优化backtrader代码

futures_backtrader.py

@@ -10,7 +10,6 @@ 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
@@ -37,7 +36,6 @@ class MyPandasData(PandasData):
     '''
 
 
-
 class TestStrategy(bt.Strategy):
     params = (
         ("num", 3),
@@ -50,10 +48,7 @@ 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
@@ -72,11 +67,6 @@ 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):
         """
@@ -117,7 +107,6 @@ 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
@@ -126,19 +115,22 @@ 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.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)))) \
+            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 + 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)))):
+                    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])
 
@@ -160,86 +152,79 @@ def to_df(lt):
     print(df)
 
 
-def backtrader(list_date, table_list, result, result_change, result_change_fall,  err_list):
+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?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)
+        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}')
     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)
+        print(result, result_change, result_change_fall, num, Volatility, rate, err_list)
     # cerebro.plot()
 
 
@@ -269,28 +254,25 @@ 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}')
-    backtrader(list_date, table_list, result, result_change, result_change_fall,
-                           err_list)
+    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 i in range(0, len(table_list), step):
-    stattime = dt.now()
-    print(stattime)
+                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.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)