OpenFin/backend/routers/reasoning.py

"""
AI Reasoning Traces — store and query the full reasoning chain behind each trade.

Endpoints:
  GET  /api/reasoning/postmortem/{trade_id}          — reasoning chain (no GPT call)
  POST /api/reasoning/postmortem/{trade_id}/analyze  — GPT-4o post-mortem analysis
"""
import json
import logging
import os

from fastapi import APIRouter, HTTPException

from services.database import (
    get_config,
    get_ai_report,
    get_mtm_trades_with_traces,
    get_pattern_scoring_history,
    get_scoring_trace,
    get_suggestion_trace,
    get_trade_entry_by_id,
    list_ai_reports,
    save_ai_report,
)

logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/reasoning", tags=["reasoning"])


# ── Helpers ───────────────────────────────────────────────────────────────────

def _bucket_summary(buckets: list) -> str:
    lines = []
    for b in buckets:
        pct = round(b.get("score", 0) / b.get("max", 1) * 100) if b.get("max") else 0
        lines.append(f"  {b.get('label', b.get('id'))}: {b.get('score')}/{b.get('max')} ({pct}%) — {(b.get('comment') or '')[:90]}")
    return "\n".join(lines)


def _rankings_summary(rankings: list) -> str:
    lines = []
    for r in rankings:
        delta = r.get("score_delta", 0)
        sign = "+" if delta >= 0 else ""
        lines.append(f"  {r.get('underlying')} {r.get('strategy')} — delta {sign}{delta} | {(r.get('rationale') or '')[:80]}")
    return "\n".join(lines)


# ── Endpoints ─────────────────────────────────────────────────────────────────

@router.get("/postmortem/{trade_id}")
def get_postmortem(trade_id: int):
    """
    Return the full AI reasoning chain for a logged trade:
    - why the pattern was suggested (suggestion trace)
    - why it was scored at that level (scoring trace with pillar breakdown)
    - score evolution across cycles (trend)
    """
    trade = get_trade_entry_by_id(trade_id)
    if not trade:
        raise HTTPException(404, f"Trade {trade_id} not found")

    scoring_trace = get_scoring_trace(trade["run_id"], trade["pattern_id"])
    suggestion_trace = get_suggestion_trace(trade["pattern_id"])
    score_history = get_pattern_scoring_history(trade["pattern_id"], limit=8)

    return {
        "trade": trade,
        "scoring_context": scoring_trace,
        "suggestion_context": suggestion_trace,
        "score_history": [
            {
                "run_id": t["run_id"],
                "created_at": t["created_at"],
                "score": t["output"].get("score"),
                "key_catalyst": t["output"].get("key_catalyst"),
                "macro_dominant": t["macro_dominant"],
                "geo_score": t["geo_score"],
                "summary": t["output"].get("summary"),
            }
            for t in score_history
        ],
    }


@router.post("/postmortem/{trade_id}/analyze")
def analyze_postmortem(trade_id: int):
    """
    Ask GPT-4o to explain why a trade did/didn't work based on the full reasoning chain.
    Returns a structured analysis with diagnostic, lessons, and next-cycle recommendations.
    """
    ai_key = get_config("openai_api_key") or ""
    if not ai_key:
        raise HTTPException(400, "Clé OpenAI non configurée")
    os.environ["OPENAI_API_KEY"] = ai_key

    from services.ai_analyzer import _chat

    trade = get_trade_entry_by_id(trade_id)
    if not trade:
        raise HTTPException(404, f"Trade {trade_id} not found")

    scoring_trace = get_scoring_trace(trade["run_id"], trade["pattern_id"])
    suggestion_trace = get_suggestion_trace(trade["pattern_id"])
    score_history = get_pattern_scoring_history(trade["pattern_id"], limit=5)

    scoring_out = scoring_trace["output"] if scoring_trace else {}
    scoring_ctx = scoring_trace["input_context"] if scoring_trace else {}
    suggestion_out = suggestion_trace["output"] if suggestion_trace else {}

    buckets_text = _bucket_summary(scoring_out.get("buckets", []))
    rankings_text = _rankings_summary(scoring_out.get("trade_rankings", []))

    score_trend = " → ".join(
        f"{t['output'].get('score', '?')}/100 ({t['macro_dominant'] or '?'} régime, géo {t['geo_score'] or '?'})"
        for t in reversed(score_history)
    )

    prompt = f"""Tu es un stratège macro-géopolitique senior qui analyse le post-mortem d'un trade options.

