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 platform import psutil # import multiprocessing # import matplotlib class MyPandasData(PandasData): lines = ('hl', 'dif', 'dea', 'macd', 'rsi_6', 'rsi_12', 'rsi_24',) params = (('hl', 7), ('dif', 8), ('dea', 9), ('macd', 10), ('rsi_6', 11), ('rsi_12', 12), ('rsi_24', 13), ) ''' 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', 3), # 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.pos_price = 0 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.hl = self.datas[0].hl self.dif = self.datas[0].dif self.dea = self.datas[0].dea self.macd = self.datas[0].macd self.rsi_6 = self.datas[0].rsi_6 self.rsi_12 = self.datas[0].rsi_12 self.rsi_24 = self.datas[0].rsi_24 # 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) self.sma60 = btind.MovingAverageSimple(self.datas[0].close, period=60) 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: vola = self.params.Volatility / 100 rate = self.params.rate / 100 lowest = np.min(self.low.get(size=self.params.num)) highest = np.max(self.high.get(size=self.params.num)) # > self.sma5[-1] # and (((lowest * (1 - vola)) < self.low[-2] < (lowest * (1 + vola))) or ( # (lowest * (1 - vola)) < self.low[-1] < (lowest * (1 + vola)))) \ if self.hl[-1] == 2 or self.hl[-1] == 1: m = -2 # self.order = self.buy() # self.pos_price = self.low[-1] while True: if (self.hl[m] == 2 or self.hl[m] == 1) and self.macd[m] > self.macd[-1] \ and 0.99 * self.sma10[m] < self.sma5[m] < 1.01 * self.sma10[m] \ and 0.99 * self.sma10[m - 1] < self.sma5[m - 1] < 1.01 * self.sma10[m - 1] \ and 0.99 * self.sma10[m - 2] < self.sma5[m - 2] < 1.01 * self.sma10[m - 2] \ and self.dataclose[0] > self.sma5[0] \ and self.dataclose[-1] > self.dataopen[-1] \ and self.volume[-1] > self.volume[-2] \ and (self.sma10[-2] - self.sma5[-2]) < (self.sma10[-1] - self.sma5[-1]) \ and self.low[-2] < self.sma5[-2] * (1 - rate) \ and self.sma5[-1] < self.sma10[-1] < self.sma20[-1] < self.sma20[-2] < self.sma20[-3]: self.order = self.buy() self.pos_price = self.low[-1] break m -= 1 if m + len(self) == 2: break # elif (self.hl[0] == 5 or self.dataclose[0] < self.sma5[0]): elif self.dataclose[0] < self.sma5[0] or self.sma5[0] < self.sma5[-1] \ or self.dataclose[0] < self.pos_price or self.high[0] > self.sma5[0] * (1 + vola): self.order = self.close() self.pos_price = 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): print('开始存数据') df = pd.DataFrame(list(lt), columns=['周期', '波动率', 'MA5斜率', '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', '盈亏对比']) df.sort_values(by=['周期', '波动率', 'MA5斜率'], ascending=True, inplace=True) df = df.reset_index(drop=True) if platform.node() == 'DanieldeMBP.lan': df.to_csv(f"/Users/daniel/Documents/策略/策略穷举-均线粘连后底分型{dt.now().strftime('%Y%m%d%H%m%S')}.csv", index=True, encoding='utf_8_sig', mode='w') else: df.to_csv(f"C:\Daniel\策略\策略穷举底分型_均线缠绕_只买一次{dt.now().strftime('%Y%m%d%H%m%S')}.csv", index=True, encoding='utf_8_sig', mode='w') print(f'结果:, \n, {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}%MA5斜率为{rate}', 'myPID is ', os.getpid()) sttime = dt.now() engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qmt_stocks_tech?charset=utf8', max_overflow=-1) for stock in table_list: # print(stock) stk_df = pd.read_sql_table(stock, engine.connect()) stk_df.time = pd.to_datetime(stk_df.time) # stk_df = stk_df[stk_df['HL'] != '-'] try: stk_df['HL'] = stk_df['HL'].map({'L': 1, 'LL': 2, 'L*': 3, 'H': 4, 'HH': 5, 'H*': 6, '-': 7}) except BaseException: print(f'{stock}数据不全，不做测试') else: 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(2017, 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', hl='HL', dif='dif', dea='dea', macd='macd', rsi_6='rsi_6', rsi_12='rsi_12', rsi_24='rsi_24', ) # 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 as e: err_list.append(stock) # print(f'{num}天波动率为{Volatility}%MA5斜率为{rate}的{stock}错误') # print(e) else: if cerebro.broker.getvalue() > 100000.0: result_change.append(cerebro.broker.getvalue() - 100000) result.append(stock) # print('recode！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！') # print(result) elif cerebro.broker.getvalue() < 100000.0: result_change_fall.append(cerebro.broker.getvalue() - 100000) # print('aaaaaaaaaaa') # print(result_change_fall) print(f'计算总数={len(result) + len(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、MA5斜率为{rate}%，结果状态为：') print('正盈利的个股为：', len(result), '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) / len(result)},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) / len(result_change_fall)},最大亏损:{np.min(result_change_fall)} 最小亏损:{np.max(result_change_fall)}') # '周期', '波动率', 'MA5斜率', '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', '盈亏对比'] 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), len(result_change) / len(result_change_fall)]) # to_df(list_date) endtime = dt.now() print(f'{num}天波动率为{Volatility}%MA5斜率为{rate},myPID is {os.getpid()}.本轮耗时为{endtime - sttime}') else: print('阿欧', len(result), len(result_change), len(result_change_fall), num, Volatility, rate, err_list) list_date.append([num, Volatility, rate, 0, len(result) / len(table_list), len(result), len(result), len(result), len(result), len(result), len(result), len(result), 0]) # cerebro.plot() # df = pd.DataFrame( # columns=['周期', '波动率', 'MA5斜率', '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) pus = psutil.Process() # pus.cpu_affinity([23, 16, 17, 18, 19, 20, 21, 22]) # pus.cpu_affinity([0, 1, 2, 3, 4, 5, 6, 7]) # pus.cpu_affinity([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) # print(type(platform.node())) # 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_tech') 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[0:500] print(f'计算个股数为：{len(table_list)}') list_date = mp.Manager().list() thread_list = [] pool = mp.Pool(processes=mp.cpu_count()) # pool = mp.Pool(processes=8) for num in range(60, 80, 20): for Volatility in range(7, 13, 1): for rate in range(3, 13, 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}%MA5斜率为{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) pool.close() pool.join() to_df(list_date) 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)