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+import os
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+import traceback
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+import numpy as np
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+from sqlalchemy import create_engine
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+import pandas as pd
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+import pymysql
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+import backtrader as bt
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+import backtrader.indicators as btind
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+import datetime
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+import math
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+from datetime import datetime as dt
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+import multiprocessing as mp
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+from backtrader.feeds import PandasData
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+import platform
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+import psutil
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+
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+
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+# import multiprocessing
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+# import matplotlib
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+
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+
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+class MyPandasData(PandasData):
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+ lines = ('hl', 'dif', 'dea', 'macd', 'rsi_6', 'rsi_12', 'rsi_24',)
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+ params = (('hl', 7),
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+ ('dif', 8),
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+ ('dea', 9),
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+ ('macd', 10),
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+ ('rsi_6', 11),
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+ ('rsi_12', 12),
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+ ('rsi_24', 13),
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+ )
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+ '''
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+ lines = ('change_pct', 'net_amount_main', 'net_pct_main', 'net_amount_xl', 'net_pct_xl', 'net_amount_l', 'net_pct_l'
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+ , 'net_amount_m', 'net_pct_m', 'net_amount_s', 'net_pct_s',)
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+ params = (('change_pct', 7),
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+ ('net_amount_main', 8),
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+ ('net_pct_main', 9),
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+ ('net_amount_xl', 10),
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+ ('net_pct_xl', 11),
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+ ('net_amount_l', 12),
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+ ('net_pct_l', 13),
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+ ('net_amount_m', 14),
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+ ('net_pct_m', 15),
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+ ('net_amount_s', 16),
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+ ('net_pct_s', 17),
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+ )
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+ '''
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+
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+
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+class TestStrategy(bt.Strategy):
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+ params = (
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+ ("num", 3),
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+ ('Volatility', 0),
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+ ('rate', 3), # 注意要有逗号！！
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+ )
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+
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+ def log(self, txt, dt=None):
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+ ''' Logging function for this strategy'''
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+ dt = dt or self.datas[0].datetime.date(0)
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+ # print('%s, %s' % (dt.isoformat(), txt))
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+
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+ def __init__(self):
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+ # self.num = num
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+ # self.Volatility = Volatility/100
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+ # Keep a reference to the "close" line in the data[0] dataseries
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+ self.pos_price = 0
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+ self.dataclose = self.datas[0].close
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+ self.dataopen = self.datas[0].open
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+ self.high = self.datas[0].high
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+ self.low = self.datas[0].low
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+ self.volume = self.datas[0].volume
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+ self.hl = self.datas[0].hl
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+ self.dif = self.datas[0].dif
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+ self.dea = self.datas[0].dea
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+ self.macd = self.datas[0].macd
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+ self.rsi_6 = self.datas[0].rsi_6
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+ self.rsi_12 = self.datas[0].rsi_12
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+ self.rsi_24 = self.datas[0].rsi_24
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+ # self.change_pct = self.datas[0].change_pct
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+ # self.net_amount_main = self.datas[0].net_amount_main
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+ # self.net_pct_main = self.datas[0].net_pct_main
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+ # self.net_amount_xl = self.datas[0].net_amount_xl
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+ # self.net_pct_xl = self.datas[0].net_pct_xl
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+ # self.net_amount_l = self.datas[0].net_amount_l
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+ # self.net_pct_l = self.datas[0].net_pct_l
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+ self.sma5 = btind.MovingAverageSimple(self.datas[0].close, period=5)
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+ self.sma10 = btind.MovingAverageSimple(self.datas[0].close, period=10)
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+ self.sma20 = btind.MovingAverageSimple(self.datas[0].close, period=20)
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+ self.sma60 = btind.MovingAverageSimple(self.datas[0].close, period=60)
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+
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+ def notify_order(self, order):
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+ """
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+ 订单状态处理
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+
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+ Arguments:
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+ order {object} -- 订单状态
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+ """
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+ if order.status in [order.Submitted, order.Accepted]:
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+ # 如订单已被处理，则不用做任何事情
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+ return
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+
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+ # 检查订单是否完成
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+ if order.status in [order.Completed]:
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+ if order.isbuy():
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+ self.buyprice = order.executed.price
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+ self.buycomm = order.executed.comm
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+ self.bar_executed = len(self)
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+
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+ # 订单因为缺少资金之类的原因被拒绝执行
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+ elif order.status in [order.Canceled, order.Margin, order.Rejected]:
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+ pass
