unable to execute strategies on resampled data

Nikhil Kumar

Hi Traders ,
I needed assistance with an issue i was facing.

Here is my code :

# This is the Implementation for 9/30 Stratergy using python BackTrader 
import backtrader as bt
from datetime import datetime
import datetime as dt
import pandas as pd
import matplotlib.pyplot as plt
from pandas_datareader import data as web

class PullBack(bt.Strategy):


    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):
        self.open = self.data.open
        self.high = self.data.high
        self.low = self.data.low
        self.close = self.data.close

        self.ema = bt.ind.ExponentialMovingAverage(self.data.close, period=9)
        self.wma = bt.ind.WeightedMovingAverage(self.data.close, period=30)

        self.order = None
        self.stop_order = None

    def next(self):
       #print("current portfolio value" + str(cerebro.broker.getvalue()))
        
        
        if not self.position:
            # Long Position
            if (self.ema[-1] > self.wma[-1]) and (self.data.high[-1] < self.ema[-1]) :
                self.buy(size=100, exectype=bt.Order.StopLimit, price=self.data.high[-1])
                self.order = self.buy(size=1, exectype=bt.Order.Limit, price=self.data.high[-1])
                self.log('BUY PRICE CREATED , %.2f ' % self.data.high[-1])
                risk = (self.data.high[-1] - self.data.low[-1])
                self.log('Risk points , %.2f' % risk)
                # Place a stop loss order
                self.sell(size=100, exectype=bt.Order.StopLimit, price=self.data.low[-1])
                target_price = self.data.high[-1] + 2 * (self.data.high[-1] - self.data.low[-1])
                self.log('TARGET PRICE ,%.2f ' % target_price)
                oc1 = self.sell(size=1, exectype=bt.Order.StopLimit, price=target_price)
                self.order = self.sell(size=1, exectype=bt.Order.Stop, price=self.data.low[-1], plimit=self.data.low[-1] , oco=oc1)
            # elif (self.ema[-1] < self.wma[-1]) and  (self.data.close[-1] > self.ema[-1] )  :
                
            #     self.order = self.sell(size=1,exectype=bt.Order.Limit,price=self.data.low[-1])
            #     #Stop loss
            #     target_price = self.data.low[-1] - 2 * (self.data.high[-1] - self.data.low[-1])
            #     self.order = self.sell(size=1,exectype=bt.Order.StopLimit,price=target_price)
            #     self.log('Short Sell initiated , %.2f' % self.data.low[-1])
                



    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            return
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log('BUY EXECUTED, %.2f' % order.executed.price)
            elif order.issell():
                self.log('Sell order EXECUTED, %.2f' % order.executed.price)

        elif order.status in [order.Cancelled]:
            self.log('Order Canceled at price, %.2f' %order.price)

        elif order.status in [ order.Margin, order.Rejected]:
            self.log('Order Rejected or Margin error')

        self.order = None

class timeAdjustment(bt.TradingCalendar):
        params = dict(
        holidays=[
            dt.date(2018, 1, 26),
            # dt.date(2018, 2, 13),
            # dt.date(2018, 3, 2),
            # dt.date(2018, 3, 29),
            # dt.date(2018, 3, 30),
            # dt.date(2018, 5, 1),
            # dt.date(2018, 8, 15),
            # dt.date(2018, 8, 22),
            # dt.date(2018, 9, 13),
            # dt.date(2018, 9, 18),
            # dt.date(2018,10,2),
            # dt.date(2018,10,18),
            # dt.date(2018,11,8),
            # dt.date(2018,11,23),
            # dt.date(2018,12,2)
        ],
        # earlydays=[
        #     (dt.date(2018, 11, 7),
        #      dt.time(17, 31), dt.time(18, 31))
        # ],
        open=dt.time(9, 16),
        close=dt.time(15, 30),
    
    )




#Variable for our starting cash
startcash = 100000

#Create an instance of cerebro
cerebro = bt.Cerebro()

#Add our strategy
cerebro.addstrategy(PullBack)

nifty_futures="C:\\Nikhil_Media\\Education & Skills\\Stratergies_Codes\\BackTestFiles\\input_data\\BANKNIFTY-IBKR (1).csv"
# nifty_futures = 'C:\\Nikhil_Media\\Education & Skills\\Stratergies_Codes\\2018data.csv'


data = bt.feeds.GenericCSVData(
    headers=True,
    dataname=nifty_futures,
    # 2020-03-13
    fromdate=datetime(2018, 1, 1),    #year-month-date
    todate=datetime(2018, 1, 31),
    nullvalue=0.0,
    dtformat=("%Y-%m-%d %H:%M:%S"),
    # tmformat=('%H:%M:%S'),
    datetime=1,
    # time=3,
    open=2,
    high=3,
    low=4,
    close=5,
    timeframe=bt.TimeFrame.Minutes,
    plot=False

