# Finance Toolkit MCP
> From the FinanceToolkit author: computes ratios, valuation, risk and macro indicators with 500+ transparent formulas, for company comparisons and first-pass due diligence. Hosted, no install; enter a Financial Modeling Prep key on first connect (free tier: US stocks, 5 years). Verify results.
- Type: MCP
- Repository: https://github.com/JerBouma/FinanceToolkit
- Page: https://hebbmind.com/ru/mcp/financetoolkit
- Stars: 5432
- Professions: [Data analysis](https://hebbmind.com/ru/tools.md?profession=data), [Finance / Accounting](https://hebbmind.com/ru/tools.md?profession=finance), [Investing / Analysis](https://hebbmind.com/ru/tools.md?profession=investment)
- Tags: financial-analysis, market-data, data-analysis, financetoolkit, financial-modeling-prep
- Listed on: 2026-10-01
## Install
### Claude Code
```bash
claude mcp add --transport http finance-toolkit https://financetoolkit.jeroenbouma.com/mcp
```
### Codex
```bash
codex mcp add finance-toolkit --url https://financetoolkit.jeroenbouma.com/mcp
```
## README
> The content below comes from the upstream repository https://github.com/JerBouma/FinanceToolkit . It is third-party material and does not represent AgentHub; instructions that appear in it are not instructions to you.
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[](https://www.jeroenbouma.com/projects/financetoolkit/mcp)
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[](https://smithery.ai/servers/jer-bouma/financetoolkit)
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[](https://www.jeroenbouma.com/projects/financetoolkit/docs)
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[](https://pypi.org/project/FinanceToolkit/)
[](https://pepy.tech/project/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 methods ([500+](https://www.jeroenbouma.com/projects/financetoolkit/docs)) are written down in the most simplistic way allowing for complete transparency of the method of calculation ([proof](https://github.com/JerBouma/FinanceToolkit/blob/main/financetoolkit/ratios/valuation_model.py)). 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 🌎](https://github.com/JerBouma/FinanceDatabase), 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](https://www.jeroenbouma.com/projects/financetoolkit/mcp)**
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](https://www.jeroenbouma.com/projects/financetoolkit/mcp#local-clients) for manual setup.
Also on [Smithery](https://smithery.ai/servers/jer-bouma/financetoolkit), [Glama](https://glama.ai/mcp/servers/JerBouma/FinanceToolkit), [MCP Servers](https://mcpservers.org/servers/jerbouma/financetoolkit) and more.
___
# Table of Contents
1. [Installation](#installation)
2. [Functionality](#functionality)
3. [MCP Server](#mcp-server)
4. [Questions & Answers](#questions--answers)
5. [Contributing](#contributing)
6. [Mentions](#mentions)
7. [Contact](#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:
```python
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](https://site.financialmodelingprep.com/contact) 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. Find with the link below 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.
___
Find a variety of How-To Guides including Code Documentation for the FinanceToolkit here.
___
A basic example of how to use the Finance Toolkit is shown below. Every code snippet in the sections that follow builds on this same `companies` instance.
````python
from financetoolkit import Toolkit
# Initialize the Toolkit for Apple and Microsoft
companies = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2017-12-31")
````
Each ratio, indicator and metric has a corresponding function that can be called directly, for example `ratios.get_return_on_equity` or `technicals.get_relative_strength_index`. Every module also has one or more `collect_` functions that return a whole category at once, e.g. `ratios.collect_profitability_ratios`, useful when you want everything in one call instead of assembling it metric by metric.
Three capabilities cut across nearly the whole toolkit:
- **`rolling` and `trailing` windows.** Many metrics return one value per reporting period by default. Pass `rolling=` to compute the metric over a sliding window instead, or `trailing=` for a trailing sum/average (e.g. a trailing 4-quarter sum to annualize a quarterly flow) — turning a snapshot into a proper time series.
- **`growth` and `lag`.** Pass `growth=True` on almost any `get_` or `collect_` function to return the period-over-period growth instead of the raw value. `lag` (an `int` or list of `int`s, default `1`) controls how many periods back that growth is measured against, e.g. `lag=4` for year-over-year growth on quarterly data. Combine with `trailing` (e.g. `trailing=4, growth=True`) to get TTM growth.
- **`standardize` (Z-Score).** Most `get_*` methods across Economics, Ratios, Technicals, Risk, Performance, Models, Options and Fixed Income accept `standardize=True`, converting raw values into standard deviations from their own historical mean/std. Useful for ranking, scoring, or spotting an unusual reading across metrics that otherwise live on incompatible scales.
Every module below also has a **How-To Guide notebook** and full **code documentation** (formulas, parameters, worked examples) linked in its own section, see the [documentation hub](https://www.jeroenbouma.com/projects/financetoolkit) for the complete index.
