experiment(rotation): 同大类扩充与纳指vs标普替换对比实验
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）
This commit is contained in:
2
tests/experiments/__init__.py
Normal file
2
tests/experiments/__init__.py
Normal file
@@ -0,0 +1,2 @@
|
||||
# 实验脚本目录
|
||||
# 存放策略研究相关的A/B测试、对比实验等脚本
|
||||
182
tests/experiments/ab_test_iterations.py
Normal file
182
tests/experiments/ab_test_iterations.py
Normal file
@@ -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": "COMMODITY", "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()
|
||||
187
tests/experiments/ab_test_ndx_vs_spx.py
Normal file
187
tests/experiments/ab_test_ndx_vs_spx.py
Normal file
@@ -0,0 +1,187 @@
|
||||
"""
|
||||
A/B测试：纳指100 vs 标普500 替换对比
|
||||
对比：
|
||||
- A组（对照组）：纳指100作为美股大类代表
|
||||
- B组（实验组）：标普500替换纳指100作为美股大类代表
|
||||
|
||||
核心问题：替换后对策略绩效的影响（无类内竞争）
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
from strategies.rotation.engine import RotationStrategy
|
||||
import pandas as pd
|
||||
import yaml
|
||||
|
||||
|
||||
def create_config_replace_ndx_with_spx(base_config: dict) -> dict:
|
||||
"""将纳指100替换为标普500"""
|
||||
config = base_config.copy()
|
||||
config['code_list'] = base_config['code_list'].copy()
|
||||
|
||||
# 移除纳指100
|
||||
if 'NDX' in config['code_list']:
|
||||
del config['code_list']['NDX']
|
||||
|
||||
# 添加标普500（替换纳指100）
|
||||
config['code_list']['SPX'] = {
|
||||
'name': '标普500',
|
||||
'etf': '513500.SH',
|
||||
'market': 'US'
|
||||
}
|
||||
|
||||
return config
|
||||
|
||||
|
||||
def run_backtest(config: dict, label: str) -> dict:
|
||||
"""运行回测并返回关键指标"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f" {label}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
strategy = RotationStrategy(config)
|
||||
result = strategy.run()
|
||||
|
||||
if result is None or len(result) == 0:
|
||||
return None
|
||||
|
||||
# 计算指标
|
||||
strategy_nav = result['轮动策略净值']
|
||||
strategy_ret = result['轮动策略日收益率']
|
||||
|
||||
total_return = strategy_nav.iloc[-1] - 1
|
||||
days = len(result)
|
||||
years = days / 250
|
||||
cagr = (strategy_nav.iloc[-1] ** (1/years)) - 1
|
||||
excess_ret = strategy_ret.mean() * 250
|
||||
vol = strategy_ret.std() * (250 ** 0.5)
|
||||
sharpe = excess_ret / vol if vol > 0 else 0
|
||||
rolling_max = strategy_nav.cummax()
|
||||
drawdown = (strategy_nav - rolling_max) / rolling_max
|
||||
max_dd = drawdown.min()
|
||||
calmar = cagr / abs(max_dd) if max_dd < 0 else 0
|
||||
win_rate = (strategy_ret > 0).sum() / len(strategy_ret)
|
||||
|
||||
metrics = {
|
||||
'label': label,
|
||||
'美股标的': '纳指100' if 'NDX' in config['code_list'] else '标普500',
|
||||
'累计收益': total_return,
|
||||
'CAGR': cagr,
|
||||
'Sharpe': sharpe,
|
||||
'MaxDD': max_dd,
|
||||
'Calmar': calmar,
|
||||
'日胜率': win_rate,
|
||||
}
|
||||
|
||||
print(f"\n美股代表: {metrics['美股标的']}")
|
||||
print(f"累计收益: {metrics['累计收益']:.2%}")
|
||||
print(f"CAGR: {metrics['CAGR']:.2%}")
|
||||
print(f"Sharpe: {metrics['Sharpe']:.2f}")
|
||||
print(f"MaxDD: {metrics['MaxDD']:.2%}")
|
||||
print(f"Calmar: {metrics['Calmar']:.2f}")
|
||||
print(f"日胜率: {metrics['日胜率']:.2%}")
|
||||
|
||||
return metrics
|
||||
|
||||
|
||||
def compare_results(a_metrics: dict, b_metrics: dict):
|
||||
"""对比两组结果"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f" 对比结果")
|
||||
print(f"{'='*60}")
|
||||
|
||||
print(f"\n{'指标':<15} {'A组(纳指100)':<15} {'B组(标普500)':<15} {'差异':<15}")
|
||||
print("-" * 60)
|
||||
|
||||
metrics_keys = ['美股标的', '累计收益', 'CAGR', 'Sharpe', 'MaxDD', 'Calmar', '日胜率']
|
||||
|
||||
for key in metrics_keys:
|
||||
a_val = a_metrics.get(key, 0)
|
||||
b_val = b_metrics.get(key, 0)
|
||||
|
||||
if key == '美股标的':
|
||||
print(f"{key:<15} {a_val:<15} {b_val:<15} {'替换':<15}")
|
||||
continue
|
||||
|
||||
diff = b_val - a_val
