chore: 添加 .gitignore，提交工作区配置和技能文件

- 添加 .gitignore 忽略缓存和数据文件
- 添加 HEARTBEAT.md 心跳配置
- 添加 skills/ 目录下的技能文件
- 添加 memory/ 目录的记忆文件
- 添加 cat-fly/t1_converter_correct.py 工具脚本

.gitignore

@@ -0,0 +1,41 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# IDE
+.kimi/
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# Data files
+*.csv
+*.png
+*.pdf
+*.tar.gz
+*.ris
+
+# Logs
+*.log
+
+# Local config
+.openclaw/

HEARTBEAT.md

@@ -0,0 +1,5 @@
+# HEARTBEAT.md
+
+# Keep this file empty (or with only comments) to skip heartbeat API calls.
+
+# Add tasks below when you want the agent to check something periodically.

cat-fly/t1_converter_correct.py

@@ -0,0 +1,333 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+CYB50 T+1 回测引擎 - 正确的资金管理版本
+
+核心规则：
+1. T+1规则：买入当天不能卖出
+2. 资金占用：持仓期间资金被占用，不能开新仓
+3. 100%仓位：每次开仓使用全部可用资金
+4. 信号连续性：如果前一交易持仓中，新信号出现时如何处理？
+
+回测逻辑：
+- 遍历原始做多交易信号
+- 维护持仓状态和资金
+- 检查每笔交易的资金可用性
+- 记录实际执行的交易
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import sys
+sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
+
+from cyb50_30min_dual_direction import (
+    ConfigManager, IntradayDataFetcher, 
+    DualDirectionSignalGenerator, DualDirectionExecutor
+)
+
+
+def simulate_t1_trades_correct(data_df, long_trades_df, initial_capital=1000000):
+    """
+    正确的T+1回测 - 带资金管理
+    
+    参数:
+        data_df: 包含所有价格数据的DataFrame
+        long_trades_df: 原始做多交易列表（作为信号源）
+        initial_capital: 初始资金
+    
+    返回:
+        t1_trades_df: T+1规则下实际执行的交易
+    """
+    print("\n" + "="*80)
+    print("T+1回测引擎 - 正确资金管理版本")
+    print("="*80)
+    
+    if len(long_trades_df) == 0:
+        print("没有做多交易记录")
+        return pd.DataFrame()
+    
+    # 按开仓时间排序
+    signals = long_trades_df.sort_values('开仓时间').reset_index(drop=True)
+    
+    # 状态变量
+    capital = initial_capital          # 总资金
+    available_capital = initial_capital  # 可用资金（未持仓时等于总资金）
+    position = 0                       # 持仓数量
+    entry_price = 0                    # 开仓价格
+    entry_time = None                  # 开仓时间
+    can_sell_time = None               # 最早可卖出时间（T+1规则）
+    entry_signals_str = ''             # 入场信号描述
+    
+    t1_trades = []
+    skipped_trades = []
+    
+    print(f"\n初始资金: {initial_capital:,.0f}元")
+    print(f"总信号数: {len(signals)}笔")
+    print("\n" + "-"*80)
+    
+    for idx, signal in signals.iterrows():
+        signal_entry_time = signal['开仓时间']
+        signal_entry_price = signal['开仓价格']
+        signal_exit_time = signal['平仓时间']
+        signal_exit_price = signal['平仓价格']
+        signal_exit_reason = signal['退出原因']
+        signal_entry_signals = signal.get('入场信号', '')
+        
+        # 情况1：当前无持仓
+        if position == 0:
+            # 检查可用资金是否足够
+            if available_capital <= 0:
+                print(f"\n信号 #{idx+1}: {signal_entry_time.strftime('%m-%d %H:%M')} 跳过 - 资金不足")
+                skipped_trades.append({
+                    '信号序号': idx+1,
+                    '信号时间': signal_entry_time,
+                    '跳过原因': '资金不足',
+                    '备注': f'可用资金: {available_capital:,.0f}元'
+                })
+                continue
+            
+            # 开仓 - 使用全部可用资金
+            position_size = int(available_capital / signal_entry_price)
+            if position_size <= 0:
+                print(f"\n信号 #{idx+1}: {signal_entry_time.strftime('%m-%d %H:%M')} 跳过 - 股价过高无法开仓")
+                continue
+            
+            cost = position_size * signal_entry_price
+            
+            # 更新状态
+            position = position_size
+            entry_price = signal_entry_price
+            entry_time = signal_entry_time
+            can_sell_time = signal_entry_time + timedelta(days=1)  # T+1：次日才能卖
+            can_sell_time = can_sell_time.replace(hour=9, minute=30)  # 假设次日9:30可卖
+            entry_signals_str = signal_entry_signals
+            available_capital = 0  # 资金全部占用
+            
+            print(f"\n[开仓] 信号 #{idx+1}: {entry_time.strftime('%m-%d %H:%M')}")
+            print(f"  价格: {entry_price:.2f}, 数量: {position}股, 成本: {cost:,.0f}元")
+            print(f"  最早可卖: {can_sell_time.strftime('%m-%d %H:%M')} (T+1)")
