1

backend/app/analysis/trend_scanner.py

@@ -16,7 +16,6 @@ from app.data.tushare_client import tushare_client
 from app.analysis.technical import add_all_indicators
 from app.analysis.breakout_signals import (
     classify_entry_signal,
-    score_trend_timing,
     score_supply_demand,
     analyze_volume_pattern,
     EntrySignal,
@@ -330,7 +329,7 @@ async def _deep_analysis(
 
     results = []
     total = len(candidates)
-    signal_counts = {"breakout": 0, "pullback": 0, "launch": 0, "none": 0}
+    signal_counts = {"breakout": 0, "breakout_confirm": 0, "pullback": 0, "launch": 0, "reversal": 0, "none": 0}
 
     for idx, row in candidates.iterrows():
         ts_code = row["ts_code"] if "ts_code" in candidates.columns else ""
@@ -358,8 +357,8 @@ async def _deep_analysis(
                 continue
             signal_counts[signal_type.value] += 1
 
-            # 趋势&时机评分
+            # 趋势评分（内联简化版）
-            trend_score = score_trend_timing(df, entry_signal)
+            trend_score = _simple_trend_score(df)
 
             # 供需评分
             sd_score = score_supply_demand(df)
@@ -419,8 +418,9 @@ async def _deep_analysis(
 
     logger.info(
         f"Phase 3 入场信号分布: "
-        f"突破={signal_counts['breakout']} 回踩={signal_counts['pullback']} "
+        f"突破={signal_counts['breakout']} 确认={signal_counts['breakout_confirm']} "
-        f"启动={signal_counts['launch']} 无信号={signal_counts['none']} "
+        f"回踩={signal_counts['pullback']} 启动={signal_counts['launch']} "
+        f"反转={signal_counts['reversal']} 无信号={signal_counts['none']} "
         f"(共分析{total}只)"
     )
 
@@ -448,6 +448,31 @@ def _get_multi_day_moneyflow(candidates: pd.DataFrame, trade_date: str) -> dict[
     return result
 
 
+def _simple_trend_score(df: pd.DataFrame) -> float:
+    """简化趋势评分 (0-100)，用于回退扫描路径"""
+    import numpy as np
+    score = 0
+    last = df.iloc[-1]
+
+    ma_cols = [c for c in ["ma5", "ma10", "ma20", "ma60"] if c in df.columns]
+    if len(ma_cols) >= 4 and not any(pd.isna(last[c]) for c in ma_cols):
+        if last["ma5"] > last["ma10"] > last["ma20"] > last["ma60"]:
+            score += 50
+        elif last["ma5"] > last["ma10"] > last["ma20"]:
+            score += 35
+        elif last["ma5"] > last["ma20"]:
+            score += 20
+
+    if len(df) >= 20:
+        recent = df.tail(20)
+        if recent["high"].iloc[10:].max() > recent["high"].iloc[:10].max():
+            score += 30
+        if recent["low"].iloc[10:].min() > recent["low"].iloc[:10].min():
+            score += 20
+
+    return min(score, 100)
+
+
 def _score_capital(stock_row: dict | pd.Series, mf_history: pd.DataFrame) -> float:
     """资金流评分 (0-100)
 

backend/app/engine/scheduler.py

@@ -79,9 +79,9 @@ def setup_scheduler():
             args=["late_session"], id=f"late_{m}", replace_existing=True
         )
 
-    # 收盘总结 15:05
+    # 收盘总结 16:00（Tushare 日线数据通常在 15:30 后更新完成）
     scheduler.add_job(
-        _run_scan, CronTrigger(hour=15, minute=5, day_of_week="mon-fri"),
+        _run_scan, CronTrigger(hour=16, minute=0, day_of_week="mon-fri"),
         args=["post_market"], id="post_market", replace_existing=True
     )
 

backend/app/engine/screener.py

@@ -15,6 +15,8 @@
 
 import logging
 
+import pandas as pd
+
 from app.analysis.market_temp import calculate_market_temperature
 from app.analysis.sector_scanner import scan_hot_sectors
 from app.analysis.trend_scanner import scan_trend_breakout
@@ -107,7 +109,7 @@ async def run_screening(trade_date: str = None) -> dict:
 
     logger.info(f"=== 筛选完成: {len(recommendations)} 只股票 ({scan_mode}) ===")
     for r in recommendations[:5]:
-        signal_map = {"breakout": "突破型", "pullback": "回踩型", "launch": "启动型"}
+        signal_map = {"breakout": "突破型", "breakout_confirm": "确认型", "pullback": "回踩型", "launch": "启动型", "reversal": "反转型"}
         signal_label = signal_map.get(r.entry_signal_type, r.entry_signal_type)
         logger.info(f"  [{signal_label}] {r.name}({r.ts_code}) {r.level} 评分={r.score} 信号={r.signal}")
 