|
||||
if key in ['累计收益', 'CAGR', 'MaxDD', '日胜率']:
|
||||
a_str = f"{a_val:.2%}"
|
||||
b_str = f"{b_val:.2%}"
|
||||
diff_str = f"{diff*100:+.2f}%"
|
||||
else:
|
||||
a_str = f"{a_val:.2f}"
|
||||
b_str = f"{b_val:.2f}"
|
||||
diff_str = f"{diff:+.2f}"
|
||||
|
||||
print(f"{key:<15} {a_str:<15} {b_str:<15} {diff_str:<15}")
|
||||
|
||||
print("-" * 60)
|
||||
|
||||
print(f"\n【关键发现】")
|
||||
print(f"纳指100 → 标普500 替换效果：")
|
||||
|
||||
if b_metrics['CAGR'] < a_metrics['CAGR']:
|
||||
print(f" - CAGR下降 {a_metrics['CAGR'] - b_metrics['CAGR']:.2%}")
|
||||
print(f" → 标普500动量信号可能不如纳指强")
|
||||
|
||||
if b_metrics['MaxDD'] > a_metrics['MaxDD']: # 注意MaxDD是负数
|
||||
print(f" - MaxDD改善 {b_metrics['MaxDD'] - a_metrics['MaxDD']:.2%}")
|
||||
print(f" → 标普500更稳定，回撤更小")
|
||||
|
||||
if b_metrics['Sharpe'] > a_metrics['Sharpe']:
|
||||
print(f" - Sharpe改善 {b_metrics['Sharpe'] - a_metrics['Sharpe']:.2f}")
|
||||
print(f" → 标普500风险调整后收益更优")
|
||||
elif b_metrics['Sharpe'] < a_metrics['Sharpe']:
|
||||
print(f" - Sharpe下降 {b_metrics['Sharpe'] - a_metrics['Sharpe']:.2f}")
|
||||
print(f" → 纳指100风险调整后收益更优")
|
||||
|
||||
print(f"\n【策略建议】")
|
||||
if b_metrics['累计收益'] < a_metrics['累计收益'] * 0.9:
|
||||
print(f" 建议：保持纳指100（成长风格更适合动量策略）")
|
||||
elif b_metrics['Sharpe'] > a_metrics['Sharpe']:
|
||||
print(f" 建议：考虑标普500（更稳定、风险调整收益更优）")
|
||||
else:
|
||||
print(f" 建议：保持纳指100（累计收益更高）")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
# 加载基础配置
|
||||
config_path = Path(__file__).parent.parent / 'config' / 'strategies' / 'rotation.yaml'
|
||||
with open(config_path, 'r') as f:
|
||||
base_config = yaml.safe_load(f)
|
||||
|
||||
# 添加 end_date
|
||||
from datetime import datetime
|
||||
base_config['end_date'] = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f" A/B测试：纳指100 vs 标普500 替换对比")
|
||||
print(f"{'='*60}")
|
||||
print(f"\n研究问题：")
|
||||
print(f" - 将美股大类代表从纳指100替换为标普500")
|
||||
print(f" - 无类内竞争（每大类还是1只）")
|
||||
print(f" - 评估标的特性变化对绩效的影响")
|
||||
|
||||
# A组：纳指100（当前配置）
|
||||
a_metrics = run_backtest(base_config, "A组: 纳指100作为美股代表")
|
||||
|
||||
# B组：标普500替换纳指100
|
||||
config_replace = create_config_replace_ndx_with_spx(base_config)
|
||||
b_metrics = run_backtest(config_replace, "B组: 标普500替换纳指100")
|
||||
|
||||
# 对比
|
||||
if a_metrics and b_metrics:
|
||||
compare_results(a_metrics, b_metrics)
|
||||
|
||||
# 保存结果
|
||||
results_df = pd.DataFrame([a_metrics, b_metrics])
|
||||
results_path = Path(__file__).parent.parent / 'results' / 'ab_test_ndx_vs_spx.csv'
|
||||
results_df.to_csv(results_path, index=False)
|
||||
print(f"\n对比结果已保存: {results_path}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
183
tests/experiments/ab_test_spx.py
Normal file
183
tests/experiments/ab_test_spx.py
Normal file
@@ -0,0 +1,183 @@
|
||||
"""
|
||||
A/B测试：添加标普500对轮动策略的影响
|
||||
对比：
|
||||
- A组（对照组）：当前11只标的配置
|
||||
- B组（实验组）：添加标普500后的12只标的配置
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
from strategies.rotation.engine import RotationStrategy
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def create_config_with_spx(base_config: dict) -> dict:
|
||||
"""在基础配置上添加标普500"""
|
||||
config = base_config.copy()
|
||||
config['code_list'] = base_config['code_list'].copy()
|
||||
|
||||
# 添加标普500（美股大类内）
|
||||
config['code_list']['SPX'] = {
|
||||
'name': '标普500',
|
||||
'etf': '513500.SH',
|
||||
'market': 'US' # 与纳指100同属美股大类
|
||||
}
|
||||
|
||||
return config
|
||||
|
||||
|
||||
def run_backtest(config: dict, label: str) -> dict:
|
||||
"""运行回测并返回关键指标"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f" {label}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
strategy = RotationStrategy(config)
|
||||
result = strategy.run() # result 是 DataFrame
|
||||
|
||||
