269 lines
12 KiB
Python
269 lines
12 KiB
Python
import asyncio
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import json
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from datetime import datetime
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import bt
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import numpy as np
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import pandas as pd
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from src.utils.backtest_until import get_local_data, convert_pandas_to_json_serializable
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from src.models import wance_data_storage_backtest, wance_data_stock
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from src.tortoises_orm_config import init_tortoise
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# MACD策略函数
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async def create_dual_ma_strategy(data, stock_code: str, short_window: int = 50, long_window: int = 200):
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# 生成MACD策略信号
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signal = await macd_strategy(data, short_window, long_window)
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# 使用bt框架构建策略
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strategy = bt.Strategy(f'{stock_code} MACD策略',
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[bt.algos.RunDaily(),
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bt.algos.SelectAll(), # 选择所有股票
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bt.algos.WeighTarget(signal), # 根据信号调整权重
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bt.algos.Rebalance()]) # 调仓
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return strategy, signal
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# 定义 MACD 策略的函数
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def macd_strategy(data, short_window=12, long_window=26, signal_window=9):
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"""
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MACD 策略，当 MACD 线穿过信号线时买入，反之卖出。
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参数:
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data: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。
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short_window: int, 短期 EMA 的窗口期。
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long_window: int, 长期 EMA 的窗口期。
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signal_window: int, 信号线 EMA 的窗口期。
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返回:
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signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，-1 表示卖出。
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"""
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# 计算短期和长期的 EMA
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short_ema = data.ewm(span=short_window, adjust=False).mean()
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long_ema = data.ewm(span=long_window, adjust=False).mean()
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# 计算 MACD 线
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macd_line = short_ema - long_ema
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# 计算信号线
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signal_line = macd_line.ewm(span=signal_window, adjust=False).mean()
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# 生成买入和卖出信号
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signal = pd.DataFrame(index=data.index, columns=data.columns)
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for column in data.columns:
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signal[column] = 0 # 初始化信号为 0
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# 买入信号：MACD 线从下方穿过信号线
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signal[column] = (macd_line[column] > signal_line[column]) & (macd_line[column].shift(1) <= signal_line[column].shift(1)).astype(int)
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# 卖出信号：MACD 线从上方穿过信号线
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signal[column] = (macd_line[column] < signal_line[column]) & (macd_line[column].shift(1) >= signal_line[column].shift(1)).astype(int) * -1 + signal[column]
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# 前向填充信号，保持持仓不变
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signal = signal.ffill().fillna(0)
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return signal
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async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, short_window,
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long_window):
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await init_tortoise()
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# 要存储的字段列表
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fields_to_store = [
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'stock_code', 'strategy_name', 'stock_close_price', 'daily_price',
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'price', 'returns', 'data_start_time', 'data_end_time',
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'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr',
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'max_drawdown', 'calmar', 'mtd', 'three_month',
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'six_month', 'ytd', 'one_year', 'three_year',
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'five_year', 'ten_year', 'incep', 'daily_sharpe',
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'daily_sortino', 'daily_mean', 'daily_vol',
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'daily_skew', 'daily_kurt', 'best_day', 'worst_day',
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'monthly_sharpe', 'monthly_sortino', 'monthly_mean',
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'monthly_vol', 'monthly_skew', 'monthly_kurt',
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'best_month', 'worst_month', 'yearly_sharpe',
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'yearly_sortino', 'yearly_mean', 'yearly_vol',
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'yearly_skew', 'yearly_kurt', 'best_year', 'worst_year',
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'avg_drawdown', 'avg_drawdown_days', 'avg_up_month',
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'avg_down_month', 'win_year_perc', 'twelve_month_win_perc'
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]
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# 准备要存储的数据
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data_to_store = {
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'stock_code': stock_code,
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'strategy_name': "MACD策略",
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'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign(
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time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')),
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'daily_price': convert_pandas_to_json_serializable(result[source_column_name].daily_prices),
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'price': convert_pandas_to_json_serializable(result[source_column_name].prices),
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'returns': convert_pandas_to_json_serializable(result[source_column_name].returns.fillna(0)),
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'data_start_time': pd.to_datetime(result.stats.loc["start"].iloc[0]).strftime('%Y%m%d'),
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'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'),
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'backtest_end_time': int(datetime.now().strftime('%Y%m%d')),
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'position': convert_pandas_to_json_serializable(signal),
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'backtest_name': f'{stock_code} MACD策略 MA{short_window}-{long_window}日',
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'indicator_type': 'MACD',
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'indicator_information': json.dumps({'short_window': short_window, 'long_window': long_window})
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}
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# 使用循环填充其他字段
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for field in fields_to_store[12:]: # 从第10个字段开始
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value = result.stats.loc[field].iloc[0]
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data_to_store[field] = 0.0 if (isinstance(value, float) and np.isnan(value)) else value
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# 检查是否存在该 backtest_name
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existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
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backtest_name=data_to_store['backtest_name']
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).first()
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if existing_record:
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# 如果存在，更新记录
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await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
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id=existing_record.id
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).update(**data_to_store)
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else:
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# 如果不存在，创建新的记录
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await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store)
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return data_to_store
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async def run_macd_backtest(field_list: list,
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stock_list: list,
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period: str = '1d',
