1、新增组合代码
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models.py
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models.py
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@ -30,8 +30,3 @@ async def stock_chart(request: BackRequest):
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result = await stock_chart_service(stock_code=request.stock_code,
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benchmark_code=request.benchmark_code)
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return result
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@router.get('/combination')
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async def combination(request: BackRequest):
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await combination_service()
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@ -1,30 +1,58 @@
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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 bt # 引入bt框架用于回测
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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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from src.models import wance_data_storage_backtest, wance_data_stock, user_combination_history # 导入数据库模型
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from src.tortoises_orm_config import init_tortoise # 初始化数据库
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from src.utils.combination_until import get_local_data # 获取本地数据的工具函数
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# 布林带策略函数
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async def create_bollinger_bands_strategy(data, stock_code: str, bollingerMA: int = 50, std_dev: int = 200):
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# 生成布林带策略信号
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signal = await bollinger_bands_strategy(data, bollingerMA, std_dev)
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# 创建布林带策略函数
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async def create_bollinger_bands_strategy(data, stock_weights_dict, bollingerMA: int = 50, std_dev: int = 200):
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"""
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创建组合布林带策略，并根据股票权重进行回测。
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# 使用bt框架构建策略
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strategy = bt.Strategy(f'{stock_code} 布林带策略',
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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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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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stock_weights_dict: dict, 每只股票的权重字典。
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bollingerMA: int, 用于计算布林带的移动平均线周期。
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std_dev: int, 用于计算布林带上下轨的标准差倍数。
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返回:
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strategy: bt.Strategy, 构建的策略。
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combined_signal: pd.DataFrame, 股票的买卖信号。
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"""
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# 创建一个数据框用于存储所有股票的买卖信号
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combined_signal = pd.DataFrame(index=data.index)
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# 遍历每只股票，根据股票权重生成布林带信号
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for stock_code in stock_weights_dict.keys():
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if f'{stock_code}' in data.columns:
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# 提取每只股票的数据
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stock_data_series = data[[f'{stock_code}']]
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stock_data_series.columns = [f'{stock_code}']
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# 调用布林带策略计算买卖信号
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signal = await bollinger_bands_strategy(stock_data_series, bollingerMA, std_dev)
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combined_signal[stock_code] = signal
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else:
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print(f"Warning: Stock code {stock_code} not found in data columns.") # 如果股票不在数据中，发出警告
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# 创建权重数据框
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weights = pd.DataFrame(index=data.index, data=[stock_weights_dict] * len(data.index))
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# 使用bt框架构建布林带策略
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strategy = bt.Strategy('组合布林带策略',
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[bt.algos.RunDaily(), # 每天运行策略
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bt.algos.SelectAll(), # 选择所有可用的股票
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bt.algos.WeighSpecified(**stock_weights_dict), # 根据给定权重进行加权
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bt.algos.Rebalance()]) # 调整投资组合
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return strategy, combined_signal # 返回构建的策略和信号
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async def bollinger_bands_strategy(df, window=20, num_std_dev=2):
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@ -39,101 +67,67 @@ async def bollinger_bands_strategy(df, window=20, num_std_dev=2):
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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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# 计算中轨线（移动平均）
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# 计算布林带的中轨（移动平均）
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middle_band = df.rolling(window=window, min_periods=1).mean()
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# 计算滚动标准差
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# 计算移动标准差
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rolling_std = df.rolling(window=window, min_periods=1).std()
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# 计算上轨线和下轨线
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# 计算布林带的上下轨
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upper_band = middle_band + (rolling_std * num_std_dev)
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lower_band = middle_band - (rolling_std * num_std_dev)
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# 初始化信号 DataFrame
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# 创建一个数据框用于存储每只股票的买卖信号
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signal = pd.DataFrame(index=df.index, columns=df.columns)
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# 生成买入信号：当价格突破下轨时
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# 根据布林带计算买卖信号
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for column in df.columns:
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signal[column] = np.where(df[column] < lower_band[column], 1, np.nan) # 买入信号
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signal[column] = np.where(df[column] < lower_band[column], 1, np.nan) # 当收盘价低于下轨，生成买入信号
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signal[column] = np.where(df[column] > upper_band[column], 0, signal[column]) # 当收盘价高于上轨，生成卖出信号
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# 生成卖出信号：当价格突破上轨时
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for column in df.columns:
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signal[column] = np.where(df[column] > upper_band[column], 0, signal[column]) # 卖出信号
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# 前向填充信号，持仓不变
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signal = signal.ffill()
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# 将剩余的 NaN 替换为 0
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signal = signal.fillna(0)
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return signal
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# 前向填充信号，并用0填充NaN值
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signal = signal.ffill().fillna(0)
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return signal # 返回信号数据框
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async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, bollingerMA,
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std_dev):
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await init_tortoise()
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async def storage_backtest_data(user_id, stock_weights, strategy_parame, strategy_name):
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"""
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存储回测结果到数据库。
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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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参数:
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user_id: int, 用户的ID。
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stock_weights: dict, 股票权重字典。
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strategy_parame: dict, 策略参数字典。
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strategy_name: str, 策略名称。
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"""
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await init_tortoise() # 初始化数据库
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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': "布林带策略",
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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} 布林带策略 MA{bollingerMA}-{std_dev}倍标准差',
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'indicator_type': 'Bollinger',
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'indicator_information': json.dumps({'bollingerMA': bollingerMA, 'std_dev': std_dev})
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}
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# 查询用户的已有记录
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existing_record = await user_combination_history.UserCombinationHistory.filter(user_id=user_id).first()
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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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# 将股票权重和策略参数转换为JSON格式
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stock_weights_json = json.dumps(stock_weights)
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strategy_parame_json = json.dumps(strategy_parame)
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# 如果记录存在，更新记录；否则，创建新记录
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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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await user_combination_history.UserCombinationHistory.filter(
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id=existing_record.id
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).update(**data_to_store)
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).update(user_id=user_id,
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last_time=int(datetime.now().strftime('%Y%m%d')),
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stock_weights=stock_weights_json,
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strategy_name=strategy_name,
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strategy_parame=strategy_parame_json)
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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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await user_combination_history.UserCombinationHistory.create(user_id=user_id,
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last_time=int(datetime.now().strftime('%Y%m%d')),
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stock_weights=stock_weights_json,
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strategy_name=strategy_name,
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strategy_parame=strategy_parame_json)
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async def run_bollinger_backtest(field_list: list,
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async def run_combined_bollinger_backtest(field_list: list,
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stock_list: list,
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stock_weights: list,
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user_id: int = 1,
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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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@ -143,69 +137,61 @@ async def run_bollinger_backtest(field_list: list,
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data_dir: str = '',
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bollingerMA: int = 50,
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std_dev: int = 200):
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"""
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运行组合布林带策略的回测。
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参数:
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field_list: list, 需要获取的字段列表。
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stock_list: list, 需要回测的股票代码列表。
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stock_weights: list, 股票权重列表。
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user_id: int, 用户ID。
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period: str, 数据周期。
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start_time: str, 开始时间。
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end_time: str, 结束时间。
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count: int, 数据数量限制。
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dividend_type: str, 分红类型。
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fill_data: bool, 是否填充数据。
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data_dir: str, 数据目录。
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bollingerMA: int, 布林带移动平均线周期。
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std_dev: int, 标准差倍数。
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返回:
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result: 回测结果。
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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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# 获取本地数据
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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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fill_data, 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} 布林带策略'