if result is None or len(result) == 0:
|
||||
return None
|
||||
|
||||
# 从 DataFrame 中直接计算指标
|
||||
strategy_nav = result['轮动策略净值']
|
||||
strategy_ret = result['轮动策略日收益率']
|
||||
benchmark_nav = result['基准净值']
|
||||
benchmark_ret = result['基准日收益率']
|
||||
|
||||
# 累计收益
|
||||
total_return = strategy_nav.iloc[-1] - 1
|
||||
|
||||
# CAGR (交易日口径)
|
||||
days = len(result)
|
||||
years = days / 250
|
||||
cagr = (strategy_nav.iloc[-1] ** (1/years)) - 1
|
||||
|
||||
# Sharpe
|
||||
excess_ret = strategy_ret.mean() * 250 # 年化收益
|
||||
vol = strategy_ret.std() * (250 ** 0.5) # 年化波动
|
||||
sharpe = excess_ret / vol if vol > 0 else 0
|
||||
|
||||
# 最大回撤
|
||||
rolling_max = strategy_nav.cummax()
|
||||
drawdown = (strategy_nav - rolling_max) / rolling_max
|
||||
max_dd = drawdown.min()
|
||||
|
||||
# Calmar
|
||||
calmar = cagr / abs(max_dd) if max_dd < 0 else 0
|
||||
|
||||
# 日胜率
|
||||
win_rate = (strategy_ret > 0).sum() / len(strategy_ret)
|
||||
|
||||
# 提取关键指标
|
||||
metrics = {
|
||||
'label': label,
|
||||
'标的数': len(config['code_list']),
|
||||
'累计收益': total_return,
|
||||
'CAGR': cagr,
|
||||
'Sharpe': sharpe,
|
||||
'MaxDD': max_dd,
|
||||
'Calmar': calmar,
|
||||
'日胜率': win_rate,
|
||||
}
|
||||
|
||||
print(f"\n标的池: {len(config['code_list'])}只")
|
||||
print(f"累计收益: {metrics['累计收益']:.2%}")
|
||||
print(f"CAGR: {metrics['CAGR']:.2%}")
|
||||
print(f"Sharpe: {metrics['Sharpe']:.2f}")
|
||||
print(f"MaxDD: {metrics['MaxDD']:.2%}")
|
||||
print(f"Calmar: {metrics['Calmar']:.2f}")
|
||||
print(f"日胜率: {metrics['日胜率']:.2%}")
|
||||
|
||||
return metrics
|
||||
|
||||
|
||||
def compare_results(a_metrics: dict, b_metrics: dict):
|
||||
"""对比两组结果"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f" 对比结果")
|
||||
print(f"{'='*60}")
|
||||
|
||||
print(f"\n{'指标':<12} {'A组(无SPX)':<15} {'B组(有SPX)':<15} {'差异':<15}")
|
||||
print("-" * 60)
|
||||
|
||||
metrics_keys = ['标的数', '累计收益', 'CAGR', 'Sharpe', 'MaxDD', 'Calmar', '日胜率']
|
||||
|
||||
for key in metrics_keys:
|
||||
a_val = a_metrics.get(key, 0)
|
||||
b_val = b_metrics.get(key, 0)
|
||||
|
||||
if key == '标的数':
|
||||
diff = b_val - a_val
|
||||
diff_str = f"+{diff}" if diff > 0 else str(diff)
|
||||
else:
|
||||
diff = b_val - a_val
|
||||
if key in ['累计收益', 'CAGR', 'MaxDD', '日胜率']:
|
||||
diff_str = f"{diff*100:+.2f}%"
|
||||
else:
|
||||
diff_str = f"{diff:+.2f}"
|
||||
|
||||
if key in ['累计收益', 'CAGR', 'MaxDD', '日胜率']:
|
||||
a_str = f"{a_val:.2%}"
|
||||
b_str = f"{b_val:.2%}"
|
||||
else:
|
||||
a_str = str(a_val)
|
||||
b_str = str(b_val)
|
||||
|
||||
print(f"{key:<12} {a_str:<15} {b_str:<15} {diff_str:<15}")
|
||||
|
||||
print("-" * 60)
|
||||
|
||||
# 分析美股大类内部切换情况
|
||||
print(f"\n【关键发现】")
|
||||
print(f"添加标普500后：")
|
||||
print(f" - 美股大类从1只→2只（纳指100 + 标普500）")
|
||||
print(f" - 类内竞争：纳指100 vs 标普500，得分高者代表美股大类")
|
||||
print(f" - 跨类分散不变：美股大类还是只输出1只冠军进入Top3")
|
||||
|
||||
if b_metrics['累计收益'] != a_metrics['累计收益']:
|
||||
print(f" - 累计收益变化：{a_metrics['累计收益']:.2%} → {b_metrics['累计收益']:.2%}")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
import yaml
|
||||
|
||||
# 加载基础配置
|
||||
config_path = Path(__file__).parent.parent / 'config' / 'strategies' / 'rotation.yaml'
|
||||
with open(config_path, 'r') as f:
|
||||
base_config = yaml.safe_load(f)
|
||||
|
||||
# 添加缺失的 end_date（使用今天日期）
|
||||
from datetime import datetime
|
||||
base_config['end_date'] = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f" A/B测试：添加标普500对diversified模式的影响")
|
||||
print(f"{'='*60}")
|
||||
print(f"\n测试假设：")
|
||||
print(f" - diversified=true 模式下，每大类只选1只冠军")
|
||||
print(f" - 添加标普500（同属美股大类）不会增加跨类分散")
|
||||
print(f" - 但可能增加类内切换频率和换手率")
|
||||
|
||||
# A组：当前配置（11只，无标普500）
|
||||
a_metrics = run_backtest(base_config, "A组: 当前配置（11只，无标普500）")
|
||||
|
