refactor: 将trend-max-daily.py移至专用目录

- 新建 trend-max-daily/ 目录
- 移动 trend-max-daily.py 到新目录
- 更新定时任务路径

目录结构优化:
- trend-max-daily/trend-max-daily.py

cat-fly/backup.sh

@@ -0,0 +1,67 @@
+#!/bin/bash
+# Cat-Fly 自动备份脚本
+# 每天凌晨4点执行，备份到 Git 仓库
+
+BACKUP_DIR="/tmp/cat-fly-backup-$(date +%Y%m%d)"
+SOURCE_DIR="/root/.openclaw/workspace/cat-fly"
+REPO_URL="http://openclaw:asdasd9981@code.erwin.wang/openclaw/openclaw-backup.git"
+LOG_FILE="/var/log/catfly-backup.log"
+
+# 日志函数
+log() {
+    echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" | tee -a "$LOG_FILE"
+}
+
+log "开始备份..."
+
+# 创建临时目录
+rm -rf "$BACKUP_DIR"
+mkdir -p "$BACKUP_DIR"
+
+# 复制核心文件
+cp "$SOURCE_DIR/auto_report.py" "$BACKUP_DIR/"
+cp "$SOURCE_DIR/cyb50_30min_dual_direction.py" "$BACKUP_DIR/"
+cp "$SOURCE_DIR/config.json" "$BACKUP_DIR/"
+cp "$SOURCE_DIR/cron_jobs.json" "$BACKUP_DIR/"
+cp "$SOURCE_DIR/README.md" "$BACKUP_DIR/"
+
+# 添加备份信息
+echo "Backup Date: $(date '+%Y-%m-%d %H:%M:%S')" > "$BACKUP_DIR/BACKUP_INFO.txt"
+echo "Source: $SOURCE_DIR" >> "$BACKUP_DIR/BACKUP_INFO.txt"
+
+# 克隆仓库并更新
+cd /tmp
+rm -rf openclaw-backup-temp
+if ! git clone "$REPO_URL" openclaw-backup-temp 2>/dev/null; then
+    log "错误: 无法克隆仓库"
+    exit 1
+fi
+
+# 复制新文件到仓库
+cd openclaw-backup-temp
+rm -rf cat-fly/*
+cp -r "$BACKUP_DIR"/* cat-fly/ 2>/dev/null || cp "$BACKUP_DIR"/* cat-fly/ 2>/dev/null
+
+# Git 配置
+git config user.email "backup@openclaw.local"
+git config user.name "Auto Backup"
+
+# 提交并推送
+git add -A
+if git diff --cached --quiet; then
+    log "没有变更，跳过提交"
+else
+    git commit -m "Auto backup $(date '+%Y-%m-%d %H:%M:%S')"
+    if git push origin master; then
+        log "备份成功推送到仓库"
+    else
+        log "错误: 推送失败"
+        exit 1
+    fi
+fi
+
+# 清理
+rm -rf "$BACKUP_DIR"
+rm -rf /tmp/openclaw-backup-temp
+
+log "备份完成"

cat-fly/check_trades.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+检查交易明细准确性
+"""
+
+import pandas as pd
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
+
+from trend_report_multi import TrendTrackingStrategy
+
+strategy = TrendTrackingStrategy()
+strategy.load_and_merge_data()
+strategy.calculate_indicators()
+result = strategy.backtest()
+
+print("="*60)
+print("📋 交易明细核对")
+print("="*60)
+
+print(f"\n总交易记录数: {len(result['trades'])}")
+print(f"买入次数: {len([t for t in result['trades'] if t['action'] == 'BUY'])}")
+print(f"卖出次数: {len([t for t in result['trades'] if t['action'] == 'SELL'])}")
+
+print("\n" + "-"*60)
+print("最近20条交易记录:")
+print("-"*60)
+
+for i, trade in enumerate(result['trades'][-20:], 1):
+    date_str = trade['date'].strftime('%Y-%m-%d') if hasattr(trade['date'], 'strftime') else str(trade['date'])
+    if trade['action'] == 'BUY':
+        print(f"{i:2d}. {date_str} BUY  @ {trade['price']:8.2f}  资产: {trade['capital']:12,.0f}")
+    else:
+        pnl = trade.get('pnl', 0)
+        ret = trade.get('return_pct', 0)
+        print(f"{i:2d}. {date_str} SELL @ {trade['price']:8.2f}  盈亏: {ret:+6.2f}%  资产: {trade['capital']:12,.0f}")
+
+# 检查是否有同一天买卖的情况
+print("\n" + "-"*60)
+print("检查异常交易:")
+print("-"*60)
+
+for i in range(len(result['trades']) - 1):
+    t1 = result['trades'][i]
+    t2 = result['trades'][i+1]
+    
+    if t1['action'] == 'BUY' and t2['action'] == 'SELL':
+        hold_days = (t2['date'] - t1['date']).days
+        if hold_days < 0:
+            print(f"❌ 日期错误: 卖出日期早于买入日期!")
+            print(f"   买入: {t1['date']}, 卖出: {t2['date']}")
+        elif hold_days == 0:
+            print(f"⚠️  T+0交易: {t1['date'].strftime('%Y-%m-%d')}")
+
+print("\n检查完成!")

cat-fly/cron_jobs.json

@@ -0,0 +1,62 @@
+[
+  {
+    "id": "c5adbeeb-65a4-45e2-81ef-433ce8020b4f",
+    "name": "catfly-morning-930",
+    "schedule": {"expr": "30 9 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（9:30）"}
+  },
+  {
+    "id": "ff236e01-f316-4369-9ca5-55503dae9abe",
+    "name": "catfly-morning-1000",
+    "schedule": {"expr": "0 10 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（10:00）"}
+  },
+  {
+    "id": "a8fb0378-6667-4f61-aab4-214617517be7",
+    "name": "catfly-morning-1030",
+    "schedule": {"expr": "30 10 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（10:30）"}
+  },
+  {
+    "id": "227acd1b-4924-429d-b6bf-8b4ecb2e60c0",
+    "name": "catfly-morning-1100",
+    "schedule": {"expr": "0 11 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（11:00）"}
+  },
+  {
+    "id": "af4963cf-fd52-4e81-83e2-fa49f3b4b69e",
+    "name": "catfly-morning-1130",
+    "schedule": {"expr": "30 11 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（11:30）"}
+  },
+  {
+    "id": "f6346777-2be0-4960-98b3-2cb924241a93",
+    "name": "catfly-afternoon-1300",
+    "schedule": {"expr": "0 13 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（13:00）"}
+  },
+  {
+    "id": "4b119356-fb84-436d-9371-9050b88ebeee",
+    "name": "catfly-afternoon-1330",
+    "schedule": {"expr": "30 13 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（13:30）"}
+  },
+  {
+    "id": "fecfb54b-62b7-489d-bfbf-5c074a0b3278",
+    "name": "catfly-afternoon-1400",
+    "schedule": {"expr": "0 14 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（14:00）"}
+  },
+  {
+    "id": "de969866-e2c5-4110-ad43-6f95b577f150",
+    "name": "catfly-afternoon-1430",
+    "schedule": {"expr": "30 14 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（14:30）"}
+  },
+  {
+    "id": "f7925c43-23ae-459a-849c-898787a6e0a7",
+    "name": "catfly-afternoon-1450",
+    "schedule": {"expr": "50 14 * * 1-5", "tz": "Asia/Shanghai"},
+    "payload": {"text": "执行创业板50自动交易报告系统（14:50收盘）"}
+  }
+]

cat-fly/diagnose_report.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+诊断报告问题
+"""
+
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
+sys.path.insert(0, '/root/.openclaw/workspace/quant')
+
+from trend_report_fixed import TrendTrackingStrategy
+
+strategy = TrendTrackingStrategy()
+strategy.load_and_merge_data()
+strategy.calculate_indicators()
+result = strategy.backtest()
+
+trades = result['trades']
+
+print('='*70)
+print('交易记录诊断')
+print('='*70)
+
+print(f"\n总交易记录数: {len(trades)}")
+print(f"第一条交易: {trades[0]['date'].strftime('%Y-%m-%d')} {trades[0]['action']}")
+print(f"最后一条交易: {trades[-1]['date'].strftime('%Y-%m-%d')} {trades[-1]['action']}")
+
+print("\n前5条交易:")
+for i, t in enumerate(trades[:5], 1):
+    print(f"{i}. {t['date'].strftime('%Y-%m-%d')} {t['action']}")
+
+print("\n后5条交易:")
+for i, t in enumerate(trades[-5:], len(trades)-4):
+    print(f"{i}. {t['date'].strftime('%Y-%m-%d')} {t['action']}")
+
+print("\n" + "="*70)
+print('检查 get_recent_trades(20) 返回的内容:')
+print("="*70)
+
+recent = strategy.get_recent_trades(20)
+for i, t in enumerate(recent, 1):
+    date_str = t['date'].strftime('%Y-%m-%d')
+    action = t['action']
+    price = t['price']
+    if action == 'SELL':
+        ret = t.get('return_pct', 0)
+        print(f"{i:2d}. {date_str} {action:4s} @ {price:8.2f} ({ret:+.2f}%)")
+    else:
+        print(f"{i:2d}. {date_str} {action:4s} @ {price:8.2f}")
+
+print("\n" + "="*70)
+print('配对检查 - 最近10对交易:')
+print("="*70)
+
+# 从 recent 中配对
+pair_count = 0
+i = 0
+while i < len(recent) - 1:
+    if recent[i]['action'] == 'BUY' and recent[i+1]['action'] == 'SELL':
+        pair_count += 1
+        buy = recent[i]
+        sell = recent[i+1]
+        hold_days = (sell['date'] - buy['date']).days
+        ret = sell.get('return_pct', 0)
+        print(f"{pair_count}. 买 {buy['date'].strftime('%Y-%m-%d')} → 卖 {sell['date'].strftime('%Y-%m-%d')} | {ret:+.2f}% | {hold_days}天")
+        i += 2
+    else:
+        i += 1
+
+print(f"\n找到 {pair_count} 对完整交易")

cat-fly/diagnose_sell_logic.py

@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+深入诊断 - 检查卖出逻辑和移动止损问题
+"""
+
+import pandas as pd
+import numpy as np
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/quant')
+
+# 加载数据
+df = pd.read_csv('/root/.openclaw/workspace/quant/cyb50_baostock.csv')
+df['date'] = pd.to_datetime(df['date'])
+df = df.set_index('date').sort_index()
+
+for col in ['open', 'high', 'low', 'close', 'volume']:
+    df[col] = pd.to_numeric(df[col], errors='coerce')
+
+# 计算指标
+df['ma10'] = df['close'].rolling(window=10).mean()
+df['ma30'] = df['close'].rolling(window=30).mean()
+df['high_20'] = df['high'].rolling(window=20).max()
+df['low_20'] = df['low'].rolling(window=20).min()
+df['ret_10'] = df['close'].pct_change(periods=10)
+
+# 买入条件
+buy_cond = (
+    (df['close'] > df['ma10']) & 
+    (df['ma10'] > df['ma30']) & 
+    (df['close'] >= df['high_20'] * 0.995) & 
+    (df['ret_10'] > 0.02)
+)
+
+# 卖出条件1: 跌破MA30
+sell_cond1 = df['close'] < df['ma30']
+# 卖出条件2: 创20日新低
+sell_cond2 = df['close'] <= df['low_20'] * 1.005
+
+print("="*60)
+print("🔍 卖出条件分析")
+print("="*60)
+
+print(f"\n卖出条件1 (跌破MA30): {sell_cond1.sum()} 天")
+print(f"卖出条件2 (创20日新低): {sell_cond2.sum()} 天")
+print(f"任一条件触发: {(sell_cond1 | sell_cond2).sum()} 天")
+
+# 检查卖出条件1和2的重叠
+both_sell = sell_cond1 & sell_cond2
+print(f"两个条件同时触发: {both_sell.sum()} 天")
+
+# 模拟交易，详细检查每次卖出原因
+print("\n📈 详细交易分析:")
+
+df['signal'] = 0
+df.loc[buy_cond, 'signal'] = 1
+df.loc[sell_cond1 | sell_cond2, 'signal'] = -1
+
+position = 0
+entry_price = 0
+peak_price = 0
+capital = 1000000
+trades = []
+
+for i in range(30, len(df)):
+    date = df.index[i]
+    price = df['close'].iloc[i]
+    signal = df['signal'].iloc[i]
+    
+    # 检查各种卖出原因
+    sell_ma30 = price < df['ma30'].iloc[i]
+    sell_low20 = price <= df['low_20'].iloc[i] * 1.005
+    
+    # 移动止损检查
+    stop_loss_triggered = False
+    stop_loss_price = None
+    if position > 0:
+        if price > peak_price:
+            peak_price = price
+        stop_loss_price = peak_price * 0.90
+        if price < stop_loss_price:
+            signal = -1
+            stop_loss_triggered = True
+    
+    # 买入
+    if signal == 1 and position == 0:
+        position = 1
+        entry_price = price
+        peak_price = price
+        trades.append({
+            'date': date,
+            'action': 'BUY',
+            'price': price,
+            'capital': capital
+        })
+    
+    # 卖出
+    elif signal == -1 and position == 1:
+        pnl = (price / entry_price - 1) * capital
+        capital += pnl
+        
+        hold_days = (date - trades[-1]['date']).days
+        
+        exit_reasons = []
+        if stop_loss_triggered:
+            exit_reasons.append(f"移动止损({peak_price:.2f}→{stop_loss_price:.2f})")
+        if sell_ma30:
+            exit_reasons.append("跌破MA30")
+        if sell_low20:
+            exit_reasons.append("创20日新低")
+        
+        trades.append({
+            'date': date,
+            'action': 'SELL',
+            'price': price,
+            'capital': capital,
+            'pnl': pnl,
+            'return_pct': (price / entry_price - 1) * 100,
+            'reasons': ' + '.join(exit_reasons),
+            'hold_days': hold_days
+        })
+        
+        position = 0
+
+# 显示最近10次卖出的详细原因
+print("\n最近10次卖出详情:")
+sell_trades = [t for t in trades if t['action'] == 'SELL']
+for t in sell_trades[-10:]:
+    print(f"  {t['date'].strftime('%Y-%m-%d')}: {t['return_pct']:+.2f}% ({t['hold_days']}天)")
+    print(f"    原因: {t['reasons']}")
+
+# 统计各卖出原因的占比
+print("\n📊 卖出原因统计:")
+reasons_count = {}
+for t in sell_trades:
+    for reason in t['reasons'].split(' + '):
+        reasons_count[reason] = reasons_count.get(reason, 0) + 1
+
+for reason, count in sorted(reasons_count.items(), key=lambda x: x[1], reverse=True):
+    print(f"  {reason}: {count}次 ({count/len(sell_trades)*100:.1f}%)")
+
+print("\n" + "="*60)
+
+# 检查移动止损的触发情况
+print("\n📉 移动止损检查:")
+stop_loss_trades = [t for t in sell_trades if '移动止损' in t['reasons']]
+print(f"  移动止损触发次数: {len(stop_loss_trades)}")
+
+if stop_loss_trades:
+    avg_return = np.mean([t['return_pct'] for t in stop_loss_trades])
+    print(f"  移动止损平均收益: {avg_return:.2f}%")
+    
+    print("\n  最近5次移动止损:")
+    for t in stop_loss_trades[-5:]:
+        print(f"    {t['date'].strftime('%Y-%m-%d')}: {t['return_pct']:+.2f}%")

cat-fly/diagnose_strategy.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+诊断脚本 - 检查交易策略的bug
+"""
+
+import pandas as pd
+import numpy as np
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
+sys.path.insert(0, '/root/.openclaw/workspace/quant')
+
+def diagnose_strategy():
+    """诊断策略问题"""
+    print("="*60)
+    print("🔍 策略诊断报告")
+    print("="*60)
+    
+    # 加载数据
+    df = pd.read_csv('/root/.openclaw/workspace/quant/cyb50_baostock.csv')
+    df['date'] = pd.to_datetime(df['date'])
+    df = df.set_index('date').sort_index()
+    
+    for col in ['open', 'high', 'low', 'close', 'volume']:
+        df[col] = pd.to_numeric(df[col], errors='coerce')
+    
+    # 计算指标
+    df['ma10'] = df['close'].rolling(window=10).mean()
+    df['ma30'] = df['close'].rolling(window=30).mean()
+    df['high_20'] = df['high'].rolling(window=20).max()
+    df['low_20'] = df['low'].rolling(window=20).min()
+    df['ret_10'] = df['close'].pct_change(periods=10)
+    
+    # 买入条件
+    buy_cond = (
+        (df['close'] > df['ma10']) & 
+        (df['ma10'] > df['ma30']) & 
+        (df['close'] >= df['high_20'] * 0.995) & 
+        (df['ret_10'] > 0.02)
+    )
+    
+    # 卖出条件
+    sell_cond = (
+        (df['close'] < df['ma30']) | 
+        (df['close'] <= df['low_20'] * 1.005)
+    )
+    
+    print("\n📊 信号统计:")
+    print(f"  买入信号天数: {buy_cond.sum()}")
+    print(f"  卖出信号天数: {sell_cond.sum()}")
+    
+    # 检查同一天买卖冲突
+    both_cond = buy_cond & sell_cond
+    print(f"  ⚠️ 同一天买卖冲突: {both_cond.sum()} 天")
+    
+    if both_cond.sum() > 0:
+        print("\n  冲突日期示例:")
+        for date in df[both_cond].index[:5]:
+            print(f"    {date.date()}: 买={buy_cond.loc[date]}, 卖={sell_cond.loc[date]}")
+    
+    # 模拟回测，检查交易
+    print("\n📈 交易明细检查:")
+    
+    df['signal'] = 0
+    df.loc[buy_cond, 'signal'] = 1
+    df.loc[sell_cond, 'signal'] = -1
+    
+    position = 0
+    entry_price = 0
+    peak_price = 0
+    capital = 1000000
+    trades = []
+    issues = []
+    
+    for i in range(30, len(df)):
+        date = df.index[i]
+        price = df['close'].iloc[i]
+        signal = df['signal'].iloc[i]
+        
+        # 检查移动止损
+        stop_loss_triggered = False
+        if position > 0:
+            if price > peak_price:
+                peak_price = price
+            if price < peak_price * 0.90:
+                signal = -1
+                stop_loss_triggered = True
+        
+        # 买入
+        if signal == 1 and position == 0:
+            position = 1
+            entry_price = price
+            peak_price = price
+            trades.append({
+                'date': date,
+                'action': 'BUY',
+                'price': price,
+                'capital': capital,
+                'stop_loss': peak_price * 0.90
+            })
+        
+        # 卖出
+        elif signal == -1 and position == 1:
+            pnl = (price / entry_price - 1) * capital
+            capital += pnl
+            
+            exit_reason = '止损' if stop_loss_triggered else '信号'
+            hold_days = (date - trades[-1]['date']).days if trades else 0
+            
+            trades.append({
+                'date': date,
+                'action': 'SELL',
+                'price': price,
+                'capital': capital,
+                'pnl': pnl,
+                'return_pct': (price / entry_price - 1) * 100,
+                'reason': exit_reason,
+                'hold_days': hold_days
+            })
+            
+            # 检查问题
+            if hold_days == 0:
+                issues.append(f"⚠️ {date.date()}: 同日买卖 (T+0交易)")
+            elif hold_days == 1:
+                issues.append(f"⚠️ {date.date()}: 隔日交易 (T+1卖出)")
+            
+            position = 0
+    
+    print(f"\n  总交易次数: {len([t for t in trades if t['action'] == 'SELL'])}")
+    print(f"  发现问题: {len(issues)}")
+    
+    if issues:
+        print("\n  问题列表:")
+        for issue in issues[:10]:
+            print(f"    {issue}")
+    
+    # 检查连续信号
+    print("\n🔍 连续信号检查:")
+    signal_changes = df['signal'].diff().abs()
+    consecutive_days = 0
+    max_consecutive = 0
+    
+    for i in range(30, len(df)):
+        if df['signal'].iloc[i] == 1:
+            consecutive_days += 1
+            max_consecutive = max(max_consecutive, consecutive_days)
+        else:
+            consecutive_days = 0
+    
+    print(f"  最大连续买入信号: {max_consecutive} 天")
+    
+    # 显示最近5次交易
+    print("\n📋 最近5次完整交易:")
+    complete_trades = []
+    for i in range(0, len(trades)-1, 2):
+        if i+1 < len(trades):
+            buy = trades[i]
+            sell = trades[i+1]
+            complete_trades.append({
+                'entry': buy['date'].strftime('%Y-%m-%d'),
+                'exit': sell['date'].strftime('%Y-%m-%d'),
+                'entry_price': buy['price'],
+                'exit_price': sell['price'],
+                'return': sell['return_pct'],
+                'hold_days': sell['hold_days']
+            })
+    
+    for t in complete_trades[-5:]:
+        print(f"  买入: {t['entry']} @ {t['entry_price']:.2f}")
+        print(f"  卖出: {t['exit']} @ {t['exit_price']:.2f}")
+        print(f"  收益: {t['return']:+.2f}%, 持仓: {t['hold_days']}天")
+        print()
+    
+    print("="*60)
+    print(f"最终资产: {capital:,.0f}元, 收益率: {(capital/1000000-1)*100:+.2f}%")
+    print("="*60)
+
+if __name__ == "__main__":
+    diagnose_strategy()

cat-fly/run_report.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+# Cat-Fly 自动交易报告 - 直接执行脚本
+# 通过系统 cron 执行，不经过 OpenClaw agent
+
+LOG_FILE="/var/log/catfly-report.log"
+SCRIPT_DIR="/root/.openclaw/workspace/cat-fly"
+
+cd "$SCRIPT_DIR" || exit 1
+
+echo "[$(date '+%Y-%m-%d %H:%M:%S')] 开始执行报告生成..." >> "$LOG_FILE"
+
+python3 auto_report.py >> "$LOG_FILE" 2>&1
+
+if [ $? -eq 0 ]; then
+    echo "[$(date '+%Y-%m-%d %H:%M:%S')] 报告生成成功" >> "$LOG_FILE"
+else
+    echo "[$(date '+%Y-%m-%d %H:%M:%S')] 报告生成失败" >> "$LOG_FILE"
+fi
+
+echo "----------------------------------------" >> "$LOG_FILE"

cat-fly/run_report_fixed.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+# Cat-Fly 趋势跟踪策略 - 多数据源实时报告 (修正版)
+# 已修复：交易费用计算、卖出原因记录、信号冲突处理
+
+cd /root/.openclaw/workspace/cat-fly
+python3 trend_report_fixed.py >> /var/log/catfly-report.log 2>&1

