returns alone don’t tell the full story

Two strategies may deliver the same return—but one may involve extreme volatility and emotional stress, while the other delivers smooth, consistent growth.

The Sharpe Ratio is one of the most important risk-adjusted performance metrics used by:

Sharpe Ratio measures how much excess return you earn for each unit of risk you take

“How efficiently is risk converted into returns?”

“How much extra return do I get for each unit of volatility?”

Institutional funds usually target Sharpe > 1

⚠️ This is why Sharpe is not perfect for:

Example 1: Sharpe Ratio from Daily Returns

import numpy as np

# Daily returns of a strategy
returns = np.array([0.01, 0.005, -0.002, 0.007, 0.004])

# Risk-free rate (annual)
risk_free_rate = 0.05

# Convert annual risk-free rate to daily
rf_daily = risk_free_rate / 252

# Excess returns
excess_returns = returns - rf_daily

# Sharpe Ratio
sharpe_ratio = np.mean(excess_returns) / np.std(excess_returns)

# Annualized Sharpe Ratio
annualized_sharpe = sharpe_ratio * np.sqrt(252)

print("Sharpe Ratio:", annualized_sharpe)


import pandas as pd
import numpy as np

# Create a DataFrame of daily returns
data = {
    "returns": [0.01, 0.005, -0.002, 0.007, 0.004]
}

df = pd.DataFrame(data)

risk_free_rate = 0.05
rf_daily = risk_free_rate / 252

df["excess_return"] = df["returns"] - rf_daily

sharpe = df["excess_return"].mean() / df["excess_return"].std()
annualized_sharpe = sharpe * np.sqrt(252)

print("Annualized Sharpe Ratio:", annualized_sharpe)