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start_time: str = '',
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end_time: str = '',
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count: int = 100,
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dividend_type: str = 'none',
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fill_data: bool = True,
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data_dir: str = '',
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short_window: int = 50,
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long_window: int = 200):
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try:
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# 初始化一个列表用于存储每只股票的回测结果字典
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results_list = []
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# 遍历每只股票的数据（每列代表一个股票的收盘价）
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data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type,
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fill_data,
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data_dir)
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for stock_code in stock_list:
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data_column_name = f'close_{stock_code}'
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source_column_name = f'{stock_code} MACD策略'
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backtest_name = f'{stock_code} MACD策略 MA{short_window}-{long_window}日'
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now_data = int(datetime.now().strftime('%Y%m%d'))
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db_result_data = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
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backtest_name=backtest_name)
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if db_result_data:
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if db_result_data[0].backtest_end_time == now_data:
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results_list.append({source_column_name: db_result_data[0]})
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elif data_column_name in data.columns:
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stock_data_series = data[[data_column_name]] # 提取该股票的收盘价 DataFrame
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stock_data_series.columns = ['close'] # 重命名列为 'close'
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# 创建MACD策略
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strategy, signal = await create_dual_ma_strategy(stock_data_series, stock_code,
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short_window=short_window,
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long_window=long_window)
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# 创建回测
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backtest = bt.Backtest(strategy=strategy, data=stock_data_series, initial_capital=100000)
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# 运行回测
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result = bt.run(backtest)
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# 存储回测结果
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data_to_store = await storage_backtest_data(source_column_name, result, signal, stock_code,
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stock_data_series,
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short_window, long_window)
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# # 绘制回测结果图表
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# result.plot()
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# # 绘制个别股票数据图表
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# plt.figure(figsize=(12, 6))
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# plt.plot(stock_data_series.index, stock_data_series['close'], label='Stock Price')
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# plt.title(f'Stock Price for {stock_code}')
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# plt.xlabel('Date')
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# plt.ylabel('Price')
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# plt.legend()
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# plt.grid(True)
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# plt.show()
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# 将结果存储为字典并添加到列表中
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results_list.append({source_column_name: data_to_store})
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else:
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print(f"数据中缺少列: {data_column_name}")
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return results_list # 返回结果列表
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except Exception as e:
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print(f"Error occurred: {e}")
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async def start_macd_backtest_service(field_list: list,
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stock_list: list,
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period: str = '1d',
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start_time: str = '',
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end_time: str = '',
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count: int = -1,
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dividend_type: str = 'none',
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fill_data: bool = True,
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data_dir: str = '',
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short_window: int = 50,
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long_window: int = 200):
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for stock_code in stock_list:
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backtest_name = f'{stock_code} MACD策略 MA{short_window}-{long_window}日'
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db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
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backtest_name=backtest_name)
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now_time = int(datetime.now().strftime('%Y%m%d'))
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if db_result and db_result[0].backtest_end_time == now_time:
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return db_result
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else:
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# 执行回测
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result = await run_macd_backtest(
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field_list=field_list,
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stock_list=stock_list,
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period=period,
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start_time=start_time,
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end_time=end_time,
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count=count,
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dividend_type=dividend_type,
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fill_data=fill_data,
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data_dir=data_dir,
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short_window=short_window,
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long_window=long_window,
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)
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return result
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async def init_backtest_db():
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MACD_list = [{"short_window": 5, "long_window": 10}, {"short_window": 10, "long_window": 30},
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{"short_window": 30, "long_window": 60}, {"short_window": 30, "long_window": 90},
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{"short_window": 70, "long_window": 140}, {"short_window": 120, "long_window": 250}]
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await init_tortoise()
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wance_db = await wance_data_stock.WanceDataStock.all()
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MACD_list_lenght = len(MACD_list)
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for stock_code in wance_db:
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for i in range(MACD_list_lenght):
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short_window = MACD_list[i]['short_window']
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long_window = MACD_list[i]['long_window']
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source_column_name = f'{stock_code} MACD策略 MA{short_window}-{long_window}日'
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result = await run_macd_backtest(field_list=['close', 'time'],
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stock_list=[stock_code.stock_code],
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short_window=short_window,
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long_window=long_window)
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print(f"回测成功 {source_column_name}")
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if __name__ == '__main__':
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# 测试类的回测
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# asyncio.run(run_macd_backtest(field_list=['close', 'time'],
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# stock_list=['601222.SH', '601677.SH'],
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# short_window=10,
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# long_window=30))
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# 初始化数据库表
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asyncio.run(init_backtest_db())
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