Help - Buy signal recognition and trade creation vs execution?

j63009567

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 matplotlib.pyplot as plt
filepath = r'C:\Users\lenovo\Desktop\Scripts\Data\varta20152021.csv'

# Import the backtrader platform
import backtrader as bt
cerebro = bt.Cerebro(stdstats=False, cheat_on_open = True)
# cerebro.addobserver(bt.observers.DrawDown)
# Create a Stratey
class SmaStrategy(bt.Strategy):
    params = (
        ('maperiod', 10),
    )

    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

        # Add a MovingAverageSimple indicator
        self.sma = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=self.params.maperiod)

        # Indicators for the plotting show
        bt.indicators.ExponentialMovingAverage(self.datas[0], period=25)
        bt.indicators.WeightedMovingAverage(self.datas[0], period=25,
                                            subplot=True)
        bt.indicators.StochasticSlow(self.datas[0])
        bt.indicators.MACDHisto(self.datas[0])
        rsi = bt.indicators.RSI(self.datas[0])
        bt.indicators.SmoothedMovingAverage(rsi, period=10)
        bt.indicators.ATR(self.datas[0], plot=False)

    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')

        # Write down: no pending order
        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:

            # Not yet ... we MIGHT BUY if ...
            if self.dataclose[0] > self.sma[0]:

                # BUY, BUY, BUY!!! (with all possible 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(exectype=bt.Order.StopTrail, trailpercent=0.007)

        else:

            if self.dataclose[0] < (self.sma[0]):
                # 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()                



if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(SmaStrategy)
    cerebro.addanalyzer(bt.analyzers.SharpeRatio)
    cerebro.addanalyzer(bt.analyzers.DrawDown)
    cerebro.addanalyzer(bt.analyzers.TradeAnalyzer)
    cerebro.addanalyzer(bt.analyzers.Returns) 
    cerebro.addanalyzer(bt.analyzers.PyFolio)
    cerebro.addanalyzer(bt.analyzers.Transactions)
    cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")


    
    # 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, filepath)

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2020,6, 30),
        # Do not pass values before this date
        todate=datetime.datetime(2021, 6, 30),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(1000000.0)

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=6000)

    # Set the commission
    cerebro.broker.setcommission(commission=0.0002)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
    
    # Run over everything
    res = cerebro.run()[0]
    
    
    print("\n")
    sharpe_data = res.analyzers.sharperatio.get_analysis()
    print("=== Sharpe Ratio ===")
    print("Sharpe: %s" % sharpe_data['sharperatio'])

#     print("Draw Down: ", res.analyzers.drawdown.get_analysis())
    draw_down_data = res.analyzers.drawdown.get_analysis()
    print("========== Draw Down ==========")
    print("max drawdown: %s %%" % draw_down_data['max']['drawdown'])
    print("max money drawdown: %s" % draw_down_data['max']['moneydown'])

          
#     print("Trade Analyzer: ", res.analyzers.tradeanalyzer.get_analysis())
    trading_data = res.analyzers.tradeanalyzer.get_analysis()
    print("========== Trading Analysis ==========")
    print("Winning Trades")
    print("win ratio: %s" % (trading_data['won']['total'] / float(trading_data['won']['total'] + trading_data['lost']['total'])))
    print("win_hits: %s" % trading_data['won']['total'])
    print("win_pnl --> total: %s, average: %s, max: %s" %
          (trading_data['won']['pnl']['total'],
          trading_data['won']['pnl']['average'],
          trading_data['won']['pnl']['max'])
         )
    print("Losing Trades")
    print("lost_hits:%s" % trading_data['lost']['total'])
    print("lost_pnl --> total: %s, average: %s, max: %s" %
          (trading_data['lost']['pnl']['total'],
          trading_data['lost']['pnl']['average'],
          trading_data['lost']['pnl']['max'])
         )
    print("Long Trades")
    print("long_hits: %s" % trading_data['long']['total'])
    print("long_pnl --> total: %s, average: %s" %
         (trading_data['long']['pnl']['total'],
          trading_data['long']['pnl']['average'])
         )
    print("Short Trades")
    print("short_hits: %s" % trading_data['short']['total'])
    print("short_pnl --> total: %s, average: %s" %
         (trading_data['short']['pnl']['total'],
          trading_data['short']['pnl']['average'])
         )
        
        
    transactions_data = res.analyzers.tradeanalyzer.get_analysis()
    print("========== SQN Analysis ==========")
    print(dict(res.analyzers.sqn.get_analysis())['sqn'])
    
    
    print('\n')
    
    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
   
    # Plot the result
    cerebro.plot(style='candle')

Testing strategy using VAR1.DE daily data from yahoo finance.

Why is the buy creation and execution distanced away by more than 1 day? As I see this happen maybe a couple of times.
Why didn't the signal detect a buy order at the start of the data? As you can see, the first buy order is on Aug 2020 but in the chart you can see that before the first buy order, there is another crossover where the close is above the sma10.

