592 lines
29 KiB
Text
592 lines
29 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Backtesting.py Library Tutorial\n",
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"\n",
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"## Introduction to Backtesting.py\n",
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"\n",
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"Backtesting.py is a Python framework for inferring viability of trading strategies on historical data. It provides a simple, fast, and flexible way to backtest trading strategies with just a few lines of code.\n",
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"\n",
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"### Key Features:\n",
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"- Simple and intuitive API\n",
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"- Fast vectorized operations\n",
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"- Built-in performance metrics\n",
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"- Interactive charts and plots\n",
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"- Support for various order types\n",
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"- Portfolio optimization capabilities"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Installation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Basic Imports and Setup"
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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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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/alex/Dev/cryptoTraining/venv/lib/python3.13/site-packages/backtesting/_plotting.py:55: UserWarning: Jupyter Notebook detected. Setting Bokeh output to notebook. This may not work in Jupyter clients without JavaScript support, such as old IDEs. Reset with `backtesting.set_bokeh_output(notebook=False)`.\n",
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" warnings.warn('Jupyter Notebook detected. '\n"
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]
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"data": {
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"application/vnd.bokehjs_load.v0+json": ""
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from backtesting import Backtest, Strategy\n",
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"from backtesting.lib import crossover\n",
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"from backtesting.test import SMA, GOOG # Sample data and indicators\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"\n",
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"# Display all columns in pandas\n",
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"pd.set_option('display.max_columns', None)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Understanding the Data Format"
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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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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Data structure:\n",
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" Open High Low Close Volume\n",
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"2004-08-19 100.00 104.06 95.96 100.34 22351900\n",
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"2004-08-20 101.01 109.08 100.50 108.31 11428600\n",
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"2004-08-23 110.75 113.48 109.05 109.40 9137200\n",
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"2004-08-24 111.24 111.60 103.57 104.87 7631300\n",
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"2004-08-25 104.96 108.00 103.88 106.00 4598900\n",
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"\n",
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"Data shape: (2148, 5)\n",
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"Date range: 2004-08-19 00:00:00 to 2013-03-01 00:00:00\n"
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]
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}
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],
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"source": [
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"# Load sample Google stock data\n",
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"data = GOOG.copy()\n",
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"print(\"Data structure:\")\n",
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"print(data.head())\n",
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"print(f\"\\nData shape: {data.shape}\")\n",
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"print(f\"Date range: {data.index[0]} to {data.index[-1]}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Required Data Format:**\n",
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"- DataFrame with DateTime index\n",
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"- Columns: 'Open', 'High', 'Low', 'Close', 'Volume' (OHLCV)\n",
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"- Data should be sorted chronologically"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Creating Your First 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"class SMACrossStrategy(Strategy):\n",
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" \"\"\"\n",
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" Simple Moving Average Crossover Strategy\n",
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" Buy when fast SMA crosses above slow SMA\n",
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" Sell when fast SMA crosses below slow SMA\n",
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" \"\"\"\n",
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" \n",
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" # Strategy parameters\n",
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" fast_sma = 10\n",
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" slow_sma = 30\n",
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" \n",
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" def init(self):\n",
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" \"\"\"Initialize indicators\"\"\"\n",
