Fixed upgrading matplotlib to 3.2.0

@run-out said in Calculate EMA on the median ((high+low/2)) ?:

To get a line for ema as you ask...

self.myema = bt.ind.EMA((self.datas[0].high / self.datas[0].low) /2 )

Thanks, so I am getting an error:

"....bt.indicators.ExponentialMovingAverage((self.datas[0].high / self.data[0].low) /2, period=100)
AttributeError: 'float' object has no attribute 'low' "

Original code for the EMA: bt.indicators.ExponentialMovingAverage(self.datas[0], period=100)

Updated to do the median: bt.indicators.ExponentialMovingAverage((self.datas[0].high / self.data[0].low) /2, period=100)

and getting that error.

Thanks for your help!

@ab_trader Thanks for all your help here and a few other posts! !

@run-out Awesome thanks! I will make those changes this weekend and get back!

@run-out If not too busy, have you had a chance to look at my code? TY!

@run-out Sure, I did get rid of the error, but not sure if I am doing the list right or not (I assume NOT, lol) because it runs, but only triggers two trades at the beginning then nothing. Thanks Again

import backtrader as bt
from datetime import datetime


class Strategy(bt.Strategy):

    def log(self, txt, dt=None):
        """ Logging function fot this strategy"""
        dt = dt or self.data.datetime[0]
        if isinstance(dt, float):
            dt = bt.num2date(dt)
        print("%s, %s" % (dt.date(), txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.bar_executed = len(self)
        self.dataclose = self.datas[0].close

        # Create an order list
        self.o_li = list()

        # To keep track of pending orders and buy price/commission
        self.order = []
        self.buyprice = None
        self.buycomm = None

        # Add EMA and TRIX
        self.ema = bt.indicators.ExponentialMovingAverage((self.datas[0].high + self.datas[0].low) / 2, period=100,
                                                          plotname="EMAMid")
        self.trix = bt.indicators.TRIX(self.datas[0], period=5, plot=True)
        self.atr = bt.indicators.ATR(period=100, movav=bt.indicators.MovAv.Exponential)

    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.Canceled, order.Margin]:
            if order.isbuy():
                self.log("BUY FAILED, Cancelled or Margin")
            self.log
        if order.status in [order.Completed, order.Canceled, order.Margin]:
            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)

        # Cleans up the order list.
        if not order.alive() and order in self.o_li:
            self.o_li.remove(order)

    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
        global price, price_limit, price_stop
        self.log('ATR: %.2f' % (self.atr[0]))

        if self.position.size == 0 and len(self.o_li) == 0:
            if self.ema[0] >= self.ema[-1] and (self.trix[0] >= self.trix[-1]):
                price = self.data.close[0]
                price_limit = price + self.atr[0]
                price_stop = price - self.atr[0]

                self.order = self.buy_bracket(
                    data=self.datas[0],
                    size=None,
                    exectype=bt.Order.Limit,
                    plimit=price,
                    stopprice=price_stop,
                    stopexec=bt.Order.Stop,
                    limitprice=price_limit,
                    limitexec=bt.Order.Limit,
                )

        if self.position.size == 0 and len(self.o_li) == 0:
            if self.ema[0] <= self.ema[-1] and (self.trix[0] <= self.trix[-1]):
                price = self.data.close[0]
                price_limit = price - self.atr[0]
                price_stop = price + self.atr[0]

                self.order = self.sell_bracket(
                    data=self.datas[0],
                    size=None,
                    exectype=bt.Order.Limit,
                    plimit=price,
                    stopprice=price_stop,
                    stopexec=bt.Order.Stop,
                    limitprice=price_limit,
                    limitexec=bt.Order.Limit,
                )

            # Store orders in a list
            self.o_li = [o for o in self.order]

            self.log(
                "LONG BUY limit Targets: Buy {:8.2f}, Target {:8.2f}, Stop {:8.2f}".format(
                    price, price_limit, price_stop
                )
            )

        if self.position.size > 0:
            if self.ema[0] <= self.ema[-1] or (self.trix[0] <= self.trix[-1]):
                print("Sell shares at {}".format(self.data.close[0]))
                self.close()
                self.o_li = list()

        if self.position.size < 0:
            if self.ema[0] >= self.ema[-1] or (self.trix[0] >= self.trix[-1]):
                print("Sell shares at {}".format(self.data.close[0]))
                self.close()
                self.o_li = list()


if __name__ == '__main__':
    # Variable for our starting cash
    startcash = 100000

    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(Strategy)

    # Create a Data Feed
    data = bt.feeds.YahooFinanceData(dataname='AAPL',
                                     fromdate=datetime(2019, 6, 1),
                                     todate=datetime(2020, 11, 9),
                                     buffered=True)

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

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

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=50)

