Ploblem for Order and execute. buy() sell()

kuuhmu

I use in sample code on Quickstart and change some data to PandaData.
data it OK .
But when i run. the result stake , price , comm is 0 like result below.
And in plot is no buy or sell mark.
finally i want to know to set backtest to exit like , after 6 tick or 6 line of data then exit order . I use it for binary option .

Result

Starting Portfolio Value: 1000.00
2017-01-09, Close, 15144.02
2017-01-09, BUY CREATE, 15144.02
2017-01-09, BUY EXECUTED, Price: 0.00, Cost: 0.00, Comm 0.00
2017-01-09, Close, 15147.62
2017-01-09, BUY CREATE, 15147.62
2017-01-09, BUY EXECUTED, Price: 0.00, Cost: 0.00, Comm 0.00
2017-01-09, Close, 15143.85
2017-01-09, BUY CREATE, 15143.85
2017-01-09, BUY EXECUTED, Price: 0.00, Cost: 0.00, Comm 0.00
2017-01-09, Close, 15149.78
2017-01-09, BUY CREATE, 15149.78
2017-01-09, BUY EXECUTED, Price: 0.00, Cost: 0.00, Comm 0.00
2017-01-09, Close, 15144.56
2017-01-09, Close, 15144.45
[<__main__.TestStrategy object at 0x0000017B7FDDD780>]
Final Portfolio Value: 1000.00

Plot

Code

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

import pandas as pd

#for test
import json
import matplotlib
import matplotlib.pyplot as plt
#matplotlib.style.use('seaborn-paper')

with open('test/r100 (1).json','r') as f:
    history = json.load(f)
history_pd = pd.DataFrame(columns=['open','high','low','close','volume','openinterest'])
history_pd.close = pd.to_numeric(pd.Series(history['history']['prices']))
history_pd.open = history_pd.close
history_pd.high = history_pd.close
history_pd.low = history_pd.close
history_pd.index = pd.to_datetime(pd.Series(history['history']['times']),unit='s')
history_pd.index.name = 'date'

#data = bt.feeds.PandasData(dataname = history_pd) 



# 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 enougth cash
        if order.status in [order.Completed, order.Canceled, order.Margin]:
            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)

        # 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')

    # 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)
    data = bt.feeds.PandasData(dataname = history_pd) 
    # 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=0.90)

    # 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()

backtrader

Your starting cash is 1000 and the value of the asset is over 15000

Starting Portfolio Value: 1000.00
2017-01-09, Close, 15144.02
2017-01-09, BUY CREATE, 15144.02

It would be very difficult for the system to execute any operation, because the cash is not enough to acquire even a single item of the asset.

Options

Easiest

Increase your cash with cerebro.broker.set_cash(x) (apparently you are already reducing it to 1000, because the default is 10000)

Advanced

Tell the system it is a future-like asset and reduce the margin to 1. You will be able to have over 15000 items of the asset in your portfolio
Create your own CommissionInfo scheme and decide how much an item of the asset costs

kuuhmu

Thank you.
finally i want to know to set backtest to exit like , after 6 tick or 6 line of data then exit order .

kuuhmu

@backtrader Is it like a stock i must buy at close value , like 15144.02 Dollas and sell when it up to 16000 .. ? Cause i cannot buy at 15144.02 because i have 1000 of money.
I use forex and binary option , sorry for i'm out understanding.

ab_trader

@kuuhmu check out discussion here
https://community.backtrader.com/topic/236/check-hold-period

backtrader

If the asset has a value of 15000 you cannot buy with 1000 monetary units.

If the value of a share of AAPL is 600 dollar you cannot buy it with 10 dollars.

Your asset is obviously something you can acquire with less money, probably through leverage

See this post for leverage: Blog - BTFD (Buy The F... Dip)
And the broker docs for the call: Docs - Broker in the method setcommission

You seem to need a leverage which goes over 15 (probably 20 would be better)

finally i want to know to set backtest to exit like , after 6 tick or 6 line of data then exit order .

You can always know the number of bars of a data feed by using len(data) and from there calculate how many bars have elapsed.

Askhat Sharipov

@kuuhmu said in Ploblem for Order and execute. buy() sell():

And in plot is no buy or sell mark.

How did you resolve the issue with missed buy sell marks using Pandas data load? Even if we place Starting Portfolio Value to 10K (now BUY, SELL will be executed), buy-sell marks would be still missed.

backtrader

His/Her marks were NOT missing, because no operations were executed.

In your case, you may want to share:

Operations log
Chart

Which would be the minimum to ascertain that you indeed are missing your marks.