Fusion Markets

How to Conduct Effective Backtesting

1. Choose Reliable and Comprehensive Historical Data

The quality of your backtesting results heavily depends on the quality of the historical data you use. It’s essential to choose a reliable data source that provides comprehensive and accurate data, including bid/ask prices, spreads, and market conditions.

For instance, if you’re backtesting a strategy on the AUD/NZD pair, you’ll need historical data that covers various market conditions, such as periods of low liquidity or high volatility. This ensures that your backtesting results are reflective of real market conditions and not skewed by inaccurate or incomplete data.

2. Select Appropriate Timeframes Covering Various Market Conditions

You need to ensure that your data covers a sufficient time frame to test your strategy effectively. If you’re developing a long-term trading strategy, backtesting on at least 10 years of historical data is recommended. This provides a broader perspective on how your strategy would have performed in trending and ranging markets during different market phases over the long-term.

The relevance of the time frame to your strategy is important in order to achieve the most accurate results. For example, a day trader might backtest their strategy on 1-minute or 5-minute charts, whereas a swing trader might backtest on daily or weekly charts.

3. Account for All Trading Costs, Including Slippage and Commissions

As mentioned earlier, there are other costs to consider in trading other than losing trades, and backtesting is no different. To obtain accurate results, it’s essential to account for all trading costs, including slippage, commissions, and spreads. These costs can significantly impact your strategy’s profitability and ignoring them can lead to an overestimation of your strategy’s success.

These additional costs affect all styles of trading – spreads and slippage can quickly eat up a day trader’s profits, and overnight swap rates over a sustained period of time can reduce a swing trader’s overall profit. By incorporating these costs into your backtesting, you can get a more realistic picture of your strategy’s potential performance.

4. Use Proper Risk Management

Risk management is a critical component of any trading strategy, and it should be an integral part of your backtesting process. Proper risk management ensures that you’re not risking more than you can afford to lose on any single trade, helping to protect your trading capital.

For example, when backtesting, ensure you incorporate stop-loss and take-profit levels to assess how they impact your strategy’s performance. You might find that adjusting your stop-loss levels slightly improves your overall risk-to-reward ratio, leading to better long-term results.

Additionally, consider position sizing as part of your risk management strategy. For instance, using a fixed percentage of your trading capital for each trade can help you manage risk more effectively. Backtesting different position sizing methods can provide insights into which approach works best for your trading style.

5. Avoid Curve Fitting and Data Dredging

Curve fitting, also known as data dredging, is a common pitfall in backtesting that occurs when a strategy is overly optimised to fit historical data. While this might result in impressive backtesting results, it often leads to poor performance in live trading, as the strategy is tailored to past data rather than being robust enough to handle future market conditions.

For example, you might develop a strategy that performs exceptionally well on the EUR/NZD pair during a specific time period. However, if the strategy is too tightly fitted to this historical data, it may fail when applied to different market conditions or time frames.

To avoid curve fitting, focus on creating a strategy that works well across different market conditions and time frames. Instead of optimising your strategy to maximise historical profits, aim for a balanced approach that considers various factors, such as risk management and market volatility. This ensures that your strategy is more likely to succeed in live trading.