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+ # self.log('Order Canceled/Margin/Rejected')
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+
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+ # 订单状态处理完成，设为空
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+ self.order = None
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+
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+ def notify_trade(self, trade):
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+ """
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+ 交易成果
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+
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+ Arguments:
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+ trade {object} -- 交易状态
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+ """
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+ if not trade.isclosed:
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+ return
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+
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+ # 显示交易的毛利率和净利润
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+ # self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm))
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+
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+ def next(self):
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+ # print(self.num,self.Volatility)
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+ # Simply log the closing price of the series from the reference
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+ # self.sma20[-2] < self.sma20[-1] < self.sma20[0] and self.sma10[-2] < self.sma10[-1] < self.sma10[0]
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+ # and (self.sma5[-1] < self.sma10[-1])
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+ # and (self.net_pct_l[0] > 10) and (self.net_pct_xl[0] > 3) \
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+ # and (self.net_amount_main[-1] > 0) and (self.net_amount_main[0] > 0)
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+
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+ if len(self) > self.params.num:
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+ vola = self.params.Volatility / 100
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+ rate = self.params.rate / 100
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+ lowest = np.min(self.low.get(size=self.params.num))
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+ highest = np.max(self.high.get(size=self.params.num))
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+
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+ # > self.sma5[-1]
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+ # and (((lowest * (1 - vola)) < self.low[-2] < (lowest * (1 + vola))) or (
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+ # (lowest * (1 - vola)) < self.low[-1] < (lowest * (1 + vola)))) \
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+ if self.hl[-1] == 2 or self.hl[-1] == 1:
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+ m = -2
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+ # self.order = self.buy()
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+ # self.pos_price = self.low[-1]
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+
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+ while True:
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+ if (self.hl[m] == 2 or self.hl[m] == 1) and self.macd[m] > self.macd[-1] \
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+ and 0.99 * self.sma10[m] < self.sma5[m] < 1.01 * self.sma10[m] \
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+ and 0.99 * self.sma10[m-1] < self.sma5[m-1] < 1.01 * self.sma10[m-1] \
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+ and 0.99 * self.sma10[m-2] < self.sma5[m-2] < 1.01 * self.sma10[m-2] \
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+ and self.dataclose[0] > self.sma5[0] \
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+ and self.dataclose[-1] > self.dataopen[-1] \
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+ and self.volume[-1] > self.volume[-2] \
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+ and (self.sma10[-2] - self.sma5[-2]) < (self.sma10[-1] - self.sma5[-1]) \
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+ and self.low[-2] < self.sma5[-2] * (1 - rate) \
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+ and self.sma5[-1] < self.sma10[-1] < self.sma20[-1] < self.sma20[-2] < self.sma20[-3]:
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+ self.order = self.buy()
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+ self.pos_price = self.low[-1]
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+ break
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+ m -= 1
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+ if m + len(self) == 2:
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+ break
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+
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+ # elif (self.hl[0] == 5 or self.dataclose[0] < self.sma5[0]):
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+ elif self.dataclose[0] < self.sma5[0] or self.sma5[0] < self.sma5[-1] \
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+ or self.dataclose[0] < self.pos_price or self.high[0] > self.sma5[0] * (1 + vola):
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+ self.order = self.close()
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+ self.pos_price = 0
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+
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+ def stop(self):
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+ # pass
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+ self.log(u'(MA趋势交易效果) Ending Value %.2f' % (self.broker.getvalue()))
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+
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+
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+def err_call_back(err):
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+ print(f'出错啦~ error：{str(err)}')
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+ traceback.format_exc(err)
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+
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+
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+def to_df(lt):
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+ print('开始存数据')
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+ df = pd.DataFrame(list(lt),
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+ 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',
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+ '最小盈利', '总亏损', '平均亏损', '最大亏损', '最小亏损', '盈亏对比'])
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+ df.sort_values(by=['周期', '波动率', 'MA5斜率'], ascending=True, inplace=True)
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+ df = df.reset_index(drop=True)
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+ if platform.node() == 'DanieldeMBP.lan':
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+ 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,
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+ encoding='utf_8_sig', mode='w')
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+ else:
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+ 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',
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+ mode='w')
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+ print(f'结果:{df}')
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+
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+
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+def backtrader(list_date, table_list, result, result_change, result_change_fall, num, Volatility, rate, err_list):
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+ print(f'{num}天波动率为{Volatility}%MA5斜率为{rate}', 'myPID is ', os.getpid())
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+ sttime = dt.now()
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+ engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qmt_stocks_tech?charset=utf8')
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+ for stock in table_list:
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+ # print(stock)
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+ stk_df = pd.read_sql_table(stock, engine)
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+ stk_df.time = pd.to_datetime(stk_df.time)
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+ # stk_df = stk_df[stk_df['HL'] != '-']
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+ try:
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+ stk_df['HL'] = stk_df['HL'].map({'L': 1,
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+ 'LL': 2,
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+ 'L*': 3,
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+ 'H': 4,
