324 lines
11 KiB
Python
324 lines
11 KiB
Python
import os
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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from concurrent.futures import ProcessPoolExecutor, as_completed
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import logging
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from numba import jit
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from datetime import datetime
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# ========== 环境配置 ==========
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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# ========== 策略参数配置 ==========
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# ========== 全局参数 ==========
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ATR_WINDOW = 5
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VOLATILITY_WINDOW = 10
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BULL_THRESHOLD = 0.83
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BEAR_THRESHOLD = 0.77
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NEUTRAL_THRESHOLD = 0.81
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HOLDING_DAYS_MAP = {
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'bull': 4,
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'bear': 2,
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'neutral': 3
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}
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# ========== 核心策略逻辑 ==========
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@jit(nopython=True)
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def calculate_technical_indicators(close, high, low, volume,
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atr_window, volatility_window):
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"""支持Numba的参数传递"""
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n = len(close)
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macd = np.zeros(n)
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signal = np.zeros(n)
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atr = np.zeros(n)
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# MACD计算
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ema12, ema26 = np.zeros(n), np.zeros(n)
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for i in range(1, n):
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ema12[i] = ema12[i - 1] * 11 / 13 + close[i] * 2 / 13
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ema26[i] = ema26[i - 1] * 25 / 27 + close[i] * 2 / 27
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macd[i] = ema12[i] - ema26[i]
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signal[i] = signal[i - 1] * 0.8 + macd[i] * 0.2
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# ATR计算
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for i in range(1, n):
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tr = max(high[i] - low[i],
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abs(high[i] - close[i - 1]),
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abs(low[i] - close[i - 1]))
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atr[i] = atr[i-1] * (atr_window-1)/atr_window + tr/atr_window
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return macd, signal, atr
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@jit(nopython=True)
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def generate_trading_signals(close, open_, high, low, volume, macd, signal, atr, threshold, volatility_window):
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"""生成交易信号"""
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n = len(close)
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signals = np.zeros(n, dtype=np.bool_)
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for i in range(3, n):
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# 基础K线形态条件
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is_red = close[i] > open_[i]
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upper_shadow = high[i] - max(close[i], open_[i])
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lower_shadow = min(close[i], open_[i]) - low[i]
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body_size = abs(close[i] - open_[i])
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cond1 = is_red and (high[i] / close[i - 1] > 1.005)
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cond2 = (body_size > upper_shadow) and (body_size > lower_shadow)
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cond3 = (high[i] / low[i] < 1.12) and (high[i] / open_[i] > 1.036)
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cond4 = close[i] < close[i - 1] * 1.10 # 排除涨停
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# 技术指标条件
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cond5 = atr[i] > np.mean(atr[i - 4:i + 1]) * 0.8
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cond6 = (macd[i] - signal[i]) > (macd[i - 1] - signal[i - 1]) * 1.2
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# 波动率条件
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llv = np.min(low[max(0, i - volatility_window + 1):i + 1])
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hhv = np.max(high[max(0, i - volatility_window + 1):i + 1])
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cond7 = (llv / hhv) < threshold
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# 量能条件
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vol_cond1 = volume[i] < np.mean(volume[max(0, i - 10):i])
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vol_cond2 = volume[i] < np.min(volume[max(0, i - 20):i - 1]) * 3.5
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signals[i] = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & vol_cond1 & vol_cond2
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return signals
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# ========== 市场状态判断 ==========
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def get_market_condition(index_data):
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"""动态判断市场状态"""
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if len(index_data) < 60:
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return 'neutral'
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ma20 = index_data['close'].rolling(20).mean().iloc[-1]
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ma60 = index_data['close'].rolling(60).mean().iloc[-1]
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if pd.isna(ma20) or pd.isna(ma60):
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return 'neutral'
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if ma20 > ma60 * 1.05:
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return 'bull'
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elif ma20 < ma60 * 0.95:
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return 'bear'
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else:
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return 'neutral'
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# ========== 数据加载处理 ==========
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def load_index_data(index_path):
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"""加载并预处理指数数据"""
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try:
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# 自动检测日期列名
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df = pd.read_csv(index_path, sep='\t', nrows=0)
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date_col = 'date' if 'date' in df.columns else 'trade_date'
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index_data = pd.read_csv(
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index_path,
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sep='\t',
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usecols=[date_col, 'open', 'high', 'low', 'close', 'volume'],
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parse_dates=[date_col],
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date_parser=lambda x: pd.to_datetime(x, format='%Y%m%d')
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)
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# index_data.rename(columns={date_col: 'trade_date'}, inplace=True)
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index_data.sort_values(date_col, inplace=True)
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logging.info(
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f"指数数据加载成功，时间范围: {index_data['trade_date'].min().date()} 至 {index_data['trade_date'].max().date()}")
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return index_data
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except Exception as e:
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logging.error(f"指数数据加载失败: {str(e)}")
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return None
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def process_stock_file(file_path, index_data):
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"""处理单个股票文件"""
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try:
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# 加载并预处理数据
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df = pd.read_csv(file_path, sep='\t',
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usecols=['trade_date', 'open', 'high', 'low', 'close', 'vol'])
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df = df.rename(columns={'vol': 'volume'})
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df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d', errors='coerce')
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df = df.dropna(subset=['trade_date']).sort_values('trade_date')
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# 对齐指数时间范围
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start_date = index_data['trade_date'].min()
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end_date = index_data['trade_date'].max()
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df = df[(df['trade_date'] >= start_date) & (df['trade_date'] <= end_date)]
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# if len(df) < StrategyConfig.MIN_TRADE_DAYS:
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# return None
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# 计算技术指标
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close = df['close'].values.astype(np.float64)
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high = df['high'].values.astype(np.float64)
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low = df['low'].values.astype(np.float64)
