aszerW
2829f80427
消除回测前视偏差(Look-Ahead Bias)： - 新增 ETFDataCache 本地缓存系统，预下载全量ETF（含已退市）基础信息和日线数据 - 改造 ETFUniverseBuilder 支持纯历史模式，每个时间点只使用当时可获得的数据 - 动量.py 新增 dynamic 模式，回测中每60交易日动态重建ETF候选池 - momentum_experiment.py 同步支持动态重建 - 新增 ETF筛选引擎文档和动态池方案文档 无前视偏差实验结果（6组对比，2015-2026）： A: 全仓1只 CAGR=3.32%, MaxDD=-63.19%, Sharpe=0.26 B: 等权3只 CAGR=3.40%, MaxDD=-49.72%, Sharpe=0.30 ← 最优 C: 反波动率3只 CAGR=1.73%, MaxDD=-38.59%, Sharpe=0.21 D: 等权5只 CAGR=2.77%, MaxDD=-42.39%, Sharpe=0.29 E: 反波动率5只 CAGR=-0.37%, MaxDD=-19.56%, Sharpe=-0.03 F: 动量>0全选等权 CAGR=2.02%, MaxDD=-43.27%, Sharpe=0.24 最优方案: B（等权3只）夏普、Calmar、CAGR三项均最高
628 lines
22 KiB
Python
628 lines
22 KiB
Python
"""
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ETF动量轮动策略 - 本地回测版本
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原始策略来源：聚宽 https://www.joinquant.com/post/1399
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核心逻辑：
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1. 加权线性回归（权重1→2递增）计算趋势得分
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2. score = 年化收益率 × R²
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3. ATR动态调整回看窗口（20~60天）
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4. 崩盘过滤：连续3天任一天跌>5%则得分归零
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5. 溢价过滤：溢价率≥5%则降权
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6. 全仓单一品种轮动
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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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# 添加项目根目录
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sys.path.insert(0, str(Path(__file__).parent))
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from dotenv import load_dotenv
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load_dotenv()
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# ==================== 策略配置 ====================
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CONFIG = {
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# 候选ETF池:
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# - dict: 手动指定 {ts_code: name}
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# - 'auto': 使用动态筛选引擎自动构建
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# - 'latest': 加载最近一次构建结果
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# - 'dynamic': 回测中定期重建，无前视偏差
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'etf_pool': 'dynamic',
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'rebuild_interval': 60, # 动态池重建间隔(交易日)
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'target_num': 1, # 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': '2015-01-01',
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'benchmark': '000300.SH', # 基准：沪深300
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}
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# ==================== 数据获取 ====================
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def fetch_all_etf_data(etf_codes: list, start_date: str, end_date: str, etf_pool: dict = None) -> dict:
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"""使用Tushare获取所有ETF的OHLCV数据"""
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import os
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import tushare as ts
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token = os.getenv("TUSHARE_TOKEN")
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if not token:
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raise ValueError("请设置环境变量 TUSHARE_TOKEN")
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pro = ts.pro_api(token)
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# 需要额外前置数据用于ATR计算
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pre_start = (pd.Timestamp(start_date) - pd.Timedelta(days=120)).strftime('%Y%m%d')
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end_str = end_date.replace('-', '')
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pool_names = etf_pool or {}
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all_data = {}
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for code in etf_codes:
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print(f" 下载 {code} ({pool_names.get(code, '')})...", end=" ")
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try:
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df = pro.fund_daily(
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ts_code=code,
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start_date=pre_start,
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end_date=end_str,
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)
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if df is None or df.empty:
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print("✗ 无数据")
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continue
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df = df.rename(columns={'trade_date': 'date', 'vol': 'volume'})
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df['date'] = pd.to_datetime(df['date'])
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df = df.set_index('date').sort_index()
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df = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
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all_data[code] = df
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print(f"✓ {len(df)} 条")
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except Exception as e:
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print(f"✗ {e}")
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return all_data
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def fetch_etf_nav_data(etf_codes: list, start_date: str, end_date: str) -> dict:
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"""获取ETF净值数据（用于溢价率计算）"""
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import os
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import tushare as ts
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token = os.getenv("TUSHARE_TOKEN")
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pro = ts.pro_api(token)
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pre_start = (pd.Timestamp(start_date) - pd.Timedelta(days=120)).strftime('%Y%m%d')
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end_str = end_date.replace('-', '')
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nav_data = {}
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for code in etf_codes:
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try:
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df = pro.fund_nav(
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ts_code=code,
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start_date=pre_start,
