Nice! Was looking at trying to make something like this myself. Thanks for sharing
Posts made by tim3lord
RE: BBand Strategy
@ab_trader I just wanted to take the past 30 days of data and use that to find the average low and highs for the BBand and trade on 1 day ticks. When backtesting, 20 days of data seemed to produce better results. I also found this to be true on Quantopian when I added the strategy there and tried many different stocks on 1 day ticks with 20 days of data.
Hello, I am excited to see that Backtrader now has a community section and I want to share a simple strategy with you all. This strategy uses Backtrader's BBand indicator and buys after the market dips into the lower band and sells on the moving average after the market hits the top band. This works great in sideways/bull markets. The idea is to buy during a low period and sell if the market dips below a moving average. Also note I am new to algotrading and programming in general so don't laugh to hard at this idea/strategy
I actually use this strategy in the Bitcoin world and have had great results trading it live in the past 3 months. I use Tradewave to run it live with a small porting of my holdings.
You can use talib to create a BBands in Tradewave or you can use a simple math formula to recreate the bbands used in Backtrader....
std = data(interval=INTERVAL).btc_usd.std(SETTING1) ma = data(interval=INTERVAL).btc_usd.ma(SETTING1) upperband = ma + (Decimal(2) * std) lowerband = ma - (Decimal(2) * std)
Note, I changed a lot of the code to work better such as not trading in low volatile markets, using a stop when the market turns and other things you can use to help make better, safer decisions. :wink:
So here is the code you can copy and paste....
import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv) # Import the backtrader platform import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): params = (('BBandsperiod', 20),) def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas.datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data dataseries self.dataclose = self.datas.close # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None self.redline = None self.blueline = None # Add a BBand indicator self.bband = bt.indicators.BBands(self.datas, period=self.params.BBandsperiod) 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 enougth cash 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) # 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) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return if self.dataclose < self.bband.lines.bot and not self.position: self.redline = True if self.dataclose > self.bband.lines.top and self.position: self.blueline = True if self.dataclose > self.bband.lines.mid and not self.position and self.redline: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose) # Keep track of the created order to avoid a 2nd order self.order = self.buy() if self.dataclose > self.bband.lines.top and not self.position: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose) # Keep track of the created order to avoid a 2nd order self.order = self.buy() if self.dataclose < self.bband.lines.mid and self.position and self.blueline: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose) self.blueline = False self.redline = False # 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() # Add a strategy cerebro.addstrategy(TestStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv)) datapath = os.path.join(modpath, 'TSLA-USD.csv') # Create a Data Feed data = bt.feeds.GenericCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2008, 4, 4), # Do not pass values before this date todate=datetime.datetime(2016, 12, 2), nullvalue=0.0, dtformat=('%m/%d/%Y'), datetime=0, high=2, low=3, open=1, close=4, volume=5, openinterest=-1) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(10000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=5) # Set the commission cerebro.broker.setcommission(commission=0.002) # 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 cerebro.plot()
Also, you can just use this script and paste it into your browsers to grab quick market cvs data from yahoo.
I will be posting more simple examples and hope others do as well. I found backtesting ideas with Backtrader can be quick and when compared to using online backtesters and more powerful. Using the right parameters without oversampling is still a difficult thing to master. Hope to keep learning and trying new things!