132 lines
4.9 KiB
Python
132 lines
4.9 KiB
Python
"""
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Expert Recommendation Engine (Senior Level)
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============================================
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Evaluates ALL markets, classifies by risk, and ensures NO "empty" recommendations.
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Prioritizes user safety by clearly labeling risk levels.
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"""
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from dataclasses import dataclass, field
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from typing import List, Optional, Any, Dict
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from .base_calculator import BaseCalculator, CalculationContext
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from .match_result_calculator import MatchResultPrediction
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from .over_under_calculator import OverUnderPrediction
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from .risk_assessor import RiskAnalysis
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@dataclass
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class ExpertPick:
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market_type: str
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pick: str
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probability: float
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confidence: float
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odds: float
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edge: float # Expected value percentage
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# Risk Classification
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risk_level: str # SAFE, MEDIUM, RISKY, SURPRISE
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reasoning: str # Why this pick? (e.g., "High xG support", "Value detected")
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@dataclass
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class ExpertResult:
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main_pick: ExpertPick
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safe_alternative: Optional[ExpertPick]
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value_picks: List[ExpertPick]
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surprise_picks: List[ExpertPick]
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market_summary: Dict[str, float] # {market: probability}
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class ExpertRecommender(BaseCalculator):
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def calculate(self, # type: ignore[override]
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ctx: CalculationContext,
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ms_res: MatchResultPrediction,
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ou_res: OverUnderPrediction,
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risk: RiskAnalysis) -> ExpertResult:
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odds_data = ctx.odds_data
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all_picks: List[ExpertPick] = []
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# ─── 1. Helper to Evaluate Pick ───
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def evaluate(market: str, pick: str, prob: float, odd_key: str):
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odd_val = float(odds_data.get(odd_key, 0))
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# If odd is missing/low, estimate it via probability (Kelly-ish estimation)
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if odd_val <= 1.01:
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odd_val = round(1.0 / (prob + 0.05), 2) # Conservative estimation
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reasoning = "Derived (No market odd)"
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else:
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reasoning = "Market Confirmed"
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implied = 1.0 / odd_val
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edge = (prob - implied) * 100
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# ─── Risk Classification ───
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if prob >= 0.75 and odd_val <= 1.45:
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level = "SAFE"
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elif edge > 5.0:
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level = "VALUE"
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elif odd_val >= 2.50 and prob >= 0.35:
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level = "SURPRISE"
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else:
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level = "MEDIUM"
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all_picks.append(ExpertPick(
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market_type=market, pick=pick, probability=prob,
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confidence=prob * 100, odds=odd_val, edge=edge,
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risk_level=level, reasoning=reasoning
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))
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# ─── 2. Evaluate All Major Markets ───
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# MS
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evaluate("MS", ms_res.ms_pick,
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ms_res.ms_home_prob if ms_res.ms_pick == "1" else (ms_res.ms_away_prob if ms_res.ms_pick == "2" else ms_res.ms_draw_prob),
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f"ms_{ms_res.ms_pick.lower()}")
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# Double Chance
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evaluate("DC", ms_res.dc_pick,
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ms_res.dc_1x_prob if ms_res.dc_pick == "1X" else (ms_res.dc_x2_prob if ms_res.dc_pick == "X2" else ms_res.dc_12_prob),
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f"dc_{ms_res.dc_pick.lower()}")
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# OU25
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evaluate("OU25", ou_res.ou25_pick,
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ou_res.over_25_prob if "Üst" in ou_res.ou25_pick else ou_res.under_25_prob,
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"ou25_o" if "Üst" in ou_res.ou25_pick else "ou25_u")
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# BTTS
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evaluate("BTTS", ou_res.btts_pick,
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ou_res.btts_yes_prob if "Var" in ou_res.btts_pick else ou_res.btts_no_prob,
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"btts_y" if "Var" in ou_res.btts_pick else "btts_n")
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# OU15
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evaluate("OU15", ou_res.ou15_pick,
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ou_res.over_15_prob if "Üst" in ou_res.ou15_pick else ou_res.under_15_prob,
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"ou15_o" if "Üst" in ou_res.ou15_pick else "ou15_u")
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# ─── 3. Sort and Select ───
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# Sort by a mix of Confidence and Edge
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all_picks.sort(key=lambda p: (p.probability * 0.6) + (max(0, p.edge/100) * 0.4), reverse=True)
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main = all_picks[0]
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# Find Safe Alternative (if main isn't Safe)
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safe_alt = next((p for p in all_picks if p.risk_level == "SAFE"), None)
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if safe_alt == main: safe_alt = None
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value_picks = [p for p in all_picks if p.risk_level == "VALUE" and p != main]
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surprise_picks = [p for p in all_picks if p.risk_level == "SURPRISE"]
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# Market Summary for UI
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market_summary = {
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"MS_Home": ms_res.ms_home_prob,
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"MS_Draw": ms_res.ms_draw_prob,
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"MS_Away": ms_res.ms_away_prob,
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"OU25_Over": ou_res.over_25_prob,
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"BTTS_Yes": ou_res.btts_yes_prob
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}
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return ExpertResult(
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main_pick=main,
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safe_alternative=safe_alt,
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value_picks=value_picks,
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surprise_picks=surprise_picks,
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market_summary=market_summary
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)
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