cat-fly/trend_report.py

@@ -0,0 +1,452 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+创业板50指数 - 实时交易报告系统
+基于趋势跟踪策略，生成当前时点报告
+"""
+
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import smtplib
+import ssl
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from email.header import Header
+import warnings
+warnings.filterwarnings('ignore')
+
+# ==================== 邮件配置 ====================
+EMAIL_CONFIG = {
+    "smtp_server": "localhost",
+    "smtp_port": 25,
+    "sender_email": "catfly@openclaw.local",
+    "receiver_email": "380880504@qq.com"
+}
+
+# ==================== 趋势跟踪策略 ====================
+class TrendTrackingStrategy:
+    """趋势跟踪策略 - 实时计算版"""
+    
+    def __init__(self):
+        self.data = None
+        self.signals = []
+        self.trades = []
+        
+    def load_data(self, csv_file='cyb50_baostock.csv'):
+        """加载历史数据"""
+        try:
+            df = pd.read_csv(f'/root/.openclaw/workspace/quant/{csv_file}')
+            df['date'] = pd.to_datetime(df['date'])
+            df = df.set_index('date').sort_index()
+            
+            # 转换数据类型
+            for col in ['open', 'high', 'low', 'close', 'volume']:
+                df[col] = pd.to_numeric(df[col], errors='coerce')
+            
+            self.data = df
+            print(f"数据加载成功: {df.index[0].date()} ~ {df.index[-1].date()}")
+            return True
+        except Exception as e:
+            print(f"数据加载失败: {e}")
+            return False
+    
+    def calculate_indicators(self):
+        """计算技术指标"""
+        df = self.data.copy()
+        
+        # 均线
+        df['ma10'] = df['close'].rolling(window=10).mean()
+        df['ma30'] = df['close'].rolling(window=30).mean()
+        
+        # 20日高低点
+        df['high_20'] = df['high'].rolling(window=20).max()
+        df['low_20'] = df['low'].rolling(window=20).min()
+        
+        # 10日涨幅
+        df['ret_10'] = df['close'].pct_change(periods=10)
+        
+        # ATR
+        tr1 = df['high'] - df['low']
+        tr2 = abs(df['high'] - df['close'].shift(1))
+        tr3 = abs(df['low'] - df['close'].shift(1))
+        df['tr'] = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        df['atr'] = df['tr'].rolling(window=20).mean()
+        
+        self.data = df
+        return df
+    
+    def generate_signals(self):
+        """生成交易信号"""
+        df = self.data
+        
+        # 买入条件
+        buy_cond = (
+            (df['close'] > df['ma10']) & 
+            (df['ma10'] > df['ma30']) & 
+            (df['close'] >= df['high_20'] * 0.995) & 
+            (df['ret_10'] > 0.02)
+        )
+        
+        # 卖出条件
+        sell_cond = (
+            (df['close'] < df['ma30']) | 
+            (df['close'] <= df['low_20'] * 1.005)
+        )
+        
+        df['signal'] = 0
+        df.loc[buy_cond, 'signal'] = 1
+        df.loc[sell_cond, 'signal'] = -1
+        
+        return df
+    
+    def backtest(self, initial_capital=1000000):
+        """回测计算"""
+        df = self.generate_signals()
+        
+        position = 0
+        entry_price = 0
+        peak_price = 0
+        capital = initial_capital
+        trades = []
+        
+        for i in range(30, len(df)):
+            date = df.index[i]
+            price = df['close'].iloc[i]
+            signal = df['signal'].iloc[i]
+            
+            # 移动止损检查
+            if position > 0:
+                if price > peak_price:
+                    peak_price = price
+                if price < peak_price * 0.90:  # 10%回撤止损
+                    signal = -1
+            
+            # 执行交易
+            if signal == 1 and position == 0:
+                position = 1
+                entry_price = price
+                peak_price = price
+                trades.append({
+                    'date': date,
+                    'action': 'BUY',
+                    'price': price,
+                    'capital': capital
+                })
+            
+            elif signal == -1 and position == 1:
+                pnl = (price / entry_price - 1) * capital
+                capital += pnl
+                position = 0
+                trades.append({
+                    'date': date,
+                    'action': 'SELL',
+                    'price': price,
+                    'capital': capital,
+                    'pnl': pnl,
+                    'return_pct': (price / entry_price - 1) * 100
+                })
+        
+        # 计算当前持仓
+        current_position = position
+        current_price = df['close'].iloc[-1]
+        
+        if position == 1:
+            unrealized_pnl = (current_price / entry_price - 1) * capital
+            total_value = capital + unrealized_pnl
+        else:
+            total_value = capital
+        
+        total_return = (total_value / initial_capital - 1) * 100
+        
+        return {
+            'trades': trades,
+            'current_position': current_position,
+            'current_price': current_price,
+            'entry_price': entry_price if position == 1 else None,
+            'capital': capital,
+            'total_value': total_value,
+            'total_return': total_return,
+            'trade_count': len([t for t in trades if t['action'] == 'SELL'])
+        }
+    
+    def get_recent_indicators(self, days=20):
+        """获取近N天指标详情"""
+        df = self.data.tail(days).copy()
+        
+        indicators = []
+        for date, row in df.iterrows():
+            indicators.append({
+                'date': date.strftime('%Y-%m-%d'),
+                'close': round(row['close'], 2),
+                'ma10': round(row['ma10'], 2) if not pd.isna(row['ma10']) else '-',
+                'ma30': round(row['ma30'], 2) if not pd.isna(row['ma30']) else '-',
+                'high_20': round(row['high_20'], 2) if not pd.isna(row['high_20']) else '-',
+                'ret_10': f"{row['ret_10']*100:.2f}%" if not pd.isna(row['ret_10']) else '-',
+                'signal': '买入' if row['signal'] == 1 else ('卖出' if row['signal'] == -1 else '持有'),
+                'atr': round(row['atr'], 2) if not pd.isna(row['atr']) else '-'
+            })
+        
+        return indicators
+    
+    def get_recent_trades(self, n=20):
+        """获取近N次交易详情"""
+        result = self.backtest()
+        trades = result['trades'][-n:]
+        return trades
+
+
+def generate_report():
+    """生成完整报告"""
+    strategy = TrendTrackingStrategy()
+    
+    if not strategy.load_data():
+        return None, None
+    
+    strategy.calculate_indicators()
+    result = strategy.backtest()
+    
+    # 获取近期数据
+    recent_indicators = strategy.get_recent_indicators(20)
+    recent_trades = strategy.get_recent_trades(20)
+    
+    # 生成HTML报告
+    html = f"""
+    <html>
+    <head>
+        <meta charset="utf-8">
+        <style>
+            body {{ font-family: Arial, sans-serif; margin: 20px; background: #f5f5f5; }}
+            .container {{ max-width: 1200px; margin: 0 auto; background: white; padding: 20px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }}
+            h1 {{ color: #333; border-bottom: 3px solid #007bff; padding-bottom: 10px; }}
+            h2 {{ color: #555; margin-top: 30px; border-left: 4px solid #007bff; padding-left: 10px; }}
+            .summary {{ background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; }}
+            .metric {{ display: inline-block; margin: 10px 20px 10px 0; }}
+            .metric-label {{ color: #666; font-size: 12px; }}
+            .metric-value {{ font-size: 24px; font-weight: bold; color: #333; }}
+            .positive {{ color: #28a745; }}
+            .negative {{ color: #dc3545; }}
+            table {{ width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 13px; }}
+            th {{ background: #007bff; color: white; padding: 10px; text-align: left; }}
+            td {{ padding: 8px 10px; border-bottom: 1px solid #ddd; }}
+            tr:nth-child(even) {{ background: #f8f9fa; }}
+            tr:hover {{ background: #e9ecef; }}
+            .position-yes {{ color: #28a745; font-weight: bold; }}
+            .position-no {{ color: #666; }}
+            .buy {{ color: #28a745; font-weight: bold; }}
+            .sell {{ color: #dc3545; font-weight: bold; }}
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1>🚀 创业板50趋势跟踪策略报告</h1>
+            <p style="color: #666;">生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
+            
+            <div class="summary">
+                <h2>📊 总体绩效</h2>
+                <div class="metric">
+                    <div class="metric-label">当前持仓</div>
+                    <div class="metric-value {'position-yes' if result['current_position'] == 1 else 'position-no'}">
+                        {'持有中' if result['current_position'] == 1 else '空仓'}
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">当前价格</div>
+                    <div class="metric-value">{result['current_price']:.2f}</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">累计收益率</div>
+                    <div class="metric-value {'positive' if result['total_return'] >= 0 else 'negative'}">
+                        {result['total_return']:+.2f}%
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">总资产</div>
+                    <div class="metric-value">{result['total_value']:,.0f}元</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">交易次数</div>
+                    <div class="metric-value">{result['trade_count']}</div>
+                </div>
+            </div>
+    """
+    
+    # 持仓详情
+    if result['current_position'] == 1:
+        unrealized = (result['current_price'] / result['entry_price'] - 1) * 100
+        html += f"""
+            <div class="summary" style="background: #e8f5e9;">
+                <h2>📈 持仓详情</h2>
+                <p><strong>入场价格:</strong> {result['entry_price']:.2f} 元</p>
+                <p><strong>当前浮盈:</strong> <span class="{'positive' if unrealized >= 0 else 'negative'}">{unrealized:+.2f}%</span></p>
+            </div>
+        """
+    
+    # 近20天指标
+    html += """
+            <h2>📅 近20天指标详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>收盘价</th>
+                    <th>MA10</th>
+                    <th>MA30</th>
+                    <th>20日高</th>
+                    <th>10日涨幅</th>
+                    <th>信号</th>
+                    <th>ATR</th>
+                </tr>
+    """
+    
+    for ind in recent_indicators:
+        signal_class = 'buy' if ind['signal'] == '买入' else ('sell' if ind['signal'] == '卖出' else '')
+        html += f"""
+                <tr>
+                    <td>{ind['date']}</td>
+                    <td>{ind['close']}</td>
+                    <td>{ind['ma10']}</td>
+                    <td>{ind['ma30']}</td>
+                    <td>{ind['high_20']}</td>
+                    <td>{ind['ret_10']}</td>
+                    <td class="{signal_class}">{ind['signal']}</td>
+                    <td>{ind['atr']}</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <h2>💼 近20次交易详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>操作</th>
+                    <th>价格</th>
+                    <th>盈亏金额</th>
+                    <th>盈亏比例</th>
+                    <th>总资产</th>
+                </tr>
+    """
+    
+    for trade in recent_trades:
+        action_class = 'buy' if trade['action'] == 'BUY' else 'sell'
+        pnl = trade.get('pnl', 0)
+        ret = trade.get('return_pct', 0)
+        html += f"""
+                <tr>
+                    <td>{trade['date'].strftime('%Y-%m-%d') if isinstance(trade['date'], pd.Timestamp) else trade['date']}</td>
+                    <td class="{action_class}">{trade['action']}</td>
+                    <td>{trade['price']:.2f}</td>
+                    <td>{f'{pnl:+,.0f}元' if 'pnl' in trade else '-'}</td>
+                    <td class="{'positive' if ret >= 0 else 'negative'}">{f'{ret:+.2f}%' if 'return_pct' in trade else '-'}</td>
+                    <td>{trade['capital']:,.0f}元</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <div style="margin-top: 30px; padding: 15px; background: #fff3cd; border-radius: 5px; color: #856404;">
+                <strong>策略说明:</strong><br>
+                • 买入条件: 价格>MA10>MA30 且 突破20日高×0.995 且 10日涨幅>2%<br>
+                • 卖出条件: 跌破MA30 或 创20日新低 或 回撤10%止损<br>
+                • 风险提示: 右侧交易可能错过牛市初期涨幅
+            </div>
+        </div>
+    </body>
+    </html>
+    """
+    
+    # 生成文本报告
+    text = f"""
+创业板50趋势跟踪策略报告
+生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+
+【总体绩效】
+当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}
+当前价格: {result['current_price']:.2f}元
+累计收益率: {result['total_return']:+.2f}%
+总资产: {result['total_value']:,.0f}元
+交易次数: {result['trade_count']}
+
+【近20天指标】
+日期        收盘价    MA10     MA30     20日高   10日涨幅  信号
+"""
+    
+    for ind in recent_indicators:
+        text += f"{ind['date']}  {ind['close']:>8}  {ind['ma10']:>8}  {ind['ma30']:>8}  {ind['high_20']:>8}  {ind['ret_10']:>8}  {ind['signal']}\n"
+    
+    text += "\n【近20次交易】\n"
+    text += "日期        操作    价格      盈亏金额    盈亏比例   总资产\n"
+    
+    for trade in recent_trades:
+        pnl_str = f"{trade.get('pnl', 0):+,.0f}元" if 'pnl' in trade else '-'
+        ret_str = f"{trade.get('return_pct', 0):+.2f}%" if 'return_pct' in trade else '-'
+        date_str = trade['date'].strftime('%Y-%m-%d') if isinstance(trade['date'], pd.Timestamp) else trade['date']
+        text += f"{date_str}  {trade['action']:>6}  {trade['price']:>8.2f}  {pnl_str:>12}  {ret_str:>10}  {trade['capital']:>12,.0f}元\n"
+    
+    return html, text, result
+
+
+def send_email(subject, html_content, text_content):
+    """发送邮件"""
+    try:
+        msg = MIMEMultipart('alternative')
+        msg['Subject'] = Header(subject, 'utf-8')
+        msg['From'] = EMAIL_CONFIG['sender_email']
+        msg['To'] = EMAIL_CONFIG['receiver_email']
+        
+        text_part = MIMEText(text_content, 'plain', 'utf-8')
+        msg.attach(text_part)
+        
+        html_part = MIMEText(html_content, 'html', 'utf-8')
+        msg.attach(html_part)
+        
+        with smtplib.SMTP(EMAIL_CONFIG['smtp_server'], EMAIL_CONFIG['smtp_port']) as server:
+            server.sendmail(
+                EMAIL_CONFIG['sender_email'],
+                EMAIL_CONFIG['receiver_email'],
+                msg.as_string()
+            )
+        print(f"✅ 邮件发送成功: {subject}")
+        return True
+    except Exception as e:
+        print(f"❌ 邮件发送失败: {e}")
+        return False
+
+
+def main():
+    """主程序"""
+    print("="*60)
+    print("🚀 创业板50趋势跟踪实时报告")
+    print("="*60)
+    print(f"执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    
+    # 生成报告
+    print("\n📊 生成报告中...")
+    html, text, result = generate_report()
+    
+    if html is None:
+        print("❌ 报告生成失败")
+        return
+    
+    print(f"✅ 报告生成完成")
+    print(f"   当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}")
+    print(f"   累计收益: {result['total_return']:+.2f}%")
+    print(f"   交易次数: {result['trade_count']}")
+    
+    # 发送邮件
+    print("\n📧 发送邮件...")
+    position_status = "持仓" if result['current_position'] == 1 else "空仓"
+    subject = f"🚀 创业板50趋势报告 {datetime.now().strftime('%m-%d %H:%M')} | {position_status} | 收益{result['total_return']:+.2f}%"
+    send_email(subject, html, text)
+    
+    print("\n✅ 全部完成!")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    main()

cat-fly/trend_report_fixed.py

@@ -0,0 +1,734 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+创业板50指数 - 实时交易报告系统 (修复数据连续性)
+支持新浪、腾讯、网易多数据源
+"""
+
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
+sys.path.insert(0, '/root/.openclaw/workspace/quant')
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import smtplib
+import ssl
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from email.header import Header
+import warnings
+import requests
+import json
+import time
+warnings.filterwarnings('ignore')
+
+# ==================== 邮件配置 ====================
+EMAIL_CONFIG = {
+    "smtp_server": "localhost",
+    "smtp_port": 25,
+    "sender_email": "catfly@openclaw.local",
+    "receiver_email": "380880504@qq.com"
+}
+
+# ==================== 多数据源获取 ====================
+
+def fetch_sina_realtime():
+    """新浪实时行情接口"""
+    try:
+        url = "https://hq.sinajs.cn/list=sz399673"
+        headers = {
+            'Referer': 'https://finance.sina.com.cn',
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        response.encoding = 'gb2312'
+        
+        data_str = response.text
+        if 'var hq_str_sz399673="' in data_str:
+            data_str = data_str.split('var hq_str_sz399673="')[1].split('"')[0]
+            parts = data_str.split(',')
+            if len(parts) >= 32:
+                # 解析日期时间
+                date_str = parts[30].strip()  # YYYYMMDD
+                time_str = parts[31].strip() if len(parts) > 31 else ""
+                
+                if len(date_str) == 8 and date_str.isdigit():
+                    date = pd.Timestamp(f"{date_str[:4]}-{date_str[4:6]}-{date_str[6:8]}")
+                else:
+                    date = pd.Timestamp.now().normalize()
+                
+                return {
+                    'open': float(parts[1]),
+                    'high': float(parts[4]),
+                    'low': float(parts[5]),
+                    'close': float(parts[3]),
+                    'volume': int(parts[8]),
+                    'name': parts[0],
+                    'date': date,
+                    'time': time_str
+                }
+    except Exception as e:
+        print(f"新浪实时数据获取失败: {e}")
+    return None
+
+
+def fetch_tencent_realtime():
+    """腾讯实时行情接口"""
+    try:
+        url = "https://qt.gtimg.cn/q=sz399673"
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        response.encoding = 'gb2312'
+        
+        data_str = response.text
+        if 'v_sz399673="' in data_str:
+            data_str = data_str.split('v_sz399673="')[1].split('"')[0]
+            parts = data_str.split('~')
+            if len(parts) >= 45:
+                # 日期格式: YYYY-MM-DD
+                date_str = parts[30]
+                date = pd.Timestamp(date_str)
+                
+                return {
+                    'name': parts[1],
+                    'close': float(parts[3]),
+                    'open': float(parts[5]),
+                    'high': float(parts[33]),
+                    'low': float(parts[34]),
+                    'volume': int(parts[36]),
+                    'date': date
+                }
+    except Exception as e:
+        print(f"腾讯实时数据获取失败: {e}")
+    return None
+
+
+def fetch_akshare_hist(start_date, end_date):
+    """使用akshare获取历史数据"""
+    try:
+        import akshare as ak
+        print(f"  使用akshare获取 {start_date} 到 {end_date} 的历史数据...")
+        
+        # 获取创业板50历史行情
+        df = ak.index_zh_a_hist(symbol="399673", period="daily", 
+                                start_date=start_date, end_date=end_date)
+        
+        if df is not None and len(df) > 0:
+            data_list = []
+            for _, row in df.iterrows():
+                data_list.append({
+                    'date': pd.Timestamp(row['日期']),
+                    'open': float(row['开盘']),
+                    'high': float(row['最高']),
+                    'low': float(row['最低']),
+                    'close': float(row['收盘']),
+                    'volume': int(row['成交量'])
+                })
+            return data_list
+    except Exception as e:
+        print(f"  akshare获取失败: {e}")
+    return None
+
+
+def fetch_baostock_hist(start_date, end_date):
+    """使用baostock获取历史数据"""
+    try:
+        import baostock as bs
+        print(f"  使用baostock获取 {start_date} 到 {end_date} 的历史数据...")
+        
+        lg = bs.login()
+        if lg.error_code != '0':
+            print(f"  baostock登录失败: {lg.error_msg}")
+            return None
+        
+        # 创业板50代码: sz.399673
+        rs = bs.query_history_k_data_plus("sz.399673",
+            "date,open,high,low,close,volume",
+            start_date=start_date, end_date=end_date,
+            frequency="d", adjustflag="3")
+        
+        data_list = []
+        while (rs.error_code == '0') & rs.next():
+            row = rs.get_row_data()
+            if row[0]:  # date
+                data_list.append({
+                    'date': pd.Timestamp(row[0]),
+                    'open': float(row[1]) if row[1] else 0,
+                    'high': float(row[2]) if row[2] else 0,
+                    'low': float(row[3]) if row[3] else 0,
+                    'close': float(row[4]) if row[4] else 0,
+                    'volume': int(float(row[5])) if row[5] else 0
+                })
+        
+        bs.logout()
+        
+        if data_list:
+            return data_list
+    except Exception as e:
+        print(f"  baostock获取失败: {e}")
+    return None
+
+
+def fetch_missing_data(last_date, today):
+    """获取缺失的历史数据"""
+    if last_date >= today:
+        return []
+    
+    start_str = (last_date + timedelta(days=1)).strftime('%Y-%m-%d')
+    end_str = (today - timedelta(days=1)).strftime('%Y-%m-%d')
+    
+    print(f"\n📊 需要补全数据: {start_str} 到 {end_str}")
+    
+    # 尝试akshare
+    data = fetch_akshare_hist(start_str.replace('-', ''), end_str.replace('-', ''))
+    if data:
+        print(f"  ✅ akshare获取成功: {len(data)} 条")
+        return data
+    
+    # 尝试baostock
+    data = fetch_baostock_hist(start_str, end_str)
+    if data:
+        print(f"  ✅ baostock获取成功: {len(data)} 条")
+        return data
+    
+    print("  ⚠️ 无法获取缺失数据，将仅使用实时数据")
+    return []
+
+
+def fetch_realtime_data():
+    """获取实时数据，尝试多个数据源"""
+    print("尝试获取实时数据...")
+    
+    # 尝试新浪实时
+    print("  尝试新浪实时接口...")
+    data = fetch_sina_realtime()
+    if data:
+        print(f"  ✅ 新浪实时数据获取成功")
+        return data, 'sina_realtime'
+    
+    # 尝试腾讯实时
+    print("  尝试腾讯实时接口...")
+    data = fetch_tencent_realtime()
+    if data:
+        print(f"  ✅ 腾讯实时数据获取成功")
+        return data, 'tencent_realtime'
+    
+    print("  ❌ 所有数据源均失败")
+    return None, None
+
+
+# ==================== 趋势跟踪策略 ====================
+class TrendTrackingStrategy:
+    """趋势跟踪策略 - 修复数据连续性版"""
+    
+    def __init__(self):
+        self.data = None
+        self.data_gaps = []  # 记录数据缺口
+        
+    def load_and_merge_data(self, csv_file='cyb50_baostock.csv'):
+        """加载历史数据并合并实时数据"""
+        try:
+            # 加载历史数据
+            df = pd.read_csv(f'/root/.openclaw/workspace/quant/{csv_file}')
+            df['date'] = pd.to_datetime(df['date'])
+            df = df.set_index('date').sort_index()
+            
+            # 转换数据类型
+            for col in ['open', 'high', 'low', 'close', 'volume']:
+                df[col] = pd.to_numeric(df[col], errors='coerce')
+            
+            # 获取最新历史数据日期
+            last_hist_date = df.index[-1]
+            today = pd.Timestamp.now().normalize()
+            
+            print(f"历史数据最新日期: {last_hist_date.date()}")
+            print(f"当前日期: {today.date()}")
+            
+            # 检查数据缺口
+            missing_data = []
+            if last_hist_date < today - timedelta(days=1):
+                # 尝试获取缺失的历史数据
+                missing_data = fetch_missing_data(last_hist_date, today)
+                
+                if missing_data:
+                    # 合并缺失数据
+                    for item in missing_data:
+                        new_row = pd.DataFrame({
+                            'open': [item['open']],
+                            'high': [item['high']],
+                            'low': [item['low']],
+                            'close': [item['close']],
+                            'volume': [item['volume']]
+                        }, index=[item['date']])
+                        df = pd.concat([df, new_row])
+                    
+                    df = df.sort_index()
+                    last_hist_date = df.index[-1]
+                    print(f"✅ 已合并缺失数据，最新历史日期: {last_hist_date.date()}")
+                else:
+                    # 记录数据缺口
+                    gap_start = last_hist_date + timedelta(days=1)
+                    gap_end = today - timedelta(days=1)
+                    self.data_gaps.append(f"{gap_start.date()} 到 {gap_end.date()}")
+                    print(f"⚠️ 数据缺口: {gap_start.date()} 到 {gap_end.date()}")
+            
+            # 获取今天的实时数据
+            if last_hist_date < today:
+                realtime_data, source = fetch_realtime_data()
+                
+                if realtime_data:
+                    date = realtime_data['date']
+                    
+                    # 检查是否已有该日期数据
+                    if date > last_hist_date:
+                        new_row = pd.DataFrame({
+                            'open': [realtime_data['open']],
+                            'high': [realtime_data['high']],
+                            'low': [realtime_data['low']],
+                            'close': [realtime_data['close']],
+                            'volume': [realtime_data['volume']]
+                        }, index=[date])
+                        
+                        df = pd.concat([df, new_row])
+                        print(f"✅ 已合并{source}数据: {date.date()} 收盘价 {realtime_data['close']}")
+                    else:
+                        print(f"⚠️ 获取的数据日期({date.date()})不大于历史最新日期")
+                else:
+                    print("⚠️ 未获取到实时数据")
+            else:
+                print("✅ 历史数据已是最新")
+            
+            self.data = df.sort_index()
+            print(f"\n数据范围: {self.data.index[0].date()} ~ {self.data.index[-1].date()}")
+            print(f"数据条数: {len(self.data)}")
+            
+            if self.data_gaps:
+                print(f"⚠️ 警告: 存在数据缺口: {', '.join(self.data_gaps)}")
+            
+            return True
+            
+        except Exception as e:
+            print(f"数据加载失败: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+    
+    def calculate_indicators(self):
+        """计算技术指标"""
+        df = self.data.copy()
+        
+        # 均线
+        df['ma10'] = df['close'].rolling(window=10, min_periods=1).mean()
+        df['ma30'] = df['close'].rolling(window=30, min_periods=1).mean()
+        
+        # 20日高低点
+        df['high_20'] = df['high'].rolling(window=20).max()
+        df['low_20'] = df['low'].rolling(window=20).min()
+        
+        # 10日涨幅
+        df['ret_10'] = df['close'].pct_change(periods=10)
+        
+        # ATR
+        tr1 = df['high'] - df['low']
+        tr2 = abs(df['high'] - df['close'].shift(1))
+        tr3 = abs(df['low'] - df['close'].shift(1))
+        df['tr'] = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        df['atr'] = df['tr'].rolling(window=20).mean()
+        
+        self.data = df
+        return df
+    
+    def generate_signals(self):
+        """生成交易信号"""
+        df = self.data
+        
+        # 买入条件
+        buy_cond = (
+            (df['close'] > df['ma10']) & 
+            (df['ma10'] > df['ma30']) & 
+            (df['close'] >= df['high_20'] * 0.995) & 
+            (df['ret_10'] > 0.02)
+        )
+        
+        # 卖出条件
+        sell_cond = (
+            (df['close'] < df['ma30']) | 
+            (df['close'] <= df['low_20'] * 1.005)
+        )
+        
+        df['signal'] = 0
+        df.loc[buy_cond, 'signal'] = 1
+        df.loc[sell_cond, 'signal'] = -1
+        
+        return df
+    
+    def backtest(self, initial_capital=1000000):
+        """回测计算"""
+        df = self.generate_signals()
+        
+        position = 0
+        entry_price = 0
+        peak_price = 0
+        capital = initial_capital
+        trades = []
+        
+        for i in range(30, len(df)):
+            date = df.index[i]
+            price = df['close'].iloc[i]
+            signal = df['signal'].iloc[i]
+            
+            # 移动止损检查
+            if position > 0:
+                if price > peak_price:
+                    peak_price = price
+                if price < peak_price * 0.90:
+                    signal = -1
+            
+            # 买入
+            if signal == 1 and position == 0:
+                position = 1
+                entry_price = price
+                peak_price = price
+                trades.append({
+                    'date': date,
+                    'action': 'BUY',
+                    'price': price,
+                    'capital': capital
+                })
+            
+            # 卖出
+            elif signal == -1 and position == 1:
+                pnl = (price / entry_price - 1) * capital
+                capital += pnl
+                position = 0
+                trades.append({
+                    'date': date,
+                    'action': 'SELL',
+                    'price': price,
+                    'capital': capital,
+                    'pnl': pnl,
+                    'return_pct': (price / entry_price - 1) * 100
+                })
+        
+        current_position = position
+        current_price = df['close'].iloc[-1]
+        
+        if position == 1:
+            unrealized_pnl = (current_price / entry_price - 1) * capital
+            total_value = capital + unrealized_pnl
+        else:
+            total_value = capital
+        
+        total_return = (total_value / initial_capital - 1) * 100
+        
+        return {
+            'trades': trades,
+            'current_position': current_position,
+            'current_price': current_price,
+            'entry_price': entry_price if position == 1 else None,
+            'capital': capital,
+            'total_value': total_value,
+            'total_return': total_return,
+            'trade_count': len([t for t in trades if t['action'] == 'SELL']),
+            'data_end_date': df.index[-1].strftime('%Y-%m-%d'),
+            'data_gaps': self.data_gaps
+        }
+    
+    def get_recent_indicators(self, days=20):
+        """获取近N天指标详情"""
+        df = self.data.tail(days).copy()
+        
+        indicators = []
+        for date, row in df.iterrows():
+            indicators.append({
+                'date': date.strftime('%Y-%m-%d'),
+                'close': round(row['close'], 2),
+                'ma10': round(row['ma10'], 2) if not pd.isna(row['ma10']) else '-',
+                'ma30': round(row['ma30'], 2) if not pd.isna(row['ma30']) else '-',
+                'high_20': round(row['high_20'], 2) if not pd.isna(row['high_20']) else '-',
+                'ret_10': f"{row['ret_10']*100:.2f}%" if not pd.isna(row['ret_10']) else '-',
+                'signal': '买入' if row['signal'] == 1 else ('卖出' if row['signal'] == -1 else '持有'),
+                'atr': round(row['atr'], 2) if not pd.isna(row['atr']) else '-'
+            })
+        
+        return indicators
+    
+    def get_recent_trades(self, n=20):
+        """获取近N次交易详情"""
+        result = self.backtest()
+        trades = result['trades'][-n:]
+        return trades
+
+
+def generate_report():
+    """生成完整报告"""
+    strategy = TrendTrackingStrategy()
+    
+    if not strategy.load_and_merge_data():
+        return None, None, None
+    
+    strategy.calculate_indicators()
+    result = strategy.backtest()
+    
+    recent_indicators = strategy.get_recent_indicators(20)
+    recent_trades = strategy.get_recent_trades(20)
+    
+    # 数据缺口警告
+    gap_warning = ""
+    if result['data_gaps']:
+        gap_warning = f"<div class='warning'>⚠️ 数据缺口: {', '.join(result['data_gaps'])} - 指标计算可能不准确</div>"
+    
+    html = f"""
+    <html>
+    <head>
+        <meta charset="utf-8">
+        <style>
+            body {{ font-family: Arial, sans-serif; margin: 20px; background: #f5f5f5; }}
+            .container {{ max-width: 1200px; margin: 0 auto; background: white; padding: 20px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }}
+            h1 {{ color: #333; border-bottom: 3px solid #007bff; padding-bottom: 10px; }}
+            h2 {{ color: #555; margin-top: 30px; border-left: 4px solid #007bff; padding-left: 10px; }}
+            .summary {{ background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; }}
+            .metric {{ display: inline-block; margin: 10px 20px 10px 0; }}
+            .metric-label {{ color: #666; font-size: 12px; }}
+            .metric-value {{ font-size: 24px; font-weight: bold; color: #333; }}
+            .positive {{ color: #28a745; }}
+            .negative {{ color: #dc3545; }}
+            .warning {{ background: #fff3cd; color: #856404; padding: 10px; border-radius: 5px; margin: 10px 0; }}
+            table {{ width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 13px; }}
+            th {{ background: #007bff; color: white; padding: 10px; text-align: left; }}
+            td {{ padding: 8px 10px; border-bottom: 1px solid #ddd; }}
+            tr:nth-child(even) {{ background: #f8f9fa; }}
+            tr:hover {{ background: #e9ecef; }}
+            .position-yes {{ color: #28a745; font-weight: bold; }}
+            .position-no {{ color: #666; }}
+            .buy {{ color: #28a745; font-weight: bold; }}
+            .sell {{ color: #dc3545; font-weight: bold; }}
+            .data-info {{ background: #e7f3ff; padding: 10px; border-radius: 5px; margin: 10px 0; color: #004085; }}
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1>🚀 创业板50趋势跟踪策略报告</h1>
+            <p style="color: #666;">生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
+            
+            {gap_warning}
+            
+            <div class="data-info">
+                <strong>数据更新:</strong> 最新数据日期: {result['data_end_date']} | 
+                数据范围: {strategy.data.index[0].date()} ~ {strategy.data.index[-1].date()}
+            </div>
+            
+            <div class="summary">
+                <h2>📊 总体绩效</h2>
+                <div class="metric">
+                    <div class="metric-label">当前持仓</div>
+                    <div class="metric-value {'position-yes' if result['current_position'] == 1 else 'position-no'}">
+                        {'持有中' if result['current_position'] == 1 else '空仓'}
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">当前价格</div>
+                    <div class="metric-value">{result['current_price']:.2f}</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">累计收益率</div>
+                    <div class="metric-value {'positive' if result['total_return'] >= 0 else 'negative'}">
+                        {result['total_return']:+.2f}%
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">总资产</div>
+                    <div class="metric-value">{result['total_value']:,.0f}元</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">交易次数</div>
+                    <div class="metric-value">{result['trade_count']}</div>
+                </div>
+            </div>
+    """
+    
+    if result['current_position'] == 1:
+        unrealized = (result['current_price'] / result['entry_price'] - 1) * 100
+        html += f"""
+            <div class="summary" style="background: #e8f5e9;">
+                <h2>📈 持仓详情</h2>
+                <p><strong>入场价格:</strong> {result['entry_price']:.2f} 元</p>
+                <p><strong>当前浮盈:</strong> <span class="{'positive' if unrealized >= 0 else 'negative'}">{unrealized:+.2f}%</span></p>
+            </div>
+        """
+    
+    # 近20天指标
+    html += """
+            <h2>📅 近20天指标详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>收盘价</th>
+                    <th>MA10</th>
+                    <th>MA30</th>
+                    <th>20日高</th>
+                    <th>10日涨幅</th>
+                    <th>信号</th>
+                    <th>ATR</th>
+                </tr>
+    """
+    
+    for ind in recent_indicators:
+        signal_class = 'buy' if ind['signal'] == '买入' else ('sell' if ind['signal'] == '卖出' else '')
+        html += f"""
+                <tr>
+                    <td>{ind['date']}</td>
+                    <td>{ind['close']}</td>
+                    <td>{ind['ma10']}</td>
+                    <td>{ind['ma30']}</td>
+                    <td>{ind['high_20']}</td>
+                    <td>{ind['ret_10']}</td>
+                    <td class="{signal_class}">{ind['signal']}</td>
+                    <td>{ind['atr']}</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <h2>💼 近20次交易详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>操作</th>
+                    <th>价格</th>
+                    <th>盈亏金额</th>
+                    <th>盈亏比例</th>
+                    <th>总资产</th>
+                </tr>
+    """
+    
+    for trade in recent_trades:
+        action_class = 'buy' if trade['action'] == 'BUY' else 'sell'
+        pnl = trade.get('pnl', 0)
+        ret = trade.get('return_pct', 0)
+        has_pnl = 'pnl' in trade
+        date_str = trade['date'].strftime('%Y-%m-%d') if hasattr(trade['date'], 'strftime') else str(trade['date'])
+        
+        # 盈亏比例样式：买入不显示样式，卖出根据盈亏显示
+        ret_class = '' if not has_pnl else ('positive' if ret >= 0 else 'negative')
+        ret_text = '-' if not has_pnl else f'{ret:+.2f}%'
+        
+        html += f"""
+                <tr>
+                    <td>{date_str}</td>
+                    <td class="{action_class}">{trade['action']}</td>
+                    <td>{trade['price']:.2f}</td>
+                    <td>{f'{pnl:+,.0f}元' if has_pnl else '-'}</td>
+                    <td class="{ret_class}">{ret_text}</td>
+                    <td>{trade['capital']:,.0f}元</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <div style="margin-top: 30px; padding: 15px; background: #fff3cd; border-radius: 5px; color: #856404;">
+                <strong>策略说明:</strong><br>
+                • 买入条件: 价格>MA10>MA30 且 突破20日高×0.995 且 10日涨幅>2%<br>
+                • 卖出条件: 跌破MA30 或 创20日新低 或 回撤10%止损<br>
+                • 数据已自动合并历史数据与实时数据
+            </div>
+        </div>
+    </body>
+    </html>
+    """
+    
+    # 文本报告
+    text = f"""
+创业板50趋势跟踪策略报告
+生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+数据最新日期: {result['data_end_date']}
+"""
+    
+    if result['data_gaps']:
+        text += f"⚠️ 警告: 数据缺口 {', '.join(result['data_gaps'])}\n"
+    
+    text += f"""
+【总体绩效】
+当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}
+当前价格: {result['current_price']:.2f}元
+累计收益率: {result['total_return']:+.2f}%
+总资产: {result['total_value']:,.0f}元
+
+【近20天指标】
+日期        收盘价    MA10     MA30     20日高   10日涨幅  信号
+"""
+    
+    for ind in recent_indicators:
+        text += f"{ind['date']}  {ind['close']:>8}  {ind['ma10']:>8}  {ind['ma30']:>8}  {ind['high_20']:>8}  {ind['ret_10']:>8}  {ind['signal']:>4}\n"
+    
+    text += "\n【近20次交易】\n"
+    for trade in recent_trades:
+        pnl_str = f"{trade.get('pnl', 0):+,.0f}元" if 'pnl' in trade else '-'
+        ret_str = f"{trade.get('return_pct', 0):+.2f}%" if 'return_pct' in trade else '-'
+        date_str = trade['date'].strftime('%Y-%m-%d') if hasattr(trade['date'], 'strftime') else str(trade['date'])
+        text += f"{date_str}  {trade['action']:>6}  {trade['price']:>8.2f}  {pnl_str:>12}  {ret_str:>10}\n"
+    
+    return html, text, result
+
+
+def send_email(subject, html_content, text_content):
+    """发送邮件"""
+    try:
+        msg = MIMEMultipart('alternative')
+        msg['Subject'] = Header(subject, 'utf-8')
+        msg['From'] = EMAIL_CONFIG['sender_email']
+        msg['To'] = EMAIL_CONFIG['receiver_email']
+        
+        text_part = MIMEText(text_content, 'plain', 'utf-8')
+        msg.attach(text_part)
+        
+        html_part = MIMEText(html_content, 'html', 'utf-8')
+        msg.attach(html_part)
+        
+        with smtplib.SMTP(EMAIL_CONFIG['smtp_server'], EMAIL_CONFIG['smtp_port']) as server:
+            server.sendmail(
+                EMAIL_CONFIG['sender_email'],
+                EMAIL_CONFIG['receiver_email'],
+                msg.as_string()
+            )
+        print(f"✅ 邮件发送成功: {subject}")
+        return True
+    except Exception as e:
+        print(f"❌ 邮件发送失败: {e}")
+        return False
+
+
+def main():
+    """主程序"""
+    print("="*60)
+    print("🚀 创业板50趋势跟踪实时报告 (修复数据连续性)")
+    print("="*60)
+    print(f"执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    
+    html, text, result = generate_report()
+    
+    if html is None:
+        print("❌ 报告生成失败")
+        return
+    
+    print(f"\n✅ 报告生成完成")
+    print(f"   数据最新日期: {result['data_end_date']}")
+    print(f"   当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}")
+    print(f"   累计收益: {result['total_return']:+.2f}%")
+    
+    print("\n📧 发送邮件...")
+    position_status = "持仓" if result['current_position'] == 1 else "空仓"
+    subject = f"🚀 创业板50趋势报告 {datetime.now().strftime('%m-%d %H:%M')} | {position_status} | 收益{result['total_return']:+.2f}%"
+    send_email(subject, html, text)
+    
+    print("\n✅ 全部完成!")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    main()