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end_date=end_str,
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)
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if df is not None and not df.empty:
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df = df.rename(columns={'nav_date': 'date', 'unit_nav': 'nav'})
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df['date'] = pd.to_datetime(df['date'])
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df = df.set_index('date').sort_index()
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nav_data[code] = df[['nav']].astype(float)
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except Exception:
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pass
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return nav_data
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# ==================== ATR计算 ====================
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def calc_atr(high: pd.Series, low: pd.Series, close: pd.Series, period: int) -> pd.Series:
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"""计算ATR（不依赖talib）"""
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prev_close = close.shift(1)
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tr = pd.concat([
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high - low,
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(high - prev_close).abs(),
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(low - prev_close).abs(),
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], axis=1).max(axis=1)
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return tr.rolling(window=period, min_periods=period).mean()
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# ==================== 核心得分计算 ====================
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def calc_weighted_momentum_score(prices: np.ndarray) -> dict:
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"""
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加权线性回归动量得分
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Args:
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prices: 价格数组（含当日价格）
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Returns:
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{'annualized_returns': float, 'r2': float, 'score': float}
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"""
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if len(prices) < 5:
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return {'annualized_returns': 0, 'r2': 0, 'score': 0}
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y = np.log(prices)
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x = np.arange(len(y))
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weights = np.linspace(1, 2, len(y)) # 近期权重更高
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# 加权线性回归
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slope, intercept = np.polyfit(x, y, 1, w=weights)
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annualized_returns = math.exp(slope * 250) - 1
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# 加权R²
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y_pred = slope * x + intercept
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ss_res = np.sum(weights * (y - y_pred) ** 2)
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ss_tot = np.sum(weights * (y - np.average(y, weights=weights)) ** 2)
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r2 = 1 - ss_res / ss_tot if ss_tot > 0 else 0
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score = annualized_returns * r2
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return {'annualized_returns': annualized_returns, 'r2': r2, 'score': score}
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def apply_crash_filter(prices: np.ndarray, score: float) -> float:
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"""崩盘过滤：连续3天有任一天跌>5%"""
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if len(prices) < 4:
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return score
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r1 = prices[-1] / prices[-2]
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r2 = prices[-2] / prices[-3]
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r3 = prices[-3] / prices[-4]
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# 条件1：任一天跌>5%
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con1 = min(r1, r2, r3) < 0.95
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# 条件2：连续下跌且累计跌>5%
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con2 = (r1 < 1) and (r2 < 1) and (r3 < 1) and (prices[-1] / prices[-4] < 0.95)
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if con1 or con2:
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return 0.0
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return score
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def calc_premium_rate(etf_price: float, nav: float) -> float:
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"""计算溢价率(%)"""
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if nav is None or nav == 0 or np.isnan(nav):
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return 0.0
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return (etf_price - nav) / nav * 100
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# ==================== 回测引擎 ====================
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def resolve_etf_pool(config: dict, ref_date: str = None, data_cache=None) -> dict:
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"""
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解析ETF池配置:
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- dict: 直接返回
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- 'auto': 调用筛选引擎构建
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- 'latest': 加载最近一次构建结果
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- 'dynamic': 用缓存数据在指定日期重建（无前视偏差）
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"""
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pool = config['etf_pool']
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if isinstance(pool, dict):
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return pool
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from scripts.build_etf_universe import build_universe, load_latest_universe
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if pool == 'latest':
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print("加载最近一次构建的动态ETF池...")
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return load_latest_universe()
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elif pool == 'auto':
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print("使用筛选引擎构建动态ETF池...")
