stock/backtrader/230202_backtrader.py

import os
import traceback

import numpy as np
from sqlalchemy import create_engine
import pandas as pd
import pymysql
import backtrader as bt
import backtrader.indicators as btind
import datetime
import math
from datetime import datetime as dt
import multiprocessing as mp
from backtrader.feeds import PandasData


# import multiprocessing
# import matplotlib

class MyPandasData(PandasData):
    lines = ()
    params = ()
    '''
    lines = ('change_pct', 'net_amount_main', 'net_pct_main', 'net_amount_xl', 'net_pct_xl', 'net_amount_l', 'net_pct_l'
             , 'net_amount_m', 'net_pct_m', 'net_amount_s', 'net_pct_s',)
    params = (('change_pct', 7),
              ('net_amount_main', 8),
              ('net_pct_main', 9),
              ('net_amount_xl', 10),
              ('net_pct_xl', 11),
              ('net_amount_l', 12),
              ('net_pct_l', 13),
              ('net_amount_m', 14),
              ('net_pct_m', 15),
              ('net_amount_s', 16),
              ('net_pct_s', 17),
              )
    '''


class TestStrategy(bt.Strategy):
    params = (
        ("num", 3),
        ('Volatility', 0),
        ('rate', 5),  # 注意要有逗号！！
    )

    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        # print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # self.num = num
        # self.Volatility = Volatility/100
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close
        self.dataopen = self.datas[0].open
        self.high = self.datas[0].high
        self.low = self.datas[0].low
        self.volume = self.datas[0].volume
        # self.change_pct = self.datas[0].change_pct
        # self.net_amount_main = self.datas[0].net_amount_main
        # self.net_pct_main = self.datas[0].net_pct_main
        # self.net_amount_xl = self.datas[0].net_amount_xl
        # self.net_pct_xl = self.datas[0].net_pct_xl
        # self.net_amount_l = self.datas[0].net_amount_l
        # self.net_pct_l = self.datas[0].net_pct_l
        self.sma5 = btind.MovingAverageSimple(self.datas[0].close, period=5)
        self.sma10 = btind.MovingAverageSimple(self.datas[0].close, period=10)
        self.sma20 = btind.MovingAverageSimple(self.datas[0].close, period=20)

    def notify_order(self, order):
        """
        订单状态处理

        Arguments:
            order {object} -- 订单状态
        """
        if order.status in [order.Submitted, order.Accepted]:
            # 如订单已被处理，则不用做任何事情
            return

        # 检查订单是否完成
        if order.status in [order.Completed]:
            if order.isbuy():
                self.buyprice = order.executed.price
                self.buycomm = order.executed.comm
            self.bar_executed = len(self)

        # 订单因为缺少资金之类的原因被拒绝执行
        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            pass
            # self.log('Order Canceled/Margin/Rejected')

        # 订单状态处理完成，设为空
        self.order = None

    def notify_trade(self, trade):
        """
        交易成果

        Arguments:
            trade {object} -- 交易状态
        """
        if not trade.isclosed:
            return

        # 显示交易的毛利率和净利润
        # self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm))

    def next(self):
        # print(self.num,self.Volatility)
        # Simply log the closing price of the series from the reference
        # self.sma20[-2] < self.sma20[-1] < self.sma20[0] and self.sma10[-2] < self.sma10[-1] < self.sma10[0]
        # and (self.sma5[-1] < self.sma10[-1])
        # and (self.net_pct_l[0] > 10) and (self.net_pct_xl[0] > 3)  \
        # and (self.net_amount_main[-1] > 0) and (self.net_amount_main[0] > 0)