cat-fly/trend_report_multi.py

@@ -0,0 +1,673 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+创业板50指数 - 实时交易报告系统 (多数据源)
+支持新浪、腾讯、东方财富接口
+"""
+
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
+sys.path.insert(0, '/root/.openclaw/workspace/quant')
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import smtplib
+import ssl
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from email.header import Header
+import warnings
+import requests
+import json
+import time
+warnings.filterwarnings('ignore')
+
+# ==================== 邮件配置 ====================
+EMAIL_CONFIG = {
+    "smtp_server": "localhost",
+    "smtp_port": 25,
+    "sender_email": "catfly@openclaw.local",
+    "receiver_email": "380880504@qq.com"
+}
+
+# ==================== 多数据源获取 ====================
+
+def fetch_sina_realtime():
+    """新浪实时行情接口"""
+    try:
+        # 新浪股票代码格式: sh000001(上证), sz399673(创业板50)
+        url = "https://hq.sinajs.cn/list=sz399673"
+        headers = {
+            'Referer': 'https://finance.sina.com.cn',
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        response.encoding = 'gb2312'
+        
+        # 解析返回数据
+        data_str = response.text
+        if 'var hq_str_sz399673="' in data_str:
+            data_str = data_str.split('var hq_str_sz399673="')[1].split('"')[0]
+            parts = data_str.split(',')
+            if len(parts) >= 32:
+                return {
+                    'open': float(parts[1]),
+                    'high': float(parts[4]),
+                    'low': float(parts[5]),
+                    'close': float(parts[3]),
+                    'volume': int(parts[8]),
+                    'name': parts[0],
+                    'time': parts[31],
+                    'date': parts[30]
+                }
+    except Exception as e:
+        print(f"新浪实时数据获取失败: {e}")
+    return None
+
+
+def fetch_tencent_realtime():
+    """腾讯实时行情接口"""
+    try:
+        # 腾讯股票代码格式: sh000001, sz399673
+        url = "https://qt.gtimg.cn/q=sz399673"
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        response.encoding = 'gb2312'
+        
+        data_str = response.text
+        if 'v_sz399673="' in data_str:
+            data_str = data_str.split('v_sz399673="')[1].split('"')[0]
+            parts = data_str.split('~')
+            if len(parts) >= 45:
+                return {
+                    'name': parts[1],
+                    'close': float(parts[3]),
+                    'open': float(parts[5]),
+                    'high': float(parts[33]),
+                    'low': float(parts[34]),
+                    'volume': int(parts[36]),
+                    'date': parts[30]
+                }
+    except Exception as e:
+        print(f"腾讯实时数据获取失败: {e}")
+    return None
+
+
+def fetch_sina_hist_t1():
+    """新浪历史数据 - T-1日"""
+    try:
+        # 新浪历史行情接口
+        today = datetime.now()
+        url = f"https://quotes.sina.cn/cn/api/quotes.php?symbol=sz399673"
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        
+        if response.status_code == 200:
+            data = response.json()
+            if 'data' in data and len(data['data']) > 0:
+                item = data['data'][0]
+                return {
+                    'date': item.get('date'),
+                    'open': float(item.get('open', 0)),
+                    'high': float(item.get('high', 0)),
+                    'low': float(item.get('low', 0)),
+                    'close': float(item.get('close', 0)),
+                    'volume': int(item.get('volume', 0))
+                }
+    except Exception as e:
+        print(f"新浪历史数据获取失败: {e}")
+    return None
+
+
+def fetch_163_hist():
+    """网易财经历史数据接口"""
+    try:
+        url = "http://quotes.money.163.com/service/chddata.html"
+        params = {
+            'code': '1399673',  # 创业板50代码
+            'start': (datetime.now() - timedelta(days=30)).strftime('%Y%m%d'),
+            'end': datetime.now().strftime('%Y%m%d'),
+            'fields': 'TCLOSE;HIGH;LOW;TOPEN;CHG;PCHG;VOTURNOVER'
+        }
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, params=params, headers=headers, timeout=10)
+        
+        if response.status_code == 200:
+            # 解析CSV格式数据
+            lines = response.text.strip().split('\n')
+            if len(lines) > 1:
+                # 取最新一条
+                latest = lines[-1].split(',')
+                if len(latest) >= 6:
+                    return {
+                        'date': latest[0],
+                        'open': float(latest[1]),
+                        'high': float(latest[2]),
+                        'low': float(latest[3]),
+                        'close': float(latest[4]),
+                        'volume': int(float(latest[5])) if latest[5] else 0
+                    }
+    except Exception as e:
+        print(f"网易历史数据获取失败: {e}")
+    return None
+
+
+def fetch_realtime_data():
+    """获取实时数据，尝试多个数据源"""
+    print("尝试获取实时数据...")
+    
+    # 尝试新浪实时
+    print("  尝试新浪实时接口...")
+    data = fetch_sina_realtime()
+    if data:
+        print(f"  ✅ 新浪实时数据获取成功")
+        return data, 'sina_realtime'
+    
+    # 尝试腾讯实时
+    print("  尝试腾讯实时接口...")
+    data = fetch_tencent_realtime()
+    if data:
+        print(f"  ✅ 腾讯实时数据获取成功")
+        return data, 'tencent_realtime'
+    
+    # 尝试获取T-1日数据
+    print("  尝试获取T-1历史数据...")
+    
+    # 网易历史数据
+    data = fetch_163_hist()
+    if data:
+        print(f"  ✅ 网易历史数据获取成功")
+        return data, '163_hist'
+    
+    print("  ❌ 所有数据源均失败")
+    return None, None
+
+
+# ==================== 趋势跟踪策略 ====================
+class TrendTrackingStrategy:
+    """趋势跟踪策略 - 实时计算版"""
+    
+    def __init__(self):
+        self.data = None
+        
+    def load_and_merge_data(self, csv_file='cyb50_baostock.csv'):
+        """加载历史数据并合并实时数据"""
+        try:
+            # 加载历史数据
+            df = pd.read_csv(f'/root/.openclaw/workspace/quant/{csv_file}')
+            df['date'] = pd.to_datetime(df['date'])
+            df = df.set_index('date').sort_index()
+            
+            # 转换数据类型
+            for col in ['open', 'high', 'low', 'close', 'volume']:
+                df[col] = pd.to_numeric(df[col], errors='coerce')
+            
+            # 获取最新历史数据日期
+            last_hist_date = df.index[-1]
+            today = pd.Timestamp.now().normalize()
+            
+            print(f"历史数据最新日期: {last_hist_date.date()}")
+            print(f"当前日期: {today.date()}")
+            
+            # 如果历史数据不是最新的，尝试获取实时数据
+            realtime_data = None
+            source = None
+            
+            if last_hist_date < today:
+                realtime_data, source = fetch_realtime_data()
+                
+                if realtime_data:
+                    # 处理日期格式
+                    if 'date' in realtime_data and realtime_data['date']:
+                        try:
+                            # 尝试不同日期格式
+                            date_str = str(realtime_data['date'])
+                            if len(date_str) == 8:  # YYYYMMDD
+                                date = pd.Timestamp(date_str)
+                            else:
+                                date = today
+                        except:
+                            date = today
+                    else:
+                        date = today
+                    
+                    # 检查是否已有该日期数据
+                    if date > last_hist_date:
+                        # 创建新数据行
+                        new_row = pd.DataFrame({
+                            'open': [realtime_data['open']],
+                            'high': [realtime_data['high']],
+                            'low': [realtime_data['low']],
+                            'close': [realtime_data['close']],
+                            'volume': [realtime_data['volume']]
+                        }, index=[date])
+                        
+                        # 合并数据
+                        df = pd.concat([df, new_row])
+                        print(f"✅ 已合并{source}数据: {date.date()} 收盘价 {realtime_data['close']}")
+                    else:
+                        print(f"⚠️ 获取的数据日期({date.date()})不大于历史最新日期")
+                else:
+                    print("⚠️ 未获取到更新的实时数据，使用历史数据")
+            else:
+                print("✅ 历史数据已是最新")
+            
+            self.data = df
+            print(f"数据范围: {df.index[0].date()} ~ {df.index[-1].date()}")
+            return True
+            
+        except Exception as e:
+            print(f"数据加载失败: {e}")
+            return False
+    
+    def calculate_indicators(self):
+        """计算技术指标"""
+        df = self.data.copy()
+        
+        # 均线
+        df['ma10'] = df['close'].rolling(window=10).mean()
+        df['ma30'] = df['close'].rolling(window=30).mean()
+        
+        # 20日高低点
+        df['high_20'] = df['high'].rolling(window=20).max()
+        df['low_20'] = df['low'].rolling(window=20).min()
+        
+        # 10日涨幅
+        df['ret_10'] = df['close'].pct_change(periods=10)
+        
+        # ATR
+        tr1 = df['high'] - df['low']
+        tr2 = abs(df['high'] - df['close'].shift(1))
+        tr3 = abs(df['low'] - df['close'].shift(1))
+        df['tr'] = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        df['atr'] = df['tr'].rolling(window=20).mean()
+        
+        self.data = df
+        return df
+    
+    def generate_signals(self):
+        """生成交易信号"""
+        df = self.data
+        
+        # 买入条件
+        buy_cond = (
+            (df['close'] > df['ma10']) & 
+            (df['ma10'] > df['ma30']) & 
+            (df['close'] >= df['high_20'] * 0.995) & 
+            (df['ret_10'] > 0.02)
+        )
+        
+        # 卖出条件
+        sell_cond = (
+            (df['close'] < df['ma30']) | 
+            (df['close'] <= df['low_20'] * 1.005)
+        )
+        
+        df['signal'] = 0
+        df.loc[buy_cond, 'signal'] = 1
+        df.loc[sell_cond, 'signal'] = -1
+        
+        return df
+    
+    def backtest(self, initial_capital=1000000):
+        """回测计算"""
+        df = self.generate_signals()
+        
+        position = 0
+        entry_price = 0
+        peak_price = 0
+        capital = initial_capital
+        trades = []
+        
+        for i in range(30, len(df)):
+            date = df.index[i]
+            price = df['close'].iloc[i]
+            signal = df['signal'].iloc[i]
+            
+            # 移动止损检查
+            if position > 0:
+                if price > peak_price:
+                    peak_price = price
+                if price < peak_price * 0.90:  # 10%回撤止损
+                    signal = -1
+            
+            # 执行交易
+            if signal == 1 and position == 0:
+                position = 1
+                entry_price = price
+                peak_price = price
+                trades.append({
+                    'date': date,
+                    'action': 'BUY',
+                    'price': price,
+                    'capital': capital
+                })
+            
+            elif signal == -1 and position == 1:
+                pnl = (price / entry_price - 1) * capital
+                capital += pnl
+                position = 0
+                trades.append({
+                    'date': date,
+                    'action': 'SELL',
+                    'price': price,
+                    'capital': capital,
+                    'pnl': pnl,
+                    'return_pct': (price / entry_price - 1) * 100
+                })
+        
+        # 计算当前持仓
+        current_position = position
+        current_price = df['close'].iloc[-1]
+        
+        if position == 1:
+            unrealized_pnl = (current_price / entry_price - 1) * capital
+            total_value = capital + unrealized_pnl
+        else:
+            total_value = capital
+        
+        total_return = (total_value / initial_capital - 1) * 100
+        
+        return {
+            'trades': trades,
+            'current_position': current_position,
+            'current_price': current_price,
+            'entry_price': entry_price if position == 1 else None,
+            'capital': capital,
+            'total_value': total_value,
+            'total_return': total_return,
+            'trade_count': len([t for t in trades if t['action'] == 'SELL']),
+            'data_end_date': df.index[-1].strftime('%Y-%m-%d')
+        }
+    
+    def get_recent_indicators(self, days=20):
+        """获取近N天指标详情"""
+        df = self.data.tail(days).copy()
+        
+        indicators = []
+        for date, row in df.iterrows():
+            indicators.append({
+                'date': date.strftime('%Y-%m-%d'),
+                'close': round(row['close'], 2),
+                'ma10': round(row['ma10'], 2) if not pd.isna(row['ma10']) else '-',
+                'ma30': round(row['ma30'], 2) if not pd.isna(row['ma30']) else '-',
+                'high_20': round(row['high_20'], 2) if not pd.isna(row['high_20']) else '-',
+                'ret_10': f"{row['ret_10']*100:.2f}%" if not pd.isna(row['ret_10']) else '-',
+                'signal': '买入' if row['signal'] == 1 else ('卖出' if row['signal'] == -1 else '持有'),
+                'atr': round(row['atr'], 2) if not pd.isna(row['atr']) else '-'
+            })
+        
+        return indicators
+    
+    def get_recent_trades(self, n=20):
+        """获取近N次交易详情"""
+        result = self.backtest()
+        trades = result['trades'][-n:]
+        return trades
+
+
+def generate_report():
+    """生成完整报告"""
+    strategy = TrendTrackingStrategy()
+    
+    if not strategy.load_and_merge_data():
+        return None, None, None
+    
+    strategy.calculate_indicators()
+    result = strategy.backtest()
+    
+    # 获取近期数据
+    recent_indicators = strategy.get_recent_indicators(20)
+    recent_trades = strategy.get_recent_trades(20)
+    
+    # 生成HTML报告
+    html = f"""
+    <html>
+    <head>
+        <meta charset="utf-8">
+        <style>
+            body {{ font-family: Arial, sans-serif; margin: 20px; background: #f5f5f5; }}
+            .container {{ max-width: 1200px; margin: 0 auto; background: white; padding: 20px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }}
+            h1 {{ color: #333; border-bottom: 3px solid #007bff; padding-bottom: 10px; }}
+            h2 {{ color: #555; margin-top: 30px; border-left: 4px solid #007bff; padding-left: 10px; }}
+            .summary {{ background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; }}
+            .metric {{ display: inline-block; margin: 10px 20px 10px 0; }}
+            .metric-label {{ color: #666; font-size: 12px; }}
+            .metric-value {{ font-size: 24px; font-weight: bold; color: #333; }}
+            .positive {{ color: #28a745; }}
+            .negative {{ color: #dc3545; }}
+            table {{ width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 13px; }}
+            th {{ background: #007bff; color: white; padding: 10px; text-align: left; }}
+            td {{ padding: 8px 10px; border-bottom: 1px solid #ddd; }}
+            tr:nth-child(even) {{ background: #f8f9fa; }}
+            tr:hover {{ background: #e9ecef; }}
+            .position-yes {{ color: #28a745; font-weight: bold; }}
+            .position-no {{ color: #666; }}
+            .buy {{ color: #28a745; font-weight: bold; }}
+            .sell {{ color: #dc3545; font-weight: bold; }}
+            .data-info {{ background: #e7f3ff; padding: 10px; border-radius: 5px; margin: 10px 0; color: #004085; }}
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1>🚀 创业板50趋势跟踪策略报告</h1>
+            <p style="color: #666;">生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
+            
+            <div class="data-info">
+                <strong>数据更新:</strong> 已合并历史数据与实时数据，最新数据日期: {result['data_end_date']}
+            </div>
+            
+            <div class="summary">
+                <h2>📊 总体绩效</h2>
+                <div class="metric">
+                    <div class="metric-label">当前持仓</div>
+                    <div class="metric-value {'position-yes' if result['current_position'] == 1 else 'position-no'}">
+                        {'持有中' if result['current_position'] == 1 else '空仓'}
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">当前价格</div>
+                    <div class="metric-value">{result['current_price']:.2f}</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">累计收益率</div>
+                    <div class="metric-value {'positive' if result['total_return'] >= 0 else 'negative'}">
+                        {result['total_return']:+.2f}%
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">总资产</div>
+                    <div class="metric-value">{result['total_value']:,.0f}元</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">交易次数</div>
+                    <div class="metric-value">{result['trade_count']}</div>
+                </div>
+            </div>
+    """
+    
+    # 持仓详情
+    if result['current_position'] == 1:
+        unrealized = (result['current_price'] / result['entry_price'] - 1) * 100
+        html += f"""
+            <div class="summary" style="background: #e8f5e9;">
+                <h2>📈 持仓详情</h2>
+                <p><strong>入场价格:</strong> {result['entry_price']:.2f} 元</p>
+                <p><strong>当前浮盈:</strong> <span class="{'positive' if unrealized >= 0 else 'negative'}">{unrealized:+.2f}%</span></p>
+            </div>
+        """
+    
+    # 近20天指标
+    html += """
+            <h2>📅 近20天指标详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>收盘价</th>
+                    <th>MA10</th>
+                    <th>MA30</th>
+                    <th>20日高</th>
+                    <th>10日涨幅</th>
+                    <th>信号</th>
+                    <th>ATR</th>
+                </tr>
+    """
+    
+    for ind in recent_indicators:
+        signal_class = 'buy' if ind['signal'] == '买入' else ('sell' if ind['signal'] == '卖出' else '')
+        html += f"""
+                <tr>
+                    <td>{ind['date']}</td>
+                    <td>{ind['close']}</td>
+                    <td>{ind['ma10']}</td>
+                    <td>{ind['ma30']}</td>
+                    <td>{ind['high_20']}</td>
+                    <td>{ind['ret_10']}</td>
+                    <td class="{signal_class}">{ind['signal']}</td>
+                    <td>{ind['atr']}</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <h2>💼 近20次交易详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>操作</th>
+                    <th>价格</th>
+                    <th>盈亏金额</th>
+                    <th>盈亏比例</th>
+                    <th>总资产</th>
+                </tr>
+    """
+    
+    for trade in recent_trades:
+        action_class = 'buy' if trade['action'] == 'BUY' else 'sell'
+        pnl = trade.get('pnl', 0)
+        ret = trade.get('return_pct', 0)
+        html += f"""
+                <tr>
+                    <td>{trade['date'].strftime('%Y-%m-%d') if isinstance(trade['date'], pd.Timestamp) else trade['date']}</td>
+                    <td class="{action_class}">{trade['action']}</td>
+                    <td>{trade['price']:.2f}</td>
+                    <td>{f'{pnl:+,.0f}元' if 'pnl' in trade else '-'}</td>
+                    <td class="{'positive' if ret >= 0 else 'negative'}">{f'{ret:+.2f}%' if 'return_pct' in trade else '-'}</td>
+                    <td>{trade['capital']:,.0f}元</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <div style="margin-top: 30px; padding: 15px; background: #fff3cd; border-radius: 5px; color: #856404;">
+                <strong>策略说明:</strong><br>
+                • 买入条件: 价格>MA10>MA30 且 突破20日高×0.995 且 10日涨幅>2%<br>
+                • 卖出条件: 跌破MA30 或 创20日新低 或 回撤10%止损<br>
+                • 数据更新: 已合并历史数据与实时市场数据(支持新浪/腾讯/网易多数据源)
+            </div>
+        </div>
+    </body>
+    </html>
+    """
+    
+    # 生成文本报告
+    text = f"""
+创业板50趋势跟踪策略报告
+生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+数据最新日期: {result['data_end_date']}
+
+【总体绩效】
+当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}
+当前价格: {result['current_price']:.2f}元
+累计收益率: {result['total_return']:+.2f}%
+总资产: {result['total_value']:,.0f}元
+交易次数: {result['trade_count']}
+
+【近20天指标】
+日期        收盘价    MA10     MA30     20日高   10日涨幅  信号  ATR
+"""
+    
+    for ind in recent_indicators:
+        text += f"{ind['date']}  {ind['close']:>8}  {ind['ma10']:>8}  {ind['ma30']:>8}  {ind['high_20']:>8}  {ind['ret_10']:>8}  {ind['signal']:>4}  {ind['atr']:>6}\n"
+    
+    text += "\n【近20次交易】\n"
+    text += "日期        操作    价格      盈亏金额     盈亏比例    总资产\n"
+    
+    for trade in recent_trades:
+        pnl_str = f"{trade.get('pnl', 0):+,.0f}元" if 'pnl' in trade else '-'
+        ret_str = f"{trade.get('return_pct', 0):+.2f}%" if 'return_pct' in trade else '-'
+        date_str = trade['date'].strftime('%Y-%m-%d') if isinstance(trade['date'], pd.Timestamp) else trade['date']
+        text += f"{date_str}  {trade['action']:>6}  {trade['price']:>8.2f}  {pnl_str:>12}  {ret_str:>10}  {trade['capital']:>12,.0f}元\n"
+    
+    return html, text, result
+
+
+def send_email(subject, html_content, text_content):
+    """发送邮件"""
+    try:
+        msg = MIMEMultipart('alternative')
+        msg['Subject'] = Header(subject, 'utf-8')
+        msg['From'] = EMAIL_CONFIG['sender_email']
+        msg['To'] = EMAIL_CONFIG['receiver_email']
+        
+        text_part = MIMEText(text_content, 'plain', 'utf-8')
+        msg.attach(text_part)
+        
+        html_part = MIMEText(html_content, 'html', 'utf-8')
+        msg.attach(html_part)
+        
+        with smtplib.SMTP(EMAIL_CONFIG['smtp_server'], EMAIL_CONFIG['smtp_port']) as server:
+            server.sendmail(
+                EMAIL_CONFIG['sender_email'],
+                EMAIL_CONFIG['receiver_email'],
+                msg.as_string()
+            )
+        print(f"✅ 邮件发送成功: {subject}")
+        return True
+    except Exception as e:
+        print(f"❌ 邮件发送失败: {e}")
+        return False
+
+
+def main():
+    """主程序"""
+    print("="*60)
+    print("🚀 创业板50趋势跟踪实时报告 (多数据源)")
+    print("="*60)
+    print(f"执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    
+    # 生成报告
+    print("\n📊 加载数据并生成报告中...")
+    html, text, result = generate_report()
+    
+    if html is None:
+        print("❌ 报告生成失败")
+        return
+    
+    print(f"✅ 报告生成完成")
+    print(f"   数据最新日期: {result['data_end_date']}")
+    print(f"   当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}")
+    print(f"   累计收益: {result['total_return']:+.2f}%")
+    print(f"   交易次数: {result['trade_count']}")
+    
+    # 发送邮件
+    print("\n📧 发送邮件...")
+    position_status = "持仓" if result['current_position'] == 1 else "空仓"
+    subject = f"🚀 创业板50趋势报告 {datetime.now().strftime('%m-%d %H:%M')} | {position_status} | 收益{result['total_return']:+.2f}%"
+    send_email(subject, html, text)
+    
+    print("\n✅ 全部完成!")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    main()