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backtest_name = f'{stock_code} 布林带策略 MA{bollingerMA}-{std_dev}倍标准差'
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now_time = int(datetime.now().strftime('%Y%m%d'))
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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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if db_result:
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if db_result[0].backtest_end_time == now_time:
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results_list.append({source_column_name: db_result[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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# 获取股票权重字典
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stock_weights_dict = stock_weights[0] if stock_weights else {}
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# 创建布林带策略
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strategy, signal = await create_bollinger_bands_strategy(stock_data_series, stock_code,
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bollingerMA=bollingerMA,
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std_dev=std_dev)
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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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strategy, signal = await create_bollinger_bands_strategy(data, stock_weights_dict, bollingerMA, std_dev)
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# 创建回测对象
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backtest = bt.Backtest(strategy, data, initial_capital=100000)
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# 策略参数
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strategy_parame = {"bollingerMA": bollingerMA, "std_dev": std_dev}
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strategy_name = "布林带策略"
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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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bollingerMA, std_dev)
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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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await storage_backtest_data(user_id, stock_weights_dict, strategy_parame, strategy_name)
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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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return result # 返回回测结果
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except Exception as e:
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print(f"Error occurred: {e}")
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print(f"Error occurred: {e}") # 输出错误信息
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async def start_bollinger_combination_service(field_list: list,
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stock_list: list,
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stock_weights: list,
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user_id: int = 1,
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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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@ -215,19 +201,33 @@ async def start_bollinger_combination_service(field_list: list,
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data_dir: str = '',
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bollingerMA: int = 50,
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std_dev: int = 200):
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for stock_code in stock_list:
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backtest_name = f'{stock_code} 布林带策略 MA{bollingerMA}-{std_dev}倍标准差'
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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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"""
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启动布林带策略回测服务。
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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_bollinger_backtest(
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参数:
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field_list: list, 需要获取的字段列表。
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stock_list: list, 需要回测的股票代码列表。
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||||
stock_weights: list, 股票权重列表。
|
||||
user_id: int, 用户ID。
|
||||
period: str, 数据周期。
|
||||
start_time: str, 开始时间。
|
||||
end_time: str, 结束时间。
|
||||
count: int, 数据数量限制。
|
||||
dividend_type: str, 分红类型。
|
||||
fill_data: bool, 是否填充数据。
|
||||
data_dir: str, 数据目录。
|
||||
bollingerMA: int, 布林带移动平均线周期。
|
||||
std_dev: int, 标准差倍数。
|
||||
|
||||
返回:
|
||||
result: 回测结果。
|
||||
"""
|
||||
# 调用回测函数并返回结果
|
||||
result = await run_combined_bollinger_backtest(
|
||||
field_list=field_list,
|
||||
stock_list=stock_list,
|
||||
stock_weights=stock_weights,
|
||||
user_id=user_id,
|
||||
period=period,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
@ -238,36 +238,14 @@ async def start_bollinger_combination_service(field_list: list,
|
||||
bollingerMA=bollingerMA,
|
||||
std_dev=std_dev,
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
async def init_backtest_db():
|
||||
bollinger_list = [{"bollingerMA": 20, "std_dev": 2}, {"bollingerMA": 30, "std_dev": 2},
|
||||
{"bollingerMA": 70, "std_dev": 2}, {"bollingerMA": 5, "std_dev": 1},
|
||||
{"bollingerMA": 20, "std_dev": 3}, {"bollingerMA": 50, "std_dev": 2.5}]
|
||||
await init_tortoise()
|
||||
wance_db = await wance_data_stock.WanceDataStock.all()
|
||||
bollinger_list_lenght = len(bollinger_list)
|
||||
|
||||
for stock_code in wance_db:
|
||||
for i in range(bollinger_list_lenght):
|
||||
bollingerMA = bollinger_list[i]['bollingerMA']
|
||||
std_dev = bollinger_list[i]['std_dev']
|
||||
source_column_name = f'{stock_code} 布林带策略 MA{bollingerMA}-{std_dev}倍标准差'
|
||||
result = await run_bollinger_backtest(field_list=['close', 'time'],
|
||||
stock_list=[stock_code.stock_code],
|
||||
bollingerMA=bollingerMA,
|
||||
std_dev=std_dev)
|
||||
|
||||
print(f"回测成功 {source_column_name}")
|
||||
return result # 返回回测结果
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 测试类的回测
|
||||
asyncio.run(run_bollinger_backtest(field_list=['close', 'time'],
|
||||
stock_list=['601222.SH', '601677.SH'],
|
||||
bollingerMA=20,
|
||||
std_dev=2))
|
||||
|
||||
# # 初始化数据库表
|
||||
# asyncio.run(init_backtest_db())
|
||||
# 主函数，启动回测服务
|
||||
asyncio.run(run_combined_bollinger_backtest(field_list=['close', 'time'],
|
||||
stock_list=["688031.SH", "600025.SH", "601222.SH"],
|
||||
user_id=1,
|
||||
stock_weights=[{"688031.SH": 0.5, "600025.SH": 0.3, "601222.SH": 0.2}],
|
||||
bollingerMA=50,
|
||||
std_dev=200))
|
||||
|
||||
@ -1,110 +0,0 @@
|
||||
import pandas as pd
|
||||
import bt
|
||||
import yfinance as yf # 这里使用 yfinance 获取股票数据，实际应用可以替换成其他数据源
|
||||
|
||||
|
||||
def fetch_stock_data(stock_list, start, end):
|
||||
"""
|
||||
获取指定股票的历史数据。
|
||||
|
||||
参数:
|
||||
stock: str, 股票代码。
|
||||
start: str, 开始时间。
|
||||
end: str, 结束时间。
|
||||
|
||||
返回:
|
||||
pd.DataFrame, 包含股票的收盘价数据。
|
||||
"""
|
||||
data = yf.download(stock_list, start=start, end=end)['Adj Close']
|
||||
return data
|
||||
|
||||
|
||||
def combine_stock_data(stock_list, start, end):
|
||||
"""
|
||||
合并多只股票的数据。
|
||||
|
||||
参数:
|
||||
stocks: list, 股票代码列表。
|
||||
start: str, 开始时间。
|
||||
end: str, 结束时间。
|
||||
|
||||
返回:
|
||||
pd.DataFrame, 合并后的股票价格数据。
|
||||
"""
|
||||
combined_data = pd.DataFrame()
|
||||
|
||||
for stock in stock_list:
|
||||
data = fetch_stock_data(stock, start, end)
|
||||
combined_data[stock] = data
|
||||
|
||||
return combined_data
|
||||
|
||||
|
||||
def create_portfolio_strategy(stock_weights):
|
||||
"""
|
||||
创建基于股票权重的投资组合策略。
|
||||
|
||||
参数:
|
||||
stock_weights: list of dict, 股票与权重的字典列表。例如：
|
||||
[{'601222.SH': 0.5, '605090.SH': 0.3, '600025.SH': 0.2}]
|
||||
|
||||
返回:
|
||||
策略对象。
|
||||
"""
|
||||
algos = [
|
||||
bt.algos.RunDaily(), # 每日运行策略
|
||||
bt.algos.SelectAll(), # 选择所有股票
|
||||
bt.algos.WeighSpecified(**stock_weights[0]), # 分配指定的权重
|
||||
bt.algos.Rebalance() # 进行调仓
|
||||
]
|
||||
|
||||
strategy = bt.Strategy('Portfolio Strategy', algos)
|
||||
|
||||
return strategy
|
||||
|
||||
|
||||
def run_portfolio_backtest(stocks_list, start, end, stock_weights):
|
||||
"""
|
||||
运行组合策略的回测。
|
||||
|
||||
参数:
|
||||
stocks: list, 股票代码列表。
|
||||
start: str, 开始时间。
|
||||
end: str, 结束时间。
|
||||
stock_weights: list of dict, 股票与权重的字典列表。例如：
|
||||
[{'601222.SH': 0.5, '605090.SH': 0.3, '600025.SH': 0.2}]
|
||||
|
||||
返回:
|
||||
回测结果。
|
||||
"""
|
||||
# 获取股票的历史数据
|
||||
data = combine_stock_data(stocks_list, start, end)
|
||||
|
||||
# 创建策略
|
||||
strategy = create_portfolio_strategy(stock_weights)
|
||||
|
||||
# 创建回测
|
||||
backtest = bt.Backtest(strategy, data)
|
||||
|
||||
# 运行回测
|
||||
result = bt.run(backtest)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# 示例使用
|
||||
if __name__ == '__main__':
|
||||
# 示例股票名
|
||||
stocks = ['601222.SH', '605090.SH', '600025.SH']
|
||||
start_date = '2021-01-01'
|
||||
end_date = '2023-01-01'
|
||||
|
||||
# 示例权重分配
|
||||
stock_weights = [{'601222.SH': 0.5, '605090.SH': 0.3, '600025.SH': 0.2}]
|
||||
|
||||
# 运行组合策略回测
|
||||
result = run_portfolio_backtest(stocks, start_date, end_date, stock_weights)
|
||||
|
||||
# 打印回测结果
|
||||
result.plot()
|
||||
result.display()
|
||||
@ -1,215 +0,0 @@
|
||||
import asyncio
|
||||
import json
|
||||
from datetime import datetime
|
||||
|
||||
import bt
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from src.utils.backtest_until import get_local_data, convert_pandas_to_json_serializable
|
||||
from src.models import wance_data_storage_backtest, wance_data_stock
|
||||
from src.tortoises_orm_config import init_tortoise
|
||||
|
||||
|
||||
# 双均线策略函数
|
||||
async def create_dual_ma_strategy(data, short_window: int = 50, long_window: int = 200):
|
||||
# 生成权重
|
||||
weights = await dual_ma_strategy(data, short_window, long_window)
|
||||
|
||||
# 使用bt框架构建组合策略
|
||||
strategy = bt.Strategy('组合双均线策略',
|
||||
[bt.algos.RunDaily(),
|
||||
bt.algos.SelectAll(),
|
||||
bt.algos.WeighTarget(weights), # 根据信号调整权重
|
||||
bt.algos.Rebalance()])
|
||||
return strategy
|
||||
|
||||
|
||||
async def dual_ma_strategy(df, short_window=20, long_window=50):
|
||||
"""
|
||||
基于双均线策略生成买卖信号。
|
||||
|
||||
参数:
|
||||
df: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。
|
||||
short_window: int, 短期均线窗口期。
|
||||
long_window: int, 长期均线窗口期。
|
||||
|
||||
返回:
|
||||
signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，0 表示卖出。
|
||||
"""
|
||||
# 计算短期均线和长期均线
|
||||
short_ma = df.rolling(window=short_window, min_periods=1).mean()
|
||||
long_ma = df.rolling(window=long_window, min_periods=1).mean()
|
||||
|
||||
# 生成买入信号
|
||||
buy_signal = (short_ma > long_ma).astype(int)
|
||||
|
||||
# 计算权重（例如：均等权重）
|
||||
weights = buy_signal.div(buy_signal.sum(axis=1), axis=0).fillna(0)
|
||||
|
||||
return weights
|
||||
|
||||
|
||||
async def storage_backtest_data(source_column_name, result, signal, stock_data_series, short_window, long_window):
|
||||
await init_tortoise()
|
||||
|
||||
# 要存储的字段列表
|
||||
fields_to_store = [
|
||||
'stock_code', 'strategy_name', 'stock_close_price', 'daily_price',
|
||||
'price', 'returns', 'data_start_time', 'data_end_time',
|
||||
'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr',
|
||||
'max_drawdown', 'calmar', 'mtd', 'three_month',
|
||||
'six_month', 'ytd', 'one_year', 'three_year',
|
||||
'five_year', 'ten_year', 'incep', 'daily_sharpe',
|
||||
'daily_sortino', 'daily_mean', 'daily_vol',
|
||||
'daily_skew', 'daily_kurt', 'best_day', 'worst_day',
|
||||
'monthly_sharpe', 'monthly_sortino', 'monthly_mean',
|
||||
'monthly_vol', 'monthly_skew', 'monthly_kurt',
|
||||
'best_month', 'worst_month', 'yearly_sharpe',
|
||||
'yearly_sortino', 'yearly_mean', 'yearly_vol',
|
||||
'yearly_skew', 'yearly_kurt', 'best_year', 'worst_year',
|
||||
'avg_drawdown', 'avg_drawdown_days', 'avg_up_month',
|
||||
'avg_down_month', 'win_year_perc', 'twelve_month_win_perc'
|
||||
]
|
||||
|
||||
# 准备要存储的数据
|
||||
data_to_store = {
|
||||
'strategy_name': "组合双均线策略",
|
||||
'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign(
|
||||
time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')),
|
||||
'daily_price': convert_pandas_to_json_serializable(result['daily_prices']),
|
||||
'price': convert_pandas_to_json_serializable(result['prices']),
|
||||
'returns': convert_pandas_to_json_serializable(result['returns'].fillna(0)),
|
||||
'data_start_time': pd.to_datetime(result.stats.loc["start"].iloc[0]).strftime('%Y%m%d'),
|
||||
'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'),
|
||||
'backtest_end_time': int(datetime.now().strftime('%Y%m%d')),
|
||||
'position': convert_pandas_to_json_serializable(signal),
|
||||
'backtest_name': f'组合双均线策略 MA{short_window}-{long_window}日',
|
||||
'indicator_type': 'SMA',
|
||||
'indicator_information': json.dumps({'short_window': short_window, 'long_window': long_window})
|
||||
}
|
||||
|
||||
# 使用循环填充其他字段
|
||||
for field in fields_to_store[12:]: # 从第10个字段开始
|
||||
value = result.stats.loc[field].iloc[0]
|
||||
data_to_store[field] = 0.0 if (isinstance(value, float) and np.isnan(value)) else value
|
||||
|
||||
# 检查是否存在该 backtest_name
|
||||
existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=data_to_store['backtest_name']
|
||||
).first()
|
||||
|
||||
if existing_record:
|
||||
# 如果存在，更新记录
|
||||
await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
id=existing_record.id
|
||||
).update(**data_to_store)
|
||||
else:
|
||||
# 如果不存在，创建新的记录
|
||||
await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store)
|
||||
|
||||
return data_to_store
|
||||
|
||||
|
||||
async def run_sma_backtest(field_list: list,
|
||||
stock_list: list,
|
||||
period: str = '1d',
|
||||
start_time: str = '',
|
||||
end_time: str = '',
|
||||
count: int = 100,
|
||||
dividend_type: str = 'none',
|
||||
fill_data: bool = True,
|
||||
data_dir: str = '',
|
||||
short_window: int = 50,
|
||||
long_window: int = 200):
|
||||
try:
|
||||
# 初始化一个列表用于存储每只股票的回测结果字典
|
||||
results_list = []
|
||||
|
||||
# 获取股票数据
|
||||
data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type,
|
||||
fill_data, data_dir)
|
||||
|
||||
# 创建组合数据
|
||||
combined_data = data[stock_list].copy() # 只选择需要的股票列
|
||||
|
||||
# 创建双均线策略
|
||||
strategy, signal = await create_dual_ma_strategy(combined_data, short_window=short_window,
|
||||
long_window=long_window)
