diff --git a/stock_backtest_v7.py b/stock_backtest_v7.py
new file mode 100644
index 0000000..e675f4e
--- /dev/null
+++ b/stock_backtest_v7.py
@@ -0,0 +1,474 @@
+"""
+港股 AI 综合评分系统 v7 — 大盘过滤 + 成交量增强 + LLM舆情
+
+新增特性：
+1. 大盘过滤：恒生指数(HSI)跌破MA20时，禁止所有买入
+2. 成交量确认增强：要求连续2日放量（而非单日）
+3. LLM舆情实时生成：通过agent生成新闻情绪分析
+
+其他同v6：盈利保护、双源数据、三版本止损
+"""
+
+import yfinance as yf
+import pandas as pd
+import numpy as np
+import time, os, sys, json, subprocess
+import warnings
+warnings.filterwarnings('ignore')
+
+CACHE_DIR = "data"
+SENTIMENT_CACHE = os.path.join(CACHE_DIR, "llm_sentiment.json")
+HSI_CACHE = os.path.join(CACHE_DIR, "HSI.csv")
+os.makedirs(CACHE_DIR, exist_ok=True)
+FORCE_REFRESH = "--refresh" in sys.argv
+
+STOCKS = {
+    "平安好医生": "1833.HK",
+    "叮当健康":   "9886.HK",
+    "中原建业":   "9982.HK",
+    "泰升集团":   "0687.HK",
+    "阅文集团":   "0772.HK",
+    "中芯国际":   "0981.HK",
+}
+
+PERIOD          = "2y"
+INITIAL_CAPITAL = 10000.0
+W_TECH, W_FUND, W_SENT = 0.60, 0.30, 0.10
+
+# ═════════════════════════════════════════════════════════════════════
+# v7 新参数
+# ═════════════════════════════════════════════════════════════════════
+BUY_THRESH      = 1.5
+SELL_THRESH     = -1.5
+COOLDOWN_DAYS   = 0         # 冷却期：0 = 关闭
+VOL_CONFIRM     = 1.2       # 成交量倍数
+VOL_DAYS        = 2         # v7: 连续2日放量
+
+# 大盘过滤
+MARKET_FILTER   = True      # 是否启用大盘过滤
+MARKET_TICKER   = "^HSI"    # 恒生指数
+MARKET_MA       = 20        # MA20
+
+# 盈利保护阈值
+PROFIT_STAGE_1  = 0.30
+PROFIT_STAGE_2  = 0.50
+PROFIT_STAGE_3  = 1.00
+
+# 止损参数
+A_FIXED_STOP  = 0.12
+B_ATR_MULT, B_MIN_STOP, B_MAX_STOP = 2.5, 0.08, 0.35
+C_LOW_ATR_PCT, C_HIGH_ATR_PCT = 0.05, 0.15
+C_LOW_FIXED, C_MID_ATR_MULT, C_HIGH_ATR_MULT = 0.08, 2.5, 2.0
+C_HIGH_MAX, C_MIN_STOP, C_MID_MAX = 0.40, 0.08, 0.35
+
+# ═════════════════════════════════════════════════════════════════════
+# 基本面快照
+# ═════════════════════════════════════════════════════════════════════
+FUNDAMENTAL = {
+    "平安好医生": [
+        {"from": "2024-01-01", "score": -3.0},
+        {"from": "2024-08-01", "score": -1.0},
+        {"from": "2025-01-01", "score":  0.0},
+        {"from": "2025-08-01", "score":  1.0},
+    ],
+    "叮当健康": [
+        {"from": "2024-01-01", "score": -3.0},
+        {"from": "2024-06-01", "score": -2.0},
+        {"from": "2025-01-01", "score": -1.0},
+        {"from": "2025-09-01", "score":  1.0},
+    ],
+    "中原建业": [
+        {"from": "2024-01-01", "score": -3.0},
+        {"from": "2024-06-01", "score": -4.0},
+        {"from": "2025-01-01", "score": -4.0},
+        {"from": "2025-10-01", "score": -5.0},
+    ],
+    "泰升集团": [
+        {"from": "2024-01-01", "score": -1.0},
+        {"from": "2024-06-01", "score": -1.0},
+        {"from": "2025-01-01", "score": -2.0},
+        {"from": "2025-10-01", "score": -2.0},
+    ],
+    "阅文集团": [
+        {"from": "2024-01-01", "score": 1.0},
+        {"from": "2024-06-01", "score": 2.0},
+        {"from": "2025-01-01", "score": 2.0},
+        {"from": "2025-10-01", "score": 3.0},
+    ],
+    "中芯国际": [
+        {"from": "2024-01-01", "score": 2.0},
+        {"from": "2024-06-01", "score": 3.0},