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volume = df['volume'].values.astype(np.float64)
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macd, signal, atr = calculate_technical_indicators(
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close, high, low, volume,
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atr_window=ATR_WINDOW,
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volatility_window=VOLATILITY_WINDOW
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)
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# # 获取市场状态
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# market_condition = get_market_condition(index_data)
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# threshold = StrategyConfig.THRESHOLDS[market_condition]
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# 生成信号
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signals = generate_trading_signals(
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close, df['open'].values, high, low, volume,
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macd, signal, atr, threshold=BULL_THRESHOLD,
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volatility_window=VOLATILITY_WINDOW
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)
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df['signal'] = signals
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return os.path.basename(file_path).split('_')[0], df
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except Exception as e:
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logging.error(f"处理文件 {os.path.basename(file_path)} 失败: {str(e)}")
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return None
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# ========== 回测分析模块 ==========
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def backtest_strategy(all_data, index_data):
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"""执行动态持仓周期回测"""
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results = []
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for stock_code, data in all_data.items():
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if data is None or 'signal' not in data.columns:
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continue
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signals = data[data['signal']]
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for idx in signals.index:
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# 动态获取市场状态
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current_date = data.iloc[idx]['trade_date']
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market_condition = get_market_condition(index_data)
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holding_days = HOLDING_DAYS_MAP.get(market_condition, 2)
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# 计算退出时间
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exit_idx = idx + holding_days + 1 # 包含买入当天
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if exit_idx >= len(data):
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continue
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# 计算收益
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entry_price = data.loc[idx, 'close']
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exit_prices = data.iloc[idx + 1:exit_idx]['close']
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max_profit = (exit_prices.max() - entry_price) / entry_price
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max_loss = (exit_prices.min() - entry_price) / entry_price
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final_return = (exit_prices.iloc[-1] - entry_price) / entry_price
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results.append({
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'code': stock_code,
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'date': current_date.strftime('%Y-%m-%d'),
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'market': market_condition,
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'holding_days': holding_days,
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'return': final_return,
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'max_profit': max_profit,
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'max_loss': max_loss
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})
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return pd.DataFrame(results) if results else pd.DataFrame()
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def analyze_results(results_df):
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"""分析回测结果"""
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if results_df.empty:
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logging.warning("无有效交易记录")
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return
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# 基础统计
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total_trades = len(results_df)
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annual_return = results_df['return'].mean() * 252
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win_rate = len(results_df[results_df['return'] > 0]) / total_trades
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profit_factor = results_df[results_df['return'] > 0]['return'].mean() / \
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abs(results_df[results_df['return'] < 0]['return'].mean())
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print(f"\n策略表现汇总:")
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print(f"总交易次数: {total_trades}")
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print(f"年化收益率: {annual_return:.2%}")
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print(f"胜率: {win_rate:.2%}")
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print(f"盈亏比: {profit_factor:.2f}")
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# 分市场状态分析
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if 'market' in results_df.columns:
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market_stats = results_df.groupby('market').agg({
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'return': ['mean', 'count'],
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'holding_days': 'mean'
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})
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print("\n分市场状态表现:")
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print(market_stats)
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# 可视化
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plt.figure(figsize=(12, 5))
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plt.subplot(121)
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results_df['return'].hist(bins=20, alpha=0.7)
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plt.title('收益率分布')
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plt.xlabel('收益率')
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plt.ylabel('频次')
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plt.subplot(122)
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if 'market' in results_df.columns:
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for condition, group in results_df.groupby('market'):
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plt.scatter(group['holding_days'], group['return'], alpha=0.5, label=condition)
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plt.legend()
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plt.axhline(0, color='red', linestyle='--')
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plt.title('持仓周期 vs 收益率')
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plt.xlabel('持仓天数')
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plt.ylabel('收益率')
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plt.tight_layout()
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plt.show()
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# ========== 主程序 ==========
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if __name__ == "__main__":
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# 配置路径
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STOCK_DIR = 'day/'
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INDEX_PATH = 'index/000001.SH.txt'
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# 加载指数数据
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logging.info("正在加载指数数据...")
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index_data = load_index_data(INDEX_PATH)
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if index_data is None:
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exit()
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# 并行处理个股数据
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logging.info("正在加载个股数据...")
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stock_files = [os.path.join(STOCK_DIR, f) for f in os.listdir(STOCK_DIR)
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if f.endswith('.txt') and not any(kw in f for kw in ['ST', '*ST', '688'])]
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all_data = {}
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with ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:
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futures = {executor.submit(process_stock_file, f, index_data): f for f in stock_files}
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for future in tqdm(as_completed(futures), total=len(futures)):
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result = future.result()
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if result:
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code, data, _ = result
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all_data[code] = data
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if not all_data:
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logging.error("没有加载到有效股票数据")
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exit()
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# 执行回测
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logging.info("开始回测...")
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results_df = backtest_strategy(all_data, index_data)
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# 分析结果
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analyze_results(results_df)
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# 保存结果
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if not results_df.empty:
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results_df.to_csv('strategy_backtest_results.csv', index=False)
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logging.info("回测结果已保存至 strategy_backtest_results.csv") |