@@ -309,7 +311,7 @@ def _build_recommendations(
 
     recommendations = []
     total = len(candidates)
-    signal_counts = {"breakout": 0, "pullback": 0, "launch": 0, "none": 0}
+    signal_counts = {"breakout": 0, "breakout_confirm": 0, "pullback": 0, "launch": 0, "reversal": 0, "none": 0}
 
     for idx, stock in enumerate(candidates):
         ts_code = stock.get("ts_code", "")
@@ -406,7 +408,7 @@ def _build_recommendations(
 
                 details = entry_signal.get("details", {})
                 st = signal_type.value
-                if st == "breakout" and details.get("resist_level"):
+                if st in ("breakout", "breakout_confirm") and details.get("resist_level"):
                     entry_price = details["resist_level"]
                     target_price = round(entry_price * 1.05, 2)
                 elif st == "pullback" and details.get("support_price"):
@@ -415,6 +417,9 @@ def _build_recommendations(
                 elif st == "launch" and details.get("resist_level"):
                     entry_price = round(details["resist_level"] * 1.01, 2)
                     target_price = round(details["resist_level"] * 1.08, 2)
+                elif st == "reversal" and tech_signal and tech_signal.support_price:
+                    entry_price = tech_signal.support_price
+                    target_price = round(entry_price * 1.08, 2)
 
             # 生成推荐理由
             reasons = _generate_reasons(stock, entry_signal, tech_signal, df, intraday)
@@ -458,8 +463,9 @@ def _build_recommendations(
 
     logger.info(
         f"Step 3 入场信号分布: "
-        f"突破={signal_counts['breakout']} 回踩={signal_counts['pullback']} "
+        f"突破={signal_counts['breakout']} 确认={signal_counts['breakout_confirm']} "
-        f"启动={signal_counts['launch']} 无信号={signal_counts['none']} "
+        f"回踩={signal_counts['pullback']} 启动={signal_counts['launch']} "
+        f"反转={signal_counts['reversal']} 无信号={signal_counts['none']} "
         f"(共分析{total}只)"
     )
 
@@ -472,21 +478,20 @@ def _build_recommendations(
 def _score_price_action(df, entry_signal: dict) -> float:
     """价格行为学评分 (0-100)
 
+    纯粹关注 K 线形态和量价配合，不重复评估趋势/均线因素。
+
     维度：
-    - 入场信号类型质量 (40): 突破型/回踩型/启动型各自的得分
+    - K线形态强度 (35): 实体占比、收盘位置、下影线
-    - K线形态强度 (30): 突破日/回踩日的K线实体占比、下影线、收盘位置
+    - 量价配合 (35): 放量/缩量与价格方向的配合度
-    - 支撑阻力位质量 (30): 关键价格位置的测试情况
+    - 入场形态质量 (30): 各信号类型的形态完成度
     """
+    import pandas as pd
     score = 0
     last = df.iloc[-1]
     details = entry_signal.get("details", {})
     signal_type = entry_signal.get("signal_type")
 
-    # 入场信号类型质量 (40)
+    # K线形态强度 (35)
-    signal_score = entry_signal.get("signal_score", 0)
-    score += signal_score * 0.40
-
-    # K线形态强度 (30)
     day_range = last["high"] - last["low"]
     if day_range > 0:
         # 实体占比（实体/全振幅）
@@ -508,24 +513,64 @@ def _score_price_action(df, entry_signal: dict) -> float:
         elif close_position > 0.4:
             score += 3
 
-    # 支撑阻力位质量 (30)
+        # 下影线（回踩型/启动型利好）
+        lower_wick = (last["open"] - last["low"]) if last["close"] > last["open"] else (last["close"] - last["low"])
+        if lower_wick > 0:
+            wick_ratio = lower_wick / day_range
+            if signal_type and signal_type.value in ("pullback", "reversal") and wick_ratio > 0.2:
+                score += 5  # 回踩型/反转型有下影线支撑
+
+    # 量价配合 (35)
+    vol_ma_col = "vol_ma5" if "vol_ma5" in df.columns else None
+    if vol_ma_col and not pd.isna(last[vol_ma_col]) and last[vol_ma_col] > 0:
+        vol_ratio = last["vol"] / last[vol_ma_col]
+        price_up = last["pct_chg"] > 0 if "pct_chg" in df.columns else last["close"] > last["open"]
+
+        if price_up and vol_ratio > 2.0:
+            score += 35  # 放量大阳
+        elif price_up and vol_ratio > 1.5:
+            score += 25
+        elif price_up and vol_ratio > 1.2:
+            score += 18
+        elif not price_up and vol_ratio < 0.7:
+            score += 25  # 缩量回调（良性）
+        elif not price_up and vol_ratio < 0.9:
+            score += 15
+        elif price_up and vol_ratio > 1.0:
+            score += 10
+    else:
+        score += 10
+
+    # 入场形态质量 (30) — 只评估形态完成度，不涉及均线/MACD
     if signal_type and signal_type.value == "breakout":
-        # 突破型：阻力位被突破的力度
         breakout_pct = details.get("breakout_pct", 0)
         vol_ratio = details.get("volume_ratio", 1)
         if breakout_pct > 2 and vol_ratio > 2:
-            score += 30  # 强力突破
+            score += 30
         elif breakout_pct > 1 and vol_ratio > 1.5:
             score += 20
         elif breakout_pct > 0:
-            score += 10
+            score += 12
+        else:
+            score += 6
+
+    elif signal_type and signal_type.value == "breakout_confirm":
+        vol_ratio = details.get("volume_ratio", 1)
+        confirm_pct = details.get("confirm_pct", 0)
+        if vol_ratio > 2 and confirm_pct > 2:
+            score += 30
+        elif vol_ratio > 1.5 and confirm_pct > 1:
+            score += 22
+        elif vol_ratio > 1.0:
+            score += 14
+        else:
+            score += 8
 