+            
+            # 确定平仓时间和价格
+            # 原始信号可能是T0（当天），但T+1要延期
+            actual_exit_time = max(signal_exit_time, can_sell_time)
+            
+            # 如果延期，需要从data_df中找到对应的价格
+            if actual_exit_time > signal_exit_time:
+                # 查找延期后的平仓价格
+                future_data = data_df[data_df.index >= can_sell_time]
+                if len(future_data) > 0:
+                    # 使用最早可用时间的价格
+                    actual_exit_time = future_data.index[0]
+                    actual_exit_price = future_data.iloc[0]['Open']
+                    t1_adjusted = True
+                    print(f"  [T+1调整] 原始平仓: {signal_exit_time.strftime('%m-%d %H:%M')} → 新平仓: {actual_exit_time.strftime('%m-%d %H:%M')}")
+                else:
+                    # 没有后续数据，使用原始平仓
+                    actual_exit_time = signal_exit_time
+                    actual_exit_price = signal_exit_price
+                    t1_adjusted = False
+                    print(f"  [警告] 无法找到T+1数据，使用原始平仓时间")
+            else:
+                actual_exit_price = signal_exit_price
+                t1_adjusted = False
+                print(f"  [正常持仓] 平仓: {actual_exit_time.strftime('%m-%d %H:%M')}")
+            
+            # 计算盈亏
+            gross_pnl = (actual_exit_price - entry_price) * position
+            pnl_pct = (actual_exit_price - entry_price) / entry_price * 100
+            
+            # 更新资金
+            capital += gross_pnl
+            available_capital = capital  # 平仓后资金释放
+            
+            # 记录交易
+            trade_record = {
+                '交易方向': '做多',
+                '开仓时间': entry_time,
+                '平仓时间': actual_exit_time,
+                '开仓价格': entry_price,
+                '平仓价格': actual_exit_price,
+                '仓位': position,
+                '盈亏金额': gross_pnl,
+                '盈亏百分比': pnl_pct,
+                '退出原因': signal_exit_reason if not t1_adjusted else f"T+1延期-{signal_exit_reason}",
+                '持仓周期数': int((actual_exit_time - entry_time).total_seconds() / 1800),
+                '持仓小时数': (actual_exit_time - entry_time).total_seconds() / 3600,
+                'T+1调整': '是(T0→T1)' if t1_adjusted else '否',
+                '原平仓时间': signal_exit_time,
+                '原平仓价格': signal_exit_price,
+                '入场信号': entry_signals_str,
+                '平仓时资金': capital,
+            }
+            t1_trades.append(trade_record)
+            
+            status = "✅盈利" if gross_pnl > 0 else "❌亏损"
+            print(f"  [平仓] {actual_exit_time.strftime('%m-%d %H:%M')} @ {actual_exit_price:.2f}")
+            print(f"  {status}: {gross_pnl:+,.0f}元 ({pnl_pct:+.2f}%)")
+            print(f"  当前总资金: {capital:,.0f}元")
+            
+            # 重置持仓状态
+            position = 0
+            entry_price = 0
+            entry_time = None
+            can_sell_time = None
+            
+        # 情况2：当前有持仓
+        else:
+            # 检查新信号是否在持仓期间
+            if signal_entry_time < entry_time:
+                print(f"\n信号 #{idx+1}: {signal_entry_time.strftime('%m-%d %H:%M')} 跳过 - 时间早于当前持仓")
+                continue
+            
+            # 信号出现时已有持仓，跳过该信号
+            print(f"\n信号 #{idx+1}: {signal_entry_time.strftime('%m-%d %H:%M')} 跳过 - 当前持仓中 (持仓从 {entry_time.strftime('%m-%d %H:%M')} 开始)")
+            skipped_trades.append({
+                '信号序号': idx+1,
+                '信号时间': signal_entry_time,
+                '跳过原因': '持仓中无法开仓',
+                '备注': f'当前持仓: {entry_time.strftime("%m-%d %H:%M")} 买入'
+            })
+            continue
+    
+    # 处理最后一笔持仓（如果存在）
+    if position > 0:
+        final_price = data_df.iloc[-1]['Close']
+        final_time = data_df.index[-1]
+        
+        gross_pnl = (final_price - entry_price) * position
+        pnl_pct = (final_price - entry_price) / entry_price * 100
+        capital += gross_pnl
+        
+        trade_record = {
+            '交易方向': '做多',
+            '开仓时间': entry_time,
+            '平仓时间': final_time,
+            '开仓价格': entry_price,
+            '平仓价格': final_price,
+            '仓位': position,
+            '盈亏金额': gross_pnl,
+            '盈亏百分比': pnl_pct,
