While browsing a variety of websites, I repeatedly observed significant fluctuations in the same financial metric among different sources. Similarly, the reported financial statements often didn't line up, and there was limited information on the methodology used to calculate each metric.

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 of calculation varies leading to different results. Therefore, collecting data from multiple sources can lead to wrong interpretation of the results given that one source could apply a different definition than another. And that is, if that definition is even available as often the underlying methods are hidden behind a paid subscription.

This is why I designed the FinanceToolkit, this is an open-source toolkit in which all relevant financial ratios (200+), indicators and performance measurements are written down in the most simplistic way allowing for complete transparency of the method of calculation (proof). This enables you to avoid dependence on metrics from other providers that do not provide their methods. With a large selection of financial statements in hand, it facilitates streamlined calculations, promoting the adoption of a consistent and universally understood methods and formulas.

Beyond Equities, it supports Options, Currencies, Cryptocurrencies, ETFs, Mutual Funds, Indices, Money Markets, Commodities, Key Economic Indicators and more, allowing you to obtain historical data as well as important performance and risk measurements such as the Sharpe Ratio and Value at Risk.

Complementing this is the Finance Database 🌎, a database featuring 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.

🔌 The Finance Toolkit is also available as an MCP Server

Query 200+ metrics from Claude, Copilot, Cursor, Windsurf or any MCP-compatible client without writing code.

• Hosted: connect to https://financetoolkit.jeroenbouma.com/mcp — OAuth handles the rest on first use.
• Local: uvx --from "financetoolkit[mcp]" financetoolkit-mcp-setup — sets up your client config and API key automatically. See MCP Server Documentation for manual setup.

Also on Smithery, Glama, MCP Servers and more.

Table of Contents

1. Installation
2. Basic Usage
3. Functionality and Metrics
4. MCP Server
5. Questions & Answers
6. Contributing
7. Mentions
8. Contact

Installation

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

<a href="https://github.com/JerBouma/FinanceToolkit" target="_blank"><img width="1415" alt="image" src="https://github.com/JerBouma/FinanceToolkit/assets/46355364/014109fe-0c68-47d4-99bd-217c69dcea8d"></a>

To install the Finance Toolkit 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",  # replace with your actual API 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.

<b><div align="center">Obtain an API Key from FinancialModelingPrep <a href="https://www.jeroenbouma.com/fmp" target="_blank">here</a>.</div></b>

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.

By default, the Finance Toolkit prioritizes Financial Modeling Prep for data retrieval. If data acquisition from Financial Modeling Prep is unsuccessful (e.g., due to plan restrictions or API key issues), the toolkit automatically switches to Yahoo Finance as a secondary source. To disable this fallback behavior and exclusively use Financial Modeling Prep, set enforce_source="FinancialModelingPrep" during Toolkit initialization. This configuration ensures that an error is raised if Financial Modeling Prep data cannot be accessed. Alternatively, you can set enforce_source="YahooFinance" to exclusively use Yahoo Finance as the data source.

Basic Usage

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.

<b><div align="center">Find a variety of How-To Guides including Code Documentation for the FinanceToolkit <a href="https://www.jeroenbouma.com/projects/financetoolkit">here</a>.</div></b>

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()

# an Options example
all_greeks = companies.options.collect_all_greeks(expiration_time_range=180)

# 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()

# a Fixed Income example
corporate_bond_yields = companies.fixedincome.get_ice_bofa_effective_yield()

# 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.

Obtaining Historical Data

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:

Obtaining Financial Statements

Obtain an Income Statement on an annual or quarterly basis. This can also be a balance statement (companies.get_balance_sheet_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.

Obtaining Financial Ratios

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.

Obtaining Financial Models

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).

Obtaining Options and Greeks

Get the Black Scholes Model for both call and put options including the relevant Greeks, in this case Delta, Gamma, Theta and Vega. This can be any of the First, Second or Third Order Greeks as found in the the options module. The get_ functions show a single Greek whereas the collect_ functions show an aggregation of Greeks. For example, see the delta of the Call options for Apple for multiple expiration times and strike prices below (Stock Price: 185.92, Volatility: 31.59%, Dividend Yield: 0.49% and Risk Free Rate: 3.95%):

Which can also be plotted together with Gamma, Theta and Vega as follows:

Obtaining Performance Metrics

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 intraday, 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.

