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新增尾盘数据统计程序

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lintaogood
2025-02-13 00:01:12 +08:00
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import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed
import logging
from numba import jit
from datetime import datetime
# ========== 环境配置 ==========
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# ========== 策略参数配置 ==========
# ========== 全局参数 ==========
ATR_WINDOW = 5
VOLATILITY_WINDOW = 10
BULL_THRESHOLD = 0.83
BEAR_THRESHOLD = 0.77
NEUTRAL_THRESHOLD = 0.81
HOLDING_DAYS_MAP = {
'bull': 4,
'bear': 2,
'neutral': 3
}
# ========== 核心策略逻辑 ==========
@jit(nopython=True)
def calculate_technical_indicators(close, high, low, volume,
atr_window, volatility_window):
"""支持Numba的参数传递"""
n = len(close)
macd = np.zeros(n)
signal = np.zeros(n)
atr = np.zeros(n)
# MACD计算
ema12, ema26 = np.zeros(n), np.zeros(n)
for i in range(1, n):
ema12[i] = ema12[i - 1] * 11 / 13 + close[i] * 2 / 13
ema26[i] = ema26[i - 1] * 25 / 27 + close[i] * 2 / 27
macd[i] = ema12[i] - ema26[i]
signal[i] = signal[i - 1] * 0.8 + macd[i] * 0.2
# ATR计算
for i in range(1, n):
tr = max(high[i] - low[i],
abs(high[i] - close[i - 1]),
abs(low[i] - close[i - 1]))
atr[i] = atr[i-1] * (atr_window-1)/atr_window + tr/atr_window
return macd, signal, atr
@jit(nopython=True)
def generate_trading_signals(close, open_, high, low, volume, macd, signal, atr, threshold, volatility_window):
"""生成交易信号"""
n = len(close)
signals = np.zeros(n, dtype=np.bool_)
for i in range(3, n):
# 基础K线形态条件
is_red = close[i] > open_[i]
upper_shadow = high[i] - max(close[i], open_[i])
lower_shadow = min(close[i], open_[i]) - low[i]
body_size = abs(close[i] - open_[i])
cond1 = is_red and (high[i] / close[i - 1] > 1.005)
cond2 = (body_size > upper_shadow) and (body_size > lower_shadow)
cond3 = (high[i] / low[i] < 1.12) and (high[i] / open_[i] > 1.036)
cond4 = close[i] < close[i - 1] * 1.10 # 排除涨停
# 技术指标条件
cond5 = atr[i] > np.mean(atr[i - 4:i + 1]) * 0.8
cond6 = (macd[i] - signal[i]) > (macd[i - 1] - signal[i - 1]) * 1.2
# 波动率条件
llv = np.min(low[max(0, i - volatility_window + 1):i + 1])
hhv = np.max(high[max(0, i - volatility_window + 1):i + 1])
cond7 = (llv / hhv) < threshold
# 量能条件
vol_cond1 = volume[i] < np.mean(volume[max(0, i - 10):i])
vol_cond2 = volume[i] < np.min(volume[max(0, i - 20):i - 1]) * 3.5
signals[i] = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & vol_cond1 & vol_cond2
return signals
# ========== 市场状态判断 ==========
def get_market_condition(index_data):
"""动态判断市场状态"""
if len(index_data) < 60:
return 'neutral'
ma20 = index_data['close'].rolling(20).mean().iloc[-1]
ma60 = index_data['close'].rolling(60).mean().iloc[-1]
if pd.isna(ma20) or pd.isna(ma60):
return 'neutral'
if ma20 > ma60 * 1.05:
return 'bull'
elif ma20 < ma60 * 0.95:
return 'bear'
else:
return 'neutral'
# ========== 数据加载处理 ==========
def load_index_data(index_path):
"""加载并预处理指数数据"""
try:
# 自动检测日期列名
df = pd.read_csv(index_path, sep='\t', nrows=0)
date_col = 'date' if 'date' in df.columns else 'trade_date'
index_data = pd.read_csv(
index_path,
sep='\t',
usecols=[date_col, 'open', 'high', 'low', 'close', 'volume'],
parse_dates=[date_col],
date_parser=lambda x: pd.to_datetime(x, format='%Y%m%d')
)
# index_data.rename(columns={date_col: 'trade_date'}, inplace=True)
index_data.sort_values(date_col, inplace=True)
logging.info(
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()}")
return index_data
except Exception as e:
logging.error(f"指数数据加载失败: {str(e)}")
return None
def process_stock_file(file_path, index_data):
"""处理单个股票文件"""
try:
# 加载并预处理数据
df = pd.read_csv(file_path, sep='\t',
usecols=['trade_date', 'open', 'high', 'low', 'close', 'vol'])
df = df.rename(columns={'vol': 'volume'})
df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d', errors='coerce')
df = df.dropna(subset=['trade_date']).sort_values('trade_date')
# 对齐指数时间范围
start_date = index_data['trade_date'].min()
end_date = index_data['trade_date'].max()
df = df[(df['trade_date'] >= start_date) & (df['trade_date'] <= end_date)]