cat-fly/trend_report_multi_source.py

@@ -0,0 +1,819 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+创业板50指数 - 多数据源交叉验证版
+确保数据准确性
+"""
+
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
+sys.path.insert(0, '/root/.openclaw/workspace/quant')
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from email.header import Header
+import warnings
+import requests
+import json
+import time
+import baostock as bs
+warnings.filterwarnings('ignore')
+
+# ==================== 邮件配置 ====================
+EMAIL_CONFIG = {
+    "smtp_server": "localhost",
+    "smtp_port": 25,
+    "sender_email": "catfly@openclaw.local",
+    "receiver_email": "380880504@qq.com"
+}
+
+# ==================== 数据源配置 ====================
+DATA_SOURCES = {
+    'baostock': {'priority': 1, 'weight': 0.4},
+    'akshare': {'priority': 2, 'weight': 0.3},
+    'sina': {'priority': 3, 'weight': 0.15},
+    'tencent': {'priority': 4, 'weight': 0.15}
+}
+
+# ==================== 多数据源获取 ====================
+
+class DataValidator:
+    """数据验证器 - 交叉验证多个数据源"""
+    
+    PRICE_TOLERANCE = 0.02  # 价格差异容忍度 2%
+    
+    @staticmethod
+    def validate_prices(sources_data):
+        """
+        验证多个数据源的价格一致性
+        返回: (validated_data, warnings)
+        """
+        if not sources_data:
+            return None, ["无可用数据源"]
+        
+        if len(sources_data) == 1:
+            return sources_data[0]['data'], []
+        
+        # 提取收盘价
+        prices = {}
+        for src in sources_data:
+            if src and 'data' in src and src['data']:
+                prices[src['source']] = src['data'].get('close', 0)
+        
+        if len(prices) < 2:
+            return sources_data[0]['data'], []
+        
+        # 计算价格统计
+        price_values = list(prices.values())
+        price_mean = np.mean(price_values)
+        price_std = np.std(price_values)
+        price_max = max(price_values)
+        price_min = min(price_values)
+        price_range = (price_max - price_min) / price_mean if price_mean > 0 else 0
+        
+        warnings = []
+        
+        # 检查价格差异
+        if price_range > DataValidator.PRICE_TOLERANCE:
+            warnings.append(f"⚠️ 价格差异过大: {price_range*100:.2f}% (容忍度: {DataValidator.PRICE_TOLERANCE*100}%)")
+            for src, price in prices.items():
+                deviation = (price - price_mean) / price_mean * 100
+                warnings.append(f"  - {src}: {price:.2f} (偏离均值: {deviation:+.2f}%)")
+        
+        # 使用加权平均价格
+        weighted_price = 0
+        total_weight = 0
+        for src in sources_data:
+            if src and 'data' in src and src['data']:
+                source_name = src['source']
+                weight = DATA_SOURCES.get(source_name, {}).get('weight', 0.25)
+                weighted_price += src['data']['close'] * weight
+                total_weight += weight
+        
+        if total_weight > 0:
+            weighted_price /= total_weight
+        
+        # 选择最接近加权平均的数据源
+        best_source = None
+        min_diff = float('inf')
+        for src in sources_data:
+            if src and 'data' in src and src['data']:
+                diff = abs(src['data']['close'] - weighted_price)
+                if diff < min_diff:
+                    min_diff = diff
+                    best_source = src
+        
+        if warnings:
+            warnings.append(f"✅ 使用加权平均价格: {weighted_price:.2f}")
+            if best_source:
+                warnings.append(f"✅ 选择数据源: {best_source['source']} (价格: {best_source['data']['close']:.2f})")
+        
+        return best_source['data'] if best_source else sources_data[0]['data'], warnings
+
+
+def fetch_sina_realtime():
+    """新浪实时行情接口"""
+    try:
+        url = "https://hq.sinajs.cn/list=sz399673"
+        headers = {
+            'Referer': 'https://finance.sina.com.cn',
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        response.encoding = 'gb2312'
+        
+        data_str = response.text
+        if 'var hq_str_sz399673="' in data_str:
+            data_str = data_str.split('var hq_str_sz399673="')[1].split('"')[0]
+            parts = data_str.split(',')
+            if len(parts) >= 32:
+                date_str = parts[30].strip()
+                if len(date_str) == 8 and date_str.isdigit():
+                    date = pd.Timestamp(f"{date_str[:4]}-{date_str[4:6]}-{date_str[6:8]}")
+                else:
+                    date = pd.Timestamp.now().normalize()
+                
+                return {
+                    'source': 'sina',
+                    'data': {
+                        'open': float(parts[1]),
+                        'high': float(parts[4]),
+                        'low': float(parts[5]),
+                        'close': float(parts[3]),
+                        'volume': int(parts[8]),
+                        'date': date
+                    }
+                }
+    except Exception as e:
+        print(f"  新浪实时数据获取失败: {e}")
+    return None
+
+
+def fetch_tencent_realtime():
+    """腾讯实时行情接口"""
+    try:
+        url = "https://qt.gtimg.cn/q=sz399673"
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        response.encoding = 'gb2312'
+        
+        data_str = response.text
+        if 'v_sz399673="' in data_str:
+            data_str = data_str.split('v_sz399673="')[1].split('"')[0]
+            parts = data_str.split('~')
+            if len(parts) >= 45:
+                date_str = parts[30]
+                date = pd.Timestamp(date_str)
+                
+                return {
+                    'source': 'tencent',
+                    'data': {
+                        'close': float(parts[3]),
+                        'open': float(parts[5]),
+                        'high': float(parts[33]),
+                        'low': float(parts[34]),
+                        'volume': int(parts[36]),
+                        'date': date
+                    }
+                }
+    except Exception as e:
+        print(f"  腾讯实时数据获取失败: {e}")
+    return None
+
+
+def fetch_akshare_hist(start_date, end_date):
+    """使用akshare获取历史数据"""
+    try:
+        import akshare as ak
+        print(f"  [akshare] 获取 {start_date} 到 {end_date} 的历史数据...")
+        
+        df = ak.index_zh_a_hist(symbol="399673", period="daily", 
+                                start_date=start_date, end_date=end_date)
+        
+        if df is not None and len(df) > 0:
+            data_list = []
+            for _, row in df.iterrows():
+                data_list.append({
+                    'date': pd.Timestamp(row['日期']),
+                    'open': float(row['开盘']),
+                    'high': float(row['最高']),
+                    'low': float(row['最低']),
+                    'close': float(row['收盘']),
+                    'volume': int(row['成交量'])
+                })
+            print(f"  [akshare] ✅ 获取成功: {len(data_list)} 条")
+            return data_list
+    except Exception as e:
+        print(f"  [akshare] ❌ 获取失败: {e}")
+    return None
+
+
+def fetch_baostock_hist(start_date, end_date):
+    """使用baostock获取历史数据"""
+    try:
+        print(f"  [baostock] 获取 {start_date} 到 {end_date} 的历史数据...")
+        
+        lg = bs.login()
+        if lg.error_code != '0':
+            print(f"  [baostock] ❌ 登录失败: {lg.error_msg}")
+            return None
+        
+        rs = bs.query_history_k_data_plus("sz.399673",
+            "date,open,high,low,close,volume",
+            start_date=start_date, end_date=end_date,
+            frequency="d", adjustflag="3")
+        
+        data_list = []
+        while (rs.error_code == '0') & rs.next():
+            row = rs.get_row_data()
+            if row[0]:
+                data_list.append({
+                    'date': pd.Timestamp(row[0]),
+                    'open': float(row[1]) if row[1] else 0,
+                    'high': float(row[2]) if row[2] else 0,
+                    'low': float(row[3]) if row[3] else 0,
+                    'close': float(row[4]) if row[4] else 0,
+                    'volume': int(float(row[5])) if row[5] else 0
+                })
+        
+        bs.logout()
+        
+        if data_list:
+            print(f"  [baostock] ✅ 获取成功: {len(data_list)} 条")
+            return data_list
+    except Exception as e:
+        print(f"  [baostock] ❌ 获取失败: {e}")
+    return None
+
+
+def fetch_multi_source_realtime():
+    """从多个源获取实时数据并交叉验证"""
+    print("\n📊 多数据源获取实时数据...")
+    
+    sources = []
+    
+    # 获取新浪数据
+    sina_data = fetch_sina_realtime()
+    if sina_data:
+        sources.append(sina_data)
+        print(f"  [新浪]    收盘价: {sina_data['data']['close']:.2f}")
+    
+    # 获取腾讯数据
+    tencent_data = fetch_tencent_realtime()
+    if tencent_data:
+        sources.append(tencent_data)
+        print(f"  [腾讯]    收盘价: {tencent_data['data']['close']:.2f}")
+    
+    # 交叉验证
+    validated_data, warnings = DataValidator.validate_prices(sources)
+    
+    if warnings:
+        print("\n  ⚠️ 数据验证警告:")
+        for w in warnings:
+            print(f"     {w}")
+    
+    if validated_data:
+        print(f"\n  ✅ 最终使用收盘价: {validated_data['close']:.2f}")
+        return validated_data, sources
+    
+    return None, []
+
+
+def fetch_missing_data_multi_source(last_date, today):
+    """使用多个源获取缺失的历史数据"""
+    if last_date >= today:
+        return [], []
+    
+    start_str = (last_date + timedelta(days=1)).strftime('%Y-%m-%d')
+    end_str = (today - timedelta(days=1)).strftime('%Y-%m-%d')
+    
+    print(f"\n📊 补全缺失数据: {start_str} 到 {end_str}")
+    
+    all_warnings = []
+    
+    # 尝试baostock (优先级高)
+    data = fetch_baostock_hist(start_str, end_str)
+    if data:
+        return data, all_warnings
+    
+    # 尝试akshare
+    data = fetch_akshare_hist(start_str.replace('-', ''), end_str.replace('-', ''))
+    if data:
+        return data, all_warnings
+    
+    all_warnings.append(f"⚠️ 无法获取 {start_str} 到 {end_str} 的历史数据")
+    return [], all_warnings
+
+
+# ==================== 趋势跟踪策略 ====================
+class TrendTrackingStrategy:
+    """趋势跟踪策略 - 多数据源验证版"""
+    
+    def __init__(self):
+        self.data = None
+        self.warnings = []
+        self.data_sources = []
+        
+    def load_and_merge_data(self, csv_file='cyb50_baostock.csv'):
+        """加载历史数据并合并实时数据"""
+        try:
+            # 加载历史数据
+            df = pd.read_csv(f'/root/.openclaw/workspace/quant/{csv_file}')
+            df['date'] = pd.to_datetime(df['date'])
+            df = df.set_index('date').sort_index()
+            
+            for col in ['open', 'high', 'low', 'close', 'volume']:
+                df[col] = pd.to_numeric(df[col], errors='coerce')
+            
+            last_hist_date = df.index[-1]
+            today = pd.Timestamp.now().normalize()
+            
+            print(f"历史数据最新日期: {last_hist_date.date()}")
+            print(f"当前日期: {today.date()}")
+            
+            # 获取缺失的历史数据
+            missing_data, warnings = fetch_missing_data_multi_source(last_hist_date, today)
+            self.warnings.extend(warnings)
+            
+            if missing_data:
+                for item in missing_data:
+                    new_row = pd.DataFrame({
+                        'open': [item['open']],
+                        'high': [item['high']],
+                        'low': [item['low']],
+                        'close': [item['close']],
+                        'volume': [item['volume']]
+                    }, index=[item['date']])
+                    df = pd.concat([df, new_row])
+                
+                df = df.sort_index()
+                last_hist_date = df.index[-1]
+                print(f"✅ 已合并历史数据，最新日期: {last_hist_date.date()}")
+            
+            # 获取今天的实时数据（多源验证）
+            if last_hist_date < today:
+                realtime_data, sources = fetch_multi_source_realtime()
+                self.data_sources = sources
+                
+                if realtime_data:
+                    date = realtime_data['date']
+                    
+                    if date > last_hist_date:
+                        new_row = pd.DataFrame({
+                            'open': [realtime_data['open']],
+                            'high': [realtime_data['high']],
+                            'low': [realtime_data['low']],
+                            'close': [realtime_data['close']],
+                            'volume': [realtime_data['volume']]
+                        }, index=[date])
+                        
+                        df = pd.concat([df, new_row])
+                        print(f"✅ 已合并实时数据: {date.date()} 收盘价 {realtime_data['close']:.2f}")
+                    else:
+                        print(f"⚠️ 实时数据日期({date.date()})不大于历史最新日期")
+                else:
+                    self.warnings.append("⚠️ 未获取到实时数据")
+            else:
+                print("✅ 历史数据已是最新")
+            
+            self.data = df.sort_index()
+            print(f"\n数据范围: {self.data.index[0].date()} ~ {self.data.index[-1].date()}")
+            print(f"数据条数: {len(self.data)}")
+            
+            return True
+            
+        except Exception as e:
+            print(f"❌ 数据加载失败: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+    
+    def calculate_indicators(self):
+        """计算技术指标"""
+        df = self.data.copy()
+        
+        # 均线
+        df['ma10'] = df['close'].rolling(window=10, min_periods=1).mean()
+        df['ma30'] = df['close'].rolling(window=30, min_periods=1).mean()
+        
+        # 20日高低点
+        df['high_20'] = df['high'].rolling(window=20).max()
+        df['low_20'] = df['low'].rolling(window=20).min()
+        
+        # 10日涨幅
+        df['ret_10'] = df['close'].pct_change(periods=10)
+        
+        # ATR
+        tr1 = df['high'] - df['low']
+        tr2 = abs(df['high'] - df['close'].shift(1))
+        tr3 = abs(df['low'] - df['close'].shift(1))
+        df['tr'] = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        df['atr'] = df['tr'].rolling(window=20).mean()
+        
+        self.data = df
+        return df
+    
+    def generate_signals(self):
+        """生成交易信号"""
+        df = self.data
+        
+        # 买入条件
+        buy_cond = (
+            (df['close'] > df['ma10']) & 
+            (df['ma10'] > df['ma30']) & 
+            (df['close'] >= df['high_20'] * 0.995) & 
+            (df['ret_10'] > 0.02)
+        )
+        
+        # 卖出条件
+        sell_cond = (
+            (df['close'] < df['ma30']) | 
+            (df['close'] <= df['low_20'] * 1.005)
+        )
+        
+        df['signal'] = 0
+        df.loc[buy_cond, 'signal'] = 1
+        df.loc[sell_cond, 'signal'] = -1
+        
+        return df
+    
+    def backtest(self, initial_capital=1000000):
+        """回测计算"""
+        df = self.generate_signals()
+        
+        position = 0
+        entry_price = 0
+        peak_price = 0
+        capital = initial_capital
+        trades = []
+        
+        for i in range(30, len(df)):
+            date = df.index[i]
+            price = df['close'].iloc[i]
+            signal = df['signal'].iloc[i]
+            
+            # 移动止损检查
+            if position > 0:
+                if price > peak_price:
+                    peak_price = price
+                if price < peak_price * 0.90:
+                    signal = -1
+            
+            # 买入
+            if signal == 1 and position == 0:
+                position = 1
+                entry_price = price
+                peak_price = price
+                trades.append({
+                    'date': date,
+                    'action': 'BUY',
+                    'price': price,
+                    'capital': capital
+                })
+            
+            # 卖出
+            elif signal == -1 and position == 1:
+                pnl = (price / entry_price - 1) * capital
+                capital += pnl
+                position = 0
+                trades.append({
+                    'date': date,
+                    'action': 'SELL',
+                    'price': price,
+                    'capital': capital,
+                    'pnl': pnl,
+                    'return_pct': (price / entry_price - 1) * 100
+                })
+        
+        current_position = position
+        current_price = df['close'].iloc[-1]
+        
+        if position == 1:
+            unrealized_pnl = (current_price / entry_price - 1) * capital
+            total_value = capital + unrealized_pnl
+        else:
+            total_value = capital
+        
+        total_return = (total_value / initial_capital - 1) * 100
+        
+        return {
+            'trades': trades,
+            'current_position': current_position,
+            'current_price': current_price,
+            'entry_price': entry_price if position == 1 else None,
+            'capital': capital,
+            'total_value': total_value,
+            'total_return': total_return,
+            'trade_count': len([t for t in trades if t['action'] == 'SELL']),
+            'data_end_date': df.index[-1].strftime('%Y-%m-%d')
+        }
+    
+    def get_recent_indicators(self, days=20):
+        """获取近N天指标详情"""
+        df = self.data.tail(days).copy()
+        
+        indicators = []
+        for date, row in df.iterrows():
+            indicators.append({
+                'date': date.strftime('%Y-%m-%d'),
+                'close': round(row['close'], 2),
+                'ma10': round(row['ma10'], 2) if not pd.isna(row['ma10']) else '-',
+                'ma30': round(row['ma30'], 2) if not pd.isna(row['ma30']) else '-',
+                'high_20': round(row['high_20'], 2) if not pd.isna(row['high_20']) else '-',
+                'ret_10': f"{row['ret_10']*100:.2f}%" if not pd.isna(row['ret_10']) else '-',
+                'signal': '买入' if row['signal'] == 1 else ('卖出' if row['signal'] == -1 else '持有'),
+                'atr': round(row['atr'], 2) if not pd.isna(row['atr']) else '-'
+            })
+        
+        return indicators
+    
+    def get_recent_trades(self, n=20):
+        """获取近N次交易详情"""
+        result = self.backtest()
+        trades = result['trades'][-n:]
+        return trades
+
+
+def generate_report():
+    """生成完整报告"""
+    strategy = TrendTrackingStrategy()
+    
+    if not strategy.load_and_merge_data():
+        return None, None, None
+    
+    strategy.calculate_indicators()
+    result = strategy.backtest()
+    
+    recent_indicators = strategy.get_recent_indicators(20)
+    recent_trades = strategy.get_recent_trades(20)
+    
+    # 数据源信息
+    source_info = ""
+    if strategy.data_sources:
+        source_info = "<div class='data-info'><strong>数据来源:</strong> "
+        for src in strategy.data_sources:
+            source_info += f"{src['source']}({src['data']['close']:.2f}) "
+        source_info += "| 已交叉验证</div>"
+    
+    # 警告信息
+    warnings_html = ""
+    if strategy.warnings:
+        warnings_html = "<div class='warning'><strong>⚠️ 警告:</strong><br>" + "<br>".join(strategy.warnings) + "</div>"
+    
+    html = f"""
+    <html>
+    <head>
+        <meta charset="utf-8">
+        <style>
+            body {{ font-family: Arial, sans-serif; margin: 20px; background: #f5f5f5; }}
+            .container {{ max-width: 1200px; margin: 0 auto; background: white; padding: 20px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }}
+            h1 {{ color: #333; border-bottom: 3px solid #007bff; padding-bottom: 10px; }}
+            h2 {{ color: #555; margin-top: 30px; border-left: 4px solid #007bff; padding-left: 10px; }}
+            .summary {{ background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; }}
+            .metric {{ display: inline-block; margin: 10px 20px 10px 0; }}
+            .metric-label {{ color: #666; font-size: 12px; }}
+            .metric-value {{ font-size: 24px; font-weight: bold; color: #333; }}
+            .positive {{ color: #28a745; }}
+            .negative {{ color: #dc3545; }}
+            .warning {{ background: #fff3cd; color: #856404; padding: 10px; border-radius: 5px; margin: 10px 0; }}
+            .data-info {{ background: #e7f3ff; padding: 10px; border-radius: 5px; margin: 10px 0; color: #004085; }}
+            table {{ width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 13px; }}
+            th {{ background: #007bff; color: white; padding: 10px; text-align: left; }}
+            td {{ padding: 8px 10px; border-bottom: 1px solid #ddd; }}
+            tr:nth-child(even) {{ background: #f8f9fa; }}
+            tr:hover {{ background: #e9ecef; }}
+            .position-yes {{ color: #28a745; font-weight: bold; }}
+            .position-no {{ color: #666; }}
+            .buy {{ color: #28a745; font-weight: bold; }}
+            .sell {{ color: #dc3545; font-weight: bold; }}
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1>🚀 创业板50趋势跟踪策略报告 (多源验证版)</h1>
+            <p style="color: #666;">生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
+            
+            {warnings_html}
+            {source_info}
+            
+            <div class="data-info">
+                <strong>数据更新:</strong> 最新数据日期: {result['data_end_date']} | 
+                数据范围: {strategy.data.index[0].date()} ~ {strategy.data.index[-1].date()}
+            </div>
+            
+            <div class="summary">
+                <h2>📊 总体绩效</h2>
+                <div class="metric">
+                    <div class="metric-label">当前持仓</div>
+                    <div class="metric-value {'position-yes' if result['current_position'] == 1 else 'position-no'}">
+                        {'持有中' if result['current_position'] == 1 else '空仓'}
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">当前价格</div>
+                    <div class="metric-value">{result['current_price']:.2f}</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">累计收益率</div>
+                    <div class="metric-value {'positive' if result['total_return'] >= 0 else 'negative'}">
+                        {result['total_return']:+.2f}%
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">总资产</div>
+                    <div class="metric-value">{result['total_value']:,.0f}元</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">交易次数</div>
+                    <div class="metric-value">{result['trade_count']}</div>
+                </div>
+            </div>
+    """
+    
+    if result['current_position'] == 1:
+        unrealized = (result['current_price'] / result['entry_price'] - 1) * 100
+        html += f"""
+            <div class="summary" style="background: #e8f5e9;">
+                <h2>📈 持仓详情</h2>
+                <p><strong>入场价格:</strong> {result['entry_price']:.2f} 元</p>
+                <p><strong>当前浮盈:</strong> <span class="{'positive' if unrealized >= 0 else 'negative'}">{unrealized:+.2f}%</span></p>
+            </div>
+        """
+    
+    # 近20天指标
+    html += """
+            <h2>📅 近20天指标详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>收盘价</th>
+                    <th>MA10</th>
+                    <th>MA30</th>
+                    <th>20日高</th>
+                    <th>10日涨幅</th>
+                    <th>信号</th>
+                    <th>ATR</th>
+                </tr>
+    """
+    
+    for ind in recent_indicators:
+        signal_class = 'buy' if ind['signal'] == '买入' else ('sell' if ind['signal'] == '卖出' else '')
+        html += f"""
+                <tr>
+                    <td>{ind['date']}</td>
+                    <td>{ind['close']}</td>
+                    <td>{ind['ma10']}</td>
+                    <td>{ind['ma30']}</td>
+                    <td>{ind['high_20']}</td>
+                    <td>{ind['ret_10']}</td>
+                    <td class="{signal_class}">{ind['signal']}</td>
+                    <td>{ind['atr']}</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <h2>💼 近20次交易详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>操作</th>
+                    <th>价格</th>
+                    <th>盈亏金额</th>
+                    <th>盈亏比例</th>
+                    <th>总资产</th>
+                </tr>
+    """
+    
+    for trade in recent_trades:
+        action_class = 'buy' if trade['action'] == 'BUY' else 'sell'
+        pnl = trade.get('pnl', 0)
+        ret = trade.get('return_pct', 0)
+        has_pnl = 'pnl' in trade
+        date_str = trade['date'].strftime('%Y-%m-%d') if hasattr(trade['date'], 'strftime') else str(trade['date'])
+        
+        ret_class = '' if not has_pnl else ('positive' if ret >= 0 else 'negative')
+        ret_text = '-' if not has_pnl else f'{ret:+.2f}%'
+        
+        html += f"""
+                <tr>
+                    <td>{date_str}</td>
+                    <td class="{action_class}">{trade['action']}</td>
+                    <td>{trade['price']:.2f}</td>
+                    <td>{f'{pnl:+,.0f}元' if has_pnl else '-'}</td>
+                    <td class="{ret_class}">{ret_text}</td>
+                    <td>{trade['capital']:,.0f}元</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <div style="margin-top: 30px; padding: 15px; background: #fff3cd; border-radius: 5px; color: #856404;">
+                <strong>策略说明:</strong><br>
+                • 买入条件: 价格>MA10>MA30 且 突破20日高×0.995 且 10日涨幅>2%<br>
+                • 卖出条件: 跌破MA30 或 创20日新低 或 回撤10%止损<br>
+                • 数据验证: 多源交叉验证，差异>2%时报警
+            </div>
+        </div>
+    </body>
+    </html>
+    """
+    
+    # 文本报告
+    text = f"""
+创业板50趋势跟踪策略报告 (多源验证版)
+生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+数据最新日期: {result['data_end_date']}
+"""
+    
+    if strategy.warnings:
+        text += "\n警告:\n" + "\n".join(strategy.warnings) + "\n"
+    
+    text += f"""
+【总体绩效】
+当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}
+当前价格: {result['current_price']:.2f}元
+累计收益率: {result['total_return']:+.2f}%
+总资产: {result['total_value']:,.0f}元
+
+【近20天指标】
+日期        收盘价    MA10     MA30     20日高   10日涨幅  信号
+"""
+    
+    for ind in recent_indicators:
+        text += f"{ind['date']}  {ind['close']:>8}  {ind['ma10']:>8}  {ind['ma30']:>8}  {ind['high_20']:>8}  {ind['ret_10']:>8}  {ind['signal']:>4}\n"
+    
+    text += "\n【近20次交易】\n"
+    for trade in recent_trades:
+        pnl_str = f"{trade.get('pnl', 0):+,.0f}元" if 'pnl' in trade else '-'
+        ret_str = f"{trade.get('return_pct', 0):+.2f}%" if 'return_pct' in trade else '-'
+        date_str = trade['date'].strftime('%Y-%m-%d') if hasattr(trade['date'], 'strftime') else str(trade['date'])
+        text += f"{date_str}  {trade['action']:>6}  {trade['price']:>8.2f}  {pnl_str:>12}  {ret_str:>10}\n"
+    
+    return html, text, result
+
+
+def send_email(subject, html_content, text_content):
+    """发送邮件"""
+    try:
+        msg = MIMEMultipart('alternative')
+        msg['Subject'] = Header(subject, 'utf-8')
+        msg['From'] = EMAIL_CONFIG['sender_email']
+        msg['To'] = EMAIL_CONFIG['receiver_email']
+        
+        text_part = MIMEText(text_content, 'plain', 'utf-8')
+        msg.attach(text_part)
+        
+        html_part = MIMEText(html_content, 'html', 'utf-8')
+        msg.attach(html_part)
+        
+        with smtplib.SMTP(EMAIL_CONFIG['smtp_server'], EMAIL_CONFIG['smtp_port']) as server:
+            server.sendmail(
+                EMAIL_CONFIG['sender_email'],
+                EMAIL_CONFIG['receiver_email'],
+                msg.as_string()
+            )
+        print(f"✅ 邮件发送成功: {subject}")
+        return True
+    except Exception as e:
+        print(f"❌ 邮件发送失败: {e}")
+        return False
+
+
+def main():
+    """主程序"""
+    print("="*60)
+    print("🚀 创业板50趋势跟踪实时报告 (多数据源交叉验证版)")
+    print("="*60)
+    print(f"执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    
+    html, text, result = generate_report()
+    
+    if html is None:
+        print("❌ 报告生成失败")
+        return
+    
+    print(f"\n✅ 报告生成完成")
+    print(f"   数据最新日期: {result['data_end_date']}")
+    print(f"   当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}")
+    print(f"   累计收益: {result['total_return']:+.2f}%")
+    
+    print("\n📧 发送邮件...")
+    position_status = "持仓" if result['current_position'] == 1 else "空仓"
+    subject = f"🚀 创业板50趋势报告 {datetime.now().strftime('%m-%d %H:%M')} | {position_status} | 收益{result['total_return']:+.2f}%"
+    send_email(subject, html, text)
+    
+    print("\n✅ 全部完成!")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    main()

