aszerW
a62cfb4cd5
问题根因： - pandas reindex(method='ffill') 只填充新增行的 NaN，不填充已存在的 NaN - 当 factor_df 中已有境外市场放假日期的 NaN 值时，reindex 无法填充 修复方案： - 改为两步操作：reindex() 然后 ffill() - ffill() 会填充所有 NaN，包括已存在的 验证结果： - 2026-04-30 HSI: None → 0.2388 ✅ - 2026-04-30 GDAXI: None → 0.5647 ✅ - 2026-05-08 HSI: None → 0.1144 ✅
401 lines
15 KiB
Python
401 lines
15 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
导出 V2 框架回测逐日明细到 JSON，供 HTML 回放器加载。
|
||
|
||
适用于 GlobalRotationStrategy（V2 正式版）
|
||
- 指数信号 + ETF 收益
|
||
- 动态短债阈值
|
||
- 强制分散化
|
||
- 交易成本
|
||
- CrossMarketAligner 数据对齐
|
||
|
||
用法:
|
||
python framework_v2/scripts/export_backtest_detail.py
|
||
"""
|
||
|
||
import sys
|
||
import json
|
||
import math
|
||
from pathlib import Path
|
||
from datetime import datetime
|
||
|
||
import numpy as np
|
||
import pandas as pd
|
||
|
||
project_root = Path(__file__).parent.parent.parent
|
||
sys.path.insert(0, str(project_root))
|
||
|
||
from dotenv import load_dotenv
|
||
load_dotenv()
|
||
|
||
from framework_v2.config import load_config
|
||
from framework_v2.strategies.rotation.rotation import GlobalRotationStrategy
|
||
from framework_v2.shared.data.alignment import CrossMarketAligner
|
||
|
||
|
||
# ==================== 辅助函数 ====================
|
||
|
||
def safe_val(v, decimals=4):
|
||
"""安全转换数值，处理 NaN/Inf"""
|
||
if v is None or (isinstance(v, float) and (math.isnan(v) or math.isinf(v))):
|
||
return None
|
||
if isinstance(v, (np.floating, float)):
|
||
return round(float(v), decimals)
|
||
if isinstance(v, (np.integer, int)):
|
||
return int(v)
|
||
return v
|
||
|
||
|
||
def main():
|
||
print("=" * 80)
|
||
print(" V2 回测逐日明细导出（GlobalRotationStrategy）")
|
||
print("=" * 80)
|
||
|
||
# 1. 加载配置
|
||
config_file = project_root / 'framework_v2' / 'strategies' / 'rotation' / 'config_simple.yaml'
|
||
print(f"\n[1] 加载配置: {config_file}")
|
||
config = load_config(str(config_file))
|
||
|
||
# 2. 初始化策略
|
||
print("[2] 初始化策略...")
|
||
strategy = GlobalRotationStrategy(config)
|
||
|
||
# 3. 获取数据
|
||
print("[3] 获取数据...")
|
||
data = strategy.get_data()
|
||
print(f" 获取 {len(data)} 个标的")
|
||
|
||
# 4. 计算因子
|
||
print("[4] 计算因子...")
|
||
factors = strategy.compute_factors(data)
|
||
print(f" 计算 {len(factors)} 个因子")
|
||
|
||
# 5. 生成信号
|
||
print("[5] 生成信号...")
|
||
signals = strategy.generate_signals(factors)
|
||
print(f" 生成 {signals.shape[0]} 个信号")
|
||
|
||
# 6. 仓位管理
|
||
print("[6] 仓位管理...")
|
||
positions = strategy.manage_positions(signals)
|
||
|
||
# 7. 准备收益率数据（使用 CrossMarketAligner）
|
||
print("[7] 准备收益率数据...")
|
||
signal_to_trade = config.asset_pools.get_signal_to_trade_mapping()
|
||
|
||
# 获取 A 股交易日历
|
||
trading_calendar = strategy._get_trading_calendar()
|
||
print(f" A 股交易日: {len(trading_calendar)} 天")
|
||
|
||
# 准备收盘价和溢价率数据
|
||
print("[7.5] 准备价格和溢价率数据...")
