.8392,0.0066,45.8195,0.4721,0.0,3.0644,-0.31,14.7223,0.018,-10.0874,0.0233,0.0152,4.1261,0.17,2.2217,0.1622,-44.6181,0.2693,0.0,4.6487,0.53,24.0102,0.1007,-7.3113,0.0168,0.0398,3.5372,0.36,3.0562,0.0632,-12.6059,0.0932,0.0,2.4876,0.4,5.504 -2022,0.2991,57.7391,0.7101,0.0,2.3379,-0.22,12.1394,0.007,1.4717,0.0035,0.3484,1.5665,0.14,-0.4771,-0.0719,-27.5124,0.271,0.0,2.8597,-0.03,-1.0176,-0.0382,-9.5653,0.0683,0.0,2.2381,-0.54,-5.3912,-0.0033,-15.7239,0.1983,0.0,2.0033,0.0,-0.2608,0.3135,59.0265,0.7114,0.0,2.3826,-0.22,12.392,-0.0028,-0.0141,0.0,0.993,1.6029,0.14,-0.2755,-0.0965,-30.7617,0.3247,0.0,2.8111,-0.03,-1.1067,-0.0389,-9.5288,0.065,0.0,2.29,-0.54,-5.3869,-0.0299,-19.4439,0.2907,0.0,1.9246,0.0,-0.2504 +2020,0.0375,60.3847,0.6813,0.0,2.6069,0.39,-23.5876,0.0258,-3.6107,0.0099,0.114,2.2767,-0.71,-2.5894,-0.1284,-0.2007,0.0,0.9535,3.4377,0.4,0.2087,-0.0144,3.9471,0.0413,0.0011,1.2,0.56,-2.196,-0.0385,0.0916,0.0,0.9251,0.9736,-0.22,0.0586,0.0966,68.8725,0.7776,0.0,2.3246,0.39,-26.9569,0.0221,-2.9601,0.0059,0.2254,2.4355,-0.71,-2.1237,-0.1283,-1.9477,0.0011,0.5959,3.668,0.4,0.9074,-0.0101,1.7434,0.0071,0.1824,1.3038,0.56,-0.9662,-0.0377,-4.0657,0.061,0.0001,1.0072,-0.22,-0.8567 +2021,0.0387,37.3908,0.4469,0.0,2.631,-0.31,11.899,0.0056,-3.3354,0.0036,0.3408,3.4945,0.18,0.9412,0.1241,-31.3999,0.1911,0.0,4.0858,0.53,16.8644,0.0949,-5.2934,0.0126,0.0752,2.9632,0.36,2.1572,0.0434,-1.5043,0.0018,0.4974,2.2138,0.4,0.9048,0.0065,45.8257,0.4721,0.0,3.065,-0.31,14.7242,0.0194,-10.2437,0.0241,0.0136,4.1239,0.18,2.3493,0.1615,-44.371,0.2684,0.0,4.6335,0.53,23.88,0.1004,-7.0302,0.0156,0.0474,3.528,0.36,2.9552,0.0642,-12.8926,0.0952,0.0,2.5132,0.4,5.6176 +2022,0.299,57.738,0.7101,0.0,2.3378,-0.22,12.1392,0.0058,1.3856,0.0031,0.3769,1.5651,0.13,-0.45,-0.0708,-27.3065,0.2687,0.0,2.8545,-0.03,-1.0125,-0.0377,-9.3844,0.0664,0.0,2.2308,-0.53,-5.2001,-0.0029,-15.7821,0.1987,0.0,2.0086,0.01,-0.1033,0.3134,59.0207,0.7113,0.0,2.383,-0.22,12.3909,-0.0039,-0.0859,0.0,0.9573,1.6011,0.13,-0.2652,-0.0954,-30.5678,0.3228,0.0,2.8057,-0.03,-1.1019,-0.0384,-9.34,0.063,0.0001,2.2826,-0.53,-5.1921,-0.0296,-19.5152,0.2912,0.0,1.9295,0.01,-0.0556 diff --git a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_4.csv b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_4.csv index a3e1859..c19e2f9 100644 --- a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_4.csv +++ b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_4.csv @@ -1,5 +1,5 @@ ,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT ,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared -2020,-0.0006,0.0129,-0.0018,-0.0075,0.007,0.0137,0.0002,0.8172,0.0,0.013,-0.0029,-0.0097,0.0031,0.0166,0.0001,0.7648 -2021,-0.0057,0.0124,-0.0032,-0.0056,0.0021,0.0065,0.0001,0.7466,-0.0018,0.0122,-0.0029,-0.004,0.0029,-0.0056,0.0001,0.8859 -2022,0.0008,0.011,-0.0024,-0.0061,0.0041,0.0036,0.0,0.7233,-0.0054,0.0112,-0.0077,-0.0037,0.0016,-0.0006,0.0001,0.7703 +2020,-0.0006,0.0129,-0.0018,-0.0075,0.007,0.0134,0.0002,0.8165,0.0,0.0131,-0.0029,-0.0098,0.0032,0.0165,0.0001,0.765 +2021,-0.0057,0.0125,-0.0032,-0.0057,0.0022,0.0065,0.0001,0.7473,-0.0018,0.0122,-0.0029,-0.004,0.003,-0.0056,0.0001,0.8859 +2022,0.0008,0.011,-0.0024,-0.0061,0.0041,0.0036,0.0,0.7238,-0.0054,0.0112,-0.0077,-0.0038,0.0017,-0.0005,0.0001,0.7706 diff --git a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_5.csv b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_5.csv index 1b1374e..7f690d0 