aszerW
1fca536c95
归档内容： - core/ (数据源、因子计算、通用工具) → archive/legacy_core/ - strategies/rotation/engine.py, portfolio.py, report.py → archive/legacy_core/ - scripts/ (run_rotation, daily_scheduler) → archive/legacy_scripts/ - examples/ → archive/legacy_examples/ - tests/ (实验、对比测试) → archive/legacy_tests/ - 单独文件 (fetch_*.py, 动量.py, 全球市场.py等) → archive/single_files/ 保留新结构： - framework/ (抽象接口) - strategies/shared/ (定制组件) - strategies/rotation/strategy.py (新策略) - 外层配置: .env, .dockerignore, build-and-push.sh, hk_ecs.pem, README.md, requirements.txt - Docker相关: Dockerfile, Dockerfile_base, docker-compose.yml 更新README反映新框架架构
113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
"""
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持仓数量 (select_num) 敏感度测试
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测试 select_num 分别为 1, 2, 3, 4, 5 时的策略表现
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基于最终精选的 11 只标的池
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"""
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import sys
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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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import matplotlib.pyplot as plt
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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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# ==================== 基础配置 ====================
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FINAL_POOL = {
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"399006.SZ": {"name": "创业板指", "market": "A", "etf": "159915.SZ"},
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"H30269.CSI": {"name": "中证红利低波", "market": "A", "etf": "512890.SH"},
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"000015.SH": {"name": "上证红利", "market": "A", "etf": "510880.SH"},
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"NDX": {"name": "纳指100", "market": "US", "etf": "513100.SH"},
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"N225": {"name": "日经225", "market": "JP", "etf": "513520.SH"},
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"GDAXI": {"name": "德国DAX", "market": "EU", "etf": "513030.SH"},
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"HSI": {"name": "恒生指数", "market": "HK", "etf": "159920.SZ"},
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"HSTECH.HK": {"name": "恒生科技", "market": "HK", "etf": "513130.SH"},
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"AU.SHF": {"name": "黄金", "market": "COMMODITY", "etf": "518880.SH"},
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"CL.NYM": {"name": "原油", "market": "COMMODITY", "etf": "160723.SZ"},
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"931862.CSI": {"name": "30年国债", "market": "BOND", "etf": "511090.SH"}
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}
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BASE_CONFIG = {
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"start_date": "2019-01-01",
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"end_date": datetime.now().strftime('%Y-%m-%d'),
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"code_list": FINAL_POOL,
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"factor_type": "weighted_momentum",
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"auto_day": False, # 使用当前设定的固定窗口
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"n_days": 25,
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"diversified": True,
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"rebalance_days": 1,
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"rebalance_threshold": 0.0,
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"trade_cost": 0.001,
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"premium_control": {"enabled": True, "default_threshold": 0.10},
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"use_cache": True,
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"ssh_tunnel": {"enabled": True, "host": "8.218.167.69", "port": 22, "username": "root", "key_path": "hk_ecs.pem", "local_port": 1080}
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}
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def run_sensitivity_test():
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test_values = [1, 2, 3, 4, 5]
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results = []
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for val in test_values:
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print(f"\n测试 select_num = {val} ...")
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cfg = BASE_CONFIG.copy()
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cfg["select_num"] = val
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strategy = RotationStrategy(cfg)
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try:
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res_df = strategy.run()
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nav = res_df['轮动策略净值']
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total_ret = nav.iloc[-1] - 1
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days = (nav.index[-1] - nav.index[0]).days
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cagr = (1 + total_ret)**(365.25/days) - 1
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daily_ret = res_df['轮动策略日收益率']
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sharpe = daily_ret.mean() / daily_ret.std() * np.sqrt(252) if daily_ret.std() > 0 else 0
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peak = nav.cummax()
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dd = (nav - peak) / peak
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max_dd = dd.min()
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results.append({
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"select_num": val,
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"total_ret": total_ret,
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"cagr": cagr,
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"max_dd": max_dd,
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"sharpe": sharpe,
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"nav": nav
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})
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except Exception as e:
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print(f"测试失败 (select_num={val}): {e}")
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# ==================== 汇总报告 ====================
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print(f"\n\n{'='*90}")
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print(f"{'持仓数量 (select_num) 敏感度测试报告':^90}")
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print(f"{'='*90}")
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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}")
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print(f"{'-'*90}")
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for r in results:
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print(f"{r['select_num']:<10} | {r['total_ret']:>12.2%} | {r['cagr']:>12.2%} | {r['max_dd']:>12.2%} | {r['sharpe']:>10.2f}")
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print(f"{'='*90}")
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# ==================== 绘图 ====================
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plt.figure(figsize=(14, 7))
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for r in results:
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plt.plot(r['nav'].index, r['nav'], label=f"select_num = {r['select_num']}")
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plt.yscale('log')
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plt.title("持仓数量对净值的影响 (select_num 1-5)", fontsize=14)
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plt.legend()
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plt.grid(True, alpha=0.3)
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output_path = Path(__file__).parent.parent / "results" / "select_num_test.png"
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plt.savefig(output_path)
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print(f"\n对比图表已保存至: {output_path}")
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if __name__ == "__main__":
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run_sensitivity_test()
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