Sharpe Ratio in Python

A measure of risk-adjusted return used to evaluate the performance of quantitative strategies.

Definition

The Sharpe Ratio is a critical metric in quantitative finance used to understand the return of an investment compared to its risk. The ratio is the average return earned in excess of the risk-free rate per unit of volatility or total risk. A higher Sharpe ratio indicates a better risk-adjusted performance.

Quantitative Formula

S = \frac{R_p - R_f}{\sigma_p}

Where $R_p$ is the portfolio return, $R_f$ is the risk-free rate, and $\sigma_p$ is the standard deviation of the portfolio's excess return.

Why It Matters in Backtesting

In algorithmic backtesting, a strategy might show massive returns, but if the Sharpe ratio is below 1.0, the algorithm is taking on too much risk for the reward. Institutional quants generally look for a Sharpe ratio > 1.5 before deploying a model to live markets.

Python Implementation

import numpy as np
  import pandas as pd
  
  def calculate_sharpe_ratio(returns: pd.Series, risk_free_rate: float = 0.02, trading_days: int = 252) -> float:
      """Calculates the annualized Sharpe Ratio."""
      excess_returns = returns - (risk_free_rate / trading_days)
      annualized_return = excess_returns.mean() * trading_days
      annualized_vol = returns.std() * np.sqrt(trading_days)
      
      return annualized_return / annualized_vol if annualized_vol != 0 else 0.0

Test this in a live environment

Stop running Jupyter notebooks locally. Paste this Sharpe Ratio code directly into Valetha's Strategy Lab and run a full historical backtest in seconds.