strategy-lab/to_explore/pyquantnews/109_Expectancy.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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    "This code calculates the expectancy ratio of a series of trades. The expectancy ratio measures the average expected return per trade by considering the win rate, loss rate, and average profit/loss of trades. It is useful in financial trading to evaluate the performance of a trading strategy. The input is a DataFrame of trades with profit or loss values. The output is a single expectancy ratio value."
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
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   "source": [
    "Define a function to calculate the expectancy ratio of trades."
   ]
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   "source": [
    "def calculate_expectancy_ratio(trades):\n",
    "    \"\"\"Calculate the expectancy ratio of trades.\n",
    "    \n",
    "    This function computes the average expected return for a series of trades \n",
    "    by considering their win rate, loss rate, and average profit/loss.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    trades : pd.DataFrame\n",
    "        DataFrame containing trade information with a 'Profit' column.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    expectancy_ratio : float\n",
    "        The calculated expectancy ratio.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Calculate the number of trades\n",
    "    num_trades = len(trades)\n",
    "    \n",
    "    # Separate winning and losing trades\n",
    "    winners = trades[trades['Profit'] > 0]\n",
    "    losers = trades[trades['Profit'] <= 0]\n",
    "    \n",
    "    # Calculate win rate and loss rate\n",
    "    win_rate = len(winners) / num_trades\n",
    "    loss_rate = len(losers) / num_trades\n",
    "    \n",
    "    # Calculate average profit for winning trades and average loss for losing trades\n",
    "    avg_win = winners['Profit'].mean()\n",
    "    avg_loss = losers['Profit'].mean()\n",
    "    \n",
    "    # Compute the expectancy ratio\n",
    "    expectancy_ratio = (win_rate * avg_win) + (loss_rate * avg_loss)\n",
    "    \n",
    "    return expectancy_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2611f9e5",
   "metadata": {},
   "source": [
    "Create a dictionary with trade data including trade numbers and corresponding profits/losses."
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "7c26a120",
   "metadata": {},
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   "source": [
    "trade_data = {\n",
    "    'Trade': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "    'Profit': [100, -50, 200, -100, 300, -150, 400, -200, 500, -250]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84b5061",
   "metadata": {},
   "source": [
    "Convert the trade data dictionary into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f057971",
   "metadata": {},
   "outputs": [],
   "source": [
    "trades = pd.DataFrame(trade_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce371e5",
   "metadata": {},
   "source": [
    "Calculate the expectancy ratio using the defined function and print the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4086f225",
   "metadata": {},
   "outputs": [],
   "source": [
    "expectancy_ratio = calculate_expectancy_ratio(trades)\n",
    "print(f\"Expectancy Ratio: {expectancy_ratio}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49299264",
   "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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