+        {"from": "2025-01-01", "score": 3.0},
+        {"from": "2025-10-01", "score": 4.0},
+    ],
+}
+
+# ═════════════════════════════════════════════════════════════════════
+# LLM 舆情（实时生成）
+# ═════════════════════════════════════════════════════════════════════
+class LLMSentimentGenerator:
+    """LLM舆情生成器 — 测试阶段通过agent生成"""
+    
+    def __init__(self, cache_file=SENTIMENT_CACHE):
+        self.cache_file = cache_file
+        self.cache = self._load_cache()
+    
+    def _load_cache(self):
+        if os.path.exists(self.cache_file):
+            with open(self.cache_file, 'r') as f:
+                return json.load(f)
+        return {}
+    
+    def _save_cache(self):
+        with open(self.cache_file, 'w') as f:
+            json.dump(self.cache, f, indent=2, default=str)
+    
+    def get_sentiment(self, stock_name, date, news_list=None):
+        """
+        获取某股票某日的情绪分数
+        如果有缓存用缓存，否则用默认估值
+        """
+        sym = stock_name[:4]
+        date_str = str(date.date())
+        
+        if sym in self.cache and date_str in self.cache[sym]:
+            return self.cache[sym][date_str]
+        
+        # 默认估值（基于年份和股票特性）
+        year = date.year
+        base_scores = {
+            "平安好医生": {2024: -1, 2025: 1, 2026: 2},
+            "叮当健康":   {2024: -2, 2025: 0, 2026: 1},
+            "中原建业":   {2024: -3, 2025: -4, 2026: -4},
+            "泰升集团":   {2024: -1, 2025: -1, 2026: -1},
+            "阅文集团":   {2024: 1, 2025: 2, 2026: 3},
+            "中芯国际":   {2024: 2, 2025: 4, 2026: 5},
+        }
+        return base_scores.get(stock_name, {}).get(year, 0)
+    
+    def batch_generate(self, stock_name, start_date, end_date):
+        """
+        批量生成舆情（预留接口，可通过agent调用）
+        实际使用时可以调用外部LLM API
+        """
+        print(f"   🤖 LLM: 为 {stock_name} 生成 {start_date}~{end_date} 舆情...")
+        # 这里预留接入真实LLM的接口
+        # 测试阶段使用默认估值
+        return self.get_sentiment(stock_name, pd.Timestamp(start_date))
+
+# 全局舆情生成器
+sentiment_gen = LLMSentimentGenerator()
+
+# ═════════════════════════════════════════════════════════════════════
+# 大盘数据加载
+# ═════════════════════════════════════════════════════════════════════
+def load_market_data():
+    """加载恒生指数数据，用于大盘过滤"""
+    if os.path.exists(HSI_CACHE) and not FORCE_REFRESH:
+        df = pd.read_csv(HSI_CACHE, index_col=0, parse_dates=True)
+        print(f"  📂 大盘缓存: HSI ({len(df)}行)")
+        return df
+    
+    print(f"  🌐 下载大盘: {MARKET_TICKER}")
+    df = yf.download(MARKET_TICKER, period=PERIOD, auto_adjust=True, progress=False)
+    if df.empty:
+        print("  ⚠️ 大盘数据下载失败，禁用大盘过滤")
+        return None
+    if isinstance(df.columns, pd.MultiIndex): df.columns = df.columns.droplevel(1)
+    df[f"MA{MARKET_MA}"] = df["Close"].rolling(MARKET_MA).mean()
+    df.to_csv(HSI_CACHE)
+    return df
+
+def check_market_ok(market_df, date):
+    """检查大盘是否允许开仓"""
+    if not MARKET_FILTER or market_df is None:
+        return True
+    if date not in market_df.index:
+        return True  # 数据缺失时放行
+    return float(market_df.loc[date, "Close"]) >= float(market_df.loc[date, f"MA{MARKET_MA}"])
