/strategy-pivot-designer

Stagnation Triggers

Four deterministic triggers detect when a strategy's backtest iteration loop has stalled. Each trigger maps directly to fields in evaluate_backtest.py output accumulated in an iteration history file.

Field Reference Mapping

Note: Dimension scores are looked up by name field, not by array index. This makes the system resilient to future dimension reordering or additions.

Trigger 1: Improvement Plateau

ID: improvement_plateau Severity: high

Condition: Over the last K iterations (default K=3), the range of total_score values is less than threshold (default 3).

Rationale: When score stops moving despite parameter changes, the strategy architecture itself has reached a local maximum.

Evidence fields:

• last_k_scores: list of total_score values for the last K iterations
• score_range: max - min of last_k_scores
• threshold: the configured threshold

Minimum iterations: K (default 3). Cannot fire with fewer iterations.

Trigger 2: Overfitting Proxy

ID: overfitting_proxy Severity: medium

Condition: ALL of the following must be true:

1. Expectancy dimension score >= 15
2. Risk Management dimension score >= 15
3. Robustness dimension score < 10
4. red_flags IDs contain over_optimized OR short_test_period

Rationale: High in-sample performance combined with low robustness and red flags for curve-fitting suggests the strategy is optimized to historical noise rather than genuine edge.

Minimum iterations: 2 (needs at least some iteration history to be meaningful).

Trigger 3: Cost Defeat

ID: cost_defeat Severity: medium

Condition: ALL of the following must be true:

1. eval.expectancy < 0.3
2. eval.profit_factor < 1.3
3. eval.inputs.slippage_tested == True

Rationale: When expectancy and profit factor are thin AND slippage has already been modeled, the edge is too narrow to survive real-world execution costs. Further parameter tuning cannot create edge that isn't there.

Minimum iterations: 2 (requires slippage to have been tested, which implies at least one refinement cycle).

Trigger 4: Tail Risk

ID: tail_risk Severity: high

Condition: EITHER of the following:

1. eval.inputs.max_drawdown_pct > 35
2. Risk Management dimension score <= 5

Rationale: Extreme drawdown or catastrophically low risk management scores indicate structural risk problems that parameter tuning alone cannot fix. Requires architectural changes to risk module.

Minimum iterations: 1 (can fire on the very first evaluation — if drawdown is extreme, early pivot is warranted).

Recommendation Decision Table

Evaluated in priority order (first match wins):

Note: abandon is evaluated first. This catches cases where scores are consistently terrible but may not trip any specific trigger threshold (e.g., all scores hovering around 25 with no single trigger matching).