|
||||
# 创建回测
|
||||
backtest = bt.Backtest(strategy=strategy, data=combined_data, initial_capital=100000)
|
||||
# 运行回测
|
||||
result = bt.run(backtest)
|
||||
|
||||
# 存储回测结果
|
||||
data_to_store = await storage_backtest_data(result, signal, combined_data, short_window, long_window)
|
||||
results_list.append(data_to_store)
|
||||
|
||||
return results_list # 返回结果列表
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
|
||||
|
||||
async def start_sma_backtest_service(field_list: list,
|
||||
stock_list: list,
|
||||
period: str = '1d',
|
||||
start_time: str = '',
|
||||
end_time: str = '',
|
||||
count: int = -1,
|
||||
dividend_type: str = 'none',
|
||||
fill_data: bool = True,
|
||||
data_dir: str = '',
|
||||
short_window: int = 50,
|
||||
long_window: int = 200):
|
||||
# 执行回测
|
||||
result = await run_sma_backtest(
|
||||
field_list=field_list,
|
||||
stock_list=stock_list,
|
||||
period=period,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
count=count,
|
||||
dividend_type=dividend_type,
|
||||
fill_data=fill_data,
|
||||
data_dir=data_dir,
|
||||
short_window=short_window,
|
||||
long_window=long_window,
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
async def init_backtest_db():
|
||||
sma_list = [{"short_window": 5, "long_window": 10}, {"short_window": 10, "long_window": 30},
|
||||
{"short_window": 30, "long_window": 60}, {"short_window": 30, "long_window": 90},
|
||||
{"short_window": 70, "long_window": 140}, {"short_window": 120, "long_window": 250}]
|
||||
await init_tortoise()
|
||||
wance_db = await wance_data_stock.WanceDataStock.all()
|
||||
sma_list_length = len(sma_list)
|
||||
|
||||
for stock_code in wance_db:
|
||||
for i in range(sma_list_length):
|
||||
short_window = sma_list[i]['short_window']
|
||||
long_window = sma_list[i]['long_window']
|
||||
source_column_name = f'{stock_code.stock_code}'
|
||||
|
||||
result = await start_sma_backtest_service(
|
||||
field_list=[source_column_name],
|
||||
stock_list=[stock_code.stock_code],
|
||||
period='1d',
|
||||
start_time='2022-01-01',
|
||||
end_time='2022-09-01',
|
||||
count=-1,
|
||||
dividend_type='none',
|
||||
fill_data=True,
|
||||
data_dir='',
|
||||
short_window=short_window,
|
||||
long_window=long_window
|
||||
)
|
||||
print(
|
||||
f"Finished backtesting for {stock_code.stock_code} with short_window: {short_window}, long_window: {long_window}")
|
||||
|
||||
|
||||
# 启动回测
|
||||
if __name__ == '__main__':
|
||||
asyncio.run(init_backtest_db())
|
||||
@ -6,23 +6,52 @@ import bt
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from src.utils.backtest_until import get_local_data, convert_pandas_to_json_serializable
|
||||
from src.models import wance_data_storage_backtest, wance_data_stock
|
||||
from src.models import wance_data_storage_backtest, wance_data_stock, user_combination_history
|
||||
from src.tortoises_orm_config import init_tortoise
|
||||
from src.utils.backtest_until import convert_pandas_to_json_serializable
|
||||
from src.utils.combination_until import get_local_data
|
||||
|
||||
|
||||
# 双均线策略函数
|
||||
async def create_dual_ma_strategy(data, stock_code: str, short_window: int = 50, long_window: int = 200):
|
||||
# 生成双均线策略信号
|
||||
signal = await dual_ma_strategy(data, short_window, long_window)
|
||||
# 组合双均线策略函数
|
||||
async def create_combined_sma_strategy(data, stock_weights_dict, short_window: int = 50, long_window: int = 200):
|
||||
"""
|
||||
创建组合双均线策略，并根据股票权重进行回测。
|
||||
|
||||
# 使用bt框架构建策略
|
||||
strategy = bt.Strategy(f'{stock_code} 双均线策略',
|
||||
[bt.algos.RunDaily(),
|
||||
参数:
|
||||
data: pd.DataFrame, 股票价格数据，行索引为日期，列为股票代码。
|
||||
stock_weights_dict: dict, 股票权重字典，键为股票代码，值为权重。
|
||||
short_window: int, 短期均线窗口期，默认为50。
|
||||
long_window: int, 长期均线窗口期，默认为200。
|
||||
|
||||
返回:
|
||||
strategy: bt.Strategy, 创建的双均线策略对象。
|
||||
combined_signal: pd.DataFrame, 每只股票的买卖信号数据框。
|
||||
"""
|
||||
combined_signal = pd.DataFrame(index=data.index) # 初始化信号数据框
|
||||
|
||||
# 遍历每只股票代码，根据股票权重生成信号
|
||||
for stock_code in stock_weights_dict.keys():
|
||||
# 确保每个股票代码唯一，不要使用重复代码
|
||||
if f'{stock_code}' in data.columns:
|
||||
stock_data_series = data[[f'{stock_code}']] # 提取对应股票的数据
|
||||
stock_data_series.columns = [f'{stock_code}'] # 重命名列
|
||||
|
||||
signal = await dual_ma_strategy(stock_data_series, short_window, long_window) # 生成信号
|
||||
combined_signal[stock_code] = signal # 存储信号
|
||||
else:
|
||||
print(f"Warning: Stock code {stock_code} not found in data columns.") # 提示未找到的股票代码
|
||||
|
||||
# 将权重转换为 DataFrame，行索引为数据的时间，列为股票代码
|
||||
weights = pd.DataFrame(index=data.index, data=[stock_weights_dict] * len(data.index))
|
||||
|
||||
# 使用bt框架构建组合策略
|
||||
strategy = bt.Strategy('组合双均线策略',
|
||||
[bt.algos.RunDaily(), # 每天运行
|
||||
bt.algos.SelectAll(), # 选择所有股票
|
||||
bt.algos.WeighTarget(signal), # 根据信号调整权重
|
||||
bt.algos.WeighSpecified(**stock_weights_dict), # 根据权重调整股票权重
|
||||
bt.algos.Rebalance()]) # 调仓
|
||||
return strategy, signal
|
||||
|
||||
return strategy, combined_signal # 返回策略和信号
|
||||
|
||||
|
||||
async def dual_ma_strategy(df, short_window=20, long_window=50):
|
||||
@ -37,36 +66,39 @@ async def dual_ma_strategy(df, short_window=20, long_window=50):
|
||||
返回:
|
||||
signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，0 表示卖出。
|
||||
"""
|
||||
# 计算短期均线和长期均线
|
||||
short_ma = df.rolling(window=short_window, min_periods=1).mean()
|
||||
long_ma = df.rolling(window=long_window, min_periods=1).mean()
|
||||
# 计算短期和长期均线
|
||||
short_ma = df.rolling(window=short_window, min_periods=1).mean() # 短期均线
|
||||
long_ma = df.rolling(window=long_window, min_periods=1).mean() # 长期均线
|
||||
|
||||
# 生成买入信号: 当短期均线从下方穿过长期均线
|
||||
buy_signal = np.where(short_ma > long_ma, 1, np.nan)
|
||||
# 生成买入和卖出信号
|
||||
buy_signal = np.where(short_ma > long_ma, 1, np.nan) # 买入信号
|
||||
sell_signal = np.where(short_ma < long_ma, 0, np.nan) # 卖出信号
|
||||
|
||||
# 生成卖出信号: 当短期均线从上方穿过长期均线
|
||||
sell_signal = np.where(short_ma < long_ma, 0, np.nan)
|
||||
|
||||
# 合并买卖信号
|
||||
# 将信号转换为DataFrame，并向前填充
|
||||
signal = pd.DataFrame(buy_signal, index=df.index, columns=df.columns)
|
||||
signal = np.where(short_ma < long_ma, 0, signal)
|
||||
signal = np.where(short_ma < long_ma, 0, signal) # 卖出信号更新
|
||||
signal = pd.DataFrame(signal, index=df.index, columns=df.columns).ffill() # 前向填充信号
|
||||
|
||||
# 前向填充信号，持仓不变
|
||||
signal = pd.DataFrame(signal, index=df.index, columns=df.columns).ffill()
|
||||
|
||||
# 将剩余的 NaN 替换为 0
|
||||
signal = signal.fillna(0)
|
||||
|
||||
return signal
|
||||
signal = signal.fillna(0) # 将 NaN 替换为 0
|
||||
return signal # 返回信号
|
||||
|
||||
|
||||
async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, short_window,
|
||||
long_window):
|
||||
await init_tortoise()
|
||||
async def storage_backtest_data(user_id, result, stock_data_series, source_column_name, signal, stock_weights,
|
||||
strategy_parame, strategy_name,short_window,long_window):
|
||||
"""
|
||||
将回测数据存储到数据库。
|
||||
|
||||
参数:
|
||||
user_id: int, 用户ID。
|
||||
stock_weights: dict, 股票权重字典。
|
||||
strategy_parame: dict, 策略参数字典。
|
||||
strategy_name: str, 策略名称。
|
||||
"""
|
||||
await init_tortoise() # 初始化数据库连接
|
||||
|
||||
# 要存储的字段列表
|
||||
fields_to_store = [
|
||||
'stock_code', 'strategy_name', 'stock_close_price', 'daily_price',
|
||||
'stock_weights', 'strategy_name', 'stock_close_price', 'daily_price',
|
||||
'price', 'returns', 'data_start_time', 'data_end_time',
|
||||
'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr',
|
||||
'max_drawdown', 'calmar', 'mtd', 'three_month',
|
||||
@ -85,7 +117,7 @@ async def storage_backtest_data(source_column_name, result, signal, stock_code,
|
||||
|
||||
# 准备要存储的数据
|
||||
data_to_store = {
|
||||
'stock_code': stock_code,
|
||||
'stock_weights': stock_weights,
|
||||
'strategy_name': "双均线策略",
|
||||
'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign(
|
||||
time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')),
|
||||
@ -96,7 +128,7 @@ async def storage_backtest_data(source_column_name, result, signal, stock_code,
|
||||
'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'),
|
||||
'backtest_end_time': int(datetime.now().strftime('%Y%m%d')),
|
||||
'position': convert_pandas_to_json_serializable(signal),
|
||||
'backtest_name': f'{stock_code} 双均线策略 MA{short_window}-{long_window}日',
|
||||
'backtest_name': f'{stock_weights} 双均线策略 MA{short_window}-{long_window}日',
|
||||
'indicator_type': 'SMA',
|
||||
'indicator_information': json.dumps({'short_window': short_window, 'long_window': long_window})
|
||||
}
|
||||
@ -107,23 +139,31 @@ async def storage_backtest_data(source_column_name, result, signal, stock_code,
|
||||
data_to_store[field] = 0.0 if (isinstance(value, float) and np.isnan(value)) else value
|
||||
|
||||
# 检查是否存在该 backtest_name
|
||||
existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=data_to_store['backtest_name']
|
||||
).first()
|
||||
existing_record = await user_combination_history.UserCombinationHistory.filter(user_id=user_id).first()
|
||||
|
||||
stock_weights_json = json.dumps(stock_weights) # 将权重字典转换为JSON格式
|
||||
strategy_parame_json = json.dumps(strategy_parame) # 将策略参数字典转换为JSON格式
|
||||
|
||||
if existing_record:
|
||||
# 如果存在，更新记录
|
||||
await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
await user_combination_history.UserCombinationHistory.filter(
|
||||
id=existing_record.id
|
||||
).update(**data_to_store)
|
||||
).update(user_id=user_id,
|
||||
last_time=int(datetime.now().strftime('%Y%m%d')), # 更新时间
|
||||
stock_weights=stock_weights_json,
|
||||
strategy_name=strategy_name,
|
||||
strategy_parame=strategy_parame_json)
|
||||
else:
|
||||
# 如果不存在，创建新的记录
|
||||
await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store)
|
||||
|
||||
await user_combination_history.UserCombinationHistory.create(user_id=user_id,
|
||||
last_time=int(datetime.now().strftime('%Y%m%d')),
|
||||
stock_weights=stock_weights_json,
|
||||
strategy_name=strategy_name,
|
||||
strategy_parame=strategy_parame_json)
|
||||
return data_to_store
|
||||
|
||||
|
||||
async def run_sma_backtest(field_list: list,
|
||||
async def run_combined_sma_backtest(field_list: list,
|
||||
stock_list: list,
|
||||
stock_weights: list,
|
||||
user_id: int = 1,
|
||||
@ -136,65 +176,59 @@ async def run_sma_backtest(field_list: list,
|
||||
data_dir: str = '',
|
||||
short_window: int = 50,
|
||||
long_window: int = 200):
|
||||
"""
|
||||
运行组合双均线策略的回测。
|
||||
|
||||
参数:
|
||||
field_list: list, 需要获取的字段列表。
|
||||
stock_list: list, 股票代码列表。
|
||||
stock_weights: list, 股票权重列表。
|
||||
user_id: int, 用户ID，默认为1。
|
||||
period: str, 数据时间周期，默认为'1d'。
|
||||
start_time: str, 开始时间，默认为''（空字符串表示不限制）。
|
||||
end_time: str, 结束时间，默认为''（空字符串表示不限制）。
|
||||
count: int, 获取的数据数量，默认为100。
|
||||
dividend_type: str, 分红类型，默认为'none'。
|
||||
fill_data: bool, 是否填充缺失数据，默认为True。
|
||||
data_dir: str, 数据目录，默认为''（空字符串表示当前目录）。
|
||||
short_window: int, 短期均线窗口期，默认为50。
|
||||
long_window: int, 长期均线窗口期，默认为200。
|
||||
|
||||
返回:
|
||||
result: bt.BacktestResult, 回测结果。
|
||||
"""
|
||||
try:
|
||||
# 初始化一个列表用于存储每只股票的回测结果字典
|
||||
results_list = []
|
||||
|
||||
# 遍历每只股票的数据（每列代表一个股票的收盘价）
|
||||
# 获取本地数据
|
||||
data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type,
|
||||
fill_data,
|
||||
data_dir)
|
||||
fill_data, data_dir)
|
||||
|
||||
for stock_code in stock_list:
|
||||
# 将 stock_weights 转换为字典形式，提取第一个字典作为权重
|
||||
stock_weights_dict = stock_weights[0] if stock_weights else {}
|
||||
|
||||
data_column_name = f'close_{stock_code}'
|
||||
source_column_name = f'{stock_code} 双均线策略'
|
||||
backtest_name = f'{stock_code} 双均线策略 MA{short_window}-{long_window}日'
|
||||
now_data = int(datetime.now().strftime('%Y%m%d'))
|
||||
db_result_data = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=backtest_name)
|
||||
# 创建组合双均线策略
|
||||
strategy, signal = await create_combined_sma_strategy(data, stock_weights_dict, short_window, long_window)
|
||||
|
||||
if db_result_data:
|
||||
if db_result_data[0].backtest_end_time == now_data:
|
||||
results_list.append({source_column_name: db_result_data[0]})
|
||||
|
||||
if data_column_name in data.columns:
|
||||
stock_data_series = data[[data_column_name]] # 提取该股票的收盘价 DataFrame
|
||||
stock_data_series.columns = ['close'] # 重命名列为 'close'
|
||||
|
||||
# 创建双均线策略
|
||||
strategy, signal = await create_dual_ma_strategy(stock_data_series, stock_code,
|
||||
short_window=short_window,
|
||||
long_window=long_window)
|
||||
# 创建回测
|
||||
backtest = bt.Backtest(strategy=strategy, data=stock_data_series, initial_capital=100000)
|
||||
backtest = bt.Backtest(strategy, data, initial_capital=100000) # 初始化回测对象
|
||||
|
||||
strategy_parame = {"short_window": short_window, "long_window": long_window} # 策略参数
|
||||
|
||||
strategy_name = "双均线策略" # 策略名称
|
||||
|
||||
# 运行回测
|
||||
result = bt.run(backtest)
|
||||
# 存储回测结果
|
||||
data_to_store = await storage_backtest_data(source_column_name, result, signal, stock_code,
|
||||
stock_data_series,
|
||||
short_window, long_window)
|
||||
# # 绘制回测结果图表
|
||||
# result.plot()
|
||||
# # 绘制个别股票数据图表
|
||||
# plt.figure(figsize=(12, 6))
|
||||
# plt.plot(stock_data_series.index, stock_data_series['close'], label='Stock Price')
|
||||
# plt.title(f'Stock Price for {stock_code}')
|
||||
# plt.xlabel('Date')
|
||||
# plt.ylabel('Price')
|
||||
# plt.legend()
|
||||
# plt.grid(True)
|
||||
# plt.show()
|
||||
# 将结果存储为字典并添加到列表中
|
||||
results_list.append({source_column_name: data_to_store})
|
||||
result = bt.run(backtest) # 执行回测
|
||||
|
||||
else:
|
||||
print(f"数据中缺少列: {data_column_name}")
|
||||
# 存储回测数据
|
||||
result = await storage_backtest_data(user_id=user_id, result=result, stock_data_series=data,
|
||||
source_column_name="组合双均线策略",
|
||||
signal=signal, stock_weights=stock_weights_dict,
|
||||
strategy_parame=strategy_parame,
|
||||
strategy_name=strategy_name,short_window=short_window,long_window=long_window)
|
||||
|
||||
return results_list # 返回结果列表
|
||||
return result # 返回回测结果
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
print(f"Error occurred: {e}") # 打印错误信息
|
||||
|
||||
|
||||
async def start_sma_combination_service(field_list: list,
|
||||
@ -210,19 +244,32 @@ async def start_sma_combination_service(field_list: list,
|
||||
data_dir: str = '',
|
||||
short_window: int = 50,
|
||||
long_window: int = 200):
|
||||
for stock_code in stock_list:
|
||||
backtest_name = f'{stock_code} 双均线策略 MA{short_window}-{long_window}日'
|
||||
db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=backtest_name)
|
||||
now_time = int(datetime.now().strftime('%Y%m%d'))
