OpenSquared 952e326590 feat: pattern convergence engine — categories, signal_direction, conviction scores ...

Phase 1 — Catégorisation:
- database.py: ADD COLUMN category + signal_direction on custom_patterns (migration);
  save_custom_pattern persists category/signal_direction; new helpers:
  get_unclassified_patterns(), update_pattern_classification(),
  get_patterns_with_last_score()
- ai_analyzer.py: PATTERN_CATEGORIES dict (8 categories: géopolitique, macro_monétaire,
  technique, commodités_supply, risk_off, flux_saisonnier, géo_économique, crédit_stress);
  classify_patterns_batch() → GPT-4o-mini batch classification
- suggest schema: added category + signal_direction fields so new patterns are
  classified from birth
- auto_cycle.py: Step 3.1 classifies all unclassified patterns after each suggestion

Phase 2 — Convergence layer (post-scoring, no extra AI call):
- ai_analyzer.py: _compute_convergence() groups scored patterns by (underlying, signal_direction);
  conviction_bonus = min(20, +5 per additional agreeing pattern); adds conviction_score,
  conviction_bonus, convergence_count, convergence_underlying, convergence_partners to each result;
  called at end of score_patterns_with_context(), re-sorts by conviction_score
- auto_cycle.py: logs convergence summary after scoring; propagates category/signal_direction
  to scored results for display

Phase optionnelle — Convergence in suggestion prompt:
- ai_analyzer.py: suggest_patterns_from_market_context() accepts convergence_block param;
  injected into prompt so AI knows which underlyings have multi-pattern agreement
- auto_cycle.py: before suggestion, loads last-cycle scores via get_patterns_with_last_score(),
  calls _compute_convergence() to build convergence block, passes to suggester

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-06-21 20:22:08 +02:00

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