Skill: ligo-gao/ssq-predictor (version 1.0.0)

--- name: ssq-predictor description: 双色球彩票预测工具，基于历史数据分析生成区间平衡的号码推荐。使用冷热号分析、遗漏值追踪、区间分布优化等算法，生成符合历史统计规律的预测号码。当用户需要双色球号码预测、彩票号码分析、或历史开奖数据统计时使用此Skill。 version: 1.0.0 --- # 双色球预测器 (SSQ Predictor) 基于历史开奖数据的区间平衡预测工具。 ## 核心功能 1. **特征工程分析** - 冷热号统计、遗漏值追踪、区间分布分析 2. **区间平衡预测** - 确保红球在三区(01-11, 12-22, 23-33)合理分布 3. **多组预测生成** - 提供6种不同分布模式的预测组合 ## 使用方法 ### 生成预测 ```bash cd ~/.openclaw/workspace/skills/ssq-predictor/scripts python3 ssq_predict.py ``` ### 在Python中调用 ```python from ssq_predict import generate_predictions results = generate_predictions(csv_path='ssq_history_100.csv', n_groups=6) for r in results: print(f"{r['name']}: 红球 {r['red']}, 蓝球 {r['blue']}") ``` ## 预测策略 ### 区间分布模式 | 模式 | 一区(01-11) | 二区(12-22) | 三区(23-33) | 说明 | |------|------------|------------|------------|------| | 均衡型(2-2-2) | 2个 | 2个 | 2个 | 最均衡分布 | | 偏态型(3-2-1) | 3个 | 2个 | 1个 | 偏小号 | | 偏态型(2-3-1) | 2个 | 3个 | 1个 | 偏中区 | | 偏态型(1-2-3) | 1个 | 2个 | 3个 | 偏大号 | | 偏态型(3-1-2) | 3个 | 1个 | 2个 | 偏一三区 | | 偏态型(2-1-3) | 2个 | 1个 | 3个 | 偏二三区 | ### 算法特点 1. **冷热号加权** - 近期热号增加权重 2. **遗漏值补偿** - 长期未出号码适当增加概率 3. **区间强制平衡** - 避免小号或大号过度集中 4. **随机扰动** - 保留随机性，避免过度拟合 ## 数据文件 需要双色球历史数据CSV文件，格式如下： ```csv # 双色球近100期开奖结果 # 数据来源：500彩票网 期号,红球1,红球2,红球3,红球4,红球5,红球6,蓝球,开奖日期 2026030,10,11,14,19,22,24,04,2026-03-19 ... ``` ## 注意事项 ⚠️ **仅供娱乐参考** - 双色球是完全随机抽奖，任何算法都无法保证中奖。请理性购彩，量力而行。 ## 文件结构 ``` ssq-predictor/ ├── SKILL.md └── scripts/ ├── ssq_features.py # 特征工程模块 └── ssq_predict.py # 预测算法 ```

path: scripts/ssq_features.py
content: #!/usr/bin/env python3
"""
双色球特征工程模块
提供冷热号分析、遗漏值、区间分布、奇偶比、大小比等特征
"""

import numpy as np
import pandas as pd
from collections import Counter
from datetime import datetime

class SSQFeatureEngineer:
    """双色球特征工程类"""
    
    def __init__(self, csv_path='ssq_history_100.csv'):
        self.data = self._load_data(csv_path)
        self.red_history = self.data[:, :6]
        self.blue_history = self.data[:, 6]
        
    def _load_data(self, csv_path):
        df = pd.read_csv(csv_path, skiprows=4)
        data = []
        for _, row in df.iterrows():
            red_balls = [int(row[f'红球{i}']) for i in range(1, 7)]
            blue_ball = int(row['蓝球'])
            data.append(red_balls + [blue_ball])
        return np.array(data)
    
    def get_hot_cold_numbers(self, periods=30):
        recent_data = self.data[-periods:]
        red_counter = Counter()
        blue_counter = Counter()
        
        for period in recent_data:
            for i in range(6):
                red_counter[period[i]] += 1
            blue_counter[period[6]] += 1
        
        hot_red = red_counter.most_common()
        cold_red = list(reversed(hot_red))
        hot_blue = blue_counter.most_common()
        cold_blue = list(reversed(hot_blue))
        
        return {
            'hot_red': hot_red,
            'cold_red': cold_red,
            'hot_blue': hot_blue,
            'cold_blue': cold_blue,
            'periods': periods
        }
    