|
||
index_close_dict = {} # 指数收盘价
|
||
etf_close_dict = {} # ETF 收盘价
|
||
etf_premium_dict = {} # ETF 溢价率（需要从 API 获取）
|
||
|
||
for signal_code, trade_code in signal_to_trade.items():
|
||
# 指数收盘价
|
||
if signal_code in data:
|
||
index_close_dict[signal_code] = data[signal_code]['close']
|
||
|
||
# ETF 收盘价
|
||
if trade_code in data:
|
||
etf_close_dict[signal_code] = data[trade_code]['close'] # 注意：用 signal_code 作为键
|
||
|
||
# 溢价率暂时设为 None（需要额外 API 支持）
|
||
# TODO: 接入 ETF 净值数据计算溢价率
|
||
|
||
# 创建对齐器
|
||
aligner = CrossMarketAligner(target_calendar=trading_calendar)
|
||
|
||
# 提取收盘价
|
||
close_dict = {}
|
||
for signal_code, trade_code in signal_to_trade.items():
|
||
if trade_code in data:
|
||
close_dict[signal_code] = data[trade_code]['close']
|
||
|
||
# 对齐收益率
|
||
returns_df = aligner.align_multi_asset(close_dict)
|
||
print(f" 收益率数据: {len(returns_df)} 天, {len(returns_df.columns)} 个标的")
|
||
|
||
# 8. 计算策略收益和净值
|
||
print("[8] 计算策略收益...")
|
||
positions_aligned = positions.reindex(trading_calendar, method='ffill')
|
||
positions_delayed = positions_aligned.shift(1).fillna(0)
|
||
strategy_returns = (positions_delayed * returns_df).sum(axis=1)
|
||
|
||
# 扣除交易成本
|
||
strategy_returns_clean, rebalance_count = strategy._apply_trade_cost(
|
||
strategy_returns, positions_aligned
|
||
)
|
||
print(f" 调仓次数: {rebalance_count}")
|
||
|
||
# 计算净值
|
||
equity_curve = (1 + strategy_returns_clean).cumprod()
|
||
print(f" 最终净值: {equity_curve.iloc[-1]:.4f}")
|
||
|
||
# 9. 构建逐日明细
|
||
print("[9] 构建逐日明细...")
|
||
|
||
# 获取展示日历
|
||
common_dates = equity_curve.index
|
||
|
||
# 因子数据（DataFrame 格式）
|
||
factor_df = pd.DataFrame(factors)
|
||
|
||
# 确保索引是 DatetimeIndex
|
||
if not isinstance(factor_df.index, pd.DatetimeIndex):
|
||
factor_df.index = pd.to_datetime(factor_df.index)
|
||
|
||
# 将因子对齐到实际展示日历（前向填充）
|
||
# 因子已经在原始数据上计算完成，这里只是将结果对齐到展示日历
|
||
# 注意：必须先 reindex 再 ffill，因为 reindex(method='ffill') 不会填充已有的 NaN
|
||
factor_df_aligned = factor_df.reindex(common_dates)
|
||
factor_df_aligned = factor_df_aligned.ffill()
|
||
|
||
# 持仓状态跟踪
|
||
holdings_state = {} # {code: {'entry_date': str, 'entry_price': float}}
|
||
prev_holdings = set()
|
||
|
||
days_list = []
|
||
|
||
# 获取配置信息
|
||
bond_code = strategy.bond_code if strategy.use_dynamic_threshold else None
|
||
bond_ratio = strategy.bond_ratio
|
||
|
||
for i, date in enumerate(common_dates):
|
||
# 当前持仓
|
||
pos_row = positions_aligned.loc[date]
|
||
current_holdings = set(pos_row[pos_row > 0].index.tolist())
|
||
|
||
# 调仓检测
|
||
added = list(current_holdings - prev_holdings)
|
||
removed = list(prev_holdings - current_holdings)
|
||
is_rebalance = len(added) > 0 or len(removed) > 0
|
||
|
||
# 更新持仓状态
|
||
for code in removed:
|
||
holdings_state.pop(code, None)
|
||
for code in added:
|
||
# 获取入场价格
|
||
entry_price = None
|
||
if code in close_dict:
|
||
ep = close_dict[code].get(date)
|
||
if pd.notna(ep):
|
||
entry_price = float(ep)
|
||
|
||
holdings_state[code] = {
|
||
'entry_date': date.strftime('%Y-%m-%d'),
|
||
'entry_price': entry_price,
|
||
}
|
||
|
||
# 动态阈值（使用对齐后的因子）
|
||
factor_scores = {}
|
||
if date in factor_df_aligned.index:
|
||
for code in factor_df_aligned.columns:
|
||
v = factor_df_aligned.loc[date, code]
|
||
if pd.notna(v):
|
||