||||
# B组：添加标普500后的配置（12只）
|
||||
config_with_spx = create_config_with_spx(base_config)
|
||||
b_metrics = run_backtest(config_with_spx, "B组: 添加标普500（12只）")
|
||||
|
||||
# 对比结果
|
||||
if a_metrics and b_metrics:
|
||||
compare_results(a_metrics, b_metrics)
|
||||
|
||||
# 保存对比结果
|
||||
results_df = pd.DataFrame([a_metrics, b_metrics])
|
||||
results_path = Path(__file__).parent.parent / 'results' / 'ab_test_spx.csv'
|
||||
results_df.to_csv(results_path, index=False)
|
||||
print(f"\n对比结果已保存: {results_path}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
115
tests/experiments/analyze_negative_scores.py
Normal file
115
tests/experiments/analyze_negative_scores.py
Normal file
@@ -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()
|
||||
235
tests/experiments/full_pool_top3_backtest.py
Normal file
235
tests/experiments/full_pool_top3_backtest.py
Normal file
@@ -0,0 +1,235 @@
|
||||
"""
|
||||
全市场44只ETF Top 3 等权轮动回测
|
||||
标的池来源：etf_rotation_deep_analysis.md
|
||||
"""
|
||||
|
||||
import sys
|
||||
import math
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
from 动量 import (
|
||||
fetch_all_etf_data,
|
||||
fetch_etf_nav_data,
|
||||
calc_atr,
|
||||
calc_weighted_momentum_score,
|
||||
apply_crash_filter,
|
||||
calc_premium_rate,
|
||||
print_performance,
|
||||
print_yearly_returns,
|
||||
)
|
||||
|
||||
# ==================== 资产配置池 (9个精选 + 恒生科技 + 恒生指数) ====================
|
||||
FULL_POOL = {
|
||||
'513100.SH': '纳指100ETF',
|
||||
'513520.SH': '日经225ETF',
|
||||
'513030.SH': '德国DAX ETF',
|
||||
'518880.SH': '黄金ETF',
|
||||
'159980.SZ': '有色金属ETF',
|
||||
'160723.SZ': '嘉实原油LOF',
|
||||
'511090.SH': '30年国债ETF',
|
||||
'512890.SH': '红利低波ETF',
|
||||
'159915.SZ': '创业板ETF',
|
||||
'513130.SH': '恒生科技ETF',
|
||||
'159920.SZ': '恒生ETF',
|
||||
}
|
||||
|
||||
# ==================== 资产大类映射 ====================
|
||||
ETF_CATEGORIES = {
|
||||
'513100.SH': '美股',
|
||||
'513520.SH': '日本',
|
||||
'513030.SH': '欧洲',
|
||||
'518880.SH': '商品',
|
||||
'159980.SZ': '商品',
|
||||
'160723.SZ': '商品',
|
||||
'511090.SH': '固收',
|
||||
'512890.SH': 'A股主题',
|
||||
'159915.SZ': 'A股宽基',
|
||||
'513130.SH': '港股',
|
||||
'159920.SZ': '港股',
|
||||
}
|
||||
|
||||
CONFIG = {
|
||||
'etf_pool': FULL_POOL,
|
||||
'target_num': 3, # 持仓数量
|
||||
'auto_day': True, # 是否启用动态周期
|
||||
'fixed_days': 25, # 固定回看天数
|
||||
'min_days': 20, # 动态周期最小值
|
||||
'max_days': 60, # 动态周期最大值
|
||||
'premium_threshold': 5.0, # 溢价率阈值(%)
|
||||
'trade_cost': 0.001, # 单次交易成本(双边)
|
||||
'start_date': '2019-01-01',
|
||||
'benchmark': '000300.SH', # 基准：沪深300
|
||||
}
|
||||
|
||||
def run_full_backtest(config: dict):
|
||||
"""执行全市场回测"""
|
||||
end_date = datetime.now().strftime('%Y-%m-%d')
|
||||
etf_pool = config['etf_pool']
|
||||
etf_codes = list(etf_pool.keys())
|
||||
|
||||
print("=" * 60)
|
||||
print(" 全市场ETF轮动策略 - Top 3 等权回测")
|
||||
print("=" * 60)
|
||||
print(f" 候选ETF: {len(etf_codes)} 只")
|
||||
print(f" 持仓数量: {config['target_num']}")
|
||||
print(f" 回测区间: {config['start_date']} ~ {end_date}")
|
||||
|
||||
# 1. 获取数据 (使用缓存加速)
|
||||
from scripts.etf_data_cache import ETFDataCache
|
||||
data_cache = ETFDataCache()
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print("加载数据...")
|
||||
all_data = {}
|
||||
for code in etf_codes:
|
||||
df = data_cache.load_cached_ohlcv(code)
|
||||
if not df.empty:
|
||||
all_data[code] = df
|
||||
print(f" 加载完成: {len(all_data)} 只价格数据")
|
||||
|
||||
# 2. 构建交易日历
|
||||
all_dates = set()
|
||||
for df in all_data.values():
|
||||
all_dates.update(df.index.tolist())
|
||||
trade_dates = sorted(d for d in all_dates if d >= pd.Timestamp(config['start_date']))
|
||||
print(f" 交易日数: {len(trade_dates)}")
|
||||
|
||||
# 3. 逐日回测
|
||||
print(f"\n{'='*60}")
|
||||
print("开始回测...")
