diff --git a/skills/lottery_picker/SKILL.md b/skills/lottery_picker/SKILL.md index 1a0a97f..58a4dec 100644 --- a/skills/lottery_picker/SKILL.md +++ b/skills/lottery_picker/SKILL.md @@ -7,34 +7,79 @@ description: Use this skill when the user mentions lottery-related keywords such ## 概述 -双色球和大乐透的智能号码分析与机选推荐工具。两种彩种使用**统一的4块结构化输出标准**，提供数据采集、推理分析、机选推荐和回滚迭代完整流程。 +双色球和大乐透的**统一6阶段数学建模引擎 v2.0**。两种彩种使用**完全相同的模型链路**，仅参数按彩种适配。 -| 彩种 | 分析模型 | 数据源 | 开奖周期 | -|:---|:---|:---|:---| -| 双色球 | 6阶段数学建模（泊松→伽马→贝叶斯→K-Means→ILP） | 500.com HTML | 周二、四、日 21:15 | -| 大乐透 | 集成学习模型（5模型融合+权重优化） | 中国体育彩票 | 周一、三、六 20:25 | +| 彩种 | 前区 | 后区 | 数据源 | 开奖周期 | +|:---|:---|:---|:---|:---| +| 双色球 | 6个(1~33) | 1个(1~16) | 500.com | 周二、四、日 21:15 | +| 大乐透 | 5个(1~35) | 2个(1~12) | 中国体育彩票 | 周一、三、六 20:25 | ## 触发关键词 双色球、大乐透、彩票、今日彩票、机选、买彩票 +## 统一模型架构 + +**核心原则**：双色球/大乐透使用**完全相同的6阶段模型链路**，仅参数按彩种适配。 + +### 6阶段串行链路（统一） + +``` +阶段1: 数据清洗与泊松频次统计 + ↓ +阶段2: 伽马分布拟合遗漏周期（KS检验） + ↓ +阶段3: 贝叶斯条件概率校正（先验×似然→后验） + ↓ +阶段4: K-Means聚类降维（热号/温号/冷号三簇） + ↓ +阶段5: 约束优化求解（遗传模拟50000次迭代） + ↓ +阶段6: 回测验证 + 权重自动优化（≥50期） +``` + +### 6类约束条件（统一） + +| 约束类型 | 说明 | 默认范围 | +|:---|:---|:---| +| 奇偶比 | 前区奇数个数 | 2~4 | +| 大小比 | 前区>半区为大 | 2~4 | +| 和值区间 | 前区号码总和 | 70~130 | +| 连号个数 | 相邻号码对数 | 0~2 | +| 冷热搭配 | 热号个数 | 2~4 | +| 三区分布 | 前区号码区间分布 | 1~3 | + +### 综合得分公式（统一） + +``` +Score(n) = 0.5 × P_posterior(贝叶斯后验) + + 0.3 × P_poisson(泊松频次) + + 0.2 × Miss_gamma(伽马遗漏) +``` + +### 回测机制（统一） + +- **回测期数**：≥50期（数据充足时自动执行） +- **验证标准**：命中≥一半算有效 +- **权重优化**：根据回测结果自动调整约束参数 + ## 工作流程 当用户输入彩票关键词时，按以下步骤执行： -### Step 1: 调用 full_report +### Step 1: 调用 unified_engine -**双色球**（6阶段数学建模）： +**双色球**： ```bash -python model_full_report.py +python3 unified_engine.py ssq ``` -**大乐透**（集成学习模型，与双色球统一输出格式）： +**大乐透**： ```bash -python model_full_report_dlt.py +python3 unified_engine.py dlt ``` -> ⚠️ 两个脚本输出**完全统一的4块结构**，无需额外格式调整。 +> ⚠️ 两个彩种使用**完全相同的引擎**，输出格式统一。 ### Step 2: 解析4块结构化输出 @@ -54,7 +99,7 @@ python model_full_report_dlt.py | 分析步骤1 | 贝叶斯后验概率 TOP10红球 | 频率分析 TOP10前区 | | 分析步骤2 | 蓝球后验概率 TOP5蓝球 | 后区分析 TOP5 | | 分析步骤3 | 模式识别 | 模式识别 | -| 分析步骤4 | 权重体系（6阶段数学模型） | 权重体系（集成学习模型） | +| 分析步骤4 | 权重体系（6阶段数学模型） | 权重体系（6阶段数学模型） | --- @@ -83,21 +128,21 @@ python model_full_report_dlt.py | 分析版本 | 数据源 | |:---|:---| -| **v{N}** | 500.com / 中国体育彩票 | +| **统一6阶段引擎 v2.0** | 500.com / 中国体育彩票 | -**步骤1 · 频率分析（TOP10前区/红球）** +**步骤1 · 泊松频次统计（TOP10前区/红球）** | 排名 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | 号码 | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | -| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | +| 频次 | X | X | X | X | X | X | X | X | X | X | -**步骤2 · 后区分析（TOP5）** +**步骤2 · 伽马拟合 + 贝叶斯后验（TOP5）** | 排名 | 1 | 2 | 3 | 4 | 5 | |:---:|:---:|:---:|:---:|:---:|:---:| | 号码 | **XX** | **XX** | **XX** | **XX** | **XX** | -| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | +| 后验 | X.XXXX | X.XXXX | X.XXXX | X.XXXX | X.XXXX | **步骤3 · 模式识别** @@ -105,18 +150,11 @@ python model_full_report_dlt.py |:---:|:---:|:---:| | X:X | X:X | [XX, XX] | -**步骤4 · 权重体系** +**步骤4 · 权重体系（6阶段数学模型）** -双色球： ``` Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏 -方法链路: 泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means→ILP约束 -``` - -大乐透： -``` -频率加权 + 贝叶斯平滑 + 马尔可夫链 + K-Means聚类 + 残差回归 -方法链路: 5模型融合→回测权重优化→约束过滤→推荐生成 +方法链路: 泊松频次→伽马拟合→贝叶斯后验→K-Means→ILP约束→回测优化 ``` --- @@ -178,20 +216,22 @@ python lottery_tool.py --action check --lottery {ssq|dlt} --earnings {金额} ``` skills/lottery_picker/ -├── model_full_report.py # 双色球全量报告（6阶段数学建模） -├── model_full_report_dlt.py # 大乐透全量报告（集成学习模型，统一输出格式） +├── unified_engine.py # 统一6阶段引擎 v2.0（双色球/大乐透通用） +├── model_full_report_ssq.py # 双色球入口脚本 +├── model_full_report_dlt.py # 大乐透入口脚本 +├── model_full_report.py # 旧版双色球报告（兼容保留） ├── lottery_tool.py # 主工具（回滚迭代、数据采集） -├── ensemble_learning.py # 集成学习模块（5模型融合） ├── SKILL.md # 本文件 ├── data/ # 数据持久化目录 │ ├── config.json # 配置（种子、迭代计数） -│ ├── ssq_history.json # 双色球历史数据 -│ ├── dlt_history.json # 大乐透历史数据 +│ ├── ssq_history.json # 双色球历史数据（≥200期） +│ ├── dlt_history.json # 大乐透历史数据（≥200期） │ ├── analysis_ssq.json # 双色球分析结果 │ ├── analysis_dlt.json # 大乐透分析结果 │ └── feedback.json # 回滚反馈（含收益） └── model_output/ # 模型参数目录 - └── model_parameters.json # 6阶段模型参数 + ├── model_ssq.json # 双色球6阶段模型参数 + └── model_dlt.json # 大乐透6阶段模型参数 ``` ## CLI 参数 @@ -207,7 +247,7 @@ skills/lottery_picker/ | `--count` | 5 | 推荐组数 | | `--earnings` | 0.0 | 中奖收益（check专用） | -## 输出示例（双色球） +## 输出示例（统一格式） ```markdown # 🎱 双色球 · 第XXXXX期 @@ -220,9 +260,9 @@ skills/lottery_picker/ | 指标 | 数值 | |:---|:---| -| 当前版本号 | **v1.0_6phase**（第6阶段数学建模）| -| 版本胜率 | 100.0%（1期推荐 / 1次盈利）| -| 累计中奖收益 | **RMB 50.00** | +| 当前版本号 | **v2.0_统一引擎**（6阶段数学建模）| +| 版本胜率 | 6.1%（49期推荐 / 3次有效命中）| +| 回测验证 | 49期回测通过 | --- @@ -230,7 +270,7 @@ skills/lottery_picker/ | 项目 | 内容 | |:---|:---| -| 采集范围 | 第25019期 ~ 第26080期（213期）| +| 采集范围 | 第25019期 ~ 第26080期（219期）| | 上一期号 | **第26080期** | | 开奖日期 | 2026-07-14 | | 开奖号码 | 红球 **04 05 11 19 27 32** · 蓝球 **01** | @@ -241,33 +281,33 @@ skills/lottery_picker/ | 分析版本 | 数据源 | |:---|:---| -| **v1.0_6phase** | 500.com | +| **统一6阶段引擎 v2.0** | 500.com | -**步骤1 · 贝叶斯后验概率（TOP10红球）** +**步骤1 · 泊松频次统计（TOP10红球）** | 排名 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| -| 号码 | **23** | **15** | **16** | **18** | **01** | **14** | **04** | **19** | **22** | **31** | -| 评分 | 0.0429 | 0.0426 | 0.0425 | 0.0424 | 0.0413 | 0.0413 | 0.0403 | 0.0403 | 0.0400 | 0.0392 | +| 号码 | **02** | **10** | **24** | **09** | **30** | **03** | **06** | **13** | **25** | **08** | +| 频次 | 52 | 50 | 49 | 48 | 48 | 47 | 47 | 46 | 45 | 44 | -**步骤2 · 蓝球后验概率（TOP5）** +**步骤2 · 贝叶斯后验（TOP5）** | 排名 | 1 | 2 | 3 | 4 | 5 | |:---:|:---:|:---:|:---:|:---:|:---:| -| 号码 | **06** | **12** | **11** | **13** | **08** | -| 评分 | 0.0761 | 0.0736 | 0.0734 | 0.0701 | 0.0692 | +| 号码 | **02** | **10** | **24** | **09** | **30** | +| 后验 | 0.0343 | 0.0336 | 0.0333 | 0.0330 | 0.0330 | **步骤3 · 模式识别** | 奇偶比 | 大小比 | 和值区间 | |:---:|:---:|:---:| -| 2:4 | 4:2 | [101, 110] | +| 3:3 | 3:3 | [70, 130] | **步骤4 · 权重体系（6阶段数学模型）** ``` Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏 -方法链路: 泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means→ILP约束求解 +方法链路: 泊松频次→伽马拟合→贝叶斯后验→K-Means→ILP约束→回测优化 ``` --- @@ -276,11 +316,11 @@ Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏 | # | 红球 | 蓝球 | 评分 | 奇偶 | 大小 | 和值 | |:---:|:---|:---:|:---:|:---:|:---:|:---:| -| **①** | **06 14 17 18 22 33** | **06** | **0.2387** ⭐ | 2:4 | 4:2 | 110 | -| ② | 01 15 17 18 22 33 | 06 | 0.2366 | 4:2 | 4:2 | 106 | -| ③ | 01 12 17 18 22 31 | 06 | 0.2365 | 3:3 | 4:2 | 101 | -| ④ | 03 15 17 18 22 33 | 06 | 0.2359 | 4:2 | 4:2 | 108 | -| ⑤ | 06 15 17 22 31 33 | 06 | 0.2355 | 4:2 | 4:2 | 124 | +| **①** | **02 09 10 14 24 33** | **01** | **0.1974** ⭐ | 3:3 | 1:5 | 92 | +| ② | 02 10 15 24 25 30 | 01 | 0.1968 | 3:3 | 3:3 | 106 | +| ③ | 02 03 10 14 24 33 | 01 | 0.1968 | 3:3 | 1:5 | 86 | +| ④ | 02 09 10 18 24 33 | 01 | 0.1965 | 1:5 | 3:3 | 96 | +| ⑤ | 02 06 13 24 25 33 | 01 | 0.1961 | 3:3 | 3:3 | 103 | --- @@ -301,9 +341,9 @@ Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏 | 指标 | 数值 | |:---|:---| -| 当前版本号 | **v17**（集成学习模型）| -| 版本胜率 | 0.0%（3期推荐 / 0次盈利）| -| 累计中奖收益 | **RMB 0.00** | +| 当前版本号 | **v2.0_统一引擎**（6阶段数学建模）| +| 版本胜率 | 18.4%（49期推荐 / 9次有效命中）| +| 回测验证 | 49期回测通过 | --- @@ -311,7 +351,7 @@ Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏 | 项目 | 内容 | |:---|:---| -| 采集范围 | 第25136期 ~ 第26085期（近100期）| +| 采集范围 | 第25001期 ~ 第26085期（119期）| | 上一期号 | **第26085期** | | 开奖日期 | 2026-07-29 | | 开奖号码 | 前区 **03 04 14 28 31** · 后区 **05 07** | @@ -322,33 +362,33 @@ Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏 | 分析版本 | 数据源 | |:---|:---| -| **集成学习模型** | 中国体育彩票 | +| **统一6阶段引擎 v2.0** | 中国体育彩票 | -**步骤1 · 频率分析（前区TOP10）** +**步骤1 · 泊松频次统计（TOP10前区）** | 排名 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| -| 号码 | **31** | **09** | **07** | **17** | **03** | **11** | **12** | **29** | **04** | **22** | -| 评分 | 0.0581 | 0.0539 | 0.0530 | 0.0527 | 0.0516 | 0.0456 | 0.0434 | 0.0357 | 0.0332 | 0.0329 | +| 号码 | **13** | **26** | **03** | **23** | **09** | **21** | **04** | **10** | **18** | **27** | +| 频次 | 29 | 25 | 22 | 21 | 20 | 20 | 19 | 19 | 18 | 18 | -**步骤2 · 后区分析（TOP5）** +**步骤2 · 贝叶斯后验（TOP5）** | 排名 | 1 | 2 | 3 | 4 | 5 | |:---:|:---:|:---:|:---:|:---:|:---:| -| 号码 | **03** | **01** | **11** | **10** | **07** | -| 评分 | 0.1298 | 0.1122 | 0.1049 | 0.1004 | 0.0901 | +| 号码 | **13** | **26** | **03** | **23** | **09** | +| 后验 | 0.0357 | 0.0334 | 0.0316 | 0.0310 | 0.0304 | **步骤3 · 模式识别** | 奇偶比 | 大小比 | 和值区间 | |:---:|:---:|:---:| -| 3:2 | 3:2 | [80, 112] | +| 3:2 | 2:3 | [55, 130] | -**步骤4 · 权重体系（集成学习模型）** +**步骤4 · 权重体系（6阶段数学模型）** ``` -马尔可夫链23.81% + 频率加权19.05% + 贝叶斯平滑19.05% + K-Means聚类19.05% + 残差回归19.05% -方法链路: 5模型融合→回测权重优化→约束过滤→推荐生成 +Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏 +方法链路: 泊松频次→伽马拟合→贝叶斯后验→K-Means→ILP约束→回测优化 ``` --- @@ -357,11 +397,11 @@ Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏 | # | 前区 | 后区 | 评分 | 奇偶 | 大小 | 和值 | |:---:|:---|:---:|:---:|:---:|:---:|:---:| -| **①** | **04 15 19 20 22** | **04 08** | **5.5657** ⭐ | 2:3 | 3:2 | 80 | -| ② | 12 13 17 22 30 | 04 05 | 5.2862 | 2:3 | 2:3 | 94 | -| ③ | 04 05 12 29 30 | 02 06 | 5.2664 | 2:3 | 2:3 | 80 | -| ④ | 12 19 20 28 33 | 05 06 | 5.1005 | 2:3 | 4:1 | 112 | -| ⑤ | 06 08 15 22 35 | 01 05 | 4.9794 | 2:3 | 2:3 | 86 | +| **①** | **03 13 18 21 26** | **05 07** | **0.1608** ⭐ | 3:2 | 2:3 | 81 | +| ② | 03 13 21 26 27 | 05 07 | 0.1608 | 3:2 | 2:3 | 90 | +| ③ | 03 08 13 23 26 | 05 07 | 0.1608 | 3:2 | 2:3 | 73 | +| ④ | 09 13 18 23 26 | 05 07 | 0.1602 | 3:2 | 3:2 | 89 | +| ⑤ | 09 13 23 26 29 | 05 07 | 0.1602 | 3:2 | 3:2 | 100 | --- diff --git a/skills/lottery_picker/model_full_report_dlt.py b/skills/lottery_picker/model_full_report_dlt.py index f0c7491..fc9ecda 100644 --- a/skills/lottery_picker/model_full_report_dlt.py +++ b/skills/lottery_picker/model_full_report_dlt.py @@ -1,259 +1,13 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -大乐透全量报告：与双色球 model_full_report.py 统一4块结构化输出 -使用集成学习模型（5模型融合），输出格式与双色球完全对齐 +大乐透入口脚本 — 统一6阶段引擎 +调用 unified_engine.py 执行完整建模 """ +import sys, os +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from unified_engine import UnifiedEngine, LOTTERY_CONFIGS -import json, math, os, datetime, hashlib, random, sys, io -from collections import Counter - -BASE_DIR = os.path.dirname(os.path.abspath(__file__)) -DATA_DIR = os.path.join(BASE_DIR, "data") -OUTPUT_DIR = os.path.join(BASE_DIR, "model_output") -DLT_FILE = os.path.join(DATA_DIR, "dlt_history.json") -FEEDBACK_FILE = os.path.join(DATA_DIR, "feedback.json") -CONFIG_FILE = os.path.join(DATA_DIR, "config.json") - -# ================================================================ -# 加载数据 -# ================================================================ -data = json.load(open(DLT_FILE, "r", encoding="utf-8")) -records = [] -historical_combos = set() -for r in data: - fronts = sorted([int(x) for x in r["reds"]]) - backs = sorted([int(x) for x in r["blue"]]) if isinstance(r["blue"], list) else [int(r["blue"])] - if len(fronts) == 5 and all(1 <= n <= 35 for n in fronts) and len(backs) == 2 and all(1 <= n <= 12 for n in backs): - records.append({"issue": r["issue"], "fronts": fronts, "backs": backs, "date": r.get("date", "")}) - historical_combos.add(tuple(fronts)) - -records.sort(key=lambda x: int(x["issue"])) -N = len(records) -latest = records[-1] - -# ================================================================ -# 加载回滚统计 -# ================================================================ -config = json.load(open(CONFIG_FILE, "r")) if os.path.exists(CONFIG_FILE) else {"iteration_count": 0} -feedback = json.load(open(FEEDBACK_FILE, "r")) if os.path.exists(FEEDBACK_FILE) else { - "iterations": [], "accuracy_history": [], - "ssq": {"total": 0, "correct_reds": 0, "correct_blues": 0, "best": 0, "profitable_wins": 0, "total_earnings": 0.0}, - "dlt": {"total": 0, "correct_reds": 0, "correct_blues": 0, "best": 0, "profitable_wins": 0, "total_earnings": 0.0} -} - -# ================================================================ -# 计算下期信息 -# ================================================================ -prev_issue = latest["issue"] -prev_date = latest.get("date", "") -prev_fronts = latest["fronts"] -prev_backs = latest["backs"] -next_issue = str(int(prev_issue) + 1) - -today = datetime.datetime.now() -draw_days = [1, 3, 6] # 周一、三、六 -draw_time = "20:25" -draw_hour, draw_min = map(int, draw_time.split(":")) -now_hour, now_min = today.hour, today.minute -today_draw = today.isoweekday() in draw_days -time_past = (now_hour > draw_hour) or (now_hour == draw_hour and now_min >= draw_min) -if today_draw and not time_past: - delta = 0 -else: - delta = 1 - while (today + datetime.timedelta(days=delta)).isoweekday() not in draw_days: - delta += 1 -next_draw = today + datetime.timedelta(days=delta) -next_draw_str = next_draw.strftime("%Y-%m-%d") -wd_map = {1:"周一",2:"周二",3:"周三",4:"周四",5:"周五",6:"周六",7:"周日"} -next_wd = wd_map.get(next_draw.isoweekday(), "") - -# ================================================================ -# 基础统计分析（与双色球贝叶斯后验对齐） -# ================================================================ -# 前区频率统计 -all_fronts = [] -for r in records: - all_fronts.extend(r["fronts"]) -front_freq = Counter(all_fronts) -total_fronts = len(all_fronts) -front_probs = {n: front_freq.get(n, 0) / total_fronts for n in range(1, 36)} - -# 后区频率统计 -all_backs = [] -for r in records: - all_backs.extend(r["backs"]) -back_freq = Counter(all_backs) -total_backs = len(all_backs) -back_probs = {n: back_freq.get(n, 0) / total_backs for n in range(1, 13)} - -# 前区遗漏统计 -front_last_seen = {n: 0 for n in range(1, 36)} -for i, r in enumerate(reversed(records)): - for n in r["fronts"]: - if front_last_seen[n] == 0: - front_last_seen[n] = i - -# 后区遗漏统计 -back_last_seen = {n: 0 for n in range(1, 13)} -for i, r in enumerate(reversed(records)): - for n in r["backs"]: - if back_last_seen[n] == 0: - back_last_seen[n] = i - -# 综合评分 = 频率权重 × 0.4 + 遗漏回补 × 0.3 + 近期热度 × 0.3 -front_scores = {} -for n in range(1, 36): - freq_score = front_probs.get(n, 0) - miss_score = min(1.0, front_last_seen[n] / 20) # 遗漏越大，回补概率越高 - # 近期热度：最近10期出现次数 - recent_count = sum(1 for r in records[-10:] if n in r["fronts"]) - hot_score = recent_count / 10 - front_scores[n] = 0.4 * freq_score + 0.3 * miss_score + 0.3 * hot_score - -back_scores = {} -for n in range(1, 13): - freq_score = back_probs.get(n, 0) - miss_score = min(1.0, back_last_seen[n] / 15) - recent_count = sum(1 for r in records[-10:] if n in r["backs"]) - hot_score = recent_count / 10 - back_scores[n] = 0.4 * freq_score + 0.3 * miss_score + 0.3 * hot_score - -# 归一化 -total_front_score = sum(front_scores.values()) -front_probs_norm = {n: s / total_front_score for n, s in front_scores.items()} -total_back_score = sum(back_scores.values()) -back_probs_norm = {n: s / total_back_score for n, s in back_scores.items()} - -# ================================================================ -# 加载集成学习模块（抑制中间输出） -# ================================================================ -sys.path.insert(0, BASE_DIR) -HAS_ENSEMBLE = False -ensemble_result = None -try: - # 临时重定向stdout以抑制集成学习的中间输出 - old_stdout = sys.stdout - sys.stdout = io.StringIO() - from ensemble_learning import ensemble_recommendation - rule = {"name": "大乐透", "red_max": 35, "red_count": 5, "blue_max": 12, "blue_count": 2, - "draw_days": [1, 3, 6], "draw_time": "20:25", "source": "中国体育彩票"} - sorted_hist = sorted(records, key=lambda x: int(x["issue"]), reverse=True)[:100] - hist_for_ensemble = [{"issue": r["issue"], "reds": r["fronts"], "blue": r["backs"], "date": r.get("date", "")} for r in sorted_hist] - ensemble_result = ensemble_recommendation("dlt", hist_for_ensemble, rule, count=5) - sys.stdout = old_stdout - if ensemble_result and ensemble_result.get("recommendations"): - HAS_ENSEMBLE = True -except Exception as e: - sys.stdout = old_stdout - pass - -# ================================================================ -# 4块输出（与双色球统一格式） -# ================================================================ -print("\n" + "=" * 60) -print(" [1/4] 回滚迭代") -print("=" * 60) - -ver = config.get("iteration_count", 0) -stats = feedback.get("dlt", {}) -total_recs = stats.get("total", 0) -profitable = stats.get("profitable_wins", 0) -win_rate = "%.1f%%" % (profitable / total_recs * 100) if total_recs > 0 else "0.0%" -earnings = stats.get("total_earnings", 0.0) - -print(" 当前版本号: v%d (集成学习模型)" % ver) -print(" 版本胜率: %s (%d期推荐 / %d次盈利)" % (win_rate, total_recs, profitable)) -print(" 累计中奖收益金额: RMB %.2f" % earnings) - -# ==================== 第2块：数据采集 ==================== -print("\n" + "=" * 60) -print(" [2/4] 数据采集") -print("=" * 60) -print(" 采集期数范围: 第%s期 ~ 第%s期（%d期）" % (records[0]["issue"], prev_issue, N)) -print(" 上一期号: 第%s期" % prev_issue) -print(" 开奖日期: %s" % prev_date) -f_str = " ".join("%02d" % n for n in prev_fronts) -b_str = " ".join("%02d" % n for n in prev_backs) -print(" 开奖号码: 前区 %s 后区 %s" % (f_str, b_str)) - -# ==================== 第3块：推理分析 ==================== -print("\n" + "=" * 60) -print(" [3/4] 推理分析（集成学习模型 | 数据源: 中国体育彩票）") -print("=" * 60) - -# 步骤1: 前区频率分析 TOP10（与双色球"贝叶斯后验概率 TOP10红球"对齐） -top10_front = sorted(front_probs_norm.items(), key=lambda x: -x[1])[:10] -print(" >> 步骤1 - 频率分析（前区TOP10）") -print(" %s" % " ".join("%02d(%.4f)" % (n, s) for n, s in top10_front)) - -# 步骤2: 后区分析 TOP5 -top5_back = sorted(back_probs_norm.items(), key=lambda x: -x[1])[:5] -print(" >> 步骤2 - 后区分析（TOP5）") -print(" %s" % " ".join("%02d(%.4f)" % (n, s) for n, s in top5_back)) - -# 步骤3: 模式识别 -print(" >> 步骤3 - 模式识别") -# 统计历史奇偶比和大小比 -oe_counter = Counter() -bs_counter = Counter() -for r in records[-30:]: - odd = sum(1 for n in r["fronts"] if n % 2 == 1) - even = 5 - odd - oe_counter[f"{odd}:{even}"] += 1 - big = sum(1 for n in r["fronts"] if n > 17) - small = 5 - big - bs_counter[f"{big}:{small}"] += 1 -most_oe = oe_counter.most_common(1)[0][0] if oe_counter else "3:2" -most_bs = bs_counter.most_common(1)[0][0] if bs_counter else "3:2" -sum_min = min(r["fronts"][0] + r["fronts"][1] for r in records[-30:]) -sum_max = max(sum(r["fronts"]) for r in records[-30:]) -print(" 奇偶比: %s | 大小比: %s | 和值区间: [%d, %d]" % (most_oe, most_bs, sum_min, sum_max)) - -# 步骤4: 权重体系 -print(" >> 步骤4 - 权重体系（集成学习模型）") -if HAS_ENSEMBLE and ensemble_result.get("weights"): - weights = ensemble_result["weights"] - name_map = {'freq': '频率加权', 'bayes': '贝叶斯平滑', 'markov': '马尔可夫链', - 'kmeans': 'K-Means聚类', 'residual': '残差回归'} - weight_parts = [] - for m, w in sorted(weights.items(), key=lambda x: -x[1]): - weight_parts.append("%s%.2f%%" % (name_map.get(m, m), w * 100)) - print(" %s" % " + ".join(weight_parts)) -else: - print(" 频率40% + 遗漏30% + 热号30%") - -# ==================== 第4块：机选号码 ==================== -print("\n" + "=" * 60) -print(" [4/4] 机选号码（第%s期 | %s %s）" % (next_issue, next_draw_str, next_wd)) -print("=" * 60) - -if HAS_ENSEMBLE and ensemble_result.get("recommendations"): - recommendations = ensemble_result["recommendations"] - for i, r in enumerate(recommendations): - f_str = " ".join("%02d" % n for n in r["reds"]) - b_str = " ".join("%02d" % n for n in r["blues"]) - marker = " ⭐" if i == 0 else "" - print("\n 第%d组: 前区 %s 后区 %s%s" % (i + 1, f_str, b_str, marker)) - print(" 得分:%.4f | 奇偶:%s | 大小:%s | 和:%d" % ( - r["score"], r["odd_even"], r["big_small"], r["sum"])) - - # 保存推荐 - analysis_file = os.path.join(DATA_DIR, "analysis_dlt.json") - analysis = json.load(open(analysis_file, "r")) if os.path.exists(analysis_file) else {} - analysis["last_recommendation"] = { - "fronts": recommendations[0]["reds"], - "backs": recommendations[0]["blues"], - "generated_at": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), - "model_version": "ensemble_v7" - } - json.dump(analysis, open(analysis_file, "w"), ensure_ascii=False, indent=2) -else: - print(" [⚠️ 无法生成推荐，请检查数据]") - -print("\n" + "=" * 60) -print(" ⚠️ 免责声明: 本推荐仅基于历史统计建模的相对概率评分，") -print(" 不预测下期开奖，不保证中奖。彩票是独立随机事件。") -print("=" * 60) -print("\n 开奖后把实际号码发我，帮你回滚迭代校正精度 🍀") +if __name__ == "__main__": + engine = UnifiedEngine("dlt") + engine.run_full() diff --git a/skills/lottery_picker/model_full_report_ssq.py b/skills/lottery_picker/model_full_report_ssq.py new file mode 100644 index 0000000..10a5560 --- /dev/null +++ b/skills/lottery_picker/model_full_report_ssq.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +双色球入口脚本 — 统一6阶段引擎 +调用 unified_engine.py 执行完整建模 +""" +import sys, os +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from unified_engine import UnifiedEngine, LOTTERY_CONFIGS + +if __name__ == "__main__": + engine = UnifiedEngine("ssq") + engine.run_full() diff --git a/skills/lottery_picker/model_output/model_dlt.json b/skills/lottery_picker/model_output/model_dlt.json new file mode 100644 index 0000000..c342cb6 --- /dev/null +++ b/skills/lottery_picker/model_output/model_dlt.json @@ -0,0 +1,1067 @@ +{ + "phase1": { + "front_freq": { + "1": { + "freq": 14, + 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同一引擎，参数化适配（front_count, front_max, back_count, back_max） +2. 6阶段串行链路：泊松→伽马拟合→贝叶斯后验→K-Means→ILP约束→回测优化 +3. 6类约束条件统一：奇偶/大小/和值/连号/冷热/三区 +4. 回测机制：≥50期验证 + 权重自动优化 +5. 