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.
2026-04-30 00:56:20 +08:00
parent e946dbe804
commit c1fbd2c7db
6 changed files with 504 additions and 14 deletions
--- a/scripts/ab_test_iterations.py
+++ b/scripts/ab_test_iterations.py
@@ -0,0 +1,182 @@
+"""
+策略迭代 A/B 对比实验脚本
+量化三个维度的改进贡献度：
+1. 标的池: 原始全市场池 vs. 精选11只核心池
+2. 评分公式: 简单斜率(slope_r2) vs. 年化收益率*R2 (weighted_momentum)
+3. 观察窗口: 固定25日窗口 vs. 动态ATR窗口 (20-60天)
+"""
+
+import sys
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from datetime import datetime
+
+# 添加项目根目录
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from strategies.rotation.engine import RotationStrategy
+import matplotlib.pyplot as plt
+
+# ==================== 标的池定义 ====================
+ORIGINAL_POOL = {
+    "000300.SH": {"name": "沪深300", "market": "A", "etf": "510300.SH"},
+    "000905.SH": {"name": "中证500", "market": "A", "etf": "510500.SH"},
+    "000852.SH": {"name": "中证1000", "market": "A", "etf": "512100.SH"},
+    "399006.SZ": {"name": "创业板指", "market": "A", "etf": "159915.SZ"},
+    "000015.SH": {"name": "上证红利", "market": "A", "etf": "510880.SH"},
+    "399986.SZ": {"name": "中证银行", "market": "A", "etf": "516310.SH"},
+    "399997.SZ": {"name": "中证白酒", "market": "A", "etf": "512690.SH"},
+    "399989.SZ": {"name": "中证医疗", "market": "A", "etf": "512170.SH"},
+    "399395.SZ": {"name": "国证有色", "market": "A", "etf": "159880.SZ"},
+    "399998.SZ": {"name": "中证煤炭", "market": "A", "etf": "515220.SH"},
+    "399967.SZ": {"name": "中证军工", "market": "A", "etf": "512660.SH"},
+    "HSTECH.HK": {"name": "恒生科技", "market": "HK", "etf": "513180.SH"},
+    "NDX": {"name": "纳指100", "market": "US", "etf": "513100.SH"},
+    "AU.SHF": {"name": "黄金", "market": "COMMODITY", "etf": "518880.SH"}
+}
+
+FINAL_POOL = {
+    "399006.SZ": {"name": "创业板指", "market": "A", "etf": "159915.SZ"},
+    "H30269.CSI": {"name": "中证红利低波", "market": "A", "etf": "512890.SH"},
+    "000015.SH": {"name": "上证红利", "market": "A", "etf": "510880.SH"},
+    "NDX": {"name": "纳指100", "market": "US", "etf": "513100.SH"},
+    "N225": {"name": "日经225", "market": "JP", "etf": "513520.SH"},
+    "GDAXI": {"name": "德国DAX", "market": "EU", "etf": "513030.SH"},
+    "HSI": {"name": "恒生指数", "market": "HK", "etf": "159920.SZ"},
+    "HSTECH.HK": {"name": "恒生科技", "market": "HK", "etf": "513130.SH"},
+    "AU.SHF": {"name": "黄金", "market": "COMMODITY", "etf": "518880.SH"},
+    "CL.NYM": {"name": "原油", "market": "COMMODITY", "etf": "160723.SZ"},
+    "931862.CSI": {"name": "30年国债", "market": "BOND", "etf": "511090.SH"}
+}
+
+# ==================== 实验配置 ====================
+ITERATIONS = [
+    {
+        "label": "1. 原始基准 (原始池+简单评分+固定窗口)",
+        "config": {
+            "code_list": ORIGINAL_POOL,
+            "factor_type": "slope_r2",
+            "auto_day": False,
+            "n_days": 25,
+            "diversified": False
+        }
+    },
+    {
+        "label": "2. 标的池优化 (精选池+简单评分+固定窗口)",
+        "config": {
+            "code_list": FINAL_POOL,
+            "factor_type": "slope_r2",
+            "auto_day": False,
+            "n_days": 25,
+            "diversified": True # 开启跨大类分散
+        }
+    },
+    {
+        "label": "3. 评分公式优化 (精选池+加权评分+固定窗口)",
+        "config": {
+            "code_list": FINAL_POOL,
+            "factor_type": "weighted_momentum",
+            "auto_day": False,
+            "n_days": 25,
+            "diversified": True
+        }
+    },
+    {
+        "label": "4. 终极版本 (精选池+加权评分+动态窗口)",
+        "config": {
+            "code_list": FINAL_POOL,
+            "factor_type": "weighted_momentum",
+            "auto_day": True,
+            "n_days": 25, # 提供默认窗口作为 fallback
+            "min_days": 20,