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return build_universe()
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elif pool == 'dynamic':
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if data_cache is None:
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from scripts.etf_data_cache import ETFDataCache
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data_cache = ETFDataCache()
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date_str = ref_date or datetime.now().strftime('%Y%m%d')
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return build_universe(ref_date=date_str, data_cache=data_cache)
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else:
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raise ValueError(f"不支持的 etf_pool 配置: {pool}")
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def run_backtest(config: dict):
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"""执行回测"""
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end_date = datetime.now().strftime('%Y-%m-%d')
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pool_mode = config['etf_pool'] if isinstance(config['etf_pool'], str) else '手动指定'
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is_dynamic = (pool_mode == 'dynamic')
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# 动态模式: 初始化缓存
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data_cache = None
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if is_dynamic:
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from scripts.etf_data_cache import ETFDataCache
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data_cache = ETFDataCache()
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print("动态重建模式: 使用本地缓存数据，无前视偏差")
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print(f" 重建间隔: {config['rebuild_interval']} 交易日")
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# 解析初始 ETF 池
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# 动态模式下用 start_date 作为初始重建日期
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init_ref_date = config['start_date'].replace('-', '') if is_dynamic else None
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etf_pool = resolve_etf_pool(config, ref_date=init_ref_date, data_cache=data_cache)
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etf_codes = list(etf_pool.keys())
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print("=" * 60)
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print(" ETF动量轮动策略 - 本地回测")
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print("=" * 60)
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print(f" ETF池模式: {pool_mode}")
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print(f" 候选ETF: {len(etf_codes)} 只")
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for code, name in etf_pool.items():
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print(f" {code} {name}")
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print(f" 持仓数量: {config['target_num']}")
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print(f" 动态周期: {'开启' if config['auto_day'] else '关闭'}")
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if config['auto_day']:
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print(f" 回看范围: {config['min_days']}~{config['max_days']} 天")
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else:
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print(f" 固定回看: {config['fixed_days']} 天")
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print(f" 回测区间: {config['start_date']} ~ {end_date}")
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# 1. 获取数据
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print(f"\n{'='*60}")
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if data_cache is not None:
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print("从本地缓存加载ETF价格数据...")
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all_data = {}
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for code in etf_codes:
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ohlcv = data_cache.load_cached_ohlcv(code)
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if not ohlcv.empty:
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all_data[code] = ohlcv
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print(f" 加载完成: {len(all_data)} 只")
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nav_data = {} # 动态模式下暂不用净值数据
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else:
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print("下载ETF价格数据...")
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all_data = fetch_all_etf_data(etf_codes, config['start_date'], end_date, etf_pool)
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print("\n下载ETF净值数据...")
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nav_data = fetch_etf_nav_data(etf_codes, config['start_date'], end_date)
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print(f" 净值数据: {len(nav_data)} 只")
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if not all_data:
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print("无数据，退出")
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return
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# 2. 构建交易日历（以A股交易日为准）
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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(all_dates)
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trade_dates = [d for d in trade_dates if d >= pd.Timestamp(config['start_date'])]
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print(f"\n交易日数: {len(trade_dates)}")
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print(f"区间: {trade_dates[0].strftime('%Y-%m-%d')} ~ {trade_dates[-1].strftime('%Y-%m-%d')}")
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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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print("=" * 60)
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max_lookback = config['max_days'] + 10
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holding = None # 当前持仓ETF代码
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daily_returns = [] # 每日收益率
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signals = [] # 信号记录
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last_rebuild_i = -config['rebuild_interval'] # 确保第一天就重建
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for i, today in enumerate(trade_dates):
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# 动态重建 ETF 池
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if is_dynamic and (i - last_rebuild_i >= config['rebuild_interval']):
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ref_str = today.strftime('%Y%m%d')
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print(f"\n [重建] {ref_str}: 重新构建ETF池...")
