(I cut out a few days in results for brevity)

length.append(self.data[0]) - this will return huge list of data feeds, I don't think you need it; if you implement your indicator in the __init()__, you don't need to check this. bt will do it for you.

@ab_trader Did all the above, but still having trouble with this last part. Made a dummy indicator class but I get an "array index out of bound exception" on the " self.dayHigh[0] = self.data1.high[-5]" line if I don't give a massive self.addminperiod. Data1 is days, but it seems self.addminperiod only cares about data0 (minutes). If I switch data1 to data0, then having self.addminperiod(5) works "correctly" since I need data from 5 previous bars ago (although of course the indicator should only care about days)

How can I sync up self.addminperiod to days?

Once I have this basic script set up I have the foundation to build on my own. Just need to fix this! Thanks again in advance.

Below is the simple code and the output.

class dummyIndicator(bt.Indicator,):
    lines = ('dayHigh',)
    params = (
        ('period', 5),
    )

    def __init__(self):
        self.addminperiod(1000)

    def next(self):
        self.dayHigh[0] = self.data1.high[-5]

class TestStrategy(bt.Strategy):
    params = (
        # changes the starting indicator value
        ('maperiod', 5),

    )

    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

        #"Unexpected argument" but code still runs
        self.dayHigh = dummyIndicator(self.data0, self.data1)

    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):

        self.log('Current Date')
        print("Indicator Value", self.dayHigh[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.data0.close[0] > self.dayHigh[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()

        else:

            if self.data0.close[0] < self.dayHigh[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__':
    DataFactory = alpaca_backtrader_api.AlpacaData
    data0 = DataFactory(dataname='DSX',
                        historical=True,
                        fromdate=datetime(2020, 1, 20),
                        timeframe=bt.TimeFrame.Minutes,
                        reversed=True)
    cerebro.adddata(data0)
    data1 = cerebro.resampledata(data0, timeframe=bt.TimeFrame.Days)
    cerebro.adddata(data1)
    cerebro.addstrategy(TestStrategy)
    cerebro.broker.setcash(1000.0)
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
    cerebro.run()
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

OUTPUT:

Starting Portfolio Value: 1000.00
2020-01-28, Current Date
Indicator Value 2.8
2020-01-28, Current Date
Indicator Value 2.8
2020-01-28, Current Date
Indicator Value 2.8
2020-01-28, Current Date
Indicator Value 2.8
2020-01-28, Current Date
Indicator Value 2.8
2020-01-28, Current Date
Indicator Value 2.8
2020-01-28, Current Date
Indicator Value 2.8
2020-01-28, Current Date
Indicator Value 2.8
2020-01-28, Current Date
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Indicator Value 2.8
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Indicator Value 2.8
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Indicator Value 2.8
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Indicator Value 2.8
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Indicator Value 2.8
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Indicator Value 2.8
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, BUY CREATE, 2.86
2020-01-29, BUY EXECUTED, Price: 2.80, Cost: 2.80, Comm 0.00
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
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Indicator Value 2.78
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Indicator Value 2.78
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Indicator Value 2.78
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Indicator Value 2.78
2020-01-29, SELL CREATE, 2.76
2020-01-29, SELL EXECUTED, Price: 2.76, Cost: 2.80, Comm 0.00
2020-01-29, OPERATION PROFIT, GROSS -0.04, NET -0.04
2020-01-29, Current Date
Indicator Value 2.78
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Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78
2020-01-29, Current Date
Indicator Value 2.78

Final Portfolio Value: 999.90

Process finished with exit code 0

I know my question might seem simple, but I am new to python (but an experienced trader). The documentation is helpful but the examples are rather complicated and confusing for a beginner.

I've marked up the code for extra clarity. Again, it's calculating a simple "baseline" number that should be from Daily Bars and the trades should be coming from Minute bars. Example: The previous 5 day's highs average to 5 dollars, the lows average to 3 dollars, so the "baseline" indicator on day 1 should be 4 dollars. If the stock price, at any minute is below 4, buy order is placed, and if price is above 4, it sells.

To help me learn, I would greatly appreciate if you could point out where exactly, specifically, the separate data feeds would be referenced in the simple code below to achieve the desired result. If we can work this small example out I'd be able to run and expand on it. Thank you again for your help.