    def get_missing_values(self):
        red_missing = {}
        blue_missing = {}
        
        for ball in range(1, 34):
            found = False
            for i in range(len(self.data) - 1, -1, -1):
                if ball in self.data[i, :6]:
                    red_missing[ball] = len(self.data) - 1 - i
                    found = True
                    break
            if not found:
                red_missing[ball] = len(self.data)
        
        for ball in range(1, 17):
            found = False
            for i in range(len(self.data) - 1, -1, -1):
                if ball == self.data[i, 6]:
                    blue_missing[ball] = len(self.data) - 1 - i
                    found = True
                    break
            if not found:
                blue_missing[ball] = len(self.data)
        
        return {'red_missing': red_missing, 'blue_missing': blue_missing}
    
    def get_zone_distribution(self, periods=10):
        recent_data = self.data[-periods:]
        zone_stats = {'zone1': 0, 'zone2': 0, 'zone3': 0}
        
        for period in recent_data:
            for i in range(6):
                ball = period[i]
                if 1 <= ball <= 11:
                    zone_stats['zone1'] += 1
                elif 12 <= ball <= 22:
                    zone_stats['zone2'] += 1
                else:
                    zone_stats['zone3'] += 1
        
        return zone_stats
    
    def generate_feature_vector(self):
        features = {}
        
        hot_cold = self.get_hot_cold_numbers(30)
        features['hot_red_top5'] = [x[0] for x in hot_cold['hot_red'][:5]]
        features['cold_red_top5'] = [x[0] for x in hot_cold['cold_red'][:5]]
        features['hot_blue_top3'] = [x[0] for x in hot_cold['hot_blue'][:3]]
        
        missing = self.get_missing_values()
        features['high_missing_red'] = [
            k for k, v in sorted(missing['red_missing'].items(), 
                                key=lambda x: x[1], reverse=True)[:5]
        ]
        features['high_missing_blue'] = [
            k for k, v in sorted(missing['blue_missing'].items(), 
                                key=lambda x: x[1], reverse=True)[:3]
        ]
        
        zone_dist = self.get_zone_distribution(10)
        features['zone_distribution'] = zone_dist
        features['dominant_zone'] = max(zone_dist, key=zone_dist.get)
        
        return features
    
    def print_feature_report(self):
        print("=" * 60)
        print("📊 双色球特征分析报告")
        print("=" * 60)
        
        print("\n🔥 冷热号分析 (最近30期)")
        print("-" * 40)
        hot_cold = self.get_hot_cold_numbers(30)
        print(f"热红球 TOP5: {[f'{x[0]:02d}({x[1]}次)' for x in hot_cold['hot_red'][:5]]}")
        print(f"冷红球 TOP5: {[f'{x[0]:02d}({x[1]}次)' for x in hot_cold['cold_red'][:5]]}")
        print(f"热蓝球 TOP3: {[f'{x[0]:02d}({x[1]}次)' for x in hot_cold['hot_blue'][:3]]}")
        
        print("\n⏱️ 遗漏值分析")
        print("-" * 40)
        missing = self.get_missing_values()
        high_missing_red = sorted(missing['red_missing'].items(), 
                                  key=lambda x: x[1], reverse=True)[:5]
        high_missing_blue = sorted(missing['blue_missing'].items(), 
                                   key=lambda x: x[1], reverse=True)[:3]
        print(f"红球遗漏 TOP5: {[f'{x[0]:02d}({x[1]}期)' for x in high_missing_red]}")
        print(f"蓝球遗漏 TOP3: {[f'{x[0]:02d}({x[1]}期)' for x in high_missing_blue]}")
        
        print("\n📍 区间分布 (最近10期)")
        print("-" * 40)
        zone_dist = self.get_zone_distribution(10)
        print(f"一区(01-11): {zone_dist['zone1']}次")
        print(f"二区(12-22): {zone_dist['zone2']}次")
        print(f"三区(23-33): {zone_dist['zone3']}次")
        
        print("\n" + "=" * 60)


if __name__ == '__main__':
    import os
    workspace = os.path.expanduser('~/.openclaw/workspace')
    csv_path = os.path.join(workspace, 'ssq_history_100.csv')
    engineer = SSQFeatureEngineer(csv_path)
    engineer.print_feature_report()


path: scripts/ssq_predict.py
content: #!/usr/bin/env python3
"""
区间平衡版双色球预测算法
确保区间分布和大小分布符合历史规律
"""

import numpy as np
import pandas as pd
import random
from datetime import datetime
from ssq_features import SSQFeatureEngineer

def balanced_predict(data, features, zone_dist=(2, 2, 2)):
    """
    生成区间平衡的双色球预测
    zone_dist: (一区个数, 二区个数, 三区个数)
    """
    # 基础频率统计
    freq_red = np.zeros(33)
    freq_blue = np.zeros(16)
    
    for period in data:
        for j in range(6):
            freq_red[period[j] - 1] += 1
        freq_blue[period[6] - 1] += 1
    