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" # Calculate moving averages\n",
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" self.sma_fast = self.I(SMA, self.data.Close, self.fast_sma)\n",
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" self.sma_slow = self.I(SMA, self.data.Close, self.slow_sma)\n",
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" \n",
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" def next(self):\n",
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" \"\"\"Define trading logic for each bar\"\"\"\n",
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" # Buy signal: fast SMA crosses above slow SMA\n",
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" if crossover(self.sma_fast, self.sma_slow):\n",
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" self.buy()\n",
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" \n",
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" # Sell signal: fast SMA crosses below slow SMA\n",
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" elif crossover(self.sma_slow, self.sma_fast):\n",
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" self.sell()\n",
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"\n",
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"print(\"Strategy class created successfully!\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Running a Backtest"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Create and run backtest\n",
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"bt = Backtest(data, SMACrossStrategy, cash=10000, commission=.002)\n",
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"results = bt.run()\n",
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"\n",
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"# Display results\n",
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"print(\"Backtest Results:\")\n",
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"print(results)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Key Strategy Methods\n",
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"\n",
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"### The `init()` Method"
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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": 260,
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"metadata": {},
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"outputs": [],
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"source": [
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"def init(self):\n",
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" \"\"\"\n",
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" Called once at the beginning of backtesting\n",
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" Used to:\n",
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" - Initialize indicators using self.I()\n",
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" - Set up any variables needed throughout the strategy\n",
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" - Prepare data transformations\n",
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" \"\"\"\n",
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" self.sma = self.I(SMA, self.data.Close, 20)\n",
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" # self.rsi = self.I(lambda x, n: RSI(x, n), self.data.Close, 14)"
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]
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},
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{
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"cell_type": "markdown",
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|
"metadata": {},
|
|
"source": [
|
|
"### The `next()` Method"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 261,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def next(self):\n",
|
|
" \"\"\"\n",
|
|
" Called for each bar of data\n",
|
|
" Contains the main trading logic\n",
|
|
" Available methods:\n",
|
|
" - self.buy() / self.sell() - Market orders\n",
|
|
" - self.position - Current position info\n",
|
|
" - self.data - Current price data\n",
|
|
" \"\"\"\n",
|
|
" if self.data.Close[-1] > self.sma[-1]:\n",
|
|
" self.buy()\n",
|
|
" elif self.data.Close[-1] < self.sma[-1]:\n",
|
|
" self.sell()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Position Management"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 262,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class PositionSizingStrategy(Strategy):\n",
|
|
" def init(self):\n",
|
|
" self.sma = self.I(SMA, self.data.Close, 20)\n",
|
|
" \n",
|
|
" def next(self):\n",
|
|
" # Check current position\n",
|
|
" if not self.position: # No position\n",
|
|
" if self.data.Close[-1] > self.sma[-1]:\n",
|
|
" # Buy with specific size (50% of equity)\n",
|
|
" self.buy(size=0.5)\n",
|
|
" \n",
|
|
" elif self.position.is_long: # Long position\n",
|
|
" if self.data.Close[-1] < self.sma[-1]:\n",
|
|
" self.position.close() # Close position\n",
|
|
" \n",
|
|
" # Access position information\n",
|
|
" if self.position:\n",
|
|
" print(f\"Position size: {self.position.size}\")\n",
|
|
" print(f\"Position value: {self.position.pl}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Advanced Order Types"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 263,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class AdvancedOrderStrategy(Strategy):\n",
|
|
" def init(self):\n",
|
|
" self.sma = self.I(SMA, self.data.Close, 20)\n",
|
|
" \n",
|
|
" def next(self):\n",
|
|
" current_price = self.data.Close[-1]\n",
|
|
" \n",
|
|
" # Market order\n",
|
|
" if current_price > self.sma[-1] and not self.position:\n",
|
|
" self.buy()\n",
|
|
" \n",
|
|
" # Limit order (buy below current price)\n",
|
|
" elif not self.position:\n",
|
|
" self.buy(limit=current_price * 0.98)\n",
|
|
" \n",
|
|
" # Stop-loss order\n",
|
|
" elif self.position.is_long:\n",
|
|
" self.sell(stop=current_price * 0.95)\n",
|
|
" \n",
|
|
" # Take-profit order\n",
|
|
" elif self.position.is_long:\n",
|
|
" self.sell(limit=current_price * 1.10)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Performance Metrics"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Run backtest and analyze results\n",