kalman-filter/kalman_daily_signals.csv

@@ -0,0 +1,76 @@
+,Signal,Price,Kalman Trend,Volatility
+2018-01-03,Sell,1562.519,-2.0355113121015873,
+2018-02-02,Sell,1476.918,-1.7419861595430093,0.25639007571993194
+2018-03-27,Sell,1610.466,-1.0415835886469165,0.39435008454746656
+2018-04-12,Sell,1581.94,-1.2681249791570712,0.383517814904589
+2018-05-08,Buy,1604.305,0.5987125837657654,0.29509261954063004
+2018-05-28,Sell,1508.221,-3.174410501451353,0.21335079722463063
+2018-08-02,Sell,1264.285,-3.651634925143716,0.25283598599496565
+2018-11-27,Sell,1067.116,-1.9200633355216745,0.33066425877691935
+2019-02-01,Buy,1027.244,0.10609817329741333,0.2294105413109952
+2019-03-28,Sell,1301.882,-3.2103710428944003,0.3388590211305272
+2019-04-16,Sell,1369.381,-1.4321553316862383,0.3048864388215655
+2019-07-23,Sell,1214.612,-0.5195605727889401,0.23949631305208738
+2019-07-24,Buy,1231.285,0.025137849499758058,0.2434619011135576
+2019-08-07,Sell,1177.666,-2.3103157263519485,0.18509640165457333
+2019-08-16,Buy,1242.894,0.2544943182145989,0.20209271424939354
+2019-09-27,Sell,1333.115,-1.8594014318099048,0.24990774067616287
+2019-10-28,Buy,1366.258,0.3152604550344319,0.21822382430719692
+2019-12-03,Sell,1379.46,-0.4412995163095275,0.18671869132267355
+2019-12-05,Buy,1415.648,0.6277196690425455,0.206326636795782
+2020-03-10,Sell,1829.119,-1.0419552970606873,0.4571238715185032
+2020-05-26,Buy,1828.709,0.08863021434275051,0.228315287229799
+2020-05-28,Sell,1783.911,-1.6991339124499223,0.2312102129270274
+2020-06-01,Buy,1869.072,0.11939359030458618,0.26171065052734377
+2020-08-13,Sell,2384.163,-7.185497333688526,0.3856995376603528
+2020-09-01,Buy,2495.351,1.3475911651522459,0.2926116794873706
+2020-09-09,Sell,2278.578,-5.195239869428842,0.33687848974068296
+2020-11-19,Sell,2476.449,-4.8785359143628915,0.2951681925507462
+2020-12-04,Buy,2571.335,0.8878809816247473,0.2717934858863099
+2021-02-24,Sell,2931.276,-9.428328081257824,0.39481923331210683
+2021-07-28,Sell,3318.334,-10.08119427301443,0.3849211009879227
+2021-08-12,Sell,3395.613,-3.472397060529297,0.3878842485873111
+2021-09-14,Buy,3281.784,0.49998126856403946,0.2940763672496989
+2021-09-17,Sell,3231.128,-0.781828960831134,0.30046236638153284
+2021-09-27,Buy,3262.039,1.0996596107732728,0.2578576890599948
+2021-10-13,Sell,3228.941,-0.40836419572671323,0.23803135383012752
+2021-10-15,Buy,3309.932,2.3366740981575087,0.24784898451930282
+2021-12-08,Sell,3476.456,-3.055754539306629,0.1894663009986254
+2021-12-14,Buy,3547.913,0.30874284682110253,0.18626405874345586
+2021-12-16,Buy,3546.362,0.7533677961495376,0.18380483611735424
+2021-12-20,Sell,3369.308,-4.9825622319902205,0.19383438797350597
+2022-03-04,Sell,2706.315,-2.5429923253410376,0.3155745474336556
+2022-03-29,Sell,2565.614,-2.872829301717552,0.39532090306209344
+2022-04-08,Sell,2567.852,-3.3680398618282146,0.392948660891435
+2022-07-18,Sell,2822.813,-0.22808368236933063,0.29796503547422304
+2022-08-16,Buy,2741.242,0.6163740273565161,0.2179498495909916
+2022-08-30,Sell,2609.234,-5.758116920805243,0.2522856553560171
+2022-10-31,Sell,2238.797,-5.2140367732118875,0.349024154490802
+2022-11-24,Sell,2327.58,-2.582775637845167,0.31252399244878665
+2022-12-06,Buy,2382.376,0.16798325364903866,0.21976116897719505
+2022-12-21,Sell,2300.698,-3.303189087857409,0.16633746068745825
+2023-01-05,Buy,2395.3,0.2968540039121761,0.20555393094036503
+2023-02-15,Sell,2527.289,-1.6879127443177468,0.17228156954750298
+2023-04-24,Sell,2201.507,-5.72215679075741,0.17268772840910118
+2023-07-20,Sell,2064.198,-1.8499009643065736,0.18823472830420732
+2023-07-31,Buy,2153.64,0.7995013622864371,0.19046202986194222
+2023-08-16,Sell,2053.811,-3.8325451047992667,0.18604400191513232
+2023-11-23,Sell,1844.577,-1.664434703508825,0.24472437210993644
+2024-01-04,Sell,1684.004,-1.2792006165234453,0.2546607649055747
+2024-03-28,Sell,1738.009,-2.948469784561251,0.28650368619202576
+2024-04-29,Buy,1827.414,1.3395406917781578,0.2998828823558075
+2024-05-27,Sell,1749.825,-1.300394628189764,0.27409926803989554
+2024-07-30,Sell,1578.294,-2.113238877409489,0.20056783822796279
+2024-09-12,Buy,1513.177,0.17056947234677095,0.1874450943268222
+2024-11-25,Sell,2184.683,-6.357767665535631,0.4310705836957812
+2024-12-18,Sell,2213.066,-1.2158007489141183,0.28026902591151126
+2025-03-06,Sell,2205.115,-1.0376875045202147,0.2704778091985956
+2025-05-30,Sell,1962.624,-1.9911213179099712,0.21240464292519487
+2025-06-11,Buy,2029.103,0.7077045181739439,0.1758700233122866
+2025-06-24,Buy,2044.78,0.7835402302193312,0.17185577905047927
+2025-10-15,Sell,3163.51,-5.254645976396086,0.4063640698395624
+2025-11-17,Sell,3263.756,-5.031104320134871,0.33719000481739203
+2025-12-05,Buy,3302.897,1.113259924809289,0.27554945226550775
+2026-01-23,Sell,3480.852,-0.4085628007292203,0.18989157155364011
+2026-02-25,Buy,3514.742,0.7462336542353936,0.22550333858114457
+2026-03-04,Sell,3310.591,-6.487809580213213,0.24431277193518378

kalman-filter/test_extract.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+import subprocess
+
+result = subprocess.run(['python3', 'cyb50_kalman_filter_daily.py'], cwd='/root/.openclaw/workspace/kalman-filter', capture_output=True, text=True, timeout=300)
+
+output = result.stdout
+lines = output.split('\n')
+
+signals = []
+in_signal_section = False
+
+for line in lines:
+    if '所有交易信号详情' in line:
+        in_signal_section = True
+        continue
+    if in_signal_section:
+        stripped = line.strip()
+        if len(stripped) > 30:
+            parts = stripped.split()
+            if len(parts) >= 3 and parts[0].isdigit() and len(parts[1]) == 10 and parts[1][4] == '-':
+                signals.append({
+                    'num': parts[0],
+                    'date': parts[1],
+                    'action': parts[2],
+                    'price': parts[3] if len(parts) > 3 else '',
+                })
+        # 获取所有信号
+        if '当前市场状态' in line:
+            break
+
+print(f'找到 {len(signals)} 个交易信号')
+
+# 取最近40个信号
+recent_signals = signals[-40:] if len(signals) > 40 else signals
+print(f'\n最近40个信号中的前10个:')
+for s in recent_signals[:10]:
+    print(f"  {s['num']:>3} {s['date']} {s['action']:>4} {s['price']}")
+
+print(f'\n最近40个信号中的后10个:')
+for s in recent_signals[-10:]:
+    print(f"  {s['num']:>3} {s['date']} {s['action']:>4} {s['price']}")
+
+# 配对
+trades = []
+for i in range(len(recent_signals) - 1):
+    if recent_signals[i]['action'] == '买入' and recent_signals[i+1]['action'] == '卖出':
+        buy = recent_signals[i]
+        sell = recent_signals[i+1]
+        trades.append(f"{buy['num']} {buy['date']}买入→{sell['date']}卖出")
+
+print(f'\n配对到 {len(trades)} 对交易')
+print('\n最近5对:')
+for t in trades[-5:]:
+    print(f'  {t}')

market-regime-identifier/hmm_diagnosis.py

@@ -0,0 +1,188 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+HMM模型诊断脚本
+验证市场环境识别器的效果
+"""
+
+import numpy as np
+import pandas as pd
+import warnings
+warnings.filterwarnings('ignore')
+
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/market-regime-identifier')
+from market_regime_hmm import MarketRegimeHMM, extract_features
+
+print("="*70)
+print("HMM模型诊断报告")
+print("="*70)
+
+# 1. 生成带标签的测试数据
+print("\n[1] 生成测试数据...")
+np.random.seed(42)
+n_days = 800
+
+# 创建有明确状态特征的数据
+segments = []
+true_states = []
+
+for i in range(8):
+    state = i % 3
+    seg_prices = []
+    price = 1000 + i * 100
+    
+    for day in range(100):
+        if state == 0:  # 震荡: 零均值，中等波动
+            ret = np.random.normal(0, 0.015)
+        elif state == 1:  # 趋势: 正漂移，低波动
+            ret = np.random.normal(0.001, 0.010)
+        else:  # 反转: 负漂移，高波动
+            ret = np.random.normal(-0.001, 0.025)
+        
+        price *= (1 + ret)
+        seg_prices.append(price)
+        true_states.append(state)
+    
+    segments.extend(seg_prices)
+
+dates = pd.date_range('2020-01-01', periods=n_days, freq='B')
+df = pd.DataFrame({
+    'open': np.array(segments) + np.random.normal(0, 2, n_days),
+    'high': np.array(segments) + np.abs(np.random.normal(5, 2, n_days)),
+    'low': np.array(segments) - np.abs(np.random.normal(5, 2, n_days)),
+    'close': segments,
+    'volume': np.random.randint(1000000, 5000000, n_days),
+    'true_state': true_states
+}, index=dates)
+
+print(f"数据天数: {n_days}")
+print(f"真实状态分布:")
+for i in range(3):
+    count = sum(1 for s in true_states if s == i)
+    print(f"  状态{i}: {count}天 ({count/n_days*100:.1f}%)")
+
+# 2. 特征提取
+print("\n[2] 特征提取...")
+features = extract_features(df)
+feature_cols = ['ret_std_5', 'momentum_10', 'vol_ratio', 'volume_change', 'intraday_trend']
+X = features[feature_cols].dropna()
+print(f"特征维度: {X.shape}")
+
+# 3. 训练模型
+print("\n[3] 训练HMM模型...")
+hmm = MarketRegimeHMM(n_components=3, n_iter=100)
+hmm.fit(X)
+
+# 4. 预测状态
+states, probs = hmm.predict(X)
+df_aligned = df.iloc[-len(states):].copy()
+df_aligned['predicted_state'] = states
+df_aligned['return'] = df_aligned['close'].pct_change()
+
+# 5. 诊断分析
+print("\n" + "="*70)
+print("诊断结果")
+print("="*70)
+
+# 5.1 转移矩阵对比
+print("\n[5.1] 转移矩阵对比")
+print("\n先验矩阵 (设定):")
+prior = np.array([
+    [0.85, 0.10, 0.05],
+    [0.15, 0.80, 0.05],
+    [0.20, 0.10, 0.70]
+])
+print(prior.round(3))
+
+print("\n学习到的矩阵:")
+learned = hmm.model.transmat_
+print(learned.round(3))
+
+print("\n差异:")
+diff = np.abs(learned - prior)
+print(diff.round(3))
+print(f"平均绝对差异: {diff.mean():.3f}")
+
+# 5.2 状态分布对比
+print("\n[5.2] 状态分布对比")
+print(f"{'状态':<10} {'真实占比':<15} {'预测占比':<15} {'差异':<10}")
+print("-"*50)
+for i in range(3):
+    true_pct = sum(1 for s in true_states if s == i) / n_days * 100
+    pred_pct = sum(1 for s in states if s == i) / len(states) * 100
+    diff_pct = abs(true_pct - pred_pct)
+    print(f"状态{i:<5} {true_pct:>6.1f}%{' '*8} {pred_pct:>6.1f}%{' '*8} {diff_pct:>5.1f}%")
+
+# 5.3 状态特征验证
+print("\n[5.3] 各状态的价格行为特征")
+print(f"{'状态':<8} {'收益率均值':<12} {'收益率标准差':<15} {'样本数':<10}")
+print("-"*50)
+for i in range(3):
+    mask = states == i
+    if mask.any():
+        rets = df_aligned.loc[mask, 'return'].dropna()
+        mean_ret = rets.mean() * 100
+        std_ret = rets.std() * 100
+        count = mask.sum()
+        print(f"状态{i:<5} {mean_ret:>+8.3f}%{' '*4} {std_ret:>8.3f}%{' '*6} {count:>5}天")
+
+# 5.4 预期 vs 实际
+print("\n[5.4] 状态定义验证")
+state_names = ['震荡', '趋势', '反转']
+expected = {
+    0: {'vol': '中高', 'ret': '接近0'},
+    1: {'vol': '低', 'ret': '正'},
+    2: {'vol': '高', 'ret': '负'}
+}
+
+for i in range(3):
+    mask = states == i
+    if mask.any():
+        rets = df_aligned.loc[mask, 'return'].dropna()
+        mean_ret = rets.mean() * 100
+        std_ret = rets.std() * 100
+        
+        print(f"\n状态{i} ({state_names[i]}):")
+        print(f"  预期: 波动{expected[i]['vol']}, 收益{expected[i]['ret']}")
+        print(f"  实际: 波动{std_ret:.2f}%, 收益{mean_ret:+.3f}%")
+        
+        # 简单判断
+        if i == 0 and abs(mean_ret) < 0.1 and std_ret > 1.0:
+            print("  ✓ 符合震荡特征")
+        elif i == 1 and mean_ret > 0.05 and std_ret < 1.5:
+            print("  ✓ 符合趋势特征")
+        elif i == 2 and std_ret > 1.8:
+            print("  ✓ 符合反转特征")
+        else:
+            print("  ✗ 特征不匹配")
+
+# 5.5 准确率估算
+print("\n[5.5] 状态识别准确率估算")
+# 基于特征匹配度估算
+matches = 0
+for i in range(len(states) - 1):
+    true_seg = i // 100
+    if states[i] == true_states[i]:
+        matches += 1
+
+accuracy = matches / len(states) * 100
+print(f"与生成标签匹配率: {accuracy:.1f}%")
+
+if accuracy >= 72:
+    print("✓ 达到目标准确率 (>72%)")
+else:
+    print("✗ 未达到目标准确率，需要优化")
+
+print("\n" + "="*70)
+print("诊断结论")
+print("="*70)
+print(f"1. 转移矩阵与先验差异: {'可接受' if diff.mean() < 0.3 else '较大'}")
+print(f"2. 状态识别准确率: {accuracy:.1f}%")
+print(f"3. 状态特征一致性: 见上文分析")
+print("\n建议:")
+if diff.mean() > 0.3:
+    print("- 转移矩阵与先验差异较大，建议检查数据特征或调整模型参数")
+if accuracy < 72:
+    print("- 准确率不足，建议增加特征维度或使用更长的训练数据")
+print("="*70)

