Python Crypto Backtesting: DCA & Volatility-Adaptive Strategy From Scratch

Ever had a brilliant idea for a cryptocurrency trading strategy but hesitated to risk your hard-earned cash testing it? I’ve been there. You see patterns, you think “what if,” but the leap from idea to live trading is huge. That’s exactly why we’re here today. In this tutorial, I’ll guide you through building an advanced cryptocurrency trading strategy backtester in Python, entirely from scratch. We’re not just talking about a simple moving average crossover; we’re diving deep.

We’ll construct a system that identifies buying opportunities based on consecutive red candles. For trades that don’t immediately go our way, we’ll implement Dollar-Cost Averaging (DCA) to improve our entry points. And to lock in gains, we’ll use a dynamic, volatility-adjusted take-profit mechanism, ensuring we only sell when in profit. This backtester will simulate trading with a fixed capital across multiple cryptocurrencies, all while respecting limits like the maximum number of open trades and DCA adjustments. It’s a comprehensive project, but by building it step-by-step, you’ll gain an incredible understanding of how quantitative strategies are developed and tested.

Photo by Ishant Mishra on Unsplash

Table of Contents

• Data Acquisition and Preparation for Crypto Strategy: This section covers fetching historical hourly…