- Controlled risk + low drawdowns – high risk systems may look amazing on paper, but nobody can stomach as much drawdown as they think they can.
- Fully end-to-end backtested + rules-based. Statistical edge = good. Random idea = bad.
- No black boxes/ML – this is a hot take but I think most ML approaches will just overfit faster. When the biggest issue in backtesting is over fitting, ML just makes the over fitting more efficient and easier to justify
- Good Sharpe ratio – the saying is "you can only eat risk-adjusted return"
- Survives parameter robustness stress tests. If it only "works" with one value it's probably overfit
- Every rule has some sort of justification besides the backtest. It needs to make sense intuitively
- Based on actual academic research, such as different risk premia extraction. Short volatility strategies are a good example of this
Everything I learned about the qualities of a robust system
byu/Impressive-Bottle229 inoptions
Posted by Impressive-Bottle229