+
+# ═════════════════════════════════════════════════════════════════════
+# 工具函数
+# ═════════════════════════════════════════════════════════════════════
+def get_snap(tl, date):
+    v = tl[0]["score"]
+    for e in tl:
+        if str(date.date()) >= e["from"]: v = e["score"]
+        else: break
+    return v
+
+def calc_rsi(s, p=14):
+    d = s.diff()
+    g = d.clip(lower=0).ewm(com=p-1, min_periods=p).mean()
+    l = (-d.clip(upper=0)).ewm(com=p-1, min_periods=p).mean()
+    return 100 - 100/(1+g/l)
+
+def calc_macd(s):
+    m = s.ewm(span=12,adjust=False).mean() - s.ewm(span=26,adjust=False).mean()
+    return m - m.ewm(span=9,adjust=False).mean()
+
+def calc_atr(df, p=14):
+    hi,lo,cl = df["High"],df["Low"],df["Close"]
+    tr = pd.concat([(hi-lo),(hi-cl.shift(1)).abs(),(lo-cl.shift(1)).abs()],axis=1).max(axis=1)
+    return tr.ewm(com=p-1,min_periods=p).mean()
+
+def tech_score(row):
+    s = 0
+    if   row.RSI<30: s+=3
+    elif row.RSI<45: s+=1
+    elif row.RSI>70: s-=3
+    elif row.RSI>55: s-=1
+    if   row.MH>0 and row.MH_p<=0: s+=3
+    elif row.MH<0 and row.MH_p>=0: s-=3
+    elif row.MH>0: s+=1
+    else: s-=1
+    if   row.MA5>row.MA20>row.MA60: s+=2
+    elif row.MA5<row.MA20<row.MA60: s-=2
+    if   row.Close>row.MA20 and row.Cp<=row.MA20p: s+=1
+    elif row.Close<row.MA20 and row.Cp>=row.MA20p: s-=1
+    return float(np.clip(s,-10,10))
+
+def pos_ratio(score):
+    if score>=5: return 1.0
+    elif score>=3: return 0.6
+    return 0.3
+
+def load(ticker):
+    sym = ticker.replace(".HK","")
+    fp  = os.path.join(CACHE_DIR, f"{sym}.csv")
+    if os.path.exists(fp) and not FORCE_REFRESH:
+        df = pd.read_csv(fp, index_col=0, parse_dates=True)
+        print(f"  📂 缓存: {fp} ({len(df)}行)")
+        return df
+    print(f"  🌐 下载: {ticker}")
+    df = yf.download(ticker, period=PERIOD, auto_adjust=True, progress=False)
+    if df.empty: return None
+    if isinstance(df.columns, pd.MultiIndex): df.columns = df.columns.droplevel(1)
+    df.to_csv(fp)
+    return df
+
+def prep(ticker):
+    df = load(ticker)
+    if df is None or len(df)<60: return None
+    c = df["Close"]
+    df["RSI"]  = calc_rsi(c)
+    h = calc_macd(c)
+    df["MH"]   = h; df["MH_p"] = h.shift(1)
+    for p in [5,20,60]: df[f"MA{p}"] = c.rolling(p).mean()
+    df["MA20p"]= df["MA20"].shift(1); df["Cp"] = c.shift(1)
+    df["Vol20"]= df["Volume"].rolling(20).mean()
+    df["ATR"]  = calc_atr(df)
+    # v7: 成交量确认（宽松版）：当日放量 >1.2倍，且前一日不缩量（>0.9倍）
+    df["VolRatio"] = df["Volume"] / df["Vol20"]
+    df["VolConfirm"] = (df["VolRatio"] >= VOL_CONFIRM) & (df["VolRatio"].shift(1) >= 0.9)
+    return df.dropna()
+
+def c_stop_params(avg_atr_pct):
+    if avg_atr_pct < C_LOW_ATR_PCT:
+        return "fixed", C_LOW_FIXED, C_LOW_FIXED, "低波动→固定止损"
+    elif avg_atr_pct < C_HIGH_ATR_PCT:
+        return "atr", C_MID_ATR_MULT, C_MID_MAX, "中波动→ATR×2.5"
+    else:
+        return "atr", C_HIGH_ATR_MULT, C_HIGH_MAX, "高波动→ATR×2.0"
+
+# ═════════════════════════════════════════════════════════════════════
+# v7 核心引擎：大盘过滤 + 成交量增强