    # Set the commission
    cerebro.broker.setcommission(commission=0.0)

    # Add Analyzers:
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="mySharpe", riskfreerate=0.001)
    cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
    cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    portvalue = cerebro.broker.getvalue()
    pnl = portvalue - startcash

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
    print('Final Portfolio Value: ${}'.format(portvalue))
    print('P/L: ${}'.format(pnl))

    # Plot the results
    cerebro.plot(style='bar', bardown='grey', barup="0.75", volume=False, grid=False)

@run-out Thanks so much for your guidance! I'm still racking my brain against the well....is the error in

# To keep track of pending orders and buy price/commission
        self.order = []
        self.buyprice = None
        self.buycomm = None

or

# Clean up the order list.
        if not order.alive() and order in self.o_li:
            self.o_li.remove(order)

        # Write down: no pending order
        self.order = None

or none of the above . LOL! Thanks again!!

@run-out I tried to implement your suggestions, I and I know I am doing something wrong with the list. I get an error :

"line 110, in next for o in self.order:
TypeError: 'NoneType' object is not iterable"

Code:

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
from datetime import datetime
import backtrader as bt

# Create a Strategy
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):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.bar_executed = len(self)
        self.dataclose = self.datas[0].close

        # Create an order list
        self.o_li = list()

        # To keep track of pending orders and buy price/commission
        self.order = []
        self.buyprice = None
        self.buycomm = None

        # Add EMAs
        self.ema = bt.indicators.ExponentialMovingAverage((self.datas[0].high + self.datas[0].low) / 2, period=50)
        self.ema2 = bt.indicators.ExponentialMovingAverage((self.datas[0].high + self.datas[0].low) / 2, period=15)
        self.atr = bt.indicators.ATR(period=50, movav=bt.indicators.MovAv.Exponential)

    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, order.Canceled, order.Margin]:
            if order.isbuy():
                self.log(
                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f, Size %.2f' %
                    (order.executed.price,
                     order.executed.value,
                     order.executed.comm,
                     order.executed.size))

                self.buyprice = order.executed.price
                self.buycomm = order.executed.comm
            else:  # Sell
                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f, Size %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm,
                          order.executed.size))
            self.bar_executed = len(self)

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

        # Clean up the order list.
        if not order.alive() and order in self.o_li:
            self.o_li.remove(order)

        # 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):
        # 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.ema[0] >= self.ema[-1] and (self.ema2[0] >= self.ema2[-1]):
                price = self.data.close[0]
                price_limit = price + self.atr[0]
                price_stop = price - self.atr[0]

                self.order = self.buy_bracket(
                    data=self.datas[0],
                    size=None,
                    exectype=bt.Order.Limit,
                    plimit=price,
                    stopprice=price_stop,
                    stopexec=bt.Order.Stop,
                    limitprice=price_limit,
                    limitexec=bt.Order.Limit,
                )

            if self.ema[0] <= self.ema[-1] and (self.ema2[0] <= self.ema2[-1]):
                price = self.data.close[0]
                price_limit = price - self.atr[0]
                price_stop = price + self.atr[0]

                self.order = self.sell_bracket(
                    data=self.datas[0],
                    size=None,
                    exectype=bt.Order.Limit,
                    plimit=price,
                    stopprice=price_stop,
                    stopexec=bt.Order.Stop,
                    limitprice=price_limit,
                    limitexec=bt.Order.Limit,
                )

        if self.position.size > 0 and (self.ema[0] <= self.ema[-1] or (self.ema2[0] <= self.ema2[-1])):
            self.close()
            for o in self.order:
                self.cancel(o)

        if self.position.size < 0 and (self.ema[0] >= self.ema[-1] or (self.ema2[0] >= self.ema2[-1])):
            self.close()
            for o in self.order:
                self.cancel(o)


if __name__ == '__main__':
    # Variable for our starting cash
    startcash = 100000

    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Create a Data Feed
    data = bt.feeds.YahooFinanceData(dataname='AAPL',
                                     fromdate=datetime(2019, 6, 1),
                                     todate=datetime(2020, 11, 9),
                                     buffered=True)

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

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

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=50)

    # Set the commission
    cerebro.broker.setcommission(commission=0.0)

    # Add Analyzers:
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="mySharpe", riskfreerate=0.001)
    cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
    cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    portvalue = cerebro.broker.getvalue()
    pnl = portvalue - startcash

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
    print('Final Portfolio Value: ${}'.format(portvalue))
    print('P/L: ${}'.format(pnl))

    # Plot the results
    cerebro.plot(style='bar', bardown='grey', barup="0.75", volume=False, grid=False)

@run-out Awesome thanks! I will make those changes this weekend and get back!

Sorry, I don't know why it didn't markdown the whole code.