market-regime-identifier/last_60_days_report.html

@@ -0,0 +1,665 @@
+
+<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>
+    
+    <div class="summary">
+        <h2>📊 最近60天统计</h2>
+        <p><strong>统计区间:</strong> 2025-12-03 ~ 2026-03-06</p>
+        <p><strong>起始价格:</strong> 3213.67</p>
+        <p><strong>结束价格:</strong> 3380.31</p>
+        <p><strong>区间涨跌:</strong> +5.19%</p>
+        <p><strong>最高价:</strong> 3547.03 (01-12)</p>
+        <p><strong>最低价:</strong> 3213.67 (12-03)</p>
+        <br>
+        <p><strong>状态分布:</strong></p>
+        <p>🟦 震荡: 45天 (75.0%)</p>
+        <p>🟩 趋势: 9天 (15.0%)</p>
+        <p>🟧 反转: 6天 (10.0%)</p>
+    </div>
+
+    
+    <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>
+            
+        <tr>
+            <td>2025-12-03</td>
+            <td>3213.67</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>57.0%</td>
+            <td>10.1%</td>
+            <td>32.9%</td>
+            <td style="color: gray;">-</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-04</td>
+            <td>3254.79</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>50.8%</td>
+            <td>12.9%</td>
+            <td>36.3%</td>
+            <td style="color: green;">+1.28%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-05</td>
+            <td>3302.90</td>
+            <td style="color: #4CAF50; font-weight: bold;">趋势</td>
+            <td>15.1%</td>
+            <td>65.7%</td>
+            <td>19.2%</td>
+            <td style="color: green;">+1.48%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-08</td>
+            <td>3399.46</td>
+            <td style="color: #4CAF50; font-weight: bold;">趋势</td>
+            <td>17.5%</td>
+            <td>68.5%</td>
+            <td>14.1%</td>
+            <td style="color: green;">+2.92%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-09</td>
+            <td>3427.43</td>
+            <td style="color: #4CAF50; font-weight: bold;">趋势</td>
+            <td>14.1%</td>
+            <td>72.7%</td>
+            <td>13.2%</td>
+            <td style="color: green;">+0.82%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-10</td>
+            <td>3418.68</td>
+            <td style="color: #4CAF50; font-weight: bold;">趋势</td>
+            <td>14.6%</td>
+            <td>74.8%</td>
+            <td>10.6%</td>
+            <td style="color: red;">-0.26%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-11</td>
+            <td>3366.70</td>
+            <td style="color: #4CAF50; font-weight: bold;">趋势</td>
+            <td>11.5%</td>
+            <td>80.6%</td>
+            <td>7.9%</td>
+            <td style="color: red;">-1.52%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-12</td>
+            <td>3397.66</td>
+            <td style="color: #4CAF50; font-weight: bold;">趋势</td>
+            <td>13.0%</td>
+            <td>83.1%</td>
+            <td>3.9%</td>
+            <td style="color: green;">+0.92%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-15</td>
+            <td>3334.31</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>45.6%</td>
+            <td>21.0%</td>
+            <td>33.4%</td>
+            <td style="color: red;">-1.86%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-16</td>
+            <td>3256.98</td>
+            <td style="color: #FF5722; font-weight: bold;">反转</td>
+            <td>25.9%</td>
+            <td>9.0%</td>
+            <td>65.1%</td>
+            <td style="color: red;">-2.32%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-17</td>
+            <td>3377.65</td>
+            <td style="color: #FF5722; font-weight: bold;">反转</td>
+            <td>21.2%</td>
+            <td>24.7%</td>
+            <td>54.2%</td>
+            <td style="color: green;">+3.70%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-18</td>
+            <td>3292.59</td>
+            <td style="color: #FF5722; font-weight: bold;">反转</td>
+            <td>37.0%</td>
+            <td>11.6%</td>
+            <td>51.4%</td>
+            <td style="color: red;">-2.52%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-19</td>
+            <td>3307.29</td>
+            <td style="color: #FF5722; font-weight: bold;">反转</td>
+            <td>26.4%</td>
+            <td>7.8%</td>
+            <td>65.8%</td>
+            <td style="color: green;">+0.45%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-22</td>
+            <td>3389.02</td>
+            <td style="color: #FF5722; font-weight: bold;">反转</td>
+            <td>34.2%</td>
+            <td>13.5%</td>
+            <td>52.2%</td>
+            <td style="color: green;">+2.47%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-23</td>
+            <td>3404.61</td>
+            <td style="color: #FF5722; font-weight: bold;">反转</td>
+            <td>20.7%</td>
+            <td>7.6%</td>
+            <td>71.7%</td>
+            <td style="color: green;">+0.46%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-24</td>
+            <td>3429.06</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>61.0%</td>
+            <td>13.1%</td>
+            <td>25.9%</td>
+            <td style="color: green;">+0.72%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-25</td>
+            <td>3437.43</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>49.7%</td>
+            <td>15.4%</td>
+            <td>34.9%</td>
+            <td style="color: green;">+0.24%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-26</td>
+            <td>3441.99</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>43.1%</td>
+            <td>15.0%</td>
+            <td>41.9%</td>
+            <td style="color: green;">+0.13%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-29</td>
+            <td>3413.77</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>78.7%</td>
+            <td>12.4%</td>
+            <td>8.9%</td>
+            <td style="color: red;">-0.82%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-30</td>
+            <td>3440.06</td>
+            <td style="color: #4CAF50; font-weight: bold;">趋势</td>
+            <td>29.2%</td>
+            <td>64.6%</td>
+            <td>6.2%</td>
+            <td style="color: green;">+0.77%</td>
+        </tr>
+    
+        <tr>
+            <td>2025-12-31</td>
+            <td>3390.24</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>75.7%</td>
+            <td>8.1%</td>
+            <td>16.2%</td>
+            <td style="color: red;">-1.45%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-05</td>
+            <td>3488.23</td>
+            <td style="color: #4CAF50; font-weight: bold;">趋势</td>
+            <td>32.4%</td>
+            <td>54.2%</td>
+            <td>13.4%</td>
+            <td style="color: green;">+2.89%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-06</td>
+            <td>3506.50</td>
+            <td style="color: #4CAF50; font-weight: bold;">趋势</td>
+            <td>19.4%</td>
+            <td>70.9%</td>
+            <td>9.7%</td>
+            <td style="color: green;">+0.52%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-07</td>
+            <td>3515.34</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>51.3%</td>
+            <td>35.6%</td>
+            <td>13.1%</td>
+            <td style="color: green;">+0.25%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-08</td>
+            <td>3473.66</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>72.8%</td>
+            <td>14.2%</td>
+            <td>13.1%</td>
+            <td style="color: red;">-1.19%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-09</td>
+            <td>3490.83</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>66.3%</td>
+            <td>17.6%</td>
+            <td>16.1%</td>
+            <td style="color: green;">+0.49%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-12</td>
+            <td>3547.03</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>64.2%</td>
+            <td>26.6%</td>
+            <td>9.2%</td>
+            <td style="color: green;">+1.61%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-13</td>
+            <td>3477.90</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>69.8%</td>
+            <td>12.8%</td>
+            <td>17.5%</td>
+            <td style="color: red;">-1.95%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-14</td>
+            <td>3502.85</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>74.8%</td>
+            <td>15.6%</td>
+            <td>9.6%</td>
+            <td style="color: green;">+0.72%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-15</td>
+            <td>3526.02</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>71.2%</td>
+            <td>15.9%</td>
+            <td>12.9%</td>
+            <td style="color: green;">+0.66%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-16</td>
+            <td>3518.73</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>56.0%</td>
+            <td>36.3%</td>
+            <td>7.7%</td>
+            <td style="color: red;">-0.21%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-19</td>
+            <td>3489.25</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>84.0%</td>
+            <td>7.9%</td>
+            <td>8.1%</td>
+            <td style="color: red;">-0.84%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-20</td>
+            <td>3419.62</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>84.7%</td>
+            <td>5.8%</td>
+            <td>9.5%</td>
+            <td style="color: red;">-2.00%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-21</td>
+            <td>3435.49</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>87.2%</td>
+            <td>4.8%</td>
+            <td>8.0%</td>
+            <td style="color: green;">+0.46%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-22</td>
+            <td>3473.18</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>85.6%</td>
+            <td>5.4%</td>
+            <td>9.0%</td>
+            <td style="color: green;">+1.10%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-23</td>
+            <td>3480.85</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>93.6%</td>
+            <td>4.0%</td>
+            <td>2.4%</td>
+            <td style="color: green;">+0.22%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-26</td>
+            <td>3448.70</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>92.7%</td>
+            <td>4.6%</td>
+            <td>2.7%</td>
+            <td style="color: red;">-0.92%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-27</td>
+            <td>3476.17</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>91.9%</td>
+            <td>3.2%</td>
+            <td>4.9%</td>
+            <td style="color: green;">+0.80%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-28</td>
+            <td>3463.33</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>93.3%</td>
+            <td>2.7%</td>
+            <td>4.1%</td>
+            <td style="color: red;">-0.37%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-29</td>
+            <td>3450.16</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>93.9%</td>
+            <td>2.9%</td>
+            <td>3.2%</td>
+            <td style="color: red;">-0.38%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-01-30</td>
+            <td>3515.28</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>89.7%</td>
+            <td>5.4%</td>
+            <td>4.8%</td>
+            <td style="color: green;">+1.89%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-02</td>
+            <td>3432.87</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>82.0%</td>
+            <td>5.1%</td>
+            <td>12.8%</td>
+            <td style="color: red;">-2.34%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-03</td>
+            <td>3487.59</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>85.8%</td>
+            <td>4.2%</td>
+            <td>10.0%</td>
+            <td style="color: green;">+1.59%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-04</td>
+            <td>3471.23</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>89.3%</td>
+            <td>3.2%</td>
+            <td>7.5%</td>
+            <td style="color: red;">-0.47%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-05</td>
+            <td>3413.58</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>79.1%</td>
+            <td>4.7%</td>
+            <td>16.3%</td>
+            <td style="color: red;">-1.66%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-06</td>
+            <td>3385.75</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>70.5%</td>
+            <td>7.9%</td>
+            <td>21.5%</td>
+            <td style="color: red;">-0.82%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-09</td>
+            <td>3493.59</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>77.4%</td>
+            <td>3.9%</td>
+            <td>18.7%</td>
+            <td style="color: green;">+3.18%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-10</td>
+            <td>3479.49</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>81.9%</td>
+            <td>2.9%</td>
+            <td>15.2%</td>
+            <td style="color: red;">-0.40%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-11</td>
+            <td>3432.86</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>79.9%</td>
+            <td>4.4%</td>
+            <td>15.7%</td>
+            <td style="color: red;">-1.34%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-12</td>
+            <td>3480.50</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>81.7%</td>
+            <td>2.2%</td>
+            <td>16.1%</td>
+            <td style="color: green;">+1.39%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-13</td>
+            <td>3423.05</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>51.1%</td>
+            <td>5.8%</td>
+            <td>43.1%</td>
+            <td style="color: red;">-1.65%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-24</td>
+            <td>3469.02</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>71.1%</td>
+            <td>2.9%</td>
+            <td>26.0%</td>
+            <td style="color: green;">+1.34%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-25</td>
+            <td>3514.74</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>78.1%</td>
+            <td>9.7%</td>
+            <td>12.2%</td>
+            <td style="color: green;">+1.32%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-26</td>
+            <td>3500.75</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>78.0%</td>
+            <td>4.7%</td>
+            <td>17.3%</td>
+            <td style="color: red;">-0.40%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-02-27</td>
+            <td>3449.46</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>74.3%</td>
+            <td>4.3%</td>
+            <td>21.4%</td>
+            <td style="color: red;">-1.47%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-03-02</td>
+            <td>3443.78</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>84.5%</td>
+            <td>3.9%</td>
+            <td>11.6%</td>
+            <td style="color: red;">-0.16%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-03-03</td>
+            <td>3362.90</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>70.6%</td>
+            <td>10.4%</td>
+            <td>18.9%</td>
+            <td style="color: red;">-2.35%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-03-04</td>
+            <td>3310.59</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>61.9%</td>
+            <td>24.0%</td>
+            <td>14.1%</td>
+            <td style="color: red;">-1.56%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-03-05</td>
+            <td>3373.47</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>67.4%</td>
+            <td>7.4%</td>
+            <td>25.2%</td>
+            <td style="color: green;">+1.90%</td>
+        </tr>
+    
+        <tr>
+            <td>2026-03-06</td>
+            <td>3380.31</td>
+            <td style="color: #2196F3; font-weight: bold;">震荡</td>
+            <td>72.4%</td>
+            <td>10.6%</td>
+            <td>17.0%</td>
+            <td style="color: green;">+0.20%</td>
+        </tr>
+    
+        </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>

market-regime-identifier/market_regime_hmm.py

@@ -105,19 +105,20 @@ class MarketRegimeHMM:
         2: '反转'
     }
     
-    # 先验转移概率矩阵
-    PRIOR_TRANSITION = np.array([
-        [0.85, 0.10, 0.05],  # 震荡 -> 震荡/趋势/反转
-        [0.15, 0.80, 0.05],  # 趋势 -> 震荡/趋势/反转
-        [0.20, 0.10, 0.70]   # 反转 -> 震荡/趋势/反转
-    ])
-    
     def __init__(self, n_components=3, n_iter=100):
+        # 先验转移概率矩阵
+        self.PRIOR_TRANSITION = np.array([
+            [0.85, 0.10, 0.05],  # 震荡 -> 震荡/趋势/反转
+            [0.15, 0.80, 0.05],  # 趋势 -> 震荡/趋势/反转
+            [0.20, 0.10, 0.70]   # 反转 -> 震荡/趋势/反转
+        ])
+        
         self.model = GaussianHMM(
             n_components=n_components,
             covariance_type='full',
             n_iter=n_iter,
-            random_state=42
+            random_state=42,
+            init_params='mc'  # 只初始化均值和协方差，不初始化转移矩阵
         )
         self.is_fitted = False
         
@@ -125,11 +126,15 @@ class MarketRegimeHMM:
         """训练HMM模型"""
         print("训练HMM模型...")
         
+        X = features.values
+        
+        # 先验状态分布（均匀分布）
+        self.model.startprob_ = np.array([1/3, 1/3, 1/3])
+        
         # 使用先验转移概率初始化
-        self.model.transmat_ = self.PRIOR_TRANSITION
+        self.model.transmat_ = self.PRIOR_TRANSITION.copy()
         
         # 拟合模型
-        X = features.values
         self.model.fit(X)
         self.is_fitted = True
         

quant/quant_report_20260306_1133.txt

@@ -0,0 +1,67 @@
+==========================================================================================
+创业板50指数 - 全策略年度收益对比（真实数据）
+==========================================================================================
+
+开始年度回测...
+------------------------------------------------------------------------------------------
+
+==========================================================================================
+年度收益对比表 (%)
+==========================================================================================
+策略         |     2018 |     2019 |     2020 |     2021 |     2022 |     2023 |     2024 |     2025 |       平均 |     跑赢年数
+------------------------------------------------------------------------------------------
+趋势跟踪       |    -9.4 |    +3.8 |   +48.3 |   +16.6 |    +7.4 |    +0.0 |   +30.5 |   +33.2 |   +16.3 |      4/8
+双均线        |   -13.7 |    +5.5 |   +40.7 |    +6.9 |    +1.6 |    -2.0 |    +1.1 |   +49.7 |   +11.2 |      3/8
+动量         |   -17.9 |    +0.0 |   +38.1 |    +1.3 |    +4.4 |    -7.0 |    +7.6 |   +38.9 |    +8.2 |      3/8
+多因子        |   -16.0 |    +2.6 |   +45.8 |    +4.2 |    -0.5 |   -12.8 |   +13.5 |   +40.3 |    +9.6 |      3/8
+RSI        |   -21.3 |    +9.6 |   +14.3 |    +6.6 |    +3.5 |   -15.6 |    -5.4 |   +13.0 |    +0.6 |      4/8
+买入持有       |   -38.7 |    +6.6 |   +76.2 |   +21.6 |    -8.9 |   -23.8 |   +21.8 |   +92.7 |   +18.4 |       --
+------------------------------------------------------------------------------------------
+
+==========================================================================================
+超额收益对比 (策略 - 买入持有)
+==========================================================================================
+策略         |     2018 |     2019 |     2020 |     2021 |     2022 |     2023 |     2024 |     2025 |     平均超额
+------------------------------------------------------------------------------------------
+趋势跟踪       |   +29.3⭐ |    -2.8 |   -27.9 |    -5.0 |   +16.3⭐ |   +23.8⭐ |    +8.7✓ |   -59.6 |    -2.1
+双均线        |   +25.0⭐ |    -1.1 |   -35.5 |   -14.7 |   +10.5⭐ |   +21.8⭐ |   -20.6 |   -43.0 |    -7.2
+动量         |   +20.8⭐ |    -6.6 |   -38.0 |   -20.4 |   +13.3⭐ |   +16.8⭐ |   -14.2 |   -53.8 |   -10.3
+多因子        |   +22.7⭐ |    -3.9 |   -30.4 |   -17.4 |    +8.4✓ |   +10.9⭐ |    -8.3 |   -52.4 |    -8.8
+RSI        |   +17.4⭐ |    +3.0✓ |   -61.8 |   -15.1 |   +12.4⭐ |    +8.2✓ |   -27.2 |   -79.8 |   -17.9
+------------------------------------------------------------------------------------------
+
+==========================================================================================
+策略评价
+==========================================================================================
+
+【趋势跟踪】
+  年均收益: +16.3%
+  年均超额: -2.1%
+  跑赢年数: 4/8 (50%)
+  评价: 不佳
+
+【双均线】
+  年均收益: +11.2%
+  年均超额: -7.2%
+  跑赢年数: 3/8 (38%)
+  评价: 不佳
+
+【动量】
+  年均收益: +8.2%
+  年均超额: -10.3%
+  跑赢年数: 3/8 (38%)
+  评价: 不佳
+
+【多因子】
+  年均收益: +9.6%
+  年均超额: -8.8%
+  跑赢年数: 3/8 (38%)
+  评价: 不佳
+
+【RSI】
+  年均收益: +0.6%
+  年均超额: -17.9%
+  跑赢年数: 4/8 (50%)
+  评价: 不佳
+
+==========================================================================================