Obtaining Risk Metrics

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.

Obtaining Technical Indicators

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.

Obtaining Fixed Income Metrics

Get access to the ICE BofA Corporate Bond benchmark indices and a variety of other bond and derivative related valuations within the fixedincome module. For example, see the Effective Yield for the ICE BofA Corporate Bond Index below for each Credit Rating:

And below a variety of Fixed Income metrics are shown all acquired from the Fixed Income module.

Understanding Key Economic Indicators

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:

Explore your own Portfolio

Through a custom XLSX, XLS or CSV file you are able to load in your own portfolio directly into the Finance Toolkit. This allows you to view your positions and performance (over time) versus a benchmark and other positions as well as your PnL development over time. Furthermore, the portfolio can be directly loaded in the core functionality of the Finance Toolkit as well making it possible to calculate all metrics and ratios for your portfolio (which is a time-weighted sum of all positions). The portfolio module is a standalone module and can be used as such by using from financetoolkit import Portfolio.

<b><div align="center">It is important to note that it requires a specific Excel template to work, see for further instructions the following notebook <a href="https://www.jeroenbouma.com/projects/financetoolkit/portfolio-notebook" target="_blank">here</a>.</div></b>

The table below shows one of the functionalities of the Portfolio module but is purposely shrunken down given the >30 assets.

In which the weights and returns can be depicted as follows:

Core Functionality and Metrics

The Finance Toolkit has the ability to collect 30+ years of financial statements and calculate 200+ financial metrics. The following list shows all of the available functionality and metrics.

<b><div align="center">Find a variety of How-To Guides including Code Documentation for the Finance Toolkit <a href="https://www.jeroenbouma.com/projects/financetoolkit">here</a>.</div></b>

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. However, there are also functions that collect multiple ratios or indicators at once such as ratios.collect_profitability_ratios. These functions are useful when you want to collect a large amount of ratios or indicators at once.

Core Functionality

These are the core functionalities of the Finance Toolkit. For any calculation, it often first collects data via these functions. For example, financial ratios require the financial statements and historical data which are obtained through the Toolkit without needing to specify this first.

Acquire a full history of both annual and quarterly financial statements, including balance sheets, income statements, and cash flow statements.

These financial statements are adjusted for the following reasons:

• The financial statements are automatically standardized (based on these files to allow for the ability to enter any type of dataset given that the names used are what all of the functionalities rely on.
• The fiscal year of each company is automatically converted to the calendar year so that all companies can be compared on the same basis. As an example, Apple's Q4 2023 is related to the period July 2023 until September 2023 which corresponds to Q3 2023. This means that in the Finance Toolkit these results are reported in the Q3 2023 column.
• When convert_currency=True (automatically enabled with a Premium FMP plan) the currency of the historical data is compared to the currency of the financial statements. If they do not match, the financial statement data is converted to the currency of the historical data. This is done to ensure that calculations such as the Price-to-Earnings Ratio (PE) have both the Share Price and Earnings denoted in the same currency.

To get insights related to the reported currency, CIK ID and SEC Links, it is possible to retrieve a statististics statement as well.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["MSFT", "MU"], api_key="FINANCIAL_MODELING_PREP_KEY", quarterly=True, start_date='2022-05-01')

balance_sheet_statements = toolkit.get_balance_sheet_statement()

balance_sheet_statements.loc['MU']

Which returns:

Obtain the profile of the specified tickers. These include important metrics such as the beta, market capitalization, currency, isin, industry, and ipo date that give an overall understanding about the company.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["MSFT", "AAPL"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.get_profile()

Which returns:

Get the quote of the specified tickers. These include important metrics such as the price, changes, day low, day high, year low, year high, market capitalization, volume, average volume, open, previous close, earnings per share (EPS), price to earnings ratio (PE), earnings announcement, shares outstanding and timestamp that give an overall understanding about the company.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["TSLA", "AAPL"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.get_quote()

Which returns:

Get the rating of the specified tickers. These scores and recommendations are categorized as follows:

• An overall rating
• Discounted Cash Flow (DCF)
• Return on Equity (ROE)
• Return on Assets (ROA)
• Debt to Equity (DE)
• Price Earnings (PE)
• Price to Book (PB)

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

rating = toolkit.get_rating()

rating.loc['AMZN', 'Rating Recommendation'].tail()

Which returns:

Obtain historical market data for the specified tickers. This contains the following columns:

• Open: The opening price for the period.
• High: The highest price for the period.
• Low: The lowest price for the period.
• Close: The closing price for the period.
• Adj Close: The adjusted closing price for the period.
• Volume: The volume for the period.
• Dividends: The dividends for the period.
• Return: The return for the period.
• Volatility: The volatility for the period.
• Excess Return: The excess return for the period. This is defined as the return minus the a predefined risk free rate. Only calculated when excess_return is True.
• Excess Volatility: The excess volatility for the period. This is defined as the volatility of the excess return. Only calculated when excess_return is True.
• Cumulative Return: The cumulative return for the period.

If a benchmark ticker is selected, it also calculates the benchmark ticker together with the results. By default this is set to “SPY” (S&P 500 Index) but can be any ticker. This is relevant for calculations for models such as CAPM, Alpha and Beta.

Important to note is that when an api_key is included in the Toolkit initialization that the data collection defaults to FinancialModelingPrep which is a more stable source and utilises your subscription. However, if this is undesired, it can be disabled by setting historical_source to YahooFinance. If data collection fails from FinancialModelingPrep it automatically reverts back to YahooFinance.

You are able to specify the period which can be daily (default), weekly, monthly, quarterly or yearly.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit("AAPL", api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.get_historical_data(period="yearly")

Which returns:

It is also possible to retrieve intraday data. This has the option to get you 1 minute, 5 minute, 15 minute, 30 minute or 1 hour data. It can also be used as part of the Risk, Performance and Technicals modules when defining intraday_period as part of the Toolkit initialization.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit("MSFT", api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.get_intraday_data(period="1min")

Which returns:

Just like the historical market data, obtain a full history for the treasury rates which also serve as risk-free rate by default allowing for calculations such as the Sharpe Ratio. This also includes normalization of the data as well as auto-adjustments for missing values. It can also be obtained from both FinancialModelingPrep and Yahoo Finance.

It returns the following columns:

• 13 Week Treasury Bond
• 5 Year Treasury Bond
• 10 Year Treasury Bond
• 30 Year Treasury Bond

By default, the Finance Toolkit uses the 10 Year Treasury Bond as risk-free rate but this can be changed by setting risk_free_rate to any of the other treasury rates.

As an example:

from financetoolkit import Toolkit

companies = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2023-08-10")

companies.get_treasury_data()

Which returns:

Obtain Earnings Calendars for any range of companies. You have the option to obtain the actual dates or to convert to the corresponding quarters and can obtain a rich history. This returns:

• Date: The date of the earnings release.
• EPS: The actual earnings-per-share.
• EPS Estimate: The estimated earnings-per-share.
• Revenue: The actual revenue.
• Revenue Estimate: The estimated revenue.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
    ["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2022-08-01", quarterly=False
)

earning_calendar = toolkit.get_earnings_calendar()

earning_calendar.loc['AMZN']

Which returns:

Furthermore, find Dividend Calendars which includes:

• Date: The date of the dividend.
• Adj Dividend: The adjusted dividend amount.
• Dividend: The dividend amount.
• Record Date: The record date of the dividend.
• Payment Date: The payment date of the dividend.
• Declaration Date: The declaration date of the dividend.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
    ["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2022-08-01", quarterly=False
)

dividend_calendar = toolkit.get_dividend_calendar()

dividend_calendar.loc['AAPL']

Which returns:

Obtain the Analyst Estimates which include estimates for Revenue, Earnings-per-Share (EPS), EBITDA, EBIT, Net Income, and SGA Expense from the past and future from a large collection of analysts.

