Backtrader Community

Nikhil Kumar

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

Appreciate it

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'))

Nikhil Kumar

Thanks for the input rajan

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

Nikhil Kumar

@Nikhil Kumar

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