futures backtrader

bk_test.py

@@ -0,0 +1,38 @@
+from jqdatasdk import *
+from datetime import datetime as dt
+import pandas as pd
+import pymysql
+from sqlalchemy import create_engine
+import time
+
+auth('18616891214', 'Ea?*7f68nD.dafcW34d!')
+
+# fre = ['30m', '1d']
+
+engine_hlfx_pool = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx_pool?charset=utf8')
+stock_pool = pd.read_sql_query(
+                    'select value from MA5_1d', engine_hlfx_pool)
+stock_pool = stock_pool.iloc[-2, 0].split(",")
+print(type(stock_pool), len(stock_pool),stock_pool)
+num_industry = get_industry(stock_pool)
+print(num_industry)
+results = []
+a = []
+for key in num_industry.values():
+    for key2 in key.values():
+        results.append(key2['industry_name'])
+results = pd.value_counts(results)
+print(results)
+
+results = results[0:3]
+results = list(results.index)
+print(results)
+
+for key,value in num_industry.items():
+    for key2 in value.values():
+        if key2['industry_name'] in results:
+            a.append(key)
+
+
+
+print(set(a))

futures_backtrader.py

@@ -0,0 +1,258 @@
+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 threading
+from datetime import datetime as dt
+import multiprocessing as mp
+from backtrader.feeds import PandasData
+
+# import multiprocessing
+import matplotlib
+
+class MyPandasData(PandasData):
+    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))
+            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[0] < (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]) \
+                    and (self.net_pct_main[-2] > 5) \
+                    and (self.change_pct[0] > 5):
+                # 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))):
+                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 backtrader(table_list, result, result_change,result_change_fall, num, Volatility, rate, err_list):
+    engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/stocks_data?charset=utf8')
+    for stock in table_list:
+        # print(stock)
+        stk_df = pd.read_sql_table(stock, engine)
+        stk_df.date = pd.to_datetime(stk_df.date)
+        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='date',
+                                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:
+                    # print('recode！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！')
+                    result_change.append((cerebro.broker.getvalue()/10000-1))
+                    result.append(stock)
+                else:
+                    result_change_fall.append((1-cerebro.broker.getvalue() / 10000))
+                    # print('aaaaaaaaaaa')
+
+
+            # cerebro.plot()
+
+if __name__ == '__main__':
+    # 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='stocks_data')
+    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]
+
+    df = pd.DataFrame(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'])
+
+
+    sttime = dt.now()
+
+    for num in range(20, 180, 10):
+        for Volatility in range(10, 20, 1):
+            for rate in range(5, 20, 1):
+                step = 400
+                thread_list = []
+                result = mp.Manager().list()
+                result_change = mp.Manager().list()
+                result_change_fall = mp.Manager().list()
+                err_list = mp.Manager().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=(table_list[i:i + step], result,
+                                                            result_change, result_change_fall, num, Volatility, rate,
+                                                            err_list))
+                    thread_list.append(p)
+                    # p.start()
+                    # p.join()
+                    # print(thread_list)
+                for thread in thread_list:
+                    thread.start()
+                for thread in thread_list:
+                    thread.join()
+
+                print(f'以{num}内最低值波动{Volatility}为支撑、{rate}%为乖离率，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)}')
+                endtime = dt.now()
+                df.loc[len(df)] = [num, Volatility, rate, len(result), len(result)/len(table_list), np.sum(result_change),
+                                                np.mean(result_change), np.max(result_change), np.min(result_change),
+                                                np.sum(result_change_fall), np.mean(result_change_fall),
+                                                np.min(result_change_fall), np.max(result_change_fall)]
+                print(df)
+                print('每轮耗时：', endtime-stattime)
+    edtime = dt.now()
+    print('总耗时：', edtime - sttime)
+    df.to_csv('/Users/daniel/Documents/策略穷举2.csv', index=True)

his_money_flow.py

@@ -23,7 +23,7 @@ engine_data = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/st
 
 fre = '1d'
 print('ready to write to mysql %s' % fre)
-for stock in stocks:
+for stock in stocks[2500:]:
     print(stock, fre)
     starttime ='2010-01-04'
     # endtime = pd.read_sql_table('stk%s_%s' % (stock, fre), con=engine).iloc[-1, 1]
@@ -43,11 +43,11 @@ for stock in stocks:
     # print(df_money)
 
     df_stock = pd.merge(df_stock, df_money, how='outer', left_index=False , on='date')
-    # df_stock.to_csv('D:\001_QuantTrade\Result.csv')
+    df_stock.to_csv('/Users/daniel/Downloads/Result.csv')
     df_stock = df_stock.dropna(axis=0)
     df_stock.reset_index(inplace=True)
     df_stock.rename(columns={'index': 'date'}, inplace=True)
-    df_stock.to_sql('stk%s_%s' % (stock, fre), con=engine_data, index=True, if_exists='replace')
+    df_stock.to_sql('stk%s_%s' % (stock, fre), con=engine_data, index=True, if_exists='append')
     # with engine.connect() as con:
     #     con.execute("ALTER TABLE `stk%s_%s` ADD PRIMARY KEY (`date`);" % (stock, fre))
     print(df_stock)