It includes the lower, average and upper bound for each estimate which gives insights whether analysts have reached a consensus on the prices or think wildly different. The larger the difference between the lower and upper bound, the more uncertain the analysts are.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
    ["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2021-05-01", quarterly=False
)

analyst_estimates = toolkit.get_analyst_estimates()

analyst_estimates.loc['AAPL']

Which returns:

Retrieve the product revenue segmentation for each company. This is for example iPhone, iPad, Mac, Wearables, Services, and Other Products for Apple and helps understand the products that grow the fastest and slowest.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
    ["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2021-05-01", quarterly=False
)

product_segmentation = toolkit.get_revenue_product_segmentation()

product_segmentation.loc['MSFT']

Which returns:

It is also possible to retrieve the geographic revenue segmentation which includes regions such as Americas, Europe, Greater China, Japan, and Rest of Asia Pacific and helps understand where companies retrieve their revenue from. As an example, a company like Microsoft might be based in the United States, their revenue streams are truly global.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
    ["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2021-05-01", quarterly=False
)

geographic_segmentation = toolkit.get_revenue_geographic_segmentation()

geographic_segmentation.loc['AAPL']

Which returns:

ESG scores, which stands for Environmental, Social, and Governance scores, are a crucial metric used by investors and organizations to assess a company’s sustainability and ethical practices. These scores provide valuable insights into a company’s performance in three key areas:

• Environmental (E): The environmental component evaluates a company’s impact on the planet and its efforts to mitigate environmental risks. It includes factors like carbon emissions, energy efficiency, water management, and waste reduction. A high environmental score indicates a company’s commitment to eco-friendly practices and reducing its ecological footprint.
• Social (S): The social component focuses on how a company interacts with its employees, customers, suppliers, and the communities in which it operates. Key factors in the social score include labor practices, diversity and inclusion, human rights, product safety, and community engagement. A strong social score reflects a company’s dedication to fostering positive relationships and contributing positively to society.
• Governance (G): Governance examines a company’s internal structures, policies, and leadership. It assesses aspects such as board independence, executive compensation, transparency, and the presence of anti-corruption measures. A high governance score signifies strong leadership and a commitment to maintaining high ethical standards and accountability

ESG scores provide investors with a holistic view of a company’s sustainability and ethical practices, allowing them to make more informed investment decisions. These scores are increasingly used to identify socially responsible investments and guide capital towards companies that prioritize long-term sustainability and responsible business practices. As the importance of ESG considerations continues to grow, companies are motivated to improve their ESG scores, not only for ethical reasons but also to attract investors who value sustainable and responsible business practices.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
    ["MSFT", "TSLA", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2022-08-01", quarterly=False
)

esg_scores = toolkit.get_esg_scores()

esg_scores.xs("MSFT", level=1, axis=1)

Which returns:

Discover Instruments

The Discovery module contains lists of companies, cryptocurrencies, forex, commodities, etfs and indices including screeners, quotes, performance metrics and more to find and select tickers to use in the Finance Toolkit. Find the Notebook here and the documentation here which includes an explanation about the functionality, the parameters and an example.

Screen stocks, obtain a list of companies, quotes, floating shares, sectors performance, biggest gainers, biggest losers, most active stocks and delisted companies.

Search Instruments

The search instruments function allows you to search for a company or financial instrument by name. It returns a dataframe with all the symbols that match the query. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

discovery.search_instruments(query='META')

Which returns:

Stock Screener

Screen stocks based on a set of criteria. This can be useful to find companies that match a specific criteria or your analysis. Further filtering can be done by utilising the Finance Toolkit and calculating the relevant ratios to filter by. This can be:

• Market capitalization (market_cap_higher, market_cap_lower)
• Price (price_higher, price_lower)
• Beta (beta_higher, beta_lower)
• Volume (volume_higher, volume_lower)
• Dividend (dividend_higher, dividend_lower)

Note that the limit is 1000 companies. Thus if you hit the 1000, it is recommended to narrow down your search to prevent companies from being excluded simply because of this limit. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

discovery.get_stock_screener(
    market_cap_higher=1000000,
    market_cap_lower=200000000000,
    price_higher=100,
    price_lower=200,
    beta_higher=1,
    beta_lower=1.5,
    volume_higher=100000,
    volume_lower=2000000,
    dividend_higher=1,
    dividend_lower=2,
    is_etf=False
)

