Which Composite Metric Wins for Consistent, Low-Risk Trading Strategies?
Hey traders,
In the world of strategy optimization, your choice of purpose function can make or break whether you uncover a robust, consistently profitable setup or just curve-fit noise. Backtesting isn't real trading (we all know the pitfalls), but it's our essential foundation for filtering parameter groups that deliver comfortable, steady gains without heart-stopping drawdowns.
I've been experimenting with these three composite metrics to score strategies during optimization:
- Option A: Very conservative - Heavily penalizes drawdowns: composite = total_pnl * (1 - max_drawdown) 2- Option B: Good balance - Emphasizes steady compounding: composite = total_pnl * profit_factor * (1 - max_drawdown)- Option C: Simple but effective - Straightforward risk-adjusted return: composite = total_pnl / (max_drawdown + 0.01)
Each has strengths: A crushes high-risk blowups, B rewards efficient equity curves, and C keeps it dead simple. But which one truly surfaces parameter sets for comfortable, consistent profits in live conditions? I've seen A overly conservative (missing winners), B shine on balanced systems, and C work wonders for quick scans - but results vary by market and timeframe.
Experienced optimizers and backtesters: What's your go-to composite (or custom formula)? How do you weight PNL, drawdown, profit factor, Sharpe, or win rate? Share your tweaks, backtest examples, or why you swear by (or ditch) these. Let's crowdsource the best purpose functions for real-world edge.
Drop your thoughts below.Best Regards,Simon
I tend to be cautious with any composite that overweights raw PNL. Drawdown control usually tells you more about survivability than total return. Option B feels balanced, but I still prefer validating results on small live accounts because optimization can hide fragility.
