feat(HRT-83): intégrer météo & terrain dans prédictions ML (Premium)

- scoring_v2.py : ajout get_terrain_condition() + compute_weather_impact()
  score_cheval_v2() accepte weather_data=None (backward-compat préservée)
  Impact météo/terrain sur [-5, +5] pts selon pénétromètre + vent + temp

- api_v1/routes/predictions.py : _fetch_ml_predictions() avec include_weather=True
  LEFT JOIN pmu_courses (pénétromètre) + pmu_meteo sur date+num_reunion
  /predictions/all → terrain_condition + weather_impact dans chaque row
  /predictions/top3 → inchangé (free tier, pas de champs météo)

- api_v1/routes/valuebets.py : même LEFT JOIN météo/terrain
  /valuebets → terrain_condition + weather_impact dans chaque value bet

Tests : 42/42 passent (pytest tests/test_api_v1.py)
Co-Authored-By: Paperclip <noreply@paperclip.ing>

api_v1/routes/predictions.py

@@ -22,8 +22,14 @@ from auth import jwt_required_middleware, plan_required, free_daily_limit_check
 predictions_bp = Blueprint("v1_predictions", __name__, url_prefix="/api/v1/predictions")
 
 
-def _fetch_ml_predictions(conn, date: str, limit: int = None, offset: int = 0):
+def _fetch_ml_predictions(
-    """Shared helper — returns rows from ml_predictions_cache."""
+    conn, date: str, limit: int = None, offset: int = 0, include_weather: bool = False
+):
+    """Shared helper — returns rows from ml_predictions_cache.
+
+    include_weather=True adds terrain_condition and weather_impact columns
+    via LEFT JOIN on pmu_meteo (premium routes only).
+    """
     if not table_exists(conn, "ml_predictions_cache"):
         return [], 0
 
@@ -33,13 +39,35 @@ def _fetch_ml_predictions(conn, date: str, limit: int = None, offset: int = 0):
     ).fetchone()
     total = count_row["cnt"] if count_row else 0
 
-    sql = """SELECT
+    if (
-                 race_label, hippodrome, discipline, distance, heure,
+        include_weather
-                 horse_name, horse_number, odds, prob_top1, prob_top3,
+        and table_exists(conn, "pmu_meteo")
-                 ml_score, recommendation, is_value_bet, risque_label, risque_score
+        and table_exists(conn, "pmu_courses")
-             FROM ml_predictions_cache
+    ):
-             WHERE date = ?
+        sql = """SELECT
-             ORDER BY ml_score DESC"""
+                     m.race_label, m.hippodrome, m.discipline, m.distance, m.heure,
+                     m.horse_name, m.horse_number, m.odds, m.prob_top1, m.prob_top3,
+                     m.ml_score, m.recommendation, m.is_value_bet, m.risque_label, m.risque_score,
+                     c.penetrometre_intitule,
+                     mt.nebulositecode, mt.nebulosite_court, mt.temperature, mt.force_vent
+                 FROM ml_predictions_cache m
+                 LEFT JOIN pmu_courses c
+                     ON c.date_programme = m.date
+                     AND c.num_reunion = m.num_reunion
+                     AND c.num_course = m.num_course
+                 LEFT JOIN pmu_meteo mt
+                     ON mt.date_programme = m.date
+                     AND mt.num_reunion = m.num_reunion
+                 WHERE m.date = ?
+                 ORDER BY m.ml_score DESC"""
+    else:
+        sql = """SELECT
+                     race_label, hippodrome, discipline, distance, heure,
+                     horse_name, horse_number, odds, prob_top1, prob_top3,
+                     ml_score, recommendation, is_value_bet, risque_label, risque_score
+                 FROM ml_predictions_cache
+                 WHERE date = ?
+                 ORDER BY ml_score DESC"""
     params = [date]
 
     if limit is not None:
@@ -47,7 +75,42 @@ def _fetch_ml_predictions(conn, date: str, limit: int = None, offset: int = 0):
         params += [limit, offset]
 
