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):

    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        if dt is None:
            dt = self.datas[0].datetime.date(0) if len(self) > 0 else 'No Data'
        print('%s, %s' % (dt, txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close
        self.log('Strategy initialized', dt='Initialization')

    def next(self):
        # Log the current date and close price
        self.log('Date: %s, Close: %.2f' % (self.datas[0].datetime.date(0), self.dataclose[0]))
        self.log('High: %.2f, Low: %.2f' % (self.datas[0].high[0], self.datas[0].low[0]))

        # Simple trading logic
        if self.dataclose[0] < 30000:
            self.log('BUY CREATE, Date: %s, Price: %.2f, Position: %d' % (self.datas[0].datetime.date(0), self.dataclose[0], self.position.size))
            self.buy()
        elif self.dataclose[0] > 30000 and self.position.size > 0:
            self.log('SELL CREATE, Date: %s, Price: %.2f, Position: %d' % (self.datas[0].datetime.date(0), self.dataclose[0], self.position.size))
            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, 'candle_test_asc.csv')

# Create a Data Feed
data = bt.feeds.GenericCSVData(
    dataname=datapath,
    fromdate=datetime.datetime(2022, 1, 1),
    todate=datetime.datetime(2022, 5, 31),
    dtformat=('%Y-%m-%d %H:%M:%S'),
    timeframe=bt.TimeFrame.Days,
    compression=1,
    datetime=0,
    open=1,
    high=2,
    low=3,
    close=4,
    volume=5,
    openinterest=-1)

    # Add the Data Feed to Cerebro
cerebro.adddata(data)

# Set our desired cash start
cerebro.broker.setcash(100000.0)

# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

# Run over everything
cerebro.run()

# Print out the final result
final_value = cerebro.broker.getvalue()
cash = cerebro.broker.getcash()
portfolio_value = final_value - cash

print('Final Portfolio Value: %.2f' % final_value)
print('Cash: %.2f' % cash)
print('Portfolio Value (Positions): %.2f' % portfolio_value)