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try:
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new_pool = resolve_etf_pool(config, ref_date=ref_str, data_cache=data_cache)
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etf_codes = list(new_pool.keys())
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# 加载新增 ETF 的数据
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for code in etf_codes:
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if code not in all_data and data_cache is not None:
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ohlcv = data_cache.load_cached_ohlcv(code)
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if not ohlcv.empty:
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all_data[code] = ohlcv
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print(f" [重建] 新池子: {len(etf_codes)} 只")
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last_rebuild_i = i
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except Exception as e:
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print(f" [重建] 失败: {e}，继续使用旧池")
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# 计算每只ETF的得分
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scores = {}
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score_details = {}
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for code in etf_codes:
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if code not in all_data:
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continue
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df = all_data[code]
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# 获取截至今日的历史数据
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hist = df[df.index <= today].tail(max_lookback + 1)
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if len(hist) < config['min_days']:
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continue
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close_arr = hist['close'].values
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if config['auto_day']:
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# 动态周期：基于ATR波动率调整
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if len(hist) < max_lookback:
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lookback = config['fixed_days']
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else:
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long_atr = calc_atr(hist['high'], hist['low'], hist['close'],
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config['max_days'])
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short_atr = calc_atr(hist['high'], hist['low'], hist['close'],
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config['min_days'])
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la = long_atr.iloc[-1]
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sa = short_atr.iloc[-1]
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if la > 0 and not np.isnan(la) and not np.isnan(sa):
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ratio = min(0.9, sa / la)
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lookback = int(config['min_days'] +
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(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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prices = close_arr[-lookback:]
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else:
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prices = close_arr[-config['fixed_days']:]
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if len(prices) < 5:
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continue
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# 计算得分
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result = calc_weighted_momentum_score(prices)
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score = result['score']
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# 崩盘过滤
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score = apply_crash_filter(close_arr, score)
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# 溢价过滤
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if code in nav_data:
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nav_df = nav_data[code]
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nav_row = nav_df[nav_df.index <= today]
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if not nav_row.empty:
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nav_val = nav_row.iloc[-1]['nav']
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etf_price = close_arr[-1]
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premium = calc_premium_rate(etf_price, nav_val)
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if premium >= config['premium_threshold']:
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score -= 1
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# 只保留有效得分 (0 < score < 6)
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if 0 < score < 6:
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scores[code] = score
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score_details[code] = result
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# 选出排名最高的标的
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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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target = ranked[0][0] # target_num=1
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else:
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target = None
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# 计算当日收益
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if holding is not None and holding in all_data:
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df_h = all_data[holding]
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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_date = prev_dates[-1]
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prev_price = df_h.loc[prev_date, 'close']
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today_price = df_h.loc[today, 'close']
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daily_ret = today_price / prev_price - 1
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else:
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daily_ret = 0.0
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else:
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daily_ret = 0.0
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else:
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daily_ret = 0.0
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# 调仓成本
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trade_cost = 0.0
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if target != holding:
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trade_cost = config['trade_cost']
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if holding is not None:
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signals.append({
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'date': today, 'action': '调仓',
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'from': holding, 'to': target or '空仓',
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'score': scores.get(target, 0) if target else 0,
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})
|
||
holding = target
|
||
|
||
daily_returns.append({
|
||
'date': today,
|
||
'daily_return': daily_ret - trade_cost if trade_cost > 0 else daily_ret,
|
||
'holding': holding or '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()
|
||
|
||
# 基准数据
|
||
benchmark_code = config['benchmark']
|
||
print(f"\n获取基准数据 {benchmark_code}...")
