openclaw
/
cyb50-quant


			
				
					
						
						
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							#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
CYB50市场状态识别 - 每日邮件发送脚本
通过本地SMTP服务器(直接投递模式)发送邮件
"""

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 smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.header import Header
from datetime import datetime
import os
import warnings
warnings.filterwarnings('ignore')

# SMTP配置
SMTP_SERVER = os.getenv('SMTP_SERVER', 'localhost')
SMTP_PORT = int(os.getenv('SMTP_PORT', '25'))
SENDER = os.getenv('SMTP_SENDER', 'kalman@erwin.wang')
RECEIVER = '380880504@qq.com'

print("="*60)
print(f"CYB50每日市场状态报告 - {datetime.now().strftime('%Y-%m-%d %H:%M')}")
print(f"SMTP: {SMTP_SERVER}:{SMTP_PORT}")
print("="*60)

# 获取数据
df = fetch_cyb50_data('2024-01-01', '2026-12-31')
if df is None:
    print("数据获取失败")
    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]

# 获取最新数据
today = df_aligned.iloc[-1]
yesterday = df_aligned.iloc[-2] if len(df_aligned) > 1 else today

state_names = ['震荡', '趋势', '反转']
colors = ['#2196F3', '#4CAF50', '#FF5722']
state_name = state_names[int(today['state'])]
state_color = colors[int(today['state'])]

# 计算涨跌
daily_change = today['close'] - yesterday['close']
daily_change_pct = daily_change / yesterday['close'] * 100

# 获取最近30天
last_30 = df_aligned.tail(30).copy()

# 生成HTML表格
html_rows = ""
for idx, row in last_30.iterrows():
    s = int(row['state'])
    html_rows += f"""
        <tr>
            <td>{idx.strftime('%m-%d')}</td>
            <td>{row['close']:.2f}</td>
            <td style="color: {colors[s]}; font-weight: bold;">{state_names[s]}</td>
            <td>{row['prob_ranging']:.1%}</td>
            <td>{row['prob_trend']:.1%}</td>
            <td>{row['prob_reversal']:.1%}</td>
        </tr>
    """

# 邮件内容
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; }}
        .today {{ background: #e3f2fd; padding: 15px; border-radius: 5px; margin: 20px 0; border-left: 4px solid #2196F3; }}
        table {{ width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 11px; }}
        th {{ background: #2196F3; color: white; padding: 8px; text-align: center; }}
        td {{ padding: 6px 8px; border-bottom: 1px solid #ddd; text-align: center; }}
        tr:nth-child(even) {{ background: #f8f9fa; }}
    </style>
</head>
<body>
    <h1>CYB50每日市场状态报告</h1>

    <div class="today">
        <h2>今日状态 ({df_aligned.index[-1].strftime('%Y-%m-%d')})</h2>
        <p><strong>收盘价:</strong> {today['close']:.2f}</p>
        <p><strong>日涨跌:</strong> <span style="color: {'green' if daily_change >= 0 else 'red'};">{daily_change:+.2f} ({daily_change_pct:+.2f}%)</span></p>
        <p><strong>市场状态:</strong> <span style="color: {state_color}; font-size: 16px; font-weight: bold;">{state_name}</span></p>
        <p><strong>状态概率:</strong> 震荡 {today['prob_ranging']:.1%} / 趋势 {today['prob_trend']:.1%} / 反转 {today['prob_reversal']:.1%}</p>
    </div>

    <h2>最近30天数据</h2>
    <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>
            </tr>
        </thead>
        <tbody>
            {html_rows}
        </tbody>
    </table>

    <hr>
    <p style="color: #666; font-size: 11px;">
        生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}<br>
        数据更新至: {df_aligned.index[-1].strftime('%Y-%m-%d')}<br>
        模型准确率: 72.10% | 创业板50指数 (sz399673)
    </p>
</body>
</html>
"""

# 发送邮件
msg = MIMEMultipart('related')
msg['Subject'] = Header(f"CYB50-Regime每日报告 [{df_aligned.index[-1].strftime('%m-%d')}] 当前{state_name}", 'utf-8')
msg['From'] = f"regime <{SENDER}>"
msg['To'] = RECEIVER
msg.attach(MIMEText(html, 'html', 'utf-8'))

try:
    with smtplib.SMTP(SMTP_SERVER, SMTP_PORT, timeout=10) as server:
        server.sendmail(SENDER, RECEIVER, msg.as_string())
    print(f"[OK] 邮件发送成功! [{df_aligned.index[-1].strftime('%Y-%m-%d')}] 当前状态: {state_name}")
except Exception as e:
    print(f"[ERROR] 邮件发送失败: {e}")
    print(f"请确保SMTP服务器已启动: python smtp_relay_server.py --direct")

print("="*60)