100644 --- a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_5.csv +++ b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_5.csv @@ -1,757 +1,757 @@ ,AAPL,MSFT 2020-01-02,-0.0195,-0.0208 -2020-01-03,-0.0036,-0.0077 +2020-01-03,-0.0035,-0.0078 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-2022-12-15,-0.0207,-0.0098 +2022-12-12,-0.0119,0.0 +2022-12-13,-0.0114,-0.0025 +2022-12-14,-0.0204,-0.0044 +2022-12-15,-0.0206,-0.0098 2022-12-16,-0.0119,-0.0113 -2022-12-19,-0.0177,-0.0172 -2022-12-20,-0.0122,-0.0025 -2022-12-21,-0.0047,-0.0168 -2022-12-22,-0.0178,-0.0155 +2022-12-19,-0.0177,-0.0171 +2022-12-20,-0.0122,-0.0026 +2022-12-21,-0.0048,-0.0168 +2022-12-22,-0.0179,-0.0156 2022-12-23,-0.0188,-0.0135 -2022-12-27,-0.0216,-0.0116 -2022-12-28,-0.026,-0.0089 -2022-12-29,-0.0004,0.0013 -2022-12-30,-0.0037,-0.0113 +2022-12-27,-0.0215,-0.0115 +2022-12-28,-0.0258,-0.0087 +2022-12-29,-0.0005,0.0013 +2022-12-30,-0.0038,-0.0114 diff --git a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_6.csv b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_6.csv index a0272c8..694d7d8 100644 --- a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_6.csv +++ b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_6.csv @@ -1,5 +1,5 @@ ,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT ,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared,Intercept,Mkt-RF Slope,SMB Slope,HML Slope,RMW Slope,CMA Slope,Mean Squared Error (MSE),R Squared 2020,,,,,,,,,,,,,,,, -2021,8.5,-0.0388,0.7778,-0.2533,-0.7,-0.5255,-0.5,-0.0864,-inf,-0.0615,0.0,-0.5876,-0.0645,-1.3373,0.0,0.1583 -2022,-1.1404,-0.1129,-0.25,0.0893,0.9524,-0.4462,-1.0,-0.0312,2.0,-0.082,1.6552,-0.075,-0.4483,-0.8929,0.0,-0.1305 +2021,8.5,-0.031,0.7778,-0.24,-0.6857,-0.5149,-0.5,-0.0848,-inf,-0.0687,0.0,-0.5918,-0.0625,-1.3394,0.0,0.158 +2022,-1.1404,-0.12,-0.25,0.0702,0.8636,-0.4462,-1.0,-0.0314,2.0,-0.082,1.6552,-0.05,-0.4333,-0.9107,0.0,-0.1302 diff --git a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_7.csv b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_7.csv index 31ede05..1f24063 100644 --- a/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_7.csv +++ b/tests/performance/csv/test_performance_controller/test_get_fama_and_french_model_7.csv @@ -2,5 +2,5 @@ ,Intercept,Intercept,Intercept,Mkt-RF Slope,Mkt-RF Slope,Mkt-RF Slope,SMB Slope,SMB Slope,SMB Slope,HML Slope,HML Slope,HML Slope,RMW Slope,RMW Slope,RMW Slope,CMA Slope,CMA Slope,CMA Slope,Mean Squared Error (MSE),Mean Squared Error (MSE),Mean Squared Error (MSE),R Squared,R Squared,R Squared,Intercept,Intercept,Intercept,Mkt-RF Slope,Mkt-RF Slope,Mkt-RF Slope,SMB Slope,SMB Slope,SMB Slope,HML Slope,HML Slope,HML Slope,RMW Slope,RMW Slope,RMW Slope,CMA Slope,CMA Slope,CMA Slope,Mean Squared Error (MSE),Mean Squared Error (MSE),Mean Squared Error (MSE),R Squared,R Squared,R Squared ,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3 2020,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -2021,8.5,,,-0.0388,,,0.7778,,,-0.2533,,,-0.7,,,-0.5255,,,-0.5,,,-0.0864,,,-inf,,,-0.0615,,,0.0,,,-0.5876,,,-0.0645,,,-1.3373,,,0.0,,,0.1583,, -2022,-1.1404,-2.3333,,-0.1129,-0.1473,,-0.25,0.3333,,0.0893,-0.1867,,0.9524,-0.4143,,-0.4462,-0.7372,,-1.0,-1.0,,-0.0312,-0.1149,,2.0,-inf,,-0.082,-0.1385,,1.6552,1.6552,,-0.075,-0.6186,,-0.4483,-0.4839,,-0.8929,-1.0361,,0.0,0.0,,-0.1305,0.0072, +2021,8.5,,,-0.031,,,0.7778,,,-0.24,,,-0.6857,,,-0.5149,,,-0.5,,,-0.0848,,,-inf,,,-0.0687,,,0.0,,,-0.5918,,,-0.0625,,,-1.3394,,,0.0,,,0.158,, +2022,-1.1404,-2.3333,,-0.12,-0.1473,,-0.25,0.3333,,0.0702,-0.1867,,0.8636,-0.4143,,-0.4462,-0.7313,,-1.0,-1.0,,-0.0314,-0.1135,,2.0,-inf,,-0.082,-0.145,,1.6552,1.6552,,-0.05,-0.6122,,-0.4333,-0.4688,,-0.9107,-1.0303,,0.0,0.0,,-0.1302,0.0073, diff --git a/tests/performance/csv/test_performance_controller/test_get_jensens_alpha.csv b/tests/performance/csv/test_performance_controller/test_get_jensens_alpha.csv index caa07a8..f7944f3 100644 --- a/tests/performance/csv/test_performance_controller/test_get_jensens_alpha.csv +++ b/tests/performance/csv/test_performance_controller/test_get_jensens_alpha.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT -2020,0.0014,0.0014 -2021,-0.0292,0.1937 -2022,-0.0155,-0.037 +2020,0.0,0.0 +2021,-0.0341,0.1913 +2022,-0.0273,-0.0478 diff --git a/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_1.csv b/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_1.csv index 508f525..40014dc 100644 --- a/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_1.csv +++ b/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_1.csv @@ -1,13 +1,13 @@ Date,AAPL,MSFT -2020Q1,0.001,0.0014 -2020Q2,0.2547,0.1159 -2020Q3,0.1088,-0.1048 -2020Q4,-0.0205,-0.0847 -2021Q1,-0.1615,-0.0045 -2021Q2,0.0119,0.043 -2021Q3,0.0326,0.0376 -2021Q4,0.1366,0.0658 -2022Q1,0.0463,-0.014 -2022Q2,0.0047,0.0298 -2022Q3,0.0769,-0.0253 -2022Q4,-0.1507,-0.0602 +2020Q1,0.0,0.0 +2020Q2,0.2553,0.1166 +2020Q3,0.1029,-0.1089 +2020Q4,-0.0244,-0.0866 +2021Q1,-0.1692,-0.0058 +2021Q2,0.0062,0.0378 +2021Q3,0.0268,0.0366 +2021Q4,0.135,0.0629 +2022Q1,0.0411,-0.0211 +2022Q2,-0.0046,0.0245 +2022Q3,0.0702,-0.0325 +2022Q4,-0.1685,-0.0783 diff --git a/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_2.csv b/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_2.csv index 4da6cc5..eb99a00 100644 --- a/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_2.csv +++ b/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_2.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT 2020,, -2021,-21.8571,137.3571 -2022,-0.4692,-1.191 +2021,-inf,inf +2022,-0.1994,-1.2499 diff --git a/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_3.csv b/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_3.csv index 360376b..af5780a 100644 --- a/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_3.csv +++ b/tests/performance/csv/test_performance_controller/test_get_jensens_alpha_3.csv @@ -2,5 +2,5 @@ ,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3 Date,,,,,, 2020,,,,,, -2021,-21.8571,,,137.3571,, -2022,-0.4692,-12.0714,,-1.191,-27.4286, +2021,-inf,,,inf,, +2022,-0.1994,-inf,,-1.2499,-inf, diff --git a/tests/performance/csv/test_performance_controller/test_get_m2_ratio.csv b/tests/performance/csv/test_performance_controller/test_get_m2_ratio.csv index 5acedbc..d6a6a93 100644 --- a/tests/performance/csv/test_performance_controller/test_get_m2_ratio.csv +++ b/tests/performance/csv/test_performance_controller/test_get_m2_ratio.