If you're a cryptocurrency trader reliant solely on technical analysis, you've probably heard of backtesting—a process that simulates trading strategies against historical data to evaluate their feasibility. But have you wondered how to interpret those backtest results effectively? With Machine Learning (ML) and Artificial Intelligence (AI) models becoming increasingly popular, understanding backtesting metrics can give you a competitive edge—even if you have no background in ML or AI
General Metrics to Consider:
- Profit Factor: Simply put, the Profit Factor measures the ratio of profits to losses. A profit factor greater than 1.5 is generally considered good. It means for every dollar risked, you’ve made a dollar and fifty cents in return.
- Drawdown: This term refers to the percentage decline from the peak of your trading equity to its lowest point. A low drawdown percentage is desired, as it means your strategy didn’t lose much money during its worst-performing period.
- Sharpe Ratio: A high Sharpe Ratio, generally above 1, indicates a better risk-adjusted performance. It measures the return generated for each unit of risk taken.
- Annualized Return: This indicates the hypothetical annual profit percentage if the strategy were deployed. Make sure to compare this to a “buy and hold” strategy to see if your trading approach adds value.
How to Interpret Results
- Look for Consistency:Results that show consistent gains with minimal drawdowns over different time periods can indicate a robust trading strategy.
- Risk-to-Reward: It’s crucial to weigh the potential returns against the drawdown risks. A strategy may yield high profits but come with significant drawdowns. Assess whether the potential return is worth the risk involved.
- Comparing Multiple Strategies: If you’ve backtested more than one strategy, compare their metrics side-by-side. Sometimes, combining elements from multiple strategies can yield a more effective hybrid approach.
AI / ML specific metrics
If you are backtesting a trading strategy that is based on AI / ML models, there are additional metrics that can help you evaluate the effectiveness an safety of your trading strategy.
Number of Signals
Because model accuracy is essential for price prediction, your trading strategy must specify a limit for accuracy, for example, you only consider price predictions from models with an average error of less than 2%. However, it’s not always possible to find models with good accuracy, which means that sometimes the trading strategy doesn’t produce signals because there are no models with good prediction accuracy. Therefore, choosing a more lenient model accuracy limit can help you achieve a trading strategy with more frequent signals.
Signal Accuracy
An advantage for AI / ML trading strategy is that you have a trading signal with every new candle, which is different from trading strategies based on technical indicators where you have to wait until the indicators satisfy a specific criterion (for example MACD crossing) in order to get a trading signal. So, in backtesting, the overall accuracy of the signals can be calculated along with the accuracy of buy signals and sell signals separately.
Buy Signal Accuracy
Measured as the number of accurate buy signals divided by the total number of buy signals
Sell Signal Accuracy
Measured as the number of accurate sell signals divided by the total number of sell signals
You don’t need to be a machine learning expert to benefit from AI-enhanced tools. Platforms like Coincharted allow you to backtest strategies using complex AI models through a no-code Graphical User Interface (GUI). It’s an exciting time to be a trader, as even those without a tech background can now access sophisticated analytical tools.
Understanding how to read backtest results can significantly improve your trading outcomes. So, dive into those metrics and use them to fine-tune your strategies for a smarter, more profitable trading journey. Start using our platform
