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A Simple Strategy Trading Two Stocks (back trader)
"""A Simple Strategy Trading Two Stocks
Original code: https://blog.csdn.net/qq_26948675/article/details/80016633
Modified based on: https://www.backtrader.com/blog/posts/2018-04-22-improving-code/improving-code.html
Replaced the local CSV files with online data from IEX.
Unfortunately, this strategy is not profitable for the two stocks picked.
"""
import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
import pandas as pd
# Import the backtrader platform
import backtrader as bt
# Data Source
import pandas_datareader.data as web
# To avoid downloading the same data more than once
import joblib
MEMORY = joblib.Memory(cachedir="cache/")
@MEMORY.cache
def get_data(symbol, start, end):
df_prices = web.DataReader(
symbol, 'iex',
start, end).reset_index()
df_prices["date"] = pd.to_datetime(df_prices.date)
return df_prices.set_index("date")
class TestStrategy(bt.Strategy):
params = (
# Standard MACD Parameters
('period', 252),
('prepend_constant', True),
)
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.dataclose_x = self.datas[0].close
self.dataclose_y = self.datas[1].close
ma1 = bt.indicators.SMA(
self.data0, period=self.p.period)
ma2 = bt.indicators.SMA(
self.data1, period=self.p.period)
# Use a built-in indicator
ma1_pct = bt.ind.PctChange(ma1, period=1) # The ma1 percentage part
ma2_pct = bt.ind.PctChange(ma2, period=1) # The ma2 percentage part
# # Use line delay notation (-x) to get a ref to the -1 point
# ma1_pct = ma1 / ma1(-1) - 1.0 # The ma1 percentage part
# ma2_pct = ma2 / ma2(-1) - 1.0 # The ma2 percentage part
self.buy_sig = ma1_pct > ma2_pct # buy signal
self.sell_sig = ma1_pct <= ma2_pct # sell signal
self.order = None
self.buyprice = None
self.buycomm = None
def notify_cashvalue(self, cash, value):
self.log('Cash %s Value %s' % (cash, value))
def notify_order(self, order):
print(type(order), 'Is Buy ', order.isbuy())
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):
# Simply log the closing price of the series from the reference
self.log('Close, %.2f' % self.dataclose_x[0])
self.log('Close, %.2f' % self.dataclose_y[0])
# Check if we are in the market
if not self.getposition(self.datas[1]):
# Not yet ... we MIGHT BUY if ...
if self.buy_sig:
# if sma[0]<top[-5]:
# BUY, BUY, BUY!!! (with default parameters)
self.log('BUY CREATE,{},{}'.format(
self.dataclose_y[0], self.dataclose_x[0]))
# Keep track of the created order to avoid a 2nd order
self.order = self.buy(self.datas[0])
self.order = self.sell(self.datas[1])
else:
# Already in the market ... we might sell
if self.sell_sig:
# SELL, SELL, SELL!!! (with all possible default parameters)
self.log('BUY CREATE,{},{}'.format(
self.dataclose_y[0], self.dataclose_x[0]))
# Keep track of the created order to avoid a 2nd order
self.log('Pos size %s' % self.position.size)
self.order = self.close(self.datas[1])
self.order = self.close(self.datas[0])
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro()
cerebro.addstrategy(TestStrategy)
# Create a Data Feed
data_1 = bt.feeds.PandasData(
dataname=get_data(
'AAPL', datetime.datetime(2016, 1, 1),
datetime.datetime(2018, 7, 6))
)
data_2 = bt.feeds.PandasData(
dataname=get_data(
'DPZ', datetime.datetime(2016, 1, 1),
datetime.datetime(2018, 7, 6))
)
# Add the Data Feed to Cerebro
cerebro.adddata(data_1)
cerebro.adddata(data_2)
# Set our desired cash start
cerebro.broker.setcash(100000.0)
cerebro.broker.setcommission(commission=0.001)
cerebro.addsizer(bt.sizers.FixedSize, stake=100)
# 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())
cerebro.plot()
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