|
||||
"""
|
||||
启动双均线组合策略服务。
|
||||
|
||||
if db_result and db_result[0].backtest_end_time == now_time:
|
||||
return db_result
|
||||
else:
|
||||
# 执行回测
|
||||
result = await run_sma_backtest(
|
||||
参数:
|
||||
field_list: list, 需要获取的字段列表。
|
||||
stock_list: list, 股票代码列表。
|
||||
stock_weights: list, 股票权重列表。
|
||||
user_id: int, 用户ID，默认为1。
|
||||
period: str, 数据时间周期，默认为'1d'。
|
||||
start_time: str, 开始时间，默认为''（空字符串表示不限制）。
|
||||
end_time: str, 结束时间，默认为''（空字符串表示不限制）。
|
||||
count: int, 获取的数据数量，默认为-1（表示不限制）。
|
||||
dividend_type: str, 分红类型，默认为'none'。
|
||||
fill_data: bool, 是否填充缺失数据，默认为True。
|
||||
data_dir: str, 数据目录，默认为''（空字符串表示当前目录）。
|
||||
short_window: int, 短期均线窗口期，默认为50。
|
||||
long_window: int, 长期均线窗口期，默认为200。
|
||||
|
||||
返回:
|
||||
result: bt.BacktestResult, 回测结果。
|
||||
"""
|
||||
result = await run_combined_sma_backtest(
|
||||
field_list=field_list,
|
||||
stock_list=stock_list,
|
||||
user_id=user_id,
|
||||
stock_weights=stock_weights,
|
||||
period=period,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
@ -233,28 +280,37 @@ async def start_sma_combination_service(field_list: list,
|
||||
short_window=short_window,
|
||||
long_window=long_window,
|
||||
)
|
||||
return result
|
||||
return result # 返回回测结果
|
||||
|
||||
|
||||
async def init_backtest_db():
|
||||
"""
|
||||
初始化回测数据库，针对每个股票和不同的均线组合运行回测。
|
||||
|
||||
"""
|
||||
# 定义短期和长期均线组合
|
||||
sma_list = [{"short_window": 5, "long_window": 10}, {"short_window": 10, "long_window": 30},
|
||||
{"short_window": 30, "long_window": 60}, {"short_window": 30, "long_window": 90},
|
||||
{"short_window": 70, "long_window": 140}, {"short_window": 120, "long_window": 250}]
|
||||
await init_tortoise()
|
||||
wance_db = await wance_data_stock.WanceDataStock.all()
|
||||
sma_list_lenght = len(sma_list)
|
||||
|
||||
await init_tortoise() # 初始化数据库连接
|
||||
wance_db = await wance_data_stock.WanceDataStock.all() # 获取所有股票数据
|
||||
sma_list_length = len(sma_list) # 获取均线组合的数量
|
||||
|
||||
# 遍历每只股票，针对每个均线组合运行回测
|
||||
for stock_code in wance_db:
|
||||
for i in range(sma_list_lenght):
|
||||
short_window = sma_list[i]['short_window']
|
||||
long_window = sma_list[i]['long_window']
|
||||
source_column_name = f'{stock_code} 双均线策略 MA{short_window}-{long_window}日'
|
||||
result = await run_sma_backtest(field_list=['close', 'time'],
|
||||
for i in range(sma_list_length):
|
||||
short_window = sma_list[i]['short_window'] # 短期均线
|
||||
long_window = sma_list[i]['long_window'] # 长期均线
|
||||
source_column_name = f'{stock_code} 双均线策略 MA{short_window}-{long_window}日' # 结果列名称
|
||||
|
||||
# 运行回测
|
||||
result = await run_combined_sma_backtest(field_list=['close', 'time'],
|
||||
stock_list=[stock_code.stock_code],
|
||||
short_window=short_window,
|
||||
long_window=long_window)
|
||||
|
||||
print(f"回测成功 {source_column_name}")
|
||||
print(f"回测成功 {source_column_name}") # 打印回测成功信息
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
@ -265,4 +321,10 @@ if __name__ == '__main__':
|
||||
# long_window=30))
|
||||
|
||||
# 初始化数据库表
|
||||
asyncio.run(init_backtest_db())
|
||||
# asyncio.run(init_backtest_db())
|
||||
asyncio.run(run_combined_sma_backtest(field_list=['close', 'time'],
|
||||
stock_list=["688031.SH", "600025.SH", "601222.SH"],
|
||||
user_id=1,
|
||||
stock_weights=[{"688031.SH": 0.5, "600025.SH": 0.3, "601222.SH": 0.2}],
|
||||
short_window=10,
|
||||
long_window=30))
|
||||
|
||||
254
src/combination/macd_strategy.py
Normal file
254
src/combination/macd_strategy.py
Normal file
@ -0,0 +1,254 @@
|
||||
import asyncio
|
||||
import json
|
||||
from datetime import datetime
|
||||
|
||||
import bt
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from src.models import wance_data_storage_backtest, wance_data_stock, user_combination_history
|
||||
from src.tortoises_orm_config import init_tortoise
|
||||
from src.utils.combination_until import get_local_data
|
||||
|
||||
|
||||
# 创建组合MACD策略函数
|
||||
async def create_combined_macd_strategy(data, stock_weights_dict, short_window: int = 12, long_window: int = 26, signal_window: int = 9):
|
||||
"""
|
||||
创建组合MACD策略，并根据股票权重进行回测。
|
||||
|
||||
参数:
|
||||
data: pd.DataFrame, 股票价格数据，行索引为日期，列为股票代码。
|
||||
stock_weights_dict: dict, 股票权重字典，键为股票代码，值为相应权重。
|
||||
short_window: int, 短期EMA窗口期，默认为12。
|
||||
long_window: int, 长期EMA窗口期，默认为26。
|
||||
signal_window: int, 信号线窗口期，默认为9。
|
||||
|
||||
返回:
|
||||
strategy: bt.Strategy, 创建的组合MACD策略。
|
||||
combined_signal: pd.DataFrame, 各股票的买卖信号。
|
||||
"""
|
||||
combined_signal = pd.DataFrame(index=data.index) # 创建一个空的DataFrame用于存放买卖信号
|
||||
|
||||
# 遍历每个股票代码，计算对应的MACD信号
|
||||
for stock_code in stock_weights_dict.keys():
|
||||
if stock_code in data.columns:
|
||||
stock_data_series = data[[stock_code]] # 获取该股票的价格数据
|
||||
stock_data_series.columns = [stock_code] # 重命名列
|
||||
|
||||
signal = await macd_strategy(stock_data_series, short_window, long_window, signal_window) # 计算MACD信号
|
||||
combined_signal[stock_code] = signal # 将信号存入combined_signal
|
||||
else:
|
||||
print(f"Warning: Stock code {stock_code} not found in data columns.") # 如果股票代码不存在，发出警告
|
||||
|
||||
# 将权重转换为 DataFrame，行索引为数据的时间，列为股票代码
|
||||
weights = pd.DataFrame(index=data.index, data=[stock_weights_dict] * len(data.index))
|
||||
|
||||
# 使用bt框架构建组合策略
|
||||
strategy = bt.Strategy('组合MACD策略',
|
||||
[bt.algos.RunDaily(), # 每日运行策略
|
||||
bt.algos.SelectAll(), # 选择所有股票
|
||||
bt.algos.WeighSpecified(**stock_weights_dict), # 按照指定的权重分配
|
||||
bt.algos.Rebalance()]) # 调整持仓
|
||||
|
||||
return strategy, combined_signal # 返回创建的策略和买卖信号
|
||||
|
||||
|
||||
# MACD策略生成买卖信号
|
||||
async def macd_strategy(df, short_window=12, long_window=26, signal_window=9):
|
||||
"""
|
||||
基于MACD策略生成买卖信号。
|
||||
|
||||
参数:
|
||||
df: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。
|
||||
short_window: int, 短期EMA窗口期，默认为12。
|
||||
long_window: int, 长期EMA窗口期，默认为26。
|
||||
signal_window: int, 信号线窗口期，默认为9。
|
||||
|
||||
返回:
|
||||
signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，-1 表示卖出。
|
||||
"""
|
||||
# 计算短期和长期EMA
|
||||
short_ema = df.ewm(span=short_window, adjust=False).mean() # 短期指数移动平均
|
||||
long_ema = df.ewm(span=long_window, adjust=False).mean() # 长期指数移动平均
|
||||
|
||||
# 计算MACD和信号线
|
||||
macd = short_ema - long_ema # MACD值
|
||||
signal_line = macd.ewm(span=signal_window, adjust=False).mean() # 信号线
|
||||
|
||||
# 生成买卖信号
|
||||
signal = np.where(macd > signal_line, 1, 0) # MACD上穿信号线 -> 买入
|
||||
signal = np.where(macd < signal_line, -1, signal) # MACD下穿信号线 -> 卖出
|
||||
signal = pd.DataFrame(signal, index=df.index, columns=df.columns).ffill() # 前向填充信号
|
||||
|
||||
signal = signal.fillna(0) # 将所有NaN替换为0
|
||||
return signal # 返回买卖信号
|
||||
|
||||
|
||||
# 保存回测数据
|
||||
async def storage_backtest_data(user_id, stock_weights, strategy_parame, strategy_name):
|
||||
"""
|
||||
将回测结果保存到数据库。
|
||||
|
||||
参数:
|
||||
user_id: int, 用户ID。
|
||||
stock_weights: list, 股票权重。
|
||||
strategy_parame: dict, 策略参数。
|
||||
strategy_name: str, 策略名称。
|
||||
"""
|
||||
await init_tortoise() # 初始化Tortoise ORM
|
||||
|
||||
# 检查是否存在该 backtest_name
|
||||
existing_record = await user_combination_history.UserCombinationHistory.filter(user_id=user_id).first()
|
||||
|
||||
# 将股票权重和策略参数转换为JSON格式
|
||||
stock_weights_json = json.dumps(stock_weights)
|
||||
strategy_parame_json = json.dumps(strategy_parame)
|
||||
|
||||
if existing_record: # 如果存在记录，则更新
|
||||
await user_combination_history.UserCombinationHistory.filter(
|
||||
id=existing_record.id
|
||||
).update(user_id=user_id,
|
||||
last_time=int(datetime.now().strftime('%Y%m%d')),
|
||||
stock_weights=stock_weights_json,
|
||||
strategy_name=strategy_name,
|
||||
strategy_parame=strategy_parame_json)
|
||||
else: # 如果不存在记录，则创建新的记录
|
||||
await user_combination_history.UserCombinationHistory.create(user_id=user_id,
|
||||
last_time=int(datetime.now().strftime('%Y%m%d')),
|
||||
stock_weights=stock_weights_json,
|
||||
strategy_name=strategy_name,
|
||||
strategy_parame=strategy_parame_json)
|
||||
|
||||
|
||||
# 运行组合MACD策略回测
|
||||
async def run_combined_macd_backtest(field_list: list,
|
||||
stock_list: list,
|
||||
stock_weights: list,
|
||||
user_id: int = 1,
|
||||
period: str = '1d',
|
||||
start_time: str = '',
|
||||
end_time: str = '',
|
||||
count: int = 100,
|
||||
dividend_type: str = 'none',
|
||||
fill_data: bool = True,
|
||||
data_dir: str = '',
|
||||
short_window: int = 12,
|
||||
long_window: int = 26,
|
||||
signal_window: int = 9):
|
||||
"""
|
||||
运行组合MACD策略的回测。
|
||||
|
||||
参数:
|
||||
field_list: list, 需要获取的字段列表。
|
||||
stock_list: list, 股票代码列表。
|
||||
stock_weights: list, 股票权重列表。
|
||||
user_id: int, 用户ID，默认为1。
|
||||
period: str, 数据时间周期，默认为'1d'。
|
||||
start_time: str, 开始时间，默认为''（空字符串表示不限制）。
|
||||
end_time: str, 结束时间，默认为''（空字符串表示不限制）。
|
||||
count: int, 获取的数据数量，默认为100。
|
||||
dividend_type: str, 分红类型，默认为'none'。
|
||||
fill_data: bool, 是否填充缺失数据，默认为True。
|
||||
data_dir: str, 数据目录，默认为''（空字符串表示当前目录）。
|
||||
short_window: int, 短期EMA窗口期，默认为12。
|
||||
long_window: int, 长期EMA窗口期，默认为26。
|
||||
signal_window: int, 信号线窗口期，默认为9。
|
||||
|
||||
返回:
|
||||
result: bt.BacktestResult, 回测结果。
|
||||
"""
|
||||
try:
|
||||
# 获取本地数据
|
||||
data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type,
|
||||
fill_data, data_dir)
|
||||
|
||||
# 将 stock_weights 转换为字典形式，提取第一个字典作为权重
|
||||
stock_weights_dict = stock_weights[0] if stock_weights else {}
|
||||
|
||||
# 创建组合MACD策略
|
||||
strategy, signal = await create_combined_macd_strategy(data, stock_weights_dict, short_window, long_window, signal_window)
|
||||
|
||||
# 创建回测
|
||||
backtest = bt.Backtest(strategy, data, initial_capital=100000) # 使用初始资本创建回测
|
||||
|
||||
# 策略参数和名称
|
||||
strategy_parame = {"short_window": short_window, "long_window": long_window, "signal_window": signal_window}
|
||||
strategy_name = "MACD策略"
|
||||
|
||||
# 运行回测
|
||||
result = bt.run(backtest) # 运行回测
|
||||
|
||||
# 保存回测数据
|
||||
await storage_backtest_data(user_id, stock_weights_dict, strategy_parame, strategy_name)
|
||||
|
||||
return result # 返回回测结果
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}") # 捕获并打印异常
|
||||
|
||||
|
||||
# 启动MACD策略服务
|
||||
async def start_macd_combination_service(field_list: list,
|
||||
stock_list: list,
|
||||
stock_weights: list,
|
||||
user_id: int = 1,
|
||||
period: str = '1d',
|
||||
start_time: str = '',
|
||||
end_time: str = '',
|
||||
count: int = -1,
|
||||
dividend_type: str = 'none',
|
||||
fill_data: bool = True,
|
||||
data_dir: str = '',
|
||||
short_window: int = 12,
|
||||
long_window: int = 26,
|
||||
signal_window: int = 9):
|
||||
"""
|
||||
启动MACD策略组合服务。
|
||||
|
||||
参数:
|
||||
field_list: list, 需要获取的字段列表。
|
||||
stock_list: list, 股票代码列表。
|
||||
stock_weights: list, 股票权重列表。
|
||||
user_id: int, 用户ID，默认为1。
|
||||
period: str, 数据时间周期，默认为'1d'。
|
||||
start_time: str, 开始时间，默认为''（空字符串表示不限制）。
|
||||
end_time: str, 结束时间，默认为''（空字符串表示不限制）。
|
||||
count: int, 获取的数据数量，默认为-1（表示获取所有可用数据）。
|
||||
dividend_type: str, 分红类型，默认为'none'。
|
||||
fill_data: bool, 是否填充缺失数据，默认为True。
|
||||
data_dir: str, 数据目录，默认为''（空字符串表示当前目录）。
|
||||
short_window: int, 短期EMA窗口期，默认为12。
|
||||
long_window: int, 长期EMA窗口期，默认为26。
|
||||
signal_window: int, 信号线窗口期，默认为9。
|
||||
|
||||
返回:
|
||||
result: bt.BacktestResult, 回测结果。
|
||||
"""
|
||||
result = await run_combined_macd_backtest(
|
||||
field_list=field_list,
|
||||
stock_list=stock_list,
|
||||
user_id=user_id,
|
||||
stock_weights=stock_weights,
|
||||
period=period,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
count=count,
|
||||
dividend_type=dividend_type,
|
||||
fill_data=fill_data,
|
||||
data_dir=data_dir,
|
||||
short_window=short_window,
|
||||
long_window=long_window,
|
||||
signal_window=signal_window
|
||||
)
|
||||
return result # 返回回测结果
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 运行回测
|
||||
asyncio.run(run_combined_macd_backtest(field_list=['close', 'time'],
|
||||
stock_list=["688031.SH", "600025.SH", "601222.SH"],
|
||||
user_id=1,
|
||||
stock_weights=[{"688031.SH": 0.5, "600025.SH": 0.3, "601222.SH": 0.2}],
|
||||
short_window=12,
|
||||
long_window=26,
|
||||
signal_window=9))
|
||||
@ -6,122 +6,130 @@ import bt
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from src.utils.backtest_until import get_local_data, convert_pandas_to_json_serializable
|
||||
from src.models import wance_data_storage_backtest, wance_data_stock
|
||||
from src.models import wance_data_storage_backtest, wance_data_stock, user_combination_history
|
||||
from src.tortoises_orm_config import init_tortoise
|
||||
from src.utils.combination_until import get_local_data
|
||||
|
||||
|
||||
# 反双均线策略函数
|
||||
async def create_dual_ma_strategy(data, stock_code: str, short_window: int = 50, long_window: int = 200):
|
||||
# 生成反双均线策略信号
|
||||
signal = await reverse_dual_ma_strategy(data, short_window, long_window)
|
||||
|
||||
# 使用bt框架构建策略
|
||||
strategy = bt.Strategy(f'{stock_code} 反双均线策略',
|
||||
[bt.algos.RunDaily(),
|
||||
bt.algos.SelectAll(), # 选择所有股票
|
||||
bt.algos.WeighTarget(signal), # 根据信号调整权重
|
||||
bt.algos.Rebalance()]) # 调仓
|
||||
return strategy, signal
|
||||
|
||||
|
||||
|
||||
# 定义反反双均线策略的函数
|
||||
def reverse_dual_ma_strategy(data, short_window=50, long_window=200):
|
||||
# 组合反双均线策略函数
|
||||
async def create_combined_sma_strategy(data, stock_weights_dict, short_window: int = 50, long_window: int = 200):
|
||||
"""
|
||||
反反双均线策略，当短期均线跌破长期均线时买入，穿过长期均线时卖出。
|
||||
创建组合反双均线策略，并根据股票权重进行回测。
|
||||
|
||||
参数:
|
||||
data: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。
|
||||
short_window: int, 短期均线的窗口期。
|
||||
long_window: int, 长期均线的窗口期。
|
||||
data: pd.DataFrame, 包含股票的历史价格数据，行索引为时间，列为股票代码。
|
||||
stock_weights_dict: dict, 包含每只股票的权重。
|
||||
short_window: int, 短期均线的计算窗口，默认为50。
|
||||
long_window: int, 长期均线的计算窗口，默认为200。
|
||||
|
||||
返回:
|
||||
strategy: bt.Strategy, 创建的组合策略。
|
||||
combined_signal: pd.DataFrame, 每只股票的买卖信号。
|
||||
"""
|
||||
# 创建一个 DataFrame 用于存储组合信号，索引为数据的日期
|
||||
combined_signal = pd.DataFrame(index=data.index)
|
||||
|
||||
# 遍历每只股票，根据其权重生成信号
|
||||
for stock_code in stock_weights_dict.keys():
|
||||
# 确保每个股票代码在数据中唯一
|
||||
if f'{stock_code}' in data.columns:
|
||||
# 选择对应股票的数据列
|
||||
stock_data_series = data[[f'{stock_code}']]
|
||||
stock_data_series.columns = [f'{stock_code}']
|
||||
|
||||
# 调用反双均线策略生成买卖信号
|
||||
signal = await reverse_dual_ma_strategy(stock_data_series, short_window, long_window)
|
||||
# 将生成的信号添加到组合信号 DataFrame 中
|
||||
combined_signal[stock_code] = signal
|
||||
else:
|
||||
print(f"Warning: Stock code {stock_code} not found in data columns.")
