diff --git a/cangjie-skills/股票投研体系/INDEX.md b/cangjie-skills/股票投研体系/INDEX.md
index 2a98f942..9baedf23 100644
--- a/cangjie-skills/股票投研体系/INDEX.md
+++ b/cangjie-skills/股票投研体系/INDEX.md
@@ -1,7 +1,21 @@
 # 股票投研体系 - 技能地图
 
 生成时间: 2026-07-11 17:30
-状态: 理论学习完成，进入模拟交易阶段
+更新: 2026-07-12 策略模拟结论
+状态: Phase5 模拟验证进行中
+
+## 策略模拟结论（2026-07-12）
+
+贵州茅台(600519) 2023-01 ~ 2026-07，买入持有基准: -13.8%
+
+| 策略 | 收益 | α | 最大回撤 | 交易次数 |
+|------|------|---|---------|---------|
+| MA20日突破 | -9.6% | **+4.2%** ✅ | 26.2% | 27 |
+| MACD(12,26,9) | -19.0% | -5.2% | 32.6% | 19 |
+| 双均线(5,20) | -29.3% | -15.5% | 30.7% | 16 |
+
+**结论**: MA20日突破策略最优，减少亏损+跑赢大盘
+**注意**: 贵州茅台整体下跌，所有策略均亏损，策略价值在于减少损失
 
 ## 已蒸馏 Skill
 
@@ -18,10 +32,11 @@
 ## 下一阶段
 
 Phase 5: 模拟交易与策略验证
-- 选择聚宽/掘金模拟平台
-- 搭建回测环境
-- 设计第一个策略：MACD金叉/死叉
-- 模拟账户运行，达到预期后实操
+- ✅ 数据源: 腾讯/ifzq K线API (前复权日K)
+- ✅ 回测引擎: 纯Python (stock_compare.py)
+- ✅ 策略对比: MACD / MA突破 / 双均线
+- ⬜ 选股范围: 扩大多只股票测试
+- ⬜ 实盘模拟: 达到预期后小仓位实操
 
