One of the most important aspects of backtesting trading strategies is acquiring accurate and reliable historical data. Without quality data, even the most sophisticated strategies can fail or lead to misleading results. In this post, we’ll look at different ways to fetch historical data for backtesting using various platforms, including both paid and free options, and I’ll introduce you to Syncra, a tool I built for working with MetaTrader and Binance data.
Backtesting is a crucial part of developing successful trading strategies. It’s how we simulate our strategies using past market data to gauge how they would have performed. But building a backtesting pipeline that gives you actionable insights can be tricky, especially if you’re just starting out. In this post, I’ll take you through the exact backtesting pipeline I use to test and optimize my trading strategies.
Step 1: Initial Research with VectorBT I start with VectorBT, a Python library that’s excellent for initial research and validation of strategies.
Intro to Backtesting We can simply define Backtesting as the process of testing your strategy against historical data. During backtesting, the aim is to validate your strategy’s robustness and look for performance metrics, increasing confidence levels about the possibility of the strategy performing consistently when live trading.
To make profit in the financial markets, you need to have a strategy that is proven and true, and would generate consistent profits for you.
The concept of trading has always fascinated me, but I lacked the resources to explore further into this deep ocean. Fortunately, I kicked off my software engineering career and it was during this time that I got to properly understand the basics of trading, especially forex trading.
Trading is a worldwide phenomenon that affects every facet of our economy, and with respect to financial trading in the markets, it plays a fundamental role in ensuring the exchange of value between individuals and companies.