|
||||
|
||||
# 将权重字典转换为 DataFrame，行索引为数据的时间，列为股票代码
|
||||
weights = pd.DataFrame(index=data.index, data=[stock_weights_dict] * len(data.index))
|
||||
|
||||
# 使用 bt 框架构建组合策略
|
||||
strategy = bt.Strategy('组合反双均线策略',
|
||||
[bt.algos.RunDaily(), # 每天运行策略
|
||||
bt.algos.SelectAll(), # 选择所有股票
|
||||
bt.algos.WeighSpecified(**stock_weights_dict), # 使用指定的权重
|
||||
bt.algos.Rebalance()]) # 调仓
|
||||
|
||||
return strategy, combined_signal
|
||||
|
||||
|
||||
# 反双均线策略生成买卖信号
|
||||
async def reverse_dual_ma_strategy(df, short_window=20, long_window=50):
|
||||
"""
|
||||
基于反双均线策略生成买卖信号。
|
||||
|
||||
参数:
|
||||
df: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。
|
||||
short_window: int, 短期均线窗口期。
|
||||
long_window: int, 长期均线窗口期。
|
||||
|
||||
返回:
|
||||
signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，0 表示卖出。
|
||||
"""
|
||||
# 计算短期均线和长期均线
|
||||
short_ma = data.rolling(window=short_window).mean()
|
||||
long_ma = data.rolling(window=long_window).mean()
|
||||
# 计算短期和长期均线
|
||||
short_ma = df.rolling(window=short_window, min_periods=1).mean()
|
||||
long_ma = df.rolling(window=long_window, min_periods=1).mean()
|
||||
|
||||
# 初始化信号 DataFrame
|
||||
signal = pd.DataFrame(index=data.index, columns=data.columns)
|
||||
# 反双均线策略的买卖信号生成规则
|
||||
buy_signal = np.where(short_ma < long_ma, 1, np.nan) # 短期均线跌破长期均线 -> 买入信号
|
||||
sell_signal = np.where(short_ma > long_ma, 0, np.nan) # 短期均线上穿长期均线 -> 卖出信号
|
||||
|
||||
# 生成买入信号：短期均线从上往下穿过长期均线
|
||||
for column in data.columns:
|
||||
signal[column] = (short_ma[column] < long_ma[column]).astype(int) # 跌破时买入，信号为1
|
||||
signal[column] = (short_ma[column] > long_ma[column]).astype(int) * -1 + signal[column] # 穿过时卖出，信号为0
|
||||
|
||||
# 前向填充信号，保持持仓不变
|
||||
signal = signal.ffill()
|
||||
# 创建信号 DataFrame
|
||||
signal = pd.DataFrame(buy_signal, index=df.index, columns=df.columns)
|
||||
# 将卖出信号应用到信号 DataFrame 中
|
||||
signal = np.where(short_ma > long_ma, 0, signal)
|
||||
signal = pd.DataFrame(signal, index=df.index, columns=df.columns).ffill() # 向前填充信号
|
||||
|
||||
signal = signal.fillna(0) # 将 NaN 转换为 0
|
||||
return signal
|
||||
|
||||
|
||||
async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, short_window,
|
||||
long_window):
|
||||
await init_tortoise()
|
||||
# 保存回测数据
|
||||
async def storage_backtest_data(user_id, stock_weights, strategy_parame, strategy_name):
|
||||
"""
|
||||
保存回测数据到数据库。
|
||||
|
||||
# 要存储的字段列表
|
||||
fields_to_store = [
|
||||
'stock_code', 'strategy_name', 'stock_close_price', 'daily_price',
|
||||
'price', 'returns', 'data_start_time', 'data_end_time',
|
||||
'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr',
|
||||
'max_drawdown', 'calmar', 'mtd', 'three_month',
|
||||
'six_month', 'ytd', 'one_year', 'three_year',
|
||||
'five_year', 'ten_year', 'incep', 'daily_sharpe',
|
||||
'daily_sortino', 'daily_mean', 'daily_vol',
|
||||
'daily_skew', 'daily_kurt', 'best_day', 'worst_day',
|
||||
'monthly_sharpe', 'monthly_sortino', 'monthly_mean',
|
||||
'monthly_vol', 'monthly_skew', 'monthly_kurt',
|
||||
'best_month', 'worst_month', 'yearly_sharpe',
|
||||
'yearly_sortino', 'yearly_mean', 'yearly_vol',
|
||||
'yearly_skew', 'yearly_kurt', 'best_year', 'worst_year',
|
||||
'avg_drawdown', 'avg_drawdown_days', 'avg_up_month',
|
||||
'avg_down_month', 'win_year_perc', 'twelve_month_win_perc'
|
||||
]
|
||||
|
||||
# 准备要存储的数据
|
||||
data_to_store = {
|
||||
'stock_code': stock_code,
|
||||
'strategy_name': "反双均线策略",
|
||||
'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign(
|
||||
time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')),
|
||||
'daily_price': convert_pandas_to_json_serializable(result[source_column_name].daily_prices),
|
||||
'price': convert_pandas_to_json_serializable(result[source_column_name].prices),
|
||||
'returns': convert_pandas_to_json_serializable(result[source_column_name].returns.fillna(0)),
|
||||
'data_start_time': pd.to_datetime(result.stats.loc["start"].iloc[0]).strftime('%Y%m%d'),
|
||||
'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'),
|
||||
'backtest_end_time': int(datetime.now().strftime('%Y%m%d')),
|
||||
'position': convert_pandas_to_json_serializable(signal),
|
||||
'backtest_name': f'{stock_code} 反双均线策略 MA{short_window}-{long_window}日',
|
||||
'indicator_type': 'reverse_SMA',
|
||||
'indicator_information': json.dumps({'short_window': short_window, 'long_window': long_window})
|
||||
}
|
||||
|
||||
# 使用循环填充其他字段
|
||||
for field in fields_to_store[12:]: # 从第10个字段开始
|
||||
value = result.stats.loc[field].iloc[0]
|
||||
data_to_store[field] = 0.0 if (isinstance(value, float) and np.isnan(value)) else value
|
||||
参数:
|
||||
user_id: int, 用户的 ID。
|
||||
stock_weights: dict, 股票权重。
|
||||
strategy_parame: dict, 策略参数。
|
||||
strategy_name: str, 策略名称。
|
||||
"""
|
||||
await init_tortoise() # 初始化 Tortoise ORM
|
||||
|
||||
# 检查是否存在该 backtest_name
|
||||
existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=data_to_store['backtest_name']
|
||||
).first()
|
||||
existing_record = await user_combination_history.UserCombinationHistory.filter(user_id=user_id).first()
|
||||
|
||||
# 将权重和策略参数转换为 JSON 格式
|
||||
stock_weights_json = json.dumps(stock_weights)
|
||||
strategy_parame_json = json.dumps(strategy_parame)
|
||||
|
||||
if existing_record:
|
||||
# 如果存在，更新记录
|
||||
await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
# 如果记录已存在，更新记录
|
||||
await user_combination_history.UserCombinationHistory.filter(
|
||||
id=existing_record.id
|
||||
).update(**data_to_store)
|
||||
).update(user_id=user_id,
|
||||
last_time=int(datetime.now().strftime('%Y%m%d')),
|
||||
stock_weights=stock_weights_json,
|
||||
strategy_name=strategy_name,
|
||||
strategy_parame=strategy_parame_json)
|
||||
else:
|
||||
# 如果不存在，创建新的记录
|
||||
await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store)
|
||||
|
||||
return data_to_store
|
||||
# 如果记录不存在，创建新的记录
|
||||
await user_combination_history.UserCombinationHistory.create(user_id=user_id,
|
||||
last_time=int(datetime.now().strftime('%Y%m%d')),
|
||||
stock_weights=stock_weights_json,
|
||||
strategy_name=strategy_name,
|
||||
strategy_parame=strategy_parame_json)
|
||||
|
||||
|
||||
async def run_reverse_reverse_SMA_backtest(field_list: list,
|
||||
# 运行组合反双均线策略回测
|
||||
async def run_combined_sma_backtest(field_list: list,
|
||||
stock_list: list,
|
||||
stock_weights: list,
|
||||
user_id: int = 1,
|
||||
period: str = '1d',
|
||||
start_time: str = '',
|
||||
end_time: str = '',
|
||||
@ -131,69 +139,63 @@ async def run_reverse_reverse_SMA_backtest(field_list: list,
|
||||
data_dir: str = '',
|
||||
short_window: int = 50,
|
||||
long_window: int = 200):
|
||||
"""
|
||||
运行组合反双均线策略的回测。
|
||||
|
||||
参数:
|
||||
field_list: list, 包含需要获取的字段，如收盘价、时间等。
|
||||
stock_list: list, 需要回测的股票列表。
|
||||
stock_weights: list, 包含每只股票的权重字典。
|
||||
user_id: int, 用户 ID，默认为 1。
|
||||
period: str, 数据时间周期，默认为 '1d'。
|
||||
start_time: str, 开始时间，默认为 ''（空字符串表示不限制）。
|
||||
end_time: str, 结束时间，默认为 ''（空字符串表示不限制）。
|
||||
count: int, 获取的数据数量，默认为 100。
|
||||
dividend_type: str, 分红类型，默认为 'none'。
|
||||
fill_data: bool, 是否填充缺失数据，默认为 True。
|
||||
data_dir: str, 数据目录，默认为 ''（空字符串表示当前目录）。
|
||||
short_window: int, 短期均线窗口，默认为 50。
|
||||
long_window: int, 长期均线窗口，默认为 200。
|
||||
|
||||
返回:
|
||||
result: bt.BacktestResult, 回测结果。
|
||||
"""
|
||||
try:
|
||||
# 初始化一个列表用于存储每只股票的回测结果字典
|
||||
results_list = []
|
||||
|
||||
# 遍历每只股票的数据（每列代表一个股票的收盘价）
|
||||
# 获取本地数据
|
||||
data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type,
|
||||
fill_data,
|
||||
data_dir)
|
||||
fill_data, data_dir)
|
||||
|
||||
for stock_code in stock_list:
|
||||
# 将 stock_weights 转换为字典形式，提取第一个字典作为权重
|
||||
stock_weights_dict = stock_weights[0] if stock_weights else {}
|
||||
|
||||
data_column_name = f'close_{stock_code}'
|
||||
source_column_name = f'{stock_code} 反双均线策略'
|
||||
backtest_name = f'{stock_code} 反双均线策略 MA{short_window}-{long_window}日'
|
||||
now_data = int(datetime.now().strftime('%Y%m%d'))
|
||||
db_result_data = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=backtest_name)
|
||||
# 创建组合反双均线策略
|
||||
strategy, signal = await create_combined_sma_strategy(data, stock_weights_dict, short_window, long_window)
|
||||
|
||||
if db_result_data:
|
||||
if db_result_data[0].backtest_end_time == now_data:
|
||||
results_list.append({source_column_name: db_result_data[0]})
|
||||
# 创建回测实例
|
||||
backtest = bt.Backtest(strategy, data, initial_capital=100000)
|
||||
|
||||
elif data_column_name in data.columns:
|
||||
stock_data_series = data[[data_column_name]] # 提取该股票的收盘价 DataFrame
|
||||
stock_data_series.columns = ['close'] # 重命名列为 'close'
|
||||
# 策略参数
|
||||
strategy_parame = {"short_window": short_window, "long_window": long_window}
|
||||
|
||||
strategy_name = "反双均线策略"
|
||||
|
||||
# 创建反双均线策略
|
||||
strategy, signal = await create_dual_ma_strategy(stock_data_series, stock_code,
|
||||
short_window=short_window,
|
||||
long_window=long_window)
|
||||
# 创建回测
|
||||
backtest = bt.Backtest(strategy=strategy, data=stock_data_series, initial_capital=100000)
|
||||
# 运行回测
|
||||
result = bt.run(backtest)
|
||||
# 存储回测结果
|
||||
data_to_store = await storage_backtest_data(source_column_name, result, signal, stock_code,
|
||||
stock_data_series,
|
||||
short_window, long_window)
|
||||
# # 绘制回测结果图表
|
||||
# result.plot()
|
||||
# # 绘制个别股票数据图表
|
||||
# plt.figure(figsize=(12, 6))
|
||||
# plt.plot(stock_data_series.index, stock_data_series['close'], label='Stock Price')
|
||||
# plt.title(f'Stock Price for {stock_code}')
|
||||
# plt.xlabel('Date')
|
||||
# plt.ylabel('Price')
|
||||
# plt.legend()
|
||||
# plt.grid(True)
|
||||
# plt.show()
|
||||
# 将结果存储为字典并添加到列表中
|
||||
results_list.append({source_column_name: data_to_store})
|
||||
|
||||
else:
|
||||
print(f"数据中缺少列: {data_column_name}")
|
||||
# 保存回测数据
|
||||
await storage_backtest_data(user_id, stock_weights_dict, strategy_parame, strategy_name)
|
||||
|
||||
return results_list # 返回结果列表
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
