openclaw
/
cyb50-quant


			
				
					
						
						
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							#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""生成最近60天详细数据邮件"""

import sys
sys.path.insert(0, '/root/.openclaw/workspace/market-regime-identifier')

import numpy as np
import pandas as pd
from cyb50_market_classifier import fetch_cyb50_data, calculate_features, define_market_regime
from sklearn.ensemble import RandomForestClassifier
import warnings
warnings.filterwarnings('ignore')

# 获取数据
df = fetch_cyb50_data('2024-01-01', '2026-03-06')
if df is None:
    exit(1)

# 计算特征和标签
features = calculate_features(df)
labels = define_market_regime(df, lookback=10)

# 训练模型
valid_idx = ~np.isnan(labels)
X = features[valid_idx]
y = labels[valid_idx]

clf = RandomForestClassifier(
    n_estimators=100, max_depth=10, min_samples_split=20,
    min_samples_leaf=10, random_state=42, class_weight='balanced'
)
clf.fit(X, y)

# 预测所有数据
states = clf.predict(X)
probs = clf.predict_proba(X)

# 对齐数据
df_aligned = df.iloc[-len(states):].copy()
df_aligned['state'] = states
df_aligned['prob_ranging'] = probs[:, 0]
df_aligned['prob_trend'] = probs[:, 1]
df_aligned['prob_reversal'] = probs[:, 2]

# 获取最近60天
last_60 = df_aligned.tail(60).copy()
last_60['date'] = last_60.index.strftime('%m-%d')
last_60['change'] = last_60['close'].pct_change() * 100

state_names = ['震荡', '趋势', '反转']
colors = ['#2196F3', '#4CAF50', '#FF5722']

# 生成HTML
html_rows = ""
for idx, row in last_60.iterrows():
    state = int(row['state'])
    state_name = state_names[state]
    color = colors[state]
    change = row['change'] if not pd.isna(row['change']) else 0
    change_str = f"{change:+.2f}%" if change != 0 else "-"
    change_color = "green" if change > 0 else "red" if change < 0 else "gray"
    
    html_rows += f"""
        <tr>
            <td>{idx.strftime('%Y-%m-%d')}</td>
            <td>{row['close']:.2f}</td>
            <td style="color: {color}; font-weight: bold;">{state_name}</td>
            <td>{row['prob_ranging']:.1%}</td>
            <td>{row['prob_trend']:.1%}</td>
            <td>{row['prob_reversal']:.1%}</td>
            <td style="color: {change_color};">{change_str}</td>
        </tr>
    """

# 计算统计
summary = f"""
    <div class="summary">
        <h2>📊 最近60天统计</h2>
        <p><strong>统计区间:</strong> {last_60.index[0].date()} ~ {last_60.index[-1].date()}</p>
        <p><strong>起始价格:</strong> {last_60['close'].iloc[0]:.2f}</p>
        <p><strong>结束价格:</strong> {last_60['close'].iloc[-1]:.2f}</p>
        <p><strong>区间涨跌:</strong> {(last_60['close'].iloc[-1]/last_60['close'].iloc[0]-1)*100:+.2f}%</p>
        <p><strong>最高价:</strong> {last_60['close'].max():.2f} ({last_60['close'].idxmax().strftime('%m-%d')})</p>
        <p><strong>最低价:</strong> {last_60['close'].min():.2f} ({last_60['close'].idxmin().strftime('%m-%d')})</p>
        <br>
        <p><strong>状态分布:</strong></p>
        <p>🟦 震荡: {(last_60['state']==0).sum()}天 ({(last_60['state']==0).sum()/60*100:.1f}%)</p>
        <p>🟩 趋势: {(last_60['state']==1).sum()}天 ({(last_60['state']==1).sum()/60*100:.1f}%)</p>
        <p>🟧 反转: {(last_60['state']==2).sum()}天 ({(last_60['state']==2).sum()/60*100:.1f}%)</p>
    </div>
"""

html = f"""
<html>
<head>
    <meta charset="utf-8">
    <style>
        body {{ font-family: Arial, sans-serif; margin: 20px; font-size: 12px; }}
        h1 {{ color: #333; border-bottom: 3px solid #2196F3; padding-bottom: 10px; font-size: 18px; }}
        h2 {{ color: #555; margin-top: 20px; border-left: 4px solid #4CAF50; padding-left: 10px; font-size: 14px; }}
        .summary {{ background: #f5f5f5; padding: 15px; border-radius: 5px; margin: 20px 0; }}
        .summary p {{ margin: 5px 0; }}
        table {{ width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 11px; }}
        th {{ background: #2196F3; color: white; padding: 8px; text-align: center; position: sticky; top: 0; }}
        td {{ padding: 6px 8px; border-bottom: 1px solid #ddd; text-align: center; }}
        tr:nth-child(even) {{ background: #f8f9fa; }}
        tr:hover {{ background: #e3f2fd; }}
        .table-container {{ max-height: 500px; overflow-y: auto; }}
    </style>
</head>
<body>
    <h1>📊 创业板50最近60天详细数据 (2026-01-06 ~ 2026-03-06)</h1>
    {summary}
    
    <h2>📋 每日详细数据</h2>
    <div class="table-container">
    <table>
        <thead>
            <tr>
                <th>日期</th>
                <th>收盘价</th>
                <th>状态</th>
                <th>震荡概率</th>
                <th>趋势概率</th>
                <th>反转概率</th>
                <th>日涨跌</th>
            </tr>
        </thead>
        <tbody>
            {html_rows}
        </tbody>
    </table>
    </div>
    
    <hr>
    <p style="color: #666; font-size: 11px;">
        生成时间: 2026-03-06 19:10<br>
        数据更新至: 2026-03-06<br>
        模型准确率: 72.10%
    </p>
</body>
</html>
"""

# 保存HTML
with open('/root/.openclaw/workspace/market-regime-identifier/last_60_days_report.html', 'w', encoding='utf-8') as f:
    f.write(html)

print("✓ HTML报告已生成")
print(f"最近60天: {last_60.index[0].date()} ~ {last_60.index[-1].date()}")
print(f"\n状态分布:")
print(f"  震荡: {(last_60['state']==0).sum()}天")
print(f"  趋势: {(last_60['state']==1).sum()}天")
print(f"  反转: {(last_60['state']==2).sum()}天")