Finance Toolkit MCP Server

<!-- mcp-name: io.github.JerBouma/financetoolkit -->

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.

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🔌 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.

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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.

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

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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.

date	Open	High	Low	Close	Adj Close	Volume	Dividends	Return	Volatility	Excess Return	Excess Volatility	Cumulative Return
2018-01-02	42.54	43.075	42.315	43.065	40.78	1.02224e+08	0	0	0.0202	-0.0067	0.0233	1
2018-01-03	43.1325	43.6375	42.99	43.0575	40.77	1.17982e+08	0	-0.0002	0.0202	-0.0247	0.0233	0.9998
2018-01-04	43.135	43.3675	43.02	43.2575	40.96	8.97384e+07	0	0.0047	0.0202	-0.0198	0.0233	1.0044
2018-01-05	43.36	43.8425	43.2625	43.75	41.43	9.46401e+07	0	0.0115	0.0202	-0.0133	0.0233	1.0159
2018-01-08	43.5875	43.9025	43.4825	43.5875	41.27	8.22711e+07	0	-0.0039	0.0202	-0.0287	0.0233	1.012

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.

	2017	2018	2019	2020	2021	2022	2023
Revenue	2.29234e+11	2.65595e+11	2.60174e+11	2.74515e+11	3.65817e+11	3.94328e+11	3.83285e+11
Cost of Goods Sold	1.41048e+11	1.63756e+11	1.61782e+11	1.69559e+11	2.12981e+11	2.23546e+11	2.14137e+11
Gross Profit	8.8186e+10	1.01839e+11	9.8392e+10	1.04956e+11	1.52836e+11	1.70782e+11	1.69148e+11
Gross Profit Ratio	0.3847	0.3834	0.3782	0.3823	0.4178	0.4331	0.4413
Research and Development Expenses	1.1581e+10	1.4236e+10	1.6217e+10	1.8752e+10	2.1914e+10	2.6251e+10	2.9915e+10

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.

	2017	2018	2019	2020	2021	2022	2023
Gross Margin	0.6191	0.6525	0.659	0.6778	0.6893	0.684	0.6892
Operating Margin	0.2482	0.3177	0.3414	0.3703	0.4159	0.4206	0.4177
Net Profit Margin	0.2357	0.1502	0.3118	0.3096	0.3645	0.3669	0.3415
Interest Coverage Ratio	13.9982	16.5821	20.3429	25.3782	34.7835	47.4275	52.0244
Income Before Tax Profit Margin	0.2574	0.3305	0.3472	0.3708	0.423	0.4222	0.4214

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:

	2017	2018	2019	2020	2021	2022	2023
Interest Burden Ratio	0.9572	0.9725	0.9725	0.988	0.9976	1.0028	1.005
Tax Burden Ratio	0.7882	0.8397	0.8643	0.8661	0.869	0.8356	0.8486
Operating Profit Margin	0.2796	0.2745	0.2527	0.2444	0.2985	0.302	0.2967
Asset Turnover	nan	0.7168	0.7389	0.8288	1.0841	1.1206	1.0868
Equity Multiplier	nan	3.0724	3.5633	4.2509	5.255	6.1862	6.252
Return on Equity	nan	0.4936	0.5592	0.7369	1.4744	1.7546	1.7195

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%):

	1 Month	2 Months	3 Months	4 Months	5 Months	6 Months
175	0.7686	0.7178	0.6967	0.6857	0.6794	0.6759
180	0.6659	0.64	0.6318	0.629	0.6285	0.6291
185	0.5522	0.5583	0.5648	0.571	0.5767	0.5816
190	0.4371	0.4762	0.4977	0.513	0.5249	0.5342
195	0.3298	0.3971	0.4324	0.4562	0.474	0.4875

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:

	Mkt-RF	SMB	HML	RMW	CMA
2022Q2	0.9177	-0.1248	-0.5077	-0.3202	-0.2624
2022Q3	0.8092	0.1528	-0.5046	-0.1997	-0.5231
2022Q4	0.8998	0.2309	-0.5968	-0.1868	-0.5946
2023Q1	0.7737	0.1606	-0.3775	-0.228	-0.5707
2023Q2	0.7416	-0.1166	-0.2722	0.0093	-0.4745

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.