factor_scores[code] = float(v)
|
||
|
||
bond_score = factor_scores.get(bond_code) if bond_code else None
|
||
if bond_score is not None:
|
||
threshold = bond_score * bond_ratio
|
||
else:
|
||
threshold = 0.0
|
||
|
||
# 排名（按动量降序，排除 BOND）
|
||
groups = config.asset_pools.by_group
|
||
bond_assets = groups.get('BOND', {})
|
||
bond_codes = set(bond_assets.keys())
|
||
|
||
non_bond_scores = {k: v for k, v in factor_scores.items() if k not in bond_codes}
|
||
sorted_codes = sorted(non_bond_scores.keys(),
|
||
key=lambda c: non_bond_scores[c], reverse=True)
|
||
rank_map = {c: r + 1 for r, c in enumerate(sorted_codes)}
|
||
|
||
# BOND 不参与排名
|
||
for code in bond_codes:
|
||
if code in factor_scores:
|
||
rank_map[code] = None
|
||
|
||
# 每标的详情
|
||
assets = {}
|
||
all_codes = factor_df.columns.tolist()
|
||
|
||
# 对齐价格到 A 股日历
|
||
index_close_aligned = {}
|
||
etf_close_aligned = {}
|
||
|
||
for code in all_codes:
|
||
if code in index_close_dict:
|
||
index_close_aligned[code] = index_close_dict[code].reindex(common_dates, method='ffill')
|
||
if code in etf_close_dict:
|
||
etf_close_aligned[code] = etf_close_dict[code].reindex(common_dates, method='ffill')
|
||
|
||
# 计算指数和 ETF 收益率
|
||
index_returns = {}
|
||
etf_returns = {}
|
||
for code in all_codes:
|
||
if code in index_close_aligned:
|
||
index_returns[code] = index_close_aligned[code].pct_change(fill_method=None)
|
||
if code in etf_close_aligned:
|
||
etf_returns[code] = etf_close_aligned[code].pct_change(fill_method=None)
|
||
|
||
for code in all_codes:
|
||
asset = {}
|
||
|
||
# 动量得分
|
||
mom = factor_scores.get(code)
|
||
asset['momentum'] = safe_val(mom, 4)
|
||
|
||
# 排名
|
||
asset['rank'] = rank_map.get(code)
|
||
|
||
# 阈值
|
||
asset['threshold'] = safe_val(threshold, 4)
|
||
asset['above_threshold'] = mom >= threshold if mom is not None else False
|
||
|
||
# 指数价格
|
||
if code in index_close_aligned:
|
||
idx_close = index_close_aligned[code].get(date)
|
||
asset['index_close'] = safe_val(idx_close, 2) if pd.notna(idx_close) else None
|
||
else:
|
||
asset['index_close'] = None
|
||
|
||
# ETF 价格
|
||
if code in etf_close_aligned:
|
||
etf_close = etf_close_aligned[code].get(date)
|
||
asset['etf_close'] = safe_val(etf_close, 3) if pd.notna(etf_close) else None
|
||
else:
|
||
asset['etf_close'] = None
|
||
|
||
# 指数收益率
|
||
if code in index_returns:
|
||
idx_ret = index_returns[code].get(date)
|
||
asset['index_return'] = safe_val(idx_ret, 6) if pd.notna(idx_ret) else None
|
||
else:
|
||
asset['index_return'] = None
|
||
|
||
# ETF 收益率（兼容 V1 命名：etf_return_ctc）
|
||
if code in etf_returns:
|
||
etf_ret = etf_returns[code].get(date)
|
||
asset['etf_return_ctc'] = safe_val(etf_ret, 6) if pd.notna(etf_ret) else None
|
||
else:
|
||
asset['etf_return_ctc'] = None
|
||
|
||
# 溢价率（暂时为 None）
|
||
asset['premium'] = None
|
||
|
||
# 持仓状态
|
||
is_held = code in current_holdings
|
||
asset['is_held'] = is_held
|
||
|
||
if is_held and code in holdings_state:
|
||
hs = holdings_state[code]
|
||
asset['entry_date'] = hs['entry_date']
|
||
asset['entry_price_etf'] = safe_val(hs['entry_price'], 4)
|
||
asset['entry_price_idx'] = None # V2 暂不记录指数进场价
|
||
|
||
entry_dt = pd.Timestamp(hs['entry_date'])
|
||
trading_days_held = len(common_dates[(common_dates >= entry_dt) & (common_dates <= date)])