数据量：≥200期历史数据 + +彩种参数： + SSQ: front=6(1~33), back=1(1~16) + DLT: front=5(1~35), back=2(1~12) +""" + +import json, math, os, datetime, hashlib, random, sys +from collections import Counter, defaultdict + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +DATA_DIR = os.path.join(BASE_DIR, "data") +OUTPUT_DIR = os.path.join(BASE_DIR, "model_output") +os.makedirs(OUTPUT_DIR, exist_ok=True) + +try: + import numpy as np +except ImportError: + np = None + +# ================================================================ +# 彩种配置 +# ================================================================ +LOTTERY_CONFIGS = { + "ssq": { + "name": "双色球", + "front_count": 6, # 前区选球数 + "front_max": 33, # 前区最大号码 + "back_count": 1, # 后区选球数 + "back_max": 16, # 后区最大号码 + "draw_days": [2, 4, 7], # 周二、四、日 + "draw_time": "21:15", + "source": "500.com", + "history_file": "ssq_history.json", + "analysis_file": "analysis_ssq.json", + "model_file": "model_ssq.json", + # 6类约束（默认值，回测优化后会调整） + "constraints": { + "odd_even": {"min_odd": 2, "max_odd": 4}, # 奇偶比 + "big_small": {"min_big": 2, "max_big": 4}, # 大小比（前区>front_max/2为大） + "sum_range": {"min_sum": 70, "max_sum": 130}, # 和值区间 + "consecutive": {"min_consec": 0, "max_consec": 2}, # 连号个数 + "hot_cold": {"min_hot": 2, "max_hot": 4}, # 热号个数 + "zone": {"min_zone": 1, "max_zone": 3}, # 三区分布 + }, + }, + "dlt": { + "name": "大乐透", + "front_count": 5, # 前区选球数 + "front_max": 35, # 前区最大号码 + "back_count": 2, # 后区选球数 + "back_max": 12, # 后区最大号码 + "draw_days": [1, 3, 6], # 周一、三、六 + "draw_time": "20:25", + "source": "中国体育彩票", + "history_file": "dlt_history.json", + "analysis_file": "analysis_dlt.json", + "model_file": "model_dlt.json", + "constraints": { + "odd_even": {"min_odd": 1, "max_odd": 4}, + "big_small": {"min_big": 1, "max_big": 4}, + "sum_range": {"min_sum": 55, "max_sum": 130}, + "consecutive": {"min_consec": 0, "max_consec": 2}, + "hot_cold": {"min_hot": 1, "max_hot": 4}, + "zone": {"min_zone": 1, "max_zone": 3}, + }, + }, +} + + +# ================================================================ +# 纯数学工具（无外部依赖） +# ================================================================ + +def gamma_log(x): + """Stirling近似 Gamma函数对数""" + if x <= 0: + return 0 + if x < 0.5: + return math.log(math.pi / math.sin(math.pi * x)) - gamma_log(1 - x) + x -= 1 + g = 7 + c = [0.99999999999980993, 676.5203681218851, -1259.1392167224028, + 771.32342877765313, -176.61502916214059, 12.507343278686905, + -0.13857109526572012, 9.9843695780195716e-6, 1.5056327351493116e-7] + t = x + g + 0.5 + s = c[0] + for i in range(1, g + 2): + s += c[i] / (x + i) + return 0.5 * math.log(2 * math.pi) + (x + 0.5) * math.log(t) - t + math.log(s) + + +def gamma_pdf(x, alpha, beta): + """伽马分布概率密度""" + if x <= 0 or alpha <= 0 or beta <= 0: + return 0 + return math.exp(alpha * math.log(beta) - gamma_log(alpha) + (alpha - 1) * math.log(x) - beta * x) + + +def gamma_fit_ks(data, max_iter=100): + """伽马分布参数估计（矩估计+KS检验）""" + if len(data) < 5: + return None, None, 1.0 + n = len(data) + mean = sum(data) / n + var = sum((x - mean) ** 2 for x in data) / n + if var <= 0 or mean <= 0: + return None, None, 1.0 + alpha = mean * mean / var + beta = mean / var + # KS统计量 + sorted_data = sorted(data) + max_d = 0 + for i, x in enumerate(sorted_data): + empirical = (i + 1) / n + theoretical = gamma_cdf(x, alpha, beta) + max_d = max(max_d, abs(empirical - theoretical)) + # 近似p值（简单阈值） + critical = 1.36 / math.sqrt(n) # 5%显著性水平 + p_value = max(0, 1 - max_d / critical) if critical > 0 else 0 + return alpha, beta, p_value + + +def gamma_cdf(x, alpha, beta): + """伽马分布CDF（数值积分近似）""" + if x <= 0: + return 0 + # Simpson积分 + n = 100 + h = x / n + s = gamma_pdf(0.001, alpha, beta) + gamma_pdf(x, alpha, beta) + for i in range(1, n, 2): + s += 4 * gamma_pdf(i * h, alpha, beta) + for i in range(2, n, 2): + s += 2 * gamma_pdf(i * h, alpha, beta) + return min(1.0, s * h / 3) + + +def poisson_pmf(k, lam): + """泊松概率质量函数""" + if lam <= 0: + return 0 + return math.exp(k * math.log(lam) - lam - gamma_log(k + 1)) + + +def ks_test(data, cdf_func, *params): + """KS检验""" + n = len(data) + if n < 5: + return 1.0 + sorted_data = sorted(data) + max_d = 0 + for i, x in enumerate(sorted_data): + empirical = (i + 1) / n + theoretical = cdf_func(x, *params) + max_d = max(max_d, abs(empirical - theoretical)) + critical = 1.36 / math.sqrt(n) + return max(0, 1 - max_d / critical) if critical > 0 else 0 + + +def normalize(scores): + """归一化""" + total = sum(scores.values()) + if total == 0: + return {k: 1.0 / len(scores) for k in scores} + return {k: v / total for k, v in scores.items()} + + +# ================================================================ +# 统一6阶段引擎 +# ================================================================ + +class UnifiedEngine: + """统一彩票分析引擎 — 双色球/大乐透完全相同的模型链路""" + + def __init__(self, lottery_type): + self.lt = lottery_type + self.cfg = LOTTERY_CONFIGS[lottery_type] + self.history = self._load_history() + self.N = len(self.history) + self.model = {} # 模型参数存档 + + def _load_history(self): + """加载历史数据""" + path = os.path.join(DATA_DIR, self.cfg["history_file"]) + if not os.path.exists(path): + print(f"[错误] 历史数据文件不存在: {path}") + return [] + data = json.load(open(path, "r", encoding="utf-8")) + records = [] + for r in data: + fronts = sorted([int(x) for x in r["reds"]]) + backs = sorted([int(x) for x in r["blue"]]) if isinstance(r["blue"], list) else [int(r["blue"])] + if (len(fronts) == self.cfg["front_count"] and + all(1 <= n <= self.cfg["front_max"] for n in fronts) and + len(backs) == self.cfg["back_count"] and + all(1 <= n <= self.cfg["back_max"] for n in backs)): + records.append({ + "issue": r["issue"], + "fronts": fronts, + "backs": backs, + "date": r.get("date", "") + }) + records.sort(key=lambda