优化backtrader代码

.idea/stock.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Py38Quant" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Py39PyTorch" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
     <orderEntry type="module" module-name="stocks_to_sql" />
     <orderEntry type="module" module-name="quantify01" />

QMT/get_macd.py

@@ -0,0 +1,117 @@
+# coding:utf-8
+from datetime import datetime as dt
+import os
+import pandas as pd
+# from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
+# from xtquant.xttype import StockAccount
+# from xtquant import xtdata, xtconstant
+import time
+from sqlalchemy import create_engine
+from jqdatasdk import *
+import pymysql
+import multiprocessing as mp
+import math
+import talib as ta
+
+pd.set_option('display.max_columns', None) # 设置显示最大行
+
+def err_call_back(err):
+    print(f'出错啦~ error：{str(err)}')
+
+def myself_kdj(df):
+    low_list = df['low'].rolling(9, min_periods=9).min()
+    low_list.fillna(value=df['low'].expanding().min(), inplace=True)
+    high_list = df['high'].rolling(9, min_periods=9).max()
+    high_list.fillna(value=df['high'].expanding().max(), inplace=True)
+    rsv = (df['close'] - low_list) / (high_list - low_list) * 100
+    df['k'] = pd.DataFrame(rsv).ewm(com=2).mean()
+    df['d'] = df['k'].ewm(com=2).mean()
+    df['j'] = 3 * df['k'] - 2 * df['d']
+    return df
+
+
+# macd指标
+def get_macd_data(data, short=0, long1=0, mid=0):
+    if short == 0:
+        short = 12
+    if long1 == 0:
+        long1 = 26
+    if mid == 0:
+        mid = 9
+    data['sema'] = pd.Series(data['close']).ewm(span=short).mean()
+    data['lema'] = pd.Series(data['close']).ewm(span=long1).mean()
+    data.fillna(0, inplace=True)
+    data['dif'] = data['sema'] - data['lema']
+    data['dea'] = pd.Series(data['dif']).ewm(span=mid).mean()
+    data['macd'] = 2 * (data['dif'] - data['dea'])
+    data.fillna(0, inplace=True)
+    return data[['dif', 'dea', 'macd']]
+
+
+# rsi指标
+# 建议用talib库的RSI方法，亲测有用
+def get_ris(data):
+    data["rsi_6"] = ta.RSI(data['close'], timeperiod=6)
+    data["rsi_12"] = ta.RSI(data['close'], timeperiod=12)
+    data["rsi_24"] = ta.RSI(data['close'], timeperiod=24)
+
+
+def get_bias(df):
+    # 计算方法：
+    # bias指标
+    # N期BIAS=(当日收盘价-N期平均收盘价)/N期平均收盘价*100%
+    df['bias_6'] = (df['close'] - df['close'].rolling(6, min_periods=1).mean()) / df['close'].rolling(6,
+                                                                                                      min_periods=1).mean() * 100
+    df['bias_12'] = (df['close'] - df['close'].rolling(12, min_periods=1).mean()) / df['close'].rolling(12,
+                                                                                                        min_periods=1).mean() * 100
+    df['bias_24'] = (df['close'] - df['close'].rolling(24, min_periods=1).mean()) / df['close'].rolling(24,
+                                                                                                        min_periods=1).mean() * 100
+    df['bias_6'] = round(df['bias_6'], 2)
+    df['bias_12'] = round(df['bias_12'], 2)
+    df['bias_24'] = round(df['bias_24'], 2)
+
+
+def get_wilr(df):
+    # 威廉指标
+    # 建议用talib库的WILLR方法，亲测有用
+    df['willr'] = ta.WILLR(df['high'], df['low'], df['close'], timeperiod=14)
+
+
+def tech_anal(datas):
+    engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qmt_stocks?charset=utf8')
+    engine_tech = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qmt_stocks_tech?charset=utf8')
+    for stock in datas:
+        stock = stock.replace('XSHG', 'SH').replace('XSHE', 'SZ')
+        df = pd.read_sql_table('%s_1d' % stock, con=engine)
+        get_macd_data(df)
+        get_ris(df)
+        get_bias(df)
+        get_wilr(df)
+        df = df.reset_index(drop=True)
+        df.to_sql('%s_1d' % stock, con=engine_tech, index=False, if_exists='append')
+        # with engine.connect() as con:
+        #     con.execute("ALTER TABLE `%s_1d` ADD PRIMARY KEY (`time`);" % stock)
+        print(stock, df)
+
+
+if __name__ == '__main__':
+    sttime = dt.now()
+
+    engine_hlfx_pool = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx_pool?charset=utf8')
+    stock_pool = pd.read_sql_query('select securities from stocks_list', engine_hlfx_pool)
+    stock_pool = stock_pool.iloc[-1, 0].split(",")
+    stock_pool.sort()
+    print(len(stock_pool))
+
+    pool = mp.Pool(processes=mp.cpu_count())
+    # step = math.ceil(len(stock_pool) / mp.cpu_count())
+    step = 100
+
+    for i in range(0, len(stock_pool), step):
+        pool.apply_async(func=tech_anal, args=(stock_pool[i:i+step],), error_callback=err_call_back)
+
+    pool.close()
+    pool.join()
+    edtime = dt.now()
+
+    print(edtime-sttime)