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT -2020,-0.0197,-0.021 -2021,1.3203,2.4214 -2022,-0.8485,-0.9037 +2020,0.0,0.0 +2021,1.3805,2.4931 +2022,-0.7398,-0.7938 diff --git a/tests/performance/csv/test_performance_controller/test_get_m2_ratio_1.csv b/tests/performance/csv/test_performance_controller/test_get_m2_ratio_1.csv index 213449d..2189b73 100644 --- a/tests/performance/csv/test_performance_controller/test_get_m2_ratio_1.csv +++ b/tests/performance/csv/test_performance_controller/test_get_m2_ratio_1.csv @@ -1,13 +1,13 @@ Date,AAPL,MSFT -2020Q1,-0.0212,-0.0201 -2020Q2,2.4771,1.6952 -2020Q3,1.1897,0.1717 -2020Q4,0.8053,0.3878 -2021Q1,-0.5756,0.3566 -2021Q2,1.0333,1.4262 -2021Q3,0.188,0.3341 -2021Q4,2.026,1.5826 -2022Q1,-0.2546,-0.6369 -2022Q2,-1.2094,-1.0301 -2022Q3,-0.1688,-0.8831 -2022Q4,-0.48,-0.0303 +2020Q1,0.0,0.0 +2020Q2,2.5143,1.7335 +2020Q3,1.2202,0.2116 +2020Q4,0.8588,0.4578 +2021Q1,-0.4702,0.4952 +2021Q2,1.1702,1.5759 +2021Q3,0.3362,0.5207 +2021Q4,2.1526,1.7153 +2022Q1,-0.1013,-0.4951 +2022Q2,-1.0631,-0.8731 +2022Q3,0.0798,-0.6234 +2022Q4,-0.288,0.1595 diff --git a/tests/performance/csv/test_performance_controller/test_get_m2_ratio_2.csv b/tests/performance/csv/test_performance_controller/test_get_m2_ratio_2.csv index 5aed365..0902f55 100644 --- a/tests/performance/csv/test_performance_controller/test_get_m2_ratio_2.csv +++ b/tests/performance/csv/test_performance_controller/test_get_m2_ratio_2.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT 2020,, -2021,-68.0203,-116.3048 -2022,-1.6427,-1.3732 +2021,inf,inf +2022,-1.5359,-1.3184 diff --git a/tests/performance/csv/test_performance_controller/test_get_m2_ratio_3.csv b/tests/performance/csv/test_performance_controller/test_get_m2_ratio_3.csv index 83b6d0d..20114fa 100644 --- a/tests/performance/csv/test_performance_controller/test_get_m2_ratio_3.csv +++ b/tests/performance/csv/test_performance_controller/test_get_m2_ratio_3.csv @@ -2,5 +2,5 @@ ,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3 Date,,,,,, 2020,,,,,, -2021,-68.0203,,,-116.3048,, -2022,-1.6427,42.0711,,-1.3732,42.0333, +2021,inf,,,inf,, +2022,-1.5359,-inf,,-1.3184,-inf, diff --git a/tests/performance/csv/test_performance_controller/test_get_treynor_ratio.csv b/tests/performance/csv/test_performance_controller/test_get_treynor_ratio.csv index a89b3f2..c05d3c6 100644 --- a/tests/performance/csv/test_performance_controller/test_get_treynor_ratio.csv +++ b/tests/performance/csv/test_performance_controller/test_get_treynor_ratio.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT -2020,-0.008,-0.008 -2021,0.2502,0.439 -2022,-0.2325,-0.2495 +2020,0.0,0.0 +2021,0.2616,0.452 +2022,-0.2027,-0.2192 diff --git a/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_1.csv b/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_1.csv index 5672212..6748ca7 100644 --- a/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_1.csv +++ b/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_1.csv @@ -1,13 +1,13 @@ Date,AAPL,MSFT -2020Q1,-0.0061,-0.0058 -2020Q2,0.4754,0.3268 -2020Q3,0.1417,0.0182 -2020Q4,0.0976,0.0421 -2021Q1,-0.0665,0.0418 -2021Q2,0.0777,0.1008 -2021Q3,0.0141,0.0257 -2021Q4,0.2198,0.1506 -2022Q1,-0.0316,-0.0801 -2022Q2,-0.1872,-0.1655 -2022Q3,-0.0219,-0.1085 -2022Q4,-0.0665,-0.0042 +2020Q1,0.0,0.0 +2020Q2,0.4826,0.3342 +2020Q3,0.1454,0.0225 +2020Q4,0.104,0.0497 +2021Q1,-0.0543,0.0581 +2021Q2,0.088,0.1114 +2021Q3,0.0252,0.0401 +2021Q4,0.2335,0.1633 +2022Q1,-0.0126,-0.0623 +2022Q2,-0.1646,-0.1403 +2022Q3,0.0104,-0.0766 +2022Q4,-0.0399,0.0222 diff --git a/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_2.csv b/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_2.csv index bd9fa36..30b6514 100644 --- a/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_2.csv +++ b/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_2.