     elif signal_type and signal_type.value == "pullback":
-        # 回踩型：支撑位的精确度
         support_ma = details.get("support_ma", "")
         shrink = details.get("volume_shrink_ratio", 1)
         if support_ma == "MA20" and shrink < 0.6:
-            score += 30  # 精确回踩 MA20 且大幅缩量
+            score += 30
         elif support_ma == "MA20":
             score += 22
         elif support_ma == "MA10" and shrink < 0.6:
@@ -534,15 +579,26 @@ def _score_price_action(df, entry_signal: dict) -> float:
             score += 10
 
     elif signal_type and signal_type.value == "launch":
-        # 启动型：整理的充分度
         range_pct = details.get("price_range_pct", 10)
-        shrink = details.get("volume_shrink_ratio", 1)
+        if range_pct < 3:
-        if range_pct < 3 and shrink < 0.4:
+            score += 30
-            score += 30  # 极度缩量窄幅整理
+        elif range_pct < 5:
-        elif range_pct < 5 and shrink < 0.6:
             score += 20
         else:
             score += 10
+
+    elif signal_type and signal_type.value == "reversal":
+        reversal_pct = details.get("reversal_pct", 0)
+        vol_ratio = details.get("volume_ratio", 1)
+        if reversal_pct > 5 and vol_ratio > 2.5:
+            score += 30
+        elif reversal_pct > 3 and vol_ratio > 2:
+            score += 22
+        elif reversal_pct > 3:
+            score += 14
+        else:
+            score += 8
+
     else:
         score += 10
 
@@ -673,10 +729,13 @@ def _generate_reasons(
 ) -> list[str]:
     """生成推荐理由"""
     import pandas as pd
+    from app.analysis.breakout_signals import EntrySignal
     reasons = []
     signal_type = entry_signal.get("signal_type")
     details = entry_signal.get("details", {})
-    signal_map = {EntrySignal.BREAKOUT: "突破型", EntrySignal.PULLBACK: "回踩型", EntrySignal.LAUNCH: "启动型"}
+    signal_map = {EntrySignal.BREAKOUT: "突破型", EntrySignal.BREAKOUT_CONFIRM: "确认型",
+                  EntrySignal.PULLBACK: "回踩型", EntrySignal.LAUNCH: "启动型",
+                  EntrySignal.REVERSAL: "反转型"}
     entry_label = signal_map.get(signal_type, "")
 
     # 入场信号
@@ -686,14 +745,21 @@ def _generate_reasons(
             breakout_pct = details.get("breakout_pct", 0)
             vol_ratio = details.get("volume_ratio", 0)
             reasons.append(f"放量突破20日阻力位（涨幅{breakout_pct:.1f}%，量比{vol_ratio:.1f}倍）")
+        elif st == "breakout_confirm":
+            vol_ratio = details.get("volume_ratio", 0)
+            confirm_pct = details.get("confirm_pct", 0)
+            reasons.append(f"突破后放量确认（确认日涨{confirm_pct:.1f}%，量比{vol_ratio:.1f}倍）")
         elif st == "pullback":
             support = details.get("support_ma", "")
             shrink = details.get("volume_shrink_ratio", 0)
             reasons.append(f"缩量回踩{support}支撑（量能收缩至{shrink:.0%}）")
         elif st == "launch":
             range_pct = details.get("price_range_pct", 0)
-            shrink = details.get("volume_shrink_ratio", 0)
+            reasons.append(f"缩量横盘整理{range_pct:.1f}%后首日放量启动")
-            reasons.append(f"高位缩量整理{range_pct:.1f}%后即将变盘（量缩至{shrink:.0%}）")
+        elif st == "reversal":
+            reversal_pct = details.get("reversal_pct", 0)
+            vol_ratio = details.get("volume_ratio", 0)
+            reasons.append(f"连续下跌后放量长阳反转（涨{reversal_pct:.1f}%，量比{vol_ratio:.1f}倍）")
 
