量化交易学习-RSI策略2

曾经终败给现在 2023-07-18 06:24 25阅读 0赞

# 加入交易信号二 RSI的“黄金交叉”与“死亡交叉” #

用Ta-Lib计算6日的RSI值和24日的RSI值

*  当RSI6>70或者RSI6向下穿过RSI24时，为卖出信号。
 *  当RSI6<30或者RSI6向上穿过RSI24时，为买入信号。

from __future__ import (absolute_import, division, print_function,
                            unicode_literals)
    
    import datetime  # For datetime objects
    import os.path  # To manage paths
    import sys  # To find out the script name (in argv[0])
    
    # Import the backtrader platform
    import backtrader as bt
    
    # Create a Stratey
    class TestStrategy(bt.Strategy):
    
        params = (
            ('RSI6period', 6),
            ('RSI24period', 24),
        )
        def log(self, txt, dt=None):
            ''' Logging function fot this strategy'''
            dt = dt or self.datas[0].datetime.date(0)
            print('%s, %s' % (dt.isoformat(), txt))
        def __init__(self):
            # Keep a reference to the "close" line in the data[0] dataseries
            self.dataclose = self.datas[0].close
            # To keep track of pending orders and buy price/commission
            self.order = None
            self.buyprice = None
            self.buycomm = None
            # 添加Ta-Lib RSI指标
            self.RSI6=bt.talib.RSI(self.data, timeperiod= self.p.RSI6period)
            self.RSI24=bt.talib.RSI(self.data, timeperiod= self.p.RSI24period)
            self.crossover = bt.ind.CrossOver(self.RSI6, self.RSI24)  # 交叉信号
    
        def notify_order(self, order):
            if order.status in [order.Submitted, order.Accepted]:
                # Buy/Sell order submitted/accepted to/by broker - Nothing to do
                return
             # Check if an order has been completed
            # Attention: broker could reject order if not enough cash
            if order.status in [order.Completed]:
                if order.isbuy():
                    self.log(
                        'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                        (order.executed.price,
                         order.executed.value,
                         order.executed.comm))
                    self.buyprice = order.executed.price
                    self.buycomm = order.executed.comm
                else: # Sell
                    self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                             (order.executed.price,
                              order.executed.value,
                              order.executed.comm))
                self.bar_executed = len(self)
            elif order.status in [order.Canceled, order.Margin, order.Rejected]:
                self.log('Order Canceled/Margin/Rejected')
            
            self.order = None
        def notify_trade(self, trade):
            if not trade.isclosed:
                return
            self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
                     (trade.pnl, trade.pnlcomm))
        def next(self):
            # Simply log the closing price of the series from the reference
            self.log('Close, %.2f' % self.dataclose[0])
            
            # Check if an order is pending ... if yes, we cannot send a 2nd one
            if self.order:
                return
    
            if not self.position:
                if self.crossover > 0: 
                    self.order = self.buy()
                elif self.RSI6[0] < 30: 
                    self.order = self.buy()
            else:
                if self.crossover < 0:
                    self.order=self.close()
                elif self.RSI6[0] > 80:
                    self.order = self.close() 
            
    # Create a cerebro entity
    cerebro = bt.Cerebro()
    # Add a strategy
    cerebro.addstrategy(TestStrategy)
    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, '600519-1.csv')
    # Create a Data Feed
    data = bt.feeds.GenericCSVData(
            dataname = datapath,
            nullvalue = 0.0,
            dtformat = ('%Y-%m-%d'),
            datetime = 0,
            open = 1,
            high = 2,
            low = 3,
            close = 4,
            volume = 5,
            openinterest = -1
            )
    # Add the Data Feed to Cerebro
    cerebro.adddata(data)
    # Set our desired cash start
    cerebro.broker.setcash(100000.0)
    # 设置交易单位大小 A股100股为一手
    cerebro.addsizer(bt.sizers.FixedSize, stake = 100)
    
    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
    # Run over everything
    cerebro.run()
    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
    # Plot the result
    cerebro.plot()

结果如下，是盈利的

Final Portfolio Value: 119192.00

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDI5ODI0MQ_size_16_color_FFFFFF_t_70]

# 天数差异 #

短期RSI对于价格的变化情况反应比较敏感，RSI取值波动较大，长期RSI值反应相对迟钝，其波动相对较小。对于交易信号一，超买和超卖现象，使用6日的RSI值和20日的RSI值之间用哪一个时间跨度得到的收益最大那。Backtrader里有Optimize功能

from __future__ import (absolute_import, division, print_function,
                            unicode_literals)
    
    import datetime  # For datetime objects
    import os.path  # To manage paths
    import sys  # To find out the script name (in argv[0])
    
