165 lines
6.7 KiB
Python
165 lines
6.7 KiB
Python
import json
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import os
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import sys
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from datetime import datetime, timedelta
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current_dir = os.path.dirname(os.path.abspath(__file__))
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# 如果当前目录不在sys.path中，就添加进去（避免重复添加）
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if current_dir not in sys.path:
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sys.path.append(current_dir)
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from predict_model_v6 import LotteryPredictorV6
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SUM12_ODDS = {
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3: 41.9, 4: 41.9, 18: 41.9, 19: 41.9,
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5: 20.38, 6: 20.38, 16: 20.38, 17: 20.38,
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7: 13.51, 8: 13.51, 14: 13.51, 15: 13.51,
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9: 9.7, 10: 9.7, 12: 9.7, 13: 9.7,
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11: 8.1
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}
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# 玩法1.1 赔率映射
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SIZE_ODDS = {"冠亚大": 2.1, "冠亚小": 1.7}
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ODD_EVEN_ODDS = {"冠亚单": 1.7, "冠亚双": 2.1}
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NUM_DETAIL_ODDS = 1.9199 * 20
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# NUM_DETAIL_ODDS = {"大": 1.9199, "小": 1.9199, "单": 1.9199, "双": 1.9199}
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# 玩法2.1/2.2/3 固定赔率
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RANK_GLH_ODDS = 1.9199 * 10 # 玩法2.2
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NUM_POS_ODDS = 9.599 * 10 # 玩法3
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# 总赔率
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TOTAL_ODDS = 0
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SUM12_ODDS_TOTAL = sum(SUM12_ODDS.values())
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SIZE_ODDS_TOTAL = sum(SIZE_ODDS.values())
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ODD_EVEN_ODDS_TOTAL = sum(ODD_EVEN_ODDS.values())
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TOTAL_ODDS += SUM12_ODDS_TOTAL + SIZE_ODDS_TOTAL + ODD_EVEN_ODDS_TOTAL
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TOTAL_ODDS += round(NUM_DETAIL_ODDS + RANK_GLH_ODDS + NUM_POS_ODDS, 4)
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# TOTAL_ODDS += round(NUM_DETAIL_ODDS * 20 + RANK_GLH_ODDS * 10 + NUM_POS_ODDS * 10, 4)
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EMPTY_TIME_WINDOWS = [
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"06:05:00", "06:10:00", "06:15:00", "06:20:00", "06:25:00", "06:30:00", "06:35:00", "06:40:00", "06:45:00",
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"06:50:00", "06:55:00", "07:00:00",
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]
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print(f"总赔率: {TOTAL_ODDS}")
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# 生成预测数据
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def generate_time_series(start_str, end_str):
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start = datetime.strptime(start_str, '%Y-%m-%d %H:%M:%S')
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end = datetime.strptime(end_str, '%Y-%m-%d %H:%M:%S')
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current = start
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series = []
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while current <= end:
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series.append(current)
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current += timedelta(minutes=5)
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return series
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def main():
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predictor = LotteryPredictorV6('data_test_predict/aggregated_stats_v7.json')
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with open("full_history_time_performance.json", "r", encoding="utf-8") as f:
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time_windows = json.load(f)
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# start_time = "2025-01-01 07:05:00"
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# end_time = "2026-01-19 06:00:00"
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start_time = "2026-01-20 07:05:00"
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end_time = "2026-01-21 06:00:00"
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time_series = generate_time_series(start_time, end_time)
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total_budget = 20.0
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results = []
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for dt in time_series:
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date_str = dt.strftime('%Y-%m-%d')
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time_str = dt.strftime('%H:%M:%S')
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raw = predictor.predict(date_str, time_str)
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# 收集所有潜在押注项及其原始概率
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candidates = [] # (type, pos/key, sub_key, prob)
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# 1. Result (数字位置) - 每个位置选 Top 3, 门槛 0.11
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for i in range(10):
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# if time_str not in EMPTY_TIME_WINDOWS and time_windows["accuracy"][time_str] < 0.6248:
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# break
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pos_key = f'pos_{i}'
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probs = raw['digital_pos'][pos_key]
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sorted_p = sorted(probs.items(), key=lambda x: x[1], reverse=True)
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for val, p in sorted_p[:5]:
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if p >= 0.1055:
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# if p >= 0.1055:
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candidates.append(('result', i, val, p))
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# 2. Result Detail (单双大小) - 互斥, 门槛 0.53
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for i in range(10):
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pos_key = f'pos_{i}'
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for cat in ['big_small', 'odd_even']:
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probs = raw['pos_detail'][pos_key][cat]
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sorted_p = sorted(probs.items(), key=lambda x: x[1], reverse=True)
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best_opt, best_p = sorted_p[0]
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if best_p >= 0.53068:
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candidates.append(('result_detail', i, best_opt, best_p))
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# 3. Winner - 门槛 0.08
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for val, p in raw['winner_prob'].items():
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if p >= 0.08582:
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candidates.append(('winner', None, val, p))
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# 4. GD1 & GD2 - 互斥, 门槛 0.53
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for cat in ['direct_gd1', 'direct_gd2']:
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probs = raw[cat]
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sorted_p = sorted(probs.items(), key=lambda x: x[1], reverse=True)
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best_opt, best_p = sorted_p[0]
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if best_p >= 0.53068:
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candidates.append(('gd', cat, best_opt, best_p))
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# 5. GLH (龙虎) - 进取型, 无门槛 (取优势方)
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for i in range(5):
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pos_key = f'pos_{i}'
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probs = raw['glh_pos'][pos_key]
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sorted_p = sorted(probs.items(), key=lambda x: x[1], reverse=True)
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best_opt, best_p = sorted_p[0]
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if best_p >= 0.53068:
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candidates.append(('glh', i, best_opt, best_p))
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# 动态资金分配: 金额 = (概率 / 总概率和) * 500
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total_prob_sum = sum(c[3] for c in candidates)
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entry = {
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"id": dt.strftime('%Y%m%d%H%M'),
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"time": dt.strftime('%Y-%m-%d %H:%M:%S'),
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"result": {i: {str(n): None for n in range(1, 11)} for i in range(10)},
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"result_detail": {i: {"单": None, "双": None, "大": None, "小": None} for i in range(10)},
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"winner": {},
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"GD1": {"冠亚大": None, "冠亚小": None},
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"GD2": {"冠亚单": None, "冠亚双": None},
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"GLH_result": {}
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}
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if total_prob_sum > 0:
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for c_type, key, sub_key, prob in candidates:
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# bet_amount = round((prob / total_prob_sum) * total_budget, 2)
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if c_type == 'result':
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# entry['result'][key][sub_key] = 10
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entry['result'][key][sub_key] = round((NUM_POS_ODDS / TOTAL_ODDS) * total_budget, 2)
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elif c_type == 'result_detail':
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entry['result_detail'][key][sub_key] = round((NUM_DETAIL_ODDS / TOTAL_ODDS) * total_budget, 2)
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elif c_type == 'winner':
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entry['winner'][sub_key] = round((SUM12_ODDS[int(sub_key)] / TOTAL_ODDS) * total_budget, 2)
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elif c_type == 'gd':
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# key is 'direct_gd1' or 'direct_gd2'
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field = 'GD1' if key == 'direct_gd1' else 'GD2'
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_odd = SIZE_ODDS_TOTAL if key == 'direct_gd1' else ODD_EVEN_ODDS_TOTAL
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entry[field][sub_key] = round((_odd / TOTAL_ODDS) * total_budget, 2)
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elif c_type == 'glh':
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entry['GLH_result'][f"pos_{key}_{sub_key}"] = round((RANK_GLH_ODDS / TOTAL_ODDS) * total_budget, 2)
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results.append(entry)
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with open('data_test_predict/betting_predictions_final_1_20.json', 'w', encoding='utf-8') as f:
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# with open('data_test_predict/betting_predictions_final_502.json', 'w', encoding='utf-8') as f:
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# with open('data_test_predict/betting_predictions_final_501.json', 'w', encoding='utf-8') as f:
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json.dump(results, f, ensure_ascii=False, indent=2)
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print(f"Generated {len(results)} predictions.")
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if __name__ == "__main__":
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main()
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