Build an Advanced Crypto Trading Backtester with Python OOP

I remember diving into cryptocurrency trading — the fast pace and potential were exhilarating, but so was the risk. Relying purely on gut feeling or simple chart patterns quickly led to inconsistent results and some painful losses. That’s when I realized the critical importance of rigorously testing trading ideas before putting real capital on the line. Simply put, you need a reliable way to simulate how a strategy would have performed historically. This tutorial is born from that necessity; we’re going to develop a sophisticated, modular cryptocurrency trading backtester from the ground up using Python and the power of Object-Oriented Programming (OOP).

Why OOP? Because building a backtester involves multiple interacting parts: fetching data, defining strategies, managing a portfolio, simulating trades and analyzing results. OOP allows us to encapsulate each piece of logic into distinct objects (like DataHandler, Strategy, Portfolio), making the system cleaner, easier to understand, maintain and crucially, extend with new features or strategies later on. We'll focus on an event-driven approach, which closely mimics how live trading unfolds, processing data bar-by-bar. We will primarily use libraries like Pandas for data manipulation, yfinance for fetching crypto data and quantstats for robust performance analysis.

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