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Finance Toolkit

★ 5,100

from JerBouma

The Finance Toolkit gives AI assistants access to 200+ financial metrics, all calculated transparently from raw financial statements, not pulled from third-party endpoints.

🔥🔥🔥🔥✓ VerifiedAccount requiredQuick setup
#automation#calendar#payments#database#documents#git#finance#news

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

  • Installation

  • Basic Usage

  • Functionality and Metrics

  • MCP Server

  • Questions & Answers

  • Contributing

  • Mentions

  • Contact

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.

Find a variety of How-To Guides including Code Documentation for the Finance Toolkit here.

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.

Financial Statements 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:

Copy & paste — that's it
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:

2022Q2 2022Q3 2022Q4 2023Q1 2023Q2 Cash and Cash Equivalents 9.157e+09 8.262e+09 9.574e+09 9.798e+09 9.298e+09 Short Term Investments 1.07e+09 1.069e+09 1.007e+09 1.02e+09 1.054e+09 Cash and Short Term Investments 1.0227e+10 9.331e+09 1.0581e+10 1.0818e+10 1.0352e+10 Accounts Receivable 6.229e+09 5.13e+09 3.318e+09 2.278e+09 2.429e+09 Inventory 5.629e+09 6.663e+09 8.359e+09 8.129e+09 8.238e+09 Other Current Assets 6.08e+08 6.44e+08 6.63e+08 6.73e+08 7.15e+08 Total Current Assets 2.2708e+10 2.1781e+10 2.2921e+10 2.1898e+10 2.1734e+10 Property, Plant and Equipment 3.7355e+10 3.9227e+10 4.0028e+10 3.9758e+10 3.9382e+10 Goodwill 1.228e+09 1.228e+09 1.228e+09 1.228e+09 1.252e+09 Intangible Assets 4.15e+08 4.21e+08 4.28e+08 4.1e+08 4.1e+08 Long Term Investments 1.646e+09 1.647e+09 1.426e+09 1.212e+09 9.73e+08 Tax Assets 6.82e+08 7.02e+08 6.72e+08 6.97e+08 7.08e+08 Other Fixed Assets 1.262e+09 1.277e+09 1.171e+09 1.317e+09 1.221e+09 Fixed Assets 4.2588e+10 4.4502e+10 4.4953e+10 4.4622e+10 4.3946e+10 Other Assets 0 0 0 0 0 Total Assets 6.5296e+10 6.6283e+10 6.7874e+10 6.652e+10 6.568e+10 Accounts Payable 2.019e+09 2.142e+09 1.789e+09 1.689e+09 1.64e+09 Short Term Debt 1.07e+08 1.03e+08 1.71e+08 2.37e+08 2.59e+08 Tax Payables 3.82e+08 4.2e+08 4.19e+08 2.41e+08 1.48e+08 Deferred Revenue 0 0 0 0 -1.64e+09 Other Current Liabilities 4.883e+09 5.294e+09 4.565e+09 3.329e+09 4.845e+09 Total Current Liabilities 7.009e+09 7.539e+09 6.525e+09 5.255e+09 5.104e+09 Long Term Debt 7.485e+09 7.413e+09 1.0719e+10 1.2647e+10 1.3589e+10 Deferred Revenue Non Current 6.63e+08 5.89e+08 5.16e+08 5.29e+08 6.32e+08 Deferred Tax Liabilities 0 0 0 0 0 Other Non Current Liabilities 8.58e+08 8.35e+08 8.08e+08 8.32e+08 9.5e+08 Total Non Current Liabilities 9.006e+09 8.837e+09 1.2043e+10 1.4008e+10 1.5171e+10 Other Liabilities 0 0 0 0 0 Capital Lease Obligations 6.29e+08 6.1e+08 6.25e+08 6.1e+08 6.03e+08 Total Liabilities 1.6015e+10 1.6376e+10 1.8568e+10 1.9263e+10 2.0275e+10 Preferred Stock 0 0 0 0 0 Common Stock 1.22e+08 1.23e+08 1.23e+08 1.23e+08 1.24e+08 Retained Earnings 4.5916e+10 4.7274e+10 4.6873e+10 4.4426e+10 4.2391e+10 Accumulated Other Comprehensive Income -3.64e+08 -5.6e+08 -4.73e+08 -3.73e+08 -3.4e+08 Other Total Shareholder Equity 3.607e+09 3.07e+09 2.783e+09 3.081e+09 3.23e+09 Total Shareholder Equity 4.9281e+10 4.9907e+10 4.9306e+10 4.7257e+10 4.5405e+10 Total Equity 4.9281e+10 4.9907e+10 4.9306e+10 4.7257e+10 4.5405e+10 Total Liabilities and Shareholder Equity 6.5296e+10 6.6283e+10 6.7874e+10 6.652e+10 6.568e+10 Minority Interest 0 0 0 0 0 Total Liabilities and Equity 6.5296e+10 6.6283e+10 6.7874e+10 6.652e+10 6.568e+10 Total Investments 2.716e+09 2.716e+09 2.433e+09 2.232e+09 2.027e+09 Total Debt 7.592e+09 7.516e+09 1.089e+10 1.2884e+10 1.3848e+10 Net Debt -1.565e+09 -7.46e+08 1.316e+09 3.086e+09 4.55e+09