|
||
asset['holding_days'] = trading_days_held
|
||
|
||
# 累计收益（区分 ETF 和指数，兼容 V1）
|
||
if hs['entry_price'] and hs['entry_price'] > 0:
|
||
if code in close_dict:
|
||
cur = close_dict[code].get(date)
|
||
if cur and pd.notna(cur):
|
||
cum_ret = float(cur) / hs['entry_price'] - 1
|
||
asset['cum_return_etf'] = safe_val(cum_ret, 4)
|
||
asset['cum_return_idx'] = safe_val(cum_ret, 4) # V2 暂不区分
|
||
else:
|
||
asset['cum_return_etf'] = None
|
||
asset['cum_return_idx'] = None
|
||
else:
|
||
asset['cum_return_etf'] = None
|
||
asset['cum_return_idx'] = None
|
||
else:
|
||
asset['cum_return_etf'] = None
|
||
asset['cum_return_idx'] = None
|
||
else:
|
||
asset['entry_date'] = None
|
||
asset['entry_price_etf'] = None
|
||
asset['entry_price_idx'] = None
|
||
asset['holding_days'] = 0
|
||
asset['cum_return_etf'] = None
|
||
asset['cum_return_idx'] = None
|
||
|
||
assets[code] = asset
|
||
|
||
# 构建当天记录
|
||
nav_val = equity_curve.loc[date] if date in equity_curve.index else None
|
||
ret_val = strategy_returns_clean.loc[date] if date in strategy_returns_clean.index else None
|
||
|
||
day_record = {
|
||
'date': date.strftime('%Y-%m-%d'),
|
||
'nav': safe_val(nav_val, 4),
|
||
'daily_return': safe_val(ret_val, 6),
|
||
'is_rebalance': is_rebalance,
|
||
'holdings': sorted(list(current_holdings)),
|
||
'added': sorted(added),
|
||
'removed': sorted(removed),
|
||
'assets': assets
|
||
}
|
||
days_list.append(day_record)
|
||
prev_holdings = current_holdings
|
||
|
||
# 10. 构建元数据（兼容 V1 格式）
|
||
codes_meta = {}
|
||
for code in all_codes:
|
||
asset_config = config.asset_pools.assets.get(code)
|
||
codes_meta[code] = {
|
||
'name': asset_config.name if asset_config else code,
|
||
'etf': asset_config.trade_source if asset_config else None,
|
||
'market': asset_config.group if asset_config else None # V1 使用 market 字段
|
||
}
|
||
|
||
output = {
|
||
'meta': {
|
||
'mode': 'V2: 指数信号 + ETF收益',
|
||
'start_date': common_dates[0].strftime('%Y-%m-%d'),
|
||
'end_date': common_dates[-1].strftime('%Y-%m-%d'),
|
||
'total_days': len(common_dates),
|
||
'select_num': strategy.select_num,
|
||
'n_days': config.factor.n_days,
|
||
'trade_cost': strategy.trade_cost,
|
||
'bond_threshold': {
|
||
'enabled': strategy.use_dynamic_threshold,
|
||
'bond_code': bond_code,
|
||
'ratio': bond_ratio
|
||
},
|
||
'codes': codes_meta
|
||
},
|
||
'days': days_list
|
||
}
|
||
|
||
# 11. 输出
|
||
output_path = project_root / 'framework_v2' / 'results' / 'backtest_detail_v2.json'
|
||
print(f"\n[10] 写入 {output_path}...")
|
||
with open(output_path, 'w', encoding='utf-8') as f:
|
||
json.dump(output, f, ensure_ascii=False)
|
||
|
||
file_size_mb = output_path.stat().st_size / 1024 / 1024
|
||
print(f" 大小: {file_size_mb:.1f} MB")
|
||
print(f" 天数: {len(days_list)}")
|
||
print(f" 标的: {len(all_codes)}")
|
||
print(" 完成!")
|
||
|
||
# 打印汇总统计
|
||
print("\n" + "=" * 80)
|
||
print(" 回测汇总")
|
||
print("=" * 80)
|
||
print(f" 总收益: {(equity_curve.iloc[-1] - 1) * 100:.2f}%")
|
||
print(f" 年化收益: {((equity_curve.iloc[-1]) ** (252 / len(common_dates)) - 1) * 100:.2f}%")
|
||
print(f" 调仓次数: {rebalance_count}")
|
||
print(f" 交易天数: {len(common_dates)}")
|
||
print(f" 输出文件: {output_path}")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|