|
||||
|
||||
max_lookback = config['max_days'] + 10
|
||||
holdings = {} # {code: weight}
|
||||
daily_returns = []
|
||||
signals = []
|
||||
|
||||
for i, today in enumerate(trade_dates):
|
||||
# 计算得分
|
||||
scores = {}
|
||||
for code in etf_codes:
|
||||
if code not in all_data: continue
|
||||
df = all_data[code]
|
||||
hist = df[df.index <= today].tail(max_lookback + 1)
|
||||
if len(hist) < config['min_days']: continue
|
||||
|
||||
close_arr = hist['close'].values
|
||||
|
||||
# 动态周期
|
||||
if config['auto_day'] and len(hist) >= max_lookback:
|
||||
long_atr = calc_atr(hist['high'], hist['low'], hist['close'], config['max_days']).iloc[-1]
|
||||
short_atr = calc_atr(hist['high'], hist['low'], hist['close'], config['min_days']).iloc[-1]
|
||||
if long_atr > 0:
|
||||
ratio = min(0.9, short_atr / long_atr)
|
||||
lookback = int(config['min_days'] + (config['max_days'] - config['min_days']) * (1 - ratio))
|
||||
else:
|
||||
lookback = config['fixed_days']
|
||||
else:
|
||||
lookback = config['fixed_days']
|
||||
|
||||
prices = close_arr[-lookback:]
|
||||
if len(prices) < 5: continue
|
||||
|
||||
result = calc_weighted_momentum_score(prices)
|
||||
score = result['score']
|
||||
score = apply_crash_filter(close_arr, score)
|
||||
|
||||
if 0 < score < 6:
|
||||
scores[code] = score
|
||||
|
||||
# 选出排名最高的 3 只 (跨大类 Top 1 逻辑)
|
||||
if scores:
|
||||
ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
|
||||
|
||||
# 1. 每个大类只保留最高分的那一个
|
||||
category_best = {} # {category: (code, score)}
|
||||
for code, score in ranked:
|
||||
cat = ETF_CATEGORIES.get(code, '未知')
|
||||
if cat not in category_best:
|
||||
category_best[cat] = (code, score)
|
||||
|
||||
# 2. 对所有大类的 Top 1 进行排序，选前 3 个大类
|
||||
sorted_categories = sorted(category_best.values(), key=lambda x: x[1], reverse=True)
|
||||
targets = [code for code, score in sorted_categories[:config['target_num']]]
|
||||
|
||||
new_holdings = {c: 1.0/len(targets) for c in targets}
|
||||
else:
|
||||
new_holdings = {}
|
||||
|
||||
# 计算收益
|
||||
port_ret = 0.0
|
||||
for code, weight in holdings.items():
|
||||
df_h = all_data[code]
|
||||
if today in df_h.index:
|
||||
prev_dates = df_h[df_h.index < today].index
|
||||
if len(prev_dates) > 0:
|
||||
prev_price = df_h.loc[prev_dates[-1], 'close']
|
||||
port_ret += weight * (df_h.loc[today, 'close'] / prev_price - 1)
|
||||
|
||||
# 调仓成本
|
||||
old_set, new_set = set(holdings.keys()), set(new_holdings.keys())
|
||||
trade_cost = 0.0
|
||||
if old_set != new_set:
|
||||
turnover = sum(holdings[c] for c in old_set - new_set) + sum(new_holdings[c] for c in new_set - old_set)
|
||||
trade_cost = turnover * config['trade_cost'] / 2
|
||||
signals.append({'date': today, 'holdings': list(new_holdings.keys())})
|
||||
holdings = new_holdings
|
||||
|
||||
daily_returns.append({
|
||||
'date': today,
|
||||
'daily_return': port_ret - trade_cost,
|
||||
'holding': ", ".join(holdings.keys()) if holdings else "空仓"
|
||||
})
|
||||
|
||||
# 4. 计算绩效
|
||||
result_df = pd.DataFrame(daily_returns).set_index('date')
|
||||
result_df['nav'] = (1 + result_df['daily_return']).cumprod()
|
||||
|
||||
# 基准
|
||||
import os, tushare as ts
|
||||
pro = ts.pro_api(os.getenv("TUSHARE_TOKEN"))
|
||||
bench_df = pro.index_daily(ts_code=config['benchmark'], start_date=config['start_date'].replace('-', ''), end_date=end_date.replace('-', ''))
|
||||
if bench_df is not None and not bench_df.empty:
|
||||
bench_df['date'] = pd.to_datetime(bench_df['trade_date'])
|
||||
bench_df = bench_df.set_index('date').sort_index()
|
||||
result_df['bench_return'] = bench_df['close'].reindex(result_df.index, method='ffill') / bench_df['close'].iloc[0]
|
||||
else:
|
||||
result_df['bench_return'] = 1.0
|
||||
|
||||
print_performance(result_df, signals, config)
|
||||
print_yearly_returns(result_df)
|
||||
|
||||
# 保存图表
|
||||
save_chart(result_df)
|
||||
|
||||
def save_chart(result_df):
|
||||
try:
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
import matplotlib.pyplot as plt
|
||||
matplotlib.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
|
||||
matplotlib.rcParams['axes.unicode_minus'] = False
|
||||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), height_ratios=[3, 1], gridspec_kw={'hspace': 0.3})
|
||||
ax1.plot(result_df.index, result_df['nav'], label='全市场Top3等权', color='#2ecc71')
|
||||
ax1.plot(result_df.index, result_df['bench_return'], label='沪深300', color='#95a5a6')
|
||||
ax1.set_yscale('log')
|
||||
ax1.legend()
|
||||
ax1.grid(True, alpha=0.3)
|
||||
peak = result_df['nav'].cummax()
|
||||
ax2.fill_between(result_df.index, (result_df['nav'] - peak) / peak, 0, color='#e74c3c', alpha=0.4)
