diff --git a/scripts/strategy_lifecycle.py b/scripts/strategy_lifecycle.py
index eacf9247..12dce8e9 100644
--- a/scripts/strategy_lifecycle.py
+++ b/scripts/strategy_lifecycle.py
@@ -898,6 +898,7 @@ def reassess_strategy(code, name, price, cost, shares, current_action,
         sr = tech["support_resistance"]
         candle = tech.get("candlestick", {})
         vol = tech.get("volume", {})
+        vol_deep = tech.get("volume_deep", {})
         ss = sr.get("strong_support")
         ws = sr.get("weak_support")
         wr = sr.get("weak_resist")
@@ -1268,6 +1269,25 @@ def reassess_strategy(code, name, price, cost, shares, current_action,
     # 融合大盘趋势、行业板块强弱、基本面估值作为修正因子
     volume_signal = vol.get("volume_signal", "")
     candlestick_sentiment = candle.get("sentiment", "neutral")
+
+    # ----- 深度量价分析（2026-07-08 新增：放量建仓/出货/洗盘检测）-----
+    deep_vol = {}
+    vol_pattern_note = ""
+    try:
+        from technical_analysis import analyze_volume_deep
+        deep_vol = analyze_volume_deep(code)
+        if deep_vol.get("volume_signal"):
+            vp = deep_vol.get("price_vol_description", "")
+            patterns = deep_vol.get("patterns", {})
+            vol_pattern_note = vp
+            # 合并到volume_signal
+            if volume_signal:
+                volume_signal += f" | {vp}" if vp else ""
+            else:
+                volume_signal = vp or ""
+    except Exception:
+        pass
+
     timing_signal = "neutral"
 
     # --- 三维分析数据装载 ---
@@ -1438,6 +1458,10 @@ def reassess_strategy(code, name, price, cost, shares, current_action,
     if action_note:
         action_parts.append(action_note)
 
+    # 量价分析描述（如有，注入action）
+    if vol_pattern_note:
+        action_parts.append(vol_pattern_note)
+
     if is_watchlist:
         # 自选股（未入场）：有止损参考+买入区，内部算RR需要止盈位
         action_parts.append(f"目标参考{new_target}")
diff --git a/scripts/technical_analysis.py b/scripts/technical_analysis.py
index b5128578..27426959 100644
--- a/scripts/technical_analysis.py
+++ b/scripts/technical_analysis.py
@@ -116,6 +116,8 @@ def get_quote(code):
     h = get(33)  # high
     l = get(34)  # low
     c = get(3)   # price / close
+    v = get(6)   # volume（手）
+    amt = get(37)  # 成交额
     if h and l and c:
         history = _load_history()
         if raw not in history:
@@ -126,8 +128,13 @@ def get_quote(code):
             days[-1]["high"] = max(days[-1]["high"], h)
             days[-1]["low"] = min(days[-1]["low"], l)
             days[-1]["close"] = c  # 盘中用最新价，收盘后是收盘价
+            if v: days[-1]["volume"] = v
+            if amt: days[-1]["amount"] = amt
         else:
-            days.append({"date": today_str, "high": h, "low": l, "close": c})
+            entry = {"date": today_str, "high": h, "low": l, "close": c}
+            if v: entry["volume"] = v
+            if amt: entry["amount"] = amt
+            days.append(entry)
         # 只保留最近 HISTORY_DAYS 天
         history[raw] = days[-HISTORY_DAYS:]
         _save_history(history)
@@ -348,6 +355,240 @@ def analyze_volume(q):
     return result
 
 
+def analyze_volume_trend(code):
+    """量价趋势分析：对比历史N日平均成交量，检测量价背离模式
+
+    从 price_history.json 读取历史数据，比较今日量价关系。
+    """
+    result = {}
+    try:
+        history = _load_history()
+        days = history.get(code, [])
+        if len(days) < 3:
+            result["trend"] = "数据不足"
+            return result
+
+        today = days[-1]
+        prev = days[-2] if len(days) >= 2 else None
+        today_vol = today.get("volume", 0)
+        today_close = today.get("close", 0)
+
+        if not today_vol or not today_close:
+            result["trend"] = "数据不足"
+            return result
+
+        # 计算N日均量
+        vols_5 = [d.get("volume", 0) for d in days[-6:-1] if d.get("volume")]
+        vols_20 = [d.get("volume", 0) for d in days[-21:-1] if d.get("volume")]
+        avg_5 = sum(vols_5) / len(vols_5) if vols_5 else 0
+        avg_20 = sum(vols_20) / len(vols_20) if vols_20 else 0
+
+        vol_ratio_vs_5 = today_vol / avg_5 if avg_5 > 0 else 0
+        vol_ratio_vs_20 = today_vol / avg_20 if avg_20 > 0 else 0
+
+        result["avg_volume_5d"] = round(avg_5, 0)
+        result["avg_volume_20d"] = round(avg_20, 0)
+        result["today_volume"] = int(today_vol)
