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 methods (500+) 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 500+ methods 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.

Installation
Functionality
MCP Server
Questions & Answers
Contributing
Mentions
Contact

Installation

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

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=['AAPL', 'MSFT'],
    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.

Obtain an API Key from FinancialModelingPrep here.

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.

The same enforce_source argument is also accepted per call on get_historical_data, get_treasury_data and the four statement functions (get_balance_sheet_statement, get_income_statement, get_cash_flow_statement and get_statistics_statement), where it overrides whatever the Toolkit was initialised with.

Functionality

This section is an introduction to the Finance Toolkit. The documentation contains many more examples: every module page opens with a Getting Started section, from the basics to creating custom ratios and working with your own datasets.

Find the Documentation, including the Getting Started guides, here.

The sections below follow one analysis of the semiconductor sector from start to finish, where each step answers the question the previous one raises: which companies make up the sector, how their shares did, what drives their profits, how much risk came with the returns, what the option market expects next, how the economy shaped all of it, whether a fund is the better way in and how the sector fits into a diversified portfolio. Every module of the Finance Toolkit appears once, but each holds far more than shown here: 500+ methods in total, all described with examples in the documentation.

Discovering the Sector

An analysis starts with a set of companies. The Discovery module finds them without needing any tickers yet, through stock screeners, sector performance, news and lists of companies, ETFs, indices, currencies, cryptocurrencies and commodities.

The screener below looks for semiconductor companies on NASDAQ that are worth more than $100 billion:

from financetoolkit import Discovery

# Initialize the standalone Discovery module
discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

# Find US semiconductor companies worth more than $100 billion
semiconductors = discovery.get_stock_screener(
    industry="Semiconductors",
    country="US",
    exchange="NASDAQ",
    market_cap_higher=100_000_000_000,
    is_etf=False,
)

# Show the five largest
semiconductors[["Name", "Market Cap", "Beta", "Price", "Dividend"]].head()

For example, the five largest companies the screener returns are shown below.

Symbol	Name	Market Cap	Beta	Price	Dividend
NVDA	NVIDIA Corporation	5084136000000	2.216	210.96	0.28
AVGO	Broadcom Inc.	1535581254973	1.476	321.68	2.6
MU	Micron Technology, Inc.	992877621000	2.226	877.55	0.565
AMD	Advanced Micro Devices, Inc.	843646776955	2.454	516.79	0
INTC	Intel Corp.	459679713584	2.23	86.96	0

And below all of them are ranked by market cap.

Following the Market

The rest of this walkthrough follows three of them: Nvidia, AMD and Intel. They are loaded into the Toolkit in one line, and the S&P 500 comes along as the benchmark:

from financetoolkit import Toolkit

# Initialize the Toolkit for Nvidia, AMD and Intel from 2018 to 2025
companies = Toolkit(
    ["NVDA", "AMD", "INTC"],
    api_key="FINANCIAL_MODELING_PREP_KEY",
    start_date="2017-12-31",
    end_date="2025-12-31",
)

Historical data comes on a daily, weekly, monthly, quarterly or yearly basis, with the return and cumulative return of each period, and so do the income statement, balance sheet and cash flow statement on an annual or quarterly basis. How far a share has travelled is one thing; whether the rally was stretched is another. The technicals module answers that with 50+ indicators, such as the Relative Strength Index (RSI), which moves between 0 and 100 and is commonly read as overbought above 70:

# Obtain weekly market data for all tickers and the benchmark
historical_data = companies.get_historical_data(period="weekly")

# The 14-week Relative Strength Index
relative_strength_index = companies.technicals.get_relative_strength_index(period="weekly", window=14)

# Show the highest RSI of each year
relative_strength_index.groupby(relative_strength_index.index.start_time.year).max()

For example, the highest weekly RSI of each year is shown below.

Date	NVDA	AMD	INTC	Benchmark
2018	66.1087	90.9719	66.7363	66.0498
2019	73.8156	81.2446	69.8225	75.9305
2020	85.2337	82.1215	78.4827	80.2378
2021	85.1593	82.0983	71.5862	74.4751
2022	58.4343	58.9426	61.2806	59.9225
2023	87.5129	77.506	76.2332	71.4291
2024	91.0702	82.1403	67.6949	80.0566
2025	73.3514	82.5281	79.7429	71.6939

And below the cumulative returns of all three are plotted against the S&P 500, with the RSI underneath.