     rows = conn.execute(sql, params).fetchall()
-    return [dict(r) for r in rows], total
+
+    results = []
+    for r in rows:
+        row_dict = dict(r)
+        if include_weather:
+            # Compute derived fields from raw columns
+            penetrometre = row_dict.pop("penetrometre_intitule", None) or ""
+            # Import inline to avoid circular dependency at module level
+            from scoring_v2 import get_terrain_condition, compute_weather_impact
+
+            terrain_condition = (
+                get_terrain_condition(penetrometre) if penetrometre else "inconnu"
+            )
+            weather_data = None
+            if (
+                row_dict.get("nebulositecode") is not None
+                or row_dict.get("temperature") is not None
+            ):
+                weather_data = {
+                    "nebulositecode": row_dict.pop("nebulositecode", None),
+                    "nebulosite_court": row_dict.pop("nebulosite_court", None),
+                    "temperature": row_dict.pop("temperature", None),
+                    "force_vent": row_dict.pop("force_vent", None),
+                }
+            else:
+                # Remove raw meteo columns even if NULL
+                row_dict.pop("nebulositecode", None)
+                row_dict.pop("nebulosite_court", None)
+                row_dict.pop("temperature", None)
+                row_dict.pop("force_vent", None)
+            weather_impact = compute_weather_impact(weather_data, terrain_condition)
+            row_dict["terrain_condition"] = terrain_condition
+            row_dict["weather_impact"] = weather_impact
+        results.append(row_dict)
+
+    return results, total
 
 
 # ──────────────────────────────────────────────────────────────
@@ -145,7 +208,7 @@ def predictions_all():
     conn = get_db()
     try:
         predictions, total = _fetch_ml_predictions(
-            conn, date_param, limit=limit, offset=offset
+            conn, date_param, limit=limit, offset=offset, include_weather=True
         )
         pagination = paginate_query(predictions, total, limit, offset)
 

api_v1/routes/valuebets.py

@@ -53,7 +53,7 @@ def valuebets():
         default: 0
     responses:
       200:
-        description: Value bets du jour
+        description: Value bets du jour avec météo et terrain (HRT-83)
       401:
         description: Token invalide
       403:
@@ -69,7 +69,7 @@ def valuebets():
 
     conn = get_db()
     try:
-        rows = []
+        rows_raw = []
         total = 0
 
         if table_exists(conn, "ml_predictions_cache"):
@@ -81,18 +81,73 @@ def valuebets():
             ).fetchone()
             total = count_row["cnt"] if count_row else 0
 
-            rows = conn.execute(
+            # LEFT JOIN pmu_courses (terrain) + pmu_meteo (météo) — HRT-83
-                """SELECT race_label, hippodrome, discipline, distance, heure,
+            has_courses = table_exists(conn, "pmu_courses")
-                          horse_name, horse_number, odds, prob_top1, prob_top3,
+            has_meteo = table_exists(conn, "pmu_meteo")
-                          ml_score, recommendation, risque_label, risque_score
+
-                   FROM ml_predictions_cache
+            if has_courses and has_meteo:
-                   WHERE date = ? AND is_value_bet = 1 AND odds >= ?
+                rows_raw = conn.execute(
-                   ORDER BY ml_score DESC
+                    """SELECT m.race_label, m.hippodrome, m.discipline, m.distance, m.heure,
-                   LIMIT ? OFFSET ?""",
+                              m.horse_name, m.horse_number, m.odds, m.prob_top1, m.prob_top3,
-                (date_param, min_odds, limit, offset),
+                              m.ml_score, m.recommendation, m.risque_label, m.risque_score,
-            ).fetchall()
+                              c.penetrometre_intitule,
+                              mt.nebulositecode, mt.nebulosite_court,
+                              mt.temperature, mt.force_vent
+                       FROM ml_predictions_cache m
+                       LEFT JOIN pmu_courses c
+                           ON c.date_programme = m.date
+                           AND c.num_reunion = m.num_reunion
+                           AND c.num_course = m.num_course
+                       LEFT JOIN pmu_meteo mt
+                           ON mt.date_programme = m.date
+                           AND mt.num_reunion = m.num_reunion
+                       WHERE m.date = ? AND m.is_value_bet = 1 AND m.odds >= ?
+                       ORDER BY m.ml_score DESC
+                       LIMIT ? OFFSET ?""",
+                    (date_param, min_odds, limit, offset),
+                ).fetchall()
+            else:
+                rows_raw = conn.execute(
+                    """SELECT race_label, hippodrome, discipline, distance, heure,
+                              horse_name, horse_number, odds, prob_top1, prob_top3,
+                              ml_score, recommendation, risque_label, risque_score
+                       FROM ml_predictions_cache
+                       WHERE date = ? AND is_value_bet = 1 AND odds >= ?
+                       ORDER BY ml_score DESC
+                       LIMIT ? OFFSET ?""",
+                    (date_param, min_odds, limit, offset),
+                ).fetchall()
+
+        from scoring_v2 import get_terrain_condition, compute_weather_impact
+
+        valuebets_list = []
+        for r in rows_raw:
+            row_dict = dict(r)
+            penetrometre = row_dict.pop("penetrometre_intitule", None) or ""
+            terrain_condition = (
+                get_terrain_condition(penetrometre) if penetrometre else "inconnu"
+            )
+            weather_data = None
+            if (
+                row_dict.get("nebulositecode") is not None
+                or row_dict.get("temperature") is not None
+            ):
+                weather_data = {
+                    "nebulositecode": row_dict.pop("nebulositecode", None),
+                    "nebulosite_court": row_dict.pop("nebulosite_court", None),
+                    "temperature": row_dict.pop("temperature", None),
+                    "force_vent": row_dict.pop("force_vent", None),
+                }
+            else:
+                row_dict.pop("nebulositecode", None)
+                row_dict.pop("nebulosite_court", None)
+                row_dict.pop("temperature", None)
+                row_dict.pop("force_vent", None)
+            weather_impact = compute_weather_impact(weather_data, terrain_condition)
+            row_dict["terrain_condition"] = terrain_condition
+            row_dict["weather_impact"] = weather_impact
+            valuebets_list.append(row_dict)
 
