fix: calibrate betting recommendations from settled outcomes

platform/backend/app/analytics/daily_card_generator.py

@@ -52,6 +52,89 @@ TEAM_ALIASES = {
   'drcongo': '剛果民主共和國',
 }
 
+RECENT_MARKET_CALIBRATION: dict[str, dict[str, Any]] = {
+  '同場串關': {
+    'settled_count': 10,
+    'hit_rate_percent': 30.0,
+    'confidence_penalty': 6.5,
+    'stake_multiplier': 0.68,
+    'min_ev_boost': 4.0,
+    'min_win_prob_boost': 0.02,
+    'severity': 'caution',
+    'note': '近 7 天同場串關 10 筆可判定、命中率 30.00%，已提高進場門檻並縮小注碼。',
+  },
+  '正確比分': {
+    'settled_count': 7,
+    'hit_rate_percent': 0.0,
+    'confidence_penalty': 11.0,
+    'stake_multiplier': 0.42,
+    'min_ev_boost': 8.0,
+    'min_win_prob_boost': 0.03,
+    'severity': 'severe',
+    'note': '近 7 天正確比分 7 筆可判定仍未命中，僅保留超高賠小倉監控。',
+  },
+  '大小球 3.5': {
+    'settled_count': 6,
+    'hit_rate_percent': 33.33,
+    'confidence_penalty': 5.5,
+    'stake_multiplier': 0.72,
+    'min_ev_boost': 3.0,
+    'min_win_prob_boost': 0.015,
+    'severity': 'caution',
+    'note': '近 7 天大小球 3.5 命中率 33.33%，進場需要更明顯的賠率優勢。',
+  },
+  '勝平負': {
+    'settled_count': 6,
+    'hit_rate_percent': 0.0,
+    'confidence_penalty': 10.0,
+    'stake_multiplier': 0.55,
+    'min_ev_boost': 5.0,
+    'min_win_prob_boost': 0.04,
+    'severity': 'severe',
+    'note': '近 7 天勝平負 6 筆可判定仍未命中，先降為保守監控，不當核心下注。',
+  },
+  '跨場串關': {
+    'settled_count': 5,
+    'hit_rate_percent': 20.0,
+    'confidence_penalty': 7.0,
+    'stake_multiplier': 0.62,
+    'min_ev_boost': 4.5,
+    'min_win_prob_boost': 0.025,
+    'severity': 'caution',
+    'note': '近 7 天跨場串關命中率 20.00%，串關只作輔助，不放大倉位。',
+  },
+  '大小球 2.5': {
+    'settled_count': 4,
+    'hit_rate_percent': 0.0,
+    'confidence_penalty': 9.0,
+    'stake_multiplier': 0.58,
+    'min_ev_boost': 5.0,
+    'min_win_prob_boost': 0.035,
+    'severity': 'severe',
+    'note': '近 7 天大小球 2.5 四筆全未命中，需等更好的盤口差距才進場。',
+  },
+  '雙重機會': {
+    'settled_count': 7,
+    'hit_rate_percent': 71.43,
+    'confidence_penalty': 0.0,
+    'stake_multiplier': 1.0,
+    'min_ev_boost': 0.0,
+    'min_win_prob_boost': 0.0,
+    'severity': 'stable',
+    'note': '近 7 天雙重機會命中率 71.43%，暫列較穩玩法，但仍不額外加碼。',
+  },
+  '大小球 1.5': {
+    'settled_count': 5,
+    'hit_rate_percent': 100.0,
+    'confidence_penalty': 0.0,
+    'stake_multiplier': 1.0,
+    'min_ev_boost': 0.0,
+    'min_win_prob_boost': 0.0,
+    'severity': 'stable',
+    'note': '近 7 天大小球 1.5 五筆全命中，暫保留原門檻；樣本仍小，不自動加碼。',
+  },
+}
+
 
 def _safe_float(value: Any, default: float = 0.0) -> float:
   try:
@@ -459,6 +542,98 @@ def _finalize_confidence_score(
   return round(_clamp(adjusted, floor, ceiling), 1)
 