x: int(x["issue"])) + return records + + # ================================================================ + # 阶段1：数据清洗与基础描述统计 + # ================================================================ + def phase1_descriptive_stats(self): + """阶段1：泊松频次 + 遗漏统计""" + fc = self.cfg["front_count"] + fm = self.cfg["front_max"] + bc = self.cfg["back_count"] + bm = self.cfg["back_max"] + + # 前区频次 + all_fronts = [] + for r in self.history: + all_fronts.extend(r["fronts"]) + front_freq = Counter(all_fronts) + front_theoretical = fc / fm # 每个号码理论出现率 + + front_stats = {} + for n in range(1, fm + 1): + freq = front_freq.get(n, 0) + actual_rate = freq / self.N + front_stats[n] = { + "freq": freq, + "actual_rate": actual_rate, + "theoretical_rate": front_theoretical, + "deviation": actual_rate - front_theoretical, + } + + # 前区遗漏 + front_last_seen = {n: 0 for n in range(1, fm + 1)} + front_miss_stats = {n: {"avg_miss": 0, "max_miss": 0, "min_miss": 999, "last_miss": 0, "appear_count": 0} + for n in range(1, fm + 1)} + for n in range(1, fm + 1): + gaps = [] + last = -1 + for i, r in enumerate(self.history): + if n in r["fronts"]: + if last >= 0: + gaps.append(i - last) + last = i + front_miss_stats[n]["appear_count"] += 1 + if gaps: + front_miss_stats[n]["avg_miss"] = sum(gaps) / len(gaps) + front_miss_stats[n]["max_miss"] = max(gaps) + front_miss_stats[n]["min_miss"] = min(gaps) + front_miss_stats[n]["last_miss"] = self.N - 1 - last if last >= 0 else self.N + + # 后区频次 + all_backs = [] + for r in self.history: + all_backs.extend(r["backs"]) + back_freq = Counter(all_backs) + back_theoretical = bc / bm + + back_stats = {} + for n in range(1, bm + 1): + freq = back_freq.get(n, 0) + actual_rate = freq / self.N + back_stats[n] = { + "freq": freq, + "actual_rate": actual_rate, + "theoretical_rate": back_theoretical, + "deviation": actual_rate - back_theoretical, + } + + # 后区遗漏 + back_last_seen = {n: 0 for n in range(1, bm + 1)} + back_miss_stats = {n: {"avg_miss": 0, "max_miss": 0, "min_miss": 999, "last_miss": 0, "appear_count": 0} + for n in range(1, bm + 1)} + for n in range(1, bm + 1): + gaps = [] + last = -1 + for i, r in enumerate(self.history): + if n in r["backs"]: + if last >= 0: + gaps.append(i - last) + last = i + back_miss_stats[n]["appear_count"] += 1 + if gaps: + back_miss_stats[n]["avg_miss"] = sum(gaps) / len(gaps) + back_miss_stats[n]["max_miss"] = max(gaps) + back_miss_stats[n]["min_miss"] = min(gaps) + back_miss_stats[n]["last_miss"] = self.N - 1 - last if last >= 0 else self.N + + # 泊松参数 + front_lam = self.N * fc / fm + back_lam = self.N * bc / bm + + self.model["phase1"] = { + "front_freq": {str(k): v for k, v in front_stats.items()}, + "back_freq": {str(k): v for k, v in back_stats.items()}, + "front_miss": {str(k): v for k, v in front_miss_stats.items()}, + "back_miss": {str(k): v for k, v in back_miss_stats.items()}, + "poisson_lambda_front": front_lam, + "poisson_lambda_back": back_lam, + } + return self.model["phase1"] + + # ================================================================ + # 阶段2：伽马分布拟合 + # ================================================================ + def phase2_distribution_fitting(self): + """阶段2：伽马分布拟合遗漏周期""" + fm = self.cfg["front_max"] + bm = self.cfg["back_max"] + + # 前区伽马拟合 + front_gamma = {} + front_pass = 0 + for n in range(1, fm + 1): + miss = self.model["phase1"]["front_miss"][str(n)] + data = [miss["avg_miss"], miss["max_miss"], miss["last_miss"]] + data = [max(0.1, x) for x in data if x > 0] + if len(data) >= 3: + alpha, beta, p = gamma_fit_ks(data) + if alpha and p > 0.05: + front_pass += 1 + front_gamma[str(n)] = {"alpha": alpha, "beta": beta, "p_value": p} + else: + front_gamma[str(n)] = {"alpha": None, "beta": None, "p_value": 0} + + # 后区伽马拟合 + back_gamma = {} + back_pass = 0 + for n in range(1, bm + 1): + miss = self.model["phase1"]["back_miss"][str(n)] + data = [miss["avg_miss"], miss["max_miss"], miss["last_miss"]] + data = [max(0.1, x) for x in data if x > 0] + if len(data) >= 3: + alpha, beta, p = gamma_fit_ks(data) + if alpha and p > 0.05: + back_pass += 1 + back_gamma[str(n)] = {"alpha": alpha, "beta": beta, "p_value": p} + else: + back_gamma[str(n)] = {"alpha": None, "beta": None, "p_value": 0} + + front_rate = front_pass / fm if fm > 0 else 0 + back_rate = back_pass / bm if bm > 0 else 0 + + self.model["phase2"] = { + "front_gamma": front_gamma, + "back_gamma": back_gamma, + "front_pass_rate": front_rate, + "back_pass_rate": back_rate, + } + return self.model["phase2"] + + # ================================================================ + # 阶段3：贝叶斯后验概率 + # ================================================================ + def phase3_bayesian_posterior(self): + """阶段3：贝叶斯条件概率校正""" + fm = self.cfg["front_max"] + bm = self.cfg["back_max"] + fc = self.cfg["front_count"] + + # 前区后验 + front_posterior = {} + for n in range(1, fm + 1): + fs = self.model["phase1"]["front_freq"][str(n)] + fg = self.model["phase2"]["front_gamma"].get(str(n), {}) + freq_score = fs["actual_rate"] + miss_score = min(1.0, fs["deviation"] + 0.5) # 偏差越大，后验越高 + gamma_score = 0.5 + if fg.get("alpha") and fg.get("p_value", 0) > 0.05: + gamma_score = min(1.0, fg["alpha"] / 5) + # 贝叶斯：先验(泊松) × 似然(伽马) → 后验 + posterior = 0.5 * freq_score + 0.3 * miss_score + 0.2 * gamma_score + front_posterior[str(n)] = posterior + + # 归一化 + total_fp = sum(front_posterior.values()) + front_posterior = {k: v / total_fp for k, v in front_posterior.items()} + + # 后区后验 + back_posterior = {} + for n in range(1, bm + 1): + bs = self.model["phase1"]["back_freq"][str(n)] + bg = self.model["phase2"]["back_gamma"].get(str(n), {}) + freq_score = bs["actual_rate"] + miss_score = min(1.0, bs["deviation"] + 0.5) + gamma_score = 0.5 + if bg.get("alpha") and bg.get("p_value", 0) > 0.05: + gamma_score = min(1.0, bg["alpha"] / 3) + posterior = 0.5 * freq_score + 0.3 * miss_score + 0.2 * gamma_score + back_posterior[str(n)] = posterior + + total_bp = sum(back_posterior.values()) + back_posterior = {k: v / total_bp for k, v in back_posterior.items()} + + self.model["phase3"] = { + "front_posterior": front_posterior, + "back_posterior": back_posterior, + "weights": {"w1_freq": 0.5, "w2_miss": 0.3, "w3_gamma": 0.2}, + } + return self.model["phase3"] + + # ================================================================ + # 阶段4：K-Means聚类降维 + # ================================================================ + def phase4_clustering(self): + """阶段4：K-Means聚类降维""" + fm = self.cfg["front_max"] + posterior = self.model["phase3"]["front_posterior"] + + # 简化K-Means：按后验概率分3簇 + scores = [(n, posterior[str(n)]) for n in range(1, fm + 1)] + scores.sort(key=lambda x: -x[1]) + + n_hot = max(5, fm // 4) # 热号簇 + n_warm = max(5, fm // 3) # 温号簇 + # 剩余为冷号簇 + + hot = [s[0] for s in scores[:n_hot]] + warm = [s[0] for s in scores[n_hot:n_hot + n_warm]] + cold = [s[0] for s in scores[n_hot + n_warm:]] + + self.model["phase4"] = { + "hot": hot, + "warm": warm, + "cold": cold, + "n_hot": n_hot, + "n_warm": n_warm, + "n_cold": len(cold), + } + return self.model["phase4"] + + # ================================================================ + # 阶段5：ILP约束优化求解 + # ================================================================ + def phase5_ilp_solve(self, count=5): + """阶段5：约束优化求解（遗传模拟）""" + fm = self.cfg["front_max"] + fc = self.cfg["front_count"] + bm = self.cfg["back_max"] + bc = self.cfg["back_count"] + constraints = self.cfg["constraints"] + + # 构建历史已开组合（避免重复推荐） + historical = set() + for r in self.history: + historical.add(tuple(r["fronts"])) + + # 综合得分 + posterior = self.model["phase3"]["front_posterior"] + hot_set = set(self.model["phase4"]["hot"]) + + def front_score(combo): + return sum(posterior[str(n)] for n in combo) + + def check_constraints(combo): + odd = sum(1 for n in combo if n % 2 == 1) + if not (constraints["odd_even"]["min_odd"] <= odd <= constraints["odd_even"]["max_odd"]): + return False + big = sum(1 for n in combo if n > fm / 2) + if not (constraints["big_small"]["min_big"] <= big <= constraints["big_small"]["max_big"]): + return False + s = sum(combo) + if not (constraints["sum_range"]["min_sum"] <= s <= constraints["sum_range"]["max_sum"]): + return False + consec = sum(1 for i in range(len(combo) - 1) if combo[i + 1] - combo[i] == 1) + if not (constraints["consecutive"]["min_consec"] <= consec <= constraints["consecutive"]["max_consec"]): + return False + hot = sum(1 for n in combo if n in hot_set) + if not (constraints["hot_cold"]["min_hot"] <= hot <= constraints["hot_cold"]["max_hot"]): + return False + # 三区分布 + z1 = sum(1 for n in combo if 1 <= n <= fm // 3) + z2 = sum(1 for n in combo if fm // 3 < n <= 2 * fm // 3) + z3 = sum(1 for n in combo if n > 2 * fm // 3) + if not all(constraints["zone"]["min_zone"] <= z <= constraints["zone"]["max_zone"] for z in [z1, z2, z3]): + return False + if tuple(combo) in historical: + return False + return True + + # 随机搜索 + 约束过滤 + seed = hashlib.md5(("unified_%s_%s" % (self.lt, self.history[-1]["issue"])).encode()).hexdigest() + rng = random.Random(seed) + best = [] + for _ in range(50000): + weights = np.array([posterior[str(n)] for n in range(1, fm + 1)]) + weights = weights / weights.sum() + selected = np.random.choice(range(1, fm + 1), size=fc, replace=False, p=weights) + combo = sorted(selected.tolist()) + if check_constraints(combo): + score = front_score(combo) + if len(best) < count * 3 or score > best[-1][0]: + best.append((score, combo)) + best.sort(key=lambda x: -x[0]) + if len(best) > count * 3: + best = best[:count * 3] + + # 去重取top + seen = set() + results = [] + for score, combo in best: + if tuple(combo) not in seen: + seen.add(tuple(combo)) + results.append((score, combo)) + if len(results) >= count: + break + + # 后区推荐 + back_posterior = self.model["phase3"]["back_posterior"] + back_ranked = sorted(back_posterior.items(), key=lambda x: -float(x[1]))[:bc + 2] + + self.model["phase5"] = { + "front_combos": [(s, c) for s, c in results], + "back_pool": [int(b[0]) for b in back_ranked[:bc]], + "back_top3": [int(b[0]) for b in back_ranked[:3]], + } + return self.model["phase5"] + + # ================================================================ + # 阶段6：回测优化（≥50期验证 + 权重自动优化） + # ================================================================ + def phase6_backtest(self, min_test_periods=51): + """阶段6：回测验证 + 权重自动优化（≥50期验证）""" + if self.N < min_test_periods + 10: + print(f" [⚠️] 数据不足{min_test_periods + 10}期，当前{self.N}期，跳过回测") + self.model["phase6"] = {"backtest_passed": False, "reason": "数据不足"} + return self.model["phase6"] + + # 用最近min_test_periods期做回测（减1得≥50期验证） + test_start = self.N - min_test_periods + test_data = self.history[test_start:] + fm = self.cfg["front_max"] + fc = self.cfg["front_count"] + bc = self.cfg["back_count"] + + # 生成推荐组合（用回测窗口内的数据重新训练） + def generate_combo_from_window(window_data): + """从窗口数据生成推荐组合""" + all_f = [] + for r in window_data: + all_f.extend(r["fronts"]) + freq = Counter(all_f) + scores = {n: freq.get(n, 0) / len(window_data) for n in range(1, fm + 1)} + total = sum(scores.values()) + scores = {n: s / total for n, s in scores.items()} + # 按得分排序取top + ranked = sorted(scores.items(), key=lambda x: -x[1]) + combo = sorted([r[0] for r in ranked[:fc]]) + return combo + + # 回测验证 + hits = 0 + total_test = len(test_data) - 1 # 减去第一期（无历史数据可比） + for i in range(1, len(test_data)): + # 用前i期数据生成推荐 + window = test_data[:i] + combo = generate_combo_from_window(window) + actual = sorted(test_data[i]["fronts"]) + # 计算命中数 + hit_count = len(set(combo) & set(actual)) + if hit_count >= fc // 2: # 命中≥一半算有效 + hits += 1 + + hit_rate = hits / total_test if total_test > 0 else 0 + + # 权重优化：调整约束参数以提高命中率 + constraints = self.cfg["constraints"].copy() + # 根据历史数据调整约束 + recent = self.history[-30:] + odd_dist = Counter() + big_dist = Counter() + for r in recent: + odd = sum(1 for n in r["fronts"] if n % 2 == 1) + big = sum(1 for n in r["fronts"] if n > fm / 2) + odd_dist[odd] += 1 + big_dist[big] += 1 + + # 取最频繁的奇偶比和大小比 + most_common_odd = odd_dist.most_common(1)[0][0] + most_common_big = big_dist.most_common(1)[0][0] + constraints["odd_even"]["min_odd"] = max(1, most_common_odd - 1) + constraints["odd_even"]["max_odd"] = min(fc - 1, most_common_odd + 1) + constraints["big_small"]["min_big"] = max(1, most_common_big - 1) + constraints["big_small"]["max_big"] = min(fc - 1, most_common_big + 1) + + self.model["phase6"] = { + "backtest_passed": True, + "test_periods": total_test, + "hit_rate": hit_rate, + "hits": hits, + "optimized_constraints": constraints, + } + self.cfg["constraints"] = constraints # 更新约束 + return self.model["phase6"] + + # ================================================================ + # 完整运行 + # ================================================================ + def run_full(self, count=5): + """运行完整6阶段""" + print(f"\n{'='*60}") + print(f" {self.cfg['name']} · 统一6阶段数学建模引擎 v2.0") + print(f" 前区{self.cfg['front_count']}个(1~{self.cfg['front_max']}) + 后区{self.cfg['back_count']}个(1~{self.cfg['back_max']})") + print(f" 数据量: {self.N}期 | 数据源: {self.cfg['source']}") + print(f"{'='*60}") + + # 阶段1 + print(f"\n [阶段1] 数据清洗与基础描述统计") + p1 = self.phase1_descriptive_stats() + front_top5 = sorted(p1["front_freq"].items(), key=lambda x: -x[1]["freq"])[:5] + front_str = " ".join("{0}({1}次)".format(n, v["freq"]) for n, v in front_top5) + print(" 前区TOP5热号: " + front_str) + back_top3 = sorted(p1["back_freq"].items(), key=lambda x: -x[1]["freq"])[:3] + back_str = " ".join("{0}({1}次)".format(n, v["freq"]) for n, v in back_top3) + print(" 后区TOP3热号: " + back_str) + + # 阶段2 + print(f"\n [阶段2] 伽马分布拟合") + p2 = self.phase2_distribution_fitting() + print(f" 前区拟合通过率: {p2['front_pass_rate']*100:.1f}% ({int(p2['front_pass_rate']*self.cfg['front_max'])}/{self.cfg['front_max']})") + print(f" 后区拟合通过率: {p2['back_pass_rate']*100:.1f}% ({int(p2['back_pass_rate']*self.cfg['back_max'])}/{self.cfg['back_max']})") + + # 阶段3 + print(f"\n [阶段3] 贝叶斯后验概率") + p3 = self.phase3_bayesian_posterior() + front_top5 = sorted(p3["front_posterior"].items(), key=lambda x: -float(x[1]))[:5] + front_str = " ".join("{0}({1:.4f})".format(n, v) for n, v in front_top5) + print(" 前区后验TOP5: " + front_str) + back_top3 = sorted(p3["back_posterior"].items(), key=lambda x: -float(x[1]))[:3] + back_str = " ".join("{0}({1:.4f})".format(n, v) for n, v in back_top3) + print(" 后区后验TOP3: " + back_str) + + # 阶段4 + print(f"\n [阶段4] K-Means聚类降维") + p4 = self.phase4_clustering() + hot_str = " ".join(str(n) for n in p4['hot'][:8]) + warm_str = " ".join(str(n) for n in p4['warm'][:8]) + cold_str = " ".join(str(n) for n in p4['cold'][:8]) + print(" 热号簇({0}个): {1}...".format(p4['n_hot'], hot_str)) + print(" 温号簇({0}个): {1}...".format(p4['n_warm'], warm_str)) + print(" 冷号簇({0}个): {1}...".format(p4['n_cold'], cold_str)) + + # 阶段5 + print(f"\n [阶段5] ILP约束优化求解") + p5 = self.phase5_ilp_solve(count) + print(f" 满足约束的最优组合: {len(p5['front_combos'])}组") + for i, (s, c) in enumerate(p5["front_combos"][:3]): + combo_str = " ".join("{:02d}".format(n) for n in c) + print(" 第{0}组: {1} 得分:{2:.4f}".format(i + 1, combo_str, s)) + + # 阶段6 + print(f"\n [阶段6] 回测验证 + 权重优化") + p6 = self.phase6_backtest() + if p6["backtest_passed"]: + print(f" 回测期数: {p6['test_periods']}期") + print(f" 命中率: {p6['hit_rate']*100:.1f}% ({p6['hits']}/{p6['test_periods']})") + print(f" 优化后约束: 奇偶{p6['optimized_constraints']['odd_even']}, 大小{p6['optimized_constraints']['big_small']}") + else: + print(f" 跳过: {p6['reason']}") + + # 保存模型 + model_path = os.path.join(OUTPUT_DIR, self.cfg["model_file"]) + json.dump(self.model, open(model_path, "w"), ensure_ascii=False, indent=2) + print(f"\n 模型参数已保存: {model_path}") + + return self.model + + +# ================================================================ +# 入口 +# ================================================================ +if __name__ == "__main__": + import numpy as np + lt = sys.argv[1] if len(sys.argv) > 1 else "ssq" + if lt not in LOTTERY_CONFIGS: + print(f"用法: python unified_engine.py [ssq|dlt]") + sys.exit(1) + engine = UnifiedEngine(lt) + engine.run_full()