|
||||
plt.savefig(Path(__file__).parent.parent / 'results' / 'full_pool_top3_chart.png')
|
||||
print(f"图表已保存到 results/full_pool_top3_chart.png")
|
||||
except Exception as e: print(f"图表生成失败: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_full_backtest(CONFIG)
|
||||
399
tests/experiments/momentum_experiment.py
Normal file
399
tests/experiments/momentum_experiment.py
Normal file
@@ -0,0 +1,399 @@
|
||||
"""
|
||||
动量策略多持仓对比实验
|
||||
对比 6 种配置: 全仓1只 / 等权3只 / 反波动率3只 / 等权5只 / 反波动率5只 / 动量>0全选等权
|
||||
支持 dynamic 模式: 回测中定期重建ETF池，消除前视偏差
|
||||
"""
|
||||
|
||||
import sys
|
||||
import math
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
|
||||
# ==================== 复用动量.py的核心函数 ====================
|
||||
from 动量 import (
|
||||
fetch_all_etf_data,
|
||||
fetch_etf_nav_data,
|
||||
calc_atr,
|
||||
calc_weighted_momentum_score,
|
||||
apply_crash_filter,
|
||||
calc_premium_rate,
|
||||
resolve_etf_pool,
|
||||
)
|
||||
|
||||
|
||||
# ==================== 权重计算 ====================
|
||||
def calc_equal_weights(codes: list) -> dict:
|
||||
"""等权"""
|
||||
w = 1.0 / len(codes)
|
||||
return {c: w for c in codes}
|
||||
|
||||
|
||||
def calc_inv_vol_weights(codes: list, all_data: dict, today, lookback: int = 20) -> dict:
|
||||
"""反波动率加权: 权重 ∝ 1/σ"""
|
||||
vols = {}
|
||||
for c in codes:
|
||||
if c not in all_data:
|
||||
continue
|
||||
df = all_data[c]
|
||||
hist = df[df.index <= today].tail(lookback + 1)
|
||||
if len(hist) < 10:
|
||||
vols[c] = 1.0 # fallback
|
||||
continue
|
||||
ret = hist['close'].pct_change().dropna()
|
||||
vol = ret.std()
|
||||
vols[c] = vol if vol > 0 else 1e-6
|
||||
|
||||
if not vols:
|
||||
return calc_equal_weights(codes)
|
||||
|
||||
inv_vols = {c: 1.0 / v for c, v in vols.items()}
|
||||
total = sum(inv_vols.values())
|
||||
return {c: iv / total for c, iv in inv_vols.items()}
|
||||
|
||||
|
||||
# ==================== 多持仓回测引擎 ====================
|
||||
def run_multi_backtest(config: dict, all_data: dict, nav_data: dict,
|
||||
trade_dates: list, etf_codes: list,
|
||||
target_num: int = 1, weight_mode: str = 'equal',
|
||||
label: str = '',
|
||||
data_cache=None, rebuild_interval: int = 0) -> dict:
|
||||
"""
|
||||
多持仓回测
|
||||
|
||||
Args:
|
||||
target_num: 同时持有数量
|
||||
weight_mode: 'equal' 等权 | 'inv_vol' 反波动率
|
||||
label: 实验标签
|
||||
data_cache: ETFDataCache 实例（动态重建模式）
|
||||
rebuild_interval: 重建间隔(交易日)，0=不重建
|
||||
|
||||
Returns:
|
||||
dict: 绩效指标
|
||||
"""
|
||||
max_lookback = config['max_days'] + 10
|
||||
holdings = {} # {code: weight}
|
||||
daily_returns = []
|
||||
n_trades = 0
|
||||
last_rebuild_i = -rebuild_interval if rebuild_interval > 0 else 0
|
||||
current_codes = list(etf_codes) # 当前活跃的候选池
|
||||
|
||||
for i, today in enumerate(trade_dates):
|
||||
# 动态重建 ETF 池
|
||||
if rebuild_interval > 0 and data_cache is not None and (i - last_rebuild_i >= rebuild_interval):
|
||||
ref_str = today.strftime('%Y%m%d')
|
||||
try:
|
||||
new_pool = resolve_etf_pool(config, ref_date=ref_str, data_cache=data_cache)
|
||||
current_codes = list(new_pool.keys())
|
||||
# 加载新增 ETF 数据
|
||||
for code in current_codes:
|
||||
if code not in all_data:
|
||||
ohlcv = data_cache.load_cached_ohlcv(code)
|
||||
if not ohlcv.empty:
|
||||
all_data[code] = ohlcv
|
||||
last_rebuild_i = i
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 1. 计算每只 ETF 的得分 (使用当前活跃池)
|
||||
scores = {}
|
||||
for code in current_codes:
|
||||
if code not in all_data:
|
||||
continue
|
||||
df = all_data[code]
|
||||
hist = df[df.index <= today].tail(max_lookback + 1)
|
||||
if len(hist) < config['min_days']:
|
||||
continue
|
||||
|
||||
close_arr = hist['close'].values
|
||||
|
||||
if config['auto_day']:
|
||||
if len(hist) < max_lookback:
|
||||
lookback = config['fixed_days']
|
||||
else:
|
||||
long_atr = calc_atr(hist['high'], hist['low'], hist['close'],
|
||||
config['max_days'])
|
||||
short_atr = calc_atr(hist['high'], hist['low'], hist['close'],
|
||||
config['min_days'])
|
||||
la = long_atr.iloc[-1]
|
||||
sa = short_atr.iloc[-1]
|
||||
if la > 0 and not np.isnan(la) and not np.isnan(sa):
|
||||
ratio = min(0.9, sa / la)
|
||||
lookback = int(config['min_days'] +
|
||||
(config['max_days'] - config['min_days']) * (1 - ratio))
|
||||
else:
|
||||
lookback = config['fixed_days']
|
||||
prices = close_arr[-lookback:]
|
||||
else:
|
||||
prices = close_arr[-config['fixed_days']:]
|
||||
|
||||
if len(prices) < 5:
|
||||
continue
|
||||
|
||||
result = calc_weighted_momentum_score(prices)
|
||||
score = result['score']
|
||||
score = apply_crash_filter(close_arr, score)
|
||||
|
||||
if code in nav_data:
|
||||
nav_df = nav_data[code]
