While browsing a variety of websites, I kept finding that the same financial metric can greatly vary per source and so do the financial statements reported while little information is given how the metric was calculated.

For example, Microsoft's Price-to-Earnings (PE) ratio on the 6th of May, 2023 is reported to be 28.93 (Stockopedia), 32.05 (Morningstar), 32.66 (Macrotrends), 33.09 (Finance Charts), 33.66 (Y Charts), 33.67 (Wall Street Journal), 33.80 (Yahoo Finance) and 34.4 (Companies Market Cap). All of these calculations are correct, however the method applied varies leading to different results. Therefore, collecting data from multiple sources can lead to wrong interpretation of the results given that one source could be applying a different calculation method than another. And that is, if it is even freely available. Often the calculation is hidden behind a paid subscription.

This is why I designed the FinanceToolkit, this is an open-source toolkit in which all relevant financial ratios (150+), indicators and performance measurements are written down in the most simplistic way allowing for complete transparency of the calculation method (proof). This allows you to not have to rely on metrics from other providers and, given a financial statement, allow for efficient manual calculations. This leads to one uniform method of calculation being applied that is available and understood by everyone.

The Finance Toolkit not only supports Equities. Even for Currencies, Cryptocurrencies, ETFs, Mutual Funds, Indices, Money Markets, Commodities, Key Economic Indicators and more, the Finance Toolkit can be used to obtain historical data as well as important performance and risk measurements such as the Sharpe Ratio and Value at Risk.

The Finance Toolkit is complimented very well with the Finance Database 🌎, a database that features 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets. By utilising both, it is possible to do a fully-fledged competitive analysis with the tickers found from the FinanceDatabase inputted into the FinanceToolkit.

1. Installation
2. Functionality
3. Basic Usage
4. Available Metrics
5. Questions & Answers
6. Contributing
7. Contact

Before installation, consider starring the project on GitHub which helps others find the project as well.

To install the FinanceToolkit it simply requires the following:

pip install financetoolkit -U

Then within Python use:

from financetoolkit import Toolkit

companies = Toolkit(
    tickers=['GOOGL', 'MSFT', 'AMZN'],
    api_key="FINANCIAL_MODELING_PREP_KEY",
)

To be able to get started, you need to obtain an API Key from FinancialModelingPrep. This is used to gain access to 30+ years of financial statement both annually and quarterly. Note that the Free plan is limited to 250 requests each day, 5 years of data and only features companies listed on US exchanges.

Through the link you are able to subscribe for the free plan and also premium plans at a 15% discount. This is an affiliate link and thus supports the project at the same time. I have chosen FinancialModelingPrep as a source as I find it to be the most transparent, reliable and at an affordable price. I have yet to find a platform offering such low prices for the amount of data offered. When you notice that the data is inaccurate or have any other issue related to the data, note that I simply provide the means to access this data and I am not responsible for the accuracy of the data itself. For this, use their contact form or provide the data yourself.

The Finance Toolkit features the following functionality, also see Basic Usage to see some of these functions in action and the Code Documentation for a detailed explanation of each function.

The dependencies of the package are on purpose very slim so that it will work well with any combination of packages and not result in conflicts.

This section is an introduction to the Finance Toolkit. Also see this notebook for a detailed Getting Started guide as well as this notebook that includes the Finance Database 🌎 and a proper financial analysis. Next to that, find below a fully-fledged code documentation as well as Jupyter Notebooks in which you can see many examples ranging from basic examples to creating custom ratios to working with your own datasets.