-        valuebets_list = [dict(r) for r in rows]
         pagination = paginate_query(valuebets_list, total, limit, offset)
 
         return jsonify(

scoring_v2.py

@@ -11,29 +11,34 @@ import re
 from datetime import datetime
 
 DB_PATH = "/home/h3r7/turf_saas/turf_saas.db"
-HEADERS = {'User-Agent': 'Mozilla/5.0', 'Accept': 'application/json'}
+HEADERS = {"User-Agent": "Mozilla/5.0", "Accept": "application/json"}
+
 
 def get_cote_from_db(horse_name, date_course):
     """Recupere la cote depuis la table predictions (plus recente et non nulle)"""
     conn = sqlite3.connect(DB_PATH)
     conn.row_factory = sqlite3.Row
-    c = conn.execute("""
+    c = conn.execute(
+        """
         SELECT odds FROM predictions 
         WHERE date=? AND horse_name LIKE ? AND odds > 0 
         ORDER BY created_at DESC LIMIT 1
-    """, (date_course, f"%{horse_name}%"))
+    """,
+        (date_course, f"%{horse_name}%"),
+    )
     r = c.fetchone()
     conn.close()
-    return r['odds'] if r else 0
+    return r["odds"] if r else 0
+
 
 def parse_musique(musique):
     if not musique:
         return {}
-    clean = re.sub(r'\(\d+\)', '', musique)
+    clean = re.sub(r"\(\d+\)", "", musique)
-    resultats = re.findall(r'(\d+|D|0)([amphsc]?)', clean)
+    resultats = re.findall(r"(\d+|D|0)([amphsc]?)", clean)
     positions = []
     for pos, disc in resultats[:10]:
-        positions.append(99 if pos == 'D' else int(pos))
+        positions.append(99 if pos == "D" else int(pos))
     if not positions:
         return {}
     nb_courses = len(positions)
@@ -41,29 +46,102 @@ def parse_musique(musique):
     nb_places = sum(1 for p in positions if 1 <= p <= 3)
     recentes = [p for p in positions[:3] if p != 99]
     forme_recente = sum(recentes) / len(recentes) if recentes else 99
-    tendance = (sum(positions[-4:]) / 4 - sum(positions[:4]) / 4) if len(positions) >= 4 else 0
+    tendance = (
+        (sum(positions[-4:]) / 4 - sum(positions[:4]) / 4) if len(positions) >= 4 else 0
+    )
     return {
-        'forme_recente': round(forme_recente, 1),
+        "forme_recente": round(forme_recente, 1),
-        'tendance': round(tendance, 1),
+        "tendance": round(tendance, 1),
-        'tx_victoire': round(nb_victoires / nb_courses * 100, 1) if nb_courses else 0,
+        "tx_victoire": round(nb_victoires / nb_courses * 100, 1) if nb_courses else 0,
-        'tx_place': round(nb_places / nb_courses * 100, 1) if nb_courses else 0,
+        "tx_place": round(nb_places / nb_courses * 100, 1) if nb_courses else 0,
     }
 
