What metrics does this skill support?

It supports a wide range of professional metrics including Sharpe, Sortino, Calmar, Information, and Omega ratios, as well as Max Drawdown, Hit Ratios, and Profit Factors.

Does it handle different return calculation methods?

Yes, it provides logic for both arithmetic and log (geometric) returns, with configurable annualization periods for daily, monthly, or intraday data.

Can it help identify backtest overfitting?

Yes, it includes implementations for the Deflated Sharpe Ratio which adjusts for multiple testing and calculates the probability that an observed Sharpe ratio is genuine rather than luck.

Is this suitable for trade-level analysis?

Absolutely. It includes a dedicated trade statistics function that computes win/loss ratios, average wins/losses, expectancy, and recovery factors based on individual trade PnL data.

How does it handle non-normal return distributions?

It provides the Sortino ratio to focus on downside risk and the Omega ratio to capture the full return distribution, which is superior for strategies with significant skew or kurtosis.

Trading Performance Analytics

Name: Trading Performance Analytics
Author: brainbytes-dev

bybrainbytes-dev

•

Ciencia de Datos y ML

Computes professional-grade risk-adjusted return metrics and drawdown statistics for quantitative trading strategies.

This skill equips Claude with specialized quantitative finance capabilities to evaluate and compare trading strategy performance. It provides robust implementations for calculating essential risk-adjusted metrics like the Sharpe, Sortino, and Calmar ratios, alongside detailed drawdown analysis and trade-level statistics. By applying advanced methodologies such as the Deflated Sharpe Ratio to adjust for backtest overfitting and the Omega Ratio for non-normal return distributions, it helps developers build more reliable and statistically sound trading systems.

Características Principales

01Trade-level performance metrics including Hit Ratio, Profit Factor, and Expectancy

02Support for arithmetic and geometric return methodologies with standardized annualization

03Comprehensive risk-adjusted ratio calculations (Sharpe, Sortino, Calmar, Information Ratio)

041 GitHub stars

05Statistical adjustment for backtest overfitting via Deflated Sharpe Ratio (Bailey and Lopez de Prado)

06Advanced drawdown analysis including peak-to-trough depth, duration, and recovery tracking

Casos de Uso

01Comparing the risk-reward profiles of different asset portfolios or investment models

02Auditing live trading performance to identify hidden risks or excessive downside volatility

03Evaluating backtest results for a new algorithmic trading strategy to determine viability

Características Principales

01Trade-level performance metrics including Hit Ratio, Profit Factor, and Expectancy

02Support for arithmetic and geometric return methodologies with standardized annualization

03Comprehensive risk-adjusted ratio calculations (Sharpe, Sortino, Calmar, Information Ratio)

041 GitHub stars

05Statistical adjustment for backtest overfitting via Deflated Sharpe Ratio (Bailey and Lopez de Prado)

06Advanced drawdown analysis including peak-to-trough depth, duration, and recovery tracking

Casos de Uso

01Comparing the risk-reward profiles of different asset portfolios or investment models

02Auditing live trading performance to identify hidden risks or excessive downside volatility

03Evaluating backtest results for a new algorithmic trading strategy to determine viability