strategy-lab/to_explore/pyquantnews/10_SharpeRatio.ipynb

This code downloads historical price data for SPY and AAPL from Yahoo Finance, calculates daily returns, and computes the Sharpe ratio for these returns. It includes a function to determine the Sharpe ratio, adjusting for a daily benchmark return. The code then plots the rolling 30-day Sharpe ratio for AAPL and visualizes the histogram of these Sharpe ratios. Additionally, it compares the rolling 30-day Sharpe ratio of AAPL against SPY and plots the histogram of the differences.

In [ ]:

import yfinance as yf
import numpy as np

Download historical price data for SPY and AAPL from Yahoo Finance

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data = yf.download(["SPY", "AAPL"], start="2020-01-01", end="2022-07-31")

Extract adjusted closing prices for SPY and AAPL

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closes = data['Adj Close']
spy_returns = closes.SPY.pct_change().dropna()
aapl_returns = closes.AAPL.pct_change().dropna()

Define a function to calculate the Sharpe ratio of a strategy

In [ ]:

def sharpe_ratio(returns, adjustment_factor=0.0):
    """
    Determines the Sharpe ratio of a strategy.
    
    Parameters
    ----------
    returns : pd.Series or np.ndarray
        Daily returns of the strategy, noncumulative.
    adjustment_factor : int, float
        Constant daily benchmark return throughout the period.

    Returns
    -------
    sharpe_ratio : float

    Note
    -----
    See https://en.wikipedia.org/wiki/Sharpe_ratio for more details.
    """

    # Adjust returns by subtracting the benchmark return

    returns_risk_adj = returns - adjustment_factor

    # Print the annualized standard deviation of the risk-adjusted returns

    print(returns_risk_adj.std() * np.sqrt(252))

    # Return the annualized Sharpe ratio

    return (
        returns_risk_adj.mean() / returns_risk_adj.std()
    ) * np.sqrt(252)

Calculate the Sharpe ratio for SPY daily returns

In [ ]:

sharpe_ratio(spy_returns)

Calculate the Sharpe ratio for AAPL daily returns

In [ ]:

sharpe_ratio(aapl_returns)

Plot the rolling 30-day Sharpe ratio for AAPL

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aapl_returns.rolling(30).apply(sharpe_ratio).plot()

Plot the histogram of the rolling 30-day Sharpe ratios for AAPL

In [ ]:

aapl_returns.rolling(30).apply(sharpe_ratio).hist(bins=50)

Compare the rolling 30-day Sharpe ratio of AAPL against SPY and plot the histogram of the differences

In [ ]:

(
    aapl_returns.rolling(30).apply(sharpe_ratio)
    - spy_returns.rolling(30).apply(sharpe_ratio)
).hist(bins=50)

PyQuant News is where finance practitioners level up with Python for quant finance, algorithmic trading, and market data analysis. Looking to get started? Check out the fastest growing, top-selling course to get started with Python for quant finance. For educational purposes. Not investment advise. Use at your own risk.