Need help to add a simple stop loss

[Jeffreylabranche]

Hi,

I'm just starting to learn Python and Backtrader. Any idea how I can add a stop loss to this strategy? I want to replace the actual exit which is based on the moving average by a 2% stop loss. Unfortunately was not able to do it based on Backtrader documentation.

Thank you in advance!

code_text

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 backtrader.feeds as btfeeds
#from backtrader_plotting import Bokeh

from matplotlib.dates import (HOURS_PER_DAY, MIN_PER_HOUR, SEC_PER_MIN,
MONTHS_PER_YEAR, DAYS_PER_WEEK,
SEC_PER_HOUR, SEC_PER_DAY,
num2date, rrulewrapper, YearLocator,
MicrosecondLocator)

#import matplotlib
#import matplotlib.pyplot as plt
#import backtrader.plot

#matplotlib.use('Qt5Agg')
#plt.switch_backend('Qt5Agg')

Import the backtrader platform

import backtrader as bt

Create a Stratey

class TestStrategy(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()

    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()

cerebro.addstrategy(TestStrategy)


# Create a Data Feed
data = btfeeds.GenericCSVData(dataname='/Users/jeffrey/Documents/Python_projects/spy_data_1min.csv',
fromdate=datetime.datetime(2022, 3, 30),
todate=datetime.datetime(2022, 4, 18),
nullvalue=0.0,
datetime=0,
#timeframe=bt.TimeFrame.Minutes,
dtformat=('%Y-%m-%d %H:%M'),
open=1,
high=2,
low=3,
close=4,
volume=5,
openinterest=-1
)    

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

# Set our desired cash start
cerebro.broker.setcash(100000.0)

# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=10)

# Set the commission - 0.1% ... divide by 100 to remove the %
cerebro.broker.setcommission(commission=0.001)

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

# Run over everything
cerebro.run()

# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

 # Plot the result
#b = Bokeh(style='bar', plot_mode='single')
cerebro.plot()
#cerebro.plot(height= 30, iplot= False)