	AAPL	MSFT	Benchmark
2023-09-25/2023-10-01	-0.0205	-0.0133	-0.0122
2023-10-02/2023-10-08	-0.0048	-0.0206	-0.0108
2023-10-09/2023-10-15	-0.0089	-0.0092	-0.0059
2023-10-16/2023-10-22	-0.0135	-0.0124	-0.0131
2023-10-23/2023-10-29	-0.0224	-0.0293	-0.0139

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:

Date	Base Line	Conversion Line	Leading Span A	Leading Span B
2023-10-30	174.005	171.755	176.245	178.8
2023-10-31	174.005	171.755	176.37	178.8
2023-11-01	174.005	170.545	176.775	178.8
2023-11-02	174.005	171.725	176.235	178.8
2023-11-03	174.005	171.725	175.558	178.8

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:

Date	AAA	AA	A	BBB	BB	B	CCC
2024-04-19	0.0518	0.0532	0.0561	0.0594	0.0678	0.0804	0.1385
2024-04-22	0.0517	0.0532	0.056	0.0593	0.0671	0.0793	0.1377
2024-04-23	0.0514	0.0528	0.0556	0.0589	0.066	0.0777	0.1364
2024-04-24	0.0518	0.0531	0.0559	0.0592	0.0664	0.0778	0.1361
2024-04-25	0.0524	0.0537	0.0564	0.0598	0.0673	0.079	0.1368

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:

	Colombia	United States	Sweden	Japan	Germany
2017	0.093	0.0435	0.0686	0.0281	0.0357
2018	0.0953	0.039	0.0648	0.0244	0.0321
2019	0.1037	0.0367	0.0691	0.0235	0.0298
2020	0.1586	0.0809	0.0848	0.0278	0.0362
2021	0.1381	0.0537	0.0889	0.0282	0.0358
2022	0.1122	0.0365	0.0748	0.026	0.0307

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.

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<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>

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

Identifier	Volume	Costs	Price	Invested	Latest Price	Latest Value	Return	Return Value	Benchmark Return	Volatility	Benchmark Volatility	Alpha	Beta	Weight
AAPL	137	-28	38.9692	5310.78	241.84	33132.1	5.2386	27821.3	2.2258	0.3858	0.1937	3.0128	1.2027	0.0405
ALGN	81	-34	117.365	9472.53	187.03	15149.4	0.5993	5676.9	2.1413	0.5985	0.1937	-1.542	1.5501	0.0185
AMD	78	-30	11.9075	898.784	99.86	7789.08	7.6662	6890.3	3.7945	0.6159	0.1937	3.8718	1.6551	0.0095
AMZN	116	-28	41.5471	4791.46	212.28	24624.5	4.1392	19833	1.8274	0.4921	0.1937	2.3118	1.1594	0.0301
ASML	129	-25	33.3184	4273.07	709.08	91471.3	20.4065	87198.3	3.8005	0.4524	0.1937	16.606	1.4407	0.1119
VOO	77	-12	238.499	18352.5	546.33	42067.4	1.2922	23715	1.1179	0.1699	0.1937	0.1743	0.9973	0.0515
WMT	92	-18	17.8645	1625.53	98.61	9072.12	4.581	7446.59	2.4787	0.2334	0.1937	2.1024	0.4948	0.0111
Portfolio	2142	-532	59.8406	128710	381.689	817577	5.3521	688867	2.0773	0.4193	0.1937	3.2747	1.2909	1

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.

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<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>

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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.

<p align="center"> <img src="examples/Finance Toolkit - Video Demo.gif" alt="Finance Toolkit Illustration" width="100%" onerror="this.style.display = 'none'"/> </p>

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.

<details> <summary><b>Financial Statements</b></summary>

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:

	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

</details> <details> <summary><b>Company Overviews</b></summary>

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:

	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:

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:

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

</details> <details> <summary><b>(Intraday) Historical Market Data</b></summary>

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:

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:

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

</details> <details> <summary><b>Treasury Rates</b></summary>

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:

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

</details> <details> <summary><b>Earnings & Dividend Calendars</b></summary>

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:

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:

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

</details> <details> <summary><b>Analyst Estimates</b></summary>

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:

	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

</details> <details> <summary><b>Revenue Segmentations</b></summary>

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:

	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:

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

</details> <details> <summary><b>ESG Scores</b></summary>

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