Building a Robust Backtesting Engine with Object-Oriented Python

Learn how to test trading strategies and analyze performance with Python and OOP.

I dove headfirst into building a backtesting system. Not some simple, theoretical model, but a robust, object-oriented system I could really sink my teeth into. I wanted something that could handle real-world data and sophisticated strategies. This wasn't just about crunching numbers; it was about building a system flexible enough to adapt and evolve. A system that could learn and grow alongside my own trading knowledge. This tutorial walks through my process, showing you how to construct your own powerful backtesting engine from the ground up.

This project isn't just about following a script, it's about understanding the underlying mechanics. It’s about gaining the insight needed to customize and improve your own backtesting system. You'll not only learn how to code it but also why it's coded that way. So, buckle up—we're going to build something special.

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Project Roadmap

Before diving into the code, let's lay out a roadmap for our project:

1. Set up our environment with necessary imports and define core data structures.

2. Create a Trade class to represent individual trades.

3. Implement a data handling class to fetch and manage market data.

4. Develop trading strategies, the brains of our backtesting system.

5. Build the Backtester class to orchestrate the entire process, execute trades, and analyze performance.

Let's start with the necessary imports and basic configurations:

import datetime
import requests
import os
import sys

import numpy as np
import pandas as pd
import yfinance as yf

from bs4 import BeautifulSoup
from collections import defaultdict
from enum import Enum
from loguru import logger
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Type, Union


LOG_DIR = os.path.join(os.path.normpath(os.getcwd()), 'logs')

In this initial setup:

1. We import necessary libraries:

   • Standard Python libraries: datetime, requests, os, sys

   • Data manipulation libraries: numpy, pandas

   • Financial data library: yfinance

   • Web scraping library: BeautifulSoup

   • Utility modules: collections, enum, loguru, typing

2. We define a LOG_DIR to store log files, which will be crucial for tracking and debugging later.

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