 
+def _recent_market_calibration(market_type: str) -> dict[str, Any] | None:
+  """Return recent post-match market calibration for public recommendation safety."""
+
+  normalized_market = str(market_type or '').strip()
+  if normalized_market in RECENT_MARKET_CALIBRATION:
+    return RECENT_MARKET_CALIBRATION[normalized_market]
+  if normalized_market.startswith('大小球 '):
+    return RECENT_MARKET_CALIBRATION.get(normalized_market)
+  return None
+
+
+def _calibrated_minimums(market_type: str, min_win_prob: float, min_ev: float) -> tuple[float, float]:
+  calibration = _recent_market_calibration(market_type)
+  if not calibration:
+    return min_win_prob, min_ev
+  return (
+    min_win_prob + _safe_float(calibration.get('min_win_prob_boost'), 0.0),
+    min_ev + _safe_float(calibration.get('min_ev_boost'), 0.0),
+  )
+
+
+def _calibration_marker(market_type: str) -> str:
+  return f'近7天玩法校準：{market_type}'
+
+
+def _apply_recent_market_calibration(item: dict[str, Any]) -> dict[str, Any]:
+  """Apply settled-result feedback to a generated pick without changing its legs."""
+
+  market_type = str(item.get('market_type') or '')
+  calibration = _recent_market_calibration(market_type)
+  if not calibration:
+    return item
+
+  marker = _calibration_marker(market_type)
+  data_checks = list(item.get('data_checks') or [])
+  if marker in data_checks:
+    return item
+
+  adjusted = dict(item)
+  factors = list(adjusted.get('confidence_factors') or [])
+  note = str(calibration.get('note') or '')
+  settled_count = int(_safe_float(calibration.get('settled_count'), 0))
+  hit_rate = _safe_float(calibration.get('hit_rate_percent'), 0.0)
+  severity = str(calibration.get('severity') or 'neutral')
+  confidence_penalty = _safe_float(calibration.get('confidence_penalty'), 0.0)
+  stake_multiplier = _safe_float(calibration.get('stake_multiplier'), 1.0)
+
+  current_confidence = _safe_float(adjusted.get('confidence_score'), 0.0)
+  if confidence_penalty > 0:
+    adjusted['confidence_score'] = round(_clamp(current_confidence - confidence_penalty, 0.0, current_confidence), 1)
+
+  current_stake = _safe_float(adjusted.get('stake_units'), 0.0)
+  if current_stake > 0 and stake_multiplier < 1.0:
+    next_stake = round(_clamp(current_stake * stake_multiplier, 0.05, current_stake), 2)
+    adjusted['stake_units'] = next_stake
+    adjusted['stake_amount_twd'] = _stake_amount_twd(next_stake)
+
+  if severity == 'severe':
+    recommendation = str(adjusted.get('recommendation') or '')
+    if recommendation == 'SAFE_SINGLE':
+      adjusted['recommendation'] = 'WATCHLIST_SINGLE'
+    elif recommendation == 'HIGH_RISK_SINGLE':
+      adjusted['recommendation'] = 'WATCHLIST_HIGH_RISK'
+    elif recommendation == 'SAFE_PARLAY':
+      adjusted['recommendation'] = 'WATCHLIST_PARLAY'
+    if str(adjusted.get('risk_level') or '') == 'core':
+      adjusted['risk_level'] = 'speculative'
+
+  adjusted['confidence_band'] = _confidence_band(
+    _safe_float(adjusted.get('confidence_score'), 0.0),
+    bool(adjusted.get('has_market_odds')),
+    str(adjusted.get('risk_level') or 'core'),
+  )
+
+  if note and note not in factors:
+    factors.append(note)
+  factors.append(f'賽後校準樣本 {settled_count} 筆，命中率 {hit_rate:.2f}%')
+  adjusted['confidence_factors'] = factors[:9]
+  adjusted['data_checks'] = data_checks + [
+    marker,
+    f'近7天樣本 {settled_count} 筆',
+    f'近7天命中率 {hit_rate:.2f}%',
+    '已套用賽後校準觀察' if severity == 'stable' else '已套用賽後校準降權',
+  ]
+
+  rationale = str(adjusted.get('rationale') or '')
+  if note and note not in rationale:
+    adjusted['rationale'] = f'{rationale} 賽後校準：{note}'
+
+  return adjusted
+
+
 
 def _market_tier_from_item(item: dict[str, Any]) -> str:
   market_type = str(item.get('market_type') or '')
@@ -530,6 +705,7 @@ def recalibrate_daily_card_confidence_payload(card_payload: dict[str, Any]) -> d
         if '信心分數已套用新版資料品質校準' not in factors:
           factors.append('信心分數已套用新版資料品質校準')
         item['confidence_factors'] = factors[:8]
+        item = _apply_recent_market_calibration(item)
       except Exception:
         pass
       next_group.append(item)
@@ -752,7 +928,8 @@ def _build_pick(
 
   ev = _ev_percent(true_prob, odds)
   edge = _edge_percent(true_prob, odds)
-  if true_prob < min_win_prob or ev < min_ev or edge < min_edge:
+  effective_min_win_prob, effective_min_ev = _calibrated_minimums(market_type, min_win_prob, min_ev)
+  if true_prob < effective_min_win_prob or ev < effective_min_ev or edge < min_edge:
     return None
 
   stake = _stake_units(true_prob, ev, risk_level, stake_cap)
@@ -811,7 +988,7 @@ def _build_pick(
     market_overround=market_overround,
   )
 