A basic example of how to use the Finance Toolkit is shown below.

from financetoolkit import Toolkit

companies = Toolkit(["AAPL", "MSFT"], api_key=API_KEY, start_date="2017-12-31")

# a Historical example
historical_data = companies.get_historical_data()

# a Financial Statement example
income_statement = companies.get_income_statement()

# a Ratios example
profitability_ratios = companies.ratios.collect_profitability_ratios()

# a Models example
extended_dupont_analysis = companies.models.get_extended_dupont_analysis()

# a Performance example
factor_asset_correlations = companies.performance.get_factor_asset_correlations(period='quarterly')

# a Risk example
value_at_risk = companies.risk.get_value_at_risk(period="weekly")

# a Technical example
ichimoku_cloud = companies.technicals.get_ichimoku_cloud()

# an Economics example
unemployment_rates = companies.economics.get_unemployment_rate()

Generally, the functions return a DataFrame with a multi-index in which all tickers, in this case Apple and Microsoft, are presented. To keep things manageable for this README, I select just Apple but in essence the list of tickers can be endless as I've seen DataFrames with thousands of tickers. The filtering is done through .loc['AAPL'] and .xs('AAPL', level=1, axis=1) based on whether it's fundamental data or historical data respectively.

Obtain historical data on a daily, weekly, monthly or yearly basis. This includes OHLC, volumes, dividends, returns, cumulative returns and volatility calculations for each corresponding period. For example, the a portion of the historical data for Apple is shown below.

And below the cumulative returns are plotted which include the S&P 500 as benchmark:

Obtain an Income Statement on an annual or quarterly basis. This can also be an income statement (companies.get_income_statement()) or cash flow statement (companies.get_cash_flow_statement()). For example, the first 5 rows of the Income Statement for Apple are shown below.

And below the Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA) are plotted for both Apple and Microsoft.

Get Profitability Ratios based on the inputted balance sheet, income and cash flow statements. This can be any of the 50+ ratios within the ratios module. The get_ functions show a single ratio whereas the collect_ functions show an aggregation of multiple ratios. For example, see some of the profitability ratios of Microsoft below.

And below a few of the profitability ratios are plotted for Microsoft.

Get an Extended DuPont Analysis based on the inputted balance sheet, income and cash flow statements. This can also be an Enterprise Value Breakdown, Weighted Average Cost of Capital (WACC), Altman Z-Score and many more models. For example, this shows the Extended DuPont Analysis for Apple:

And below each component of the Extended Dupont Analysis is plotted including the resulting Return on Equity (ROE).

Get the correlations with the factors as defined by Fama-and-French. These include market, size, value, operating profitability and investment. The beauty of all functionality here is that it can be based on any period as the function accepts the period 'weekly', 'monthly', 'quarterly' and 'yearly'. For example, this shows the quarterly correlations for Apple:

And below the correlations with each factor are plotted over time for both Apple and Microsoft.

Get the Value at Risk for each week. Here, the days within each week are considered for the Value at Risk. This makes it so that you can understand within each period what is the expected Value at Risk (VaR) which can again be any period but also based on distributions such as Historical, Gaussian, Student-t, Cornish-Fisher.

And below the Value at Risk (VaR) for Apple, Microsoft and the benchmark (S&P 500) are plotted also demonstrating the impact of COVID-19.

Get the Ichimoku Cloud parameters based on the historical market data. This can be any of the 30+ technical indicators within the technicals module. The get_ functions show a single indicator whereas the collect_ functions show an aggregation of multiple indicators. For example, see some of the parameters for Apple below:

And below the Ichimoku Cloud parameters are plotted for Apple and Microsoft side-by-side.

Get insights for 60+ countries into key economic indicators such as the Consumer Price Index (CPI), Gross Domestic Product (GDP), Unemployment Rates and 3-month and 10-year Government Interest Rates. This is done through the economics module and can be used as a standalone module as well by using from financetoolkit import Economics. For example see a selection of the countries below:

And below these Unemployment Rates are plotted over time:

The Finance Toolkit has the ability to calculate 150+ financial metrics. The following list shows all of the available metrics. Note that the Finance Toolkit is not limited to these ratios and indicators as it is possible to add custom ratios as well. See this Notebook for more information.

Each ratio and indicator has a corresponding function that can be called directly for example ratios.get_return_on_equity or technicals.get_relative_strength_index.