# if len(df) < StrategyConfig.MIN_TRADE_DAYS:
# return None
# 计算技术指标
close = df['close'].values.astype(np.float64)
high = df['high'].values.astype(np.float64)
low = df['low'].values.astype(np.float64)
volume = df['volume'].values.astype(np.float64)
macd, signal, atr = calculate_technical_indicators(
close, high, low, volume,
atr_window=ATR_WINDOW,
volatility_window=VOLATILITY_WINDOW
)
# # 获取市场状态
# market_condition = get_market_condition(index_data)
# threshold = StrategyConfig.THRESHOLDS[market_condition]
# 生成信号
signals = generate_trading_signals(
close, df['open'].values, high, low, volume,
macd, signal, atr, threshold=BULL_THRESHOLD,
volatility_window=VOLATILITY_WINDOW
)
df['signal'] = signals
return os.path.basename(file_path).split('_')[0], df
except Exception as e:
logging.error(f"处理文件 {os.path.basename(file_path)} 失败: {str(e)}")
return None
# ========== 回测分析模块 ==========
def backtest_strategy(all_data, index_data):
"""执行动态持仓周期回测"""
results = []
for stock_code, data in all_data.items():
if data is None or 'signal' not in data.columns:
continue
signals = data[data['signal']]
for idx in signals.index:
# 动态获取市场状态
current_date = data.iloc[idx]['trade_date']
market_condition = get_market_condition(index_data)
holding_days = HOLDING_DAYS_MAP.get(market_condition, 2)
# 计算退出时间
exit_idx = idx + holding_days + 1 # 包含买入当天
if exit_idx >= len(data):
continue
# 计算收益
entry_price = data.loc[idx, 'close']
exit_prices = data.iloc[idx + 1:exit_idx]['close']
max_profit = (exit_prices.max() - entry_price) / entry_price
max_loss = (exit_prices.min() - entry_price) / entry_price
final_return = (exit_prices.iloc[-1] - entry_price) / entry_price
results.append({
'code': stock_code,
'date': current_date.strftime('%Y-%m-%d'),
'market': market_condition,
'holding_days': holding_days,
'return': final_return,
'max_profit': max_profit,
'max_loss': max_loss
})
return pd.DataFrame(results) if results else pd.DataFrame()
def analyze_results(results_df):
"""分析回测结果"""
if results_df.empty:
logging.warning("无有效交易记录")
return
# 基础统计
total_trades = len(results_df)
annual_return = results_df['return'].mean() * 252
win_rate = len(results_df[results_df['return'] > 0]) / total_trades
profit_factor = results_df[results_df['return'] > 0]['return'].mean() / \
abs(results_df[results_df['return'] < 0]['return'].mean())
print(f"\n策略表现汇总:")
print(f"总交易次数: {total_trades}")
print(f"年化收益率: {annual_return:.2%}")
print(f"胜率: {win_rate:.2%}")
print(f"盈亏比: {profit_factor:.2f}")
# 分市场状态分析
if 'market' in results_df.columns:
market_stats = results_df.groupby('market').agg({
'return': ['mean', 'count'],
'holding_days': 'mean'
})
print("\n分市场状态表现:")
print(market_stats)
# 可视化
plt.figure(figsize=(12, 5))
plt.subplot(121)
results_df['return'].hist(bins=20, alpha=0.7)
plt.title('收益率分布')
plt.xlabel('收益率')
plt.ylabel('频次')
plt.subplot(122)
if 'market' in results_df.columns:
for condition, group in results_df.groupby('market'):
plt.scatter(group['holding_days'], group['return'], alpha=0.5, label=condition)
plt.legend()
plt.axhline(0, color='red', linestyle='--')
plt.title('持仓周期 vs 收益率')
plt.xlabel('持仓天数')
plt.ylabel('收益率')
plt.tight_layout()
plt.show()
# ========== 主程序 ==========
if __name__ == "__main__":
# 配置路径
STOCK_DIR = '/day/'
INDEX_PATH = '/index/000001.SH.txt'
# 加载指数数据
logging.info("正在加载指数数据...")
index_data = load_index_data(INDEX_PATH)
if index_data is None:
exit()
# 并行处理个股数据
logging.info("正在加载个股数据...")
stock_files = [os.path.join(STOCK_DIR, f) for f in os.listdir(STOCK_DIR)
if f.endswith('.txt') and not any(kw in f for kw in ['ST', '*ST', '688'])]
all_data = {}
with ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:
futures = {executor.submit(process_stock_file, f, index_data): f for f in stock_files}
for future in tqdm(as_completed(futures), total=len(futures)):
result = future.result()
if result:
code, data, _ = result
all_data[code] = data
if not all_data:
logging.error("没有加载到有效股票数据")
exit()
# 执行回测
logging.info("开始回测...")
results_df = backtest_strategy(all_data, index_data)
# 分析结果
analyze_results(results_df)
# 保存结果
if not results_df.empty:
results_df.to_csv('strategy_backtest_results.csv', index=False)
logging.info("回测结果已保存至 strategy_backtest_results.csv")