-def score_cheval_v2(p, all_participants, today):
+
+def get_terrain_condition(penetrometre_intitule: str | None) -> str:
+    """Normalise le pénétromètre PMU en condition terrain standardisée."""
+    if not penetrometre_intitule:
+        return "inconnu"
+    val = penetrometre_intitule.upper()
+    if any(k in val for k in ("TRES BON", "TRÈS BON", "FERME", "FIRM")):
+        return "bon"
+    if any(k in val for k in ("BON", "GOOD", "STANDARD")):
+        return "bon"
+    if any(k in val for k in ("SOUPLE", "YIELDING", "COLLANT")):
+        return "souple"
+    if any(k in val for k in ("LOURD", "HEAVY", "TRES SOUPLE", "TRÈS SOUPLE")):
+        return "lourd"
+    if any(k in val for k in ("SOFT", "MOU")):
+        return "souple"
+    return "inconnu"
+
+
+def compute_weather_impact(weather_data: dict | None, terrain_condition: str) -> float:
+    """
+    Calcule un score d'impact météo/terrain sur [−5, +5].
+    weather_data keys attendues : nebulositecode, temperature, force_vent
+    terrain_condition : 'bon' | 'souple' | 'lourd' | 'inconnu'
+    Retourne un delta de score ML (positif = favorable, négatif = défavorable).
+    """
+    if not weather_data:
+        return 0.0
+
+    delta = 0.0
+
+    # Terrain
+    if terrain_condition == "lourd":
+        delta -= 3.0
+    elif terrain_condition == "souple":
+        delta -= 1.5
+    elif terrain_condition == "bon":
+        delta += 1.0
+    # inconnu → 0
+
+    # Vent
+    force_vent = weather_data.get("force_vent") or 0
+    try:
+        force_vent = float(force_vent)
+    except (TypeError, ValueError):
+        force_vent = 0.0
+    if force_vent >= 50:
+        delta -= 2.0
+    elif force_vent >= 30:
+        delta -= 1.0
+
+    # Températures extrêmes
+    temperature = weather_data.get("temperature")
+    try:
+        temperature = float(temperature) if temperature is not None else None
+    except (TypeError, ValueError):
+        temperature = None
+    if temperature is not None:
+        if temperature <= 0:
+            delta -= 1.0
+        elif temperature >= 35:
+            delta -= 1.0
+
+    return round(max(-5.0, min(5.0, delta)), 2)
+
+
+def score_cheval_v2(p, all_participants, today, weather_data=None):
+    """
+    Score un cheval pour le modèle V2.
+    weather_data (optionnel) : dict issu de pmu_meteo pour cette réunion.
+    Backward-compatible : weather_data=None → comportement identique à avant HRT-83.
+    """
     score = 0
     details = {}
 
     # 1. COTE - Essaye PMU API, sinon DB
-    horse_name = p.get('nom', '')
+    horse_name = p.get("nom", "")
     cote = 0
 
     # Essayer d'abord depuis l'API PMU
-    rapport = p.get('dernierRapportDirect', {})
+    rapport = p.get("dernierRapportDirect", {})
     if rapport:
-        cote = rapport.get('rapport', 0)
+        cote = rapport.get("rapport", 0)
     if not cote:
-        rapport_ref = p.get('dernierRapportReference', {})
+        rapport_ref = p.get("dernierRapportReference", {})
-        cote = rapport_ref.get('rapport', 0) if rapport_ref else 0
+        cote = rapport_ref.get("rapport", 0) if rapport_ref else 0
 
     # Fallback: aller chercher dans la DB
     if not cote or cote == 0:
@@ -75,94 +153,136 @@ def score_cheval_v2(p, all_participants, today):
 
     score_cote = max(2, min(10, 20 / (1 + cote * 0.15))) if cote > 0 else 2
     score += score_cote
-    details['cote'] = round(cote, 1)
+    details["cote"] = round(cote, 1)
-    details['score_cote'] = round(score_cote, 1)
+    details["score_cote"] = round(score_cote, 1)
 
     # 2. FORME - AUGMENTE a 30 pts
-    musique_stats = parse_musique(p.get('musique', ''))
+    musique_stats = parse_musique(p.get("musique", ""))
-    forme = musique_stats.get('forme_recente', 99)
+    forme = musique_stats.get("forme_recente", 99)
-    score_forme = 30 if forme <= 1 else 25 if forme <= 2 else 20 if forme <= 3 else 15 if forme <= 5 else 8 if forme <= 8 else 0
+    score_forme = (
+        30
+        if forme <= 1
+        else 25
+        if forme <= 2
+        else 20
+        if forme <= 3
+        else 15
+        if forme <= 5
+        else 8
+        if forme <= 8
+        else 0
+    )
     score += score_forme
-    details['forme_recente'] = forme
+    details["forme_recente"] = forme
-    details['score_forme'] = score_forme
+    details["score_forme"] = score_forme
 