|
||||
|
||||
# 启动反双均线策略服务
|
||||
async def start_reverse_SMA_combination_service(field_list: list,
|
||||
stock_list: list,
|
||||
stock_weights: list,
|
||||
user_id: int = 1,
|
||||
period: str = '1d',
|
||||
start_time: str = '',
|
||||
end_time: str = '',
|
||||
@ -203,19 +205,32 @@ async def start_reverse_SMA_combination_service(field_list: list,
|
||||
data_dir: str = '',
|
||||
short_window: int = 50,
|
||||
long_window: int = 200):
|
||||
for stock_code in stock_list:
|
||||
backtest_name = f'{stock_code} 反双均线策略 MA{short_window}-{long_window}日'
|
||||
db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=backtest_name)
|
||||
now_time = int(datetime.now().strftime('%Y%m%d'))
|
||||
"""
|
||||
启动反双均线策略服务，运行回测并返回结果。
|
||||
|
||||
if db_result and db_result[0].backtest_end_time == now_time:
|
||||
return db_result
|
||||
else:
|
||||
# 执行回测
|
||||
result = await run_reverse_reverse_SMA_backtest(
|
||||
参数:
|
||||
field_list: list, 包含需要获取的字段。
|
||||
stock_list: list, 需要回测的股票列表。
|
||||
stock_weights: list, 股票权重列表。
|
||||
user_id: int, 用户 ID，默认为 1。
|
||||
period: str, 数据时间周期，默认为 '1d'。
|
||||
start_time: str, 开始时间，默认为 ''（空字符串表示不限制）。
|
||||
end_time: str, 结束时间，默认为 ''（空字符串表示不限制）。
|
||||
count: int, 获取的数据数量，默认为 -1（表示全部）。
|
||||
dividend_type: str, 分红类型，默认为 'none'。
|
||||
fill_data: bool, 是否填充缺失数据，默认为 True。
|
||||
data_dir: str, 数据目录，默认为 ''（空字符串表示当前目录）。
|
||||
short_window: int, 短期均线窗口，默认为 50。
|
||||
long_window: int, 长期均线窗口，默认为 200。
|
||||
|
||||
返回:
|
||||
result: bt.BacktestResult, 回测结果。
|
||||
"""
|
||||
result = await run_combined_sma_backtest(
|
||||
field_list=field_list,
|
||||
stock_list=stock_list,
|
||||
user_id=user_id,
|
||||
stock_weights=stock_weights,
|
||||
period=period,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
@ -229,33 +244,11 @@ async def start_reverse_SMA_combination_service(field_list: list,
|
||||
return result
|
||||
|
||||
|
||||
async def init_backtest_db():
|
||||
reverse_SMA_list = [{"short_window": 5, "long_window": 10}, {"short_window": 10, "long_window": 30},
|
||||
{"short_window": 30, "long_window": 60}, {"short_window": 30, "long_window": 90},
|
||||
{"short_window": 70, "long_window": 140}, {"short_window": 120, "long_window": 250}]
|
||||
await init_tortoise()
|
||||
wance_db = await wance_data_stock.WanceDataStock.all()
|
||||
reverse_SMA_list_lenght = len(reverse_SMA_list)
|
||||
|
||||
for stock_code in wance_db:
|
||||
for i in range(reverse_SMA_list_lenght):
|
||||
short_window = reverse_SMA_list[i]['short_window']
|
||||
long_window = reverse_SMA_list[i]['long_window']
|
||||
source_column_name = f'{stock_code} 反双均线策略 MA{short_window}-{long_window}日'
|
||||
result = await run_reverse_reverse_SMA_backtest(field_list=['close', 'time'],
|
||||
stock_list=[stock_code.stock_code],
|
||||
short_window=short_window,
|
||||
long_window=long_window)
|
||||
|
||||
print(f"回测成功 {source_column_name}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 测试类的回测
|
||||
# asyncio.run(run_reverse_SMA_backtest(field_list=['close', 'time'],
|
||||
# stock_list=['601222.SH', '601677.SH'],
|
||||
# short_window=10,
|
||||
# long_window=30))
|
||||
|
||||
# 初始化数据库表
|
||||
asyncio.run(init_backtest_db())
|
||||
# 运行回测
|
||||
asyncio.run(run_combined_sma_backtest(field_list=['close', 'time'],
|
||||
stock_list=["688031.SH", "600025.SH", "601222.SH"],
|
||||
user_id=1,
|
||||
stock_weights=[{"688031.SH": 0.5, "600025.SH": 0.3, "601222.SH": 0.2}],
|
||||
short_window=10,
|
||||
long_window=30))
|
||||
|
||||
@ -34,7 +34,3 @@ async def stock_chart(request: BackRequest):
|
||||
benchmark_code=request.benchmark_code)
|
||||
return result
|
||||
|
||||
|
||||
@router.get('/combination')
|
||||
async def combination(request: BackRequest):
|
||||
await combination_service()
|
||||
|
||||
@ -2,144 +2,137 @@ import asyncio
|
||||
import json
|
||||
from datetime import datetime
|
||||
|
||||
import bt
|
||||
import bt # 引入bt框架以进行回测
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from src.utils.backtest_until import get_local_data, convert_pandas_to_json_serializable
|
||||
from src.models import wance_data_storage_backtest, wance_data_stock
|
||||
from src.tortoises_orm_config import init_tortoise
|
||||
from src.models import wance_data_storage_backtest, wance_data_stock, user_combination_history # 导入数据库模型
|
||||
from src.tortoises_orm_config import init_tortoise # 初始化数据库
|
||||
from src.utils.combination_until import get_local_data # 获取本地数据的工具函数
|
||||
|
||||
|
||||
# RSI策略函数
|
||||
async def create_dual_ma_strategy(data, stock_code: str, short_window: int = 50, long_window: int = 200,
|
||||
overbought: int = 70, oversold: int = 30):
|
||||
# 生成RSI策略信号
|
||||
signal = await rsi_strategy(data, short_window, long_window, overbought, oversold)
|
||||
|
||||
# 使用bt框架构建策略
|
||||
strategy = bt.Strategy(f'{stock_code} RSI策略',
|
||||
[bt.algos.RunDaily(),
|
||||
bt.algos.SelectAll(), # 选择所有股票
|
||||
bt.algos.WeighTarget(signal), # 根据信号调整权重
|
||||
bt.algos.Rebalance()]) # 调仓
|
||||
return strategy, signal
|
||||
|
||||
|
||||
async def rsi_strategy(df, short_window=14, long_window=28, overbought=70, oversold=30):
|
||||
# 创建RSI策略函数
|
||||
async def create_rsi_strategy(data, stock_weights_dict, rsi_period: int = 14, overbought: float = 70,
|
||||
oversold: float = 30):
|
||||
"""
|
||||
基于RSI的策略生成买卖信号。
|
||||
创建组合RSI策略，并根据股票权重进行回测。
|
||||
|
||||
参数:
|
||||
data: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。
|
||||
stock_weights_dict: dict, 每只股票的权重字典。
|
||||
rsi_period: int, 用于计算RSI的周期，默认值为14。
|
||||
overbought: float, 超买阈值，默认值为70。
|
||||
oversold: float, 超卖阈值，默认值为30。
|
||||
|
||||
返回:
|
||||
strategy: bt.Strategy, 构建的RSI策略。
|
||||
combined_signal: pd.DataFrame, 股票的买卖信号。
|
||||
"""
|
||||
# 创建一个数据框用于存储所有股票的买卖信号
|
||||
combined_signal = pd.DataFrame(index=data.index)
|
||||
|
||||
# 遍历每只股票，生成RSI信号
|
||||
for stock_code in stock_weights_dict.keys():
|
||||
if f'{stock_code}' in data.columns:
|
||||
# 提取每只股票的数据
|
||||
stock_data_series = data[[f'{stock_code}']]
|
||||
stock_data_series.columns = [f'{stock_code}']
|
||||
|
||||
# 调用RSI策略计算买卖信号
|
||||
signal = await rsi_strategy(stock_data_series, rsi_period, overbought, oversold)
|
||||
combined_signal[stock_code] = signal # 存储信号
|
||||
else:
|
||||
print(f"Warning: Stock code {stock_code} not found in data columns.") # 股票未在数据中，发出警告
|
||||
|
||||
# 创建权重数据框
|
||||
weights = pd.DataFrame(index=data.index, data=[stock_weights_dict] * len(data.index))
|
||||
|
||||
# 使用bt框架构建RSI策略
|
||||
strategy = bt.Strategy('组合RSI策略',
|
||||
[bt.algos.RunDaily(), # 每天运行策略
|
||||
bt.algos.SelectAll(), # 选择所有可用的股票
|
||||
bt.algos.WeighSpecified(**stock_weights_dict), # 根据给定权重进行加权
|
||||
bt.algos.Rebalance()]) # 调整投资组合
|
||||
|
||||
return strategy, combined_signal # 返回构建的策略和信号
|
||||
|
||||
|
||||
async def rsi_strategy(df, period=14, overbought=70, oversold=30):
|
||||
"""
|
||||
基于RSI策略生成买卖信号。
|
||||
|
||||
参数:
|
||||
df: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。
|
||||
short_window: int, 短期RSI的窗口期。
|
||||
long_window: int, 长期RSI的窗口期。
|
||||
overbought: int, 超买水平。
|
||||
oversold: int, 超卖水平。
|
||||
period: int, RSI计算的周期，默认值为14。
|
||||
overbought: float, 超买阈值，默认值为70。
|
||||
oversold: float, 超卖阈值，默认值为30。
|
||||
|
||||
返回:
|
||||
signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，0 表示卖出。
|
||||
"""
|
||||
delta = df.diff().fillna(0)
|
||||
# 计算价格变化量
|
||||
delta = df.diff()
|
||||
# 计算上涨和下跌的平均值
|
||||
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean() # 上涨的平均值
|
||||
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean() # 下跌的平均值
|
||||
|
||||
gain = (delta.where(delta > 0, 0).rolling(window=short_window).mean()).fillna(0)
|
||||
loss = (-delta.where(delta < 0, 0).rolling(window=short_window).mean()).fillna(0)
|
||||
|
||||
short_rsi = (100 - (100 / (1 + (gain / loss)))).fillna(0)
|
||||
|
||||
long_gain = (delta.where(delta > 0, 0).rolling(window=long_window).mean()).fillna(0)
|
||||
long_loss = (-delta.where(delta < 0, 0).rolling(window=long_window).mean()).fillna(0)
|
||||
|
||||
long_rsi = (100 - (100 / (1 + (long_gain / long_loss)))).fillna(0)
|
||||
# 计算相对强弱指数RS和RSI
|
||||
rs = gain / loss
|
||||
rsi = 100 - (100 / (1 + rs)) # 计算RSI
|
||||
|
||||
# 创建一个数据框用于存储每只股票的买卖信号
|
||||
signal = pd.DataFrame(index=df.index, columns=df.columns)
|
||||
|
||||
# 根据RSI计算买卖信号
|
||||
for column in df.columns:
|
||||
signal[column] = np.where((short_rsi[column] < 30) & (long_rsi[column] < 30) & (short_rsi[column] != 0) & (long_rsi[column] != 0), 1, 0)
|
||||
signal[column] = np.where((short_rsi[column] > 70) & (long_rsi[column] > 70) & (short_rsi[column] != 0) & (long_rsi[column] != 0), 0, signal[column])
|
||||
signal[column] = np.where(rsi[column] < oversold, 1, np.nan) # 当RSI低于超卖阈值，生成买入信号
|
||||
signal[column] = np.where(rsi[column] > overbought, 0, signal[column]) # 当RSI高于超买阈值，生成卖出信号
|
||||
|
||||
return signal.ffill().fillna(0)
|
||||
# 前向填充信号，并用0填充NaN值
|
||||
signal = signal.ffill().fillna(0)
|
||||
return signal # 返回信号数据框
|
||||
|
||||
|
||||
async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, short_window: int,
|
||||
long_window: int, overbought: int = 70,
|
||||
oversold: int = 30):
|
||||
await init_tortoise()
|
||||
async def storage_backtest_data(user_id, stock_weights, strategy_parame, strategy_name):
|
||||
"""
|
||||
存储回测结果到数据库。
|
||||
|
||||
# 要存储的字段列表
|
||||
fields_to_store = [
|
||||
'stock_code', 'strategy_name', 'stock_close_price', 'daily_price',
|
||||
'price', 'returns', 'data_start_time', 'data_end_time',
|
||||
'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr',
|
||||
'max_drawdown', 'calmar', 'mtd', 'three_month',
|
||||
'six_month', 'ytd', 'one_year', 'three_year',
|
||||
'five_year', 'ten_year', 'incep', 'daily_sharpe',
|
||||
'daily_sortino', 'daily_mean', 'daily_vol',
|
||||
'daily_skew', 'daily_kurt', 'best_day', 'worst_day',
|
||||
'monthly_sharpe', 'monthly_sortino', 'monthly_mean',
|
||||
'monthly_vol', 'monthly_skew', 'monthly_kurt',
|
||||
'best_month', 'worst_month', 'yearly_sharpe',
|
||||
