tessl/pypi-quantstats
Portfolio analytics for quants - comprehensive statistical analysis, risk assessment, and performance visualization for quantitative finance
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risk-assessment.mddocs/
Risk Assessment
Advanced risk metrics and measures for comprehensive portfolio risk evaluation including Value at Risk (VaR), tail risk measures, drawdown analysis, stress testing indicators, and risk-adjusted performance metrics.
Capabilities
Value at Risk (VaR) and Expected Shortfall
Quantify potential losses at specific confidence levels and tail risk measures.
def value_at_risk(returns, sigma=1, confidence=0.95, prepare_returns=True):
"""
Calculate Value at Risk (VaR) at specified confidence level.
Parameters:
- returns: pandas Series of returns
- sigma: int, number of standard deviations (1=parametric, 2=cornish-fisher)
- confidence: float, confidence level (e.g., 0.95 for 95%)
- prepare_returns: bool, whether to prepare returns data
Returns:
float: VaR as negative percentage
"""
def var(returns, sigma=1, confidence=0.95, prepare_returns=True):
"""
Alias for value_at_risk function.
Parameters:
- returns: pandas Series of returns
- sigma: int, number of standard deviations
- confidence: float, confidence level
- prepare_returns: bool, whether to prepare returns data
Returns:
float: VaR as negative percentage
"""
def conditional_value_at_risk(returns, sigma=1, confidence=0.95, prepare_returns=True):
"""
Calculate Conditional Value at Risk (CVaR) - expected loss beyond VaR.
Parameters:
- returns: pandas Series of returns
- sigma: int, number of standard deviations
- confidence: float, confidence level
- prepare_returns: bool, whether to prepare returns data
Returns:
float: CVaR as negative percentage
"""
def cvar(returns, sigma=1, confidence=0.95, prepare_returns=True):
"""
Alias for conditional_value_at_risk function.
Parameters:
- returns: pandas Series of returns
- sigma: int, number of standard deviations
- confidence: float, confidence level
- prepare_returns: bool, whether to prepare returns data
Returns:
float: CVaR as negative percentage
"""
def expected_shortfall(returns, sigma=1, confidence=0.95):
"""
Calculate Expected Shortfall (ES) - another name for CVaR.
Parameters:
- returns: pandas Series of returns
- sigma: int, number of standard deviations
- confidence: float, confidence level
Returns:
float: Expected shortfall as negative percentage
"""Tail Risk Measures
Assess extreme risk scenarios and tail behavior of return distributions.
def tail_ratio(returns, cutoff=0.95, prepare_returns=True):
"""
Calculate tail ratio - ratio of right tail to left tail returns.
Parameters:
- returns: pandas Series of returns
- cutoff: float, percentile cutoff (e.g., 0.95 for 95th percentile)
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Tail ratio
"""
def outlier_win_ratio(returns, quantile=0.99, prepare_returns=True):
"""
Calculate ratio of outlier wins to total wins.
Parameters:
- returns: pandas Series of returns
- quantile: float, quantile threshold for outliers
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Outlier win ratio
"""
def outlier_loss_ratio(returns, quantile=0.01, prepare_returns=True):
"""
Calculate ratio of outlier losses to total losses.
Parameters:
- returns: pandas Series of returns
- quantile: float, quantile threshold for outliers
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Outlier loss ratio
"""
def outliers(returns, quantile=0.95):
"""
Identify outlier returns beyond specified quantile.
Parameters:
- returns: pandas Series of returns
- quantile: float, quantile threshold
Returns:
pandas Series: Boolean series identifying outliers
"""
def remove_outliers(returns, quantile=0.95):
"""
Remove outlier returns beyond specified quantile.
Parameters:
- returns: pandas Series of returns
- quantile: float, quantile threshold
Returns:
pandas Series: Returns with outliers removed
"""Ulcer Index and Pain Measures
Metrics focusing on downside volatility and sustained losses.
def ulcer_index(returns):
"""
Calculate Ulcer Index - measure of downside risk and drawdown magnitude.
Parameters:
- returns: pandas Series of returns
Returns:
float: Ulcer Index percentage
"""
def ulcer_performance_index(returns, rf=0):
"""
Calculate Ulcer Performance Index - risk-adjusted return using Ulcer Index.
Parameters:
- returns: pandas Series of returns
- rf: float, risk-free rate
Returns:
float: Ulcer Performance Index
"""
def upi(returns, rf=0):
"""
Alias for ulcer_performance_index function.
Parameters:
- returns: pandas Series of returns
- rf: float, risk-free rate
Returns:
float: UPI value
"""
def serenity_index(returns, rf=0):
"""
Calculate Serenity Index - CAGR to Ulcer Index ratio.
