Overfitting Your Trading Strategy Leads to Disaster
Overfitting is a common pitfall in the development of algorithmic trading strategies. It occurs when a model is too closely tailored to historical data, capturing noise rather than actual market trends. While overfitting may lead to impressive backtest results, it ultimately results in poor performance in live trading. In this post, we will discuss why overfitting is detrimental and how to avoid it when developing trading strategies.
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.