+        result["volume_ratio_vs_5d"] = round(vol_ratio_vs_5, 2)
+        result["volume_ratio_vs_20d"] = round(vol_ratio_vs_20, 2)
+
+        # 最近3日的收盘价和成交量趋势
+        if len(days) >= 3:
+            recent_close = [d.get("close", 0) for d in days[-4:-1]]
+            recent_vol = [d.get("volume", 0) for d in days[-4:-1]]
+            if all(recent_close) and all(recent_vol):
+                price_up = today_close > recent_close[-1]
+                vol_up = today_vol > recent_vol[-1]
+
+                # 量价模式判定
+                if vol_ratio_vs_5 >= 1.8:
+                    # 明显放量
+                    if price_up:
+                        result["trend"] = "放量上攻"
+                        result["action"] = "buy_conformation"
+                    else:
+                        # 价格下跌但大幅放量 = 恐慌？还是承接收筹？
+                        # 看今日K线：如果是阳线（低开高走）= 承接
+                        # 简单判断：如果close > open = 有承接
+                        result["trend"] = "放量下跌"
+                        result["action"] = "watch"
+                elif vol_ratio_vs_5 <= 0.6:
+                    # 明显缩量
+                    if price_up:
+                        result["trend"] = "缩量上涨"
+                        result["action"] = "divergence"
+                    else:
+                        result["trend"] = "缩量回调"
+                        result["action"] = "healthy_pullback"
+                elif vol_ratio_vs_5 >= 1.3:
+                    # 温和放量
+                    if price_up:
+                        result["trend"] = "温和放量上涨"
+                        result["action"] = "bullish"
+                    else:
+                        result["trend"] = "温和放量下跌"
+                        result["action"] = "bearish"
+                else:
+                    # 正常量
+                    if price_up:
+                        result["trend"] = "正常量上涨"
+                        result["action"] = "neutral_bullish"
+                    else:
+                        result["trend"] = "正常量下跌"
+                        result["action"] = "neutral_bearish"
+
+        # 量价背离检测：价格走高但成交量逐日递减
+        if len(days) >= 5:
+            close_5 = [d.get("close", 0) for d in days[-5:]]
+            vol_5 = [d.get("volume", 0) for d in days[-5:]]
+            if all(close_5) and all(vol_5):
+                close_trend = close_5[-1] - close_5[0]
+                vol_trend = vol_5[-1] - vol_5[0]
+                # 价格涨但量跌 = 顶背离
+                if close_trend > 0 and vol_trend < 0 and abs(vol_trend) > sum(vol_5) * 0.3:
+                    result["divergence"] = "顶背离(价涨量缩)"
+                # 价格跌但量涨 = 底背离
+                elif close_trend < 0 and vol_trend > 0 and abs(vol_trend) > sum(vol_5) * 0.3:
+                    result["divergence"] = "底背离(价跌量增)"
+
+    except Exception as e:
+        result["trend_error"] = str(e)
+
+    return result
+
+
+def analyze_volume_deep(code):
+    """深度量价分析：从日K线分析量价配合/背离/建仓/出货
+
+    使用 mtf_cache 表的日K线数据做历史量价分析。
+    """
+    import sqlite3
+    from pathlib import Path
+
+    DATA_DIR = Path(__file__).parent / "data"
+    try:
+        conn = sqlite3.connect(str(DATA_DIR / "mofin.db"))
+        row = conn.execute("SELECT cache_json FROM mtf_cache WHERE code=?", (code,)).fetchone()
+        conn.close()
+        if not row:
+            return {"volume_signal": "数据不足"}
+        data = json.loads(row[0])
+    except Exception:
+        return {"volume_signal": "数据不足"}
+
+    daily = data.get("daily", [])
+    if len(daily) < 5:
+        return {"volume_signal": "数据不足"}
+
+    closes = [d["close"] for d in daily]
+    volume = [d["volume"] for d in daily]
+    n = len(daily)
+
+    # 基准：最近20日均量（不足20日则用全部）
+    lookback = min(20, n - 1)
+    avg_vol_20d = sum(volume[-lookback-1:-1]) / lookback if lookback > 0 else volume[-1]
+
+    # 最近N日的量比
+    recent = min(5, n)
+    recent_vol_ratios = []
+    for i in range(recent):
+        vol = volume[-i-1] if i+1 <= n else volume[0]
+        recent_vol_ratios.append(round(vol / avg_vol_20d, 2) if avg_vol_20d > 0 else 1)
+
+    today_ratio = recent_vol_ratios[0] if recent_vol_ratios else 1
+    recent_max_ratio = max(recent_vol_ratios) if recent_vol_ratios else 1
+
+    # 量价配合度
+    signals = []
+    patterns = {}
+
+    # 1. 放量检测（量比 > 2x）
+    if today_ratio > 2.0:
