Robustness Checks for Evaluating Trading Robots Before Purchase
Summary
This guide presents a checklist for evaluating a trading robot with a platform’s strategy tester before buying or using it live. It recommends testing random execution delays, different broker conditions, other symbols and time frames, unfavorable historical periods, longer out-of-sample intervals, and forward results after parameter optimization. These checks probe whether performance depends on ideal execution, a particular market, or a conveniently chosen date range.
The article illustrates the process with a three-moving-average system. Its results change across historical periods, and testing on a later interval is described as more favorable; the example also reports no major effect from randomized delays and no obvious errors on other symbols. These are demonstrations for one robot, not general evidence of profitability. The article advises scrutiny of unusually high profits, excessive parameters, and elaborate money-management rules, and notes that neither historical testing nor a successful evaluation can guarantee future returns or rule out software errors in live trading.
Key ideas
- Random execution delays can reveal order-handling problems or dependence on ideal fills.
- Changing brokers, symbols, and time frames helps identify sensitivity to trading conditions and market selection.
- Testing adverse and extended historical periods can expose dependence on a favorable sample.
- Forward testing evaluates optimized parameters on data not used to select them.
- High reported profits, many parameters, and complex money management warrant additional scrutiny.
- Backtests cannot guarantee future performance or uncover every live software failure.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.