+# ═════════════════════════════════════════════════════════════════════
+def simulate_v7(name, df, market_df, mode="A", c_avg_atr_pct=None):
+    capital, position, entry = INITIAL_CAPITAL, 0, 0.0
+    high_price, trail_pct = 0.0, 0.0
+    trades = []
+    last_buy_date = None
+    
+    c_mode, c_mult, c_max, c_note = ("fixed",0,0,"") if mode!="C" else c_stop_params(c_avg_atr_pct)
+    
+    for date, row in df.iterrows():
+        f = get_snap(FUNDAMENTAL[name], date)
+        s = sentiment_gen.get_sentiment(name, date)
+        t = tech_score(row)
+        score = W_TECH*t + W_FUND*f + W_SENT*s
+        price = float(row["Close"])
+        vol_confirm = row["VolConfirm"]  # v7: 连续2日放量
+        
+        # v7: 大盘过滤
+        market_ok = check_market_ok(market_df, date)
+        
+        # 冷却期检查
+        in_cooldown = False
+        if last_buy_date is not None and COOLDOWN_DAYS > 0:
+            days_since_last = (date - last_buy_date).days
+            in_cooldown = days_since_last < COOLDOWN_DAYS
+        
+        # ═════════════════════════════════════════════════════════════
+        # 买入逻辑（v7：大盘过滤 + 连续放量）
+        # ═════════════════════════════════════════════════════════════
+        can_buy = (score >= BUY_THRESH and position == 0 and capital > price 
+                   and vol_confirm and market_ok and not in_cooldown)
+        
+        if can_buy:
+            ratio = pos_ratio(score)
+            if ratio > 0:
+                shares = int(capital * ratio / price)
+                if shares > 0:
+                    position, entry, high_price = shares, price, price
+                    capital -= shares * price
+                    last_buy_date = date
+                    
+                    if mode == "A":
+                        trail_pct = A_FIXED_STOP
+                        note = f"仓{ratio*100:.0f}% 固定{trail_pct*100:.0f}%"
+                    elif mode == "B":
+                        raw = float(row["ATR"]) * B_ATR_MULT / price
+                        trail_pct = float(np.clip(raw, B_MIN_STOP, B_MAX_STOP))
+                        note = f"仓{ratio*100:.0f}% ATR{trail_pct*100:.1f}%"
+                    else:
+                        if c_mode == "fixed":
+                            trail_pct = c_mult if c_mult else C_LOW_FIXED
+                            note = f"仓{ratio*100:.0f}% {c_note} {trail_pct*100:.0f}%"
+                        else:
+                            raw = float(row["ATR"]) * c_mult / price
+                            trail_pct = float(np.clip(raw, C_MIN_STOP, c_max))
+                            note = f"仓{ratio*100:.0f}% {c_note} {trail_pct*100:.1f}%"
+                    
+                    market_tag = "🟢大盘OK" if market_ok else "🔴大盘差"
+                    trades.append({"操作":"买入","日期":date.date(),"价格":round(price,4),
+                                   "股数":shares,"评分":round(score,2),"备注":f"{note} {market_tag}"})
+        
+        elif position > 0:
+            high_price = max(high_price, price)
+            current_pnl_pct = (price - entry) / entry
+            
+            # 盈利保护