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+ 'HH': 5,
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+ 'H*': 6,
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+ '-': 7})
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+ except BaseException:
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+ print(f'{stock}数据不全，不做测试')
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+ else:
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+ if len(stk_df) > 60:
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+ cerebro = bt.Cerebro()
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+ cerebro.addstrategy(TestStrategy, num=num, Volatility=Volatility, rate=rate)
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+ cerebro.addsizer(bt.sizers.FixedSize, stake=10000)
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+ data = MyPandasData(dataname=stk_df,
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+ fromdate=datetime.datetime(2017, 1, 1),
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+ todate=datetime.datetime(2022, 10, 30),
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+ datetime='time',
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+ open='open_back',
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+ close='close_back',
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+ high='high_back',
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+ low='low_back',
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+ volume='volume_back',
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+ hl='HL',
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+ dif='dif',
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+ dea='dea',
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+ macd='macd',
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+ rsi_6='rsi_6',
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+ rsi_12='rsi_12',
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+ rsi_24='rsi_24',
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+ )
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+ # print('取值完成')
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+ cerebro.adddata(data, name=stock)
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+ cerebro.broker.setcash(100000.0)
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+ cerebro.broker.setcommission(0.005)
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+ cerebro.addanalyzer(bt.analyzers.PyFolio)
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+ # 策略执行前的资金
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+ # print('启动资金: %.2f' % cerebro.broker.getvalue())
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+ try:
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+ # 策略执行
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+ cerebro.run()
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+ except IndexError as e:
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+ err_list.append(stock)
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+ # print(f'{num}天波动率为{Volatility}%MA5斜率为{rate}的{stock}错误')
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+ # print(e)
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+ else:
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+ if cerebro.broker.getvalue() > 100000.0:
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+ result_change.append(cerebro.broker.getvalue() - 100000)
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+ result.append(stock)
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+ # print('recode！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！')
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+ # print(result)
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+ elif cerebro.broker.getvalue() < 100000.0:
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+ result_change_fall.append(cerebro.broker.getvalue() - 100000)
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+ # print('aaaaaaaaaaa')
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+ # print(result_change_fall)
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+ print(f'计算总数={len(result) + len(result_change_fall)}')
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+ if len(result) * len(result_change) * len(result_change_fall) != 0:
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+ print(f'以{num}内最低值波动{Volatility}为支撑、MA5斜率为{rate}%，结果状态为：')
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+ print('正盈利的个股为：', len(result), '成功率为：', len(result) / len(table_list))
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+ print(
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+ 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)}')
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+ print(
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+ 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)}')
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+
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+ # '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']
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+ list_date.append([num, Volatility, rate, len(result), len(result) / len(table_list), np.nansum(result_change),
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+ np.nanmean(result_change), np.nanmax(result_change), np.min(result_change),
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+ np.nansum(result_change_fall), np.nanmean(result_change_fall),
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+ np.nanmin(result_change_fall), np.nanmax(result_change_fall),
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+ len(result_change) / len(result_change_fall)])
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+ to_df(list_date)
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+ endtime = dt.now()
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+ 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}')
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+ else:
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+ print('阿欧', len(result), len(result_change), len(result_change_fall), num, Volatility, rate, err_list)
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+ list_date.append([num, Volatility, rate, 0, len(result) / len(table_list), len(result),
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+ len(result), len(result), len(result), len(result), len(result), len(result), 0])
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+ to_df(list_date)
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+ # cerebro.plot()
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+
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+
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+# df = pd.DataFrame(
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+# 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',
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+# '平均亏损', '最大亏损', '最小亏损'])
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+if __name__ == '__main__':
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+ starttime = dt.now()
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+ print(starttime)
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+ pus = psutil.Process()
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+ # pus.cpu_affinity([23, 16, 17, 18, 19, 20, 21, 22])
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+ # pus.cpu_affinity([0, 1, 2, 3, 4, 5, 6, 7])
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+ # pus.cpu_affinity([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
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+ # print(type(platform.node()))
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+ # engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx?charset=utf8', poolclass=NullPool)
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+
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+ # stocks = pd.read_sql_query(
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+ # 'select value from MA5_1d', engine_hlfx)
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+
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+ fre = '1d'
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+ db = pymysql.connect(host='localhost',
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+ user='root',
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+ port=3307,
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+ password='r6kEwqWU9!v3',
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+ database='qmt_stocks_tech')
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+ cursor = db.cursor()
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+ cursor.execute("show tables like '%%%s%%' " % fre)
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+ 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()
|
|
|
+
|
|
|
+ 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)
|