The Ratios Module contains over 50+ ratios that can be used to analyse companies. These ratios are divided into 5 categories which are efficiency, liquidity, profitability, solvency and valuation. Each ratio is calculated using the data from the Toolkit module. Find the documentation here which includes an explanation about the ratio, the parameters and an example.

All of these ratios can be calculated based on (lagged) growth as well as trailing (e.g. TTM) metrics. This is embedded in all ratios as well as the financial statements themselves which means it is possible to calculate revenue growth and 12-month (TTM) Price-to-Earnings with the parameters growth=True and trailing=4 respectively. Note that trailing is based on periods therefore TTM can only be calculated by setting quarterly=True in the Toolkit initialization.

• Asset Turnover Ratio
• Inventory Turnover Ratio
• Days of Inventory Outstanding
• Days of Sales Outstanding
• Operating Cycle
• Accounts Payables Turnover Ratio
• Days of Accounts Payable Outstanding
• Cash Conversion Cycle (CCC)
• Cash Conversion Efficiency (CCE)
• Receivables Turnover
• SGA to Revenue Ratio
• Fixed Asset Turnover
• Operating Ratio

• Current Ratio
• Quick Ratio
• Cash Ratio
• Working Capital
• Operating Cash Flow Ratio
• Operating Cash Flow Sales Ratio
• Short Term Coverage Ratio

• Gross Margin
• Operating Margin
• Net Profit Margin
• Interest Burden Ratio
• Income Before Tax Profit Margin
• Effective Tax Rate
• Return on Assets (RoA)
• Return on Equity (RoE)
• Return on Invested Capital (RoIC)
• Income Quality Ratio
• Return on Tangible Assets (RoTA)
• Return on Capital Employed (RoCE)
• Net Income per EBT
• Free Cash Flow Operating Cash Flow Ratio
• Tax Burden Ratio
• EBT to EBIT
• EBIT to Revenue

• Debt to Assets Ratio
• Debt to Equity Ratio
• Interest Coverage Ratio
• Equity Multiplier
• Debt Service Coverage Ratio
• Free Cash Flow Yield
• Net Debt to EBITDA Ratio
• Cash Flow Coverage Ratio
• CAPEX Coverage Ratio
• CAPEX Dividend Coverage Ratio

• Earnings per Share (EPS)
• Revenue per Share (RPS)
• Price Earnings Ratio (PE)
• Price to Earnings Growth Ratio (PEG)
• Book Value per Share
• Price to Book Ratio (PB)
• Interest Debt per Share
• CAPEX per Share
• Dividend Yield
• Weighted Dividend Yield
• Price to Cash Flow Ratio (P/CF)
• Price to Free Cash Flow Ratio (P/FCF)
• Market Capitalization
• Enterprise Value
• EV to Sales Ratio
• EV to EBITDA Ratio
• EV to Operating Cashflow Ratio
• EV to EBIT
• Earnings Yield
• Dividend Payout Ratio
• Reinvestment Rate
• Tangible Asset Value
• Net Current Asset Value

The Models module is meant to execute well-known models such as DUPONT and the Discounted Cash Flow (DCF) model. These models are also directly related to the data retrieved from the Toolkit module. Find the documentation here which includes an explanation about the model, the parameters and an example.

• DuPont Analysis
• Extended DuPont Analysis
• Enterprise Value Breakdown
• Weighted Average Cost of Capital (WACC)
• Intrinsic Valuation
• Altman Z-Score
• Piotroski F-Score
• Present Value of Growth Opportunities (PVGO)

The Performance module is meant to calculate important performance metrics such as Sharpe Ratio, Sortino Ratio, Treynor Ratio, Information Ratio, Jensen's Alpha, Beta, Capital Asset Pricing Model, Factor Models and more. Find the documentation here which includes an explanation about the performance metrics, the parameters and an example.

