aszerW
c1fbd2c7db
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.
116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
"""
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分析历史 Top 3 标的中存在负分的情况 (正式版)
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"""
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import sys
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import yaml
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import pandas as pd
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import numpy as np
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from pathlib import Path
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from datetime import datetime
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from dotenv import load_dotenv
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# 加载环境变量
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load_dotenv()
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# 添加项目根目录
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from strategies.rotation.engine import RotationStrategy
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from core.factors.momentum import compute_factors
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def load_config(config_path: str) -> dict:
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with open(config_path, "r", encoding="utf-8") as f:
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return yaml.safe_load(f)
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def analyze_negative_scores():
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config_path = "config/strategies/rotation.yaml"
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config = load_config(config_path)
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# 强制不使用过滤，以获取完整数据
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config['diversified'] = True
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config['select_num'] = 3
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strategy = RotationStrategy(config)
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# 使用策略内部方法获取数据
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with strategy.data_source:
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index_data, etf_data, etf_nav_data, benchmark_data, valid_codes, index_ohlcv_data = strategy.data_source.fetch_all(
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config['code_list'],
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config['benchmark']['code'],
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config["start_date"],
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datetime.now().strftime('%Y-%m-%d')
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)
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# 手动计算因子 (不带过滤)
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# 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
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factor_data, valid_codes = compute_factors(
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index_data,
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valid_codes,
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n=config["n_days"],
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factor_type=config["factor_type"],
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auto_day=config.get("auto_day", False),
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index_ohlcv_data=index_ohlcv_data
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)
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score_cols = [c for c in factor_data.columns if c.startswith("得分_")]
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code_config = config['code_list']
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total_days = len(factor_data)
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results = []
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last_top_3 = set()
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rebalance_count = 0
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for date, row in factor_data.iterrows():
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scores = row[score_cols].dropna()
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if scores.empty: continue
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# 模拟 diversified 逻辑下的 Top 3 (不带 >0 过滤)
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cat_best = {}
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for col_name, s in scores.items():
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code = col_name.replace("得分_", "")
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cat = code_config.get(code, {}).get("market", "未知")
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if cat not in cat_best or s > cat_best[cat][1]:
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cat_best[cat] = (code, s)
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sorted_cats = sorted(cat_best.values(), key=lambda x: x[1], reverse=True)
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top_3_raw = sorted_cats[:3]
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current_top_3_codes = set(code for code, s in top_3_raw)
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# 判断是否发生调仓（目标持仓集合发生变化）
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if current_top_3_codes != last_top_3:
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rebalance_count += 1
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# 统计调仓日这 3 只中得分 <= 0 的数量
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neg_count = sum(1 for code, s in top_3_raw if s <= 0)
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results.append({
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"date": date,
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"neg_count": neg_count,
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"top_1_score": top_3_raw[0][1],
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"top_2_score": top_3_raw[1][1] if len(top_3_raw)>1 else np.nan,
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"top_3_score": top_3_raw[2][1] if len(top_3_raw)>2 else np.nan,
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"top_1_name": code_config.get(top_3_raw[0][0], {}).get('name')
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})
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last_top_3 = current_top_3_codes
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neg_df = pd.DataFrame(results)
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print(f"\n{'='*60}")
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print(f"调仓日 (Rebalance Day) Top 3 标的出现负分情况分析")
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print(f"{'='*60}")
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print(f"总调仓次数: {rebalance_count}")
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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%})")
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if not neg_df.empty:
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print(f"\n调仓日负分详细分布:")
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print(f" - 只有 1 只标的为负: {len(neg_df[neg_df['neg_count']==1])} 次")
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print(f" - 有 2 只标的为负: {len(neg_df[neg_df['neg_count']==2])} 次")
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print(f" - 全部 3 只标的均为负: {len(neg_df[neg_df['neg_count']==3])} 次")
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print(f"\n最近 10 次涉及负分的调仓详情:")
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neg_df['date'] = pd.to_datetime(neg_df['date'])
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print(neg_df[neg_df['neg_count']>0][['date', 'neg_count', 'top_1_score', 'top_1_name']].tail(10))
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if __name__ == "__main__":
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analyze_negative_scores()
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