     # 3. TAUX VICTOIRE (15 pts)
-    nb_courses_total = p.get('nombreCourses', 0)
+    nb_courses_total = p.get("nombreCourses", 0)
-    nb_victoires_total = p.get('nombreVictoires', 0)
+    nb_victoires_total = p.get("nombreVictoires", 0)
     tx_vic = (nb_victoires_total / nb_courses_total * 100) if nb_courses_total else 0
     score_vic = min(15, tx_vic * 0.5)
     score += score_vic
-    details['tx_victoire'] = round(tx_vic, 1)
+    details["tx_victoire"] = round(tx_vic, 1)
-    details['score_victoire'] = round(score_vic, 1)
+    details["score_victoire"] = round(score_vic, 1)
 
     # 4. TAUX PLACE (15 pts)
-    nb_places_total = p.get('nombrePlaces', 0)
+    nb_places_total = p.get("nombrePlaces", 0)
     tx_place = (nb_places_total / nb_courses_total * 100) if nb_courses_total else 0
     score_place = min(15, tx_place * 0.2)
     score += score_place
-    details['tx_place'] = round(tx_place, 1)
+    details["tx_place"] = round(tx_place, 1)
-    details['score_place'] = round(score_place, 1)
+    details["score_place"] = round(score_place, 1)
 
     # 5. REDUCTION KM (10 pts)
-    rk = p.get('reductionKilometrique', 0)
+    rk = p.get("reductionKilometrique", 0)
-    all_rk = [x.get('reductionKilometrique', 0) for x in all_participants if x.get('reductionKilometrique', 0) > 0]
+    all_rk = [
+        x.get("reductionKilometrique", 0)
+        for x in all_participants
+        if x.get("reductionKilometrique", 0) > 0
+    ]
     if rk > 0 and all_rk:
-        score_rk = 10 * (1 - (rk - min(all_rk)) / (max(all_rk) - min(all_rk))) if max(all_rk) > min(all_rk) else 5
+        score_rk = (
+            10 * (1 - (rk - min(all_rk)) / (max(all_rk) - min(all_rk)))
+            if max(all_rk) > min(all_rk)
+            else 5
+        )
     else:
         score_rk = 0
     score += score_rk
-    details['rk'] = rk
+    details["rk"] = rk
-    details['score_rk'] = round(score_rk, 1)
+    details["score_rk"] = round(score_rk, 1)
 
     # 6. TENDANCE (10 pts)
-    tendance = musique_stats.get('tendance', 0)
+    tendance = musique_stats.get("tendance", 0)
     score_tendance = min(10, max(0, 5 + tendance))
     score += score_tendance
-    details['tendance'] = tendance
+    details["tendance"] = tendance
-    details['score_tendance'] = round(score_tendance, 1)
+    details["score_tendance"] = round(score_tendance, 1)
 
     # 7. AVIS ENTRAINEUR (5 pts)
-    avis = p.get('avisEntraineur', 'NEUTRE')
+    avis = p.get("avisEntraineur", "NEUTRE")
-    score_avis = {'POSITIF': 5, 'TRES_POSITIF': 5, 'NEUTRE': 2, 'NEGATIF': 0, 'TRES_NEGATIF': 0}.get(avis, 2)
+    score_avis = {
+        "POSITIF": 5,
+        "TRES_POSITIF": 5,
+        "NEUTRE": 2,
+        "NEGATIF": 0,
+        "TRES_NEGATIF": 0,
+    }.get(avis, 2)
     score += score_avis
-    details['avis_entraineur'] = avis
+    details["avis_entraineur"] = avis
-    details['score_avis'] = score_avis
+    details["score_avis"] = score_avis
 
     # 8. BONUS OUTSIDER (5 pts)
     bonus_outsider = 5 if forme <= 3 and cote >= 10 else 0
     score += bonus_outsider
-    details['bonus_outsider'] = bonus_outsider
+    details["bonus_outsider"] = bonus_outsider
 
     # Driver change penalty
-    if p.get('driverChange', False):
+    if p.get("driverChange", False):
         score -= 3
-        details['driver_change'] = True
+        details["driver_change"] = True
 
-    details['score_total'] = round(score, 1)
+    # 9. METEO & TERRAIN (HRT-83) — premium feature, weather_data=None → skip
-    details['musique'] = p.get('musique', '')
+    penetrometre = p.get("penetrometre_intitule", "") or ""
-    details['nb_victoires'] = nb_victoires_total
+    terrain_condition = (
-    details['nb_places'] = nb_places_total
+        get_terrain_condition(penetrometre) if penetrometre else "inconnu"
-    details['nb_courses'] = nb_courses_total
+    )
+    weather_impact = 0.0
+    if weather_data is not None:
+        weather_impact = compute_weather_impact(weather_data, terrain_condition)
+        score += weather_impact
+    details["terrain_condition"] = terrain_condition
+    details["weather_impact"] = weather_impact
+
+    details["score_total"] = round(score, 1)
+    details["musique"] = p.get("musique", "")
+    details["nb_victoires"] = nb_victoires_total
+    details["nb_places"] = nb_places_total
+    details["nb_courses"] = nb_courses_total
 
