Developing and backtesting an algorithmic trading strategy is a critical step in ensuring its potential success in live markets. However, several common pitfalls can undermine the reliability and accuracy of your backtesting results. We take you through essential tips to help you avoid these pitfalls and create a robust trading strategy.
What is backtesting?
Backtesting is a method of evaluating the performance of a trading strategy or model by applying it to historical data and analysing the outcomes. The process involves simulating the execution of trades based on the strategy or model using historical data, and then analysing the results to determine the strategy's performance. Learn more about backtesting here.
6 ways to improve your backtesting
Avoid Overfitting and Over-optimization
Overfitting occurs when a strategy is tailored too closely to a specific set of historical data, resulting in poor performance in other scenarios. To avoid overfitting, use out-of-sample testing and cross-validation techniques to ensure your strategy is generalizable and robust. Additionally, resist the temptation to fine-tune your strategy parameters excessively, as this can lead to over-optimization and reduced performance in live markets.
Quick tip: Once you have found a successful strategy, try it on different trading pairs and candle time frames. If your results are drastically worse after trying this, your strategy is likely a victim of overfitting.
Prevent the Use of Future Information
Survivorship bias and look-ahead bias are common pitfalls that can skew your backtesting results. Survivorship bias arises when your data set only includes assets that have survived over time, ignoring those that have failed or been delisted. To address this bias, use a comprehensive historical data set that includes both successful and unsuccessful assets. Look-ahead bias occurs when a backtest uses future information that wouldn't have been available at the time of the trade. Prevent look-ahead bias by ensuring your strategy only uses information available at the time of the trade.
TradingStrategy naturally makes it difficult to use future information. For example, the standard code line to get candles, inside `decide_trades`, will only return the most recent ‘already closed’ candles.
Utilise a Comprehensive Data Set
Test your strategy using a data set that covers multiple market cycles, including bullish, bearish, and ranging conditions. A comprehensive data set will help you evaluate your strategy's robustness and adaptability to different market environments. Learn more about Trading Strategy’s comprehensive DEX market data sets here.
Account for Real-World Execution Conditions
In live DeFi trading, execution conditions can be less than ideal. Trades may fail, gas fees can be unpredictable, and other unexpected events may occur. TradingStrategy accounts for some of these real-world factors by incorporating safety mechanisms such as slippage protection, price impact protection, and gas limits.
Implement Risk Management
Consider the potential for large drawdowns and the possibility that assets can go to zero. Incorporate risk management tools, such as stop losses, into your strategy to limit potential losses and protect your trading capital.
Adding a stop loss is only a couple extra lines of code. See documentation example here.
Evaluate Strategy Success Using Multiple Performance Metrics
Relying solely on returns to evaluate the success of a trading strategy can be misleading and insufficient. Assess your strategy's performance using a variety of metrics, including risk-adjusted returns (e.g., Sharpe ratio, Sortino ratio), drawdowns, volatility, consistency, and risk of ruin. Comparing your strategy's performance against market benchmarks can also provide valuable insights.