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"""
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V2 Betting Engine — FastAPI Router
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Async endpoint that orchestrates: DB → Features → Model → Quant → Response.
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Mounted as a sub-router on the existing main.py app, so both V20+ (legacy)
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and V2 endpoints coexist.
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"""
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from __future__ import annotations
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import logging
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import time
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from typing import Any
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from fastapi import APIRouter, HTTPException
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from core.quant import (
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MarketPick,
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RiskResult,
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analyze_market,
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assess_risk,
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)
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from data.database import get_session
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from features.extractor import MatchFeatures, extract_features
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from models.betting_engine import get_predictor
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from schemas.response import (
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BetAdvice,
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BetSummaryRow,
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DataQuality,
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EngineBreakdown,
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MarketProbs,
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MatchInfo,
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PickDetail,
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PredictionResponse,
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RiskAssessment,
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)
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/v2", tags=["V2 Betting Engine"])
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# ═══════════════════════════════════════════════════════════════════════════
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# Endpoints
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# ═══════════════════════════════════════════════════════════════════════════
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@router.post("/analyze/{match_id}", response_model=PredictionResponse)
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async def analyze_match_v2(match_id: str) -> PredictionResponse:
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"""
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Full single-match analysis pipeline:
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1. Extract leakage-free features from PostgreSQL
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2. Run calibrated ensemble predictions (MS, OU25, BTTS)
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3. Calculate edges via implied probability comparison
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4. Apply Fractional Kelly staking
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5. Grade & rank picks
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6. Assess risk
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7. Return SingleMatchPredictionPackage
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"""
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started_at = time.perf_counter()
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# ─── Step 1: Feature extraction ───────────────────────────────────
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async with get_session() as session:
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feats = await extract_features(session, match_id)
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if feats is None:
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raise HTTPException(
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status_code=404,
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detail=f"Match {match_id} not found or insufficient data.",
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)
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# ─── Step 2: Model predictions ────────────────────────────────────
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predictor = get_predictor()
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X = feats.to_model_array()
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all_probs = predictor.predict_all(X, feats)
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# ─── Step 3: Quantitative analysis per market ─────────────────────
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ms_odds_map = {"1": feats.odds_home, "X": feats.odds_draw, "2": feats.odds_away}
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ou25_odds_map = {"Under": feats.odds_under25, "Over": feats.odds_over25}
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btts_odds_map = {"No": feats.odds_btts_no, "Yes": feats.odds_btts_yes}
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ms_pick = analyze_market("MS", all_probs["MS"], ms_odds_map, feats.data_quality_score)
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ou25_pick = analyze_market("OU25", all_probs["OU25"], ou25_odds_map, feats.data_quality_score)
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btts_pick = analyze_market("BTTS", all_probs["BTTS"], btts_odds_map, feats.data_quality_score)
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all_picks = [ms_pick, ou25_pick, btts_pick]
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# ─── Step 4: Select main pick (highest play_score among playable) ─
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playable_picks = [p for p in all_picks if p.playable]
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playable_picks.sort(key=lambda p: p.play_score, reverse=True)
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main_pick: MarketPick | None = playable_picks[0] if playable_picks else None
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supporting = playable_picks[1:] if len(playable_picks) > 1 else []
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# Value pick: best playable with odds >= 1.60
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value_candidates = [p for p in playable_picks if p.odds >= 1.60]
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value_pick: MarketPick | None = value_candidates[0] if value_candidates else None
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# If value_pick IS the main_pick, try the next candidate
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if value_pick and main_pick and value_pick.market == main_pick.market:
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value_pick = value_candidates[1] if len(value_candidates) > 1 else None
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# Aggressive pick: highest edge regardless of playability
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all_picks_by_edge = sorted(all_picks, key=lambda p: p.edge, reverse=True)
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aggressive = all_picks_by_edge[0] if all_picks_by_edge and all_picks_by_edge[0].edge > 0 else None
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# ─── Step 5: Risk assessment ──────────────────────────────────────
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implied_prob_fav = max(feats.implied_prob_home, feats.implied_prob_away)
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risk = assess_risk(
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missing_players_impact=feats.missing_players_impact,
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data_quality_score=feats.data_quality_score,
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elo_diff=feats.elo_diff,
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implied_prob_fav=implied_prob_fav,
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)
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# ─── Step 6: Build response ───────────────────────────────────────
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elapsed_ms = int((time.perf_counter() - started_at) * 1000)
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response = PredictionResponse(
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model_version="v2.betting_engine",
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match_info=MatchInfo(
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match_id=match_id,
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match_name=feats.match_name,
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home_team=feats.home_team_name,
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away_team=feats.away_team_name,
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league=feats.league_name,
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match_date_ms=feats.match_date_ms,
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),
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data_quality=DataQuality(
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label=_quality_label(feats.data_quality_score),
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score=feats.data_quality_score,
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flags=feats.data_quality_flags,
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),
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risk=RiskAssessment(
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level=risk.level,
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score=risk.score,
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is_surprise_risk=risk.is_surprise_risk,
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surprise_type=risk.surprise_type,
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warnings=risk.warnings,
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),
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engine_breakdown=EngineBreakdown(
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team=round(feats.elo_diff / 100.0, 2),
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player=round(-feats.missing_players_impact, 2),
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odds=round(implied_prob_fav, 2),