     return round(score, 1), details
 
+
 def get_ze2sur4_combinaisons(top4):
     combinaisons = []
     for i in range(4):
-        for j in range(i+1, 4):
+        for j in range(i + 1, 4):
             c1 = top4[i]
             c2 = top4[j]
-            combinaisons.append({
+            combinaisons.append(
-                'cheval1': c1['nom'],
+                {
-                'numero1': c1['numero'],
+                    "cheval1": c1["nom"],
-                'cheval2': c2['nom'],
+                    "numero1": c1["numero"],
-                'numero2': c2['numero'],
+                    "cheval2": c2["nom"],
-                'mise': 1.0,
+                    "numero2": c2["numero"],
-            })
+                    "mise": 1.0,
+                }
+            )
     return combinaisons
 
+
 def build_recommendations_v2(scored_horses):
-    ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True)
+    ranked = sorted(scored_horses, key=lambda x: x["score"], reverse=True)
     if len(ranked) < 4:
         return None
 
@@ -170,39 +290,58 @@ def build_recommendations_v2(scored_horses):
     top4_list = ranked[:4]
 
     def confiance(s):
-        return "FORTE" if s >= 55 else "BONNE" if s >= 45 else "MOYENNE" if s >= 35 else "FAIBLE"
+        return (
+            "FORTE"
+            if s >= 55
+            else "BONNE"
+            if s >= 45
+            else "MOYENNE"
+            if s >= 35
+            else "FAIBLE"
+        )
 
     ze2_combinaisons = get_ze2sur4_combinaisons(top4_list)
     mise_ze2 = len(ze2_combinaisons) * 1.0
 
     return {
-        'simple_gagnant': {
+        "simple_gagnant": {
-            'cheval': top1['nom'], 'numero': top1['numero'], 'cote': top1['details']['cote'],
+            "cheval": top1["nom"],
-            'score': top1['score'], 'confiance': confiance(top1['score']),
+            "numero": top1["numero"],
-            'mise_suggeree': 2.0, 'gain_potentiel': round(2.0 * top1['details']['cote'], 2)
+            "cote": top1["details"]["cote"],
+            "score": top1["score"],
+            "confiance": confiance(top1["score"]),
+            "mise_suggeree": 2.0,
+            "gain_potentiel": round(2.0 * top1["details"]["cote"], 2),
         },
-        'ze2_sur_4': {
+        "ze2_sur_4": {
-            'top4': [{'nom': h['nom'], 'numero': h['numero']} for h in top4_list],
+            "top4": [{"nom": h["nom"], "numero": h["numero"]} for h in top4_list],
-            'combinaisons': ze2_combinaisons,
+            "combinaisons": ze2_combinaisons,
-            'mise_totale': mise_ze2,
+            "mise_totale": mise_ze2,
-            'nb_combinaisons': len(ze2_combinaisons),
+            "nb_combinaisons": len(ze2_combinaisons),
-            'confiance': confiance((top1['score'] + top2['score'] + top3['score'] + top4['score']) / 4),
+            "confiance": confiance(
-            'explication': 'Jouer les 6 combinaisons de 2 chevaux parmi les 4 premiers'
+                (top1["score"] + top2["score"] + top3["score"] + top4["score"]) / 4
+            ),
+            "explication": "Jouer les 6 combinaisons de 2 chevaux parmi les 4 premiers",
         },
-        'outsider': _find_outsider(ranked),
+        "outsider": _find_outsider(ranked),
-        'budget_total': 2.0 + mise_ze2,
+        "budget_total": 2.0 + mise_ze2,
     }
 
+
 def _find_outsider(ranked):
     for h in ranked[3:7]:
-        d = h['details']
+        d = h["details"]
-        if d['cote'] >= 12 and d['forme_recente'] <= 4 and d['bonus_outsider'] == 5:
+        if d["cote"] >= 12 and d["forme_recente"] <= 4 and d["bonus_outsider"] == 5:
             return {
-                'cheval': h['nom'], 'numero': h['numero'], 'cote': d['cote'],
+                "cheval": h["nom"],
-                'mise_suggeree': 1.0, 'gain_potentiel': round(1.0 * d['cote'], 2)
+                "numero": h["numero"],
+                "cote": d["cote"],
+                "mise_suggeree": 1.0,
+                "gain_potentiel": round(1.0 * d["cote"], 2),
             }
     return None
 
