pyadverse 0.0.3

Risk Metrics library for financial analysis

pyAdverse

pyadverse is a Python library for calculating various financial risk metrics from return data.

Installation

Install the package from PyPI:

pip install pyadverse

Example Usage

import numpy as np
from pyadverse import RiskMetrics

# Sample daily return data
returns = np.array([0.01, -0.02, 0.03, -0.01, 0.015, -0.005, 0.007, -0.03])

# Create the RiskMetrics object
risk = RiskMetrics(returns)

print("VaR (95%):", risk.value_at_risk(0.95))  # alpha = 0.95
print("CVaR (95%):", risk.conditional_var(0.95))  # alpha = 0.95
print("Max Drawdown:", risk.max_drawdown())  # no parameters
print("Expected Drawdown (0.01):", risk.expected_drawdown(0.01))  # threshold = 0.01
print("Conditional Drawdown (95%):", risk.conditional_drawdown(0.95))  # alpha = 0.95
print("Lower Partial Moment (threshold=0.0, order=2):", risk.lower_partial_moment(0.0, 2))  # threshold = 0.0, order = 2
print("Variance:", risk.variance())  # no parameters
print("Standard Deviation:", risk.std_deviation())  # no parameters
print("Skewness:", risk.skewness())  # no parameters
print("Kurtosis:", risk.kurtosis())  # no parameters
print("Volatility (annualized):", risk.volatility())  # assumes 252 trading days

Risk Metrics Descriptions

• Value at Risk (VaR): Estimates the maximum potential loss at a given confidence level (e.g., 95%).

• Conditional Value at Risk (CVaR): Measures the expected loss given that the loss is beyond the VaR threshold.

• Max Drawdown: The maximum observed loss from a peak to a trough before a new peak is attained.

• Expected Drawdown: The average drawdown that exceeds a user-defined threshold.

• Conditional Drawdown (CDaR): The average of the worst-case drawdowns, defined by a confidence percentile.

• Lower Partial Moment (LPM): Measures downside risk. The higher the order, the more sensitive to extreme losses.

• Variance: The average squared deviation from the mean return; basic risk indicator.

• Standard Deviation: Square root of the variance; common measure of return volatility.

• Skewness: Measures asymmetry of the return distribution. Negative skew indicates more left tail risk.

• Kurtosis: Measures "tailedness" of the return distribution. Higher values mean more outlier-prone.

• Volatility: Annualized standard deviation of returns (assuming 252 trading days).