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>
This commit is contained in:
OpenSquared
@@ -880,9 +880,172 @@ TEMPLATE DE NOTATION:
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p["score"] = min(total, 100)
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all_scored.sort(key=lambda x: x.get("score", 0), reverse=True)
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# ── Phase convergence: compute cross-pattern conviction bonuses ────────────
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all_scored, _conv_block = _compute_convergence(all_scored, patterns)
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all_scored.sort(key=lambda x: x.get("conviction_score", x.get("score", 0)), reverse=True)
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return all_scored[:top_n]
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# ── Pattern convergence ───────────────────────────────────────────────────────
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PATTERN_CATEGORIES = {
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"géopolitique": "Conflits armés, sanctions, élections, alliances militaires, tensions régionales",
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"macro_monétaire": "Inflation, taux directeurs Fed/BCE, dollar index, yield curve, QE/QT",
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"technique": "RSI, Bollinger, momentum, cassures de support/résistance, patterns chartistes",
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"commodités_supply":"OPEC, stocks pétrole/gaz, récoltes, disruptions mines, logistique supply chain",
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"risk_off": "Catastrophes naturelles, crises bancaires, pandémies, chocs de volatilité",
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"flux_saisonnier": "COT flows, saisonnalité, rééquilibrages fin trimestre, options expiry",
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"géo_économique": "Chaînes d'approvisionnement, protectionnisme, dédollarisation, reshoring",
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"crédit_stress": "Spreads HY, CDS souverains, stress bancaire, risque de défaut",
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}
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def classify_patterns_batch(patterns: List[Dict]) -> List[Dict]:
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"""Classify unclassified patterns via GPT-4o-mini → [{id, category, signal_direction}]."""
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if not get_client() or not patterns:
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return []
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compact = [
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{
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"id": p.get("id"),
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"name": p.get("name", ""),
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"description": (p.get("description") or "")[:200],
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"triggers": (p.get("triggers") or [])[:3],
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"asset_class": p.get("asset_class", ""),
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}
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for p in patterns
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]
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cats_desc = "\n".join(f"- {c}: {d}" for c, d in PATTERN_CATEGORIES.items())
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system = "Tu es un classificateur de patterns financiers géopolitiques. Réponds UNIQUEMENT en JSON valide."
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user = f"""Classifie chaque pattern selon UNE de ces catégories:
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{cats_desc}
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signal_direction:
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- "bullish": anticipe une hausse du sous-jacent
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- "bearish": anticipe une baisse du sous-jacent
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- "volatility": anticipe un mouvement sans direction claire (straddle, vol play)
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- "neutral": direction incertaine / stratégie de range
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Patterns à classifier:
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{json.dumps(compact, ensure_ascii=False)}
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Retourne UNIQUEMENT ce JSON:
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{{"classifications": [{{"id": "<id>", "category": "<catégorie>", "signal_direction": "<direction>"}}]}}"""
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try:
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res = _chat(system, user, model="gpt-4o-mini", json_mode=True, max_tokens=2000)
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return res.get("classifications", []) if res else []
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except Exception as _e:
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import logging as _log2
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_log2.getLogger(__name__).warning(f"[Classify] Batch classification failed: {_e}")
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return []
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def _compute_convergence(
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scored_patterns: List[Dict],
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original_patterns: List[Dict],
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) -> tuple:
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"""
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Group scored patterns by (underlying, signal_direction).
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Apply conviction_bonus = min(20, +5 per additional agreeing pattern).
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Returns (enriched_list, convergence_summary_block_str).