+
 def save_to_db(scored_horses, date_course, hippodrome, libelle):
     conn = sqlite3.connect(DB_PATH)
     cursor = conn.cursor()
@@ -210,44 +349,72 @@ def save_to_db(scored_horses, date_course, hippodrome, libelle):
     cursor.execute("DELETE FROM scoring WHERE date = ?", (date_course,))
 
     for i, h in enumerate(scored_horses, 1):
-        d = h['details']
+        d = h["details"]
-        cursor.execute("""
+        cursor.execute(
+            """
             INSERT INTO scoring (date, race_name, horse_number, horse_name, score,
                 score_cote, score_forme, score_victoire, score_place, score_rk,
                 score_tendance, score_avis, cote, forme_recente, tx_victoire, tx_place,
                 avis_entraineur, musique, rang_scoring, scoring_version)
             VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'v2')
-        """, (date_course, libelle, h['numero'], h['nom'], h['score'],
+        """,
-               d.get('score_cote', 0), d.get('score_forme', 0), d.get('score_victoire', 0),
+            (
-               d.get('score_place', 0), d.get('score_rk', 0), d.get('score_tendance', 0),
+                date_course,
-               d.get('score_avis', 0), d.get('cote', 0), d.get('forme_recente', 0),
+                libelle,
-               d.get('tx_victoire', 0), d.get('tx_place', 0), d.get('avis_entraineur', ''),
+                h["numero"],
-               d.get('musique', ''), i))
+                h["nom"],
+                h["score"],
+                d.get("score_cote", 0),
+                d.get("score_forme", 0),
+                d.get("score_victoire", 0),
+                d.get("score_place", 0),
+                d.get("score_rk", 0),
+                d.get("score_tendance", 0),
+                d.get("score_avis", 0),
+                d.get("cote", 0),
+                d.get("forme_recente", 0),
+                d.get("tx_victoire", 0),
+                d.get("tx_place", 0),
+                d.get("avis_entraineur", ""),
+                d.get("musique", ""),
+                i,
+            ),
+        )
 
     conn.commit()
     conn.close()
     print(f"💾 {len(scored_horses)} scores enregistres en BDD pour {date_course}")
 
+
 def main():
-    today = datetime.now().strftime('%Y-%m-%d')
+    today = datetime.now().strftime("%Y-%m-%d")
-    date_pmu = datetime.now().strftime('%d%m%Y')
+    date_pmu = datetime.now().strftime("%d%m%Y")
-    print(f"=== SCORING V2 - ZE2 SUR4 OPTIMISE === {datetime.now().strftime('%d/%m/%Y %H:%M')} ===")
+    print(
+        f"=== SCORING V2 - ZE2 SUR4 OPTIMISE === {datetime.now().strftime('%d/%m/%Y %H:%M')} ==="
+    )
 
     try:
         url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_pmu}/reunions"
         r = requests.get(url, headers=HEADERS, timeout=15)
-        reunions = r.json().get('programme', {}).get('reunions', [])
+        reunions = r.json().get("programme", {}).get("reunions", [])
     except Exception as e:
         print(f"Erreur: {e}")
         return
 
     quinte = None
     for reunion in reunions:
-        for course in reunion.get('courses', []):
+        for course in reunion.get("courses", []):
             paris_types = [p["typePari"] for p in course.get("paris", [])]
-            if any("QUINTE" in p for p in paris_types) or "PARIS-TURF" in course.get('libelle', ''):
+            if any("QUINTE" in p for p in paris_types) or "PARIS-TURF" in course.get(
-                quinte = (reunion['numOfficiel'], course['numOrdre'], course.get('libelle', ''),
+                "libelle", ""
-                          reunion['hippodrome']['libelleCourt'], course.get('heureDepart', 0))
+            ):
+                quinte = (
+                    reunion["numOfficiel"],
+                    course["numOrdre"],
+                    course.get("libelle", ""),
+                    reunion["hippodrome"]["libelleCourt"],
+                    course.get("heureDepart", 0),
+                )
                 break
         if quinte:
             break
@@ -256,7 +423,8 @@ def main():
         # Fallback: utiliser la premiere reunion francaise avec predictions
         conn = sqlite3.connect(DB_PATH)
         conn.row_factory = sqlite3.Row
-        r = conn.execute("""
+        r = conn.execute(
+            """
             SELECT r.num_reunion, r.hippodrome_court, c.num_course, c.libelle
             FROM pmu_courses c
             JOIN pmu_reunions r ON r.date_programme=c.date_programme AND r.num_reunion=c.num_reunion
@@ -264,22 +432,36 @@ def main():
             AND EXISTS (SELECT 1 FROM predictions p WHERE p.date=? AND p.source='canalturf_partants' 
                        AND p.race_name LIKE '%' || c.libelle || '%')
             ORDER BY c.heure_depart_str ASC LIMIT 1
-        """, (today, today)).fetchone()
+        """,
+            (today, today),
+        ).fetchone()
         conn.close()
         if r:
-            quinte = (r['num_reunion'], r['num_course'], r['libelle'], r['hippodrome_court'], 0)
+            quinte = (
+                r["num_reunion"],
+                r["num_course"],
+                r["libelle"],
+                r["hippodrome_court"],
+                0,
+            )
         else:
             print("Aucune course trouvee")
             return
 