Zooming in on Competitors

Nvidia's shares outran the other two by far, which raises the question whether its business did as well. From the financial statements the Toolkit calculates 80+ ratios in five categories: efficiency, liquidity, profitability, solvency and valuation. Each ratio has its own function, and a collect_ function returns a whole category at once:

# Collect all Profitability Ratios for all tickers
profitability_ratios = companies.ratios.collect_profitability_ratios()

# Show the first ratios for Nvidia
profitability_ratios.loc["NVDA"].head()

For example, some of Nvidia's profitability ratios are shown below.

	2017	2018	2019	2020	2021	2022	2023	2024	2025
Gross Margin	0.5993	0.6121	0.6199	0.6234	0.6493	0.5693	0.7272	0.7499	0.7107
Operating Margin	0.3305	0.3247	0.2607	0.2718	0.3731	0.1566	0.5412	0.6242	0.6038
Net Profit Margin	0.3137	0.3534	0.2561	0.2598	0.3623	0.1619	0.4885	0.5585	0.556
EBITDA Margin	0.3509	0.347	0.2956	0.3376	0.4167	0.2138	0.566	0.6385	0.617
Free Cash Flow Margin	0.2995	0.2683	0.3913	0.2815	0.3021	0.1412	0.4435	0.4663	0.4477

And below a few of them are shown with their latest value and their course over the years.

A high return on equity can come from margins, from turnover or from debt. Models combine the ratios to tell these apart: the Extended DuPont Analysis splits the return on equity into five components that multiply into it: interest burden, tax burden, operating margin, asset turnover and equity multiplier.

# Get the Extended DuPont Analysis for all tickers
extended_dupont_analysis = companies.models.get_extended_dupont_analysis()

# Show the results for Nvidia
extended_dupont_analysis.loc["NVDA"]

For example, the components for Nvidia are shown below.

	2017	2018	2019	2020	2021	2022	2023	2024	2025
Interest Burden Ratio	0.9956	1.0242	1.0436	0.9729	0.99	0.9898	1.0257	1.0316	1.0848
Tax Burden Ratio	0.9534	1.0629	0.9414	0.9825	0.981	1.0447	0.88	0.8674	0.8488
Operating Profit Margin	0.3305	0.3247	0.2607	0.2718	0.3731	0.1566	0.5412	0.6242	0.6038
Asset Turnover	0.9215	0.9551	0.7134	0.7233	0.7376	0.6319	1.1397	1.4718	1.3564
Equity Multiplier	1.5931	1.4592	1.4205	1.5846	1.6775	1.7525	1.6428	1.4499	1.3456
Return on Equity	0.4605	0.4926	0.2595	0.2978	0.4483	0.1793	0.9146	1.1918	1.0149

And below each component is shown for every fiscal year, with the latest value on top, multiplying into the return on equity.

Other models include the Weighted Average Cost of Capital, Altman Z-Score, Piotroski F-Score and Intrinsic Valuation. Almost every function also accepts growth=True for the growth from one period to the next, trailing=4 for trailing twelve-month figures and standardize=True for Z-Scores.

Weighing Risk and Return

Strong businesses do not make for smooth shares. The market data feeds the performance and risk modules, which calculate their metrics for any period, from intraday to yearly:

# The annualized Sharpe Ratio for each year
sharpe_ratio = companies.performance.get_sharpe_ratio(period="yearly", annualize=True)

# The deepest fall within each year
maximum_drawdown = companies.risk.get_maximum_drawdown(period="yearly", within_period=True)

# Show the Sharpe Ratios
sharpe_ratio

For example, the yearly Sharpe Ratios are shown below.

Date	NVDA	AMD	INTC
2018	-0.613	1.1009	0.161
2019	1.5458	1.9143	1.048
2020	1.656	1.419	-0.0445
2021	2.0003	1.2366	0.2976
2022	-0.8393	-1.0532	-1.5306
2023	2.6986	1.9195	1.8358
2024	2.0851	-0.2615	-1.5749
2025	0.8325	1.1753	1.2061

And below both are plotted for each year, the maximum drawdown together with the S&P 500.

Together the two modules cover 70+ metrics, such as Value at Risk, GARCH, Jensen's Alpha, the Capital Asset Pricing Model and the Fama-French factors. Most of them also accept rolling=<n> for a value that moves through time.

What the Option Market Expects

The past shows how much risk came with the returns; the option market puts a price on the risk ahead. The options module prices options with Black-Scholes, the Binomial Model and others, calculates the Greeks up to the third order and reads the live option chains: the put-c

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