═══ TRADE ANALYSÉ ═══
Pattern    : {trade.get("pattern_name")}
Instrument : {trade.get("underlying")} — {trade.get("strategy")}
Entrée     : {trade.get("entry_date")} @ {trade.get("entry_price") or "N/A"}
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"}
Profil     : {trade.get("matched_profile")} | Gain prévu: {trade.get("expected_move_pct") or "N/A"}%

═══ CONTEXTE AU MOMENT DU SCORING ═══
Régime macro : {scoring_trace.get("macro_dominant") if scoring_trace else "N/A"} | Biais asset: {scoring_ctx.get("asset_bias", "N/A")}
Scores macro : {json.dumps(scoring_ctx.get("macro_scores", {}), ensure_ascii=False)}
Risque géo   : {scoring_trace.get("geo_score") if scoring_trace else "N/A"}/100
Gain prévu   : {scoring_ctx.get("expected_move_pct") or "N/A"}%

═══ POURQUOI CE PATTERN A ÉTÉ CRÉÉ ═══
{suggestion_out.get("macro_fit") or "N/A"}
{suggestion_out.get("description") or ""}

═══ SCORE DÉTAILLÉ PAR PILIER ═══
Score global : {scoring_out.get("score", 0)}/100 (confiance {scoring_out.get("confidence", 0)}%)
{buckets_text or "Non disponible"}
Catalyseur clé : {scoring_out.get("key_catalyst") or "N/A"}
Synthèse       : {scoring_out.get("summary") or "N/A"}
Contra-signal fort : {"OUI" if scoring_out.get("has_strong_contra") else "non"}

═══ CLASSEMENT DES TRADES AU SCORING ═══
{rankings_text or "Non disponible"}

═══ ÉVOLUTION DU SCORE DANS LE TEMPS ═══
{score_trend or "Premier scoring — pas d'historique"}

Analyse ce trade en JSON :
{{
  "diagnostic": "<2-3 phrases: qu'explique la performance (bonne ou mauvaise) de ce trade ?>",
  "what_worked": "<ce qui était correct dans l'analyse initiale>",
  "what_missed": "<ce que l'IA a sous/sur-estimé, ou n'a pas anticipé>",
  "regime_alignment": "<le régime macro était-il vraiment favorable ? a-t-il évolué depuis ?>",
  "contra_assessment": "<les contra-signals détectés étaient-ils le vrai risque ? ou un faux signal ?>",
  "lesson": "<1 règle précise à retenir pour scorer ce type de pattern plus finement>",
  "next_cycle": "<comment enrichir le contexte et les critères pour ce pattern dans les prochains cycles ?>"
}}"""

    try:
        result = _chat(
            "Tu es un stratège macro-géopolitique senior. Post-mortem concis et actionnable. JSON uniquement.",
            prompt,
            model="gpt-4o",
            json_mode=True,
            max_tokens=900,
        )
    except Exception as e:
        logger.error(f"[Postmortem] GPT-4o call failed: {e}")
        raise HTTPException(503, "GPT-4o indisponible")

    if not result:
        raise HTTPException(503, "GPT-4o n'a pas retourné de réponse")

    return {
        "trade_id": trade_id,
        "trade": trade,
        "scoring_context": scoring_trace,
        "suggestion_context": suggestion_trace,
        "analysis": result,
    }


# ── Portfolio AI Report ────────────────────────────────────────────────────────

def _trade_summary_block(label: str, trades: list) -> str:
    if not trades:
        return f"{label} : aucun trade pricé"
    lines = [f"{label} :"]
    for t in trades:
        pnl = t.get("pnl_pct")
        sc = t.get("scoring_context") or {}
        sc_out = sc.get("output", {}) if isinstance(sc, dict) else {}
        sg = t.get("suggestion_context") or {}
        sg_out = sg.get("output", {}) if isinstance(sg, dict) else {}
        macro = sc.get("macro_dominant") if isinstance(sc, dict) else "?"
        geo = sc.get("geo_score") if isinstance(sc, dict) else "?"
        catalyst = sc_out.get("key_catalyst") or "N/A"
        macro_fit = sg_out.get("macro_fit") or sg_out.get("description") or "N/A"
        trend = " → ".join(str(s) for s in (t.get("score_trend") or [])) or "N/A"
        buckets = sc_out.get("buckets", [])
        weak = [b.get("label", b.get("id", "")) for b in buckets if b.get("max") and b.get("score", 0) / b["max"] < 0.4]
        lines.append(
            f"  • {t.get('pattern_name')} | {t.get('underlying')} {t.get('strategy')}"
            f" | P&L {'+' if (pnl or 0) >= 0 else ''}{(pnl or 0):.1f}%"
            f" | Score entrée {t.get('score_at_entry')}/100 | Régime {macro} | Géo {geo}"
            f"\n    Thèse : {macro_fit[:120]}"
            f"\n    Catalyseur : {catalyst}"
            f"\n    Trend score : {trend}"
            + (f"\n    Piliers faibles : {', '.join(weak)}" if weak else "")
        )
    return "\n".join(lines)


