| name | online-learning |
| description | Mutable parameter families with bounds, shadow→canary→promote pipeline, and statistical gates for parameter promotion. |
Online Learning
The system mutates a small set of bounded parameters weekly based on closed-trade stats and post-mortems. Hard risk caps are NEVER mutated by this loop.
Mutable parameter families
| Family | Examples | Bounds |
|---|
| sizing_aggression | r1_max_pos_pct, r3_dollar_floor | conservative ≤ value ≤ aggressive |
| stop_distances | default_stop_atr_mult, option_premium_stop_pct | 0.5× ≤ value ≤ 2.0× current |
| entry_filters | min_setup_quality, min_iv_percentile_to_skip | exposed ranges per family |
| regime_thresholds | crisis-overlay vol percentile | 0.75 ≤ value ≤ 0.95 |
| candidate_count | max_candidates_per_scout | 1 ≤ value ≤ 12 |
Pipeline
- Spec — Learning Critic proposes a parameter change with rationale, expected impact, and min_canary_trades.
- Synthesis — System validates bounds, anti-thrashing (no change if same param touched in last 4 weeks).
- Implementation — New value committed as a
param_versions row with mode=CANARY.
- Validation — N trades run with the canary version. Statistical gates checked:
- Wilson 95% CI lower bound on win rate ≥ control's mean
- Profit factor ≥ control's profit factor × 0.95
- Max drawdown ≤ control's max drawdown × 1.1
- Analysis — If gates pass, promote to
mode=ACTIVE. If fail, revert.
Shadow vs canary
- Shadow — proposed parameter runs in parallel with control on the SAME trades; only logged, not actuated. Used to gather signal cheaply.
- Canary — parameter actually drives a slice (e.g. 25%) of trades. Statistically gated.
Hard rules
- N_canary_min ≥ 20 trades for any parameter family before considering promotion.
- No more than 1 active canary per family at a time.
- Risk hard-caps NEVER mutated; only "soft" parameters above.
- If a canary fails its drawdown gate within the first 5 trades, immediate auto-revert.