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT 2020,, -2021,-32.275,-55.875 -2022,-1.9293,-1.5683 +2021,inf,inf +2022,-1.7748,-1.485 diff --git a/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_3.csv b/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_3.csv index d279cc4..acbc2da 100644 --- a/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_3.csv +++ b/tests/performance/csv/test_performance_controller/test_get_treynor_ratio_3.csv @@ -2,5 +2,5 @@ ,Lag 1,Lag 2,Lag 3,Lag 1,Lag 2,Lag 3 Date,,,,,, 2020,,,,,, -2021,-32.275,,,-55.875,, -2022,-1.9293,28.0625,,-1.5683,30.1875, +2021,inf,,,inf,, +2022,-1.7748,-inf,,-1.485,-inf, diff --git a/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index.csv b/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index.csv index 0d49413..364e4e3 100644 --- a/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index.csv +++ b/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT 2020,0.0,0.0 -2021,8.8915,19.9954 -2022,-4.5217,-5.0081 +2021,9.2967,20.5877 +2022,-3.9425,-4.3992 diff --git a/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_1.csv b/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_1.csv index 4c56b64..7bb4ce9 100644 --- a/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_1.csv +++ b/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_1.csv @@ -1,13 +1,13 @@ Date,AAPL,MSFT 2020Q1,0.0,0.0 -2020Q2,23.2178,12.6509 -2020Q3,3.2153,0.4719 -2020Q4,3.3971,1.3591 -2021Q1,-1.5684,1.5695 -2021Q2,3.4551,4.6026 -2021Q3,0.5266,1.2096 -2021Q4,13.6133,7.264 -2022Q1,-0.7314,-2.0703 -2022Q2,-2.7119,-2.6308 -2022Q3,-0.4217,-1.813 -2022Q4,-1.439,-0.1063 +2020Q2,23.5672,12.9369 +2020Q3,3.2977,0.5818 +2020Q4,3.6225,1.6042 +2021Q1,-1.281,2.1798 +2021Q2,3.9128,5.0857 +2021Q3,0.9419,1.885 +2021Q4,14.4638,7.873 +2022Q1,-0.291,-1.6096 +2022Q2,-2.3839,-2.2297 +2022Q3,0.1994,-1.2797 +2022Q4,-0.8634,0.5591 diff --git a/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_2.csv b/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_2.csv index 2e8544c..c02374a 100644 --- a/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_2.csv +++ b/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_2.csv @@ -1,4 +1,4 @@ Date,AAPL,MSFT 2020,, 2021,inf,inf -2022,-1.5085,-1.2505 +2022,-1.4241,-1.2137 diff --git a/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_3.csv b/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_3.csv index 776944e..88f8802 100644 --- a/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_3.csv +++ b/tests/performance/csv/test_performance_controller/test_get_ulcer_performance_index_3.csv @@ -3,4 +3,4 @@ Date,,,,,, 2020,,,,,, 2021,inf,,,inf,, -2022,-1.5085,-inf,,-1.2505,-inf, +2022,-1.4241,-inf,,-1.2137,-inf, diff --git a/tests/test_toolkit_controller.py b/tests/test_toolkit_controller.py index 682e15d..2ecf95a 100644 --- a/tests/test_toolkit_controller.py +++ b/tests/test_toolkit_controller.py @@ -41,7 +41,8 @@ def test_toolkit_cash(recorder): def test_toolkit_historical(recorder): toolkit = Toolkit( - tickers=["AAPL", "MSFT"], start_date="2010-01-01", end_date="2020-01-01" + tickers=["AAPL", "MSFT"], + historical=historical_dataset, ) recorder.capture(toolkit.get_historical_data(period="yearly").round(0)) @@ -67,6 +68,7 @@ def test_toolkit_ratios(recorder): def test_toolkit_models(recorder): toolkit = Toolkit( tickers=["AAPL", "MSFT"], + historical=historical_dataset, balance=balance_dataset, income=income_dataset, cash=cash_dataset,