@router.get("/portfolio-report")
def get_portfolio_report_data(days: int = 90):
    """Return raw MTM + traces data (no GPT-4o call) for the report page."""
    data = get_mtm_trades_with_traces(days=days, limit_movers=5)
    return data


@router.post("/portfolio-report/generate")
def generate_portfolio_report(days: int = 90):
    """
    Generate a GPT-4o AI report: key highlights, explanations for top movers,
    macro regime assessment, and actionable next-cycle recommendations.
    """
    ai_key = get_config("openai_api_key") or ""
    if not ai_key:
        raise HTTPException(400, "Clé OpenAI non configurée")
    os.environ["OPENAI_API_KEY"] = ai_key

    from services.ai_analyzer import _chat

    data = get_mtm_trades_with_traces(days=days, limit_movers=5)
    winners = data["winners"]
    losers  = data["losers"]

    winners_block = _trade_summary_block("TOP GAINS", winners)
    losers_block  = _trade_summary_block("TOP PERTES", losers)

    avg_pnl = data.get("avg_pnl_pct")
    avg_str = f"{avg_pnl:+.1f}%" if avg_pnl is not None else "N/A"

    prompt = f"""Tu es un stratège macro-géopolitique senior. Génère un rapport synthétique sur notre portefeuille options.

═══ STATISTIQUES GLOBALES ═══
Période     : {days} derniers jours
Trades total: {data['total_trades']} | Pricés: {data['priced_count']} | P&L moyen: {avg_str}

═══ {winners_block}

═══ {losers_block}

Génère un rapport JSON structuré :
{{
  "headline": "<1 phrase résumant la performance de la période>",
  "regime_assessment": "<le régime macro a-t-il bien servi nos thèses ? convergence ou divergence ?>",
  "winners_analysis": "<pourquoi ces trades ont marché — pattern commun, catalyseur, régime ? 3-4 phrases>",
  "losers_analysis": "<pourquoi ces trades ont déçu — mauvaise thèse, mauvais timing, contra-signal manqué ? 3-4 phrases>",
  "key_lessons": ["<leçon 1>", "<leçon 2>", "<leçon 3>"],
  "blind_spots": "<ce que notre système de scoring n'a pas bien capturé cette période>",
  "next_cycle_priorities": "<3 priorités concrètes pour améliorer les prochains cycles : patterns à surveiller, ajustements de scoring, régimes à anticiper>",
  "risk_watch": "<1-2 risques macro-géopolitiques à surveiller de près qui pourraient impacter nos positions actuelles>"
}}"""

    try:
        result = _chat(
            "Tu es un stratège macro senior. Rapport synthétique et actionnable. JSON uniquement.",
            prompt,
            model="gpt-4o",
            json_mode=True,
            max_tokens=1200,
        )
    except Exception as e:
        logger.error(f"[PortfolioReport] GPT-4o call failed: {e}")
        raise HTTPException(503, "GPT-4o indisponible")

    if not result:
        raise HTTPException(503, "GPT-4o n'a pas retourné de réponse")

    stats = {
        "total_trades": data["total_trades"],
        "priced_count": data["priced_count"],
        "avg_pnl_pct": avg_pnl,
    }

    report_id = save_ai_report(
        days=days,
        stats=stats,
        winners=winners,
        losers=losers,
        report=result,
    )

    return {
        "id": report_id,
        "days": days,
        "stats": stats,
        "winners": winners,
        "losers": losers,
        "report": result,
    }


@router.get("/reports")
def list_reports(report_type: str = "portfolio", limit: int = 20):
    """List archived AI reports (newest first), summary only."""
    reports = list_ai_reports(report_type=report_type, limit=limit)
    return {
        "reports": [
            {
                "id": r["id"],
                "days": r["days"],
                "created_at": r["created_at"],
                "stats": r["stats"],
                "headline": r["report"].get("headline", ""),
            }
            for r in reports
        ]
    }


@router.get("/reports/{report_id}")
def get_report(report_id: int):
    """Retrieve a full archived AI report by ID."""
    report = get_ai_report(report_id)
    if not report:
        raise HTTPException(404, f"Report {report_id} not found")
    return report