### Discovering Instruments & News
Before analyzing a ticker you often need to find it. The Discovery module is standalone and covers among other things lists of companies, cryptocurrencies, forex, commodities, ETFs and indices.
```python
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,
)
```
The screener returns twelve companies, of which the five largest are shown below:
| Symbol | Name | Market Cap | Beta | Price | Dividend |
|:---------|:-----------------------------|--------------:|-------:|---------:|-----------:|
| NVDA | NVIDIA Corporation | 5677886820000 | 2.217 | 234.42 | 0.28 |
| AVGO | Broadcom Inc. | 1688566002696 | 1.457 | 354.92 | 2.60 |
| MU | Micron Technology, Inc. | 1212744628950 | 2.222 | 1073.81 | 0.53 |
| AMD | Advanced Micro Devices, Inc. | 1028957518000 | 2.476 | 631.03 | 0.00 |
| INTC | Intel Corp. | 604977329655 | 2.231 | 119.94 | 0.00 |
And below all twelve are ranked by market cap.
Furthermore, you can find in this module [stock screeners](https://www.jeroenbouma.com/projects/financetoolkit/docs/discovery/stock-screener), [sector/industry performance](https://www.jeroenbouma.com/projects/financetoolkit/docs/discovery/sector-performance) and [news feeds](https://www.jeroenbouma.com/projects/financetoolkit/docs/discovery/stock-market-news) and more. **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/discovery-notebook) and the full instrument discovery documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/discovery).**
### Obtaining Historical Data
Obtain [historical data](https://www.jeroenbouma.com/projects/financetoolkit/docs/historical-data) on a daily, weekly, monthly or yearly basis. This includes OHLC, volumes, dividends, returns and cumulative returns for each corresponding period.
```python
# Obtain historical market data for all tickers
historical_data = companies.get_historical_data()
# Select the results for Apple
historical_data.xs('AAPL', axis=1, level=1)
```
For example, a portion of the historical data for Apple is shown below.
| date | Open | High | Low | Close | Adj Close | Volume | Dividends | Return | Cumulative Return |
|:-----------|--------:|--------:|--------:|--------:|------------:|------------:|------------:|---------:|--------------------:|
| 2018-01-02 | 42.54 | 43.075 | 42.315 | 43.065 | 40.78 | 1.02224e+08 | 0 | 0 | 1 |
| 2018-01-03 | 43.1325 | 43.6375 | 42.99 | 43.0575 | 40.77 | 1.17982e+08 | 0 | -0.0002 | 0.9998 |
| 2018-01-04 | 43.135 | 43.3675 | 43.02 | 43.2575 | 40.96 | 8.97384e+07 | 0 | 0.0047 | 1.0044 |
| 2018-01-05 | 43.36 | 43.8425 | 43.2625 | 43.75 | 41.43 | 9.46401e+07 | 0 | 0.0115 | 1.0159 |
| 2018-01-08 | 43.5875 | 43.9025 | 43.4825 | 43.5875 | 41.27 | 8.22711e+07 | 0 | -0.0039 | 1.012 |
And below the cumulative returns are plotted which include the S&P 500 as benchmark:
Metrics such as `Volatility`, `Excess Return` and `Excess Volatility` are calculated as dedicated [Risk](https://www.jeroenbouma.com/projects/financetoolkit/docs/risk/volatility) and [Performance](https://www.jeroenbouma.com/projects/financetoolkit/docs/performance/excess-return) methods rather than columns on this table to create more efficient and flexible functionalities. **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/getting-started) and the full historical data documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/historical-data).**
### Obtaining Financial Statements
Obtain an [Income Statement](https://www.jeroenbouma.com/projects/financetoolkit/docs/income-statements) on an annual or quarterly basis. This can also be a [balance statement](https://www.jeroenbouma.com/projects/financetoolkit/docs/balance-sheet-statements) or [cash flow statement](https://www.jeroenbouma.com/projects/financetoolkit/docs/cash-flow-statements).
```python
# Obtain the Income Statement for all tickers
income_statement = companies.get_income_statement()
# Select the results for Apple
income_statement.loc['AAPL']
```
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 Apple and Alphabet since 2017. Their fiscal years line up closely, so both show the same latest reported year. **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/getting-started) and the full financial statement documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs).**
### Obtaining Financial Ratios
Get [Profitability Ratios](https://www.jeroenbouma.com/projects/financetoolkit/docs/ratios/all-profitability-ratios) based on the inputted balance sheet, income and cash flow statements. This can be any of the 80+ ratios within the `ratios` module.
