Initial commit — GeoOptions Intelligence Cockpit v2.0
Stack: FastAPI + React/TypeScript + SQLite + GPT-4o Features: Radar géopolitique, Marchés, Régime Macro, Journal de Bord MTM, Rapport IA, Super Contexte (base de raisonnement évolutive), Boucle feedback IA. Deploy: Docker + docker-compose + nginx pour openfin.open-squared.tech Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
OpenSquared
332
backend/routers/reasoning.py
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332
backend/routers/reasoning.py
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"""
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AI Reasoning Traces — store and query the full reasoning chain behind each trade.
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Endpoints:
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GET /api/reasoning/postmortem/{trade_id} — reasoning chain (no GPT call)
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POST /api/reasoning/postmortem/{trade_id}/analyze — GPT-4o post-mortem analysis
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"""
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import json
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import logging
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import os
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from fastapi import APIRouter, HTTPException
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from services.database import (
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get_config,
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get_ai_report,
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get_mtm_trades_with_traces,
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get_pattern_scoring_history,
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get_scoring_trace,
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get_suggestion_trace,
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get_trade_entry_by_id,
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list_ai_reports,
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save_ai_report,
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)
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/reasoning", tags=["reasoning"])
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# ── Helpers ───────────────────────────────────────────────────────────────────
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def _bucket_summary(buckets: list) -> str:
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lines = []
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for b in buckets:
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pct = round(b.get("score", 0) / b.get("max", 1) * 100) if b.get("max") else 0
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lines.append(f" {b.get('label', b.get('id'))}: {b.get('score')}/{b.get('max')} ({pct}%) — {(b.get('comment') or '')[:90]}")
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return "\n".join(lines)
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def _rankings_summary(rankings: list) -> str:
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lines = []
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for r in rankings:
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delta = r.get("score_delta", 0)
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sign = "+" if delta >= 0 else ""
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lines.append(f" {r.get('underlying')} {r.get('strategy')} — delta {sign}{delta} | {(r.get('rationale') or '')[:80]}")
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return "\n".join(lines)
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# ── Endpoints ─────────────────────────────────────────────────────────────────
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@router.get("/postmortem/{trade_id}")
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def get_postmortem(trade_id: int):
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"""
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Return the full AI reasoning chain for a logged trade:
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- why the pattern was suggested (suggestion trace)
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- why it was scored at that level (scoring trace with pillar breakdown)
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- score evolution across cycles (trend)
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"""
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trade = get_trade_entry_by_id(trade_id)
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if not trade:
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raise HTTPException(404, f"Trade {trade_id} not found")
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scoring_trace = get_scoring_trace(trade["run_id"], trade["pattern_id"])
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suggestion_trace = get_suggestion_trace(trade["pattern_id"])
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score_history = get_pattern_scoring_history(trade["pattern_id"], limit=8)
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return {
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"trade": trade,
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"scoring_context": scoring_trace,
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"suggestion_context": suggestion_trace,
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"score_history": [
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{
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"run_id": t["run_id"],
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"created_at": t["created_at"],
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"score": t["output"].get("score"),
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"key_catalyst": t["output"].get("key_catalyst"),
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"macro_dominant": t["macro_dominant"],
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"geo_score": t["geo_score"],
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"summary": t["output"].get("summary"),
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}
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for t in score_history
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],
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}
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@router.post("/postmortem/{trade_id}/analyze")
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def analyze_postmortem(trade_id: int):
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"""
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Ask GPT-4o to explain why a trade did/didn't work based on the full reasoning chain.
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Returns a structured analysis with diagnostic, lessons, and next-cycle recommendations.
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"""
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ai_key = get_config("openai_api_key") or ""
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if not ai_key:
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raise HTTPException(400, "Clé OpenAI non configurée")
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os.environ["OPENAI_API_KEY"] = ai_key
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from services.ai_analyzer import _chat
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trade = get_trade_entry_by_id(trade_id)
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if not trade:
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raise HTTPException(404, f"Trade {trade_id} not found")
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scoring_trace = get_scoring_trace(trade["run_id"], trade["pattern_id"])
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suggestion_trace = get_suggestion_trace(trade["pattern_id"])
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score_history = get_pattern_scoring_history(trade["pattern_id"], limit=5)
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scoring_out = scoring_trace["output"] if scoring_trace else {}
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scoring_ctx = scoring_trace["input_context"] if scoring_trace else {}
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suggestion_out = suggestion_trace["output"] if suggestion_trace else {}
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buckets_text = _bucket_summary(scoring_out.get("buckets", []))
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rankings_text = _rankings_summary(scoring_out.get("trade_rankings", []))
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score_trend = " → ".join(
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f"{t['output'].get('score', '?')}/100 ({t['macro_dominant'] or '?'} régime, géo {t['geo_score'] or '?'})"
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for t in reversed(score_history)
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)
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prompt = f"""Tu es un stratège macro-géopolitique senior qui analyse le post-mortem d'un trade options.