         if len(self) > self.params.num:
            lowest = np.min(self.low.get(size=self.params.num))
            highest = np.max(self.high.get(size=self.params.num))
            vola = self.params.Volatility / 100
            rate = self.params.rate / 100
            # print(f'{self.params.num}日天最低值：{lowest},波动率为{self.params.Volatility/100}')
            if (self.dataclose[0] > self.dataopen[0]) \
                    and (((lowest * (1 - vola)) < self.low[-2] < (lowest * (1 + vola))) or (
                    (lowest * (1 - vola)) < self.low[-1] < (lowest * (1 + vola)))) \
                    and (self.dataclose[0] > self.sma5[0]) and self.sma5[0] > self.sma5[-1] \
                    and (not self.position) and (self.sma5[0] > self.sma10[0]):
                # self.log('BUY CREATE, %.2f' % self.dataclose[0])
                self.order = self.buy()
            elif self.dataclose < self.sma5[0] or self.sma5[0] < self.sma10[0] \
                    or (self.dataclose[0] > (self.sma5[0] * (1 + rate))) or \
                    (((highest * (1 - vola)) < self.high[-2] < (highest * (1 + vola))) or (
                            (highest * (1 - vola)) < self.high[-1] < (highest * (1 + vola)))):
                self.order = self.close()
                # self.log('Close, %.2f' % self.dataclose[0])



    def stop(self):
        # pass
        self.log(u'(MA趋势交易效果) Ending Value %.2f' % (self.broker.getvalue()))


def err_call_back(err):
    print(f'出错啦~ error：{str(err)}')
    traceback.format_exc(err)


def to_df(lt):
    df = pd.DataFrame(list(lt), columns=['周期', '波动率', '乖离率', '盈利个数', '盈利比例', '总盈利', '平均盈利', '最大盈利',
                               '最小盈利', '总亏损', '平均亏损', '最大亏损', '最小亏损'])
    df.sort_values(by=['周期', '波动率', '乖离率'], ascending=True, inplace=True)
    df = df.reset_index(drop=True)
    df.to_csv(f"D:\Daniel\策略\策略穷举{dt.now().strftime('%Y%m%d')}.csv", index=True, encoding='utf-8', mode='w')
    print(df)


def backtrader(list_date, table_list, result, result_change, result_change_fall, num, Volatility, rate, err_list):
    print(f'{num}天波动率为{Volatility}%乖离率为{rate}', 'myPID is ', os.getpid())
    sttime = dt.now()
    engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qmt_stocks_tech?charset=utf8')
    for stock in table_list:
        # print(stock)
        stk_df = pd.read_sql_table(stock, engine)
        stk_df.time = pd.to_datetime(stk_df.time)

        if len(stk_df) > 60:
            cerebro = bt.Cerebro()
            cerebro.addstrategy(TestStrategy, num=num, Volatility=Volatility, rate=rate)
            cerebro.addsizer(bt.sizers.FixedSize, stake=10000)
            data = MyPandasData(dataname=stk_df,
                                fromdate=datetime.datetime(2010, 1, 1),
                                todate=datetime.datetime(2022, 10, 30),
                                datetime='time',
                                open='open_back',
                                close='close_back',
                                high='high_back',
                                low='low_back',
                                volume='volume_back',
                                # change_pct='change_pct',
                                # net_amount_main='net_amount_main',
                                # net_pct_main='net_pct_main',
                                # net_amount_xl='net_amount_xl',
                                # net_pct_xl='net_pct_xl',
                                # net_amount_l='net_amount_l',
                                # net_pct_l='net_pct_l',
                                # net_amount_m='net_amount_m',
                                # net_pct_m='net_pct_m',
                                # net_amount_s='net_amount_s',
                                # net_pct_s='net_pct_s',
                                )
            # print('取值完成')
            cerebro.adddata(data, name=stock)
            cerebro.broker.setcash(100000.0)
            cerebro.broker.setcommission(0.005)
            cerebro.addanalyzer(bt.analyzers.PyFolio)
            # 策略执行前的资金
            # print('启动资金: %.2f' % cerebro.broker.getvalue())
            try:
                # 策略执行
                cerebro.run()
            except IndexError:
                err_list.append(stock)
            else:
                if cerebro.broker.getvalue() > 100000.0:
                    result_change.append((cerebro.broker.getvalue() / 10000 - 1))
                    result.append(stock)
                    # print('recode！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！')
                    # print(result)
                else:
                    result_change_fall.append((1 - cerebro.broker.getvalue() / 10000))
                    # print('aaaaaaaaaaa')
                    # print(result_change_fall)