'yearly_sortino', 'yearly_mean', 'yearly_vol',
|
||||
'yearly_skew', 'yearly_kurt', 'best_year', 'worst_year',
|
||||
'avg_drawdown', 'avg_drawdown_days', 'avg_up_month',
|
||||
'avg_down_month', 'win_year_perc', 'twelve_month_win_perc'
|
||||
]
|
||||
参数:
|
||||
user_id: int, 用户的ID。
|
||||
stock_weights: dict, 股票权重字典。
|
||||
strategy_parame: dict, 策略参数字典。
|
||||
strategy_name: str, 策略名称。
|
||||
"""
|
||||
await init_tortoise() # 初始化数据库
|
||||
|
||||
# 准备要存储的数据
|
||||
data_to_store = {
|
||||
'stock_code': stock_code,
|
||||
'strategy_name': "RSI策略",
|
||||
'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign(
|
||||
time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')),
|
||||
'daily_price': convert_pandas_to_json_serializable(result[source_column_name].daily_prices),
|
||||
'price': convert_pandas_to_json_serializable(result[source_column_name].prices),
|
||||
'returns': convert_pandas_to_json_serializable(result[source_column_name].returns.fillna(0)),
|
||||
'data_start_time': pd.to_datetime(result.stats.loc["start"].iloc[0]).strftime('%Y%m%d'),
|
||||
'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'),
|
||||
'backtest_end_time': int(datetime.now().strftime('%Y%m%d')),
|
||||
'position': convert_pandas_to_json_serializable(signal),
|
||||
'backtest_name': f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}-overbought{overbought}-oversold{oversold}',
|
||||
'indicator_type': 'RSI',
|
||||
'indicator_information': json.dumps(
|
||||
{'short_window': short_window, 'long_window': long_window, 'overbought': overbought, 'oversold': oversold})
|
||||
}
|
||||
# 查询用户的已有记录
|
||||
existing_record = await user_combination_history.UserCombinationHistory.filter(user_id=user_id).first()
|
||||
|
||||
# 使用循环填充其他字段
|
||||
for field in fields_to_store[12:]: # 从第12个字段开始
|
||||
value = result.stats.loc[field].iloc[0]
|
||||
|
||||
if isinstance(value, float):
|
||||
if np.isnan(value):
|
||||
data_to_store[field] = 0.0 # NaN 处理为 0
|
||||
elif np.isinf(value): # 判断是否为无穷大或无穷小
|
||||
if value > 0:
|
||||
data_to_store[field] = 99999.9999 # 正无穷处理
|
||||
else:
|
||||
data_to_store[field] = -99999.9999 # 负无穷处理
|
||||
else:
|
||||
data_to_store[field] = value # 正常的浮点值
|
||||
else:
|
||||
data_to_store[field] = value # 非浮点类型保持不变
|
||||
|
||||
# 检查是否存在该 backtest_name
|
||||
existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=data_to_store['backtest_name']
|
||||
).first()
|
||||
# 将股票权重和策略参数转换为JSON格式
|
||||
stock_weights_json = json.dumps(stock_weights)
|
||||
strategy_parame_json = json.dumps(strategy_parame)
|
||||
|
||||
# 如果记录存在，更新记录；否则，创建新记录
|
||||
if existing_record:
|
||||
# 如果存在，更新记录
|
||||
await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
await user_combination_history.UserCombinationHistory.filter(
|
||||
id=existing_record.id
|
||||
).update(**data_to_store)
|
||||
).update(user_id=user_id,
|
||||
last_time=int(datetime.now().strftime('%Y%m%d')),
|
||||
stock_weights=stock_weights_json,
|
||||
strategy_name=strategy_name,
|
||||
strategy_parame=strategy_parame_json)
|
||||
else:
|
||||
# 如果不存在，创建新的记录
|
||||
await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store)
|
||||
|
||||
return data_to_store
|
||||
await user_combination_history.UserCombinationHistory.create(user_id=user_id,
|
||||
last_time=int(datetime.now().strftime('%Y%m%d')),
|
||||
stock_weights=stock_weights_json,
|
||||
strategy_name=strategy_name,
|
||||
strategy_parame=strategy_parame_json)
|
||||
|
||||
|
||||
async def run_rsi_backtest(field_list: list,
|
||||
async def run_combined_rsi_backtest(field_list: list,
|
||||
stock_list: list,
|
||||
stock_weights: list,
|
||||
user_id: int = 1,
|
||||
period: str = '1d',
|
||||
start_time: str = '',
|
||||
end_time: str = '',
|
||||
@ -147,74 +140,65 @@ async def run_rsi_backtest(field_list: list,
|
||||
dividend_type: str = 'none',
|
||||
fill_data: bool = True,
|
||||
data_dir: str = '',
|
||||
short_window: int = 50,
|
||||
long_window: int = 200,
|
||||
overbought: int = 70,
|
||||
oversold: int = 30
|
||||
):
|
||||
rsi_period: int = 14,
|
||||
overbought: float = 70,
|
||||
oversold: float = 30):
|
||||
"""
|
||||
运行组合RSI策略的回测。
|
||||
|
||||
参数:
|
||||
field_list: list, 需要获取的字段列表。
|
||||
stock_list: list, 需要回测的股票代码列表。
|
||||
stock_weights: list, 股票权重列表。
|
||||
user_id: int, 用户ID，默认为1。
|
||||
period: str, 数据周期，默认为'1d'。
|
||||
start_time: str, 开始时间，默认为''。
|
||||
end_time: str, 结束时间，默认为''。
|
||||
count: int, 数据数量限制，默认为100。
|
||||
dividend_type: str, 分红类型，默认为'none'。
|
||||
fill_data: bool, 是否填充数据，默认为True。
|
||||
data_dir: str, 数据目录，默认为''。
|
||||
rsi_period: int, RSI计算周期，默认为14。
|
||||
overbought: float, 超买阈值，默认为70。
|
||||
oversold: float, 超卖阈值，默认为30。
|
||||
|
||||
返回:
|
||||
result: 回测结果。
|
||||
"""
|
||||
try:
|
||||
# 初始化一个列表用于存储每只股票的回测结果字典
|
||||
results_list = []
|
||||
|
||||
# 遍历每只股票的数据（每列代表一个股票的收盘价）
|
||||
# 获取本地数据
|
||||
data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type,
|
||||
fill_data,
|
||||
data_dir)
|
||||
fill_data, data_dir)
|
||||
|
||||
for stock_code in stock_list:
|
||||
|
||||
data_column_name = f'close_{stock_code}'
|
||||
source_column_name = f'{stock_code} RSI策略'
|
||||
backtest_name = f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}'
|
||||
now_data = int(datetime.now().strftime('%Y%m%d'))
|
||||
db_result_data = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=backtest_name)
|
||||
|
||||
if db_result_data:
|
||||
if db_result_data[0].backtest_end_time == now_data:
|
||||
results_list.append({source_column_name: db_result_data[0]})
|
||||
|
||||
# elif data_column_name in data.columns:
|
||||
if data_column_name in data.columns:
|
||||
stock_data_series = data[[data_column_name]] # 提取该股票的收盘价 DataFrame
|
||||
stock_data_series.columns = ['close'] # 重命名列为 'close'
|
||||
# 获取股票权重字典
|
||||
stock_weights_dict = stock_weights[0] if stock_weights else {}
|
||||
|
||||
# 创建RSI策略
|
||||
strategy, signal = await create_dual_ma_strategy(stock_data_series, stock_code,
|
||||
short_window=short_window, long_window=long_window)
|
||||
# 创建回测
|
||||
backtest = bt.Backtest(strategy=strategy, data=stock_data_series, initial_capital=100000)
|
||||
strategy, signal = await create_rsi_strategy(data, stock_weights_dict, rsi_period, overbought, oversold)
|
||||
|
||||
# 创建回测对象
|
||||
backtest = bt.Backtest(strategy, data, initial_capital=100000)
|
||||
|
||||
# 策略参数
|
||||
strategy_parame = {"rsi_period": rsi_period, "overbought": overbought, "oversold": oversold}
|
||||
strategy_name = "RSI策略"
|
||||
|
||||
# 运行回测
|
||||
result = bt.run(backtest)
|
||||
|
||||
# 存储回测结果
|
||||
data_to_store = await storage_backtest_data(source_column_name, result, signal, stock_code,
|
||||
stock_data_series, short_window, long_window, overbought,
|
||||
oversold)
|
||||
# # 绘制回测结果图表
|
||||
# result.plot()
|
||||
# # 绘制个别股票数据图表
|
||||
# plt.figure(figsize=(12, 6))
|
||||
# plt.plot(stock_data_series.index, stock_data_series['close'], label='Stock Price')
|
||||
# plt.title(f'Stock Price for {stock_code}')
|
||||
# plt.xlabel('Date')
|
||||
# plt.ylabel('Price')
|
||||
# plt.legend()
|
||||
# plt.grid(True)
|
||||
# plt.show()
|
||||
# 将结果存储为字典并添加到列表中
|
||||
results_list.append({source_column_name: data_to_store})
|
||||
await storage_backtest_data(user_id, stock_weights_dict, strategy_parame, strategy_name)
|
||||
|
||||
else:
|
||||
print(f"数据中缺少列: {data_column_name}")
|
||||
|
||||
return results_list # 返回结果列表
|
||||
return result # 返回回测结果
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
print(f"Error occurred: {e}") # 输出错误信息
|
||||
|
||||
|
||||
async def start_rsi_combination_service(field_list: list,
|
||||
stock_list: list,
|
||||
stock_weights: list,
|
||||
user_id: int = 1,
|
||||
period: str = '1d',
|
||||
start_time: str = '',
|
||||
end_time: str = '',
|
||||
@ -222,24 +206,37 @@ async def start_rsi_combination_service(field_list: list,
|
||||
dividend_type: str = 'none',
|
||||
fill_data: bool = True,
|
||||
data_dir: str = '',
|
||||
short_window: int = 50,
|
||||
long_window: int = 200,
|
||||
overbought: int = 70,
|
||||
oversold: int = 30
|
||||
):
|
||||
for stock_code in stock_list:
|
||||
backtest_name = f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}'
|
||||
db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter(
|
||||
backtest_name=backtest_name)
|
||||
now_time = int(datetime.now().strftime('%Y%m%d'))
|
||||
rsi_period: int = 14,
|
||||
overbought: float = 70,
|
||||
oversold: float = 30):
|
||||
"""
|
||||
启动RSI组合策略服务。
|
||||
|
||||
if db_result and db_result[0].backtest_end_time == now_time:
|
||||
return db_result
|
||||
else:
|
||||
# 执行回测
|
||||
result = await run_rsi_backtest(
|
||||
参数:
|
||||
field_list: list, 需要获取的字段列表。
|
||||
stock_list: list, 需要回测的股票代码列表。
|
||||
stock_weights: list, 股票权重列表。
|
||||
user_id: int, 用户ID，默认为1。
|
||||
period: str, 数据周期，默认为'1d'。
|
||||
start_time: str, 开始时间，默认为''。
|
||||
end_time: str, 结束时间，默认为''。
|
||||
count: int, 数据数量限制，默认为-1。
|
||||
dividend_type: str, 分红类型，默认为'none'。
|
||||
fill_data: bool, 是否填充数据，默认为True。
|
||||
data_dir: str, 数据目录，默认为''。
|
||||
rsi_period: int, RSI计算周期，默认为14。
|
||||
overbought: float, 超买阈值，默认为70。
|
||||
oversold: float, 超卖阈值，默认为30。
|
||||
|
||||
返回:
|
||||
result: 回测结果。
|
||||
"""
|
||||
# 调用回测函数并返回结果
|
||||
result = await run_combined_rsi_backtest(
|
||||
field_list=field_list,
|
||||
stock_list=stock_list,
|
||||
stock_weights=stock_weights,
|
||||
user_id=user_id,
|
||||
period=period,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
@ -247,47 +244,19 @@ async def start_rsi_combination_service(field_list: list,
|
||||
dividend_type=dividend_type,
|
||||
fill_data=fill_data,
|
||||
data_dir=data_dir,
|
||||
short_window=short_window,
|
||||
long_window=long_window,
|
||||
rsi_period=rsi_period,
|
||||
overbought=overbought,
|
||||
oversold=oversold
|
||||
oversold=oversold,
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
async def init_backtest_db():
|
||||
sma_list = [{"short_window": 3, "long_window": 6}, {"short_window": 6, "long_window": 12},
|
||||
