aszerW
6b59855c28
技术修复： - SOCKS5代理IPv6问题：socks5:// → socks5h:// (hybrid_source.py, yfinance_source.py) 目录整理： - scripts/ → 仅保留策略入口(daily_scheduler, run_rotation, run_cci_screener) - 实验脚本移至 tests/experiments/ - 工具脚本移至 tests/utils/ - 实验记录新增 docs/experiments/ - results/ 添加到 gitignore 实验结果： 实验001 - 同大类扩充（添加标普500）： ├─ 累计收益: 1467.35% → 1176.26% (-291%) ├─ CAGR: 48.10% → 43.82% (-4.28%) ├─ 调仓次数: 459 → 501 (+42次) └─ 结论: 添加同大类标的不增加跨类分散，反而侵蚀收益 实验002 - 纳指vs标普替换对比： ├─ 累计收益: 1467.35% → 1118.77% (-348%) ├─ CAGR: 48.10% → 42.87% (-5.22%) ├─ Sharpe: 2.21 → 2.08 (-0.13) ├─ MaxDD: -17.33% → -15.14% (+2.18%) └─ 结论: 纳指100优于标普500，成长风格更适合动量策略 策略建议： - 保持纳指100作为美股大类代表 - 不添加同大类新标的（避免类内切换成本） - 新增标的应优先考虑新大类（增加跨类分散）
236 lines
8.4 KiB
Python
236 lines
8.4 KiB
Python
"""
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全市场44只ETF Top 3 等权轮动回测
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标的池来源：etf_rotation_deep_analysis.md
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"""
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import sys
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import math
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import warnings
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from pathlib import Path
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from datetime import datetime
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import numpy as np
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import pandas as pd
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warnings.filterwarnings("ignore")
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from dotenv import load_dotenv
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load_dotenv()
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from 动量 import (
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fetch_all_etf_data,
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fetch_etf_nav_data,
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calc_atr,
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calc_weighted_momentum_score,
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apply_crash_filter,
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calc_premium_rate,
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print_performance,
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print_yearly_returns,
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)
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# ==================== 资产配置池 (9个精选 + 恒生科技 + 恒生指数) ====================
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FULL_POOL = {
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'513100.SH': '纳指100ETF',
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'513520.SH': '日经225ETF',
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'513030.SH': '德国DAX ETF',
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'518880.SH': '黄金ETF',
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'159980.SZ': '有色金属ETF',
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'160723.SZ': '嘉实原油LOF',
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'511090.SH': '30年国债ETF',
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'512890.SH': '红利低波ETF',
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'159915.SZ': '创业板ETF',
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'513130.SH': '恒生科技ETF',
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'159920.SZ': '恒生ETF',
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}
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# ==================== 资产大类映射 ====================
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ETF_CATEGORIES = {
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'513100.SH': '美股',
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'513520.SH': '日本',
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'513030.SH': '欧洲',
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'518880.SH': '商品',
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'159980.SZ': '商品',
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'160723.SZ': '商品',
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'511090.SH': '固收',
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'512890.SH': 'A股主题',
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'159915.SZ': 'A股宽基',
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'513130.SH': '港股',
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'159920.SZ': '港股',
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}
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CONFIG = {
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'etf_pool': FULL_POOL,
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'target_num': 3, # 持仓数量
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'auto_day': True, # 是否启用动态周期
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'fixed_days': 25, # 固定回看天数
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'min_days': 20, # 动态周期最小值
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'max_days': 60, # 动态周期最大值
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'premium_threshold': 5.0, # 溢价率阈值(%)
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'trade_cost': 0.001, # 单次交易成本(双边)
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'start_date': '2019-01-01',
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'benchmark': '000300.SH', # 基准：沪深300
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}
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def run_full_backtest(config: dict):
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"""执行全市场回测"""
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end_date = datetime.now().strftime('%Y-%m-%d')
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etf_pool = config['etf_pool']
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etf_codes = list(etf_pool.keys())
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print("=" * 60)
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print(" 全市场ETF轮动策略 - Top 3 等权回测")
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print("=" * 60)
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print(f" 候选ETF: {len(etf_codes)} 只")
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print(f" 持仓数量: {config['target_num']}")
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print(f" 回测区间: {config['start_date']} ~ {end_date}")
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# 1. 获取数据 (使用缓存加速)
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from scripts.etf_data_cache import ETFDataCache
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data_cache = ETFDataCache()
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print(f"\n{'='*60}")
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print("加载数据...")
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all_data = {}
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for code in etf_codes:
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df = data_cache.load_cached_ohlcv(code)
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if not df.empty:
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all_data[code] = df
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print(f" 加载完成: {len(all_data)} 只价格数据")
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# 2. 构建交易日历
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all_dates = set()
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for df in all_data.values():
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all_dates.update(df.index.tolist())
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trade_dates = sorted(d for d in all_dates if d >= pd.Timestamp(config['start_date']))
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print(f" 交易日数: {len(trade_dates)}")
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# 3. 逐日回测
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print(f"\n{'='*60}")
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print("开始回测...")
