Backtesting Strategies: Pitfalls and Best Practices
Backtesting is a crucial part of developing algorithmic trading strategies. It allows traders to test their models against historical data before risking real capital in live markets. However, there are numerous pitfalls that can lead to misleading results if not approached correctly. In this post, we will explore common mistakes in backtesting and share best practices for ensuring that your backtest is robust, reliable, and reflective of real-world conditions.
Algorithmic & Quantitative Trading: Which OS is Best?
When I first began my journey in quantitative finance, algorithmic trading, and machine learning, I wish someone had emphasised the importance of raw computational power and its role in testing and executing trading strategies effectively.