|
|
"bt = Backtest(data, SMACrossStrategy, cash=10000, commission=.002)\n",
|
|
"results = bt.run()\n",
|
|
"\n",
|
|
"print(\"Key Performance Metrics:\")\n",
|
|
"print(f\"Total Return: {results['Return [%]']:.2f}%\")\n",
|
|
"print(f\"Buy & Hold Return: {results['Buy & Hold Return [%]']:.2f}%\")\n",
|
|
"print(f\"Max Drawdown: {results['Max. Drawdown [%]']:.2f}%\")\n",
|
|
"print(f\"Sharpe Ratio: {results['Sharpe Ratio']:.2f}\")\n",
|
|
"print(f\"Number of Trades: {results['# Trades']}\")\n",
|
|
"print(f\"Win Rate: {results['Win Rate [%]']:.2f}%\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Visualization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Plot backtest results\n",
|
|
"bt.plot(show_legend=False, resample=False, plot_pl=True, relative_equity=False)\n",
|
|
"\n",
|
|
"# The plot includes:\n",
|
|
"# - Price chart with entry/exit points\n",
|
|
"# - Portfolio equity curve\n",
|
|
"# - Drawdown periods\n",
|
|
"# - Trade markers"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Parameter Optimization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Optimize strategy parameters\n",
|
|
"optimization_results = bt.optimize(\n",
|
|
" fast_sma=range(5, 20, 2), # Test fast SMA from 5 to 18\n",
|
|
" slow_sma=range(20, 50, 5), # Test slow SMA from 20 to 45\n",
|
|
" maximize='Sharpe Ratio', # Optimization objective\n",
|
|
" constraint=lambda p: p.fast_sma < p.slow_sma # Constraint\n",
|
|
")\n",
|
|
"\n",
|
|
"print(\"Optimal Parameters:\")\n",
|
|
"print(optimization_results)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Complete Example: RSI Strategy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def RSI(close, period=14):\n",
|
|
" \"\"\"Calculate RSI indicator - fixed for backtesting library\"\"\"\n",
|
|
" # Convert to pandas Series if it's an _Array object\n",
|
|
" if hasattr(close, '__array__'):\n",
|
|
" close = pd.Series(close)\n",
|
|
" \n",
|
|
" delta = close.diff()\n",
|
|
" gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()\n",
|
|
" loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()\n",
|
|
" rs = gain / loss\n",
|
|
" rsi = 100 - (100 / (1 + rs))\n",
|
|
" \n",
|
|
" # Return as numpy array for backtesting compatibility\n",
|
|
" return rsi.values\n",
|
|
"\n",
|
|
"class RSIStrategy(Strategy):\n",
|
|
" \"\"\"\n",
|
|
" RSI Mean Reversion Strategy\n",
|
|
" Buy when RSI < 30 (oversold)\n",
|
|
" Sell when RSI > 70 (overbought)\n",
|
|
" \"\"\"\n",
|
|
" \n",
|
|
" rsi_period = 14\n",
|
|
" oversold = 30\n",
|
|
" overbought = 70\n",
|
|
" \n",
|
|
" def init(self):\n",
|
|
" self.rsi = self.I(RSI, self.data.Close, self.rsi_period)\n",
|
|
" \n",
|
|
" def next(self):\n",
|
|
" # Buy when oversold and no position\n",
|
|
" if self.rsi[-1] < self.oversold and not self.position:\n",
|
|
" self.buy()\n",
|
|
" \n",
|
|
" # Sell when overbought and have long position\n",
|
|
" elif self.rsi[-1] > self.overbought and self.position.is_long:\n",
|
|
" self.position.close()\n",
|
|
"\n",
|
|
"# Run RSI strategy\n",
|
|
"bt_rsi = Backtest(data, RSIStrategy, cash=10000, commission=.002)\n",
|
|
"results_rsi = bt_rsi.run()\n",
|
|
"\n",
|
|
"print(\"RSI Strategy Results:\")\n",
|
|
"print(f\"Return: {results_rsi['Return [%]']:.2f}%\")\n",
|
|
"print(f\"Sharpe Ratio: {results_rsi['Sharpe Ratio']:.2f}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Best Practices and Tips\n",
|
|
"\n",
|
|
"### 1. Data Quality"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Always check for data issues\n",
|
|
"def validate_data(df):\n",
|
|
" print(f\"Missing values: {df.isnull().sum().sum()}\")\n",
|
|
" print(f\"Duplicate dates: {df.index.duplicated().sum()}\")\n",
|
|
" \n",
|
|
" # Check for unrealistic price movements\n",
|
|
" returns = df['Close'].pct_change()\n",
|
|
" extreme_moves = returns[abs(returns) > 0.2]\n",
|
|
" if len(extreme_moves) > 0:\n",
|
|
" print(f\"Extreme price movements detected: {len(extreme_moves)}\")\n",
|
|
"\n",
|
|
"validate_data(data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 2. Avoiding Look-Ahead Bias"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 269,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# WRONG - Uses future data\n",
|
|
"def bad_strategy_next(self):\n",
|
|
" if self.data.Close[-1] > self.data.Close[0:].mean(): # Uses all data\n",
|
|
" self.buy()\n",
|
|
"\n",
|
|
"# CORRECT - Only uses past data\n",
|
|
"def good_strategy_next(self):\n",
|
|
" if self.data.Close[-1] > self.data.Close[:-1].mean(): # Excludes current bar\n",
|
|
" self.buy()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 3. Transaction Costs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 270,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Always include realistic transaction costs\n",
|
|
"bt = Backtest(\n",
|
|
" data, \n",
|
|
" Strategy, \n",
|
|
" cash=10000,\n",
|
|
" commission=0.002, # 0.2% per trade\n",
|
|
" trade_on_close=True, # More realistic execution\n",
|
|
" exclusive_orders=True # Prevent multiple orders per bar\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Common Pitfalls to Avoid\n",
|
|
"\n",
|
|
"1. **Overfitting**: Don't optimize too many parameters\n",
|
|
"2. **Look-ahead bias**: Only use historical data available at each point\n",
|
|
"3. **Survivorship bias**: Include delisted stocks in your universe\n",
|
|
"4. **Ignoring transaction costs**: Always include realistic fees\n",
|
|
"5. **In-sample testing**: Always test on out-of-sample data\n",
|
|
"\n",
|
|
"## Next Steps\n",
|
|
"\n",
|
|
"- Explore more complex strategies (multi-asset, machine learning)\n",
|
|
"- Learn about walk-forward analysis\n",
|
|
"- Study risk management techniques\n",
|
|
"- Implement portfolio-level backtesting\n",
|
|
"- Use alternative data sources\n",
|
|
"\n",
|
|
"## Resources\n",
|
|
"\n",
|
|
"- [Official Documentation](https://kernc.github.io/backtesting.py/)\n",
|
|
"- [GitHub Repository](https://github.com/kernc/backtesting.py)\n",
|
|
"- [Example Strategies](https://kernc.github.io/backtesting.py/doc/examples/)\n",
|
|
"\n",
|
|
"Happy backtesting! 📈"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "venv",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|