• Alpha
• Jensen's Alpha
• Beta
• Capital Asset Pricing Model (CAPM)
• Factor Asset Correlations
• Factor Correlations
• Fama-French 5 Factor Model (FF)
• Treynor Ratio
• Sharpe Ratio
• Sortino Ratio
• Ulcer Performance Index (UPI)
• M2 Ratio
• Tracking Error
• Information Error
• Compound Annual Growth Rate (CAGR)

The Risk module is meant to calculate important risk metrics such as Value at Risk (VaR), Conditional Value at Risk (cVaR), Maximum Drawdown, Correlations, Beta, GARCH, EWMA and more. Find the documentation here which includes an explanation about the risk metrics, the parameters and an example.

• Black Scholes Model
• Value at Risk (VaR) with distributions Historical, Gaussian, Student-t, Cornish-Fisher.
• Conditional Value at Risk (cVaR) with distributions Historical, Gaussian, Laplace, Logistic.
• Entropic Value at Risk (eVaR) with a Gaussian distribution.
• GARCH (Generalized Autoregressive Conditional Heteroskedasticity) with Forecasting
• Ulcer Index (UI)
• Maximum Drawdown (MDD)
• Skewness
• Kurtosis

The Technicals Module contains 30+ Technical Indicators that can be used to analyse companies. These ratios are divided into 4 categories which are breadth, momentum, overlap and volatility. Each indicator is calculated using the data from the Toolkit module. Find the documentation here which includes an explanation about the indicator, the parameters and an example.

• McClellan Oscillator
• Advancers/Decliners Ratio
• On-Balance Volume (OBV)
• Accumulation/Distribution Line (ADL)
• Chaikin Oscillator

• Money Flow Index
• Williams %R
• Aroon Indicator
• Commodity Channel Index
• Relative Vigor Index
• Force Index
• Ultimate Oscillator
• Percentage Price Oscillator
• Detrended Price Oscillator
• Average Directional Index (ADX)
• Chande Momentum Oscillator (CMO)
• Ichimoku Cloud
• Stochastic Oscillator
• Moving Average Convergence Divergence (MACD)
• Relative Strength Index (RSI)
• Balance of Power (BOP)

• Simple Moving Average (SMA)
• Exponential Moving Average (EMA)
• Double Exponential Moving Average (DEMA)
• Triple Exponential Moving Average (TRIX)
• Triangular Moving Average (TMA)

• True Range (TR)
• Average True Range (ATR)
• Keltners Channels
• Bollinger Bands

The Economics Module contains a variety of Key Economic Indicators that help in understanding the health and performance of more than 60 different countries. This module can be called directly via the Toolkit but also separately if desired through from financetoolkit import Economics. Find the documentation here which includes an explanation about each indicator, the parameters and an example.

• Gross Domestic Product (GDP) including Growth and Forecasts
• Consumer Confidence Index (CCI)
• Business Confidence Index (BCI)
• Composite Leading Indicator (CLI)
• Consumer Price Index (CPI)
• Producer Price Index (PPI)
• House and Rent Prices
• Share Prices

• Long Term Interest Rates (10 year)
• Short Term Interest Rates (3 month)
• Narrow Money (M1)
• Broad Money (M3)
• Purchasing Power Parity (PPP)
• Exchange Rates

• Main Refinancing Operations (ECB)
• Marginal Lending Facility (ECB)
• Deposit Facility (ECB)
• Effective Federal Funds Rate (FED)
• Overnight Bank Funding Rate (FED)
• Tri-Party General Collateral Rate (FED)
• Broad General Collateral Rate (FED)
• Secured Overnight Financing Rate (FED)

• Renewable Energy as % of Total Energy
• Environmental Tax as % of GDP
• Greenhouse Gas Emissions
• Crude Oil Production and Prices

• Deficit as % of GDP
• Revenue as % of GDP
• Spending as % of GDP
• Debt as % of GDP
• Financial Wealth as % of GDP
• Production Costs as % of GDP
• Central Spending based on the System of National accounts (SNA)
• Trust in Government

• Unemployment Rates
• Labour Productivity
• Income Inequality

• Population
• Young Population
• Working Age Population
• Elderly Population
• Fertility Rates
• Old-Age Dependency Ratio
• Poverty Rate

This section includes frequently asked questions and is meant to clear up confusion about certain results and/or deviations from other sources. If you have any questions that are not answered here, feel free to reach out to me via the contact details below.