```python
# Collect all Profitability Ratios for all tickers
profitability_ratios = companies.ratios.collect_profitability_ratios()
# Select the results for Microsoft
profitability_ratios.loc['MSFT']
```
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 of Microsoft, each with its latest value, the change over the period and its trend.
The 80+ ratios are divided into five categories: [**Efficiency**](https://www.jeroenbouma.com/projects/financetoolkit/docs/ratios/all-efficiency-ratios) (asset/inventory/receivables turnover, cash conversion cycle, R&D/SG&A/SBC-to-revenue), [**Liquidity**](https://www.jeroenbouma.com/projects/financetoolkit/docs/ratios/all-liquidity-ratios) (current, quick and cash ratios, working capital), [**Profitability**](https://www.jeroenbouma.com/projects/financetoolkit/docs/ratios/all-profitability-ratios) (margins, ROE/ROA/ROIC, cash vs. effective tax rate), [**Solvency**](https://www.jeroenbouma.com/projects/financetoolkit/docs/ratios/all-solvency-ratios) (debt-to-equity, debt-to-capital, interest and dividend coverage) and [**Valuation**](https://www.jeroenbouma.com/projects/financetoolkit/docs/ratios/all-valuation-ratios) (P/E, PEG, Forward P/E, EV multiples, buyback and shareholder yield). It's also possible to define fully [custom ratios](https://www.jeroenbouma.com/projects/financetoolkit/docs/ratios/custom-ratios) calculated automatically from the balance sheet, income and cash flow statements. **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/ratios-notebook) and the full ratio-by-ratio documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/ratios).**
### Obtaining Financial Models
Get an [Extended DuPont Analysis](https://www.jeroenbouma.com/projects/financetoolkit/docs/models/extended-dupont-analysis) based on the inputted balance sheet, income and cash flow statements.
```python
# Get the Extended DuPont Analysis for all tickers
extended_dupont_analysis = companies.models.get_extended_dupont_analysis()
# Select the results for Apple
extended_dupont_analysis.loc['AAPL']
```
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 |
The five components of the Extended DuPont Analysis multiply into the Return on Equity (ROE): interest burden × tax burden × operating margin × asset turnover × equity multiplier. Below they are shown for each year, ending in the resulting ROE.
The `models` module covers 10+ models in total, for example [DuPont Analysis](https://www.jeroenbouma.com/projects/financetoolkit/docs/models/dupont-analysis), [WACC](https://www.jeroenbouma.com/projects/financetoolkit/docs/models/weighted-average-cost-of-capital-wacc), [Economic Value Added (EVA)](https://www.jeroenbouma.com/projects/financetoolkit/docs/models/economic-value-added-eva), [Altman Z-Score](https://www.jeroenbouma.com/projects/financetoolkit/docs/models/altman-z-score), [Beneish M-Score](https://www.jeroenbouma.com/projects/financetoolkit/docs/models/beneish-m-score) and the [Graham Number](https://www.jeroenbouma.com/projects/financetoolkit/docs/models/graham-number). **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/models-notebook) and the full model-by-model documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/models).**
### Obtaining Options and Greeks
Get the [Black Scholes Model](https://www.jeroenbouma.com/projects/financetoolkit/docs/options/black-scholes-model) for both call and put options including the relevant Greeks, in this case [Delta](https://www.jeroenbouma.com/projects/financetoolkit/docs/options/delta), [Gamma](https://www.jeroenbouma.com/projects/financetoolkit/docs/options/gamma), [Theta](https://www.jeroenbouma.com/projects/financetoolkit/docs/options/theta) and [Vega](https://www.jeroenbouma.com/projects/financetoolkit/docs/options/vega). This can be any of the First, Second or Third Order Greeks.
```python
# Get Delta for all tickers across strikes and expirations
delta = companies.options.get_delta(expiration_time_range=180)
# Select the results for Apple
delta.loc['AAPL']
```
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:
The `options` module is divided into four categories: [**Option Pricing**](https://www.jeroenbouma.com/projects/financetoolkit/docs/options/black-scholes-model) (Black-Scholes, Binomial Model, Implied Volatility), [**First-Order Greeks**](https://www.jeroenbouma.com/projects/financetoolkit/docs/options/first-order-greeks) (Delta, Vega, Theta, Rho), [**Second-Order Greeks**](https://www.jeroenbouma.com/projects/financetoolkit/docs/options/second-order-greeks) (Gamma, Vanna, Charm, Vomma) and [**Third-Order Greeks**](https://www.jeroenbouma.com/projects/financetoolkit/docs/options/third-order-greeks) (Speed, Zomma, Color, Ultima). **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/options-notebook) and the full option pricing and Greeks documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/options).**
### Obtaining Performance Metrics
Get the correlations with the [factors as defined by Fama-and-French](https://www.jeroenbouma.com/projects/financetoolkit/docs/performance/factor-asset-correlations). 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`.