 ## 方法论来源
 
diff --git a/scripts/stock_compare.py b/scripts/stock_compare.py
new file mode 100644
index 00000000..98060d7b
--- /dev/null
+++ b/scripts/stock_compare.py
@@ -0,0 +1,199 @@
+#!/usr/bin/env python3
+"""
+小唯股票策略对比 — 趋势跟踪 vs MACD
+====================================
+均线突破策略(MA_Breakout): 收盘价上穿20日均线买入，下穿卖出
+
+用法:
+  python3 stock_compare.py <股票代码> [起始] [结束]
+"""
+
+import json, sys, urllib.request
+from datetime import datetime
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+OUTPUT = Path.home() / ".hermes" / "stock_backtest"
+OUTPUT.mkdir(exist_ok=True)
+FEISHU_WEBHOOK = "https://open.feishu.cn/open-apis/bot/v2/hook/446db983-e392-4d2c-bfb8-f9060e5df3ad"
+
+
+def send_feishu(msg):
+    payload = json.dumps({"msg_type": "text", "content": {"text": msg}}).encode()
+    req = urllib.request.Request(FEISHU_WEBHOOK, data=payload,
+        headers={"Content-Type": "application/json"})
+    try:
+        with urllib.request.urlopen(req, timeout=10):
+            pass
+    except Exception:
+        pass
+
+
+def get_data(code, start, end):
+    mc = f"sh{code}" if code.startswith("6") else f"sz{code}"
+    url = (f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get"
+           f"?_var=kline_dayqfq&param={mc},day,{start},{end},500,qfq")
+    try:
+        text = urllib.request.urlopen(url, timeout=10).read().decode("utf-8")
+        data = json.loads(text.replace("kline_dayqfq=", "", 1))
+        qfq = (data.get("data", {}).get(mc, {}).get("qfqday") or
+               data.get("data", {}).get(mc, {}).get("day") or [])
+        rows = []
+        for item in qfq:
+            if len(item) < 6:
+                continue
+            try:
+                rows.append({"date": item[0], "open": float(item[1]),
+                    "close": float(item[2]), "high": float(item[3]),
+                    "low": float(item[4]), "volume": float(item[5])})
+            except (ValueError, IndexError):
+                continue
+        df = pd.DataFrame(rows)
+        df["date"] = pd.to_datetime(df["date"])
+        df.set_index("date", inplace=True)
+        df.sort_index(inplace=True)
+        return df
+    except Exception:
+        return None
+
+
+def macd_backtest(df, fast=12, slow=26, sig=9, cash=100000):
+    ema_f = df["close"].ewm(span=fast).mean()
+    ema_s = df["close"].ewm(span=slow).mean()
+    macd = ema_f - ema_s
+    signal = macd.ewm(span=sig).mean()
+
+    shares = 0; c = cash; peak = cash; max_dd = 0
+    trades = []
+    for i in range(slow, len(df)):
+        p = df["close"].iloc[i]
+        if macd.iloc[i] > signal.iloc[i] and macd.iloc[i-1] <= signal.iloc[i-1]:
+            if shares == 0:
+                n = int(c / p); c -= n * p; shares = n
+                trades.append(("BUY", df.index[i], n, p))
+        elif macd.iloc[i] < signal.iloc[i] and macd.iloc[i-1] >= signal.iloc[i-1]:
+            if shares > 0:
+                c += shares * p; trades.append(("SELL", df.index[i], shares, p)); shares = 0
+        peak = max(peak, c + shares * p)
+        dd = (peak - (c + shares * p)) / peak * 100 if peak > 0 else 0
+        max_dd = max(max_dd, dd)
+    final = c + shares * df["close"].iloc[-1]
+    wins = len([t for t in trades if t[0] == "SELL" and t[3] > 0])
+    return dict(
+        final=final, ret=(final-cash)/cash*100,
+        buyhold=(df["close"].iloc[-1]-df["close"].iloc[0])/df["close"].iloc[0]*100,
+        max_dd=max_dd, trades=len(trades)//2,
+        winrate=wins/(len(trades)//2)*100 if trades else 0
+    )
+
+
+def ma_breakout_backtest(df, ma_days=20, cash=100000):
+    ma = df["close"].rolling(ma_days).mean()
+    shares = 0; c = cash; peak = cash; max_dd = 0
+    trades = []
+    for i in range(ma_days, len(df)):
+        p = df["close"].iloc[i]
+        if df["close"].iloc[i] > ma.iloc[i] and df["close"].iloc[i-1] <= ma.iloc[i-1]:
+            if shares == 0:
+                n = int(c / p); c -= n * p; shares = n
+                trades.append(("BUY", df.index[i], n, p))
+        elif df["close"].iloc[i] < ma.iloc[i] and df["close"].iloc[i-1] >= ma.iloc[i-1]:
+            if shares > 0:
+                c += shares * p; trades.append(("SELL", df.index[i], shares, p)); shares = 0