Which returns:

Company List

The stock list function returns a complete list of all the symbols that can be used in the Finance Toolkit. These are over 60.000 symbols. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

stock_list = discovery.get_stock_list()

# The total list equals over 60.000 rows
stock_list.iloc[38000:38010]

Which returns:

Floating Shares

Returns the shares float for each company. The shares float is the number of shares available for trading for each company. It also includes the number of shares outstanding and the date. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

shares_float = discovery.get_stock_shares_float()

shares_float.iloc[50000:50010]

Which returns:

Sectors Performance

Returns the sectors performance for each sector. This features the sector performance over the last months. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

sectors_performance = discovery.get_sectors_performance()

sectors_performance.tail()

Which returns:

Biggest Gainers

Returns the biggest gainers for the day. This includes the symbol, the name, the price, the change and the change percentage. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

biggest_gainers = discovery.get_biggest_gainers()

biggest_gainers.head(10)

Which returns:

Biggest Losers

Returns the biggest losers for the day. This includes the symbol, the name, the price, the change and the change percentage. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

biggest_losers = discovery.get_biggest_losers()

biggest_losers.head(10)

Which returns:

Most Active

Returns the most active stocks for the day. This includes the symbol, the name, the price, the change and the change percentage. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

most_active_stocks = discovery.get_most_active_stocks()

most_active_stocks.head(10)

Which returns:

Delisted Companies

The delisted stocks function returns a complete list of all delisted stocks including the IPO and delisted date. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

delisted_stocks = discovery.get_delisted_stocks()

delisted_stocks.head(10)

Which returns:

Obtain cryptocurrency lists and cryptocurrency quotes that can be used in the Finance Toolkit.

Cryptocurrency List

The crypto list function returns a complete list of all crypto symbols that can be used in the Finance Toolkit. These are over 4.000 symbols. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

crypto_list = discovery.get_crypto_list()

crypto_list.head(10)

Which returns:

Obtain forex lists and forex quotes that can be used in the Finance Toolkit.

Forex List

The forex list function returns a complete list of all forex symbols that can be used in the Finance Toolkit. These are over 1.000 symbols. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

forex_list = discovery.get_forex_list()

forex_list.head(10)

Which returns:

Obtain commodity lists and company quotes that can be used in the Finance Toolkit.

Commodity List

The commodity list function returns a complete list of all commodity symbols that can be used in the Finance Toolkit. These are over 1.000 symbols. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

commodity_list = discovery.get_commodity_list()

commodity_list.head(10)

Which returns:

Obtain ETF and Index lists and quotes that can be used in the Finance Toolkit.

ETF List

The etf list function returns a complete list of all etf symbols that can be used in the Finance Toolkit. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

etf_list = discovery.get_etf_list()

etf_list.head(10)

Which returns:

Index List

The index list function returns a complete list of all etf symbols that can be used in the Finance Toolkit. Find the documentation here.

As an example:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

index_list = discovery.get_index_list()

index_list.head(10)

Which returns:

Financial Ratios

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 Notebook here and the documentation here which includes an explanation about the ratio, the parameters and an example.

It is also possible to define custom ratios and calculate these automatically based on the balance sheet, income and cash flow statements. With this, it is possible to calculate any collection of custom ratios without needing to understanding the backend of the Finance Toolkit. Learn how here.

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.

The efficiency ratios are used to assess how well a company utilizes its assets and liabilities to generate revenue. They provide insight into the company’s operational efficiency and its ability to manage its assets and liabilities.

All ratios can be called by using get_ or collect_ to get a single ratio or to obtain all ratios of the category respectively. E.g. get_asset_turnover_ratio or collect_efficiency_ratios. As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

# Collect all Ratios
toolkit.ratios.collect_efficiency_ratios()

# Get an Individual Ratio
toolkit.ratios.get_asset_turnover_ratio()

Asset Turnover Ratio

The asset turnover ratio is calculated by dividing the company’s net sales (revenue) by its average total assets. It measures how well a company utilizes its assets to generate revenue. A higher asset turnover ratio indicates that the company is generating more revenue per unit of assets, which is generally seen as a positive sign of operational efficiency. See documentation here.