@run-out Sure, this is just a simple strategy for my learning purposes of BT and python:

`from future import (absolute_import, division, print_function,
unicode_literals)
from datetime import datetime
import backtrader as bt

Create a Strategy

class TestStrategy(bt.Strategy):
params = (('order_pct', 0.95), ("buy_price_adjust", 0.0), ("buy_limit_adjust", 0.02), ("buy_stop_adjust", 0.02))

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):
    # Keep a reference to the "close" line in the data[0] dataseries
    self.bar_executed = len(self)
    self.dataclose = self.datas[0].close

    # Create an order list
    self.o_li = list()

    # To keep track of pending orders and buy price/commission
    self.order = None
    self.buyprice = None
    self.buycomm = None

    # Add EMA and TRIX
    self.ema = bt.indicators.ExponentialMovingAverage((self.datas[0].high + self.datas[0].low) / 2, period=50,
                                                      plotname="EMAMid")
    self.ema2 = bt.indicators.ExponentialMovingAverage((self.datas[0].high + self.datas[0].low) / 2, period=15,
                                                      plotname="EMAMid")
    self.atr = bt.indicators.ATR(period=50, movav=bt.indicators.MovAv.Exponential)

    # Indicators for plotting
    bt.indicators.ExponentialMovingAverage(self.datas[0], period=50)

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, order.Canceled, order.Margin]:
        if order.isbuy():
            self.log(
                'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f, Size %.2f' %
                (order.executed.price,
                 order.executed.value,
                 order.executed.comm,
                 order.executed.size))

            self.buyprice = order.executed.price
            self.buycomm = order.executed.comm
        else:  # Sell
            self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f, Size %.2f' %
                     (order.executed.price,
                      order.executed.value,
                      order.executed.comm,
                      order.executed.size))
        self.bar_executed = len(self)

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

    # Clean up the order list.
    if not order.alive() and order in self.o_li:
        self.o_li.remove(order)

    # 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):
    # 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.ema[0] >= self.ema[-1] and (self.ema2[0] >= self.ema2[-1]):
            price = self.data.close[0] * (1.0 - self.p.buy_price_adjust)
            price_limit = price + self.atr[0]
            price_stop = price - self.atr[0]

            self.long_buy_order = self.buy_bracket(
                data=self.datas[0],
                size=None,
                exectype=bt.Order.Limit,
                plimit=price,
                stopprice=price_stop,
                stopexec=bt.Order.Stop,
                limitprice=price_limit,
                limitexec=bt.Order.Limit,
            )

        if self.ema[0] <= self.ema[-1] and (self.ema2[0] <= self.ema2[-1]):
            price = self.data.close[0] * (1.0 - self.p.buy_price_adjust)
            price_limit = price - self.atr[0]
            price_stop = price + self.atr[0]

            self.short_sell_order = self.sell_bracket(
                data=self.datas[0],
                size=None,
                exectype=bt.Order.Limit,
                plimit=price,
                stopprice=price_stop,
                stopexec=bt.Order.Stop,
                limitprice=price_limit,
                limitexec=bt.Order.Limit,
            )

    if self.position.size > 0 and (self.ema[0] <= self.ema[-1] or (self.ema2[0] <= self.ema2[-1])):
        self.close()

    if self.position.size < 0 and (self.ema[0] >= self.ema[-1] or (self.ema2[0] >= self.ema2[-1])):
        self.close()

if name == 'main':
# Variable for our starting cash
startcash = 100000

# Create a cerebro entity
cerebro = bt.Cerebro()

# Add a strategy
cerebro.addstrategy(TestStrategy)

# Create a Data Feed
data = bt.feeds.YahooFinanceData(dataname='AAPL',
                                 fromdate=datetime(2019, 6, 1),
                                 todate=datetime(2020, 11, 9),
                                 buffered=True)

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

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

# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=50)

# Set the commission
cerebro.broker.setcommission(commission=0.0)

# Add Analyzers:
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="mySharpe", riskfreerate=0.001)
cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')

# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

# Run over everything
cerebro.run()

portvalue = cerebro.broker.getvalue()
pnl = portvalue - startcash

# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
print('Final Portfolio Value: ${}'.format(portvalue))
print('P/L: ${}'.format(pnl))

# Plot the results
cerebro.plot(style='bar', bardown='grey', barup="0.75", volume=False, grid=False)`

@run-out I am still fairly new to BT and python. Basically there are 3 signals generated - long, short or out. I have just learned on how to put in brackets if I want to use a PT and SL. But there are times when none of those gets hit and a signal to get out of the position hits (goes flat) and that leaves the PT and SL open if I use the brackets. So I assume I need to do it another way (issue PT and SL separately, and have them cancelled when the position == 0) instead of the brackets, unless there was a way to cancel the brackets if I went flat. Make sense?

Hi @Rumen-Nenov...did you get this figured out? I am at a similar junction you were when you wrote this. Thanks!

@vladisld Thanks for your help! Figured it out....feel a little dumb, still a n00b with BT :-)