140 lines
4.9 KiB
Python
140 lines
4.9 KiB
Python
import json
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import os
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import sys
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import pandas as pd
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import numpy as np
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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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import argparse
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from predict_model_v14_profit_maximizer import LotteryPredictorV14Profit
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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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# 1. 加载数据
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def get_history_records(data_dirs):
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all_records = []
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for d in data_dirs:
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if not os.path.exists(d): continue
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for f in os.listdir(d):
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if f.endswith('.json') and f != 'stat_result.json':
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with open(os.path.join(d, f), 'r', encoding='utf-8') as file:
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try:
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content = json.load(file)
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if isinstance(content, list):
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all_records.extend([item for item in content if isinstance(item, dict)])
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except:
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continue
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return all_records
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def generate_time_series(start, end):
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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(next_period_time, df):
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entry = {
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"id": next_period_time.strftime('%Y%m%d%H%M'),
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"time": next_period_time.strftime('%Y-%m-%d %H:%M:%S'),
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"result": {i: {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 next_period_time.strftime('%H:%M:%S') in EMPTY_TIME_WINDOWS:
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return entry
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result = []
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predictor = LotteryPredictorV14Profit()
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next_period_time = pd.to_datetime(next_period_time)
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start_time, _ = predictor.check_init_daily_pnl_tracker(next_period_time)
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time_series = generate_time_series(start_time, next_period_time)
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for dt in time_series:
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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: {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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current_time = pd.to_datetime(dt)
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recent_x = df[df['time'] < current_time].tail(10000)
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predictions = predictor.predict(current_time, recent_x)
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for pos, nums_bet in predictions.items():
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if not nums_bet:
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continue
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entry['result'][pos] = nums_bet
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last_result = df[df['time'] == current_time]
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if last_result.size != 0:
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predictor.update_result(last_result, entry)
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result.append(entry)
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return result[-1]
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if __name__ == "__main__":
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data_dirs = ["./history_data", "./current_data"]
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all_records = get_history_records(data_dirs)
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parser = argparse.ArgumentParser(description="下一期时间 next_period_time, 例: 2026-01-02 06:00:00")
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# 必选位置参数（直接传入值，无需前缀）
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parser.add_argument("--next_period_time", type=str, help="下一期时间, 例: 2026-01-02 06:00:00", default="2026-01-02 07:05:00")
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args = parser.parse_args()
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fmt = "%Y-%m-%d %H:%M:%S"
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next_period_time = datetime.strptime(args.next_period_time, fmt)
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df = pd.DataFrame(all_records).drop_duplicates(subset=['id']).sort_values('time')
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df['time'] = pd.to_datetime(df['time'])
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result = main(next_period_time, df)
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print(json.dumps(result, ensure_ascii=False))
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