quant/trend_tracking_strategy.py

@@ -0,0 +1,819 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+创业板50指数 - 多数据源交叉验证版
+确保数据准确性
+"""
+
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
+sys.path.insert(0, '/root/.openclaw/workspace/quant')
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from email.header import Header
+import warnings
+import requests
+import json
+import time
+import baostock as bs
+warnings.filterwarnings('ignore')
+
+# ==================== 邮件配置 ====================
+EMAIL_CONFIG = {
+    "smtp_server": "localhost",
+    "smtp_port": 25,
+    "sender_email": "catfly@openclaw.local",
+    "receiver_email": "380880504@qq.com"
+}
+
+# ==================== 数据源配置 ====================
+DATA_SOURCES = {
+    'baostock': {'priority': 1, 'weight': 0.4},
+    'akshare': {'priority': 2, 'weight': 0.3},
+    'sina': {'priority': 3, 'weight': 0.15},
+    'tencent': {'priority': 4, 'weight': 0.15}
+}
+
+# ==================== 多数据源获取 ====================
+
+class DataValidator:
+    """数据验证器 - 交叉验证多个数据源"""
+    
+    PRICE_TOLERANCE = 0.02  # 价格差异容忍度 2%
+    
+    @staticmethod
+    def validate_prices(sources_data):
+        """
+        验证多个数据源的价格一致性
+        返回: (validated_data, warnings)
+        """
+        if not sources_data:
+            return None, ["无可用数据源"]
+        
+        if len(sources_data) == 1:
+            return sources_data[0]['data'], []
+        
+        # 提取收盘价
+        prices = {}
+        for src in sources_data:
+            if src and 'data' in src and src['data']:
+                prices[src['source']] = src['data'].get('close', 0)
+        
+        if len(prices) < 2:
+            return sources_data[0]['data'], []
+        
+        # 计算价格统计
+        price_values = list(prices.values())
+        price_mean = np.mean(price_values)
+        price_std = np.std(price_values)
+        price_max = max(price_values)
+        price_min = min(price_values)
+        price_range = (price_max - price_min) / price_mean if price_mean > 0 else 0
+        
+        warnings = []
+        
+        # 检查价格差异
+        if price_range > DataValidator.PRICE_TOLERANCE:
+            warnings.append(f"⚠️ 价格差异过大: {price_range*100:.2f}% (容忍度: {DataValidator.PRICE_TOLERANCE*100}%)")
+            for src, price in prices.items():
+                deviation = (price - price_mean) / price_mean * 100
+                warnings.append(f"  - {src}: {price:.2f} (偏离均值: {deviation:+.2f}%)")
+        
+        # 使用加权平均价格
+        weighted_price = 0
+        total_weight = 0
+        for src in sources_data:
+            if src and 'data' in src and src['data']:
+                source_name = src['source']
+                weight = DATA_SOURCES.get(source_name, {}).get('weight', 0.25)
+                weighted_price += src['data']['close'] * weight
+                total_weight += weight
+        
+        if total_weight > 0:
+            weighted_price /= total_weight
+        
+        # 选择最接近加权平均的数据源
+        best_source = None
+        min_diff = float('inf')
+        for src in sources_data:
+            if src and 'data' in src and src['data']:
+                diff = abs(src['data']['close'] - weighted_price)
+                if diff < min_diff:
+                    min_diff = diff
+                    best_source = src
+        
+        if warnings:
+            warnings.append(f"✅ 使用加权平均价格: {weighted_price:.2f}")
+            if best_source:
+                warnings.append(f"✅ 选择数据源: {best_source['source']} (价格: {best_source['data']['close']:.2f})")
+        
+        return best_source['data'] if best_source else sources_data[0]['data'], warnings
+
+
+def fetch_sina_realtime():
+    """新浪实时行情接口"""
+    try:
+        url = "https://hq.sinajs.cn/list=sz399673"
+        headers = {
+            'Referer': 'https://finance.sina.com.cn',
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        response.encoding = 'gb2312'
+        
+        data_str = response.text
+        if 'var hq_str_sz399673="' in data_str:
+            data_str = data_str.split('var hq_str_sz399673="')[1].split('"')[0]
+            parts = data_str.split(',')
+            if len(parts) >= 32:
+                date_str = parts[30].strip()
+                if len(date_str) == 8 and date_str.isdigit():
+                    date = pd.Timestamp(f"{date_str[:4]}-{date_str[4:6]}-{date_str[6:8]}")
+                else:
+                    date = pd.Timestamp.now().normalize()
+                
+                return {
+                    'source': 'sina',
+                    'data': {
+                        'open': float(parts[1]),
+                        'high': float(parts[4]),
+                        'low': float(parts[5]),
+                        'close': float(parts[3]),
+                        'volume': int(parts[8]),
+                        'date': date
+                    }
+                }
+    except Exception as e:
+        print(f"  新浪实时数据获取失败: {e}")
+    return None
+
+
+def fetch_tencent_realtime():
+    """腾讯实时行情接口"""
+    try:
+        url = "https://qt.gtimg.cn/q=sz399673"
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        }
+        response = requests.get(url, headers=headers, timeout=10)
+        response.encoding = 'gb2312'
+        
+        data_str = response.text
+        if 'v_sz399673="' in data_str:
+            data_str = data_str.split('v_sz399673="')[1].split('"')[0]
+            parts = data_str.split('~')
+            if len(parts) >= 45:
+                date_str = parts[30]
+                date = pd.Timestamp(date_str)
+                
+                return {
+                    'source': 'tencent',
+                    'data': {
+                        'close': float(parts[3]),
+                        'open': float(parts[5]),
+                        'high': float(parts[33]),
+                        'low': float(parts[34]),
+                        'volume': int(parts[36]),
+                        'date': date
+                    }
+                }
+    except Exception as e:
+        print(f"  腾讯实时数据获取失败: {e}")
+    return None
+
+
+def fetch_akshare_hist(start_date, end_date):
+    """使用akshare获取历史数据"""
+    try:
+        import akshare as ak
+        print(f"  [akshare] 获取 {start_date} 到 {end_date} 的历史数据...")
+        
+        df = ak.index_zh_a_hist(symbol="399673", period="daily", 
+                                start_date=start_date, end_date=end_date)
+        
+        if df is not None and len(df) > 0:
+            data_list = []
+            for _, row in df.iterrows():
+                data_list.append({
+                    'date': pd.Timestamp(row['日期']),
+                    'open': float(row['开盘']),
+                    'high': float(row['最高']),
+                    'low': float(row['最低']),
+                    'close': float(row['收盘']),
+                    'volume': int(row['成交量'])
+                })
+            print(f"  [akshare] ✅ 获取成功: {len(data_list)} 条")
+            return data_list
+    except Exception as e:
+        print(f"  [akshare] ❌ 获取失败: {e}")
+    return None
+
+
+def fetch_baostock_hist(start_date, end_date):
+    """使用baostock获取历史数据"""
+    try:
+        print(f"  [baostock] 获取 {start_date} 到 {end_date} 的历史数据...")
+        
+        lg = bs.login()
+        if lg.error_code != '0':
+            print(f"  [baostock] ❌ 登录失败: {lg.error_msg}")
+            return None
+        
+        rs = bs.query_history_k_data_plus("sz.399673",
+            "date,open,high,low,close,volume",
+            start_date=start_date, end_date=end_date,
+            frequency="d", adjustflag="3")
+        
+        data_list = []
+        while (rs.error_code == '0') & rs.next():
+            row = rs.get_row_data()
+            if row[0]:
+                data_list.append({
+                    'date': pd.Timestamp(row[0]),
+                    'open': float(row[1]) if row[1] else 0,
+                    'high': float(row[2]) if row[2] else 0,
+                    'low': float(row[3]) if row[3] else 0,
+                    'close': float(row[4]) if row[4] else 0,
+                    'volume': int(float(row[5])) if row[5] else 0
+                })
+        
+        bs.logout()
+        
+        if data_list:
+            print(f"  [baostock] ✅ 获取成功: {len(data_list)} 条")
+            return data_list
+    except Exception as e:
+        print(f"  [baostock] ❌ 获取失败: {e}")
+    return None
+
+
+def fetch_multi_source_realtime():
+    """从多个源获取实时数据并交叉验证"""
+    print("\n📊 多数据源获取实时数据...")
+    
+    sources = []
+    
+    # 获取新浪数据
+    sina_data = fetch_sina_realtime()
+    if sina_data:
+        sources.append(sina_data)
+        print(f"  [新浪]    收盘价: {sina_data['data']['close']:.2f}")
+    
+    # 获取腾讯数据
+    tencent_data = fetch_tencent_realtime()
+    if tencent_data:
+        sources.append(tencent_data)
+        print(f"  [腾讯]    收盘价: {tencent_data['data']['close']:.2f}")
+    
+    # 交叉验证
+    validated_data, warnings = DataValidator.validate_prices(sources)
+    
+    if warnings:
+        print("\n  ⚠️ 数据验证警告:")
+        for w in warnings:
+            print(f"     {w}")
+    
+    if validated_data:
+        print(f"\n  ✅ 最终使用收盘价: {validated_data['close']:.2f}")
+        return validated_data, sources
+    
+    return None, []
+
+
+def fetch_missing_data_multi_source(last_date, today):
+    """使用多个源获取缺失的历史数据"""
+    if last_date >= today:
+        return [], []
+    
+    start_str = (last_date + timedelta(days=1)).strftime('%Y-%m-%d')
+    end_str = (today - timedelta(days=1)).strftime('%Y-%m-%d')
+    
+    print(f"\n📊 补全缺失数据: {start_str} 到 {end_str}")
+    
+    all_warnings = []
+    
+    # 尝试baostock (优先级高)
+    data = fetch_baostock_hist(start_str, end_str)
+    if data:
+        return data, all_warnings
+    
+    # 尝试akshare
+    data = fetch_akshare_hist(start_str.replace('-', ''), end_str.replace('-', ''))
+    if data:
+        return data, all_warnings
+    
+    all_warnings.append(f"⚠️ 无法获取 {start_str} 到 {end_str} 的历史数据")
+    return [], all_warnings
+
+
+# ==================== 趋势跟踪策略 ====================
+class TrendTrackingStrategy:
+    """趋势跟踪策略 - 多数据源验证版"""
+    
+    def __init__(self):
+        self.data = None
+        self.warnings = []
+        self.data_sources = []
+        
+    def load_and_merge_data(self, csv_file='cyb50_baostock.csv'):
+        """加载历史数据并合并实时数据"""
+        try:
+            # 加载历史数据
+            df = pd.read_csv(f'/root/.openclaw/workspace/quant/{csv_file}')
+            df['date'] = pd.to_datetime(df['date'])
+            df = df.set_index('date').sort_index()
+            
+            for col in ['open', 'high', 'low', 'close', 'volume']:
+                df[col] = pd.to_numeric(df[col], errors='coerce')
+            
+            last_hist_date = df.index[-1]
+            today = pd.Timestamp.now().normalize()
+            
+            print(f"历史数据最新日期: {last_hist_date.date()}")
+            print(f"当前日期: {today.date()}")
+            
+            # 获取缺失的历史数据
+            missing_data, warnings = fetch_missing_data_multi_source(last_hist_date, today)
+            self.warnings.extend(warnings)
+            
+            if missing_data:
+                for item in missing_data:
+                    new_row = pd.DataFrame({
+                        'open': [item['open']],
+                        'high': [item['high']],
+                        'low': [item['low']],
+                        'close': [item['close']],
+                        'volume': [item['volume']]
+                    }, index=[item['date']])
+                    df = pd.concat([df, new_row])
+                
+                df = df.sort_index()
+                last_hist_date = df.index[-1]
+                print(f"✅ 已合并历史数据，最新日期: {last_hist_date.date()}")
+            
+            # 获取今天的实时数据（多源验证）
+            if last_hist_date < today:
+                realtime_data, sources = fetch_multi_source_realtime()
+                self.data_sources = sources
+                
+                if realtime_data:
+                    date = realtime_data['date']
+                    
+                    if date > last_hist_date:
+                        new_row = pd.DataFrame({
+                            'open': [realtime_data['open']],
+                            'high': [realtime_data['high']],
+                            'low': [realtime_data['low']],
+                            'close': [realtime_data['close']],
+                            'volume': [realtime_data['volume']]
+                        }, index=[date])
+                        
+                        df = pd.concat([df, new_row])
+                        print(f"✅ 已合并实时数据: {date.date()} 收盘价 {realtime_data['close']:.2f}")
+                    else:
+                        print(f"⚠️ 实时数据日期({date.date()})不大于历史最新日期")
+                else:
+                    self.warnings.append("⚠️ 未获取到实时数据")
+            else:
+                print("✅ 历史数据已是最新")
+            
+            self.data = df.sort_index()
+            print(f"\n数据范围: {self.data.index[0].date()} ~ {self.data.index[-1].date()}")
+            print(f"数据条数: {len(self.data)}")
+            
+            return True
+            
+        except Exception as e:
+            print(f"❌ 数据加载失败: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+    
+    def calculate_indicators(self):
+        """计算技术指标"""
+        df = self.data.copy()
+        
+        # 均线
+        df['ma10'] = df['close'].rolling(window=10, min_periods=1).mean()
+        df['ma30'] = df['close'].rolling(window=30, min_periods=1).mean()
+        
+        # 20日高低点
+        df['high_20'] = df['high'].rolling(window=20).max()
+        df['low_20'] = df['low'].rolling(window=20).min()
+        
+        # 10日涨幅
+        df['ret_10'] = df['close'].pct_change(periods=10)
+        
+        # ATR
+        tr1 = df['high'] - df['low']
+        tr2 = abs(df['high'] - df['close'].shift(1))
+        tr3 = abs(df['low'] - df['close'].shift(1))
+        df['tr'] = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        df['atr'] = df['tr'].rolling(window=20).mean()
+        
+        self.data = df
+        return df
+    
+    def generate_signals(self):
+        """生成交易信号"""
+        df = self.data
+        
+        # 买入条件
+        buy_cond = (
+            (df['close'] > df['ma10']) & 
+            (df['ma10'] > df['ma30']) & 
+            (df['close'] >= df['high_20'] * 0.995) & 
+            (df['ret_10'] > 0.02)
+        )
+        
+        # 卖出条件
+        sell_cond = (
+            (df['close'] < df['ma30']) | 
+            (df['close'] <= df['low_20'] * 1.005)
+        )
+        
+        df['signal'] = 0
+        df.loc[buy_cond, 'signal'] = 1
+        df.loc[sell_cond, 'signal'] = -1
+        
+        return df
+    
+    def backtest(self, initial_capital=1000000):
+        """回测计算"""
+        df = self.generate_signals()
+        
+        position = 0
+        entry_price = 0
+        peak_price = 0
+        capital = initial_capital
+        trades = []
+        
+        for i in range(30, len(df)):
+            date = df.index[i]
+            price = df['close'].iloc[i]
+            signal = df['signal'].iloc[i]
+            
+            # 移动止损检查
+            if position > 0:
+                if price > peak_price:
+                    peak_price = price
+                if price < peak_price * 0.90:
+                    signal = -1
+            
+            # 买入
+            if signal == 1 and position == 0:
+                position = 1
+                entry_price = price
+                peak_price = price
+                trades.append({
+                    'date': date,
+                    'action': 'BUY',
+                    'price': price,
+                    'capital': capital
+                })
+            
+            # 卖出
+            elif signal == -1 and position == 1:
+                pnl = (price / entry_price - 1) * capital
+                capital += pnl
+                position = 0
+                trades.append({
+                    'date': date,
+                    'action': 'SELL',
+                    'price': price,
+                    'capital': capital,
+                    'pnl': pnl,
+                    'return_pct': (price / entry_price - 1) * 100
+                })
+        
+        current_position = position
+        current_price = df['close'].iloc[-1]
+        
+        if position == 1:
+            unrealized_pnl = (current_price / entry_price - 1) * capital
+            total_value = capital + unrealized_pnl
+        else:
+            total_value = capital
+        
+        total_return = (total_value / initial_capital - 1) * 100
+        
+        return {
+            'trades': trades,
+            'current_position': current_position,
+            'current_price': current_price,
+            'entry_price': entry_price if position == 1 else None,
+            'capital': capital,
+            'total_value': total_value,
+            'total_return': total_return,
+            'trade_count': len([t for t in trades if t['action'] == 'SELL']),
+            'data_end_date': df.index[-1].strftime('%Y-%m-%d')
+        }
+    
+    def get_recent_indicators(self, days=20):
+        """获取近N天指标详情"""
+        df = self.data.tail(days).copy()
+        
+        indicators = []
+        for date, row in df.iterrows():
+            indicators.append({
+                'date': date.strftime('%Y-%m-%d'),
+                'close': round(row['close'], 2),
+                'ma10': round(row['ma10'], 2) if not pd.isna(row['ma10']) else '-',
+                'ma30': round(row['ma30'], 2) if not pd.isna(row['ma30']) else '-',
+                'high_20': round(row['high_20'], 2) if not pd.isna(row['high_20']) else '-',
+                'ret_10': f"{row['ret_10']*100:.2f}%" if not pd.isna(row['ret_10']) else '-',
+                'signal': '买入' if row['signal'] == 1 else ('卖出' if row['signal'] == -1 else '持有'),
+                'atr': round(row['atr'], 2) if not pd.isna(row['atr']) else '-'
+            })
+        
+        return indicators
+    
+    def get_recent_trades(self, n=20):
+        """获取近N次交易详情"""
+        result = self.backtest()
+        trades = result['trades'][-n:]
+        return trades
+
+
+def generate_report():
+    """生成完整报告"""
+    strategy = TrendTrackingStrategy()
+    
+    if not strategy.load_and_merge_data():
+        return None, None, None
+    
+    strategy.calculate_indicators()
+    result = strategy.backtest()
+    
+    recent_indicators = strategy.get_recent_indicators(20)
+    recent_trades = strategy.get_recent_trades(20)
+    
+    # 数据源信息
+    source_info = ""
+    if strategy.data_sources:
+        source_info = "<div class='data-info'><strong>数据来源:</strong> "
+        for src in strategy.data_sources:
+            source_info += f"{src['source']}({src['data']['close']:.2f}) "
+        source_info += "| 已交叉验证</div>"
+    
+    # 警告信息
+    warnings_html = ""
+    if strategy.warnings:
+        warnings_html = "<div class='warning'><strong>⚠️ 警告:</strong><br>" + "<br>".join(strategy.warnings) + "</div>"
+    
+    html = f"""
+    <html>
+    <head>
+        <meta charset="utf-8">
+        <style>
+            body {{ font-family: Arial, sans-serif; margin: 20px; background: #f5f5f5; }}
+            .container {{ max-width: 1200px; margin: 0 auto; background: white; padding: 20px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }}
+            h1 {{ color: #333; border-bottom: 3px solid #007bff; padding-bottom: 10px; }}
+            h2 {{ color: #555; margin-top: 30px; border-left: 4px solid #007bff; padding-left: 10px; }}
+            .summary {{ background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; }}
+            .metric {{ display: inline-block; margin: 10px 20px 10px 0; }}
+            .metric-label {{ color: #666; font-size: 12px; }}
+            .metric-value {{ font-size: 24px; font-weight: bold; color: #333; }}
+            .positive {{ color: #28a745; }}
+            .negative {{ color: #dc3545; }}
+            .warning {{ background: #fff3cd; color: #856404; padding: 10px; border-radius: 5px; margin: 10px 0; }}
+            .data-info {{ background: #e7f3ff; padding: 10px; border-radius: 5px; margin: 10px 0; color: #004085; }}
+            table {{ width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 13px; }}
+            th {{ background: #007bff; color: white; padding: 10px; text-align: left; }}
+            td {{ padding: 8px 10px; border-bottom: 1px solid #ddd; }}
+            tr:nth-child(even) {{ background: #f8f9fa; }}
+            tr:hover {{ background: #e9ecef; }}
+            .position-yes {{ color: #28a745; font-weight: bold; }}
+            .position-no {{ color: #666; }}
+            .buy {{ color: #28a745; font-weight: bold; }}
+            .sell {{ color: #dc3545; font-weight: bold; }}
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1>🚀 创业板50趋势跟踪策略报告 (多源验证版)</h1>
+            <p style="color: #666;">生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
+            
+            {warnings_html}
+            {source_info}
+            
+            <div class="data-info">
+                <strong>数据更新:</strong> 最新数据日期: {result['data_end_date']} | 
+                数据范围: {strategy.data.index[0].date()} ~ {strategy.data.index[-1].date()}
+            </div>
+            
+            <div class="summary">
+                <h2>📊 总体绩效</h2>
+                <div class="metric">
+                    <div class="metric-label">当前持仓</div>
+                    <div class="metric-value {'position-yes' if result['current_position'] == 1 else 'position-no'}">
+                        {'持有中' if result['current_position'] == 1 else '空仓'}
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">当前价格</div>
+                    <div class="metric-value">{result['current_price']:.2f}</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">累计收益率</div>
+                    <div class="metric-value {'positive' if result['total_return'] >= 0 else 'negative'}">
+                        {result['total_return']:+.2f}%
+                    </div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">总资产</div>
+                    <div class="metric-value">{result['total_value']:,.0f}元</div>
+                </div>
+                <div class="metric">
+                    <div class="metric-label">交易次数</div>
+                    <div class="metric-value">{result['trade_count']}</div>
+                </div>
+            </div>
+    """
+    
+    if result['current_position'] == 1:
+        unrealized = (result['current_price'] / result['entry_price'] - 1) * 100
+        html += f"""
+            <div class="summary" style="background: #e8f5e9;">
+                <h2>📈 持仓详情</h2>
+                <p><strong>入场价格:</strong> {result['entry_price']:.2f} 元</p>
+                <p><strong>当前浮盈:</strong> <span class="{'positive' if unrealized >= 0 else 'negative'}">{unrealized:+.2f}%</span></p>
+            </div>
+        """
+    
+    # 近20天指标
+    html += """
+            <h2>📅 近20天指标详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>收盘价</th>
+                    <th>MA10</th>
+                    <th>MA30</th>
+                    <th>20日高</th>
+                    <th>10日涨幅</th>
+                    <th>信号</th>
+                    <th>ATR</th>
+                </tr>
+    """
+    
+    for ind in recent_indicators:
+        signal_class = 'buy' if ind['signal'] == '买入' else ('sell' if ind['signal'] == '卖出' else '')
+        html += f"""
+                <tr>
+                    <td>{ind['date']}</td>
+                    <td>{ind['close']}</td>
+                    <td>{ind['ma10']}</td>
+                    <td>{ind['ma30']}</td>
+                    <td>{ind['high_20']}</td>
+                    <td>{ind['ret_10']}</td>
+                    <td class="{signal_class}">{ind['signal']}</td>
+                    <td>{ind['atr']}</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <h2>💼 近20次交易详情</h2>
+            <table>
+                <tr>
+                    <th>日期</th>
+                    <th>操作</th>
+                    <th>价格</th>
+                    <th>盈亏金额</th>
+                    <th>盈亏比例</th>
+                    <th>总资产</th>
+                </tr>
+    """
+    
+    for trade in recent_trades:
+        action_class = 'buy' if trade['action'] == 'BUY' else 'sell'
+        pnl = trade.get('pnl', 0)
+        ret = trade.get('return_pct', 0)
+        has_pnl = 'pnl' in trade
+        date_str = trade['date'].strftime('%Y-%m-%d') if hasattr(trade['date'], 'strftime') else str(trade['date'])
+        
+        ret_class = '' if not has_pnl else ('positive' if ret >= 0 else 'negative')
+        ret_text = '-' if not has_pnl else f'{ret:+.2f}%'
+        
+        html += f"""
+                <tr>
+                    <td>{date_str}</td>
+                    <td class="{action_class}">{trade['action']}</td>
+                    <td>{trade['price']:.2f}</td>
+                    <td>{f'{pnl:+,.0f}元' if has_pnl else '-'}</td>
+                    <td class="{ret_class}">{ret_text}</td>
+                    <td>{trade['capital']:,.0f}元</td>
+                </tr>
+        """
+    
+    html += """
+            </table>
+            
+            <div style="margin-top: 30px; padding: 15px; background: #fff3cd; border-radius: 5px; color: #856404;">
+                <strong>策略说明:</strong><br>
+                • 买入条件: 价格>MA10>MA30 且 突破20日高×0.995 且 10日涨幅>2%<br>
+                • 卖出条件: 跌破MA30 或 创20日新低 或 回撤10%止损<br>
+                • 数据验证: 多源交叉验证，差异>2%时报警
+            </div>
+        </div>
+    </body>
+    </html>
+    """
+    
+    # 文本报告
+    text = f"""
+创业板50趋势跟踪策略报告 (多源验证版)
+生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+数据最新日期: {result['data_end_date']}
+"""
+    
+    if strategy.warnings:
+        text += "\n警告:\n" + "\n".join(strategy.warnings) + "\n"
+    
+    text += f"""
+【总体绩效】
+当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}
+当前价格: {result['current_price']:.2f}元
+累计收益率: {result['total_return']:+.2f}%
+总资产: {result['total_value']:,.0f}元
+
+【近20天指标】
+日期        收盘价    MA10     MA30     20日高   10日涨幅  信号
+"""
+    
+    for ind in recent_indicators:
+        text += f"{ind['date']}  {ind['close']:>8}  {ind['ma10']:>8}  {ind['ma30']:>8}  {ind['high_20']:>8}  {ind['ret_10']:>8}  {ind['signal']:>4}\n"
+    
+    text += "\n【近20次交易】\n"
+    for trade in recent_trades:
+        pnl_str = f"{trade.get('pnl', 0):+,.0f}元" if 'pnl' in trade else '-'
+        ret_str = f"{trade.get('return_pct', 0):+.2f}%" if 'return_pct' in trade else '-'
+        date_str = trade['date'].strftime('%Y-%m-%d') if hasattr(trade['date'], 'strftime') else str(trade['date'])
+        text += f"{date_str}  {trade['action']:>6}  {trade['price']:>8.2f}  {pnl_str:>12}  {ret_str:>10}\n"
+    
+    return html, text, result
+
+
+def send_email(subject, html_content, text_content):
+    """发送邮件"""
+    try:
+        msg = MIMEMultipart('alternative')
+        msg['Subject'] = Header(subject, 'utf-8')
+        msg['From'] = EMAIL_CONFIG['sender_email']
+        msg['To'] = EMAIL_CONFIG['receiver_email']
+        
+        text_part = MIMEText(text_content, 'plain', 'utf-8')
+        msg.attach(text_part)
+        
+        html_part = MIMEText(html_content, 'html', 'utf-8')
+        msg.attach(html_part)
+        
+        with smtplib.SMTP(EMAIL_CONFIG['smtp_server'], EMAIL_CONFIG['smtp_port']) as server:
+            server.sendmail(
+                EMAIL_CONFIG['sender_email'],
+                EMAIL_CONFIG['receiver_email'],
+                msg.as_string()
+            )
+        print(f"✅ 邮件发送成功: {subject}")
+        return True
+    except Exception as e:
+        print(f"❌ 邮件发送失败: {e}")
+        return False
+
+
+def main():
+    """主程序"""
+    print("="*60)
+    print("🚀 创业板50趋势跟踪实时报告 (多数据源交叉验证版)")
+    print("="*60)
+    print(f"执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    
+    html, text, result = generate_report()
+    
+    if html is None:
+        print("❌ 报告生成失败")
+        return
+    
+    print(f"\n✅ 报告生成完成")
+    print(f"   数据最新日期: {result['data_end_date']}")
+    print(f"   当前持仓: {'持有中' if result['current_position'] == 1 else '空仓'}")
+    print(f"   累计收益: {result['total_return']:+.2f}%")
+    
+    print("\n📧 发送邮件...")
+    position_status = "持仓" if result['current_position'] == 1 else "空仓"
+    subject = f"🚀 创业板50趋势报告 {datetime.now().strftime('%m-%d %H:%M')} | {position_status} | 收益{result['total_return']:+.2f}%"
+    send_email(subject, html, text)
+    
+    print("\n✅ 全部完成!")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    main()

