feat(strategy): finalize global rotation system with advanced risk controls

Summary of updates:
1. Core Logic (engine.py): Added 'score > 0' filtering to support automatic cash positions during market downturns.
2. Experimental Analysis: Added scripts/analyze_negative_scores.py, scripts/test_select_num.py, and scripts/ab_test_iterations.py.
3. Documentation: Created docs/strategy_evolution_report.md detailing the evolution from benchmark to the final 47% CAGR version.
4. Configuration: Finalized rotation.yaml with 11 core assets and optimal risk parameters.

strategies/rotation/engine.py

@@ -97,27 +97,27 @@ class RotationStrategy(BacktestStrategy):
 
         if not diversified:
             if select_num == 1:
-                daily_target = (
-                    result[score_cols]
-                    .idxmax(axis=1)
-                    .str.replace("得分_", "", regex=False)
-                )
+                def top_1_filter(row):
+                    scores = pd.to_numeric(row[score_cols], errors="coerce").dropna()
+                    if scores.empty: return ""
+                    best_code = scores.idxmax()
+                    if scores[best_code] <= 0: return "" # 强制过滤负分
+                    return best_code.replace("得分_", "")
+                daily_target = result.apply(top_1_filter, axis=1)
             else:
                 def top_n_codes(row):
-                    scores = pd.to_numeric(row[score_cols], errors="coerce")
-                    scores = scores.dropna()
-                    if len(scores) == 0:
-                        return ""
+                    scores = pd.to_numeric(row[score_cols], errors="coerce").dropna()
+                    scores = scores[scores > 0] # 强制只保留正分标的
+                    if scores.empty: return ""
                     top = scores.nlargest(min(select_num, len(scores))).index.tolist()
                     return ",".join([c.replace("得分_", "") for c in top])
                 daily_target = result.apply(top_n_codes, axis=1)
         else:
             # 强制分散化：每个大类只选 Top 1
             def top_n_diversified(row):
-                scores = pd.to_numeric(row[score_cols], errors="coerce")
-                scores = scores.dropna()
-                if len(scores) == 0:
-                    return ""
+                scores = pd.to_numeric(row[score_cols], errors="coerce").dropna()
+                scores = scores[scores > 0] # 强制只保留正分标的
+                if scores.empty: return ""
                 
                 # 建立 category -> (code, score) 的映射
                 cat_best = {}