Parameters:
- returns: pandas Series of returns
- rf: float, risk-free rate
Returns:
float: Serenity Index
"""Risk of Ruin
Probability and metrics related to catastrophic losses.
def risk_of_ruin(returns, prepare_returns=True):
"""
Calculate risk of ruin - probability of losing entire capital.
Parameters:
- returns: pandas Series of returns
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Risk of ruin probability (0-1)
"""
def ror(returns):
"""
Alias for risk_of_ruin function.
Parameters:
- returns: pandas Series of returns
Returns:
float: Risk of ruin probability
"""Performance vs Risk Ratios
Ratios that balance return generation against various risk measures.
def smart_sharpe(returns, rf=0.0, periods=252, annualize=True):
"""
Calculate Smart Sharpe ratio with autocorrelation penalty.
Parameters:
- returns: pandas Series of returns
- rf: float, risk-free rate
- periods: int, number of periods per year
- annualize: bool, whether to annualize the result
Returns:
float: Smart Sharpe ratio
"""
def smart_sortino(returns, rf=0, periods=252, annualize=True):
"""
Calculate Smart Sortino ratio with autocorrelation penalty.
Parameters:
- returns: pandas Series of returns
- rf: float, risk-free rate
- periods: int, number of periods per year
- annualize: bool, whether to annualize the result
Returns:
float: Smart Sortino ratio
"""
def adjusted_sortino(returns, rf=0, periods=252, annualize=True, smart=False):
"""
Calculate Adjusted Sortino ratio using downside deviation.
Parameters:
- returns: pandas Series of returns
- rf: float, risk-free rate
- periods: int, number of periods per year
- annualize: bool, whether to annualize the result
- smart: bool, whether to apply autocorrelation penalty
Returns:
float: Adjusted Sortino ratio
"""
def recovery_factor(returns, rf=0.0, prepare_returns=True):
"""
Calculate Recovery Factor - net profit to maximum drawdown ratio.
Parameters:
- returns: pandas Series of returns
- rf: float, risk-free rate
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Recovery factor
"""
def risk_return_ratio(returns, prepare_returns=True):
"""
Calculate risk-return ratio.
Parameters:
- returns: pandas Series of returns
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Risk-return ratio
"""Advanced Risk Ratios
Sophisticated risk-adjusted performance measures.
def payoff_ratio(returns, prepare_returns=True):
"""
Calculate payoff ratio - average win to average loss ratio.
Parameters:
- returns: pandas Series of returns
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Payoff ratio
"""
def profit_ratio(returns, prepare_returns=True):
"""
Calculate profit ratio - gross profit to gross loss ratio.
Parameters:
- returns: pandas Series of returns
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Profit ratio
"""
def profit_factor(returns, prepare_returns=True):
"""
Calculate profit factor - total positive returns to total negative returns ratio.
Parameters:
- returns: pandas Series of returns
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Profit factor
"""
def win_loss_ratio(returns, prepare_returns=True):
"""
Calculate win-loss ratio - ratio of winning periods to losing periods.
Parameters:
- returns: pandas Series of returns
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Win-loss ratio
"""
def cpc_index(returns, prepare_returns=True):
"""
Calculate Common Performance Criteria (CPC) Index.
Parameters:
- returns: pandas Series of returns
- prepare_returns: bool, whether to prepare returns data
Returns:
float: CPC Index
"""
def common_sense_ratio(returns, prepare_returns=True):
"""
Calculate Common Sense Ratio.
Parameters:
- returns: pandas Series of returns
- prepare_returns: bool, whether to prepare returns data
Returns:
float: Common Sense Ratio
"""Probabilistic Risk Measures
Risk measures incorporating probability distributions and confidence intervals.
def probabilistic_sortino_ratio(returns, benchmark, periods=252.0, rf=0.0):
"""
Calculate probabilistic Sortino ratio with confidence intervals.
Parameters:
- returns: pandas Series of portfolio returns
- benchmark: pandas Series of benchmark returns
- periods: float, number of periods per year
- rf: float, risk-free rate
Returns:
float: Probabilistic Sortino ratio
"""
def probabilistic_adjusted_sortino_ratio(returns, benchmark, periods=252.0, rf=0.0):
"""
Calculate probabilistic adjusted Sortino ratio.
Parameters:
- returns: pandas Series of portfolio returns
- benchmark: pandas Series of benchmark returns
- periods: float, number of periods per year
- rf: float, risk-free rate
Returns:
float: Probabilistic adjusted Sortino ratio
"""
def implied_volatility(returns, periods=252, annualize=True):
"""
Calculate implied volatility from returns.
Parameters:
- returns: pandas Series of returns
- periods: int, number of periods per year
- annualize: bool, whether to annualize the result
Returns:
float: Implied volatility
"""Install with Tessl CLI
npx tessl i tessl/pypi-quantstats