#setting cerebro
cerebro = bt.Cerebro()

#using alpaca
store = alpaca_backtrader_api.AlpacaStore(
    key_id=ALPACA_API_KEY,
    secret_key=ALPACA_SECRET_KEY,
    paper=ALPACA_PAPER
)
if not ALPACA_PAPER:
    broker = alpaca_backtrader_api.AlpacaBroker()
    cerebro.setbroker(broker)

length = []

class TestStrategy(bt.Strategy):

    #this is the logging function
    def log(self, txt, dt=None):
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    #keeping references to certain data
    def __init__(self):
        self.datalow = self.datas[0].low
        self.datahigh = self.datas[0].high
        self.order = None
        self.buyprice = None
        self.buycomm = None

    #order notifications
    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, %.8f' % order.executed.price)
                self.log('Current Shares: %.8f' % self.getposition(data=dataMinutes).size)
            elif order.issell():
                self.log('SELL EXECUTED, %.8f' % order.executed.price)
                self.log('Current Shares: %.8f' % self.getposition(data=dataMinutes).size)

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

    #trade notifications
    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log('OPERATION PROFIT, GROSS %.8f, NET %.f' %
                 (trade.pnl, trade.pnlcomm))

    #is called for each bar of data
    def next(self):

        daysAgo = 5
        length.append(self.data[0])

        #This is the indicator of sorts. It calculates "baseline" and decides
        #whether to start placing orders or not.
        #This should use Daily Data only.
        if len(length) >= daysAgo + 1:
            last5highavg = (self.datahigh[-1] + self.datahigh[-2] + self.datahigh[-3] + self.datahigh[-4] + self.datahigh[-5]) / 5
            last5lowavg = (self.datalow[-1] + self.datalow[-2] + self.datalow[-3] + self.datalow[-4] + self.datalow[-5]) / 5

            baseline = (last5highavg + last5lowavg) / 2

            #order logic. This should be based on Minute Bars
            if self.order:
                return
            if not self.position:
                if self.data[0] < baseline:
                    self.log('BUY CREATE, %.8f' % self.data[0])
                    self.order = self.buy(exectype=bt.Order.Limit, price=self.data[0], size=310)
            else:
                if self.data[0] > baseline:
                    self.log('SELL CREATE, %.8f' % self.data[0])
                    self.order = self.sell(exectype=bt.Order.Limit, price=self.data[0])
        else:
            print("Fewer than 5 days")

#adding the strategy and setting cash
cerebro.addstrategy(TestStrategy)
cerebro.broker.setcash(1000.0)

#data feed resampling from minutes to days
DataFactory = alpaca_backtrader_api.AlpacaData
dataMinutes = DataFactory(dataname='DSX', historical=True, fromdate=datetime(2020, 1, 15), todate=datetime(2020, 1, 31), timeframe=bt.TimeFrame.Minutes)
cerebro.resampledata(dataMinutes, timeframe=bt.TimeFrame.Days, compression=1)

#dataMinutes = DataFactory(dataname='DSX', historical=True, fromdate=datetime(2020, 1, 15), todate=datetime(2020, 1, 31), timeframe=bt.TimeFrame.Days)

#printing the results
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

Thank you again for your quick response and the heads up. I'll take a look at the indicator docs and get back to you soon!

@ab_trader I regenerated them immediately after posting. The above keys are disabled. How can I delete/edit the post?

@ab_trader Hi, thanks for your prompt response. Still very unclear though. Hoping you can still help me out with this.

@ab_trader said in Evaluate Weekly Trend, Execute Trades on Minute basis:

Add minute data feed, resample it to daily bars.

In my above code snippet I already the minute feed. In the below code snipped I resample it to daily bars.

You will have two data feeds. Check your indicator against second data feed and issue your orders using first data feed.

How would you access the separate data feeds?

Given my code snippet above, is there a practical way to access the Minute feed only when making orders and the Day feed only when checking the indicator? A passed argument, perhaps? Unsure on practical implementation.

Below, modified the original code snippet with the minute feed resampled to daily bars. However, it only issues orders based on the incorrect Day feed.

import alpaca_backtrader_api
import backtrader as bt
import pandas as pd
from datetime import datetime

ALPACA_API_KEY = MY_ALPACA_API_KEY
ALPACA_SECRET_KEY = MY_ALPACA_SECRET_KEY
ALPACA_PAPER = True

cerebro = bt.Cerebro()

store = alpaca_backtrader_api.AlpacaStore(
    key_id=ALPACA_API_KEY,
    secret_key=ALPACA_SECRET_KEY,
    paper=ALPACA_PAPER
)
if not ALPACA_PAPER:
    broker = alpaca_backtrader_api.AlpacaBroker()
    cerebro.setbroker(broker)

length = []


class TestStrategy(bt.Strategy):

    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.datalow = self.datas[0].low
        self.datahigh = self.datas[0].high
        self.order = None
        self.buyprice = None
        self.buycomm = None


    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, %.8f' % order.executed.price)
                self.log('Current Shares: %.8f' % self.getposition(data=dataMinutes).size)
            elif order.issell():
                self.log('SELL EXECUTED, %.8f' % order.executed.price)
                self.log('Current Shares: %.8f' % self.getposition(data=dataMinutes).size)