Company Overviews 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:

Copy & paste — that's it
from financetoolkit import Toolkit

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

toolkit.get_profile()

Which returns:

MSFT AAPL Symbol MSFT AAPL Price 316.48 174.49 Beta 0.903706 1.286802 Average Volume 28153120 57348456 Market Capitalization 2353183809372 2744500935588 Last Dividend 2.7199999999999998 0.96 Range 213.43-366.78 124.17-198.23 Changes -0.4 0.49 Company Name Microsoft Corporation Apple Inc. Currency USD USD CIK 789019 320193 ISIN US5949181045 US0378331005 CUSIP 594918104 37833100 Exchange NASDAQ Global Select NASDAQ Global Select Exchange Short Name NASDAQ NASDAQ Industry Software - Infrastructure Consumer Electronics Website https://www.microsoft.com https://www.apple.com CEO Mr. Satya Nadella Mr. Timothy D. Cook Sector Technology Technology Country US US Full Time Employees 221000 164000 Phone 425 882 8080 408 996 1010 Address One Microsoft Way One Apple Park Way City Redmond Cupertino State WA CA ZIP Code 98052-6399 95014 DCF Difference 4.56584 4.15176 DCF 243.594 150.082 IPO Date 1986-03-13 1980-12-12

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:

Copy & paste — that's it
from financetoolkit import Toolkit

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

toolkit.get_quote()

Which returns:

TSLA AAPL Symbol TSLA AAPL Name Tesla, Inc. Apple Inc. Price 215.49 174.49 Changes Percentage -1.7015 0.2816 Change -3.73 0.49 Day Low 212.36 171.96 Day High 217.58 175.1 Year High 313.8 198.23 Year Low 101.81 124.17 Market Capitalization 682995534313 2744500935588 Price Average 50 Days 258.915 187.129 Price Average 200 Days 196.52345 161.4698 Exchange NASDAQ NASDAQ Volume 136276584 61172150 Average Volume 133110158 57348456 Open 214.12 172.3 Previous Close 219.22 174 EPS 3.08 5.89 PE 69.96 29.62 Earnings Announcement 2023-10-17T20:00:00.000+0000 2023-10-25T10:59:00.000+0000 Shares Outstanding 3169499904 15728700416 Timestamp 2023-08-18 20:00:00 2023-08-18 20:00:01

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:

Copy & paste — that's it
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:

date Rating Recommendation 2023-08-01 00:00:00 Strong Buy 2023-08-02 00:00:00 Strong Buy 2023-08-03 00:00:00 Strong Buy 2023-08-04 00:00:00 Strong Buy 2023-08-07 00:00:00 Strong Buy

(Intraday) Historical Market Data 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:

Copy & paste — that's it
from financetoolkit import Toolkit

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

toolkit.get_historical_data(period="yearly")

Which returns:

Date Open High Low Close Adj Close Volume Dividends Return Volatility Excess Return Excess Volatility Cumulative Return 2013 19.7918 20.0457 19.7857 20.0364 17.5889 2.23084e+08 0.108929 0 0.240641 0 0.244248 1 2014 28.205 28.2825 27.5525 27.595 24.734 1.65614e+08 0.461429 0.406225 0.216574 0.384525 0.219536 1.40623 2015 26.7525 26.7575 26.205 26.315 23.9886 1.63649e+08 0.5075 -0.0301373 0.267373 -0.0528273 0.269845 1.36385 2016 29.1625 29.3 28.8575 28.955 26.9824 1.22345e+08 0.5575 0.124804 0.233383 0.100344 0.240215 1.53406 2017 42.63 42.6475 42.305 42.3075 40.0593 1.04e+08 0.615 0.484644 0.176058 0.460594 0.17468 2.27753 2018 39.6325 39.84 39.12 39.435 37.9 1.40014e+08 0.705 -0.0539019 0.287421 -0.0807619 0.289905 2.15477 2019 72.4825 73.42 72.38 73.4125 71.615 1.00806e+08 0.76 0.889578 0.261384 0.870388 0.269945 4.0716 2020 134.08 134.74 131.72 132.69 130.559 9.91166e+07 0.8075 0.823067 0.466497 0.813897 0.470743 7.4228 2021 178.09 179.23 177.26 177.57 175.795 6.40623e+07 0.865 0.346482 0.251019 0.331362 0.251429 9.99467 2022 128.41 129.95 127.43 129.93 129.378 7.70342e+07 0.91 -0.264042 0.356964 -0.302832 0.377293 7.35566 2023 187.84 188.51 187.68 188.108 188.108 4.72009e+06 0.71 0.453941 0.213359 0.412901 0.22327 10.6947

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:

Copy & paste — that's it
from financetoolkit import Toolkit

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

toolkit.get_intraday_data(period="1min")

Which returns:

date Open High Low Close Volume Return Volatility Cumulative Return 2024-01-19 15:45 397.64 397.88 397.63 397.88 49202 0.0006 0.0005 1.0266 2024-01-19 15:46 397.86 397.93 397.788 397.82 68913 -0.0002 0.0005 1.0264 2024-01-19 15:47 397.81 397.97 397.76 397.78 62605 -0.0001 0.0005 1.0263 2024-01-19 15:48 397.78 397.85 397.675 397.845 62146 0.0002 0.0005 1.0265 2024-01-19 15:49 397.85 397.97 397.8 397.94 72700 0.0002 0.0005 1.0267 2024-01-19 15:50 397.92 398.27 397.9 398.04 140754 0.0003 0.0005 1.027 2024-01-19 15:51 398.04 398.15 397.96 398 122208 -0.0001 0.0005 1.0269 2024-01-19 15:52 397.99 398.26 397.98 398.05 83546 0.0001 0.0005 1.027 2024-01-19 15:53 398.04 398.12 397.98 398.09 85098 0.0001 0.0005 1.0271 2024-01-19 15:54 398.1 398.52 398.03 398.45 187358 0.0009 0.0005 1.028 2024-01-19 15:55 398.45 398.62 398.25 398.335 237902 -0.0003 0.0005 1.0278 2024-01-19 15:56 398.33 398.44 398.3 398.415 149157 0.0002 0.0005 1.028 2024-01-19 15:57 398.42 398.5 398.29 398.43 181074 0 0.0005 1.028 2024-01-19 15:58 398.46 398.47 398.29 398.35 278802 -0.0002 0.0005 1.0278 2024-01-19 15:59 398.35 398.66 398.22 398.66 586344 0.0008 0.0005 1.0286

Treasury Rates 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:

Copy & paste — that's it
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:

date 13 Week 5 Year 10 Year 30 Year 2023-10-16 0.0533 0.0472 0.0471 0.0487 2023-10-17 0.0534 0.0487 0.0485 0.0495 2023-10-18 0.0533 0.0492 0.049 0.05 2023-10-19 0.0531 0.0496 0.0499 0.051 2023-10-20 0.053 0.0491 0.0496 0.0512

Earnings & Dividend Calendars 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:

Copy & paste — that's it
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:

date EPS Estimated EPS Revenue Estimated Revenue Fiscal Date Ending Time 2022-10-27 0.17 0.22 1.27101e+11 nan 2022-09-30 amc 2023-02-02 0.25 0.18 1.49204e+11 1.5515e+11 2022-12-31 amc 2023-04-27 0.31 0.21 1.27358e+11 1.24551e+11 2023-03-31 amc 2023-08-03 0.65 0.35 1.34383e+11 1.19573e+11 2023-06-30 amc 2023-10-25 nan 0.56 nan 1.41407e+11 2023-09-30 amc 2024-01-31 nan nan nan nan 2023-12-30 amc 2024-04-25 nan nan nan nan 2024-03-30 amc 2024-08-01 nan nan nan nan 2024-06-30 amc

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:

Copy & paste — that's it
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:

date Adj Dividend Dividend Record Date Payment Date Declaration Date 2022-08-05 0.23 0.23 2022-08-08 2022-08-11 2022-07-28 2022-11-04 0.23 0.23 2022-11-07 2022-11-10 2022-10-27 2023-02-10 0.23 0.23 2022-12-28 2023-02-16 2022-12-19 2023-05-12 0.24 0.24 2023-05-15 2023-05-18 2023-05-04 2023-08-11 0.24 0.24 2023-08-14 2023-08-17 2023-08-03

Analyst Estimates 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:

Copy & paste — that's it
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:

2021 2022 2023 2024 Estimated Revenue Low 2.98738e+11 3.07919e+11 3.3871e+11 2.93633e+11 Estimated Revenue High 4.48107e+11 4.61878e+11 5.08066e+11 4.4045e+11 Estimated Revenue Average 3.73422e+11 3.84898e+11 4.23388e+11 3.67042e+11 Estimated EBITDA Low 8.50991e+10 1.00742e+11 1.10816e+11 1.07415e+11 Estimated EBITDA High 1.27649e+11 1.51113e+11 1.66224e+11 1.61122e+11 Estimated EBITDA Average 1.06374e+11 1.25928e+11 1.3852e+11 1.34269e+11 Estimated EBIT Low 7.62213e+10 9.05428e+10 9.9597e+10 9.81566e+10 Estimated EBIT High 1.14332e+11 1.35814e+11 1.49396e+11 1.47235e+11 Estimated EBIT Average 9.52766e+10 1.13178e+11 1.24496e+11 1.22696e+11 Estimated Net Income Low 6.54258e+10 7.62265e+10 8.38492e+10 8.23371e+10 Estimated Net Income High 9.81387e+10 1.1434e+11 1.25774e+11 1.23506e+11 Estimated Net Income Average 8.17822e+10 9.52832e+10 1.04811e+11 1.02921e+11 Estimated SGA Expense Low 1.48491e+10 1.85317e+10 2.03848e+10 2.04857e+10 Estimated SGA Expense High 2.22737e+10 2.77975e+10 3.05772e+10 3.07286e+10 Estimated SGA Expense Average 1.85614e+10 2.31646e+10 2.5481e+10 2.56072e+10 Estimated EPS Average 4.26 5.465 6.01 6.2612 Estimated EPS High 5.12 6.56 7.21 7.5135 Estimated EPS Low 3.4 4.37 4.81 5.009 Number of Analysts 14 16 12 10

Revenue Segmentations 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:

Copy & paste — that's it
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:

2022Q2 2022Q3 2022Q4 2023Q1 2023Q2 Devices 1.581e+09 1.448e+09 1.43e+09 1.282e+09 1.361e+09 Enterprise Services 1.902e+09 1.876e+09 1.862e+09 2.007e+09 1.977e+09 Gaming 3.455e+09 3.61e+09 4.758e+09 3.607e+09 3.491e+09 Linked In Corporation 3.712e+09 3.663e+09 3.876e+09 3.697e+09 3.909e+09 Office Products And Cloud Services 1.1639e+10 1.1548e+10 1.1837e+10 1.2438e+10 1.2905e+10 Other Products And Services 1.403e+09 1.348e+09 1.359e+09 1.428e+09 -3.924e+09 Search And News Advertising 2.926e+09 2.928e+09 3.223e+09 3.045e+09 3.012e+09 Server Products And Cloud Services 1.8839e+10 1.8388e+10 1.9594e+10 2.0025e+10 2.1963e+10 Windows 6.408e+09 5.313e+09 4.808e+09 5.328e+09 6.058e+09

