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

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    "<div style=\"background-color:#000;\"><img src=\"pqn.png\"></img></div>"
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   "cell_type": "markdown",
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   "source": [
    "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."
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   "cell_type": "code",
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   "source": [
    "import yfinance as yf\n",
    "import numpy as np"
   ]
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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "Download historical price data for SPY and AAPL from Yahoo Finance"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a83f550",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = yf.download([\"SPY\", \"AAPL\"], start=\"2020-01-01\", end=\"2022-07-31\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2018e200",
   "metadata": {},
   "source": [
    "Extract adjusted closing prices for SPY and AAPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c977a8bc",
   "metadata": {},
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   "source": [
    "closes = data['Adj Close']\n",
    "spy_returns = closes.SPY.pct_change().dropna()\n",
    "aapl_returns = closes.AAPL.pct_change().dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9741f742",
   "metadata": {},
   "source": [
    "Define a function to calculate the Sharpe ratio of a strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f618f290",
   "metadata": {},
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   "source": [
    "def sharpe_ratio(returns, adjustment_factor=0.0):\n",
    "    \"\"\"\n",
    "    Determines the Sharpe ratio of a strategy.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    returns : pd.Series or np.ndarray\n",
    "        Daily returns of the strategy, noncumulative.\n",
    "    adjustment_factor : int, float\n",
    "        Constant daily benchmark return throughout the period.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    sharpe_ratio : float\n",
    "\n",
    "    Note\n",
    "    -----\n",
    "    See https://en.wikipedia.org/wiki/Sharpe_ratio for more details.\n",
    "    \"\"\"\n",
    "\n",
    "    # Adjust returns by subtracting the benchmark return\n",
    "\n",
    "    returns_risk_adj = returns - adjustment_factor\n",
    "\n",
    "    # Print the annualized standard deviation of the risk-adjusted returns\n",
    "\n",
    "    print(returns_risk_adj.std() * np.sqrt(252))\n",
    "\n",
    "    # Return the annualized Sharpe ratio\n",
    "\n",
    "    return (\n",
    "        returns_risk_adj.mean() / returns_risk_adj.std()\n",
    "    ) * np.sqrt(252)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035cea96",
   "metadata": {},
   "source": [
    "Calculate the Sharpe ratio for SPY daily returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23dfb39c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpe_ratio(spy_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19aebd83",
   "metadata": {},
   "source": [
    "Calculate the Sharpe ratio for AAPL daily returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12292244",
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpe_ratio(aapl_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77252a24",
   "metadata": {},
   "source": [
    "Plot the rolling 30-day Sharpe ratio for AAPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75215b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl_returns.rolling(30).apply(sharpe_ratio).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac119f9",
   "metadata": {},
   "source": [
    "Plot the histogram of the rolling 30-day Sharpe ratios for AAPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bae6269a",
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl_returns.rolling(30).apply(sharpe_ratio).hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b70d6ff",
   "metadata": {},
   "source": [
    "Compare the rolling 30-day Sharpe ratio of AAPL against SPY and plot the histogram of the differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0796ca2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "(\n",
    "    aapl_returns.rolling(30).apply(sharpe_ratio)\n",
    "    - spy_returns.rolling(30).apply(sharpe_ratio)\n",
    ").hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef5bf53",
   "metadata": {},
   "source": [
    "<a href=\"https://pyquantnews.com/\">PyQuant News</a> 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 <a href=\"https://gettingstartedwithpythonforquantfinance.com/\">get started with Python for quant finance</a>. For educational purposes. Not investment advise. Use at your own risk."
   ]
  }
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