1

app/services/altcoin_confirm.py

@@ -1727,12 +1727,15 @@ def confirm_burst(symbol, cand):
         short_entry_plan = None
         if trade_side == "short":
             entry_action = "即刻买入" if short_1h.get("retest_rejected") else "等回踩"
-            retest_zone = short_1h.get("retest_zone") or []
+            retest_zone = short_1h.get("retest_zone") or short_1h.get("breakdown_level") or 0
             if entry_action == "即刻买入":
                 entry_price = round(float(price), 6)
                 entry_method = "空头1H破位反抽失败，当前可开空"
             else:
-                zone_mid = sum(float(x or 0) for x in retest_zone[:2]) / 2 if len(retest_zone) >= 2 else float(short_1h.get("breakdown_level") or price)
+                if isinstance(retest_zone, (list, tuple)) and len(retest_zone) >= 2:
+                    zone_mid = sum(float(x or 0) for x in retest_zone[:2]) / 2
+                else:
+                    zone_mid = float(retest_zone or price)
                 entry_price = round(max(float(price), zone_mid), 6)
                 entry_method = f"等反抽到${entry_price:.4f}(箱体下沿反压)"
             stop_loss = round(float(short_1h.get("stop_level") or price * 1.055), 6)

app/services/altcoin_screener.py

@@ -1217,10 +1217,10 @@ def layer1_coarse_filter():
 
             short_breakdown_1h = detect_breakdown_retest_short_1h(h1_df, change_24h=change)
             if short_breakdown_1h.get("detected"):
-                quality = float(short_breakdown_1h.get("quality") or 0)
+                quality_score = float(short_breakdown_1h.get("quality_score") or short_breakdown_1h.get("score") or 0)
-                if quality >= 0.48:
+                if quality_score >= 5:
                     anomalies.extend(short_breakdown_1h.get("signals") or ["1H破位反抽做空结构"])
-                    anomaly_score += int(short_breakdown_1h.get("score") or 4)
+                    anomaly_score += int(quality_score or 4)
 
         # 布林收窄检测（4H级别）
         if h4_df is not None and len(h4_df) >= 20:

app/strategies/short_breakdown.py

@@ -72,6 +72,7 @@ def detect_breakdown_retest_short_1h(df, *, change_24h: float = 0.0) -> dict:
             "stop_level": round(stop_level, 8),
             "target_1": round(target_1, 8),
             "quality": quality,
+            "quality_score": quality_score,
             "score": quality_score,
             "retest_rejected": latest_rejected,
             "relative_weakness": bool(below_ema or float(change_24h or 0) <= -3),

tests/test_multi_strategy_infra.py

@@ -20,7 +20,7 @@ from app.strategies.altcoin_breakout import (
     build_volume_ignition_1h_signal,
 )
 from app.strategies.box_retest_4h import build_box_retest_1h_signal
-from app.strategies.short_breakdown import build_breakdown_retest_short_1h_signal
+from app.strategies.short_breakdown import build_breakdown_retest_short_1h_signal, detect_breakdown_retest_short_1h
 
 
 def test_factor_roles_never_promote_unknown_to_trigger():
@@ -136,6 +136,27 @@ def test_breakdown_retest_short_strategy_is_independent_short_signal():
     assert signal["factor_roles"]["breakdown_retest_1h_short"] == "trigger"
 
 
+def test_short_breakdown_detector_exposes_numeric_quality_score():
+    import pandas as pd
+
+    rows = []
+    price = 1.0
+    for i in range(64):
+        rows.append({"timestamp": i, "open": price, "high": 1.02, "low": 0.98, "close": price, "volume": 100})
+    rows.extend([
+        {"timestamp": 64, "open": 1.0, "high": 1.01, "low": 0.95, "close": 0.97, "volume": 180},
+        {"timestamp": 65, "open": 0.97, "high": 1.01, "low": 0.96, "close": 0.99, "volume": 140},
+        {"timestamp": 66, "open": 0.99, "high": 1.015, "low": 0.955, "close": 0.965, "volume": 180},
+        {"timestamp": 67, "open": 0.965, "high": 0.98, "low": 0.94, "close": 0.955, "volume": 200},
+    ])
+    result = detect_breakdown_retest_short_1h(pd.DataFrame(rows), change_24h=-4)
+
+    assert result["detected"] is True
+    assert result["quality"] in {"良好", "优质"}
+    assert isinstance(result["quality_score"], int)
+    assert result["quality_score"] >= 5
+
+
 def test_strategy_evaluation_recommends_promote_or_pause():
     strong = evaluate_strategy_decision({
         "signal_count": 24,