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add backtesting
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‎Chapter5/time_series.ipynb

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"source": [
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"[Link to tsmoothie](https://bit.ly/3L8KXky)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "f2e850ef",
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"metadata": {},
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"source": [
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"### Backtesting: Assess Trading Strategy Performance Effortlessly in Python"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0ff27227",
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"metadata": {
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"tags": [
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"hide-cell"
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]
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},
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"outputs": [],
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"source": [
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"!pip install -U backtesting"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0288e5d6",
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"metadata": {},
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"source": [
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"Evaluating trading strategies' effectiveness is crucial for financial decision-making, but it's challenging due to the complexities of historical data analysis and strategy testing. \n",
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"\n",
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"Backtesting allows users to simulate trades based on historical data and visualize the outcomes through interactive plots in three lines of code.\n",
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"\n",
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"To see how Backtesting works, let's create our first strategy to backtest on these Google data, a simple moving average (MA) cross-over strategy."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4f410242",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Open</th>\n",
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" <th>High</th>\n",
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" <th>Low</th>\n",
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" <th>Close</th>\n",
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" <th>Volume</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2013-02-25</th>\n",
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" <td>802.3</td>\n",
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" <td>808.41</td>\n",
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" <td>790.49</td>\n",
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" <td>790.77</td>\n",
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" <td>2303900</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013-02-26</th>\n",
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" <td>795.0</td>\n",
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" <td>795.95</td>\n",
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" <td>784.40</td>\n",
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" <td>790.13</td>\n",
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" <td>2202500</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013-02-27</th>\n",
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" <td>794.8</td>\n",
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" <td>804.75</td>\n",
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" <td>791.11</td>\n",
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" <td>799.78</td>\n",
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" <td>2026100</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013-02-28</th>\n",
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" <td>801.1</td>\n",
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" <td>806.99</td>\n",
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" <td>801.03</td>\n",
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" <td>801.20</td>\n",
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" <td>2265800</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013-03-01</th>\n",
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" <td>797.8</td>\n",
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" <td>807.14</td>\n",
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" <td>796.15</td>\n",
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" <td>806.19</td>\n",
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" <td>2175400</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Open High Low Close Volume\n",
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"2013-02-25 802.3 808.41 790.49 790.77 2303900\n",
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"2013-02-26 795.0 795.95 784.40 790.13 2202500\n",
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"2013-02-27 794.8 804.75 791.11 799.78 2026100\n",
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"2013-02-28 801.1 806.99 801.03 801.20 2265800\n",
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"2013-03-01 797.8 807.14 796.15 806.19 2175400"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from backtesting.test import GOOG\n",
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"\n",
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"GOOG.tail()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b3230aaf",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"\n",
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"def SMA(values, n):\n",
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" \"\"\"\n",
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" Return simple moving average of `values`, at\n",
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" each step taking into account `n` previous values.\n",
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" \"\"\"\n",
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" return pd.Series(values).rolling(n).mean()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "cbb93184",
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"metadata": {},
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"outputs": [],
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"source": [
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"from backtesting import Strategy\n",
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"from backtesting.lib import crossover\n",
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"\n",
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"\n",
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"class SmaCross(Strategy):\n",
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" # Define the two MA lags as *class variables*\n",
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" # for later optimization\n",
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" n1 = 10\n",
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" n2 = 20\n",
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"\n",
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" def init(self):\n",
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" # Precompute the two moving averages\n",
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" self.sma1 = self.I(SMA, self.data.Close, self.n1)\n",
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" self.sma2 = self.I(SMA, self.data.Close, self.n2)\n",
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"\n",
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" def next(self):\n",
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" # If sma1 crosses above sma2, close any existing\n",
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" # short trades, and buy the asset\n",
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" if crossover(self.sma1, self.sma2):\n",
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" self.position.close()\n",
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" self.buy()\n",
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"\n",
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" # Else, if sma1 crosses below sma2, close any existing\n",
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" # long trades, and sell the asset\n",
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" elif crossover(self.sma2, self.sma1):\n",
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" self.position.close()\n",
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" self.sell()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "94bf31a7",
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"metadata": {},
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"source": [
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"To assess the performance of our investment strategy, we will instantiate a `Backtest` object, using Google stock data as our asset of interest and incorporating the `SmaCross` strategy class. We'll start with an initial cash balance of 10,000 units and set the broker's commission to a realistic rate of 0.2%."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "4c73c152",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Start 2004-08-19 00:00:00\n",
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"End 2013-03-01 00:00:00\n",
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"Duration 3116 days 00:00:00\n",
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"Exposure Time [%] 97.067039\n",
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"Equity Final [$] 68221.96986\n",
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"Equity Peak [$] 68991.21986\n",
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"Return [%] 582.219699\n",
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"Buy & Hold Return [%] 703.458242\n",
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"Return (Ann.) [%] 25.266427\n",
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"Volatility (Ann.) [%] 38.383008\n",
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"Sharpe Ratio 0.658271\n",
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"Sortino Ratio 1.288779\n",
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"Calmar Ratio 0.763748\n",
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"Max. Drawdown [%] -33.082172\n",
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"Avg. Drawdown [%] -5.581506\n",
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"Max. Drawdown Duration 688 days 00:00:00\n",
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"Avg. Drawdown Duration 41 days 00:00:00\n",
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"# Trades 94\n",
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"Win Rate [%] 54.255319\n",
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"Best Trade [%] 57.11931\n",
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"Worst Trade [%] -16.629898\n",
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"Avg. Trade [%] 2.074326\n",
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"Max. Trade Duration 121 days 00:00:00\n",
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"Avg. Trade Duration 33 days 00:00:00\n",
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"Profit Factor 2.190805\n",
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"Expectancy [%] 2.606294\n",
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"SQN 1.990216\n",
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"_strategy SmaCross\n",
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"_equity_curve ...\n",
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"_trades Size EntryB...\n",
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"dtype: object"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from backtesting import Backtest\n",
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"\n",
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"bt = Backtest(GOOG, SmaCross, cash=10_000, commission=.002)\n",
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"stats = bt.run()\n",
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"stats"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f3c91c05",
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"metadata": {},
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"source": [
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"Plot the outcomes:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7a5f7bf7",
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"metadata": {},
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"outputs": [],
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"source": [
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"bt.plot()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "00f1fa47",
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"metadata": {},
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"source": [
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"![](../img/backtesting.png)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f9e0ead1",
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"metadata": {},
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"source": [
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"[Link to Backtesting](https://bit.ly/3zwi0g3)."
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]
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}
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],
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"metadata": {

‎Chapter5/visualization.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "13c7d4b2",
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"metadata": {
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"tags": [
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"hide-cell"
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]
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"outputs": [
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"name": "stdout",
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"text": [
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"!pip install pygwalker -q"
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]
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"PyGWalker simplifies the process of creating visualizations by allowing users to drag and drop variables to create charts without writing much code."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7d24c9e6",
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"metadata": {},
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"source": [
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"You can use PyGWalker without changing your existing workflow. For example, you can call up PyGWalker with the Dataframe loaded in this way:\n"
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]
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},
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"import pandas as pd"
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]
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"cell_type": "markdown",
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"id": "7d24c9e6",
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"metadata": {},
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"source": [
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"You can use pygwalker without changing your existing workflow. For example, you can call up Graphic Walker with the dataframe loaded in this way:\n"
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]
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},
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"9 6 "
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}

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