Hello Algotrading!
A classic Simple Moving Average Crossover strategy, can be easily implemented and in different ways. The results and the chart are the same for the three snippets presented below.
from datetime import datetime import backtrader as bt # Create a subclass of Strategy to define the indicators and logic class SmaCross(bt.Strategy): # list of parameters which are configurable for the strategy params = dict( pfast=10, # period for the fast moving average pslow=30 # period for the slow moving average ) def __init__(self): sma1 = bt.ind.SMA(period=self.p.pfast) # fast moving average sma2 = bt.ind.SMA(period=self.p.pslow) # slow moving average self.crossover = bt.ind.CrossOver(sma1, sma2) # crossover signal def next(self): if not self.position: # not in the market if self.crossover > 0: # if fast crosses slow to the upside self.buy() # enter long elif self.crossover < 0: # in the market & cross to the downside self.close() # close long position cerebro = bt.Cerebro() # create a "Cerebro" engine instance # Create a data feed data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1), todate=datetime(2012, 12, 31)) cerebro.adddata(data) # Add the data feed cerebro.addstrategy(SmaCross) # Add the trading strategy cerebro.run() # run it all cerebro.plot() # and plot it with a single command
from datetime import datetime import backtrader as bt # Create a subclass of Strategy to define the indicators and logic class SmaCross(bt.Strategy): # list of parameters which are configurable for the strategy params = dict( pfast=10, # period for the fast moving average pslow=30 # period for the slow moving average ) def __init__(self): sma1 = bt.ind.SMA(period=self.p.pfast) # fast moving average sma2 = bt.ind.SMA(period=self.p.pslow) # slow moving average self.crossover = bt.ind.CrossOver(sma1, sma2) # crossover signal def next(self): if not self.position: # not in the market if self.crossover > 0: # if fast crosses slow to the upside self.order_target_size(target=1) # enter long elif self.crossover < 0: # in the market & cross to the downside self.order_target_size(target=0) # close long position cerebro = bt.Cerebro() # create a "Cerebro" engine instance # Create a data feed data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1), todate=datetime(2012, 12, 31)) cerebro.adddata(data) # Add the data feed cerebro.addstrategy(SmaCross) # Add the trading strategy cerebro.run() # run it all cerebro.plot() # and plot it with a single command
from datetime import datetime import backtrader as bt # Create a subclass of SignaStrategy to define the indicators and signals class SmaCross(bt.SignalStrategy): # list of parameters which are configurable for the strategy params = dict( pfast=10, # period for the fast moving average pslow=30 # period for the slow moving average ) def __init__(self): sma1 = bt.ind.SMA(period=self.p.pfast) # fast moving average sma2 = bt.ind.SMA(period=self.p.pslow) # slow moving average crossover = bt.ind.CrossOver(sma1, sma2) # crossover signal self.signal_add(bt.SIGNAL_LONG, crossover) # use it as LONG signal cerebro = bt.Cerebro() # create a "Cerebro" engine instance # Create a data feed data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1), todate=datetime(2012, 12, 31)) cerebro.adddata(data) # Add the data feed cerebro.addstrategy(SmaCross) # Add the trading strategy cerebro.run() # run it all cerebro.plot() # and plot it with a single command