    # Import the backtrader platform
    import backtrader as bt
    
    # Create a Stratey
    class TestStrategy(bt.Strategy):
    
        params = (
            ('RSIperiod', 6),
            ('printlog', False),
        )
        def log(self, txt, dt=None,doprint = False):
            ''' Logging function fot this strategy'''
            if self.params.printlog or doprint:
                dt = dt or self.datas[0].datetime.date(0)
                print('%s, %s' % (dt.isoformat(), txt))
            
        def __init__(self):
            # Keep a reference to the "close" line in the data[0] dataseries
            self.dataclose = self.datas[0].close
            # To keep track of pending orders and buy price/commission
            self.order = None
            self.buyprice = None
            self.buycomm = None
            # 添加Ta-Lib RSI指标
            self.RSI=bt.talib.RSI(
                    self.data, timeperiod= self.p.RSIperiod)
            # 
        def notify_order(self, order):
            if order.status in [order.Submitted, order.Accepted]:
                # Buy/Sell order submitted/accepted to/by broker - Nothing to do
                return
             # Check if an order has been completed
            # Attention: broker could reject order if not enough cash
            if order.status in [order.Completed]:
                if order.isbuy():
                    self.log(
                        'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                        (order.executed.price,
                         order.executed.value,
                         order.executed.comm))
                    self.buyprice = order.executed.price
                    self.buycomm = order.executed.comm
                else: # Sell
                    self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                             (order.executed.price,
                              order.executed.value,
                              order.executed.comm))
                self.bar_executed = len(self)
            elif order.status in [order.Canceled, order.Margin, order.Rejected]:
                self.log('Order Canceled/Margin/Rejected')
            
            self.order = None
        def notify_trade(self, trade):
            if not trade.isclosed:
                return
            self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
                     (trade.pnl, trade.pnlcomm))
        def next(self):
            # Simply log the closing price of the series from the reference
            self.log('Close, %.2f' % self.dataclose[0])
            
            # Check if an order is pending ... if yes, we cannot send a 2nd one
            if self.order:
                
                return
            # Check if we are in the market
            if not self.position:
    
    
                if self.RSI[0] < 30:
                    # BUY, BUY, BUY!!! (with default parameters)
                    self.log('BUY CREATE, %.2f' % self.dataclose[0])
                    
                    # Keep track of the created order to avoid a 2nd order
                    self.order = self.buy()
            # 如果已经在场内，则可以进行卖出操作
            else:
                
                # Already in the market ... we might sell
                if self.RSI[0] > 70:
                    
                    # SELL, SELL, SELL!!! (with all possible default parameters)
                    self.log('SELL CREATE, %.2f' % self.dataclose[0])
                    
                    # Keep track of the created order to avoid a 2nd order
                    self.order = self.sell()
        def stop(self):
            self.log('(RSI period %2d) Ending Value %.2f' %
                     (self.p.RSIperiod, self.broker.getvalue()), doprint = True)
                
    # Create a cerebro entity
    cerebro = bt.Cerebro()
    # Add a strategy
    strats = cerebro.optstrategy(
            TestStrategy,
            RSIperiod = range(2, 10))
    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, 'C:/Users/taohj/python-work/CopperBat-量化/600519-1.csv')
    # Create a Data Feed
    data = bt.feeds.GenericCSVData(
            dataname = datapath,
            nullvalue = 0.0,
            dtformat = ('%Y-%m-%d'),
            datetime = 0,
            open = 1,
            high = 2,
            low = 3,
            close = 4,
            volume = 5,
            openinterest = -1
            )
    # Add the Data Feed to Cerebro
    cerebro.adddata(data)
    # Set our desired cash start
    cerebro.broker.setcash(100000.0)
    # 设置交易单位大小 A股100股为一手
    cerebro.addsizer(bt.sizers.FixedSize, stake = 100)
    # Run over everything
    cerebro.run(maxcpus = 1)

结果如下

2015-07-01, (RSI period  2) Ending Value 107234.00
    2015-07-01, (RSI period  3) Ending Value 105259.00
    2015-07-01, (RSI period  4) Ending Value 105226.00
    2015-07-01, (RSI period  5) Ending Value 106260.00
    2015-07-01, (RSI period  6) Ending Value 104142.00
    2015-07-01, (RSI period  7) Ending Value 105521.00
    2015-07-01, (RSI period  8) Ending Value 102944.00
    2015-07-01, (RSI period  9) Ending Value 102944.00

看来选2日的RSI值作为交易信号一的时间跨度有更高的收益。  
当然超买线和超卖线，可以取值70和30，也可以取值80和20，或者90和10.  
交易信号二的短线RSI和长线RSi也可以取不同的时间跨度。

希望大家多给我意见。本人没有什么编程背景，有任何问题，欢迎在本文下方留言，或者将问题发送至邮箱： taohjk@hotmail.com  
谢谢大家！

免责声明  
本内容仅作为学术参考和代码实例，仅供参考，不对投资决策提供任何建议。本人不承担任何人因使用本系列中任何策略、观点等内容造成的任何直接或间接损失。

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