vwap_test

import backtrader as bt
import alpaca_backtrader_api
import logging
from datetime import datetime
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams["figure.figsize"] = (10, 6) # (w, h)
plt.ioff()

'''
Author: B. Bradford

MIT License

Copyright (c) B. Bradford

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

That they contact me for shipping information for the purpose of sending a
local delicacy of their choice native to whatever region they are domiciled.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''

class VolumeWeightedAveragePrice(bt.Indicator):
    plotinfo = dict(subplot=False)

    params = (('period', 30), )

    alias = ('VWAP', 'VolumeWeightedAveragePrice',)
    lines = ('VWAP',)
    plotlines = dict(VWAP=dict(alpha=0.50, linestyle='-.', linewidth=2.0))



    def __init__(self):
        # Before super to ensure mixins (right-hand side in subclassing)
        # can see the assignment operation and operate on the line
        cumvol = bt.ind.SumN(self.data.volume, period = self.p.period)
        typprice = ((self.data.close + self.data.high + self.data.low)/3) * self.data.volume
        cumtypprice = bt.ind.SumN(typprice, period=self.p.period)
        self.lines[0] = cumtypprice / cumvol

        super(VolumeWeightedAveragePrice, self).__init__()


# Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('maperiod', 30),
    )

    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 the vmap indicator
        self.vwap = VolumeWeightedAveragePrice(self.datas[0], period=self.params.maperiod, subplot=True)

        # Add a MovingAverageSimple indicator
        #self.sma = bt.indicators.SimpleMovingAverage(
        #    self.datas[0], period=self.params.maperiod)

    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')

        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.vwap[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.vwap[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()


cerebro = bt.Cerebro()

# TQQQ 5Min data starts on: 2019-12-12 09:30:00
# TQQQ 5Min data starts on: 2019-12-12 15:55:00
start = datetime.strptime('2019-12-12 09:30:00', '%Y-%m-%d %H:%M:%S')
end = datetime.strptime('2019-12-12 15:55:00', '%Y-%m-%d %H:%M:%S')

data = bt.feeds.GenericCSVData(
    fromdate=start,
    todate=end,
    dataname=f"./tqqq_2019_12_12_5Min.cvs",
    dtformat=('%Y-%m-%d %H:%M:%S'),
    openinterest=-1,
    nullvalue=0.0,
    timeframe=bt.TimeFrame.Minutes,
    plot=False
)
cerebro.adddata(data)

# add strategy
#cerebro.addstrategy(St, **eval('dict(' + args.strat + ')'))
cerebro.addstrategy(TestStrategy)
# set the cash
cerebro.broker.setcash(10000)
# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
results = cerebro.run()
# Print out the final result
#print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.plot()

# Basic performance evaluation ... final value ... minus starting cash
#pnl = cerebro.broker.get_value() - 10000
#print('Profit ... or Loss: {:.2f}'.format(pnl))