Strategy Fitness Scorecard — Methodology

The Strategy Fitness Scorecard rates every active strategy each session against its own historical baseline. The output is a single edge label (Hot / Warm / Cool / Cold) plus a recommended position-size adjustment.

Inputs. Data comes from the scorecards_v view, which is a per-(strategy, session) rollup of card_outcomes ⋈ setups ⋈ regime_classification. Only resolved outcomes (exit_status = profit_target or stop_loss) contribute. The current-regime/VIX context is sourced from regime_classification ORDER BY trade_date DESC LIMIT 1.

Filter (v1). For each strategy, the algorithm aggregates the rows in scorecards_v where the row's regime matches today's regime OR the row's VIX is within ±2 points of today's VIX. The GEX dimension specified in earlier drafts is deferred to Phase 1.6 (no GEX time series available in v1).

Algorithm.

1. n = sum of trades_count across filtered rows.
2. If n < 20, label = Cool, size = 0.5×, win-rate displayed as insufficient.
3. win_rate = sum of wins_count ÷ n.
4. overall_win_rate = strategy's all-regime win rate.
5. edge_score = win_rate − overall_win_rate.
6. Label by inclusive band (1e-9 IEEE 754 tolerance applied):
   • Hot if edge_score ≥ +0.10 → size 1.0×
   • Warm if edge_score ≥ +0.03 → size 0.75×
   • Cool if edge_score ≥ −0.05 → size 0.5×
   • Cold otherwise → size 0.0×

Sample-size handling. The cumulative n=20floor is per strategy, across all matching sessions. Below the floor, the algorithm returns Cool with a null win-rate. Between n=20 and n<40, the win-rate is rendered with a "near floor" suffix to flag thin samples to the trader.

Algorithm version. v1.0 — June 2026 launch.