How do you deal with companies that have different fiscal years?

For any financial statement, I make sure to line it up with the corresponding calendar period. For example, Apple's Q4 2023 relates to July to September of 2023. This corresponds to the calendar period Q3 which is why I normalize Apple's numbers to Q3 2023 instead. This is done to allow for comparison between companies that have different fiscal years.

Why do the numbers in the financial statements sometimes deviate from the data from FinancialModelingPrep?

When looking at a company such as Hyundai Motor Company (ticker: 005380.KS), you will notice that the financial statements are reported in KRW (South Korean won). As this specific ticker is listed on the Korean Exchange, the historical market data will also be reported in KRW. However, if you use the ticker HYMTF, which is listed on the American OTC market, the historical market data will be reported in USD. To deal with this discrepancy, the end of year or end of quarter exchange rate is retrieved which is used to convert the financial statements to USD. This is done to prevent ratio calculations such as the Free Cash Flow Yield (which is based on the market capitalization) or Price Earnings Ratio (which is based on the stock price) from being incorrect. This can be disabled by setting convert_currency=False in the Toolkit initialization. It is recommended to always use the ticker that is listed on the exchange where the company is based.

How can I get TTM (Trailing Twelve Months) and Growth metrics?

Most functions will have the option to define the trailing parameter. This lets you define the number of periods that you want to use to calculate the trailing metrics. For example, if you want to calculate the trailing 12-month (TTM) Price-to-Earnings Ratio, you can set trailing=4 when you have set quarterly=True in the Toolkit initialization. The same goes for growth metrics which can be calculated by setting growth=True. This will calculate the growth for each period based on the previous period. This also includes a lag parameter in which you can define lagged growth. Furthermore, you can also combine the trailing and growth parameters to get trailing growth. For example, set trailing=4 and growth=True for the Price-to-Earnings Ratio which will then calculate the TTM growth.

How can I save the data periodically so that I don't have to retrieve it every single time again?

The Toolkit initialization has the ability to add custom datasets which can serve as input for external datasets from different providers but also to prevent downloading all of the same data again. Have a look at this comment that explains this further.

What is the "Benchmark" that is automatically obtained when acquiring historical data?

This is related to the benchmark_ticker parameter which is set to "SPY" (S&P 500) by default. This is important when calculating performance metrics such as the Sharpe Ratio or Treynor Ratio that require a market return. This can be disabled by setting benchmark_ticker=None in the Toolkit initialization.

Are you part of FinancialModelingPrep?

No, I am not. I've merely picked them as the primary data provider given that they have a generous free tier and fair pricing compared to other providers. Therefore, any questions related to the data should go through their contact form. When it comes to any type of ratios, performance metrics, risk metrics, technical indicators or economic indicators, feel free to reach out to me as this is my own work.

First off all, thank you for taking the time to contribute (or at least read the Contributing Guidelines)! 🚀

The goal of the Finance Toolkit is to make any type of financial calculation as transparent and efficient as possible. I want to make these type of calculations as accessible to anyone as possible and seeing how many websites exists that do the same thing (but instead you have to pay) gave me plenty of reasons to work on this.

If you have any questions about the FinanceToolkit or would like to share with me what you have been working on, feel free to reach out to me via:

• Website: https://jeroenbouma.com/
• Twitter: https://twitter.com/JerBouma
• LinkedIn: https://www.linkedin.com/in/boumajeroen/
• Email: jer.bouma@gmail.com
• Discord: add me on Discord JerBouma

If you'd like to support my efforts, either help me out by contributing to the package or Sponsor Me.