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"""
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from collections import defaultdict
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pat_meta = {str(p.get("id", "")): p for p in original_patterns}
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# Collect underlyings + direction per pattern
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def _pat_underlyings(sp: Dict):
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underlyings = set()
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rec = sp.get("recommended_trade") or {}
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if rec.get("underlying"):
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underlyings.add(rec["underlying"])
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for tr in (sp.get("trade_rankings") or []):
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if tr.get("underlying"):
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underlyings.add(tr["underlying"])
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return underlyings
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# Build (underlying, direction) → list of pattern entries
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groups: Dict = defaultdict(list)
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for sp in scored_patterns:
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pid = str(sp.get("pattern_id", ""))
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orig = pat_meta.get(pid, {})
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direction = (orig.get("signal_direction") or "neutral").lower()
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if direction not in ("bullish", "bearish", "volatility", "neutral"):
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direction = "neutral"
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category = orig.get("category") or ""
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name = (orig.get("name") or sp.get("geo_trigger") or "")[:50]
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score = sp.get("score", 0)
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for und in _pat_underlyings(sp):
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groups[(und, direction)].append({
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"pid": pid, "name": name, "category": category, "score": score,
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})
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# Enrich each scored pattern
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enriched = []
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for sp in scored_patterns:
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pid = str(sp.get("pattern_id", ""))
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orig = pat_meta.get(pid, {})
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direction = (orig.get("signal_direction") or "neutral").lower()
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if direction not in ("bullish", "bearish", "volatility", "neutral"):
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direction = "neutral"
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best_count, best_und, best_partners = 0, "", []
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for und in _pat_underlyings(sp):
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group = groups.get((und, direction), [])
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others = [x for x in group if x["pid"] != pid]
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if len(others) > best_count:
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best_count, best_und, best_partners = len(others), und, others
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conviction_bonus = min(20, 5 * best_count)
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conviction_score = min(100, sp.get("score", 0) + conviction_bonus)
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enriched.append({
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**sp,
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"conviction_score": conviction_score,
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"conviction_bonus": conviction_bonus,
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"convergence_count": best_count,
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"convergence_underlying": best_und,
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"convergence_partners": [
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{"name": p["name"], "category": p["category"], "score": p["score"]}
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for p in best_partners[:5]
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],
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})
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# Build convergence block for injection into NEXT suggestion prompt
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conv_lines = []
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for (und, direction), pats in groups.items():
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if len(pats) >= 2:
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avg_sc = sum(p["score"] for p in pats) / len(pats)
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cats = ", ".join(sorted({p["category"] for p in pats if p["category"]}))
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conv_lines.append((len(pats), avg_sc, und, direction, pats, cats))
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conv_lines.sort(key=lambda x: (-x[0], -x[1]))
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if not conv_lines:
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conv_block = ""
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else:
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_dir_sym = {"bullish": "▲", "bearish": "▼", "volatility": "⟷", "neutral": "↔"}
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lines = [
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"\n## 🎯 CONVERGENCE SIGNAUX — Underlyings à forte conviction inter-patterns",
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"Ces sous-jacents sont ciblés par plusieurs patterns actifs dans la MÊME direction :",
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]
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for count, avg_sc, und, direction, pats, cats in conv_lines[:8]:
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sym = _dir_sym.get(direction, "↔")
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pat_names = " | ".join(p["name"][:30] for p in pats[:4])
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lines.append(f" {sym} {und} {direction.upper()}: {count} patterns convergents (score moy. {avg_sc:.0f}/100)")
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lines.append(f" Catégories: {cats or 'N/A'} | Patterns: {pat_names}")
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lines += [
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"",
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"⚠️ Ces convergences signalent une FORTE conviction collective — privilégie des patterns",
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"complémentaires sur ces underlyings ou signale un contra-signal si le contexte l'inverse.",
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]
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conv_block = "\n".join(lines)
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return enriched, conv_block
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# ── Suggest new patterns from live market context ─────────────────────────────
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# ── Temporal news utilities ───────────────────────────────────────────────────
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@@ -1045,6 +1208,7 @@ def suggest_patterns_from_market_context(
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fred_block: str = "",
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price_discovery_block: str = "",
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portfolio_context_block: str = "",
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convergence_block: str = "",
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run_id: str = "",
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) -> List[Dict]:
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"""Ask GPT-4o to propose new patterns based on current geo/market + macro regime context."""
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@@ -1195,6 +1359,7 @@ Règles supplémentaires:
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fred_section = f"\n{fred_block}\n" if fred_block else ""
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pd_section = f"\n{price_discovery_block}\n" if price_discovery_block else ""
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portfolio_section = f"\n{portfolio_context_block}\n" if portfolio_context_block else ""
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convergence_section = f"\n{convergence_block}\n" if convergence_block else ""
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user = f"""Tu es un stratège géopolitique et financier senior, expert en options.
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{macro_block}{geo_block}{lessons_block}{reliability_block}{iv_block}
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@@ -1205,6 +1370,7 @@ Règles supplémentaires:
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{pd_section}
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{tech_block_section}
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{portfolio_section}
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{convergence_section}
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## Calendrier économique à venir
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{cal_block}
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@@ -1233,6 +1399,8 @@ Retourne UNIQUEMENT ce JSON:
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"triggers": ["<trigger1>", "<trigger2>"],
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"keywords": ["<kw1>", "<kw2>", "<kw3>"],
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"asset_class": "<energy|metals|agriculture|indices|equities|forex>",
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"category": "<géopolitique|macro_monétaire|technique|commodités_supply|risk_off|flux_saisonnier|géo_économique|crédit_stress>",
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"signal_direction": "<bullish|bearish|volatility|neutral>",
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"expected_move_pct": <float, RENDEMENT OPTION MOYEN en % pour ce pattern, levier inclus. Typiquement 50-300%.>,
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"probability": <float 0-1>,
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"horizon_days": <int>,
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