Thanks a lot, now data is set to 1-Minute.
Signals are generating but it is displaying out of the boundary, how can I set signal position near to price or cross over.
'''
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
import pandas as pd
import random
import json
Import the backtrader platform
import backtrader as bt
class PandasData(btfeeds.DataBase):
params = (
('fromdate', datetime.datetime(2019, 12, 6)),
('todate', datetime.datetime(2019, 12, 10)),
('nullvalue', 0.0),
('dtformat', ('%Y-%m-%dT%H:%M:%S%z')),
#('tmformat', ('%H.%M.%S')),
('timeframe', bt.TimeFrame.Minutes),
('datetime', 0),
('open', 1),
('high', 2),
('low', 3),
('close', 4),
('volume', 5),
('openinterest', -1)
)
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, './BANKNIFTY19DECFUT.csv')
dataframe = pd.read_csv(datapath,
skiprows=0,
header=0,
parse_dates=True,
index_col=0)
data = bt.feeds.PandasData(dataname=dataframe,
name='BANKNIFTY',
fromdate=datetime.datetime(2019, 12, 6),
todate=datetime.datetime(2019, 12, 9),
timeframe=bt.TimeFrame.Minutes, compression=1,
sessionstart=datetime.time(9, 0), sessionend=datetime.time(16, 0)
)
print(dataframe)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Broker
cerebro.broker = bt.brokers.BackBroker(**eval('dict(' + args.broker + ')'))
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=1)
# Set the commission
cerebro.broker.setcommission(commission=0.0)
#print(cerebro.broker.getposition(data=None, broker=None))
# 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(start=datetime.date(2019, 12, 6), end=datetime.date(2019, 12, 10))
'''