|
||
import os, tushare as ts
|
||
pro = ts.pro_api(os.getenv("TUSHARE_TOKEN"))
|
||
bench_df = pro.index_daily(
|
||
ts_code=benchmark_code,
|
||
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()
|
||
bench_close = bench_df['close'].reindex(result_df.index, method='ffill')
|
||
result_df['bench_return'] = bench_close / bench_close.iloc[0]
|
||
else:
|
||
result_df['bench_return'] = 1.0
|
||
|
||
# 5. 输出绩效报告
|
||
print_performance(result_df, signals, config)
|
||
|
||
# 6. 年度收益统计
|
||
print_yearly_returns(result_df)
|
||
|
||
# 7. 生成图表
|
||
save_chart(result_df, config)
|
||
|
||
return result_df
|
||
|
||
|
||
# ==================== 绩效报告 ====================
|
||
def print_performance(result_df: pd.DataFrame, signals: list, config: dict):
|
||
"""打印绩效报告"""
|
||
nav = result_df['nav']
|
||
total_return = nav.iloc[-1] / nav.iloc[0] - 1
|
||
|
||
# 年化收益
|
||
days = (result_df.index[-1] - result_df.index[0]).days
|
||
cagr = (1 + total_return) ** (365 / days) - 1 if days > 0 else 0
|
||
|
||
# 夏普比率
|
||
daily_rets = result_df['daily_return']
|
||
sharpe = daily_rets.mean() / daily_rets.std() * np.sqrt(252) if daily_rets.std() > 0 else 0
|
||
|
||
# 最大回撤
|
||
peak = nav.cummax()
|
||
drawdown = (nav - peak) / peak
|
||
max_dd = drawdown.min()
|
||
dd_end = drawdown.idxmin()
|
||
dd_start = nav[:dd_end].idxmax()
|
||
|
||
# 日胜率
|
||
win_rate = (daily_rets > 0).sum() / (daily_rets != 0).sum() if (daily_rets != 0).sum() > 0 else 0
|
||
|
||
# 基准收益
|
||
bench_return = result_df['bench_return'].iloc[-1] - 1
|
||
bench_cagr = (1 + bench_return) ** (365 / days) - 1 if days > 0 else 0
|
||
|
||
# 调仓次数
|
||
n_trades = len(signals)
|
||
years = days / 365
|
||
|
||
# Calmar比率
|
||
calmar = cagr / abs(max_dd) if max_dd != 0 else 0
|
||
|
||
print(f"\n{'='*70}")
|
||
print(f" 绩效评估报告")
|
||
print(f"{'='*70}")
|
||
print(f" 回测区间: {result_df.index[0].strftime('%Y-%m-%d')} ~ {result_df.index[-1].strftime('%Y-%m-%d')}")
|
||
print(f" 交易天数: {len(result_df)}")
|
||
print(f"{'─'*70}")
|
||
print(f" {'指标':<30s} {'动量策略':>12s} {'基准(沪深300)':>14s}")
|
||
print(f"{'─'*70}")
|
||
print(f" {'累计收益':<28s} {total_return:>11.2%} {bench_return:>13.2%}")
|
||
print(f" {'CAGR(年化)':<27s} {cagr:>11.2%} {bench_cagr:>13.2%}")
|
||
print(f" {'年化夏普比率':<26s} {sharpe:>11.2f} {'--':>13s}")
|
||
print(f" {'最大回撤':<28s} {max_dd:>11.2%} {'--':>13s}")
|
||
print(f" {'Calmar比率':<27s} {calmar:>11.2f} {'--':>13s}")
|
||
print(f" {'日胜率':<28s} {win_rate:>11.2%} {'--':>13s}")
|
||
print(f" {'调仓次数':<28s} {n_trades:>9d}次 {'--':>13s}")
|
||
if years > 0:
|
||
print(f" {'年均调仓':<28s} {n_trades/years:>9.1f}次 {'--':>13s}")
|
||
print(f" {'最大回撤区间':<26s} {dd_start.strftime('%Y-%m-%d')} ~ {dd_end.strftime('%Y-%m-%d')}")
|
||
print(f"{'='*70}")
|
||
|
||
# 最新持仓信号
|
||
last_row = result_df.iloc[-1]
|
||
print(f"\n 最新持仓: {last_row['holding']}", end="")
|
||
if last_row['holding'] != '空仓':
|
||
pool = config['etf_pool'] if isinstance(config['etf_pool'], dict) else {}
|
||
name = pool.get(last_row['holding'], '')
|
||
print(f" ({name})", end="")
|
||
print(f"\n 最新净值: {last_row['nav']:.4f}")
|
||
|
||
|
||
# ==================== 年度收益统计 ====================
|
||