+            "max_days": 60,
+            "diversified": True
+        }
+    }
+]
+
+COMMON_CONFIG = {
+    "start_date": "2019-01-01",
+    "end_date": datetime.now().strftime('%Y-%m-%d'),
+    "select_num": 3,
+    "rebalance_days": 1,
+    "rebalance_threshold": 0.0,
+    "trade_cost": 0.001,
+    "premium_control": {"enabled": True, "default_threshold": 0.10},
+    "use_cache": True,
+    "ssh_tunnel": {"enabled": True, "host": "8.218.167.69", "port": 22, "username": "root", "key_path": "hk_ecs.pem", "local_port": 1080}
+}
+
+def run_experiment():
+    results = []
+    
+    for i, item in enumerate(ITERATIONS):
+        print(f"\n{'='*80}")
+        print(f"运行实验 {item['label']}")
+        print(f"{'='*80}")
+        
+        cfg = COMMON_CONFIG.copy()
+        cfg.update(item['config'])
+        
+        strategy = RotationStrategy(cfg)
+        try:
+            res_df = strategy.run()
+            
+            # 计算指标
+            nav = res_df['轮动策略净值']
+            total_ret = nav.iloc[-1] - 1
+            days = (nav.index[-1] - nav.index[0]).days
+            cagr = (1 + total_ret)**(365.25/days) - 1
+            
+            daily_ret = res_df['轮动策略日收益率']
+            sharpe = daily_ret.mean() / daily_ret.std() * np.sqrt(252) if daily_ret.std() > 0 else 0
+            
+            peak = nav.cummax()
+            dd = (nav - peak) / peak
+            max_dd = dd.min()
+            
+            results.append({
+                "label": item['label'],
+                "total_ret": total_ret,
+                "cagr": cagr,
+                "max_dd": max_dd,
+                "sharpe": sharpe,
+                "nav": nav
+            })
+            print(f"完成: CAGR={cagr:.2%}, MaxDD={max_dd:.2%}, Sharpe={sharpe:.2f}")
+        except Exception as e:
+            print(f"实验失败: {e}")
+            import traceback
+            traceback.print_exc()
+
+    # ==================== 汇总报告 ====================
+    print(f"\n\n{'='*100}")
+    print(f"{'策略迭代对比报告':^100}")
+    print(f"{'='*100}")
+    print(f"{'版本':<40} | {'累计收益':>10} | {'年化(CAGR)':>10} | {'最大回撤':>10} | {'夏普比率':>8} | {'贡献增量':>10}")
+    print(f"{'-'*100}")
+    
+    prev_cagr = 0
+    for i, r in enumerate(results):
+        delta = f"+{(r['cagr'] - prev_cagr)*100:>.2f}%" if i > 0 else "-"
+        print(f"{r['label']:<40} | {r['total_ret']:>10.2%} | {r['cagr']:>10.2%} | {r['max_dd']:>10.2%} | {r['sharpe']:>8.2f} | {delta:>10}")
+        prev_cagr = r['cagr']
+    print(f"{'='*100}")
+
+    # ==================== 绘图 ====================
+    plt.figure(figsize=(15, 8))
+    for r in results:
+        plt.plot(r['nav'].index, r['nav'], label=r['label'], linewidth=1.5)
+    
+    plt.yscale('log')
+    plt.title("策略迭代 A/B 对比 - 净值曲线 (对数坐标)", fontsize=14)
+    plt.legend()
+    plt.grid(True, alpha=0.3)
+    
+    output_path = Path(__file__).parent.parent / "results" / "ab_test_iterations.png"
+    plt.savefig(output_path)
+    print(f"\n对比图表已保存至: {output_path}")
+
+if __name__ == "__main__":
+    run_experiment()
--- a/scripts/analyze_negative_scores.py
+++ b/scripts/analyze_negative_scores.py
@@ -0,0 +1,115 @@
+"""
+分析历史 Top 3 标的中存在负分的情况 (正式版)
+"""
+import sys
+import yaml
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from datetime import datetime
+from dotenv import load_dotenv
+
+# 加载环境变量
+load_dotenv()
+
+# 添加项目根目录
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from strategies.rotation.engine import RotationStrategy
+from core.factors.momentum import compute_factors
+
+def load_config(config_path: str) -> dict:
+    with open(config_path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+def analyze_negative_scores():
+    config_path = "config/strategies/rotation.yaml"