    if len(result) * len(result_change) * len(result_change_fall) != 0:
        print(f'以{num}内最低值波动{Volatility}为支撑、乖离率为{rate}%，结果状态为：')
        print('正盈利的个股为：', len(result_change), '成功率为：', len(result) / len(table_list))
        print(
            f'总盈利:{np.sum(result_change)} 平均盈利:{np.mean(result_change)},最大盈利:{np.max(result_change)}, 最小盈利:{np.min(result_change)}')
        print(
            f'总亏损:{np.sum(result_change_fall)},平均亏损:{np.mean(result_change_fall)},最大亏损:{np.min(result_change_fall)} 最小亏损:{np.max(result_change_fall)}')

        list_date.append([num, Volatility, rate, len(result), len(result) / len(table_list), np.nansum(result_change),
                          np.nanmean(result_change), np.nanmax(result_change), np.min(result_change),
                          np.nansum(result_change_fall), np.nanmean(result_change_fall),
                          np.nanmin(result_change_fall), np.nanmax(result_change_fall)])
        to_df(list_date)
        endtime = dt.now()
        print(f'{num}天波动率为{Volatility}%乖离率为{rate},myPID is {os.getpid()}.本轮耗时为{endtime - sttime}')
    else:
        print(result, result_change, result_change_fall, num, Volatility, rate, err_list)
    # cerebro.plot()


df = pd.DataFrame(
    columns=['周期', '波动率', '盈利个数', '盈利比例', '总盈利', '平均盈利', '最大盈利', '最小盈利', '总亏损',
             '平均亏损', '最大亏损', '最小亏损'])
if __name__ == '__main__':
    starttime = dt.now()
    print(starttime)
    # engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx?charset=utf8', poolclass=NullPool)

    # stocks = pd.read_sql_query(
    #                     'select value from MA5_1d', engine_hlfx)

    fre = '1d'
    db = pymysql.connect(host='localhost',
                         user='root',
                         port=3307,
                         password='r6kEwqWU9!v3',
                         database='qmt_stocks')
    cursor = db.cursor()
    cursor.execute("show tables like '%%%s%%' " % fre)
    table_list = [tuple[0] for tuple in cursor.fetchall()]
    # print(table_list)
    # table_list = table_list[0:100]

    list_date = mp.Manager().list()
    thread_list = []
    pool = mp.Pool(processes=mp.cpu_count())
    for num in range(60, 100, 20):
        for Volatility in range(5, 7, 1):
            for rate in range(7, 9, 1):
                step = math.ceil(len(table_list) / mp.cpu_count())
                result = []
                result_change = []
                result_change_fall = []
                err_list = []
                print(f'{num}天波动率为{Volatility}%乖离率为{rate}')
                # for i in range(0, len(table_list), step):
                stattime = dt.now()
                # thd = threading.local()
                # print(i)
                # p = mp.Process(target=backtrader, args=(df, table_list, result, result_change, result_change_fall,
                #                                         num, Volatility, rate, err_list))
                # thread_list.append(p)
                pool.apply_async(func=backtrader,
                                 args=(list_date, table_list, result, result_change, result_change_fall,
                                       num, Volatility, rate, err_list,), error_callback=err_call_back)
                # p.start()
                # p.join()
                # print(thread_list)
    # for thread in thread_list:
    #     thread.start()
    # for thread in thread_list:
    #     thread.join()
    pool.close()
    pool.join()

    edtime = dt.now()
    print('总耗时：', edtime - starttime)
    # df.to_csv(r'C:\Users\Daniel\Documents\策略穷举2.csv', index=True)