-  return {
+  return _apply_recent_market_calibration({
     'match_id': match_id,
     'match_label': match_label,
     'market_type': market_type,
@@ -839,7 +1016,7 @@ def _build_pick(
         f'{fair_note}{quality_note}{longshot_note}建議上限約新台幣 {_stake_amount_twd(stake):,} 元（{stake:.2f}u），且需等待最新傷停、先發與盤口刷新一致後才進入檢驗單。'
       ),
     'data_checks': enriched_checks,
-  }
+  })
 
 
 def _minimum_value_odds(true_prob: float, min_ev_percent: float) -> float:
@@ -870,19 +1047,20 @@ def _build_conditional_pick(
 ) -> dict[str, Any] | None:
   """尚無即時盤口時，產生可執行的預掛進場條件，而不是假裝已有市場賠率。"""
 
-  if true_prob < min_win_prob:
+  effective_min_win_prob, effective_min_ev = _calibrated_minimums(market_type, min_win_prob, min_ev)
+  if true_prob < effective_min_win_prob:
     return None
 
-  target_odds = _minimum_value_odds(true_prob, min_ev)
+  target_odds = _minimum_value_odds(true_prob, effective_min_ev)
   if target_odds <= 1 or target_odds > max_target_odds:
     return None
 
   implied = (1.0 / target_odds) * 100
   edge = (true_prob * 100) - implied
-  stake = _stake_units(true_prob, min_ev, risk_level, stake_cap)
+  stake = _stake_units(true_prob, effective_min_ev, risk_level, stake_cap)
   confidence = _confidence_score(
     true_prob,
-    min_ev,
+    effective_min_ev,
     edge,
     market_tier,
     decimal_odds=target_odds,
@@ -907,28 +1085,28 @@ def _build_conditional_pick(
     odds_source_kind=odds_source_kind,
     target_odds=target_odds,
     true_prob=true_prob,
-    ev_percent=min_ev,
+    ev_percent=effective_min_ev,
     edge_percent=edge,
   )
   quality_note = _quality_note(data_quality, has_market_odds)
   confidence_band = _confidence_band(confidence, has_market_odds, risk_level)
   confidence_factors = _confidence_factors(
     true_prob=true_prob,
-    ev_percent=min_ev,
+    ev_percent=effective_min_ev,
     edge_percent=edge,
     data_quality=data_quality,
     has_market_odds=has_market_odds,
     market_tier=market_tier,
   )
 
-  return {
+  return _apply_recent_market_calibration({
     'match_id': match_id,
     'match_label': match_label,
     'market_type': market_type,
     'selection': f'{selection}｜預掛條件',
     'target_odds': round(target_odds, 2),
     'win_prob': round(true_prob * 100, 2),
-    'ev_percent': round(min_ev, 2),
+    'ev_percent': round(effective_min_ev, 2),
     'stake_units': stake,
     'stake_amount_twd': _stake_amount_twd(stake),
     'unit_size_twd': DEFAULT_UNIT_STAKE_TWD,
@@ -945,11 +1123,11 @@ def _build_conditional_pick(
     'edge_percent': round(edge, 2),
     'rationale': (
       f'此場尚未取得可用即時盤口，因此先給「預掛進場條件」。模型勝率 {true_prob * 100:.2f}%，'
-      f'只有當平台賠率達到 {target_odds:.2f} 以上，才符合至少 {min_ev:.2f}% 的正期望值門檻；'
+      f'只有當平台賠率達到 {target_odds:.2f} 以上，才符合至少 {effective_min_ev:.2f}% 的正期望值門檻；'
       f'若實際賠率低於此數字，直接跳過，不追價。建議監控上限約新台幣 {_stake_amount_twd(stake):,} 元（{stake:.2f}u）。{quality_note}'
     ),
     'data_checks': data_checks + [odds_source_label, '尚待盤口確認', '預掛最低賠率', '不得低於目標賠率下注'] + _quality_checks(data_quality, has_market_odds),
-  }
+  })
 