YH_backtrader.py

@@ -0,0 +1,306 @@
+import os
+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
+from numba import jit, cuda, njit
+
+
+# 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):
+        print('__init__', dt.now())
+        print(f'{self.params.num}天波动率为{self.params.Volatility}%乖离率为{self.params.rate}', 'myPID is ', os.getpid())
+        # 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)
+        self.yx = self.dataclose[0] > self.dataopen[0]
+        self.lowest = btind.Lowest(self.params.num)
+        self.highest = btind.Highest(self.params.num)
+        self.vola = self.params.Volatility / 100
+        self.rate = self.params.rate / 100
+
+    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))
+
+    @njit
+    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:
+
+            # print(f'{self.params.num}日天最低值：{lowest},波动率为{self.params.Volatility/100}')
+            if self.yx \
+                    and (((self.lowest[0] * (1 - self.vola)) < self.low[-2] < (self.lowest[0] * (1 + self.vola))) or (
+                    (self.lowest[0] * (1 - self.vola)) < self.low[-1] < (self.lowest[0] * (1 + self.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 + self.rate))) or \
+                    (((self.highest[0] * (1 - self.vola)) < self.high[-2] < (self.highest[0] * (1 + self.vola))) or (
+                            (self.highest[0] * (1 - self.vola)) < self.high[-1] < (self.highest[0] * (1 + self.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)}')
+
+
+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(r'D:\Daniel\策略\策略穷举.csv', index=True, encoding='utf-8', mode='w')
+    print(df)
+
+
+def backtrader(list_date, table_list, result, result_change, result_change_fall,  err_list):
+    sttime = dt.now()
+    engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qmt_stocks?charset=utf8')
+
+    cerebro = bt.Cerebro()
+    # cerebro.addstrategy(TestStrategy, num=num, Volatility=Volatility, rate=rate)
+    cerebro.addsizer(bt.sizers.FixedSize, stake=10000)
+
+    cerebro.broker.setcash(100000.0)
+    cerebro.broker.setcommission(0.005)
+
+    for stock in table_list:
+        # print(stock)
+        stk_df = pd.read_sql_table(stock, engine)
+        stk_df.time = pd.to_datetime(stk_df.time)
+        data = MyPandasData(dataname=stk_df,
+                            fromdate=datetime.datetime(2010, 1, 1),
+                            todate=datetime.datetime(2022, 12, 31),
+                            datetime='time',
+                            open='open',
+                            close='close',
+                            high='high',
+                            low='low',
+                            volume='volume',
+                            # 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',
+                            )
+
+        cerebro.adddata(data, name=stock)
+
+    print('取值完成', dt.now())
+    cerebro.optstrategy(TestStrategy, num=range(60, 80, 20), Volatility=range(3, 7), rate=range(5, 12))
+    print('最优参定义', dt.now())
+
+    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
+        print('开始执行', dt.now())
+        cerebro.run(maxcpus=None)
+    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(3, 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}')
+    backtrader(list_date, table_list, result, result_change, result_change_fall,
+                           err_list)
+                # for i in range(0, len(table_list), step):
+    stattime = dt.now()
+    print(stattime)
+                # 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)

futures_backtrader.py

@@ -10,6 +10,7 @@ import math
 from datetime import datetime as dt
 import multiprocessing as mp
 from backtrader.feeds import PandasData
+from numba import jit, cuda, njit
 