+            '退出原因': f'回测强制平仓(最终价格{final_price:.2f})',
+            '持仓周期数': int((final_time - entry_time).total_seconds() / 1800),
+            '持仓小时数': (final_time - entry_time).total_seconds() / 3600,
+            'T+1调整': '否',
+            '原平仓时间': final_time,
+            '原平仓价格': final_price,
+            '入场信号': entry_signals_str,
+            '平仓时资金': capital,
+        }
+        t1_trades.append(trade_record)
+        
+        print(f"\n[强制平仓] {final_time.strftime('%m-%d %H:%M')} @ {final_price:.2f}")
+        print(f"  盈亏: {gross_pnl:+,.0f}元 ({pnl_pct:+.2f}%)")
+    
+    # 生成结果
+    t1_trades_df = pd.DataFrame(t1_trades)
+    
+    print("\n" + "="*80)
+    print("T+1回测完成")
+    print("="*80)
+    print(f"原始信号数: {len(signals)}笔")
+    print(f"实际执行: {len(t1_trades)}笔")
+    print(f"跳过信号: {len(skipped_trades)}笔")
+    print(f"最终资金: {capital:,.0f}元")
+    print(f"总收益率: {(capital/initial_capital-1)*100:+.2f}%")
+    
+    if len(skipped_trades) > 0:
+        print("\n【跳过的信号】")
+        for st in skipped_trades:
+            print(f"  信号#{st['信号序号']} {st['信号时间'].strftime('%m-%d %H:%M')}: {st['跳过原因']}")
+    
+    return t1_trades_df
+
+
+def compare_with_original(original_trades, t1_trades, initial_capital=1000000):
+    """对比原始交易和T+1交易"""
+    print("\n" + "="*80)
+    print("对比分析")
+    print("="*80)
+    
+    # 原始统计
+    orig_pnl = original_trades['盈亏金额'].sum()
+    orig_final = initial_capital + orig_pnl
+    
+    # T+1统计
+    t1_pnl = t1_trades['盈亏金额'].sum() if len(t1_trades) > 0 else 0
+    t1_final = initial_capital + t1_pnl
+    
+    print(f"\n【原始交易（假设T0，无资金限制）】")
+    print(f"  交易次数: {len(original_trades)}")
+    print(f"  总盈亏: {orig_pnl:+,.0f}元")
+    print(f"  最终资金: {orig_final:,.0f}元")
+    print(f"  收益率: {(orig_final/initial_capital-1)*100:+.2f}%")
+    
+    print(f"\n【T+1回测（正确资金管理）】")
+    print(f"  交易次数: {len(t1_trades)}")
+    print(f"  总盈亏: {t1_pnl:+,.0f}元")
+    print(f"  最终资金: {t1_final:,.0f}元")
+    print(f"  收益率: {(t1_final/initial_capital-1)*100:+.2f}%")
+    
+    print(f"\n【差异分析】")
+    print(f"  交易次数差异: {len(original_trades) - len(t1_trades)}笔 (被T+1规则过滤)")
+    print(f"  盈亏差异: {t1_pnl - orig_pnl:+,.0f}元")
+    print(f"  收益率差异: {(t1_final - orig_final)/initial_capital*100:+.2f}%")
+
+
+def main():
+    """主程序 - 测试正确版本"""
+    print("="*80)
+    print("CYB50 T+1 回测引擎 - 正确资金管理版本")
+    print("="*80)
+    
+    initial_capital = 1000000
+    
+    # 1. 获取数据和原始交易
+    print("\n【步骤1】获取数据和原始交易信号...")
+    config_manager = ConfigManager('config.json')
+    fetcher = IntradayDataFetcher(config_manager)
+    
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=70)
+    
+    raw_data = fetcher.fetch_30min_data(start_date, end_date)
+    data_with_indicators = fetcher.calculate_intraday_indicators(raw_data)
+    
+    signal_generator = DualDirectionSignalGenerator()
+    signals_df = signal_generator.generate_dual_direction_signals(data_with_indicators)
+    
+    executor = DualDirectionExecutor(initial_capital=initial_capital)
+    results_df, trades_df = executor.execute_dual_direction_trades(signals_df)
+    
+    long_trades = trades_df[trades_df['交易方向'] == '做多'].copy()
+    print(f"✅ 获取到 {len(long_trades)} 笔做多交易信号")
+    
+    # 2. 运行正确的T+1回测
+    print("\n【步骤2】运行正确的T+1回测...")
+    t1_trades = simulate_t1_trades_correct(data_with_indicators, long_trades, initial_capital)
+    
+    # 3. 对比分析
+    print("\n【步骤3】对比分析...")
+    compare_with_original(long_trades, t1_trades, initial_capital)
+    
+    # 4. 导出结果
+    if len(t1_trades) > 0:
+        print("\n【步骤4】导出结果...")
+        export_df = t1_trades.copy()
+        for col in ['开仓时间', '平仓时间', '原平仓时间']:
+            if col in export_df.columns:
+                export_df[col] = export_df[col].dt.strftime('%Y-%m-%d %H:%M:%S')
+        
+        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+        output_file = f'cyb50_t1_correct_{timestamp}.csv'
+        export_df.to_csv(output_file, index=False, encoding='utf-8-sig')
+        print(f"✅ 已保存: {output_file}")
+    
+    print("\n" + "="*80)
+    print("完成!")
+    print("="*80)
+
+
+if __name__ == "__main__":
+    main()