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max_lookback = config['max_days'] + 10
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holdings = {} # {code: weight}
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daily_returns = []
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signals = []
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for i, today in enumerate(trade_dates):
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# 计算得分
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scores = {}
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for code in etf_codes:
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if code not in all_data: continue
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df = all_data[code]
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hist = df[df.index <= today].tail(max_lookback + 1)
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if len(hist) < config['min_days']: continue
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close_arr = hist['close'].values
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# 动态周期
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if config['auto_day'] and len(hist) >= max_lookback:
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long_atr = calc_atr(hist['high'], hist['low'], hist['close'], config['max_days']).iloc[-1]
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short_atr = calc_atr(hist['high'], hist['low'], hist['close'], config['min_days']).iloc[-1]
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if long_atr > 0:
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ratio = min(0.9, short_atr / long_atr)
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lookback = int(config['min_days'] + (config['max_days'] - config['min_days']) * (1 - ratio))
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else:
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lookback = config['fixed_days']
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else:
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lookback = config['fixed_days']
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prices = close_arr[-lookback:]
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if len(prices) < 5: continue
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result = calc_weighted_momentum_score(prices)
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score = result['score']
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score = apply_crash_filter(close_arr, score)
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if 0 < score < 6:
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scores[code] = score
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# 选出排名最高的 3 只 (跨大类 Top 1 逻辑)
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if scores:
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ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
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# 1. 每个大类只保留最高分的那一个
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category_best = {} # {category: (code, score)}
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for code, score in ranked:
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cat = ETF_CATEGORIES.get(code, '未知')
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if cat not in category_best:
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category_best[cat] = (code, score)
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# 2. 对所有大类的 Top 1 进行排序，选前 3 个大类
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sorted_categories = sorted(category_best.values(), key=lambda x: x[1], reverse=True)
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targets = [code for code, score in sorted_categories[:config['target_num']]]
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new_holdings = {c: 1.0/len(targets) for c in targets}
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else:
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new_holdings = {}
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# 计算收益
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port_ret = 0.0
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for code, weight in holdings.items():
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df_h = all_data[code]
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if today in df_h.index:
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prev_dates = df_h[df_h.index < today].index
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if len(prev_dates) > 0:
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prev_price = df_h.loc[prev_dates[-1], 'close']
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port_ret += weight * (df_h.loc[today, 'close'] / prev_price - 1)
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# 调仓成本
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old_set, new_set = set(holdings.keys()), set(new_holdings.keys())
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trade_cost = 0.0
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if old_set != new_set:
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turnover = sum(holdings[c] for c in old_set - new_set) + sum(new_holdings[c] for c in new_set - old_set)
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trade_cost = turnover * config['trade_cost'] / 2
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signals.append({'date': today, 'holdings': list(new_holdings.keys())})
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holdings = new_holdings
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daily_returns.append({
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'date': today,
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'daily_return': port_ret - trade_cost,
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'holding': ", ".join(holdings.keys()) if holdings else "空仓"
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})
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# 4. 计算绩效
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result_df = pd.DataFrame(daily_returns).set_index('date')
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result_df['nav'] = (1 + result_df['daily_return']).cumprod()
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# 基准
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import os, tushare as ts
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pro = ts.pro_api(os.getenv("TUSHARE_TOKEN"))
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bench_df = pro.index_daily(ts_code=config['benchmark'], start_date=config['start_date'].replace('-', ''), end_date=end_date.replace('-', ''))
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if bench_df is not None and not bench_df.empty:
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bench_df['date'] = pd.to_datetime(bench_df['trade_date'])
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bench_df = bench_df.set_index('date').sort_index()
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result_df['bench_return'] = bench_df['close'].reindex(result_df.index, method='ffill') / bench_df['close'].iloc[0]
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else:
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result_df['bench_return'] = 1.0
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print_performance(result_df, signals, config)
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print_yearly_returns(result_df)
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# 保存图表
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save_chart(result_df)
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def save_chart(result_df):
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try:
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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matplotlib.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
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matplotlib.rcParams['axes.unicode_minus'] = False
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), height_ratios=[3, 1], gridspec_kw={'hspace': 0.3})
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ax1.plot(result_df.index, result_df['nav'], label='全市场Top3等权', color='#2ecc71')
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ax1.plot(result_df.index, result_df['bench_return'], label='沪深300', color='#95a5a6')
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ax1.set_yscale('log')
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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peak = result_df['nav'].cummax()
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ax2.fill_between(result_df.index, (result_df['nav'] - peak) / peak, 0, color='#e74c3c', alpha=0.4)
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plt.savefig(Path(__file__).parent.parent / 'results' / 'full_pool_top3_chart.png')
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print(f"图表已保存到 results/full_pool_top3_chart.png")
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except Exception as e: print(f"图表生成失败: {e}")
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if __name__ == "__main__":
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run_full_backtest(CONFIG)
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