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:

Copy & paste — that's it
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:

2020 2021 2022 2023 Americas 4.631e+10 5.1496e+10 4.9278e+10 3.5383e+10 Asia Pacific 8.225e+09 9.81e+09 9.535e+09 5.63e+09 China 2.1313e+10 2.5783e+10 2.3905e+10 1.5758e+10 Europe 2.7306e+10 2.9749e+10 2.7681e+10 2.0205e+10 Japan 8.285e+09 7.107e+09 6.755e+09 4.821e+09

ESG Scores 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:

Copy & paste — that's it
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:

date Environmental Score Social Score Governance Score ESG Score 2022Q3 72.42 58.39 61.13 63.98 2022Q4 72.22 58.05 61.27 63.85 2023Q1 72.6 58.74 61.88 64.41 2023Q2 73.54 60.73 63.44 65.9

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.

Companies 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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

discovery.search_instruments(query='META')

Which returns:

Symbol Name Currency Exchange Exchange Code META Meta Platforms, Inc. USD NASDAQ Global Select NASDAQ META.L WisdomTree Industrial Metals Enhanced USD London Stock Exchange LSE METAUSD Metadium USD USD CCC CRYPTO META.MI WisdomTree Industrial Metals Enhanced EUR Milan MIL META.JK PT Nusantara Infrastructure Tbk IDR Jakarta Stock Exchange JKT

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:

Copy & paste — that's it
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:

Symbol Name Market Cap Sector Industry Beta Price Dividend Volume Exchange Exchange Code Country NKE NIKE, Inc. 163403295604 Consumer Cyclical Footwear & Accessories 1.079 107.36 1.48 1045865 New York Stock Exchange NYSE US SAF.PA Safran SA 66234006559 Industrials Aerospace & Defense 1.339 160.16 1.35 119394 Paris EURONEXT FR ROST Ross Stores, Inc. 46724188589 Consumer Cyclical Apparel Retail 1.026 138.785 1.34 169879 NASDAQ Global Select NASDAQ US HES Hess Corporation 44694706090 Energy Oil & Gas E&P 1.464 145.51 1.75 123147 New York Stock Exchange NYSE US

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:

Copy & paste — that's it
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:

Symbol Name Price Exchange Exchange Code LEO.V Lion Copper and Gold Corp. 0.09 Toronto Stock Exchange Ventures TSX LEOF.TA Lewinsky-Ofer Ltd. 263.1 Tel Aviv TLV LEON Leone Asset Management, Inc. 0.066 Other OTC OTC LEON.SW Leonteq AG 34.35 Swiss Exchange SIX LER.AX Leaf Resources Limited 0.014 Australian Securities Exchange ASX LERTHAI.BO LERTHAI FINANCE LIMITED 265 Bombay Stock Exchange BSE LES.WA Less S.A. 0.22 Warsaw Stock Exchange WSE LESAF Le Saunda Holdings Limited 0.071 Other OTC PNK LESHAIND.BO Lesha Industries Limited 4.68 Bombay Stock Exchange BSE LESL Leslie's, Inc. 6.91 NASDAQ Global Select NASDAQ

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:

Copy & paste — that's it
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:

Symbol Date Free Float Float Shares Outstanding Shares OPY.AX NaT 51.4746 119853548 2.3284e+08 OPYGY NaT 4.49504 60892047 1.35465e+09 OQAL 2024-01-01 13:12:23 0 0 226543 OQLGF 2023-12-31 21:48:07 0.6765 1150607 1.70082e+08 OR 2024-01-02 05:18:03 99.3281 183921869 1.85166e+08 OR-R.BK 2024-01-01 05:29:30 23.153 2778360000 1.2e+10 OR.BK 2024-01-02 03:52:39 22.7847 2734164000 1.2e+10 OR.PA 2024-01-02 07:57:35 45.2727 242084445 5.34725e+08 OR.SW 2023-12-31 13:38:10 45.2727 355743960 7.8578e+08 OR.TO 2023-12-31 17:56:33 99.3317 183928535 1.85166e+08

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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

sectors_performance = discovery.get_sectors_performance()

sectors_performance.tail()

Which returns:

Date Utilities Basic Materials Communication Services Consumer Cyclical Consumer Defensive Energy Financial Services Healthcare Industrials Real Estate Technology 2023-12-27 0.13511 0.40986 -0.23963 0.10358 0.48048 -0.27499 0.30153 0.75715 0.30234 0.35946 0.02372 2023-12-28 0.80513 -0.45131 -0.15858 -0.45874 0.03828 -0.81641 0.02954 -0.01345 0.22808 0.59612 -0.15283 2023-12-29 -0.01347 -0.14525 -0.15072 -0.58879 0.18141 -0.42463 -0.34718 -0.082 -0.2181 -0.52222 -0.57062 2024-01-01 -0.01347 -0.14536 -0.15074 -0.58877 0.18141 -0.41917 -0.34753 -0.08193 -0.21821 -0.52216 -0.5708 2024-01-02 -0.01347 -0.14536 -0.15074 -0.58877 0.18141 -0.41917 -0.34779 -0.08193 -0.21823 -0.52281 -0.57073

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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

biggest_gainers = discovery.get_biggest_gainers()

biggest_gainers.head(10)

Which returns:

Symbol Name Change Price Change % AAME Atlantic American Corporation 0.3001 2.4501 13.9581 ADAP Adaptimmune Therapeutics plc 0.1029 0.793 14.9109 ADTX Aditxt, Inc. 1.81 6.63 37.5519 AFMD Affimed N.V. 0.0861 0.625 15.977 AIH Aesthetic Medical International Holdings Group Limited 0.1016 0.6896 17.2789 ANTE AirNet Technology Inc. 0.1229 0.8299 17.3833 APRE Aprea Therapeutics, Inc. 1.04 4.7 28.4153 ASTR Astra Space, Inc. 0.55 2.28 31.7919 BHG Bright Health Group, Inc. 2.37 7.63 45.057 BROG Brooge Energy Limited 0.73 3.68 24.7458

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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

biggest_losers = discovery.get_biggest_losers()

biggest_losers.head(10)

Which returns:

Symbol Name Change Price Change % AGAE Allied Gaming & Entertainment Inc. -0.2 1.06 -15.873 AVTX Avalo Therapeutics, Inc. -2.7339 9.1 -23.1023 BAYAR Bayview Acquisition Corp Right -0.03 0.12 -20 BBLG Bone Biologics Corporation -1.48 4.52 -24.6667 BKYI BIO-key International, Inc. -0.6 3 -16.6667 BREA Brera Holdings PLC Class B Ordinary Shares -0.2064 0.6112 -25.2446 BTBT Bit Digital, Inc. -0.86 4.23 -16.8959 BTCS BTCS Inc. -0.69 1.63 -29.7414 BTDR Bitdeer Technologies Group -3.36 9.86 -25.416 BYN Banyan Acquisition Corporation -2.035 10.9 -15.7325

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:

Copy & paste — that's it
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:

Symbol Name Change Price Change % AAPL Apple Inc. -1.05 192.53 -0.5424 ADTX Aditxt, Inc. 1.81 6.63 37.5519 AMD Advanced Micro Devices, Inc. -1.35 147.41 -0.9075 AMZN Amazon.com, Inc. -1.44 151.94 -0.9388 BAC Bank of America Corporation -0.21 33.67 -0.6198 BITF Bitfarms Ltd. -0.41 2.91 -12.3494 BITO ProShares Bitcoin Strategy ETF -0.33 20.49 -1.585 CAN Canaan Inc. -0.5 2.31 -17.7936 CLSK CleanSpark, Inc. -2.08 11.03 -15.8657 DISH DISH Network Corporation 0.11 5.77 1.9435

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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

delisted_stocks = discovery.get_delisted_stocks()

delisted_stocks.head(10)

Which returns:

Symbol Name Exchange IPO Date Delisted Date AAIC Arlington Asset Investment Corp. NYSE 1997-12-23 2023-12-14 ABCM Abcam plc NASDAQ 2010-12-03 2023-12-12 ADZ DB Agriculture Short ETN AMEX 2008-04-16 2023-10-27 AENZ Aenza S.A.A. NYSE 2013-07-24 2023-12-08 AKUMQ Akumin Inc NASDAQ 2018-03-08 2023-10-25 ALTMW Kinetik Holdings Inc - Warrants (09/11/2023) NASDAQ 2017-05-01 2023-11-07 ARCE Arco Platform Limited NASDAQ 2018-09-26 2023-12-07 ARTEW Artemis Strategic Investment Corporation NASDAQ 2021-11-22 2023-11-03 ASPAU Abri SPAC I, Inc. NASDAQ 2021-08-10 2023-11-02 AVID Avid Technology, Inc. NASDAQ 1993-03-12 2023-11-07

Cryptocurrencies** 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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

crypto_list = discovery.get_crypto_list()

crypto_list.head(10)

Which returns:

Symbol Name Currency Exchange .ALPHAUSD .Alpha USD USD CCC 00USD 00 Token USD USD CCC 0NEUSD Stone USD USD CCC 0X0USD 0x0.ai USD USD CCC 0X1USD 0x1.tools: AI Multi-tool Plaform USD USD CCC 0XAUSD 0xApe USD USD CCC 0XBTCUSD 0xBitcoin USD USD CCC 0XENCRYPTUSD Encryption AI USD USD CCC 0XGASUSD 0xGasless USD USD CCC 0XMRUSD 0xMonero USD USD CCC

Forex** 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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

forex_list = discovery.get_forex_list()

forex_list.head(10)

Which returns:

Symbol Name Currency Exchange AEDAUD AED/AUD AUD CCY AEDBHD AED/BHD BHD CCY AEDCAD AED/CAD CAD CCY AEDCHF AED/CHF CHF CCY AEDDKK AED/DKK DKK CCY AEDEUR AED/EUR EUR CCY AEDGBP AED/GBP GBP CCY AEDILS AED/ILS ILS CCY AEDINR AED/INR INR CCY AEDJOD AED/JOD JOD CCY

Commodities** 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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

commodity_list = discovery.get_commodity_list()

commodity_list.head(10)

Which returns:

Symbol Name Currency Exchange ALIUSD Aluminum Futures USD COMEX BZUSD Brent Crude Oil USD ICE CCUSD Cocoa USD ICE CLUSD Crude Oil USD CME CTUSX Cotton USX ICE DCUSD Class III Milk Futures USD CME DXUSD US Dollar USD ICE ESUSD E-Mini S&P 500 USD CME GCUSD Gold Futures USD CME GFUSX Feeder Cattle Futures USX CME

ETFs & Indices** 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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

etf_list = discovery.get_etf_list()

etf_list.head(10)

Which returns:

Symbol Name Price Exchange Exchange Code 01002T.TW Cathay No.1 REIT 17.29 Taiwan TAI 020Y.L iShares IV Public Limited Company - iShares Euro Government Bond 20yr Target Duration UCITS ETF 3.9522 London Stock Exchange LSE 069500.KS KODEX 200 36390 KSE KSC 069660.KS KOSEF 200 36370 KSE KSC 091160.KS Kodex Semicon 36840 KSE KSC 091170.KS Kodex Banks 6695 KSE KSC 091180.KS Kodex Autos 19450 KSE KSC 091220.KS Mirae Asset TIGER Banks ETF 6845 KSE KSC 091230.KS Mirae Asset TIGER Semicon ETF 38400 KSE KSC 098560.KS Mirae Asset TIGER Media & Telecom ETF 7335 KSE KSC

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:

Copy & paste — that's it
from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

index_list = discovery.get_index_list()

index_list.head(10)

Which returns:

Symbol Name Currency Exchange 000001.SS SSE Composite Index CNY Shanghai 399967.SZ CSI NATIONAL DEFENSE CNY Shenzhen 512.HK CES CHINA HK MAINLAND INDEX HKD HKSE DX-Y.NYB US Dollar/USDX - Index - Cash USD ICE Futures FTSEMIB.MI FTSE MIB Index EUR Milan IAR.BA MERVAL ARGENTINA USD Buenos Aires IDX30.JK IDX30 IDR Jakarta Stock Exchange IMOEX.ME MOEX Russia Index RUB MCX ITLMS.MI FTSE Italia All-Share Index EUR Milan KOSPI200.KS KOSPI 200