How can you conduct reliable backtests?

2 Define your trading strategy

The next step is to define your trading strategy clearly and precisely. This includes specifying the entry and exit rules, the position size and risk management, the time frame and market conditions, and the indicators and signals you will use. You should also state the objectives and assumptions of your strategy, such as the expected return, drawdown, win rate, and Sharpe ratio. You should avoid overfitting your strategy to the historical data by using too many parameters, filters, or optimizations.

3 Select and prepare your data

The third step is to select and prepare your data for backtesting. You should choose a data set that matches your trading style and frequency, and that covers a sufficient period and sample size. You should also ensure that your data is clean, accurate, and consistent, and that it accounts for any adjustments, splits, dividends, commissions, or slippage. You should also perform some exploratory data analysis to understand the characteristics and trends of your data, and to identify any outliers or anomalies.

4 Run and evaluate your backtest

The fourth step is to run and evaluate your backtest using your platform and strategy. You should monitor the progress and performance of your backtest, and check for any errors or warnings. You should also analyze the results and metrics of your backtest, such as the equity curve, the trade statistics, the drawdowns, the risk-adjusted returns, and the performance ratios. You should also compare your backtest results with your objectives and assumptions, and with other benchmarks or strategies.

5 Validate and refine your backtest

The final step is to validate and refine your backtest using various methods and techniques. You should test the robustness and reliability of your backtest by performing sensitivity analysis, walk-forward analysis, Monte Carlo simulation, and out-of-sample testing. You should also review and improve your backtest by identifying and correcting any errors or biases, such as data snooping, curve fitting, look-ahead bias, or survivorship bias. You should also optimize your strategy parameters and settings using appropriate methods and criteria.