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referee=0.0,
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),
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main_pick=_pick_to_detail(main_pick, feats) if main_pick else None,
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value_pick=_pick_to_detail(value_pick, feats) if value_pick else None,
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bet_advice=BetAdvice(
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playable=main_pick is not None,
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suggested_stake_units=main_pick.stake_units if main_pick else 0.0,
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reason=(
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f"Best value: {main_pick.market} {main_pick.pick} "
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f"(edge {main_pick.edge:.1%}, grade {main_pick.bet_grade})"
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if main_pick
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else "no_playable_edge_found"
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),
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),
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bet_summary=[_pick_to_summary(p) for p in all_picks],
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supporting_picks=[_pick_to_detail(p, feats) for p in supporting],
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aggressive_pick=_pick_to_detail(aggressive, feats) if aggressive else None,
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market_board={
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"MS": MarketProbs(
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pick=ms_pick.pick,
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confidence=round(ms_pick.probability * 100, 1),
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probs=all_probs["MS"],
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).model_dump(),
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"OU25": MarketProbs(
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pick=ou25_pick.pick,
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confidence=round(ou25_pick.probability * 100, 1),
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probs=all_probs["OU25"],
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).model_dump(),
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"BTTS": MarketProbs(
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pick=btts_pick.pick,
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confidence=round(btts_pick.probability * 100, 1),
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probs=all_probs["BTTS"],
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).model_dump(),
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},
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reasoning_factors=_build_reasoning(feats, main_pick, risk, elapsed_ms),
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)
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logger.info(
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"V2 analyze %s → %s in %dms (main: %s %s, edge: %s)",
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match_id,
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response.bet_advice.reason,
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elapsed_ms,
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main_pick.market if main_pick else "NONE",
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main_pick.pick if main_pick else "",
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f"{main_pick.edge:.1%}" if main_pick else "N/A",
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)
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return response
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@router.get("/health")
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async def v2_health():
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predictor = get_predictor()
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return {
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"status": "healthy",
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"engine": "v2.betting_engine",
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"models_loaded": predictor.is_ready,
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}
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# ═══════════════════════════════════════════════════════════════════════════
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# Helpers
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# ═══════════════════════════════════════════════════════════════════════════
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def _quality_label(score: float) -> str:
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if score >= 0.8:
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return "HIGH"
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if score >= 0.5:
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return "MEDIUM"
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return "LOW"
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def _pick_to_detail(pick: MarketPick, feats: MatchFeatures) -> PickDetail:
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implied = {
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"MS": {"1": feats.implied_prob_home, "X": feats.implied_prob_draw, "2": feats.implied_prob_away},
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"OU25": {"Over": feats.implied_prob_over25, "Under": feats.implied_prob_under25},
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"BTTS": {"Yes": feats.implied_prob_btts_yes, "No": feats.implied_prob_btts_no},
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}
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raw_conf = pick.probability * 100.0
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market_implied = implied.get(pick.market, {}).get(pick.pick, 0.33)
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return PickDetail(
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market=pick.market,
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pick=pick.pick,
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probability=pick.probability,
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confidence=round(raw_conf, 1),
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odds=pick.odds,
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raw_confidence=round(raw_conf, 1),
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calibrated_confidence=round(raw_conf, 1),
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min_required_confidence=round(market_implied * 100, 1),
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edge=pick.edge,
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play_score=pick.play_score,
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playable=pick.playable,
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bet_grade=pick.bet_grade,
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stake_units=pick.stake_units,
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decision_reasons=pick.decision_reasons,
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)
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def _pick_to_summary(pick: MarketPick) -> BetSummaryRow:
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return BetSummaryRow(
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market=pick.market,
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pick=pick.pick,
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raw_confidence=round(pick.probability * 100, 1),
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calibrated_confidence=round(pick.probability * 100, 1),
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bet_grade=pick.bet_grade,
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playable=pick.playable,
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stake_units=pick.stake_units,
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play_score=pick.play_score,
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reasons=pick.decision_reasons,
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)
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def _build_reasoning(
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feats: MatchFeatures,
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main_pick: MarketPick | None,
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risk: RiskResult,
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elapsed_ms: int,
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) -> list[str]:
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reasons: list[str] = []
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reasons.append(f"ELO: {feats.home_elo:.0f} vs {feats.away_elo:.0f} (diff: {feats.elo_diff:+.0f})")
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reasons.append(
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f"Form (last 5): Home {feats.home_avg_goals_scored:.1f}GF/{feats.home_avg_goals_conceded:.1f}GA "
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f"— Away {feats.away_avg_goals_scored:.1f}GF/{feats.away_avg_goals_conceded:.1f}GA"
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)
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reasons.append(
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f"Implied probs: H={feats.implied_prob_home:.0%} D={feats.implied_prob_draw:.0%} "
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f"A={feats.implied_prob_away:.0%}"
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)
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if feats.missing_players_impact > 0:
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reasons.append(f"Missing player impact: {feats.missing_players_impact:.2f}")
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if main_pick:
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reasons.append(
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f"Best edge: {main_pick.market} {main_pick.pick} "
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f"→ {main_pick.edge:+.1%} (grade {main_pick.bet_grade})"
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)
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reasons.append(f"Risk: {risk.level} (score {risk.score:.2f})")
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reasons.append(f"Data quality: {feats.data_quality_score:.0%}")
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reasons.append(f"Inference time: {elapsed_ms}ms")
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return reasons
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