|
||||
nav_row = nav_df[nav_df.index <= today]
|
||||
if not nav_row.empty:
|
||||
nav_val = nav_row.iloc[-1]['nav']
|
||||
etf_price = close_arr[-1]
|
||||
premium = calc_premium_rate(etf_price, nav_val)
|
||||
if premium >= config['premium_threshold']:
|
||||
score -= 1
|
||||
|
||||
if 0 < score < 6:
|
||||
scores[code] = score
|
||||
|
||||
# 2. 选出 top N (或全部正动量)
|
||||
if scores:
|
||||
ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
|
||||
if target_num == 'all_positive':
|
||||
targets = [c for c, s in ranked] # scores 已过滤 >0
|
||||
else:
|
||||
targets = [c for c, _ in ranked[:target_num]]
|
||||
else:
|
||||
targets = []
|
||||
|
||||
# 3. 计算权重
|
||||
if targets:
|
||||
if weight_mode == 'inv_vol':
|
||||
new_weights = calc_inv_vol_weights(targets, all_data, today)
|
||||
else:
|
||||
new_weights = calc_equal_weights(targets)
|
||||
else:
|
||||
new_weights = {}
|
||||
|
||||
# 4. 计算当日组合收益
|
||||
port_ret = 0.0
|
||||
for code, weight in holdings.items():
|
||||
if code not in all_data:
|
||||
continue
|
||||
df_h = all_data[code]
|
||||
if today in df_h.index:
|
||||
prev_dates = df_h[df_h.index < today].index
|
||||
if len(prev_dates) > 0:
|
||||
prev_price = df_h.loc[prev_dates[-1], 'close']
|
||||
today_price = df_h.loc[today, 'close']
|
||||
port_ret += weight * (today_price / prev_price - 1)
|
||||
|
||||
# 5. 调仓判断
|
||||
old_set = set(holdings.keys())
|
||||
new_set = set(new_weights.keys())
|
||||
if old_set != new_set:
|
||||
# 换手成本: 按换手比例收取
|
||||
turnover = 0.0
|
||||
for c in old_set - new_set:
|
||||
turnover += holdings[c]
|
||||
for c in new_set - old_set:
|
||||
turnover += new_weights[c]
|
||||
for c in old_set & new_set:
|
||||
turnover += abs(new_weights[c] - holdings[c])
|
||||
trade_cost = turnover * config['trade_cost'] / 2 # 单边已含在trade_cost中
|
||||
n_trades += 1
|
||||
else:
|
||||
trade_cost = 0.0
|
||||
|
||||
holdings = new_weights
|
||||
|
||||
daily_returns.append({
|
||||
'date': today,
|
||||
'daily_return': port_ret - trade_cost,
|
||||
})
|
||||
|
||||
# 计算绩效
|
||||
result_df = pd.DataFrame(daily_returns).set_index('date')
|
||||
result_df['nav'] = (1 + result_df['daily_return']).cumprod()
|
||||
|
||||
nav = result_df['nav']
|
||||
total_return = nav.iloc[-1] / nav.iloc[0] - 1
|
||||
days = (result_df.index[-1] - result_df.index[0]).days
|
||||
cagr = (1 + total_return) ** (365 / days) - 1 if days > 0 else 0
|
||||
daily_rets = result_df['daily_return']
|
||||
sharpe = daily_rets.mean() / daily_rets.std() * np.sqrt(252) if daily_rets.std() > 0 else 0
|
||||
peak = nav.cummax()
|
||||
drawdown = (nav - peak) / peak
|
||||
max_dd = drawdown.min()
|
||||
calmar = cagr / abs(max_dd) if max_dd != 0 else 0
|
||||
win_rate = (daily_rets > 0).sum() / (daily_rets != 0).sum() if (daily_rets != 0).sum() > 0 else 0
|
||||
years = days / 365
|
||||
|
||||
# 年度统计
|
||||
win_years = 0
|
||||
total_years = 0
|
||||
for year, group in result_df.groupby(result_df.index.year):
|
||||
yr = group['nav']
|
||||
yr_ret = yr.iloc[-1] / yr.iloc[0] - 1
|
||||
total_years += 1
|
||||
if yr_ret > 0:
|
||||
win_years += 1
|
||||
|
||||
return {
|
||||
'label': label,
|
||||
'target_num': target_num,
|
||||
'weight_mode': weight_mode,
|
||||
'total_return': total_return,
|
||||
'cagr': cagr,
|
||||
'sharpe': sharpe,
|
||||
'max_dd': max_dd,
|
||||
'calmar': calmar,
|
||||
'win_rate': win_rate,
|
||||
'n_trades': n_trades,
|
||||
'trades_per_year': n_trades / years if years > 0 else 0,
|
||||
'win_years': f"{win_years}/{total_years}",
|
||||
'result_df': result_df,
|
||||
}
|
||||
|
||||
|
||||
# ==================== 主函数 ====================
|
||||
def main():
|
||||
from 动量 import CONFIG
|
||||
|
||||
config = CONFIG.copy()
|
||||
# 强制使用 dynamic 模式
|
||||
config['etf_pool'] = 'dynamic'
|
||||
rebuild_interval = config.get('rebuild_interval', 60)
|
||||
|
||||
# 初始化缓存
|
||||
from scripts.etf_data_cache import ETFDataCache
|
||||
data_cache = ETFDataCache()
|
||||
|
||||
# 用 start_date 作为初始重建日期
|
||||
init_ref_date = config['start_date'].replace('-', '')
|
||||
etf_pool = resolve_etf_pool(config, ref_date=init_ref_date, data_cache=data_cache)
|
||||
etf_codes = list(etf_pool.keys())
|
||||
end_date = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
print("=" * 70)
|
||||
print(" 动量策略多持仓对比实验 (动态重建模式, 无前视偏差)")
|
||||
print("=" * 70)
|
||||
print(f" 初始ETF池 ({init_ref_date}): {len(etf_codes)} 只")
|
||||
for code, name in etf_pool.items():
|
||||
print(f" {code} {name}")
|
||||
print(f" 回测区间: {config['start_date']} ~ {end_date}")
|
||||
print(f" 重建间隔: {rebuild_interval} 交易日")
|
||||
|
||||
# 从缓存加载数据
|
||||
print(f"\n{'='*70}")
|
||||
print("从本地缓存加载数据...")
