What data should be collected to evaluate a trading robot's performance?
Collecting data about a trading robot’s operation is the foundation for analyzing its effectiveness and making improvements to the strategy. The more information you collect, the more accurately you can assess the algorithm’s performance.
Main types of data:
- Trade results:
- Date and time of trade opening and closing.
- Instrument, volume, price, and result (profit or loss).
- Stop-loss and take-profit levels used.
- Strategy performance:
- Total profit and loss.
- Maximum drawdown.
- Sharpe Ratio and win/loss ratio indicators.
- Market data:
- Current and historical quotes.
- Instrument liquidity and volatility.
- Technical data:
- Robot response time to market events.
- Error logs and missed signals.
Tips for organizing data:
- Structure your data:
- Store information in tables or databases.
- Separate data by instrument and strategy.
- Update regularly:
- Save data after each trading session.
- Analyze information weekly or monthly.
- Automate the process:
- Set up scripts or functions to eliminate the human factor.
Examples of useful metrics:
- Total return for the period.
- Average profit per trade.
- Frequency of successful trades.
- Risk-reward ratio.
The collected data will help you better understand the trading robot’s operation and adapt it to changing market conditions.