+        signals.append(f"量比{today_ratio:.1f}倍放量")
+        patterns["volume_surge"] = True
+        # 放量方向
+        if len(closes) >= 2 and closes[-1] > closes[-2]:
+            patterns["surge_direction"] = "放量上涨"
+            if today_ratio > 2.5 and closes[-1] > closes[-2] * 1.03:
+                signals[-1] += "↑主力买入"
+            else:
+                signals[-1] += "↑"
+        elif len(closes) >= 2 and closes[-1] < closes[-2]:
+            patterns["surge_direction"] = "放量下跌"
+            if today_ratio > 2.5 and closes[-1] < closes[-2] * 0.97:
+                signals[-1] += "↓主力出货"
+            else:
+                signals[-1] += "↓"
+        else:
+            patterns["surge_direction"] = "放量平盘"
+    elif today_ratio < 0.5:
+        signals.append(f"量比{today_ratio:.1f}倍缩量")
+        patterns["volume_shrink"] = True
+    else:
+        signals.append(f"量比{today_ratio:.1f}倍正常")
+        patterns["volume_normal"] = True
+
+    # 2. 量价趋势分析（近5日 vs 前5日）
+    if len(daily) >= 10:
+        recent5_vol = sum(volume[-5:]) / 5
+        prev5_vol = sum(volume[-10:-5]) / 5
+        vol_trend = "增" if recent5_vol > prev5_vol * 1.3 else ("减" if recent5_vol < prev5_vol * 0.7 else "稳")
+        recent5_price = closes[-5:]
+        price_trend = "涨" if recent5_price[-1] > recent5_price[0] else ("跌" if recent5_price[-1] < recent5_price[0] * 0.95 else "平")
+
+        if vol_trend == "增" and price_trend == "涨":
+            patterns["accumulation"] = True  # 量价齐升=建仓
+            signals.append(f"近5日{vol_trend}量{price_trend}价=建仓型")
+        elif vol_trend == "增" and price_trend == "跌":
+            patterns["distribution"] = True  # 放量下跌=出货
+            signals.append(f"近5日{vol_trend}量{price_trend}价=⚠️出货型")
+        elif vol_trend == "减" and price_trend == "涨":
+            patterns["divergence"] = True  # 量缩价涨=背离
+            signals.append(f"近5日{vol_trend}量{price_trend}价=⬆量价背离")
+        elif vol_trend == "减" and price_trend == "跌":
+            patterns["washout"] = True  # 缩量下跌=洗盘末端
+            signals.append(f"近5日{vol_trend}量{price_trend}价=洗盘特征")
+        else:
+            signals.append(f"近5日{vol_trend}量{price_trend}价")
+    else:
+        vol_trend = price_trend = "?"
+
+    # 3. 寻找历史放量区间（主力活动痕迹）
+    surge_days = []
+    for i in range(max(0, n - 60), n):
+        vol_ratio = volume[i] / avg_vol_20d if avg_vol_20d > 0 else 0
+        if vol_ratio > 2.0:
+            surge_days.append({
+                "date": daily[i].get("date", ""),
+                "ratio": round(vol_ratio, 1),
+                "close": closes[i],
+                "direction": "涨" if (i > 0 and closes[i] > closes[i-1]) else "跌"
+            })
+
+    # 汇总描述
+    vol_level = "放量" if today_ratio > 2.0 else ("缩量" if today_ratio < 0.5 else "正常")
+    price_vol = f"{vol_level}"
+    if patterns.get("accumulation"):
+        price_vol = f"量价齐升(建仓特征) | {signals[-1]}"
+    elif patterns.get("distribution"):
+        price_vol = f"放量下跌⚠️ | {signals[-1]}"
+    elif patterns.get("washout"):
+        price_vol = f"缩量回踩(洗盘末端) | {signals[-1]}"
+    elif patterns.get("divergence"):
+        price_vol = f"量价背离 | {signals[-1]}"
+
+    return {
+        "volume_signal": " ; ".join(signals) if signals else "正常",
+        "volume_ratio": today_ratio,
+        "avg_volume_20d": int(avg_vol_20d),
+        "recent_ratios": recent_vol_ratios,
+        "surge_count_60d": len(surge_days),
+        "price_vol_description": price_vol,
+        "patterns": patterns,
+        "surge_days": surge_days[-5:] if surge_days else [],
+    }
+
+
 def full_analysis(code):
     """完整技术分析（带30秒缓存，避免分钟级波动）"""
     import time
@@ -364,6 +605,12 @@ def full_analysis(code):
     sr = calc_support_resistance(q)
     candle = analyze_candlestick(q)
     vol = analyze_volume(q)
+    # 深度量价分析（使用日K线历史数据）
+    vol_deep = {}
+    try:
+        vol_deep = analyze_volume_deep(code)
+    except Exception:
+        pass  # graceful degradation
 
     # 多周期+均线分析（整合 multi_timeframe）
     mtf = {}
@@ -403,6 +650,7 @@ def full_analysis(code):
         "support_resistance": sr,
         "candlestick": candle,
         "volume": vol,
+        "volume_deep": vol_deep,
         "multi_tf": mtf,
         "analyzed_at": datetime.now().strftime("%H:%M"),
     }