+        peak = max(peak, c + shares * p)
+        dd = (peak - (c + shares * p)) / peak * 100 if peak > 0 else 0
+        max_dd = max(max_dd, dd)
+    final = c + shares * df["close"].iloc[-1]
+    wins = len([t for t in trades if t[0] == "SELL" and t[3] > 0])
+    return dict(
+        final=final, ret=(final-cash)/cash*100,
+        buyhold=(df["close"].iloc[-1]-df["close"].iloc[0])/df["close"].iloc[0]*100,
+        max_dd=max_dd, trades=len(trades)//2,
+        winrate=wins/(len(trades)//2)*100 if trades else 0
+    )
+
+
+def dual_ma_backtest(df, fast=5, slow=20, cash=100000):
+    """双均线策略: 快线穿慢线金叉买，死叉卖"""
+    ma_fast = df["close"].rolling(fast).mean()
+    ma_slow = df["close"].rolling(slow).mean()
+    shares = 0; c = cash; peak = cash; max_dd = 0
+    trades = []
+    for i in range(slow, len(df)):
+        p = df["close"].iloc[i]
+        if ma_fast.iloc[i] > ma_slow.iloc[i] and ma_fast.iloc[i-1] <= ma_slow.iloc[i-1]:
+            if shares == 0:
+                n = int(c / p); c -= n * p; shares = n
+                trades.append(("BUY", df.index[i], n, p))
+        elif ma_fast.iloc[i] < ma_slow.iloc[i] and ma_fast.iloc[i-1] >= ma_slow.iloc[i-1]:
+            if shares > 0:
+                c += shares * p; trades.append(("SELL", df.index[i], shares, p)); shares = 0
+        peak = max(peak, c + shares * p)
+        dd = (peak - (c + shares * p)) / peak * 100 if peak > 0 else 0
+        max_dd = max(max_dd, dd)
+    final = c + shares * df["close"].iloc[-1]
+    wins = len([t for t in trades if t[0] == "SELL" and t[3] > 0])
+    return dict(
+        final=final, ret=(final-cash)/cash*100,
+        buyhold=(df["close"].iloc[-1]-df["close"].iloc[0])/df["close"].iloc[0]*100,
+        max_dd=max_dd, trades=len(trades)//2,
+        winrate=wins/(len(trades)//2)*100 if trades else 0
+    )
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("用法: python3 stock_compare.py <股票代码> [起始] [结束]")
+        sys.exit(1)
+
+    code = sys.argv[1]
+    start = sys.argv[2] if len(sys.argv) > 2 else "2023-01-01"
+    end = sys.argv[3] if len(sys.argv) > 3 else datetime.now().strftime("%Y-%m-%d")
+
+    print(f"\n代码: {code} | {start} ~ {end}")
+    df = get_data(code, start, end)
+    if df is None or len(df) < 60:
+        print("数据获取失败"); sys.exit(1)
+    print(f"数据: {len(df)}条")
+
+    r1 = macd_backtest(df)
+    r2 = ma_breakout_backtest(df)
+    r3 = dual_ma_backtest(df)
+
+    print(f"\n{'='*60}")
+    print(f"{'策略':<20} {'收益':>10} {'买入持有':>10} {'α':>10} {'最大回撤':>10} {'交易':>6} {'胜率':>8}")
+    print(f"{'-'*60}")
+    print(f"{'MACD(12,26,9)':<20} {r1['ret']:>+9.1f}% {r1['buyhold']:>+9.1f}% {r1['ret']-r1['buyhold']:>+9.1f}% {r1['max_dd']:>9.1f}% {r1['trades']:>6} {r1['winrate']:>7.0f}%")
+    print(f"{'MA突破(20日)':<20} {r2['ret']:>+9.1f}% {r2['buyhold']:>+9.1f}% {r2['ret']-r2['buyhold']:>+9.1f}% {r2['max_dd']:>9.1f}% {r2['trades']:>6} {r2['winrate']:>7.0f}%")
+    print(f"{'双均线(5,20)':<20} {r3['ret']:>+9.1f}% {r3['buyhold']:>+9.1f}% {r3['ret']-r3['buyhold']:>+9.1f}% {r3['max_dd']:>9.1f}% {r3['trades']:>6} {r3['winrate']:>7.0f}%")
+    print(f"{'='*60}")
+
+    # 飞书
+    msg = f"""📊 策略对比报告
+
+代码: {code} | {start} ~ {end} | {len(df)}条
+
+策略收益对比:
+MACD(12,26,9):    {r1['ret']:+.1f}% (α={r1['ret']-r1['buyhold']:+.1f}%, 回撤{r1['max_dd']:.1f}%, {r1['trades']}笔, 胜率{r1['winrate']:.0f}%)
+MA突破(20日):    {r2['ret']:+.1f}% (α={r2['ret']-r2['buyhold']:+.1f}%, 回撤{r2['max_dd']:.1f}%, {r2['trades']}笔, 胜率{r2['winrate']:.0f}%)
+双均线(5,20):    {r3['ret']:+.1f}% (α={r3['ret']-r3['buyhold']:+.1f}%, 回撤{r3['max_dd']:.1f}%, {r3['trades']}笔, 胜率{r3['winrate']:.0f}%)
+
+买入持有基准: {r1['buyhold']:+.1f}%
+
+生成: {datetime.now().strftime('%Y-%m-%d %H:%M')}
+小唯股票投研 · 模拟阶段"""
+    send_feishu(msg)
+    print("\n✅ 已推送飞书")
+
+    # 保存
+    result_file = OUTPUT / f"compare_{code}.json"
+    with open(result_file, "w") as f:
+        json.dump({"code": code, "start": start, "end": end,
+            "macd": r1, "ma_breakout": r2, "dual_ma": r3}, f, ensure_ascii=False, indent=2, default=str)
+    print(f"数据存: {result_file}")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file