Tips to Optimize your strategy
Last updated
Last updated
Define Practical and Relevant Parameters: When setting ranges for your parameters, ensure they reflect realistic trading conditions and avoid overly broad values that could lead to impractical results. Narrow, well-thought-out ranges save time by focusing the optimization on meaningful configurations. This approach helps prevent the Optimiser from wasting resources on unlikely scenarios and improves the relevance of the results.
Example: If you're optimizing a moving average period, consider a range of 5 to 50 rather than 1 to 500. This focus prevents overfitting and extreme and unrealistic scenarios that are unlikely to offer valuable insights.
Validate Strategy Robustness: To ensure your strategy performs well under various market conditions, test it on out-of-sample data that was not used during the optimization phase. This helps validate that the strategy is not just tailored to historical data but can adapt to new, unseen market conditions, which is crucial for real-world trading success.
Example: Backtest your strategy during a bull rally then during a summer consolidation phase with crushed volatility and compare the backtest data. You want your strategy to tank the crush volatility and not get liquidated in this context.
Optimize Critical Performance Criteria: Focus on the most important metrics that align with your trading goals. Use the Optimiser’s weighting feature to give more importance to parameters that significantly impact your strategy’s success. This allows for a more targeted optimization process, improving aspects like profitability, risk management, and overall strategy stability.
Examples of Key Metrics:
Maximize Sharpe Ratio for risk-adjusted returns.
Minimize Drawdown to reduce potential losses.
Improve Number of trades in order to reduce overfitting chance.
Explore Various Optimization Techniques: Different strategies may respond better to different optimization methods. Experiment with methods like Random Search, Simulated Annealing, and Combinatorial optimization to find the best fit for your specific strategy. Each method has unique strengths, and testing multiple approaches can reveal which one optimizes your parameters most effectively.
Method Characteristics:
Random Search: Good for broad exploration.
Simulated Annealing: Efficient for refining parameter values.
Combinatorial: It is the final optimisation step, involving to meticulously fine-tuning the parameters within a very narrow range.
Use High-Quality Data: Ensure your backtest engine is fed with accurate and reliable data to generate trustworthy results. High-quality data is crucial for realistic backtesting. That's why we partnered with high quality data provider such as VeloData for example.
Have a high number of trades: You need as much trades as possible in your strategy so it reduce the risk of having an overfitting result, the more data (trades) you have, more reliable the result will be.
Monitor and Adjust: Continuously refine your optimization process. Market conditions change, so regularly update your parameters and test with new data to keep your strategy effective and relevant.
By carefully setting realistic ranges, using out-of-sample data, prioritizing key metrics, and experimenting with various methods, you can effectively use the Optimiser tool to enhance your trading strategies and achieve better trading outcomes.