Inventory Turnover Ratio

The inventory turnover ratio is calculated by dividing the cost of goods sold (COGS) by the average inventory value. It indicates how many times a company’s inventory is sold and replaced over a period. A higher inventory turnover ratio suggests that a company is effectively managing its inventory by quickly converting it into sales. See documentation here.

Days of Inventory Outstanding

The days sales in inventory ratio (DSI) is calculated by dividing the average inventory by the cost of goods sold (COGS) and then multiplying by the number of days in the period. It represents the average number of days it takes for a company to sell its inventory. A lower DSI indicates that the company is selling its inventory more quickly. See documentation here.

Days of Sales Outstanding

The days of sales outstanding (DSO) ratio is calculated by dividing the accounts receivable by the total credit sales and then multiplying by the number of days in the period. It represents the average number of days it takes for a company to collect payment on its credit sales. A lower DSO indicates that the company is collecting payments more quickly. See documentation here.

Operating Cycle

The operating cycle represents the total time required to purchase inventory, convert it into finished goods, sell the goods to customers, and collect the accounts receivable. It is calculated by adding the days sales in inventory (DSI) and the days of sales outstanding (DSO). See documentation here.

Accounts Payables Turnover Ratio

The accounts payable turnover ratio indicates how many times, on average, a company pays off its accounts payable during a specific period. A higher turnover ratio is generally favorable, as it suggests that the company is efficiently managing its payments to suppliers. See documentation here.

Days of Accounts Payable Outstanding

The days payables outstanding (DPO) ratio is used to assess how efficiently a company manages its accounts payable. It calculates the average number of days it takes for a company to pay its suppliers after receiving an invoice. A higher DPO ratio indicates that the company is taking longer to pay its suppliers, which may have implications for its relationships with suppliers. Find the documentation here.

Cash Conversion Cycle (CCC)

The Cash Conversion Cycle (CCC) is an important measure of a company’s liquidity management and efficiency in managing its working capital. It takes into account the time it takes to sell inventory, collect payments from customers, and pay suppliers. A shorter CCC indicates that a company is able to quickly convert its investments into cash, which can be a positive sign of efficient operations. Find the documentation here.

Cash Conversion Efficiency (CCE)

The cash conversion efficiency ratio is calculated by dividing the operating cash flow by the revenue. It indicates how much of a company’s sales are converted into cash. A higher cash conversion efficiency ratio is generally favorable, as it suggests that the company is able to convert its sales into cash more efficiently. Find the documentation here.

Receivables Turnover

The receivables turnover ratio is an important measure of how well a company manages its accounts receivable. It indicates how quickly a company collects payments from its customers. A higher turnover ratio is generally favorable as it suggests that the company is collecting payments more quickly, which improves its cash flow and working capital management. Find the documentation here.

SGA to Revenue Ratio

The SG&A to revenue ratio is calculated by dividing the total SG&A expenses by the company’s revenue and then multiplying by 100 to express it as a percentage. It provides insight into the efficiency of a company’s cost management and its ability to control its overhead costs. Find the documentation here.

Fixed Asset Turnover

The Fixed Asset Turnover ratio is calculated by dividing the company’s net sales by the average fixed assets. It indicates how well a company is utilizing its fixed assets to generate revenue. A higher ratio suggests more efficient utilization of fixed assets. Find the documentation here.

Operating Ratio

The operating ratio is calculated by dividing the company’s operating expenses by its net sales and multiplying by 100 to express it as a percentage. It provides insight into how efficiently a company is managing its operations. Find the documentation here.

The liquidity ratios are used to assess a company’s ability to meet its short-term obligations using its short-term assets. They provide insight into the company’s short-term financial health and its ability to cover its current obligations using its liquid assets.

All ratios can be called by using get_ or collect_ to get a single ratio or to obtain all ratios of the category respectively. E.g. get_current_ratio or collect_liquidity_ratios. As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

# Collect all Ratios
toolkit.ratios.collect_liquidity_ratios()

# Get an Individual Ratio
toolkit.ratios.get_current_ratio()

Current Ratio

The current ratio is calculated