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═══ TRADE ANALYSÉ ═══
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Pattern : {trade.get("pattern_name")}
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Instrument : {trade.get("underlying")} — {trade.get("strategy")}
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Entrée : {trade.get("entry_date")} @ {trade.get("entry_price") or "N/A"}
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Score entrée: {trade.get("score_at_entry")}/100 | Trade Score: {trade.get("trade_score") or "N/A"} | EV nette: {trade.get("ev_net") or "N/A"}
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Profil : {trade.get("matched_profile")} | Gain prévu: {trade.get("expected_move_pct") or "N/A"}%
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═══ CONTEXTE AU MOMENT DU SCORING ═══
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Régime macro : {scoring_trace.get("macro_dominant") if scoring_trace else "N/A"} | Biais asset: {scoring_ctx.get("asset_bias", "N/A")}
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Scores macro : {json.dumps(scoring_ctx.get("macro_scores", {}), ensure_ascii=False)}
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Risque géo : {scoring_trace.get("geo_score") if scoring_trace else "N/A"}/100
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Gain prévu : {scoring_ctx.get("expected_move_pct") or "N/A"}%
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═══ POURQUOI CE PATTERN A ÉTÉ CRÉÉ ═══
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{suggestion_out.get("macro_fit") or "N/A"}
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{suggestion_out.get("description") or ""}
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═══ SCORE DÉTAILLÉ PAR PILIER ═══
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Score global : {scoring_out.get("score", 0)}/100 (confiance {scoring_out.get("confidence", 0)}%)
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{buckets_text or "Non disponible"}
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Catalyseur clé : {scoring_out.get("key_catalyst") or "N/A"}
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Synthèse : {scoring_out.get("summary") or "N/A"}
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Contra-signal fort : {"OUI" if scoring_out.get("has_strong_contra") else "non"}
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═══ CLASSEMENT DES TRADES AU SCORING ═══
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{rankings_text or "Non disponible"}
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═══ ÉVOLUTION DU SCORE DANS LE TEMPS ═══
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{score_trend or "Premier scoring — pas d'historique"}
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Analyse ce trade en JSON :
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{{
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"diagnostic": "<2-3 phrases: qu'explique la performance (bonne ou mauvaise) de ce trade ?>",
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"what_worked": "<ce qui était correct dans l'analyse initiale>",
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"what_missed": "<ce que l'IA a sous/sur-estimé, ou n'a pas anticipé>",
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"regime_alignment": "<le régime macro était-il vraiment favorable ? a-t-il évolué depuis ?>",
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"contra_assessment": "<les contra-signals détectés étaient-ils le vrai risque ? ou un faux signal ?>",
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"lesson": "<1 règle précise à retenir pour scorer ce type de pattern plus finement>",
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"next_cycle": "<comment enrichir le contexte et les critères pour ce pattern dans les prochains cycles ?>"
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}}"""
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try:
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result = _chat(
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"Tu es un stratège macro-géopolitique senior. Post-mortem concis et actionnable. JSON uniquement.",
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prompt,
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model="gpt-4o",
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json_mode=True,
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max_tokens=900,
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)
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except Exception as e:
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logger.error(f"[Postmortem] GPT-4o call failed: {e}")
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raise HTTPException(503, "GPT-4o indisponible")
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if not result:
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raise HTTPException(503, "GPT-4o n'a pas retourné de réponse")
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return {
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"trade_id": trade_id,
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"trade": trade,
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"scoring_context": scoring_trace,
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"suggestion_context": suggestion_trace,
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"analysis": result,
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}
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# ── Portfolio AI Report ────────────────────────────────────────────────────────
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def _trade_summary_block(label: str, trades: list) -> str:
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if not trades:
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return f"{label} : aucun trade pricé"
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lines = [f"{label} :"]
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for t in trades:
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pnl = t.get("pnl_pct")
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sc = t.get("scoring_context") or {}
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sc_out = sc.get("output", {}) if isinstance(sc, dict) else {}
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sg = t.get("suggestion_context") or {}
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sg_out = sg.get("output", {}) if isinstance(sg, dict) else {}
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macro = sc.get("macro_dominant") if isinstance(sc, dict) else "?"
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geo = sc.get("geo_score") if isinstance(sc, dict) else "?"