trend-quality-evaluator/backtest_report.py

@@ -0,0 +1,215 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+趋势质量评估器 - 历史回测 (2017-至今)
+生成完整回测报告
+"""
+
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/trend-quality-evaluator')
+
+import numpy as np
+import pandas as pd
+from trend_quality_evaluator import fetch_stock_data, TrendQualityEvaluator
+from datetime import datetime
+import warnings
+warnings.filterwarnings('ignore')
+
+print("="*70)
+print("趋势质量评估器 - 历史回测报告")
+print("回测区间: 2017-01-01 至 2026-03-06")
+print("="*70)
+
+# 获取数据
+df = fetch_stock_data("399673", "2017-01-01", "2026-03-06", "d")
+if df is None:
+    print("数据获取失败")
+    exit(1)
+
+print(f"\n✓ 数据获取成功: {len(df)}条")
+print(f"  日期范围: {df.index[0].date()} ~ {df.index[-1].date()}")
+
+# 初始化评估器
+evaluator = TrendQualityEvaluator()
+
+# 逐日评估
+print("\n正在进行历史回测...")
+results = []
+
+# 需要至少60天数据计算MA60
+start_idx = 60
+for i in range(start_idx, len(df)):
+    current_df = df.iloc[:i+1]
+    
+    try:
+        score = evaluator.evaluate(current_df)
+        results.append({
+            'date': df.index[i],
+            'close': df['close'].iloc[i],
+            'total_score': score.total_score,
+            'adx_score': score.adx_score,
+            'ma_slope_score': score.ma_slope_score,
+            'volatility_score': score.volatility_score,
+            'timeframe_score': score.timeframe_score,
+            'volume_score': score.volume_score,
+            'is_tradeable': score.is_tradeable,
+            'adx_value': score.adx_value,
+            'ma_slope': score.ma_slope,
+            'volatility_ratio': score.volatility_ratio,
+            'volume_ratio': score.volume_ratio
+        })
+    except Exception as e:
+        pass
+
+    if i % 100 == 0:
+        print(f"  进度: {i}/{len(df)} ({i/len(df)*100:.1f}%)")
+
+results_df = pd.DataFrame(results)
+results_df = results_df.set_index('date')
+
+print(f"\n✓ 回测完成: {len(results_df)}个交易日")
+
+# 计算未来收益率（用于验证评分有效性）
+print("\n计算未来收益验证...")
+results_df['future_5d_return'] = results_df['close'].pct_change(5).shift(-5) * 100
+results_df['future_10d_return'] = results_df['close'].pct_change(10).shift(-10) * 100
+results_df['future_20d_return'] = results_df['close'].pct_change(20).shift(-20) * 100
+
+# ============================================
+# 生成报告
+# ============================================
+print("\n" + "="*70)
+print("回测报告")
+print("="*70)
+
+# 1. 评分分布统计
+print("\n【一、评分分布统计】")
+print("-"*50)
+
+total_days = len(results_df)
+tradeable_days = results_df['is_tradeable'].sum()
+
+print(f"总交易天数: {total_days}")
+print(f"可交易天数(≥60分): {tradeable_days} ({tradeable_days/total_days*100:.1f}%)")
+print(f"不可交易天数(<60分): {total_days-tradeable_days} ({(total_days-tradeable_days)/total_days*100:.1f}%)")
+
+# 分数段分布
+print(f"\n分数段分布:")
+bins = [0, 40, 60, 70, 80, 100]
+labels = ['0-40(混乱)', '40-60(较差)', '60-70(及格)', '70-80(良好)', '80-100(优秀)']
+results_df['score_bin'] = pd.cut(results_df['total_score'], bins=bins, labels=labels, include_lowest=True)
+score_dist = results_df['score_bin'].value_counts().sort_index()
+
+for label, count in score_dist.items():
+    pct = count / total_days * 100
+    bar = '█' * int(pct / 2)
+    print(f"  {label:15s}: {count:4d}天 ({pct:5.1f}%) {bar}")
+
+# 2. 各因子得分统计
+print("\n【二、各因子得分统计】")
+print("-"*50)
+
+factors = [
+    ('adx_score', 'ADX趋势强度', 30),
+    ('ma_slope_score', '均线斜率', 25),
+    ('volatility_score', '波动率收缩', 20),
+    ('timeframe_score', '时间框架共振', 15),
+    ('volume_score', '成交量确认', 10)
+]
+
+for col, name, max_score in factors:
+    mean_score = results_df[col].mean()
+    pct_of_max = mean_score / max_score * 100
+    print(f"{name:15s}: 平均{mean_score:5.1f}/{max_score}分 ({pct_of_max:4.1f}%)")
+
+# 3. 评分有效性验证
+print("\n【三、评分有效性验证】")
+print("-"*50)
+print("不同评分区间的未来收益表现:")
+print(f"{'评分区间':<15} {'5日后收益':<12} {'10日后收益':<12} {'20日后收益':<12} {'样本数':<8}")
+print("-"*60)
+
+for label in labels:
+    mask = results_df['score_bin'] == label
+    if mask.sum() > 0:
+        r5 = results_df[mask]['future_5d_return'].mean()
+        r10 = results_df[mask]['future_10d_return'].mean()
+        r20 = results_df[mask]['future_20d_return'].mean()
+        count = mask.sum()
+        print(f"{label:<15} {r5:>+10.2f}% {r10:>+10.2f}% {r20:>+10.2f}% {count:>6d}")
+
+# 4. 可交易 vs 不可交易对比
+print("\n【四、可交易性验证】")
+print("-"*50)
+
+tradeable_mask = results_df['is_tradeable']
+not_tradeable_mask = ~results_df['is_tradeable']
+
+print(f"{'指标':<20} {'可交易(≥60分)':<15} {'不可交易(<60分)':<15} {'差异':<10}")
+print("-"*60)
+
+for period, col in [('5日', 'future_5d_return'), ('10日', 'future_10d_return'), ('20日', 'future_20d_return')]:
+    t_mean = results_df[tradeable_mask][col].mean()
+    nt_mean = results_df[not_tradeable_mask][col].mean()
+    diff = t_mean - nt_mean
+    print(f"{period+'收益':<20} {t_mean:>+13.2f}% {nt_mean:>+14.2f}% {diff:>+9.2f}%")
+
+# 5. 年度最佳/最差月份
+print("\n【五、年度统计】")
+print("-"*50)
+
+results_df['year'] = results_df.index.year
+yearly_stats = results_df.groupby('year').agg({
+    'total_score': 'mean',
+    'is_tradeable': 'sum',
+    'future_20d_return': 'mean'
+}).round(2)
+yearly_stats['tradeable_pct'] = (yearly_stats['is_tradeable'] / results_df.groupby('year').size() * 100).round(1)
+
+print(f"{'年份':<8} {'平均评分':<10} {'可交易天数':<12} {'可交易比例':<12} {'20日收益':<10}")
+print("-"*60)
+for year, row in yearly_stats.iterrows():
+    print(f"{year:<8} {row['total_score']:>8.1f}    {int(row['is_tradeable']):>8}天     {row['tradeable_pct']:>8.1f}%     {row['future_20d_return']:>+7.2f}%")
+
+# 6. 最新评分
+print("\n【六、最新评分】")
+print("-"*50)
+latest = results_df.iloc[-1]
+print(f"日期: {results_df.index[-1].strftime('%Y-%m-%d')}")
+print(f"收盘价: {latest['close']:.2f}")
+print(f"总分: {latest['total_score']:.1f}分")
+print(f"是否可交易: {'✅ 是' if latest['is_tradeable'] else '❌ 否'}")
+print(f"\n各因子得分:")
+for col, name, max_score in factors:
+    print(f"  {name:15s}: {latest[col]:.1f}/{max_score}分")
+
+# 7. 关键发现
+print("\n【七、关键发现】")
+print("-"*50)
+
+# 计算胜率
+tradeable_returns = results_df[tradeable_mask]['future_20d_return'].dropna()
+win_rate = (tradeable_returns > 0).mean() * 100
+
+print(f"1. 可交易信号胜率(20日): {win_rate:.1f}%")
+
+# 最佳评分日期
+best_day = results_df.loc[results_df['total_score'].idxmax()]
+print(f"2. 历史最高评分: {best_day['total_score']:.1f}分 ({results_df['total_score'].idxmax().strftime('%Y-%m-%d')})")
+
+# 评分与收益相关性
+corr_20d = results_df['total_score'].corr(results_df['future_20d_return'])
+print(f"3. 评分与20日收益相关性: {corr_20d:.3f}")
+
+# 连续可交易日统计
+results_df['tradeable_change'] = results_df['is_tradeable'].astype(int).diff()
+entry_points = results_df[results_df['tradeable_change'] == 1].index
+print(f"4. 历史可交易入场信号次数: {len(entry_points)}次")
+
+print("\n" + "="*70)
+print("回测报告生成完成")
+print("="*70)
+
+# 保存结果
+results_df.to_csv('/root/.openclaw/workspace/trend-quality-evaluator/backtest_results.csv')
+print("\n✓ 详细数据已保存: backtest_results.csv")

trend-quality-evaluator/optimization_results.csv

@@ -0,0 +1,568 @@
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+30,35,1.003,20,0.7,20,1.3,10,27.344110854503462,0.12801568765689958,0.5755949879235729,1.0990434247347685,-0.02335296508234941,52.71186440677966,0.11462344305252134,40.07962032387622
+30,30,1.001,30,0.7,15,1.8,10,27.113163972286376,0.117355605576794,0.5796652592575033,1.083031425597562,-0.04529461464894968,52.991452991452995,0.1192768634619027,39.95431595722148
+25,30,1.003,25,0.7,20,1.8,10,26.74364896073903,0.14288412126951258,0.6281541736251055,1.079605967308087,-0.049781242427112904,52.24913494809689,0.11994067357335297,39.635568523526416
+25,30,1.001,25,0.7,20,1.8,10,28.314087759815244,0.13933999074287437,0.5758890516474588,1.0693445575027922,-0.06520073723314357,52.864157119476275,0.11886667150446763,39.36292014202206
+25,30,1.002,25,0.7,20,1.8,10,27.66743648960739,0.13172566174636915,0.5811950236219998,1.0656590177309908,-0.0697630340828419,52.84280936454849,0.11891440377304048,39.234121185725634
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+30,35,1.001,20,0.7,20,1.8,10,26.46651270207852,0.1946073262617311,0.6184004097011352,1.0471361362305078,-0.09380876400552274,53.239929947460595,0.11326712235940826,38.2793051393118
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trend-quality-evaluator/optimize_parameters.py

@@ -0,0 +1,368 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+趋势质量评估器 - 参数优化测试
+测试不同参数组合的回测表现，寻找最优配置
+"""
+
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/trend-quality-evaluator')
+
+import numpy as np
+import pandas as pd
+from trend_quality_evaluator import fetch_stock_data
+from dataclasses import dataclass
+from typing import List, Tuple
+import itertools
+import warnings
+warnings.filterwarnings('ignore')
+
+print("="*70)
+print("趋势质量评估器 - 参数优化测试")
+print("="*70)
+
+# 获取数据
+df = fetch_stock_data("399673", "2017-01-01", "2026-03-06", "d")
+if df is None:
+    print("数据获取失败")
+    exit(1)
+
+print(f"\n✓ 数据获取成功: {len(df)}条")
+print(f"  日期范围: {df.index[0].date()} ~ {df.index[-1].date()}")
+
+# 定义参数搜索空间
+param_grid = {
+    'adx_threshold': [20, 25, 30],           # ADX阈值
+    'adx_weight': [25, 30, 35],              # ADX权重
+    'ma_slope_threshold': [1.001, 1.002, 1.003],  # 均线斜率阈值
+    'ma_slope_weight': [20, 25, 30],         # 均线斜率权重
+    'volatility_threshold': [0.7, 0.8, 0.9], # 波动率比率阈值
+    'volatility_weight': [15, 20, 25],       # 波动率权重
+    'volume_threshold': [1.3, 1.5, 1.8],     # 成交量阈值
+    'volume_weight': [8, 10, 12],            # 成交量权重
+}
+
+# 总权重必须是100
+@dataclass
+class StrategyConfig:
+    """策略配置"""
+    adx_threshold: float
+    adx_weight: int
+    ma_slope_threshold: float
+    ma_slope_weight: int
+    volatility_threshold: float
+    volatility_weight: int
+    volume_threshold: float
+    volume_weight: int
+    timeframe_weight: int = 15  # 固定15分
+    
+    @property
+    def total_weight(self) -> int:
+        return self.adx_weight + self.ma_slope_weight + self.volatility_weight + self.volume_weight + self.timeframe_weight
+    
+    @property
+    def trade_threshold(self) -> int:
+        return 60  # 固定60分阈值
+
+
+def calculate_indicators(df: pd.DataFrame) -> pd.DataFrame:
+    """预计算所有技术指标"""
+    data = df.copy()
+    
+    # ADX
+    high, low, close = data['high'], data['low'], data['close']
+    plus_dm = high.diff()
+    minus_dm = low.diff().abs()
+    plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0)
+    minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0)
+    
+    tr1 = high - low
+    tr2 = (high - close.shift()).abs()
+    tr3 = (low - close.shift()).abs()
+    tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+    atr = tr.rolling(14).mean()
+    
+    plus_di = 100 * (plus_dm.rolling(14).mean() / atr)
+    minus_di = 100 * (minus_dm.rolling(14).mean() / atr)
+    dx = (abs(plus_di - minus_di) / (plus_di + minus_di + 1e-10)) * 100
+    data['adx'] = dx.rolling(14).mean()
+    
+    # MA20斜率
+    data['ma20'] = data['close'].rolling(20).mean()
+    data['ma20_slope'] = data['ma20'] / data['ma20'].shift(5)
+    
+    # ATR比率
+    data['atr14'] = tr.rolling(14).mean()
+    data['atr50'] = tr.rolling(50).mean()
+    data['atr_ratio'] = data['atr14'] / data['atr50']
+    
+    # 成交量
+    data['volume_ma20'] = data['volume'].rolling(20).mean()
+    data['volume_ratio'] = data['volume'] / data['volume_ma20']
+    
+    # 日线突破
+    data['price_above_ma20'] = data['close'] > data['ma20']
+    
+    return data
+
+
+def evaluate_strategy(data: pd.DataFrame, config: StrategyConfig) -> dict:
+    """
+    评估策略配置
+    返回各项绩效指标
+    """
+    scores = []
+    
+    # 从第60天开始（需要足够数据计算MA60）
+    for i in range(60, len(data)):
+        row = data.iloc[i]
+        
+        # 计算各因子得分
+        # 1. ADX得分
+        if row['adx'] >= config.adx_threshold:
+            adx_score = config.adx_weight
+        else:
+            adx_score = config.adx_weight * (row['adx'] / config.adx_threshold)
+        
+        # 2. 均线斜率得分
+        if row['ma20_slope'] >= config.ma_slope_threshold * 1.5:
+            ma_score = config.ma_slope_weight
+        elif row['ma20_slope'] >= config.ma_slope_threshold:
+            ma_score = config.ma_slope_weight * 0.7
+        elif row['ma20_slope'] >= 1.0:
+            ma_score = config.ma_slope_weight * 0.3
+        else:
+            ma_score = 0
+        
+        # 3. 波动率得分
+        if row['atr_ratio'] <= config.volatility_threshold * 0.75:
+            vol_score = config.volatility_weight
+        elif row['atr_ratio'] <= config.volatility_threshold:
+            vol_score = config.volatility_weight * 0.7
+        elif row['atr_ratio'] <= 1.0:
+            vol_score = config.volatility_weight * 0.3
+        else:
+            vol_score = 0
+        
+        # 4. 成交量得分
+        if row['volume_ratio'] >= config.volume_threshold * 1.3:
+            vol_score_trade = config.volume_weight
+        elif row['volume_ratio'] >= config.volume_threshold:
+            vol_score_trade = config.volume_weight * 0.7
+        elif row['volume_ratio'] >= 1.0:
+            vol_score_trade = config.volume_weight * 0.3
+        else:
+            vol_score_trade = 0
+        
+        # 5. 时间框架得分（简化版：价格突破MA20）
+        tf_score = config.timeframe_weight if row['price_above_ma20'] else 0
+        
+        total_score = adx_score + ma_score + vol_score + vol_score_trade + tf_score
+        is_tradeable = total_score >= config.trade_threshold
+        
+        scores.append({
+            'date': data.index[i],
+            'close': row['close'],
+            'total_score': total_score,
+            'is_tradeable': is_tradeable,
+            'adx': row['adx'],
+            'ma20_slope': row['ma20_slope'],
+            'atr_ratio': row['atr_ratio'],
+            'volume_ratio': row['volume_ratio']
+        })
+    
+    scores_df = pd.DataFrame(scores)
+    scores_df = scores_df.set_index('date')
+    
+    # 计算未来收益
+    scores_df['future_5d_return'] = scores_df['close'].pct_change(5).shift(-5) * 100
+    scores_df['future_10d_return'] = scores_df['close'].pct_change(10).shift(-10) * 100
+    scores_df['future_20d_return'] = scores_df['close'].pct_change(20).shift(-20) * 100
+    
+    # 计算绩效指标
+    t_mask = scores_df['is_tradeable']
+    total_days = len(scores_df)
+    tradeable_days = t_mask.sum()
+    
+    if tradeable_days == 0:
+        return None
+    
+    returns_5d = scores_df[t_mask]['future_5d_return'].dropna()
+    returns_10d = scores_df[t_mask]['future_10d_return'].dropna()
+    returns_20d = scores_df[t_mask]['future_20d_return'].dropna()
+    
+    results = {
+        'config': config,
+        'total_days': total_days,
+        'tradeable_days': tradeable_days,
+        'tradeable_pct': tradeable_days / total_days * 100,
+        'avg_score': scores_df['total_score'].mean(),
+        'return_5d': returns_5d.mean() if len(returns_5d) > 0 else 0,
+        'return_10d': returns_10d.mean() if len(returns_10d) > 0 else 0,
+        'return_20d': returns_20d.mean() if len(returns_20d) > 0 else 0,
+        'win_rate_20d': (returns_20d > 0).mean() * 100 if len(returns_20d) > 0 else 0,
+        'sharpe_20d': returns_20d.mean() / returns_20d.std() if len(returns_20d) > 0 and returns_20d.std() > 0 else 0,
+        'excess_return_20d': returns_20d.mean() - scores_df[~t_mask]['future_20d_return'].mean() if len(returns_20d) > 0 else 0,
+        'scores_df': scores_df
+    }
+    
+    return results
+
+
+# 预计算指标
+print("\n预计算技术指标...")
+data = calculate_indicators(df)
+print("✓ 指标计算完成")
+
+# 生成参数组合（只测试权重和为100的组合）
+print("\n生成参数组合...")
+valid_configs = []
+
+for adx_t, adx_w, ma_t, ma_w, vol_t, vol_w, volu_t, volu_w in itertools.product(
+    param_grid['adx_threshold'],
+    param_grid['adx_weight'],
+    param_grid['ma_slope_threshold'],
+    param_grid['ma_slope_weight'],
+    param_grid['volatility_threshold'],
+    param_grid['volatility_weight'],
+    param_grid['volume_threshold'],
+    param_grid['volume_weight']
+):
+    config = StrategyConfig(
+        adx_threshold=adx_t,
+        adx_weight=adx_w,
+        ma_slope_threshold=ma_t,
+        ma_slope_weight=ma_w,
+        volatility_threshold=vol_t,
+        volatility_weight=vol_w,
+        volume_threshold=volu_t,
+        volume_weight=volu_w
+    )
+    
+    if config.total_weight == 100:  # 只保留权重和为100的组合
+        valid_configs.append(config)
+
+print(f"✓ 有效参数组合: {len(valid_configs)}个")
+
+# 运行回测
+print(f"\n开始回测 ({len(valid_configs)}个组合)...")
+print("-"*70)
+
+all_results = []
+for i, config in enumerate(valid_configs):
+    result = evaluate_strategy(data, config)
+    if result:
+        all_results.append(result)
+    
+    if (i + 1) % 50 == 0:
+        print(f"  进度: {i+1}/{len(valid_configs)} ({(i+1)/len(valid_configs)*100:.1f}%)")
+
+print(f"\n✓ 回测完成: {len(all_results)}个有效结果")
+
+# 排序并选择最佳配置
+print("\n" + "="*70)
+print("参数优化结果")
+print("="*70)
+
+# 按不同目标排序
+sort_by = [
+    ('20日收益', lambda r: r['return_20d'], True),
+    ('超额收益', lambda r: r['excess_return_20d'], True),
+    ('胜率', lambda r: r['win_rate_20d'], True),
+    ('夏普比率', lambda r: r['sharpe_20d'], True),
+]
+
+best_configs = {}
+for name, key_func, reverse in sort_by:
+    sorted_results = sorted(all_results, key=key_func, reverse=reverse)
+    best_configs[name] = sorted_results[0]
+    
+    print(f"\n【最佳配置 - {name}】")
+    best = sorted_results[0]
+    print(f"  ADX阈值: {best['config'].adx_threshold}, 权重: {best['config'].adx_weight}")
+    print(f"  MA斜率阈值: {best['config'].ma_slope_threshold}, 权重: {best['config'].ma_slope_weight}")
+    print(f"  波动率阈值: {best['config'].volatility_threshold}, 权重: {best['config'].volatility_weight}")
+    print(f"  成交量阈值: {best['config'].volume_threshold}, 权重: {best['config'].volume_weight}")
+    print(f"  时间框架权重: {best['config'].timeframe_weight}")
+    print(f"\n  绩效指标:")
+    print(f"    可交易比例: {best['tradeable_pct']:.1f}%")
+    print(f"    5日收益: {best['return_5d']:+.2f}%")
+    print(f"    10日收益: {best['return_10d']:+.2f}%")
+    print(f"    20日收益: {best['return_20d']:+.2f}%")
+    print(f"    超额收益: {best['excess_return_20d']:+.2f}%")
+    print(f"    胜率(20日): {best['win_rate_20d']:.1f}%")
+    print(f"    夏普比率: {best['sharpe_20d']:.2f}")
+
+# 综合评分最佳（平衡收益、胜率、夏普）
+print(f"\n【综合最佳配置】")
+print("-"*50)
+
+# 计算综合得分（标准化后加权）
+max_return = max(r['return_20d'] for r in all_results)
+max_winrate = max(r['win_rate_20d'] for r in all_results)
+max_sharpe = max(r['sharpe_20d'] for r in all_results)
+max_excess = max(r['excess_return_20d'] for r in all_results)
+
+for r in all_results:
+    # 综合得分 = 收益*0.3 + 超额*0.3 + 胜率*0.2 + 夏普*0.2
+    r['composite_score'] = (
+        (r['return_20d'] / max_return if max_return > 0 else 0) * 0.3 +
+        (r['excess_return_20d'] / max_excess if max_excess > 0 else 0) * 0.3 +
+        (r['win_rate_20d'] / max_winrate if max_winrate > 0 else 0) * 0.2 +
+        (r['sharpe_20d'] / max_sharpe if max_sharpe > 0 else 0) * 0.2
+    ) * 100
+
+best_composite = max(all_results, key=lambda r: r['composite_score'])
+
+print(f"  ADX阈值: {best_composite['config'].adx_threshold}, 权重: {best_composite['config'].adx_weight}")
+print(f"  MA斜率阈值: {best_composite['config'].ma_slope_threshold}, 权重: {best_composite['config'].ma_slope_weight}")
+print(f"  波动率阈值: {best_composite['config'].volatility_threshold}, 权重: {best_composite['config'].volatility_weight}")
+print(f"  成交量阈值: {best_composite['config'].volume_threshold}, 权重: {best_composite['config'].volume_weight}")
+print(f"\n  绩效指标:")
+print(f"    可交易比例: {best_composite['tradeable_pct']:.1f}%")
+print(f"    5日收益: {best_composite['return_5d']:+.2f}%")
+print(f"    10日收益: {best_composite['return_10d']:+.2f}%")
+print(f"    20日收益: {best_composite['return_20d']:+.2f}%")
+print(f"    超额收益: {best_composite['excess_return_20d']:+.2f}%")
+print(f"    胜率(20日): {best_composite['win_rate_20d']:.1f}%")
+print(f"    夏普比率: {best_composite['sharpe_20d']:.2f}")
+print(f"    综合得分: {best_composite['composite_score']:.1f}")
+
+# 保存结果
+print("\n" + "="*70)
+print("保存结果...")
+
+results_summary = []
+for r in all_results:
+    results_summary.append({
+        'adx_threshold': r['config'].adx_threshold,
+        'adx_weight': r['config'].adx_weight,
+        'ma_slope_threshold': r['config'].ma_slope_threshold,
+        'ma_slope_weight': r['config'].ma_slope_weight,
+        'volatility_threshold': r['config'].volatility_threshold,
+        'volatility_weight': r['config'].volatility_weight,
+        'volume_threshold': r['config'].volume_threshold,
+        'volume_weight': r['config'].volume_weight,
+        'tradeable_pct': r['tradeable_pct'],
+        'return_5d': r['return_5d'],
+        'return_10d': r['return_10d'],
+        'return_20d': r['return_20d'],
+        'excess_return_20d': r['excess_return_20d'],
+        'win_rate_20d': r['win_rate_20d'],
+        'sharpe_20d': r['sharpe_20d'],
+        'composite_score': r['composite_score']
+    })
+
+results_df = pd.DataFrame(results_summary)
+results_df = results_df.sort_values('composite_score', ascending=False)
+results_df.to_csv('/root/.openclaw/workspace/trend-quality-evaluator/optimization_results.csv', index=False)
+
+# 保存最佳配置详情
+best_composite['scores_df'].to_csv('/root/.openclaw/workspace/trend-quality-evaluator/best_config_backtest.csv')
+
+print("✓ 优化结果已保存: optimization_results.csv")
+print("✓ 最佳配置回测详情: best_config_backtest.csv")
+
+print("\n" + "="*70)
+print("参数优化测试完成！")
+print("="*70)

trend-quality-evaluator/param_optimization_results.csv

@@ -0,0 +1,9 @@
+name,adx_t,adx_w,ma_t,ma_w,vol_t,vol_w,volu_t,volu_w,trade_pct,avg_score,ret_20d,win_rate,sharpe
+原配置,25,30,1.002,25,0.8,20,1.5,10,40.70321811680572,47.407718860343316,3.1967278907747194,61.58357771260997,0.3395518262908878
+高ADX权重,25,35,1.002,20,0.8,20,1.5,10,42.01430274135876,49.800165448310736,3.1702252768447283,61.50568181818182,0.33902154073779456
+高MA权重,25,25,1.002,30,0.8,20,1.5,10,39.7497020262217,45.015272272375874,3.1534993103078,61.56156156156156,0.3367947313886168
+宽松波动率,25,30,1.002,25,0.9,20,1.5,10,43.4445768772348,49.35289168513473,3.145568167006351,61.53846153846154,0.338054673744046
+严格成交量,25,30,1.002,25,0.8,15,2.0,15,39.45172824791419,46.87732553495594,3.1151085679989015,61.17824773413897,0.32885246562076004
+综合优化2,20,35,1.003,25,0.8,15,2.0,10,43.3253873659118,52.5689736719791,2.992371035317209,60.74380165289256,0.3191922035207343
+低ADX阈值,20,30,1.002,25,0.8,20,1.5,10,42.19308700834327,49.4834349505777,2.9628928189242223,60.396039603960396,0.3144661580242694
+综合优化1,22,32,1.001,28,0.85,18,1.8,12,44.75566150178784,52.283595464643604,2.844091773403937,59.813084112149525,0.3033519660406973