Outcome Attribution

A/B Testing

Controlled experiments can isolate the causal effect of individual changes.

Open question: Can bucketed fingerprinting approximate A/B test-like isolation for strategy design decisions, even without true experimental control?

Software Root Cause Analysis

System failures can be traced to specific code changes or configuration decisions.

Open question: Can the decision fingerprint concept transfer to software engineering — attributing system quality to specific architectural choices at the bucketed level?

Value Bucketing Prevents Overfitting

Attributing to 'RSI < 30' risks overfitting. Attributing to 'RSI oversold' (< 35) captures the structural insight without the precision that would be noise. Bucket boundaries must be wide enough to accumulate meaningful sample sizes but narrow enough to distinguish genuinely different decision profiles.

Regime-Conditioned Evidence

A pattern that works in trending markets but fails in ranging markets must produce two separate attribution records, not one averaged record. Averaging across regimes destroys the most valuable signal — regime conditionality is the primary axis of variation in strategy effectiveness.

Survivorship Bias Prevention Through Negative Evidence

Attribution must include failed strategies, not just successful ones. Rejected strategies contribute 'this pattern does not work' evidence. Zero-trade strategies contribute 'this signal combination never fires' evidence. Without negative evidence, the system learns what works but not what to avoid.

Exploration Budget as Attribution Hygiene

If 100% of generation follows attribution signals, the system can only observe strategies that its learning has already endorsed. A 25% exploration budget generates strategies outside known-good patterns, providing the counterfactual evidence needed to validate whether attribution signals are genuine or artifacts of the observation distribution.

Can parameter-level attribution identify interaction effects between indicators — i.e., combinations that are more (or less) effective than the individual indicators would predict?

How should attribution weight recent evidence versus historical evidence? A 90-day rolling window discards old data entirely, which may lose persistent structural insights.

Can attribution evidence distinguish between parameters that are genuinely causal (the strategy works because of this indicator) and parameters that are merely correlated (the strategy works despite this indicator)?

What is the right aggregation cadence for attribution — weekly is the current design, but faster aggregation could accelerate learning while slower aggregation would improve statistical reliability.

How should the system handle attribution signals that conflict with domain expertise — e.g., when attribution data suggests that a theoretically unsound indicator combination has a high success rate?

Regime-conditioned attribution is essential — pooling attribution evidence across regimes produces misleading signals because most patterns are regime-dependent.

Production data clearly shows regime-dependent strategy performance: mean reversion strategies show +$504 avg PnL in ranging but -$57 in trending. Pooled attribution would average these into a weak positive signal, losing the regime-conditional insight.

Counterargument: Some behavioral families (directional momentum, scalp) appear relatively regime-insensitive. For these families, regime conditioning may fragment already-small sample sizes without adding signal.

Attribution should feed generation through soft weight adjustments (boost and deprioritize) rather than hard filters (block and require), because hard filters collapse exploration.

The learning bias system already follows this principle — negative annotations reduce family weights but never eliminate families. The outcome attribution design extends this to parameter-level weights.

Counterargument: For truly catastrophic patterns (consistently negative across all regimes with high sample size), hard exclusion may be more efficient than gradual deprioritization.

Causal vs correlational patterns: attribution aggregates observe correlation (strategies with pattern X succeed Y% of the time) but cannot establish causation. A pattern may succeed because of the indicator choice, or because strategies with that indicator tend to be generated during favorable regimes.

Bucket boundary sensitivity: the choice of bucket boundaries (e.g., RSI 'oversold' at < 35) is currently based on domain heuristics. Different bucket boundaries could produce different attribution signals. There is no systematic process for evaluating whether the current boundaries are optimal.

Qualification duration attribution: the design includes qualification_days as an outcome variable, but it is unclear whether faster qualification is genuinely better (the strategy is clearly good) or merely reflects easier market conditions during the qualification period.

Attribution decay: as market regimes evolve, historical attribution evidence may become stale. The current design uses a 90-day rolling window with binary in/out, but exponential decay or regime-conditional windowing might better capture the relevance of historical evidence.

Decision Science Researchers

The decision fingerprint concept — reducing complex decisions to compact, bucketed representations suitable for cross-case aggregation — may transfer to other domains where individual decisions are too noisy for learning but structural patterns across decisions contain signal.

System Architects

The sparse, one-way evidence accumulation pattern (observe continuously, aggregate weekly, inject into generation prompts) avoids the tight feedback loops that cause instability in systems where learning directly controls action.