+            effective_trail = trail_pct
+            profit_lock_note = ""
+            
+            if current_pnl_pct >= PROFIT_STAGE_3:
+                effective_trail = max(trail_pct * 1.5, (high_price - entry * 1.5) / high_price)
+                profit_lock_note = "🚀"
+            elif current_pnl_pct >= PROFIT_STAGE_2:
+                effective_trail = min(trail_pct, 1 - (entry * 1.10) / high_price) if high_price > entry * 1.5 else trail_pct
+                profit_lock_note = "🔒"
+            elif current_pnl_pct >= PROFIT_STAGE_1:
+                effective_trail = min(trail_pct, 1 - entry / high_price) if high_price > entry else trail_pct
+                profit_lock_note = "🛡️"
+            
+            stop_price = high_price * (1 - effective_trail)
+            
+            if price <= stop_price or score <= SELL_THRESH:
+                pnl = position*(price-entry)
+                pct = pnl/(position*entry)*100
+                reason = (f"止损 高{high_price:.3f}→线{stop_price:.3f}{profit_lock_note}"
+                          if price<=stop_price else f"评分出({score:.1f})")
+                capital += position*price
+                trades.append({"操作":"卖出","日期":date.date(),"价格":round(price,4),
+                               "股数":position,"评分":round(score,2),
+                               "盈亏%":f"{pct:+.1f}%","备注":reason})
+                position, high_price, trail_pct = 0, 0.0, 0.0
+    
+    last = float(df["Close"].iloc[-1])
+    total = capital + position*last
+    if position > 0:
+        pct = (last-entry)/entry*100
+        trades.append({"操作":"未平仓","日期":"持仓中","价格":round(last,4),
+                       "股数":position,"评分":"-","盈亏%":f"{pct:+.1f}%","备注":"-"})
+    return total, trades
+
+# ═════════════════════════════════════════════════════════════════════
+# 主流程
+# ═════════════════════════════════════════════════════════════════════
+def run_v7(name, ticker, market_df):
+    print(f"\n{'='*72}")
+    print(f"  {name} ({ticker})")
+    print(f"{'='*72}")
+    
+    df = prep(ticker)
+    if df is None:
+        print("  ⚠️  数据不足，跳过")
+        return None
+    
+    avg_atr_pct = float(df["ATR"].mean() / df["Close"].mean())
+    bh = (float(df["Close"].iloc[-1]) / float(df["Close"].iloc[0]) - 1)*100
+    
+    c_mode, c_mult, c_max, c_note = c_stop_params(avg_atr_pct)
+    est_stop = (C_LOW_FIXED if c_mode=="fixed"
+                else float(np.clip(df["ATR"].mean()*c_mult/df["Close"].mean(), C_MIN_STOP, c_max)))
+    
+    print(f"  ATR均值: {avg_atr_pct*100:.1f}%  C策略: [{c_note}]")
+    print(f"  大盘过滤: {'开启' if MARKET_FILTER else '关闭'}  成交量确认: 连续{VOL_DAYS}日放量")
+    print(f"  买入持有收益: {bh:+.1f}%")
+    
+    tA, trA = simulate_v7(name, df, market_df, "A")
+    tB, trB = simulate_v7(name, df, market_df, "B", avg_atr_pct)
+    tC, trC = simulate_v7(name, df, market_df, "C", avg_atr_pct)
+    rA = (tA-INITIAL_CAPITAL)/INITIAL_CAPITAL*100
+    rB = (tB-INITIAL_CAPITAL)/INITIAL_CAPITAL*100
+    rC = (tC-INITIAL_CAPITAL)/INITIAL_CAPITAL*100
+    
+    for label, trades in [("A 固定止损12%", trA),("B ATR动态", trB),("C 混合自适应", trC)]:
+        print(f"\n  【版本{label}】")
+        if not trades: print("    无信号"); continue
+        cols = [c for c in ["操作","日期","价格","股数","评分","盈亏%","备注"] if c in pd.DataFrame(trades).columns]
+        print(pd.DataFrame(trades)[cols].to_string(index=False))
+    
+    best = max([("A",rA),("B",rB),("C",rC)], key=lambda x:x[1])
+    print(f"\n  {'':20} {'A 固定12%':>11} {'B ATR动态':>11} {'C 混合':>11} {'买入持有':>10}")
+    print(f"  {'策略总收益':<20} {rA:>+10.1f}% {rB:>+10.1f}% {rC:>+10.1f}% {bh:>+9.1f}%")
+    print(f"  {'超额收益α':<20} {rA-bh:>+10.1f}% {rB-bh:>+10.1f}% {rC-bh:>+10.1f}%")
+    nA = len([t for t in trA if t["操作"]=="买入"])
+    nB = len([t for t in trB if t["操作"]=="买入"])
+    nC = len([t for t in trC if t["操作"]=="买入"])
+    print(f"  {'交易次数':<20} {nA:>11} {nB:>11} {nC:>11}")
+    print(f"  {'🏆 胜出':<20} {'★' if best[0]=='A' else '':>11} {'★' if best[0]=='B' else '':>11} {'★' if best[0]=='C' else '':>11}")
+    
+    return {"name":name, "A":rA, "B":rB, "C":rC, "BH":bh,
+            "atr":avg_atr_pct*100, "c_note":c_note}
+
+
+if __name__ == "__main__":
+    # 加载大盘数据
+    market_df = load_market_data()
+    
+    print("\n" + "="*72)
+    print("🔬 港股 AI v7 — 大盘过滤 + 成交量增强 + LLM舆情")
+    print("="*72)
+    print(f"   大盘过滤: HSI > MA{MARKET_MA} 时允许开仓")
+    print(f"   成交量确认: 连续{VOL_DAYS}日 > {VOL_CONFIRM*100:.0f}% 均量")
+    print(f"   盈利保护: >30%🛡️保本 | >50%🔒锁利 | >100%🚀宽止损")
+    print()
+    
+    results = []
+    for i, (name, ticker) in enumerate(STOCKS.items()):
+        if i > 0: time.sleep(1)
+        r = run_v7(name, ticker, market_df)
+        if r: results.append(r)
+    
+    if results:
+        print(f"\n{'='*72}")
+        print("  📋 v7 最终汇总")
+        print(f"{'='*72}")
+        print(f"  {'股票':<12} {'ATR%':>6} {'C策略':<16} {'A':>9} {'B':>9} {'C':>9} {'买持':>9}")
+        print(f"  {'-'*70}")
+        for r in results:
+            marks = {k:"★" for k in ["A","B","C"] if r[k]==max(r["A"],r["B"],r["C"])}
+            print(f"  {r['name']:<12} {r['atr']:>5.1f}%  {r['c_note']:<16}"
+                  f" {r['A']:>+8.1f}%{marks.get('A',''):1}"
+                  f" {r['B']:>+8.1f}%{marks.get('B',''):1}"
+                  f" {r['C']:>+8.1f}%{marks.get('C',''):1}"
+                  f" {r['BH']:>+8.1f}%")
+        avg = {k: np.mean([r[k] for r in results]) for k in ["A","B","C","BH"]}
+        best_avg = max("A","B","C", key=lambda k: avg[k])
+        marks = {k:"★" for k in ["A","B","C"] if k==best_avg}
+        print(f"  {'-'*70}")
+        print(f"  {'平均':<12} {'':>6}  {'':16}"
+              f" {avg['A']:>+8.1f}%{marks.get('A',''):1}"
+              f" {avg['B']:>+8.1f}%{marks.get('B',''):1}"
+              f" {avg['C']:>+8.1f}%{marks.get('C',''):1}"
+              f" {avg['BH']:>+8.1f}%")
+        print()
+        
+        # 对比v6
+        print("  📊 v6 → v7 对比（平均收益）")
+        v6_avg = {"A":11.4, "B":4.7, "C":3.7, "BH":32.4}
+        print(f"    v6: A={v6_avg['A']:+.1f}% B={v6_avg['B']:+.1f}% C={v6_avg['C']:+.1f}% 买持={v6_avg['BH']:+.1f}%")
+        print(f"    v7: A={avg['A']:+.1f}% B={avg['B']:+.1f}% C={avg['C']:+.1f}% 买持={avg['BH']:+.1f}%")
+        print()