https://github.com/tzabcoder/pyfinrisk
Python financial risk management framework.
https://github.com/tzabcoder/pyfinrisk
finance finance-management python3 risk-analysis risk-management valueatrisk
Last synced: 3 months ago
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Python financial risk management framework.
- Host: GitHub
- URL: https://github.com/tzabcoder/pyfinrisk
- Owner: tzabcoder
- License: mit
- Created: 2025-02-22T18:59:02.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-03-26T11:12:25.000Z (7 months ago)
- Last Synced: 2025-03-26T12:25:07.043Z (7 months ago)
- Topics: finance, finance-management, python3, risk-analysis, risk-management, valueatrisk
- Language: Python
- Homepage:
- Size: 340 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pyFinRisk
pyFinRisk (Python Financial Risk) is a Python package for quantifying and measuring financial risk assumed by a portfolio of assets. The risk tools focus heavily on Value at Risk (VaR).
**Overview**
---
Value at Risk (VaR) is a method of assessing financial risk using standard statistical techniques summarizing the worst-loss over a target horizon that will not be exceeded with a given confidence interval.
Ex: Take the 99% confidence interval. VaR is the cutoff such that the probability of experiencing a greater loss than X is less than 1%.
* **Portfolio VaR** - Portfolio VaR measures the total value at risk of a portfolio of components (risk factors).
$$
\sigma_p^2 = \begin{bmatrix} w_1 & w_2 & ... & w_n \end{bmatrix}
\begin{bmatrix} \sigma_1^2 & \sigma_{12} & ... & \sigma_{1N} \\
... & ... & ... & ... \\
\sigma_{N1} & \sigma_{N2} & ... & \sigma_N^2 \end{bmatrix}
\begin{bmatrix} w_1 \\ w_2 \\ ... \\ w_n \end{bmatrix}
$$
$$
σ_p^2 = w'Σw
$$
where, Σ is the covariance matrix and w is a vector of weights.
$$
Portfolio\hspace{0.1cm}VaR = ασ_pw = α\sqrt{w'Σw}
$$
* **Conditional VaR** - Value at Risk measures the estimated average loss in extreme scenarios beyond the VaR limit.
$$
CVaR_α=E[X|X<=VaR_α]
$$
* **Marginal VaR** - *Change* in Portfolio VaR resulting from taking additional (marginal) exposure to a given risk factor. Partial (linear) derivative with respect to the component's position.
$$
ΔVaR_i=\frac{δVaR}{δw_i}=α(β_i*σ_p)=\frac{VaR}{W}*β_i
$$
where $β_i$ is the component beta, and $W$ is the complete portfolio value (marked-to-market).
* **Incremental VaR** - *Change* in Portfolio VaR resulting from the modification of a portfolio component.
$$
IVaR_i = ΔVaR_i*a
$$
where a is a change, either positive or negative, in the component. It must be noted that this is only an approximation.
* **Component VaR** - *Change* in Portfolio VaR resulting from the removal of a risk factor.
$$
CVaR_i=(\Delta VaR_i)w_iW=\frac{VaR_i*{\beta_i}}{W}w_iW=VaR_i*{\beta_i}w_i
$$
where $w_i$ is the weight of the risk factor and $β_i$ is the component beta. It must be noted that this is only an approximation and works well with larger portfolios.
**Features**
---
For equity-based portfolios:
* Beta
* Individual (positional) VaR
* Portfolio VaR
* Conditional VaR
* Marginal VaR
* Incremental VaR
* Component VaR
* First-Order Statistical Measures
* Metric Plotting
**Quick Start**
---
For a comprehensive demonstration of usage, view the .py files in the examples folder. For test calculations, see the docs folder for the spreadsheet.
```python
# Global Imports
import yfinance as yf
# Local Imports
from RiskEngine.StockRiskEngine import StockRiskEngine
# Create the portfolio details (each ticker index corresponds with
# the shares index)
tickers = [
'AAPL','NVDA','GOOG','UNH','LLY',
'GAP','HD','COST','KO','AMGN',
'VZ','BMY','META','DIS','JPM',
'MA','NKE','PG','WMT','PM'
]
shares = [
40, 40, 40, 40, 40,
30, 30, 30, 30, 30,
20, 20, 20, 20, 20,
10, 10, 10, 10, 10
]
prices = []
for ticker in tickers:
# Download the past 1 years worth of data
prices.append(yf.download(ticker, period='1y', interval='1d', auto_adjust=False)['Close'][ticker].to_list())
portfolio_details = {
'Symbols' : tickers,
'Shares' : shares,
'Prices' : prices
}
# Download the market prices
market_prices = yf.download('SPY', period='1y', interval='1d', auto_adjust=False)['Close'][ticker].to_list()
# Create the risk engine
riskEngine = StockRiskEngine(portfolio_details, market_prices)
# Display and plot the portfolio statistics
riskEngine.DisplayPortfolioStatistics(plot=True)
# Calculate the Individual VaR for a component
individual_var = riskEngine.IndividualVAR(symbol='JPM', confidence_interval=0.95)
# Calculate the Portfolio VaR
portfolio_var_pct = riskEngine.PortfolioVAR(confidence_interval=0.99) # Returns %
portfolio_var_dollar = riskEngine.PortfolioVAR(confidence_interval=0.99, dollar_based=True) # Returns $
# Calculate the Marginal VaR
marginal_var = riskEngine.MarginalLocalVAR(symbol='AAPL')
# Calculate the Incremental VaR
incremental_var = riskEngine.IncrementalLocalVAR(symbol='COST', weight_change=0.05)
# Calculate the Component VaR
component_var = riskEngine.ComponentLocalVAR(symbol='LLY')
```
**Testing**
---
For running the unit tests, execute the following command from the top-level directory:
`python -m test.test`
For running the example, execute the following command from the top-level directory:
`python -m examples.stock_risk_ex`
**Contributing**
---
If you would like to contribute to this project or notice any issues, please [raise an issue](https://github.com/tzabcoder/pyFinRisk/issues) or [create a pull request](https://github.com/tzabcoder/pyFinRisk/pulls).