+    config = load_config(config_path)
+    
+    # 强制不使用过滤，以获取完整数据
+    config['diversified'] = True
+    config['select_num'] = 3
+    
+    strategy = RotationStrategy(config)
+    
+    # 使用策略内部方法获取数据
+    with strategy.data_source:
+        index_data, etf_data, etf_nav_data, benchmark_data, valid_codes, index_ohlcv_data = strategy.data_source.fetch_all(
+            config['code_list'], 
+            config['benchmark']['code'], 
+            config["start_date"], 
+            datetime.now().strftime('%Y-%m-%d')
+        )
+    
+    # 手动计算因子 (不带过滤)
+    # 注意：为了分析原始得分，我们将 compute_factors 内部调用的过滤函数暂时跳过或分析结果
+    factor_data, valid_codes = compute_factors(
+        index_data, 
+        valid_codes, 
+        n=config["n_days"], 
+        factor_type=config["factor_type"],
+        auto_day=config.get("auto_day", False),
+        index_ohlcv_data=index_ohlcv_data
+    )
+    
+    score_cols = [c for c in factor_data.columns if c.startswith("得分_")]
+    code_config = config['code_list']
+    
+    total_days = len(factor_data)
+    results = []
+    
+    last_top_3 = set()
+    rebalance_count = 0
+    
+    for date, row in factor_data.iterrows():
+        scores = row[score_cols].dropna()
+        if scores.empty: continue
+        
+        # 模拟 diversified 逻辑下的 Top 3 (不带 >0 过滤)
+        cat_best = {}
+        for col_name, s in scores.items():
+            code = col_name.replace("得分_", "")
+            cat = code_config.get(code, {}).get("market", "未知")
+            if cat not in cat_best or s > cat_best[cat][1]:
+                cat_best[cat] = (code, s)
+        
+        sorted_cats = sorted(cat_best.values(), key=lambda x: x[1], reverse=True)
+        top_3_raw = sorted_cats[:3]
+        current_top_3_codes = set(code for code, s in top_3_raw)
+        
+        # 判断是否发生调仓（目标持仓集合发生变化）
+        if current_top_3_codes != last_top_3:
+            rebalance_count += 1
+            # 统计调仓日这 3 只中得分 <= 0 的数量
+            neg_count = sum(1 for code, s in top_3_raw if s <= 0)
+            
+            results.append({
+                "date": date,
+                "neg_count": neg_count,
+                "top_1_score": top_3_raw[0][1],
+                "top_2_score": top_3_raw[1][1] if len(top_3_raw)>1 else np.nan,
+                "top_3_score": top_3_raw[2][1] if len(top_3_raw)>2 else np.nan,
+                "top_1_name": code_config.get(top_3_raw[0][0], {}).get('name')
+            })
+            last_top_3 = current_top_3_codes
+
+    neg_df = pd.DataFrame(results)
+    
+    print(f"\n{'='*60}")
+    print(f"调仓日 (Rebalance Day) Top 3 标的出现负分情况分析")
+    print(f"{'='*60}")
+    print(f"总调仓次数: {rebalance_count}")
+    print(f"涉及负分(<=0)的调仓次数: {len(neg_df[neg_df['neg_count']>0])} ({len(neg_df[neg_df['neg_count']>0])/rebalance_count:.1%})")
+    
+    if not neg_df.empty:
+        print(f"\n调仓日负分详细分布:")
+        print(f"  - 只有 1 只标的为负: {len(neg_df[neg_df['neg_count']==1])} 次")
+        print(f"  - 有 2 只标的为负:   {len(neg_df[neg_df['neg_count']==2])} 次")
+        print(f"  - 全部 3 只标的均为负: {len(neg_df[neg_df['neg_count']==3])} 次")
+        
+        print(f"\n最近 10 次涉及负分的调仓详情:")
+        neg_df['date'] = pd.to_datetime(neg_df['date'])
+        print(neg_df[neg_df['neg_count']>0][['date', 'neg_count', 'top_1_score', 'top_1_name']].tail(10))
+
+if __name__ == "__main__":
+    analyze_negative_scores()
--- a/scripts/test_select_num.py
+++ b/scripts/test_select_num.py
@@ -0,0 +1,112 @@
+"""
+持仓数量 (select_num) 敏感度测试
+测试 select_num 分别为 1, 2, 3, 4, 5 时的策略表现
+基于最终精选的 11 只标的池
+"""
+
+import sys
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from datetime import datetime
+import matplotlib.pyplot as plt
+
+# 添加项目根目录