)


cerebro.adddata(data)
cerebro.resampledata(data,timeframe=bt.TimeFrame.Minutes,compression=30,name='30_Min_data')
# cerebro.resampledata(data,timeframe=bt.TimeFrame.Minutes,compression=60,name='60_min_bar')
# cerebro.addcalendar(timeAdjustment)

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


# Add the analyzers we are interested in
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")
cerebro.broker.setcommission(commission=0.0005)


# Run over everything
strategies=cerebro.run()

#Get final portfolio Value

portvalue = cerebro.broker.getvalue()
print("final and last port value:  "+str(portvalue))
pnl = portvalue - startcash

#Print out the final result

print('Final Portfolio Value: ${}'.format(portvalue))
print('P/L: ${}'.format(pnl))

#Finally plot the end results

cerebro.plot(style='candlestick',fmt_x_data = ('%Y-%m-%d %H:%M:%S'))

Ideally I want my strategy to run on resampled data , but in the end what i get is the below plot .

(

As you can see no Buy sell indiactors have been displayed.
May i know why this happening ??

I also tried commenting
cerebro.add(data)

But this change gives me an empty plot .

Thanks

rajanprabu

You need to use resampled data in your strategy which is data1, but you are referencing the data aka data0 which is the input data.

Nikhil Kumar

Thanks for the input rajan

Nikhil Kumar

I made the changes you mentioned ie reference data by data[1]
. I tried using data1 but it gave me an error saying it was undefined.
However after using data[1] I got this error

  File "c:\Nikhil_Media\Education & Skills\Stratergies_Codes\BackTestFiles\Resampled_930Stratergy.py", line 18, in __init__
    self.open = self.data[1].open
AttributeError: 'float' object has no attribute 'open'

Below is the modified code
Appreciate your assistance

# This is the Implementation for 9/30 Stratergy using python BackTrader 
import backtrader as bt
from datetime import datetime
import datetime as dt
import pandas as pd
import matplotlib.pyplot as plt
from pandas_datareader import data as web

class PullBack(bt.Strategy):


    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):
        self.open = self.data[1].open
        self.high = self.data[1].high
        self.low = self.data[1].low
        self.close = self.data[1].close

        self.ema = bt.ind.ExponentialMovingAverage(self.data[1].close, period=9)
        self.wma = bt.ind.WeightedMovingAverage(self.data[1].close, period=30)

        self.order = None
        self.stop_order = None

    def next(self):
       #print("current portfolio value" + str(cerebro.broker.getvalue()))
        
        
        if not self.position:
            # Long Position
            if (self.ema[-1] > self.wma[-1]) and (self.data[1].close[-1] < self.ema[-1]) :
                print('close price : {}, ema : {}'.format(self.data[1].close[-1],self.ema[-1]))
                print('current close price {}'.format(self.data[1].high[0]))
                # self.buy(size=100, exectype=bt.Order.StopLimit, price=self.data.high[-1])
                self.order = self.buy(size=1, exectype=bt.Order.Limit, price=self.data[1].high[-1])
                self.log('BUY PRICE CREATED , %.2f ' % self.data[1].high[-1])
                risk = (self.data[1].high[-1] - self.data[1].low[-1])
                self.log('Risk points , %.2f' % risk)
                # Place a stop loss order
                # self.sell(size=100, exectype=bt.Order.StopLimit, price=self.data.low[-1])
                target_price = self.data[1].high[-1] + 2 * (self.data[1].high[-1] - self.data[1].low[-1])
                self.log('TARGET PRICE ,%.2f ' % target_price)
                oc1 = self.sell(size=1, exectype=bt.Order.StopLimit, price=target_price)
                self.order = self.sell(size=1, exectype=bt.Order.Stop, price=self.data[1].low[-1], plimit=self.data[1].low[-1] , oco=oc1)

            # elif (self.ema[-1] < self.wma[-1]) and  (self.data.close[-1] > self.ema[-1] )  :
                
            #     self.order = self.sell(size=1,exectype=bt.Order.Limit,price=self.data.low[-1])
            #     #Stop loss
            #     target_price = self.data.low[-1] - 2 * (self.data.high[-1] - self.data.low[-1])
            #     self.order = self.buy(size=1,exectype=bt.Order.StopLimit,price=target_price)
            #     self.log('Short Sell initiated , %.2f' % self.data.low[-1])