     # 供需分析
     if len(df) >= 10:
@@ -734,10 +800,14 @@ def _generate_risk_note(
 
     if entry_type == "breakout":
         notes.append("突破型需警惕假突破，关注量能是否持续")
+    elif entry_type == "breakout_confirm":
+        notes.append("确认型需观察后续量能是否跟上，防止冲高回落")
     elif entry_type == "pullback":
         notes.append("回踩型可能继续下探支撑，注意止损纪律")
     elif entry_type == "launch":
         notes.append("启动型整理可能延长，注意时间成本")
+    elif entry_type == "reversal":
+        notes.append("反转型可能二次探底，确认底部后再加仓")
 
     if market.temperature < 30:
         notes.append("市场情绪偏冷，系统性风险较高")

frontend/.next/app-build-manifest.json

@@ -11,6 +11,11 @@
       "static/css/app/layout.css",
       "static/chunks/app/layout.js"
     ],
+    "/stock/[code]/page": [
+      "static/chunks/webpack.js",
+      "static/chunks/main-app.js",
+      "static/chunks/app/stock/[code]/page.js"
+    ],
     "/recommendations/page": [
       "static/chunks/webpack.js",
       "static/chunks/main-app.js",
@@ -20,11 +25,6 @@
       "static/chunks/webpack.js",
       "static/chunks/main-app.js",
       "static/chunks/app/sectors/page.js"
-    ],
-    "/_not-found/page": [
-      "static/chunks/webpack.js",
-      "static/chunks/main-app.js",
-      "static/chunks/app/_not-found/page.js"
     ]
   }
 }

frontend/.next/react-loadable-manifest.json

@@ -1 +1,20 @@
-{}
+{
+  "components/capital-flow.tsx -> echarts": {
+    "id": "components/capital-flow.tsx -> echarts",
+    "files": [
+      "static/chunks/_app-pages-browser_node_modules_echarts_index_js.js"
+    ]
+  },
+  "components/kline-chart.tsx -> echarts": {
+    "id": "components/kline-chart.tsx -> echarts",
+    "files": [
+      "static/chunks/_app-pages-browser_node_modules_echarts_index_js.js"
+    ]
+  },
+  "components/score-radar.tsx -> echarts": {
+    "id": "components/score-radar.tsx -> echarts",
+    "files": [
+      "static/chunks/_app-pages-browser_node_modules_echarts_index_js.js"
+    ]
+  }
+}

frontend/.next/server/app-paths-manifest.json

@@ -1,6 +1,6 @@
 {
-  "/_not-found/page": "app/_not-found/page.js",
   "/page": "app/page.js",
   "/recommendations/page": "app/recommendations/page.js",
+  "/stock/[code]/page": "app/stock/[code]/page.js",
   "/sectors/page": "app/sectors/page.js"
 }

frontend/.next/server/middleware-react-loadable-manifest.js

@@ -1 +1 @@
-self.__REACT_LOADABLE_MANIFEST="{}"
+self.__REACT_LOADABLE_MANIFEST="{\"components/capital-flow.tsx -> echarts\":{\"id\":\"components/capital-flow.tsx -> echarts\",\"files\":[\"static/chunks/_app-pages-browser_node_modules_echarts_index_js.js\"]},\"components/kline-chart.tsx -> echarts\":{\"id\":\"components/kline-chart.tsx -> echarts\",\"files\":[\"static/chunks/_app-pages-browser_node_modules_echarts_index_js.js\"]},\"components/score-radar.tsx -> echarts\":{\"id\":\"components/score-radar.tsx -> echarts\",\"files\":[\"static/chunks/_app-pages-browser_node_modules_echarts_index_js.js\"]}}"

frontend/.next/server/server-reference-manifest.json

@@ -1,5 +1,5 @@
 {
   "node": {},
   "edge": {},
-  "encryptionKey": "t8NqvDMYNuIMnc0JzSsolcrzKZh3QqUqStILpaN7mCE="
+  "encryptionKey": "xV9IIi0vV+SB9UvVKH8lRLbKebnLKLutavDhD7b36pc="
 }

frontend/.next/server/webpack-runtime.js

@@ -125,7 +125,7 @@
 /******/ 	
 /******/ 	/* webpack/runtime/getFullHash */
 /******/ 	(() => {
-/******/ 		__webpack_require__.h = () => ("37ba5b2074c7bffb")
+/******/ 		__webpack_require__.h = () => ("c0150b0528f1b8db")
 /******/ 	})();
 /******/ 	
 /******/ 	/* webpack/runtime/hasOwnProperty shorthand */