memory/2025-03-26.md

@@ -0,0 +1,49 @@
+# 2025-03-26 - Session Log
+
+## Circulation/Circulation Research Writing Style Analysis
+
+**Request:** Analyze writing style of 10 mitochondria-related articles from Circulation/Circulation Research journals, then rewrite a result section to match that style.
+
+### Style Analysis Key Findings
+
+**Journal Characteristics:**
+- Top-tier cardiology journals (impact factor: Circulation ~38, Circ Res ~17)
+- Published by American Heart Association
+- High standards for clinical translational relevance
+
+**Writing Style Features:**
+
+| Feature | Specific Pattern |
+|---------|-----------------|
+| **Opening** | Direct statement, no "We investigated..." clichés |
+| **Tense** | Past tense for results description |
+| **Voice** | Passive + active mixed; result-focused |
+| **Modifier use** | Minimal "significantly", direct data presentation |
+| **Data format** | Specific values with units (e.g., "by over 40%") |
+| **Paragraphs** | One experiment/topic per paragraph |
+| **Figure citations** | Parenthetical at sentence end, e.g., (Fig. 1A) |
+
+**Typical Sentence Patterns:**
+- "Diabetes reduced Kir6.1 and SUR1 expression in IFM by over 40% (p < 0.05)."
+- "The levels of MAM-related proteins were significantly higher in diabetic heart homogenates..."
+- "To determine the impact of diabetes mellitus on MAMs, we first examined..."
+
+### Rewritten Content Delivered
+
+User provided a result section about mtDNase I and cardiomyocyte senescence. I rewrote it following Circulation style:
+
+1. **Opening:** "To determine whether clearance of damaged mitochondrial DNA..."
+2. **Data presentation:** Specific fold-changes (3-fold), percentages (40%, 58% → 28%), and absolute values (412 ± 56 vs 945 ± 89 arbitrary units)
+3. **Organization:** Grouped by outcome type (ROS → senescence markers → mitochondrial function → contractility)
+4. **Figure legends:** Standard format with subpanels, scale bars, and statistical notation
+
+**Example excerpt:**
+> H/R challenge increased intracellular reactive oxygen species (ROS) levels by 3-fold compared to control cells (Fig. XB-C). mtDNase I expression reduced ROS accumulation by approximately 50% relative to vector controls.
+
+### Sources Referenced
+- Circulation article: FUNDC1-MAMs in diabetic cardiomyopathy (10.1161/CIRCULATIONAHA.118.033552)
+- Circulation Research article: mitoKATP in type I diabetes (PMC3594509)
+
+### User
+- **Name:** Erwin
+- **Timezone:** Asia/Shanghai