def print_yearly_returns(result_df: pd.DataFrame):
|
||
"""按年统计收益"""
|
||
nav = result_df['nav']
|
||
bench = result_df['bench_return']
|
||
|
||
# 按年分组
|
||
yearly_data = []
|
||
for year, group in result_df.groupby(result_df.index.year):
|
||
year_nav = group['nav']
|
||
year_ret = year_nav.iloc[-1] / year_nav.iloc[0] - 1
|
||
|
||
year_bench = group['bench_return']
|
||
bench_ret = year_bench.iloc[-1] / year_bench.iloc[0] - 1
|
||
|
||
# 年内最大回撤
|
||
peak = year_nav.cummax()
|
||
dd = (year_nav - peak) / peak
|
||
max_dd = dd.min()
|
||
|
||
# 年内夏普
|
||
daily_rets = group['daily_return']
|
||
sharpe = daily_rets.mean() / daily_rets.std() * np.sqrt(252) if daily_rets.std() > 0 else 0
|
||
|
||
# 超额收益
|
||
excess = year_ret - bench_ret
|
||
|
||
yearly_data.append({
|
||
'year': year,
|
||
'return': year_ret,
|
||
'bench_return': bench_ret,
|
||
'excess': excess,
|
||
'max_dd': max_dd,
|
||
'sharpe': sharpe,
|
||
'trade_days': len(group),
|
||
})
|
||
|
||
print(f"\n{'='*90}")
|
||
print(f" 年度收益统计")
|
||
print(f"{'='*90}")
|
||
print(f" {'年份':<6s} {'策略收益':>10s} {'基准收益':>10s} {'超额收益':>10s} {'最大回撤':>10s} {'夏普比率':>10s} {'交易天数':>10s}")
|
||
print(f"{'─'*90}")
|
||
|
||
for d in yearly_data:
|
||
print(f" {d['year']:<6d} {d['return']:>9.2%} {d['bench_return']:>9.2%} {d['excess']:>9.2%} {d['max_dd']:>9.2%} {d['sharpe']:>9.2f} {d['trade_days']:>8d}")
|
||
|
||
print(f"{'─'*90}")
|
||
|
||
# 汇总
|
||
total_ret = nav.iloc[-1] / nav.iloc[0] - 1
|
||
total_bench = bench.iloc[-1] / bench.iloc[0] - 1
|
||
win_years = sum(1 for d in yearly_data if d['return'] > 0)
|
||
beat_years = sum(1 for d in yearly_data if d['excess'] > 0)
|
||
total_years = len(yearly_data)
|
||
|
||
print(f" {'合计':<6s} {total_ret:>9.2%} {total_bench:>9.2%} {total_ret - total_bench:>9.2%}")
|
||
print(f" 盈利年份: {win_years}/{total_years} | 跑赢基准年份: {beat_years}/{total_years}")
|
||
print(f"{'='*90}")
|
||
|
||
|
||
# ==================== 图表生成 ====================
|
||
def save_chart(result_df: pd.DataFrame, config: dict):
|
||
"""生成净值曲线图"""
|
||
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})
|
||
|
||
# 净值曲线
|
||
ax1.plot(result_df.index, result_df['nav'], label='动量策略', linewidth=1.5, color='#e74c3c')
|
||
ax1.plot(result_df.index, result_df['bench_return'], label='沪深300', linewidth=1, color='#95a5a6')
|
||
ax1.set_title('ETF动量轮动策略 净值曲线', fontsize=14, fontweight='bold')
|
||
ax1.legend(loc='upper left')
|
||
ax1.grid(True, alpha=0.3)
|
||
ax1.set_ylabel('净值')
|
||
|
||
# 回撤曲线
|
||
peak = result_df['nav'].cummax()
|
||
drawdown = (result_df['nav'] - peak) / peak
|
||
ax2.fill_between(result_df.index, drawdown, 0, alpha=0.4, color='#e74c3c')
|
||
ax2.set_title('回撤', fontsize=12)
|
||
ax2.set_ylabel('回撤')
|
||
ax2.grid(True, alpha=0.3)
|
||
|
||
chart_path = Path(__file__).parent / 'results' / 'momentum_chart.png'
|
||
chart_path.parent.mkdir(exist_ok=True)
|
||
fig.savefig(chart_path, dpi=150, bbox_inches='tight')
|
||
plt.close(fig)
|
||
print(f"\n报告图表已保存: {chart_path}")
|
||
except Exception as e:
|
||
print(f"\n图表生成失败: {e}")
|
||
|
||
|
||
# ==================== 主入口 ====================
|
||
if __name__ == "__main__":
|
||
run_backtest(CONFIG)
|