Please let me know your thoughts, thanks a lot! Would really appreciate the help especially on the coding part, as I most likely typed something wrong somewhere.. though this is taken from the quickstarter guide. The cheat is not incorporated yet.

j63009567

@j63009567 said in Help - Buy signal recognition and trade creation vs execution?:

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 matplotlib.pyplot as plt
filepath = r'C:\Users\lenovo\Desktop\Scripts\Data\varta20152021.csv'

# Import the backtrader platform
import backtrader as bt
cerebro = bt.Cerebro(stdstats=False, cheat_on_open = True)
# cerebro.addobserver(bt.observers.DrawDown)
# Create a Stratey
class SmaStrategy(bt.Strategy):
    params = (
        ('maperiod', 10),
    )

    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

        # Add a MovingAverageSimple indicator
        self.sma = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=self.params.maperiod)

        # Indicators for the plotting show
        bt.indicators.ExponentialMovingAverage(self.datas[0], period=25)
        bt.indicators.WeightedMovingAverage(self.datas[0], period=25,
                                            subplot=True)
        bt.indicators.StochasticSlow(self.datas[0])
        bt.indicators.MACDHisto(self.datas[0])
        rsi = bt.indicators.RSI(self.datas[0])
        bt.indicators.SmoothedMovingAverage(rsi, period=10)
        bt.indicators.ATR(self.datas[0], plot=False)

    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')

        # Write down: no pending order
        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:

            # Not yet ... we MIGHT BUY if ...
            if self.dataclose[0] > self.sma[0]:

                # BUY, BUY, BUY!!! (with all possible 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(exectype=bt.Order.StopTrail, trailpercent=0.007)

        else:

            if self.dataclose[0] < (self.sma[0]):
                # 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()                



if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(SmaStrategy)
    cerebro.addanalyzer(bt.analyzers.SharpeRatio)
    cerebro.addanalyzer(bt.analyzers.DrawDown)
    cerebro.addanalyzer(bt.analyzers.TradeAnalyzer)
    cerebro.addanalyzer(bt.analyzers.Returns) 
    cerebro.addanalyzer(bt.analyzers.PyFolio)
    cerebro.addanalyzer(bt.analyzers.Transactions)
    cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")


    
    # 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, filepath)

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2020,6, 30),
        # Do not pass values before this date
        todate=datetime.datetime(2021, 6, 30),
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(1000000.0)

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=6000)

    # Set the commission
    cerebro.broker.setcommission(commission=0.0002)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
    
    # Run over everything
    res = cerebro.run()[0]
    
    
    print("\n")
    sharpe_data = res.analyzers.sharperatio.get_analysis()
    print("=== Sharpe Ratio ===")
    print("Sharpe: %s" % sharpe_data['sharperatio'])

#     print("Draw Down: ", res.analyzers.drawdown.get_analysis())
    draw_down_data = res.analyzers.drawdown.get_analysis()
    print("========== Draw Down ==========")
    print("max drawdown: %s %%" % draw_down_data['max']['drawdown'])
    print("max money drawdown: %s" % draw_down_data['max']['moneydown'])

          
#     print("Trade Analyzer: ", res.analyzers.tradeanalyzer.get_analysis())
    trading_data = res.analyzers.tradeanalyzer.get_analysis()
    print("========== Trading Analysis ==========")
    print("Winning Trades")
    print("win ratio: %s" % (trading_data['won']['total'] / float(trading_data['won']['total'] + trading_data['lost']['total'])))
    print("win_hits: %s" % trading_data['won']['total'])
    print("win_pnl --> total: %s, average: %s, max: %s" %
          (trading_data['won']['pnl']['total'],
          trading_data['won']['pnl']['average'],
          trading_data['won']['pnl']['max'])
         )
    print("Losing Trades")
    print("lost_hits:%s" % trading_data['lost']['total'])
    print("lost_pnl --> total: %s, average: %s, max: %s" %
          (trading_data['lost']['pnl']['total'],
          trading_data['lost']['pnl']['average'],
          trading_data['lost']['pnl']['max'])
         )
    print("Long Trades")
    print("long_hits: %s" % trading_data['long']['total'])
    print("long_pnl --> total: %s, average: %s" %
         (trading_data['long']['pnl']['total'],
          trading_data['long']['pnl']['average'])
         )
    print("Short Trades")
    print("short_hits: %s" % trading_data['short']['total'])
    print("short_pnl --> total: %s, average: %s" %
         (trading_data['short']['pnl']['total'],
          trading_data['short']['pnl']['average'])
         )
        
        
    transactions_data = res.analyzers.tradeanalyzer.get_analysis()
    print("========== SQN Analysis ==========")
    print(dict(res.analyzers.sqn.get_analysis())['sqn'])
    
    
    print('\n')
    
    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
   
    # Plot the result
    cerebro.plot(style='candle')

Testing strategy using VAR1.DE daily data from yahoo finance.

Why is the buy creation and execution distanced away by more than 1 day? As I see this happen maybe a couple of times.
Why didn't the signal detect a buy order at the start of the data? As you can see, the first buy order is on Aug 2020 but in the chart you can see that before the first buy order, there is another crossover where the close is above the sma10.

Please let me know your thoughts, thanks a lot! Would really appreciate the help especially on the coding part, as I most likely typed something wrong somewhere.. though this is taken from the quickstarter guide. The cheat is not incorporated yet.

![alt text]( image url)

As you can see the create and execute is randomly distanced by a day. Please help me!

run-out

@j63009567 The answer is because you are using a stop trail order:

# Keep track of the created order to avoid a 2nd order
                self.order = self.buy(exectype=bt.Order.StopTrail, trailpercent=0.007)

You can read more about how they operate here in the docs

j63009567

@run-out

I thought the trailing stop loss would automatically execute?

I think there is something error with my code, as the trailing stop loss doesn't seem to be hitting

I added a bracket order, and the limit price is not executing despite the price reaching that target on the graph, as we have a position already?

A sell is only detected when close < sma