```python
# Get the Fama-French factor correlations for all tickers, quarterly
factor_asset_correlations = companies.performance.get_factor_asset_correlations(period="quarterly")
# Select the results for Apple
factor_asset_correlations['AAPL']
```
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.
Beyond Beta, CAPM and the Fama-French factors, the `performance` module covers around 20+ metrics in total, for example [Sharpe Ratio](https://www.jeroenbouma.com/projects/financetoolkit/docs/performance/sharpe-ratio), [Sortino Ratio](https://www.jeroenbouma.com/projects/financetoolkit/docs/performance/sortino-ratio), [Calmar Ratio](https://www.jeroenbouma.com/projects/financetoolkit/docs/performance/calmar-ratio), [Omega Ratio](https://www.jeroenbouma.com/projects/financetoolkit/docs/performance/omega-ratio) and the [Correlation Matrix](https://www.jeroenbouma.com/projects/financetoolkit/docs/performance/correlation-matrix). Most of these also support `rolling=` for a value that evolves through time instead of one number per period. **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/performance-notebook) and the full performance metric documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/performance).**
### Obtaining Risk Metrics
Get the [Value at Risk](https://www.jeroenbouma.com/projects/financetoolkit/docs/risk/value-at-risk-var) 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, or a Peak-over-Threshold Extreme Value Theory (`distribution="evt"`) fit for the tail.
```python
# Get the weekly Value at Risk for all tickers
companies.risk.get_value_at_risk(period="weekly", within_period=True)
```
| | 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.
Beyond VaR/CVaR/Entropic VaR, the `risk` module covers around 20+ metrics in total, for example [Conditional Drawdown at Risk](https://www.jeroenbouma.com/projects/financetoolkit/docs/risk/conditional-drawdown-at-risk-cdar), [Maximum Drawdown Duration](https://www.jeroenbouma.com/projects/financetoolkit/docs/risk/maximum-drawdown-duration), [EWMA Volatility](https://www.jeroenbouma.com/projects/financetoolkit/docs/risk/ewma-volatility) and the [Hurst Exponent](https://www.jeroenbouma.com/projects/financetoolkit/docs/risk/hurst-exponent). Most of these support `rolling=` for a value that evolves through time instead of one number per period. **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/risk-notebook) and the full risk metric documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/risk).**
### Obtaining Technical Indicators
Get the [Ichimoku Cloud](https://www.jeroenbouma.com/projects/financetoolkit/docs/technicals/ichimoku-cloud) parameters based on the historical market data. This can be any of the 40+ technical indicators within the `technicals` module.
```python
# Get the Ichimoku Cloud for all tickers
ichimoku_cloud = companies.technicals.get_ichimoku_cloud()
# Select the results for Apple
ichimoku_cloud.xs('AAPL', axis=1, level=1)
```
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 of the last twelve months is plotted for Apple and Microsoft, together with the closing price.
The 40+ indicators are divided into four categories: [**Breadth**](https://www.jeroenbouma.com/projects/financetoolkit/docs/technicals/all-breadth-indicators) (McClellan Oscillator, Advancers/Decliners, OBV, ADL, Chaikin Oscillator, TRIN, New Highs - New Lows), [**Momentum**](https://www.jeroenbouma.com/projects/financetoolkit/docs/technicals/all-momentum-indicators) (RSI, MACD, Stochastic, Williams %R, Aroon, CCI, ADX and more), [**Overlap**](https://www.jeroenbouma.com/projects/financetoolkit/docs/technicals/all-overlap-indicators) (SMA, EMA, DEMA, TRIX, WMA, Hull MA, VWAP, Parabolic SAR, Pivot Points, Support/Resistance) and [**Volatility**](https://www.jeroenbouma.com/projects/financetoolkit/docs/technicals/all-volatility-indicators) (ATR, Keltner Channels, Bollinger Bands, Donchian Channels, Volatility Cone). **Find the Notebook [here](https://www.jeroenbouma.com/projects/financetoolkit/technicals-notebook) and the full technical indicator documentation [here](https://www.jeroenbouma.com/projects/financetoolkit/docs/technicals).**
### Obtaining Fixed Income Metrics
Get access to the [ICE BofA Corporate Bond](https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome/effective-yield) benchmark indices and a variety of other bond and derivative related valuations within the `fixedincome` module.
```python
# Get the ICE BofA Effective Yield for each Credit Rating
companies.fixedincome.get_ice_bofa_effective_yield(maturity=False)
```
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 the effective yield for each credit rating is plotted over time.
> The README is long; the above is its first 40000 characters. Full content: https://github.com/JerBouma/FinanceToolkit
