Prepare Backtrader CSV data and try running the sample script
How to use Backtrader
If the sample script is used as it is, an error will occur.
backtrader Document_Quickstart https://www.backtrader.com/docu/quickstart/quickstart/
After the installation, when I run the sample script on this page, I get an error saying "The CSV file does not exist in the specified path".
I tried searching, but the corresponding folder itself was not found locally. Apparently, when I download backtrader with PIP, the data folder used to run the sample script and the sample folder itself are not included.
Prepare CSV data
https://github.com/mementum/backtrader/blob/master/datas/orcl-1995-2014.txt Therefore, copy the contents of Oracle's stock price "orcl-1995-2014.txt" from this Git page and make it a text file. Save this in the same directory as the sample script and rewrite the file path specification part in the script. (For Jupyter notebook, the directory where the ipynb file is saved: C: \ Users \ user name by default)
datapath.py
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
#Rewrite this as follows
datapath = 'C:\\Users\\xxxx\\orcl-1995-2014.txt'
Try running it with Jupyter notebook
Start Jupyter notebook from the virtual environment of Anaconda and execute the following sample script with the file path rewritten.
quickstart.py
%matplotlib notebook
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 the backtrader platform
import backtrader as bt
# Create a Stratey
class TestStrategy(bt.Strategy):
params = (
('maperiod', 15),
)
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()
# 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[0]))
#datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
datapath = 'C:\\Users\\xxxx\\orcl-1995-2014.txt'
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(1000.0)
# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=10)
# Set the commission
cerebro.broker.setcommission(commission=0.0)
# 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()
When executed in Jupyter notebook, the trading result (omitted) of the strategy and the plot of the indicator are displayed.
In the case of Jupyter notebook, if you write % matplotlib notebook at the beginning of the sample script, a scaling button and chart scroll button will be added to the plot screen. By pressing this, you can enlarge the part of the price movement that you are interested in. You can also move the place you want to see at the same magnification. It is not displayed this time, but it is convenient when you want to analyze candlesticks in detail.
(Added 2020.0207)
It's different from the above script, but when you zoom in on the candlestick, it looks like this. Switch to cerebro.plot (style ='candle') at the end of the script.
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