{"short_window": 12, "long_window": 24}, {"short_window": 14, "long_window": 18},
|
||||
{"short_window": 15, "long_window": 10}]
|
||||
await init_tortoise()
|
||||
wance_db = await wance_data_stock.WanceDataStock.all()
|
||||
sma_list_lenght = len(sma_list)
|
||||
|
||||
for stock_code in wance_db:
|
||||
for i in range(sma_list_lenght):
|
||||
short_window = sma_list[i]['short_window']
|
||||
long_window = sma_list[i]['long_window']
|
||||
source_column_name = f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}'
|
||||
result = await start_rsi_backtest_service(field_list=['close', 'time'],
|
||||
stock_list=[stock_code.stock_code],
|
||||
short_window=short_window,
|
||||
long_window=long_window,
|
||||
overbought=70,
|
||||
oversold=30)
|
||||
|
||||
print(f"回测成功 {source_column_name}")
|
||||
return result # 返回回测结果
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 测试类的回测
|
||||
asyncio.run(run_rsi_backtest(field_list=['close', 'time'],
|
||||
stock_list=['601222.SH', '601677.SH'],
|
||||
count=-1,
|
||||
short_window=10,
|
||||
long_window=30,
|
||||
# 主函数，启动回测服务
|
||||
asyncio.run(run_combined_rsi_backtest(field_list=['close', 'time'],
|
||||
stock_list=["688031.SH", "600025.SH", "601222.SH"],
|
||||
user_id=1,
|
||||
stock_weights=[{"688031.SH": 0.5, "600025.SH": 0.3, "601222.SH": 0.2}],
|
||||
rsi_period=14,
|
||||
overbought=70,
|
||||
oversold=30
|
||||
))
|
||||
|
||||
# # 初始化数据库表
|
||||
# asyncio.run(init_backtest_db())
|
||||
oversold=30))
|
||||
|
||||
@ -28,7 +28,7 @@ async def start_combination_service(field_list: list,
|
||||
overbought: int = 70,
|
||||
oversold: int = 30,
|
||||
signal_window: int = 9,
|
||||
user_id: int = 1):
|
||||
user_id: int = 1) -> object:
|
||||
# 数据检查
|
||||
await data_check(field_list=field_list,
|
||||
stock_list=stock_list,
|
||||
|
||||
@ -10,4 +10,4 @@ class BackObservedData(Model):
|
||||
|
||||
|
||||
class Meta:
|
||||
table = with_table_name("user_combination_history")
|
||||
table = with_table_name("back_observed_data")
|
||||
@ -3,11 +3,12 @@ from src.models import with_table_name
|
||||
|
||||
|
||||
class UserCombinationHistory(Model):
|
||||
id = fields.IntField()
|
||||
id = fields.IntField(pk=True, )
|
||||
last_time = fields.CharField(max_length=10, null=True, description='最后一次使用的时间', )
|
||||
stock_code = fields.CharField(max_length=20, null=True, description='股票代码', )
|
||||
stock_name = fields.CharField(max_length=20, null=True, description='股票名称', )
|
||||
stock_weights = fields.JSONField(null=True, description='权重存储', )
|
||||
strategy = fields.JSONField(null=True, description='用户使用的策略参数', )
|
||||
strategy_name = fields.CharField(max_length=50, null=True, description='用户使用的策略', )
|
||||
strategy_parame = fields.JSONField(null=True, description='用户使用的策略参数', )
|
||||
user_id = fields.IntField(description='用户的id', )
|
||||
|
||||
class Meta:
|
||||
table = with_table_name("user_combination_history")
|
||||
@ -18,7 +18,6 @@ async def get_local_data(field_list: list, stock_list: list, period: str, start_
|
||||
data_dir=data_dir)
|
||||
return await data_processing(result)
|
||||
|
||||
|
||||
async def data_processing(result_local):
|
||||
# 初始化一个空的列表，用于存储每个股票的数据框
|
||||
df_list = []
|
||||
@ -27,9 +26,11 @@ async def data_processing(result_local):
|
||||
for stock_code, df in result_local.items():
|
||||
# 确保 df 是一个 DataFrame
|
||||
if isinstance(df, pd.DataFrame):
|
||||
# 将时间戳转换为日期时间格式，只保留年-月-日
|
||||
df['time'] = pd.to_datetime(df['time'], unit='ms').dt.date
|
||||
# 将 'time' 列设置为索引，保留为日期格式
|
||||
# 将时间戳转换为 UTC 日期时间格式
|
||||
df['time'] = pd.to_datetime(df['time'], unit='ms', utc=True)
|
||||
# 转换为指定时区（例如东八区）
|
||||
df['time'] = df['time'].dt.tz_convert('Asia/Shanghai')
|
||||
# 将 'time' 列设置为索引
|
||||
df.set_index('time', inplace=True)
|
||||
# 将 'close' 列重命名为 'close_股票代码'
|
||||
df.rename(columns={'close': f'close_{stock_code}'}, inplace=True)
|
||||
@ -41,13 +42,14 @@ async def data_processing(result_local):
|
||||
# 使用 pd.concat() 将所有 DataFrame 合并为一个大的 DataFrame，保留所有列
|
||||
combined_df = pd.concat(df_list, axis=1)
|
||||
|
||||
# 确保返回的 DataFrame 索引是日期格式
|
||||
# 确保索引是日期时间格式
|
||||
combined_df.index = pd.to_datetime(combined_df.index)
|
||||
|
||||
return combined_df
|
||||
|
||||
|
||||
|
||||
|
||||
def convert_pandas_to_json_serializable(data: pd.Series) -> str:
|
||||
"""
|
||||
将 Pandas Series 或 DataFrame 中的 Timestamp 索引转换为字符串，并返回 JSON 可序列化的结果。
|
||||
|
||||
166
src/utils/combination_until.py
Normal file
166
src/utils/combination_until.py
Normal file
@ -0,0 +1,166 @@
|
||||
import json
|
||||
from datetime import datetime
|
||||
|
||||
import numpy as np
|
||||
from xtquant import xtdata
|
||||
import pandas as pd
|
||||
|
||||
from src.models import wance_data_stock
|
||||
|
||||
# 数据的列名
|
||||
columns = ['open', 'high', 'low', 'close', 'volume', 'amount', 'settelmentPrice',
|
||||
'openInterest', 'preClose', 'suspendFlag']
|
||||
|
||||
|
||||
# 获取本地数据并进行处理
|
||||
async def get_local_data(field_list: list, stock_list: list, period: str, start_time: str, end_time: str,
|
||||
count: int, dividend_type: str, fill_data: bool, data_dir: str):
|
||||
list = []
|
||||
for field in stock_list:
|
||||
result = await wance_data_stock.WanceDataStock.filter(stock_code=field)
|
||||
list.append(int(result[0].time_start))
|
||||
start_time = str(max(list))
|
||||
result = xtdata.get_local_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time,
|
||||
end_time=end_time, count=count, dividend_type=dividend_type, fill_data=fill_data,
|
||||
data_dir=data_dir)
|
||||
return await data_processing(result)
|
||||
|
||||
async def data_processing(result_local):
|
||||
# 初始化一个空的列表，用于存储每个股票的数据框
|
||||
df_list = []
|
||||
# 用于存储每个股票的最早时间
|
||||
start_times = []
|
||||
|
||||
# 遍历字典中的 DataFrame
|
||||
for stock_code, df in result_local.items():
|
||||
# 确保 df 是一个 DataFrame
|
||||
if isinstance(df, pd.DataFrame):
|
||||
# 将时间戳转换为 UTC 日期时间格式
|
||||
df['time'] = pd.to_datetime(df['time'], unit='ms', utc=True)
|
||||
# 转换为指定时区（例如东八区）
|
||||
df['time'] = df['time'].dt.tz_convert('Asia/Shanghai')
|
||||
# 记录每个股票的最早时间
|
||||
start_times.append(df['time'].min())
|
||||
# 将 'time' 列设置为索引
|
||||
df.set_index('time', inplace=True)
|
||||
# 将 'close' 列重命名为 'close_股票代码'
|
||||
df.rename(columns={'close': f'{stock_code}'}, inplace=True)
|
||||
# 将所有列添加到列表中
|
||||
df_list.append(df) # 保留所有字段，包括重命名后的 'close_股票代码'
|
||||
else:
|
||||
print(f"数据格式错误: {stock_code} 不包含 DataFrame")
|
||||
|
||||
# 找到所有股票数据中最晚的开始时间
|
||||
latest_start_time = max(start_times)
|
||||
|
||||
# 使用 pd.concat() 将所有 DataFrame 合并为一个大的 DataFrame，保留所有列
|
||||
combined_df = pd.concat(df_list, axis=1)
|
||||
|
||||
# 确保索引是日期时间格式
|
||||
combined_df.index = pd.to_datetime(combined_df.index)
|
||||
|
||||
# 根据最新的开始时间切割数据
|
||||
combined_df = combined_df[combined_df.index >= latest_start_time]
|
||||
|
||||
# 填充 NaN 值为 0
|
||||
combined_df.fillna(0, inplace=True)
|
||||
|
||||
return combined_df
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def convert_pandas_to_json_serializable(data: pd.Series) -> str:
|
||||
"""
|
||||
将 Pandas Series 或 DataFrame 中的 Timestamp 索引转换为字符串，并返回 JSON 可序列化的结果。
|
||||
|
||||
参数:
|
||||
data: pd.Series 或 pd.DataFrame, 带有时间戳索引的 pandas 数据
|
||||
|
||||
返回:
|
||||
JSON 字符串，键为日期字符串，值为原数据的值。
|
||||
"""
|
||||
# 判断数据类型
|
||||
if isinstance(data, (pd.Series, pd.DataFrame)):
|
||||
# 如果索引是时间戳类型，则转换为 YYYYMMDD 格式
|
||||
if isinstance(data.index, pd.DatetimeIndex):
|
||||
data.index = data.index.strftime('%Y%m%d')
|
||||
|
||||
# 处理 NaN 和 None 的情况，替换为 0 或其他合适的默认值
|
||||
data = data.replace([np.nan, None], 0)
|
||||
|
||||
# 将索引重置为普通列，然后转换为字典
|
||||
json_serializable_data = data.rename_axis('date').reset_index().to_dict(orient='records')
|
||||
|
||||
# 将字典转换为 JSON 格式字符串
|
||||
json_string = json.dumps(json_serializable_data)
|
||||
return json_string
|
||||
else:
|
||||
raise ValueError("输入必须为 Pandas Series 或 DataFrame")
|
||||
|
||||
|
||||
def clean_value(value):
|
||||
"""清理数据值，使其适合 JSON 序列化。"""
|
||||
if pd.isna(value) or np.isinf(value):
|
||||
return 0 # 或者可以使用 None 代替
|
||||
return value
|
||||
|
||||
|
||||
def to_json_serializable(data):
|
||||
"""
|
||||
将 Pandas DataFrame 转换为 JSON 可序列化的格式。
|
||||
- 将索引（如果是时间戳）转换为 'YYYYMMDD' 格式。
|
||||
- 将数据按时间升序排列。
|
||||
- 返回数组形式的结构，内部仍使用字典存放每一行数据。
|
||||
"""
|
||||
result = []
|
||||
|
||||
if isinstance(data, pd.DataFrame):
|
||||
# 将索引（时间戳）转换为 YYYYMMDD 格式
|
||||
data.index = data.index.strftime('%Y%m%d')
|
||||
|
||||
# 按时间（索引）升序排序
|
||||
data = data.sort_index(ascending=True)
|
||||
|
||||
# 遍历 DataFrame 的每一行
|
||||
for idx, row in data.iterrows():
|
||||
# 创建一个字典来存放当前行的数据
|
||||
row_dict = {'date': idx} # 将日期作为键
|
||||
# 清理行中的每个值
|
||||
for col in row.index:
|
||||
row_dict[col] = clean_value(row[col]) # 清理每个值
|
||||
result.append(row_dict) # 将字典添加到结果数组中
|
||||
|
||||
return result
|
||||
|
||||
elif isinstance(data, pd.Series):
|
||||
# 将 Series 转换为数组形式（每一行的数据以字典形式存放）
|
||||
for idx in data.index:
|
||||
result.append({str(idx.strftime('%Y%m%d')): clean_value(data[idx])})
|
||||
return result
|
||||
|
||||
else:
|
||||
raise TypeError("输入数据必须是 Pandas 的 DataFrame 或 Series")
|
||||
|
||||
async def data_check(field_list: list,
|
||||
stock_list: list,
|
||||
period: str = '1d',
|
||||
start_time: str = '',
|
||||
end_time: str = '',
|
||||
count: int = -1,
|
||||
dividend_type: str = 'none',
|
||||
fill_data: bool = True,
|
||||
data_dir: str = '', ):
|
||||
result_data = xtdata.get_local_data(field_list=[], stock_list=stock_list, period=period, start_time=start_time,
|
||||
end_time=end_time, count=count, dividend_type=dividend_type, fill_data=fill_data,
|
||||
data_dir=data_dir)
|
||||
time_now = int(datetime.now().strftime('%Y%m%d'))
|
||||
for i in stock_list:
|
||||
close = int(result_data.get(i).index[-1])
|
||||
if close != 0 and close < time_now:
|
||||
xtdata.download_history_data(stock_code=i,
|
||||
period='1d',
|
||||
start_time='',
|
||||
end_time='',
|
||||
incrementally=True)
|
||||
Loading…
Reference in New Issue
Block a user