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catalyst = sc_out.get("key_catalyst") or "N/A"
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macro_fit = sg_out.get("macro_fit") or sg_out.get("description") or "N/A"
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trend = " → ".join(str(s) for s in (t.get("score_trend") or [])) or "N/A"
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buckets = sc_out.get("buckets", [])
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weak = [b.get("label", b.get("id", "")) for b in buckets if b.get("max") and b.get("score", 0) / b["max"] < 0.4]
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lines.append(
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f" • {t.get('pattern_name')} | {t.get('underlying')} {t.get('strategy')}"
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f" | P&L {'+' if (pnl or 0) >= 0 else ''}{(pnl or 0):.1f}%"
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f" | Score entrée {t.get('score_at_entry')}/100 | Régime {macro} | Géo {geo}"
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f"\n Thèse : {macro_fit[:120]}"
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f"\n Catalyseur : {catalyst}"
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f"\n Trend score : {trend}"
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+ (f"\n Piliers faibles : {', '.join(weak)}" if weak else "")
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)
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return "\n".join(lines)
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@router.get("/portfolio-report")
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def get_portfolio_report_data(days: int = 90):
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"""Return raw MTM + traces data (no GPT-4o call) for the report page."""
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data = get_mtm_trades_with_traces(days=days, limit_movers=5)
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return data
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@router.post("/portfolio-report/generate")
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def generate_portfolio_report(days: int = 90):
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"""
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Generate a GPT-4o AI report: key highlights, explanations for top movers,
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macro regime assessment, and actionable next-cycle recommendations.
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"""
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ai_key = get_config("openai_api_key") or ""
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if not ai_key:
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raise HTTPException(400, "Clé OpenAI non configurée")
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os.environ["OPENAI_API_KEY"] = ai_key
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from services.ai_analyzer import _chat
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data = get_mtm_trades_with_traces(days=days, limit_movers=5)
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winners = data["winners"]
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losers = data["losers"]
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winners_block = _trade_summary_block("TOP GAINS", winners)
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losers_block = _trade_summary_block("TOP PERTES", losers)
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avg_pnl = data.get("avg_pnl_pct")
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avg_str = f"{avg_pnl:+.1f}%" if avg_pnl is not None else "N/A"
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prompt = f"""Tu es un stratège macro-géopolitique senior. Génère un rapport synthétique sur notre portefeuille options.
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═══ STATISTIQUES GLOBALES ═══
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Période : {days} derniers jours
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Trades total: {data['total_trades']} | Pricés: {data['priced_count']} | P&L moyen: {avg_str}
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═══ {winners_block}
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═══ {losers_block}
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Génère un rapport JSON structuré :
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{{
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"headline": "<1 phrase résumant la performance de la période>",
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"regime_assessment": "<le régime macro a-t-il bien servi nos thèses ? convergence ou divergence ?>",
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"winners_analysis": "<pourquoi ces trades ont marché — pattern commun, catalyseur, régime ? 3-4 phrases>",
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"losers_analysis": "<pourquoi ces trades ont déçu — mauvaise thèse, mauvais timing, contra-signal manqué ? 3-4 phrases>",
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"key_lessons": ["<leçon 1>", "<leçon 2>", "<leçon 3>"],
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"blind_spots": "<ce que notre système de scoring n'a pas bien capturé cette période>",
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"next_cycle_priorities": "<3 priorités concrètes pour améliorer les prochains cycles : patterns à surveiller, ajustements de scoring, régimes à anticiper>",
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"risk_watch": "<1-2 risques macro-géopolitiques à surveiller de près qui pourraient impacter nos positions actuelles>"
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}}"""
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try:
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result = _chat(
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"Tu es un stratège macro senior. Rapport synthétique et actionnable. JSON uniquement.",
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prompt,
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model="gpt-4o",
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json_mode=True,
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max_tokens=1200,
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)
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except Exception as e:
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logger.error(f"[PortfolioReport] GPT-4o call failed: {e}")
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raise HTTPException(503, "GPT-4o indisponible")
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if not result:
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raise HTTPException(503, "GPT-4o n'a pas retourné de réponse")
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stats = {
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"total_trades": data["total_trades"],
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"priced_count": data["priced_count"],
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"avg_pnl_pct": avg_pnl,
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}
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report_id = save_ai_report(
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days=days,
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stats=stats,
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winners=winners,
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losers=losers,
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report=result,
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)
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return {
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"id": report_id,
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"days": days,
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"stats": stats,
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"winners": winners,
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"losers": losers,
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"report": result,
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}
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@router.get("/reports")
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def list_reports(report_type: str = "portfolio", limit: int = 20):
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"""List archived AI reports (newest first), summary only."""
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reports = list_ai_reports(report_type=report_type, limit=limit)
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return {
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"reports": [
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{
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"id": r["id"],
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"days": r["days"],
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"created_at": r["created_at"],
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"stats": r["stats"],
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"headline": r["report"].get("headline", ""),
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}
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for r in reports
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]
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}
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@router.get("/reports/{report_id}")
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def get_report(report_id: int):
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"""Retrieve a full archived AI report by ID."""
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report = get_ai_report(report_id)
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if not report:
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raise HTTPException(404, f"Report {report_id} not found")
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return report
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Reference in New Issue
Block a user