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from strategies.rotation.engine import RotationStrategy
+
+# ==================== 基础配置 ====================
+FINAL_POOL = {
+    "399006.SZ": {"name": "创业板指", "market": "A", "etf": "159915.SZ"},
+    "H30269.CSI": {"name": "中证红利低波", "market": "A", "etf": "512890.SH"},
+    "000015.SH": {"name": "上证红利", "market": "A", "etf": "510880.SH"},
+    "NDX": {"name": "纳指100", "market": "US", "etf": "513100.SH"},
+    "N225": {"name": "日经225", "market": "JP", "etf": "513520.SH"},
+    "GDAXI": {"name": "德国DAX", "market": "EU", "etf": "513030.SH"},
+    "HSI": {"name": "恒生指数", "market": "HK", "etf": "159920.SZ"},
+    "HSTECH.HK": {"name": "恒生科技", "market": "HK", "etf": "513130.SH"},
+    "AU.SHF": {"name": "黄金", "market": "COMMODITY", "etf": "518880.SH"},
+    "CL.NYM": {"name": "原油", "market": "COMMODITY", "etf": "160723.SZ"},
+    "931862.CSI": {"name": "30年国债", "market": "BOND", "etf": "511090.SH"}
+}
+
+BASE_CONFIG = {
+    "start_date": "2019-01-01",
+    "end_date": datetime.now().strftime('%Y-%m-%d'),
+    "code_list": FINAL_POOL,
+    "factor_type": "weighted_momentum",
+    "auto_day": False, # 使用当前设定的固定窗口
+    "n_days": 25,
+    "diversified": True,
+    "rebalance_days": 1,
+    "rebalance_threshold": 0.0,
+    "trade_cost": 0.001,
+    "premium_control": {"enabled": True, "default_threshold": 0.10},
+    "use_cache": True,
+    "ssh_tunnel": {"enabled": True, "host": "8.218.167.69", "port": 22, "username": "root", "key_path": "hk_ecs.pem", "local_port": 1080}
+}
+
+def run_sensitivity_test():
+    test_values = [1, 2, 3, 4, 5]
+    results = []
+    
+    for val in test_values:
+        print(f"\n测试 select_num = {val} ...")
+        cfg = BASE_CONFIG.copy()
+        cfg["select_num"] = val
+        
+        strategy = RotationStrategy(cfg)
+        try:
+            res_df = strategy.run()
+            
+            nav = res_df['轮动策略净值']
+            total_ret = nav.iloc[-1] - 1
+            days = (nav.index[-1] - nav.index[0]).days
+            cagr = (1 + total_ret)**(365.25/days) - 1
+            
+            daily_ret = res_df['轮动策略日收益率']
+            sharpe = daily_ret.mean() / daily_ret.std() * np.sqrt(252) if daily_ret.std() > 0 else 0
+            
+            peak = nav.cummax()
+            dd = (nav - peak) / peak
+            max_dd = dd.min()
+            
+            results.append({
+                "select_num": val,
+                "total_ret": total_ret,
+                "cagr": cagr,
+                "max_dd": max_dd,
+                "sharpe": sharpe,
+                "nav": nav
+            })
+        except Exception as e:
+            print(f"测试失败 (select_num={val}): {e}")
+
+    # ==================== 汇总报告 ====================
+    print(f"\n\n{'='*90}")
+    print(f"{'持仓数量 (select_num) 敏感度测试报告':^90}")
+    print(f"{'='*90}")
+    print(f"{'持仓数':<10} | {'累计收益':>12} | {'年化(CAGR)':>12} | {'最大回撤':>12} | {'夏普比率':>10}")
+    print(f"{'-'*90}")
+    
+    for r in results:
+        print(f"{r['select_num']:<10} | {r['total_ret']:>12.2%} | {r['cagr']:>12.2%} | {r['max_dd']:>12.2%} | {r['sharpe']:>10.2f}")
+    print(f"{'='*90}")
+
+    # ==================== 绘图 ====================
+    plt.figure(figsize=(14, 7))
+    for r in results:
+        plt.plot(r['nav'].index, r['nav'], label=f"select_num = {r['select_num']}")
+    
+    plt.yscale('log')
+    plt.title("持仓数量对净值的影响 (select_num 1-5)", fontsize=14)
+    plt.legend()
+    plt.grid(True, alpha=0.3)
+    
+    output_path = Path(__file__).parent.parent / "results" / "select_num_test.png"
+    plt.savefig(output_path)
+    print(f"\n对比图表已保存至: {output_path}")
+
+if __name__ == "__main__":
+    run_sensitivity_test()