Backtesting Strategies: Purpose, Biases, and Software Choices
Summary
This article introduces algorithmic strategy backtesting as the application of trading rules to historical data to generate signals and estimate trade and portfolio profit and loss. It describes using backtests to screen candidate strategies, test models of costs and market behavior, tune parameters, and check an implementation against expected performance. It also notes that execution and market microstructure make results less reliable as trading frequency rises.
The article emphasizes that backtests are idealized and can overstate live performance. It discusses optimization bias and sensitivity analysis, look-ahead bias from future information or unlagged extremes, survivorship bias from omitting delisted assets, and psychological tolerance bias. It recommends limiting parameter count, checking performance across parameter values, using data that includes failed or delisted assets, and respecting information availability at each simulated time. It also compares software languages and packages in broad terms, noting trade-offs among development speed, customization, and execution speed. The supplied text is incomplete in places and does not provide the promised detailed implementation guidance or a worked strategy evaluation.
Key ideas
- Backtesting applies a strategy to historical data to assess its signals and accumulated profit and loss.
- Backtests can screen ideas, evaluate models, tune parameters, and verify implementations.
- Optimization, look-ahead, survivorship, and psychological tolerance biases can distort results.
- Sensitivity analysis and fewer parameters can help expose curve fitting.
- More realistic evaluation requires appropriate historical universes and careful modeling of market microstructure and execution.
- Software choice depends on the strategy’s complexity, development needs, and speed requirements.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.