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


    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log('OPERATION PROFIT, GROSS %.8f, NET %.f' %
                 (trade.pnl, trade.pnlcomm))

    def next(self):

        daysAgo = 5
        length.append(self.data[0])

        if len(length) >= daysAgo + 1:
            self.log('Low, %.2f' % self.datahigh[0])
            last5highavg = (self.datahigh[-1] + self.datahigh[-2] + self.datahigh[-3] + self.datahigh[-4] + self.datahigh[-5]) / 5
            last5lowavg = (self.datalow[-1] + self.datalow[-2] + self.datalow[-3] + self.datalow[-4] + self.datalow[-5]) / 5
            print(self.datas[0].datetime.date(0),  "---> Above trade was made today")

            baseline = (last5highavg + last5lowavg) / 2

            print(
                "current price:",
                self.data[0],
                "baseline:", baseline, )

            if self.order:
                return

            if not self.position:

                if self.data[0] < baseline:
                    self.log('BUY CREATE, %.8f' % self.data[0])
                    self.order = self.buy(exectype=bt.Order.Limit, price=self.data[0], size=310)

            else:

                if self.data[0] > baseline:
                    self.log('SELL CREATE, %.8f' % self.data[0])
                    self.order = self.sell(exectype=bt.Order.Limit, price=self.data[0])

        else:
            print("Fewer than 5 days")


cerebro.addstrategy(TestStrategy)


DataFactory = alpaca_backtrader_api.AlpacaData
dataMinutes = DataFactory(dataname='DSX', historical=True, fromdate=datetime(2020, 1, 15), todate=datetime(2020, 1, 31), timeframe=bt.TimeFrame.Minutes)

#Added the resampling code snippet here
cerebro.resampledata(dataMinutes, timeframe=bt.TimeFrame.Days, compression=1)


cerebro.broker.setcash(1000.0)

print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

Hi, noob here looking for help.

Confused with datafeed timeframes. Hacked together a simple example from the tutorials to illustrate:

The below program uses an "indicator," baseline, to determine trades. If current price is below baseline, it buys, if above, it sells.

Baseline is average of the last 5 day's highs and last 5 day's lows, divided by 2.

However, I can't seem to figure out how to get those daily highs and lows, while executing trades on a minute basis. I can get them if I set the datafeed to daily though. If I set it to minutes, it just takes the last 5 prices on a minute basis, instead of daily highs and lows...

I know this may sound like a simple question, but if someone could give advice/amend the below code to work, I will be able to continue my backtrader journey. Thank you!

#Making sure 5 days have passed
length = []

class TestStrategy(bt.Strategy):

    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.datalow = self.datas[0].low
        self.datahigh = self.datas[0].high
        self.order = None
        self.buyprice = None
        self.buycomm = None


    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, %.8f' % order.executed.price)
                self.log('Current Shares: %.8f' % self.getposition(data=dataMinutes).size)
            elif order.issell():
                self.log('SELL EXECUTED, %.8f' % order.executed.price)
                self.log('Current Shares: %.8f' % self.getposition(data=dataMinutes).size)

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


    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log('OPERATION PROFIT, GROSS %.8f, NET %.f' %
                 (trade.pnl, trade.pnlcomm))

    def next(self):
        ################################RelevantCode

        #Needs 5 previous days of data for the indicator to work
        daysAgo = 5
        length.append(self.data[0])

        #If there's 5 days of data....
        if len(length) >= daysAgo + 1:
            self.log('Low, %.2f' % self.datahigh[0])

            #average the daily highs of past 5 days
            last5highavg = (self.datahigh[-1] + self.datahigh[-2] + self.datahigh[-3] + self.datahigh[-4] + self.datahigh[-5]) / 5

            #average the daily lows of past 5 days
            last5lowavg = (self.datalow[-1] + self.datalow[-2] + self.datalow[-3] + self.datalow[-4] + self.datalow[-5]) / 5
          
            #calculate indicator on a daily basis
            baseline = (last5highavg + last5lowavg) / 2

           ################################RelevantCode

            if self.order:
                return

            if not self.position:

                if self.data[0] < baseline:
                    self.log('BUY CREATE, %.8f' % self.data[0])
                    self.order = self.buy(exectype=bt.Order.Limit, price=self.data[0], size=310)

            else:

                if self.data[0] > baseline:
                    self.log('SELL CREATE, %.8f' % self.data[0])
                    self.order = self.sell(exectype=bt.Order.Limit, price=self.data[0])

        else:
            print("Fewer than 5 days")


cerebro.addstrategy(TestStrategy)


#Alpaca data. I chose minutes for execution....
DataFactory = alpaca_backtrader_api.AlpacaData
dataMinutes = DataFactory(dataname='DSX', historical=True, fromdate=datetime(2020, 1, 15), todate=datetime(2020, 1, 31), timeframe=bt.TimeFrame.Minutes)
cerebro.adddata(dataMinutes)

cerebro.broker.setcash(1000.0)

print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())