297 lines
13 KiB
Python
297 lines
13 KiB
Python
import asyncio
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import json
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import time
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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 xtquant import xtdata
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import matplotlib.pyplot as plt
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from src.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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# RSI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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overbought: int = 70, oversold: int = 30):
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# 生成RSI策略信号
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signal = await rsi_strategy(data, short_window, long_window, overbought, oversold)
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# 使用bt框架构建策略
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strategy = bt.Strategy(f'{stock_code} RSI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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async def rsi_strategy(df, short_window=14, long_window=28, overbought=70, oversold=30):
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"""
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基于RSI的策略生成买卖信号。
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参数:
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df: 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RSI的窗口期。
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long_window: int, 长期RSI的窗口期。
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overbought: int, 超买水平。
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oversold: int, 超卖水平。
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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，0 表示卖出。
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"""
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delta = df.diff().fillna(0)
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gain = (delta.where(delta > 0, 0).rolling(window=short_window).mean()).fillna(0)
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loss = (-delta.where(delta < 0, 0).rolling(window=short_window).mean()).fillna(0)
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short_rsi = (100 - (100 / (1 + (gain / loss)))).fillna(0)
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long_gain = (delta.where(delta > 0, 0).rolling(window=long_window).mean()).fillna(0)
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long_loss = (-delta.where(delta < 0, 0).rolling(window=long_window).mean()).fillna(0)
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long_rsi = (100 - (100 / (1 + (long_gain / long_loss)))).fillna(0)
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signal = pd.DataFrame(index=df.index, columns=df.columns)
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for column in df.columns:
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signal[column] = np.where((short_rsi[column] < 30) & (long_rsi[column] < 30) & (short_rsi[column] != 0) & (long_rsi[column] != 0), 1, 0)
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signal[column] = np.where((short_rsi[column] > 70) & (long_rsi[column] > 70) & (short_rsi[column] != 0) & (long_rsi[column] != 0), 0, signal[column])
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return signal.ffill().fillna(0)
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async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, short_window: int,
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long_window: int, overbought: int = 70,
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oversold: int = 30):
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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': "RSI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} RSI策略 RSI{short_window}-RSI{long_window}-overbought{overbought}-oversold{oversold}',
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'indicator_type': 'RSI',
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'indicator_information': json.dumps(
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{'short_window': short_window, 'long_window': long_window, 'overbought': overbought, 'oversold': oversold})
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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12个字段开始
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value = result.stats.loc[field].iloc[0]
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if isinstance(value, float):
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if np.isnan(value):
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data_to_store[field] = 0.0 # NaN 处理为 0
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elif np.isinf(value): # 判断是否为无穷大或无穷小
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if value > 0:
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data_to_store[field] = 99999.9999 # 正无穷处理
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else:
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data_to_store[field] = -99999.9999 # 负无穷处理
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else:
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data_to_store[field] = value # 正常的浮点值
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else:
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data_to_store[field] = value # 非浮点类型保持不变
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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_rsi_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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overbought: int = 70,
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oversold: int = 30
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):
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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} RSI策略'
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backtest_name = f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}'
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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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if 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RSI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, 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, short_window, long_window, overbought,
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oversold)
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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_rsi_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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overbought: int = 70,
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oversold: int = 30
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):
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for stock_code in stock_list:
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backtest_name = f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}'
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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_rsi_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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overbought=overbought,
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oversold=oversold
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)
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return result
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async def init_backtest_db():
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sma_list = [{"short_window": 3, "long_window": 6}, {"short_window": 6, "long_window": 12},
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{"short_window": 12, "long_window": 24}, {"short_window": 14, "long_window": 18},
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{"short_window": 15, "long_window": 10}]
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await init_tortoise()
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wance_db = await wance_data_stock.WanceDataStock.all()
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sma_list_lenght = len(sma_list)
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for stock_code in wance_db:
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for i in range(sma_list_lenght):
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short_window = sma_list[i]['short_window']
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long_window = sma_list[i]['long_window']
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source_column_name = f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}'
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result = await start_rsi_backtest_service(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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overbought=70,
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oversold=30)
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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_rsi_backtest(field_list=['close', 'time'],
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stock_list=['601222.SH', '601677.SH'],
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count=-1,
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short_window=10,
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long_window=30,
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overbought=70,
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oversold=30
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))
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# # 初始化数据库表
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# asyncio.run(init_backtest_db())
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