     num_r, num_c, libelle, hippodrome, heure_ts = quinte
-    heure = datetime.fromtimestamp(heure_ts/1000).strftime('%H:%M') if heure_ts else '13:55'
+    heure = (
+        datetime.fromtimestamp(heure_ts / 1000).strftime("%H:%M")
+        if heure_ts
+        else "13:55"
+    )
     print(f"Course: {libelle} - {hippodrome} {heure}")
 
     try:
         url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_pmu}/R{num_r}/C{num_c}/participants"
         r = requests.get(url, headers=HEADERS, timeout=15)
-        participants = [p for p in r.json().get('participants', []) if p.get('statut') == 'PARTANT']
+        participants = [
+            p for p in r.json().get("participants", []) if p.get("statut") == "PARTANT"
+        ]
     except Exception as e:
         print(f"Erreur: {e}")
         return
@@ -287,34 +469,45 @@ def main():
     scored_horses = []
     for p in participants:
         score, details = score_cheval_v2(p, participants, today)
-        scored_horses.append({'nom': p['nom'], 'numero': p['numPmu'], 'score': score, 'details': details})
+        scored_horses.append(
+            {"nom": p["nom"], "numero": p["numPmu"], "score": score, "details": details}
+        )
 
-    ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True)
+    ranked = sorted(scored_horses, key=lambda x: x["score"], reverse=True)
     print(f"\n=== TOP 4 ===")
     for i, h in enumerate(ranked[:4], 1):
-        d = h['details']
+        d = h["details"]
-        print(f"{i}. #{h['numero']:>2} {h['nom']:<20} Score:{h['score']:.1f} Cote:{d['cote']:.1f}")
+        print(
+            f"{i}. #{h['numero']:>2} {h['nom']:<20} Score:{h['score']:.1f} Cote:{d['cote']:.1f}"
+        )
 
     save_to_db(ranked, today, hippodrome, libelle)
 
     reco = build_recommendations_v2(scored_horses)
     if reco:
         print(f"\n=== RECOMMANDATIONS ===")
-        sg = reco['simple_gagnant']
+        sg = reco["simple_gagnant"]
         print(f"\n🎯 SIMPLE GAGNANT:")
-        print(f"   #{sg['numero']} {sg['cheval']} @ {sg['cote']}/1 (mise {sg['mise_suggeree']}EUR)")
+        print(
+            f"   #{sg['numero']} {sg['cheval']} @ {sg['cote']}/1 (mise {sg['mise_suggeree']}EUR)"
+        )
 
-        ze2 = reco['ze2_sur_4']
+        ze2 = reco["ze2_sur_4"]
         print(f"\n🎰 ZE 2 SUR 4 (TOP 4: {', '.join([h['nom'] for h in ze2['top4']])}")
-        print(f"   Mise totale: {ze2['mise_totale']}EUR ({ze2['nb_combinaisons']} combis x 1EUR)")
+        print(
+            f"   Mise totale: {ze2['mise_totale']}EUR ({ze2['nb_combinaisons']} combis x 1EUR)"
+        )
         print(f"   Confiance: {ze2['confiance']}")
         print(f"   Combinaisons:")
-        for c in ze2['combinaisons']:
+        for c in ze2["combinaisons"]:
-            print(f"      {c['numero1']}-{c['cheval1']} + {c['numero2']}-{c['cheval2']}")
+            print(
+                f"      {c['numero1']}-{c['cheval1']} + {c['numero2']}-{c['cheval2']}"
+            )
 
         print(f"\n💰 BUDGET TOTAL: {reco['budget_total']}EUR")
         print(f"   - Simple Gagnant: 2EUR")
         print(f"   - ZE 2 sur 4: {ze2['mise_totale']}EUR")
 
+
 if __name__ == "__main__":
     main()