    # 冷热号调整
    hot_red = features['hot_red_top5']
    for ball in hot_red:
        freq_red[ball - 1] += 1.5
    
    # 遗漏值调整
    high_missing_red = features['high_missing_red']
    for ball in high_missing_red[:3]:
        freq_red[ball - 1] += 1
    
    # 添加随机扰动
    freq_red += np.random.normal(0, 0.4, 33)
    blue_scores = freq_blue + np.random.normal(0, 0.3, 16)
    
    # 按指定区间分布选择
    zones = [
        (list(range(1, 12)), zone_dist[0]),    # 一区 01-11
        (list(range(12, 23)), zone_dist[1]),   # 二区 12-22
        (list(range(23, 34)), zone_dist[2])    # 三区 23-33
    ]
    
    selected_red = []
    for zone_balls, count in zones:
        zone_scores = [(ball, freq_red[ball-1]) for ball in zone_balls]
        zone_scores.sort(key=lambda x: x[1], reverse=True)
        
        for ball, _ in zone_scores[:count]:
            selected_red.append(ball)
    
    red_balls = sorted(selected_red)
    blue_ball = int(np.argmax(blue_scores)) + 1
    
    return red_balls, blue_ball


def generate_predictions(csv_path='ssq_history_100.csv', n_groups=6):
    """生成多组区间平衡的预测"""
    engineer = SSQFeatureEngineer(csv_path)
    features = engineer.generate_feature_vector()
    data = engineer.data
    
    # 定义不同的区间分布模式
    zone_patterns = [
        (2, 2, 2),  # 均衡型
        (3, 2, 1),  # 偏态型
        (2, 3, 1),  # 偏态型
        (1, 2, 3),  # 偏态型
        (3, 1, 2),  # 偏态型
        (2, 1, 3),  # 偏态型
    ]
    
    pattern_names = [
        '均衡型(2-2-2)',
        '偏态型(3-2-1)',
        '偏态型(2-3-1)',
        '偏态型(1-2-3)',
        '偏态型(3-1-2)',
        '偏态型(2-1-3)'
    ]
    
    results = []
    for i, (pattern, name) in enumerate(zip(zone_patterns[:n_groups], pattern_names[:n_groups])):
        random.seed(100 + i * 100)
        np.random.seed(100 + i * 100)
        
        red, blue = balanced_predict(data, features, pattern)
        zone1 = sum(1 for x in red if x <= 11)
        zone2 = sum(1 for x in red if 12 <= x <= 22)
        zone3 = sum(1 for x in red if x >= 23)
        small = sum(1 for x in red if x <= 16)
        big = 6 - small
        
        results.append({
            'name': name,
            'red': red,
            'blue': blue,
            'zone_dist': f"{zone1}-{zone2}-{zone3}",
            'size_dist': f"{small}-{big}"
        })
    
    return results


def main():
    print("=" * 70)
    print("🎱 双色球区间平衡预测")
    print("=" * 70)
    print(f"预测时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
    print("=" * 70)
    
    import os
    workspace = os.path.expanduser('~/.openclaw/workspace')
    csv_path = os.path.join(workspace, 'ssq_history_100.csv')
    results = generate_predictions(csv_path=csv_path)
    
    print("\n🎯 预测结果:")
    print("-" * 70)
    
    for r in results:
        red_str = ' '.join([f'{x:02d}' for x in r['red']])
        print(f"\n{r['name']}")
        print(f"  红球: [{red_str}]")
        print(f"  蓝球: [{r['blue']:02d}]")
        print(f"  区间: {r['zone_dist']} (一区-二区-三区)")
        print(f"  大小: {r['size_dist']} (小号-大号)")
    
    print("\n" + "=" * 70)


if __name__ == '__main__':
    main()


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