 
 # import multiprocessing
@@ -36,6 +37,7 @@ class MyPandasData(PandasData):
     '''
 
 
+
 class TestStrategy(bt.Strategy):
     params = (
         ("num", 3),
@@ -48,7 +50,10 @@ class TestStrategy(bt.Strategy):
         dt = dt or self.datas[0].datetime.date(0)
         # print('%s, %s' % (dt.isoformat(), txt))
 
+
     def __init__(self):
+        print('__init__', dt.now())
+        print(f'{self.params.num}天波动率为{self.params.Volatility}%乖离率为{self.params.rate}', 'myPID is ', os.getpid())
         # self.num = num
         # self.Volatility = Volatility/100
         # Keep a reference to the "close" line in the data[0] dataseries
@@ -67,6 +72,11 @@ class TestStrategy(bt.Strategy):
         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.yx = self.dataclose[0] > self.dataopen[0]
+        self.lowest = btind.Lowest(self.params.num)
+        self.highest = btind.Highest(self.params.num)
+        self.vola = self.params.Volatility / 100
+        self.rate = self.params.rate / 100
 
     def notify_order(self, order):
         """
@@ -107,6 +117,7 @@ class TestStrategy(bt.Strategy):
         # 显示交易的毛利率和净利润
         # self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm))
 
+    @njit
     def next(self):
         # print(self.num,self.Volatility)
         # Simply log the closing price of the series from the reference
@@ -115,22 +126,19 @@ class TestStrategy(bt.Strategy):
         # 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)))) \
+            if self.yx \
+                    and (((self.lowest[0] * (1 - self.vola)) < self.low[-2] < (self.lowest[0] * (1 + self.vola))) or (
+                    (self.lowest[0] * (1 - self.vola)) < self.low[-1] < (self.lowest[0] * (1 + self.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)))):
+                    or (self.dataclose[0] > (self.sma5[0] * (1 + self.rate))) or \
+                    (((self.highest[0] * (1 - self.vola)) < self.high[-2] < (self.highest[0] * (1 + self.vola))) or (
+                            (self.highest[0] * (1 - self.vola)) < self.high[-1] < (self.highest[0] * (1 + self.vola)))):
                 self.order = self.close()
                 # self.log('Close, %.2f' % self.dataclose[0])
 
@@ -152,79 +160,86 @@ def to_df(lt):
     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())
+def backtrader(list_date, table_list, result, result_change, result_change_fall,  err_list):
     sttime = dt.now()
     engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qmt_stocks?charset=utf8')
+
+    cerebro = bt.Cerebro()
+    # cerebro.addstrategy(TestStrategy, num=num, Volatility=Volatility, rate=rate)
+    cerebro.addsizer(bt.sizers.FixedSize, stake=10000)
+
+    cerebro.broker.setcash(100000.0)
+    cerebro.broker.setcommission(0.005)
+
     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',
-                                close='close',
-                                high='high',
-                                low='low',
-                                volume='volume',
-                                # 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}')
+        data = MyPandasData(dataname=stk_df,
+                            fromdate=datetime.datetime(2010, 1, 1),
+                            todate=datetime.datetime(2022, 12, 31),
+                            datetime='time',
+                            open='open',
+                            close='close',
+                            high='high',
+                            low='low',
+                            volume='volume',
+                            # 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',
+                            )
+
+        cerebro.adddata(data, name=stock)
+
+    print('取值完成', dt.now())
+    cerebro.optstrategy(TestStrategy, num=range(60, 80, 20), Volatility=range(3, 7), rate=range(5, 12))
+    print('最优参定义', dt.now())
+
+    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
+        print('开始执行', dt.now())
+        cerebro.run(maxcpus=None)
+    except IndexError:
+        err_list.append(stock)
     else:
-        print(result, result_change, result_change_fall, num, Volatility, rate, err_list)
+        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()
 
 
@@ -254,25 +269,28 @@ if __name__ == '__main__':
     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(3, 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 num in range(60, 100, 20):
+    #     for Volatility in range(3, 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}')
+    backtrader(list_date, table_list, result, result_change, result_change_fall,
+                           err_list)
                 # for i in range(0, len(table_list), step):
-                stattime = dt.now()
+    stattime = dt.now()
+    print(stattime)
                 # 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.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)