 
 def _sort_picks(items: list[dict[str, Any]]) -> list[dict[str, Any]]:
@@ -1016,6 +1194,7 @@ def _build_cross_match_parlays(safe_singles: list[dict[str, Any]]) -> list[dict[
       combined_prob *= discount
       min_prob = 0.22 if size == 2 else 0.12
       min_ev = 2.5 if size == 2 else 5.0
+      min_prob, min_ev = _calibrated_minimums('跨場串關', min_prob, min_ev)
       required_combo_odds = _minimum_value_odds(combined_prob, min_ev)
       target_combo_odds = max(combined_odds, required_combo_odds)
       max_combo_odds = 8.5 if size == 2 else 18.0
@@ -1066,7 +1245,7 @@ def _build_cross_match_parlays(safe_singles: list[dict[str, Any]]) -> list[dict[
         edge_percent=edge,
       )
       parlays.append(
-        {
+        _apply_recent_market_calibration({
           'match_id': f'PARLAY-CROSS-MATCH-{size}-{len(parlays) + 1}',
           'match_label': ' + '.join(item['match_label'] for item in legs),
           'market_type': '跨場串關',
@@ -1106,7 +1285,7 @@ def _build_cross_match_parlays(safe_singles: list[dict[str, Any]]) -> list[dict[
               f'建議上限約新台幣 {_stake_amount_twd(stake_units):,} 元（{stake_units:.2f}u）。串關只作輔助倉位，不能取代單關主策略。'
             ),
           'data_checks': ['單關門檻通過', combo_source_label, '跨場去相關', f'相關性 {discount:.2f} 折減', '串關總賠率門檻', '串關 EV 重新計算'] + _quality_checks(combo_data_quality, combo_has_market_odds),
-        }
+        })
       )
 
   return _sort_picks(parlays)[:6]
@@ -1145,11 +1324,12 @@ def _build_same_game_parlays(picks_by_match: dict[str, list[dict[str, Any]]]) ->
     combo_odds = first['target_odds'] * second['target_odds']
     combo_prob = (first['win_prob'] / 100) * (second['win_prob'] / 100)
     combo_prob *= 0.68
-    required_combo_odds = _minimum_value_odds(combo_prob, 8.0)
+    min_prob, min_ev = _calibrated_minimums('同場串關', 0.16, 8.0)
+    required_combo_odds = _minimum_value_odds(combo_prob, min_ev)
     target_combo_odds = max(combo_odds, required_combo_odds)
     ev = _ev_percent(combo_prob, target_combo_odds)
     edge = _edge_percent(combo_prob, target_combo_odds)
-    if combo_prob < 0.16 or target_combo_odds > 12.0 or ev < 8.0 or edge <= 0:
+    if combo_prob < min_prob or target_combo_odds > 12.0 or ev < min_ev or edge <= 0:
       continue
 
     sgp_has_market_odds = False
@@ -1192,7 +1372,7 @@ def _build_same_game_parlays(picks_by_match: dict[str, list[dict[str, Any]]]) ->
     )
 
     sgps.append(
-      {
+      _apply_recent_market_calibration({
         'match_id': match_id,
         'match_label': f"{first['match_label']}【同場】",
         'market_type': '同場串關',
@@ -1232,7 +1412,7 @@ def _build_same_game_parlays(picks_by_match: dict[str, list[dict[str, Any]]]) ->
             f'建議上限約新台幣 {_stake_amount_twd(sgp_stake_units):,} 元（{sgp_stake_units:.2f}u）。此類組合波動極大，只允許娛樂型小注，不可當核心下注。'
           ),
         'data_checks': ['同場不同市場', sgp_source_label or '模型推算最低門檻', '相關性 0.68 折減', '預掛總賠率門檻', '高 EV 門檻', '小倉位上限'] + _quality_checks(sgp_data_quality, sgp_has_market_odds),
-      }
+      })
     )
 
   return _sort_picks(sgps)[:4]
@@ -1598,6 +1778,13 @@ def generate_daily_card(target_date: str, matches: list[dict[str, Any]]) -> dict
     )
 
   all_items = [*safe_singles, *high_risk_singles, *safe_parlays, *sgp_lotteries]
+  calibrated_count = sum(
+    1
+    for item in all_items
+    if any(str(check).startswith('近7天玩法校準') for check in (item.get('data_checks') or []))
+  )
+  if calibrated_count:
+    summary = f'{summary} 已套用近 7 天賽後玩法校準 {calibrated_count} 組，近期低命中玩法會自動提高門檻並縮小注碼。'
   live_market_count = sum(1 for item in all_items if bool(item.get('has_market_odds')))
   pre_market_count = len(all_items) - live_market_count
   quality_counts: dict[str, int] = {}
@@ -1616,6 +1803,7 @@ def generate_daily_card(target_date: str, matches: list[dict[str, Any]]) -> dict
       **quality_counts,
       'live_market_count': live_market_count,
       'pre_market_count': pre_market_count,
+      'recent_market_calibrated_count': calibrated_count,
     },
     'execution_policy': (
       '已有實盤盤口的候選可進入下注前檢查；尚未取得實盤者只能加入賠率監控，'