#!/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"""
| {idx.strftime('%m-%d')} |
{row['close']:.2f} |
{state_names[s]} |
{row['prob_ranging']:.1%} |
{row['prob_trend']:.1%} |
{row['prob_reversal']:.1%} |
"""
# 邮件内容
html = f"""
CYB50每日市场状态报告
今日状态 ({df_aligned.index[-1].strftime('%Y-%m-%d')})
收盘价: {today['close']:.2f}
日涨跌: {daily_change:+.2f} ({daily_change_pct:+.2f}%)
市场状态: {state_name}
状态概率: 震荡 {today['prob_ranging']:.1%} / 趋势 {today['prob_trend']:.1%} / 反转 {today['prob_reversal']:.1%}
最近30天数据
| 日期 |
收盘价 |
状态 |
震荡概率 |
趋势概率 |
反转概率 |
{html_rows}
生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}
数据更新至: {df_aligned.index[-1].strftime('%Y-%m-%d')}
模型准确率: 72.10% | 创业板50指数 (sz399673)
"""
# 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)