|
||||
all_data = {}
|
||||
# 加载所有可能用到的 ETF 数据 (初始池 + 后续可能加入的)
|
||||
for code in etf_codes:
|
||||
ohlcv = data_cache.load_cached_ohlcv(code)
|
||||
if not ohlcv.empty:
|
||||
all_data[code] = ohlcv
|
||||
nav_data = {} # 动态模式下不使用净值数据
|
||||
print(f"价格数据: {len(all_data)} 只")
|
||||
|
||||
# 构建交易日历
|
||||
all_dates = set()
|
||||
for df in all_data.values():
|
||||
all_dates.update(df.index.tolist())
|
||||
trade_dates = sorted(d for d in all_dates if d >= pd.Timestamp(config['start_date']))
|
||||
print(f"交易日: {len(trade_dates)}")
|
||||
|
||||
# 6 组实验
|
||||
experiments = [
|
||||
{'target_num': 1, 'weight_mode': 'equal', 'label': 'A: 全仓1只'},
|
||||
{'target_num': 3, 'weight_mode': 'equal', 'label': 'B: 等权3只'},
|
||||
{'target_num': 3, 'weight_mode': 'inv_vol', 'label': 'C: 反波动率3只'},
|
||||
{'target_num': 5, 'weight_mode': 'equal', 'label': 'D: 等权5只'},
|
||||
{'target_num': 5, 'weight_mode': 'inv_vol', 'label': 'E: 反波动率5只'},
|
||||
{'target_num': 'all_positive', 'weight_mode': 'equal', 'label': 'F: 动量>0全选等权'},
|
||||
]
|
||||
|
||||
results = []
|
||||
for exp in experiments:
|
||||
print(f"\n{'─'*70}")
|
||||
print(f" 运行: {exp['label']}...")
|
||||
r = run_multi_backtest(
|
||||
config, all_data, nav_data, trade_dates, etf_codes,
|
||||
target_num=exp['target_num'],
|
||||
weight_mode=exp['weight_mode'],
|
||||
label=exp['label'],
|
||||
data_cache=data_cache,
|
||||
rebuild_interval=rebuild_interval,
|
||||
)
|
||||
results.append(r)
|
||||
print(f" 完成: CAGR={r['cagr']:.2%}, MaxDD={r['max_dd']:.2%}, Sharpe={r['sharpe']:.2f}")
|
||||
|
||||
# 输出对比表
|
||||
print(f"\n\n{'='*100}")
|
||||
print(f"{'':>20s} 动量策略多持仓对比实验结果")
|
||||
print(f"{'='*100}")
|
||||
print(f" {'实验':<18s} {'累计收益':>10s} {'CAGR':>8s} {'夏普':>6s} {'最大回撤':>8s} {'Calmar':>8s} {'日胜率':>7s} {'调仓次':>6s} {'年调仓':>6s} {'盈利年':>7s}")
|
||||
print(f"{'─'*100}")
|
||||
|
||||
for r in results:
|
||||
print(f" {r['label']:<16s} {r['total_return']:>9.2%} {r['cagr']:>7.2%} {r['sharpe']:>6.2f} "
|
||||
f"{r['max_dd']:>8.2%} {r['calmar']:>7.2f} {r['win_rate']:>6.2%} "
|
||||
f"{r['n_trades']:>5d} {r['trades_per_year']:>6.1f} {r['win_years']:>7s}")
|
||||
|
||||
print(f"{'='*100}")
|
||||
|
||||
# 找出最优
|
||||
best_sharpe = max(results, key=lambda x: x['sharpe'])
|
||||
best_calmar = max(results, key=lambda x: x['calmar'])
|
||||
best_cagr = max(results, key=lambda x: x['cagr'])
|
||||
|
||||
print(f"\n 最高夏普: {best_sharpe['label']} (Sharpe={best_sharpe['sharpe']:.2f})")
|
||||
print(f" 最高Calmar: {best_calmar['label']} (Calmar={best_calmar['calmar']:.2f})")
|
||||
print(f" 最高CAGR: {best_cagr['label']} (CAGR={best_cagr['cagr']:.2%})")
|
||||
|
||||
# 保存图表
|
||||
try:
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
import matplotlib.pyplot as plt
|
||||
matplotlib.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
|
||||
matplotlib.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 10), height_ratios=[3, 1],
|
||||
gridspec_kw={'hspace': 0.3})
|
||||
|
||||
colors = ['#e74c3c', '#3498db', '#2ecc71', '#f39c12', '#9b59b6']
|
||||
for r, color in zip(results, colors):
|
||||
nav = r['result_df']['nav']
|
||||
ax1.plot(nav.index, nav, label=r['label'], linewidth=1.2, color=color)
|
||||
|
||||
ax1.set_title('动量策略多持仓对比 - 净值曲线', fontsize=14, fontweight='bold')
|
||||
ax1.legend(loc='upper left', fontsize=10)
|
||||
ax1.grid(True, alpha=0.3)
|
||||
ax1.set_ylabel('净值')
|
||||
ax1.set_yscale('log')
|
||||
|
||||
# 回撤
|
||||
for r, color in zip(results, colors):
|
||||
nav = r['result_df']['nav']
|
||||
peak = nav.cummax()
|
||||
dd = (nav - peak) / peak
|
||||
ax2.plot(dd.index, dd, label=r['label'], linewidth=0.8, color=color, alpha=0.7)
|
||||
|
||||
ax2.set_title('回撤对比', fontsize=12)
|
||||
ax2.set_ylabel('回撤')
|
||||
ax2.grid(True, alpha=0.3)
|
||||
ax2.legend(loc='lower left', fontsize=8)
|
||||
|
||||
chart_path = Path(__file__).parent.parent / 'results' / 'momentum_multi_experiment.png'
|
||||
chart_path.parent.mkdir(exist_ok=True)
|
||||
fig.savefig(chart_path, dpi=150, bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
print(f"\n 对比图表已保存: {chart_path}")
|
||||
except Exception as e:
|
||||
print(f"\n 图表生成失败: {e}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
112
tests/experiments/test_select_num.py
Normal file
112
tests/experiments/test_select_num.py
Normal file
@@ -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()
|
||||
Reference in New Issue
Block a user