                



    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            return
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log('BUY EXECUTED, %.2f' % order.executed.price)
            elif order.issell():
                self.log('Sell order EXECUTED, %.2f' % order.executed.price)

        elif order.status in [order.Cancelled]:
            self.log('Order Canceled at price, %.2f' %order.price)

        elif order.status in [ order.Margin, order.Rejected]:
            self.log('Order Rejected or Margin error')

        self.order = None

class timeAdjustment(bt.TradingCalendar):
        params = dict(
        holidays=[
            dt.date(2018, 1, 26),
            # dt.date(2018, 2, 13),
            # dt.date(2018, 3, 2),
            # dt.date(2018, 3, 29),
            # dt.date(2018, 3, 30),
            # dt.date(2018, 5, 1),
            # dt.date(2018, 8, 15),
            # dt.date(2018, 8, 22),
            # dt.date(2018, 9, 13),
            # dt.date(2018, 9, 18),
            # dt.date(2018,10,2),
            # dt.date(2018,10,18),
            # dt.date(2018,11,8),
            # dt.date(2018,11,23),
            # dt.date(2018,12,2)
        ],
        # earlydays=[
        #     (dt.date(2018, 11, 7),
        #      dt.time(17, 31), dt.time(18, 31))
        # ],
        open=dt.time(9, 16),
        close=dt.time(15, 30),
    
    )




#Variable for our starting cash
startcash = 100000

#Create an instance of cerebro
cerebro = bt.Cerebro()

#Add our strategy
cerebro.addstrategy(PullBack)

nifty_futures="C:\\Nikhil_Media\\Education & Skills\\Stratergies_Codes\\BackTestFiles\\input_data\\BankNifty30min.csv"
# nifty_futures = 'C:\\Nikhil_Media\\Education & Skills\\Stratergies_Codes\\2018data.csv'

# Note : For custom created csv's ( like resampled ones ) always mention which columns are not present in the 
#  data feed
data = bt.feeds.GenericCSVData(
    headers=True,
    dataname=nifty_futures,
    # 2020-03-13
    fromdate=datetime(2019, 1, 1),    #year-month-date
    todate=datetime(2019, 1, 31),
    nullvalue=0.0,
    dtformat=("%Y-%m-%d %H:%M:%S"),
    # tmformat=('%H:%M:%S'),
    datetime=0,
    # time=3,
    open=1,
    high=2,
    low=3,
    close=4,
    volume = -1,
    openinterest = -1,
    timeframe=bt.TimeFrame.Minutes,
    # plot=False

)


cerebro.adddata(data)
# cerebro.resampledata(data,timeframe=bt.TimeFrame.Minutes,compression=30,name='30_Min_data')
# cerebro.resampledata(data,timeframe=bt.TimeFrame.Minutes,compression=60,name='60_min_bar')
# cerebro.addcalendar(timeAdjustment)

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


# Add the analyzers we are interested in
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")
cerebro.broker.setcommission(commission=0.0005)


# Run over everything
strategies=cerebro.run()

#Get final portfolio Value

portvalue = cerebro.broker.getvalue()
print("final and last port value:  "+str(portvalue))
pnl = portvalue - startcash

#Print out the final result

print('Final Portfolio Value: ${}'.format(portvalue))
print('P/L: ${}'.format(pnl))

#Finally plot the end results

cerebro.plot(style='candlestick',fmt_x_data = ('%Y-%m-%d %H:%M:%S'))

rajanprabu

@Nikhil-Kumar
you should first read Platform concepts and handling MultiTimeframe in backtrader. It will save lots of time for you. That would have clarified that datas[0] is not data0. You need to use data1 rather than data[1].

ab_trader

@rajanprabu said in unable to execute strategies on resampled data:

That would have clarified that datas[0] is not data0. You need to use data1 rather than data[1].

self.data0 is a shortcut for self.datas[0] as well as self.data1 is a shortcut for self.datas[1].

@Nikhil-Kumar said in unable to execute strategies on resampled data:

I made the changes you mentioned ie reference data by data[1]

you commented out resampling of the data, therefore the script has no data1.

rajanprabu

@ab_trader Thank you.. perhaps its me who has to read the docs again.

Nikhil Kumar

Thanks @rajanprabu and @ab_trader for your assistance and knowledge .

Appreciate it