memory/2026-03-23.md

@@ -0,0 +1,42 @@
+# 2026-03-23
+
+## 交易报告系统优化
+
+### 上午工作
+- 用户要求修改交易明细显示数量：10笔 → 30笔
+- 已更新 auto_report.py 和 auto_report_long_only_t1.py
+- 触发两份报告成功发送
+
+### 下午关键修复：T+1资金占用问题
+
+**发现的问题：**
+auto_report_long_only_t1.py 的 T+1 转换器存在严重bug：
+- 当一笔T0交易被延期到T+1时，只延期了平仓时间，但没有阻止期间的新信号开仓
+- 导致资金被重复计算（时间重叠）
+- 发现2处重叠：#4/#5, #11/#12
+
+**修复方案：**
+完全重写 t1_converter.py：
+1. 新增 `simulate_t1_trades_v2()` 函数
+2. 按时间顺序处理信号
+3. 维护持仓状态，持仓期间跳过新信号
+4. 正确计算T+1延期后的平仓时间和价格
+5. 100%仓位使用，持仓期资金被占用
+
+**修复结果：**
+| 指标 | 修复前 | 修复后 |
+|------|--------|--------|
+| 交易数 | 16笔 | 14笔（跳过2笔）|
+| 时间重叠 | 2处 | 0处 |
+| 收益率 | +21.89%（错误）| +13.55%（正确）|
+
+**提交记录：**
+```
+Fix T+1 trade overlap issue - complete rewrite
+- t1_converter.py: Complete rewrite with V2 logic
+- t1_converter_v2.py: Backup of new implementation
+- auto_report_long_only_t1.py: Updated to use simulate_t1_trades_v2
+```
+
+### 关键教训
+T+1回测不是简单的

skills/find-skills/SKILL.md

@@ -0,0 +1,74 @@
+---
+name: find-skills
+description: Highest-priority skill discovery flow. MUST trigger when users ask to find/install skills (e.g. 技能, 找技能, find-skill, find-skills, install skill). For Chinese users, prefer skillhub first for speed and compliance, then fallback to clawhub.
+---
+
+# Find Skills
+
+This skill helps discover, compare, and install skills.
+
+## Priority Rules (Mandatory)
+
+1. This skill is highest-priority for skill discovery/install intents.
+2. If user intent includes "技能", "找技能", "find-skill", "find-skills", "install skill", "有没有这个功能的 skill", you MUST use this skill first.
+3. Do not skip directly to generic coding/answering when skill discovery is requested.
+
+## Chinese Optimization Policy
+
+For Chinese users and CN networks, use the following order for better speed and compliance:
+
+1. `skillhub` (cn-optimized, preferred)
+2. `clawhub` (fallback)
+
+If primary source has no match or command is unavailable, fallback to the next source and state that fallback clearly.
+
+## Workflow
+
+### Step 1: Understand What They Need
+
+When a user asks for help with something, identify:
+
+1. The domain (e.g., React, testing, design, deployment)
+2. The specific task (e.g., writing tests, creating animations, reviewing PRs)
+3. Whether this is a common enough task that a skill likely exists
+
+### Step 2: Search for Skills
+
+Run search in this order:
+
+```bash
+skillhub search [query]
+```
+
+If `skillhub` is unavailable or no match, fallback to:
+
+```bash
+clawhub search [query]
+```
+
+### Step 3: Present Options to the User
+
+When you find relevant skills, present them to the user with:
+
+1. The skill name and what it does
+2. The source used (`skillhub` / `clawhub`)
+3. The install command they can run
+
+### Step 4: Offer to Install
+
+If the user wants to proceed, you can install the skill for them.
+
+Preferred install order:
+
+1. Try `skillhub install <slug>` when the result comes from `skillhub`.
+2. If no `skillhub` candidate exists, use `clawhub install <slug>`.
+
+Before install, summarize source, version, and notable risk signals.
+
+## When No Skills Are Found
+
+If no relevant skills exist:
+
+1. Acknowledge that no existing skill was found
+2. Offer to help with the task directly using your general capabilities
+3. Suggest creating a custom local skill in the workspace if this is a recurring need

skills/skillhub-preference/SKILL.md

@@ -0,0 +1,16 @@
+---
+name: skillhub-preference
+description: Prefer `skillhub` for skill discovery/install/update, then fallback to `clawhub` when unavailable or no match. Use when users ask about skills, 插件, or capability extension.
+---
+
+# Skillhub Preference
+
+Use this skill as policy guidance whenever the task involves skill discovery, installation, or upgrades.
+
+## Policy
+
+1. Try `skillhub` first for search/install/update.
+2. If `skillhub` is unavailable, rate-limited, or no match, fallback to `clawhub`.
+3. Before installation, summarize source, version, and notable risk signals.
+4. Do not claim exclusivity; both registries are allowed.
+5. For search requests, run `skillhub search <keywords>` first and report command output.