I backtest my own strategy, and customize my own sizer like following:

    class MySizer(bt.Sizer):
        def _getsizing(self, comminfo, cash, data, isbuy):
            size = cash*0.96/data.close[1]
            return size

the above is for the long position, but how do I customize the short position ? maybe for the using the leverage . Thanks .

@ramoslin02 Or how can I set or what should I do to make sure that all my money buy all the position. any method or what parameter I need to use in the buy method ?

self.order = self.buy( some parameter here ？） ## thanks 

@backtrader
Thanks. And how do I get the market price for I use the following method

self.order = self.buy()

for I need to calculate the size . Or how can I set that I will buy all the size I can.

@backtrader said in self.buy(size=1.5) does not work:

Blog - Cheat On Open
ok, thanks for your reply , but still I still don't solve my problem. Here is my code below.

import backtrader as bt
import pandas as pd
from backtrader.indicators import CrossOver
from backtrader.indicators import CrossDown
from backtrader.order import Order

pd.set_option('expand_frame_repr',False)
pd.set_option('display.max_rows',10)

def resample(rule_type='15T'):
    df = pd.read_hdf('/Users/Lins/Python/QuantTrade/BTDemo/program/BTCUSDT_1min_data.h5')
    df['datetime'] = df['open_time']
    df = df.resample(rule=rule_type,on='open_time',base=0,label='left',closed='left').agg({
        'open':'first',
        'close':'last',
        'volume':'sum',
        'high':'max',
        'low':'min',
    })

    df['datetime'] = df.index
    df = df[df['volume'] > 0]
    return df

class ThreeDaysBreakoutIndicator(bt.Indicator):
    lines=('up','down',)

    def __init__(self):
        self.addminperiod(4)
        self.plotinfo.plotmaster = self.data
    def next(self):
        self.up[0] = max(max(self.data.open.get(ago=-1,size=3)), max(self.data.close.get(ago=-1,size=3)))
        self.down[0] = min(min(self.data.open.get(ago=-1,size=3)),min(self.data.close.get(ago=-1,size=3)))

class ThreeDaysBreakoutStrategy(bt.Strategy):

    def log(self,txt,dt=None):
        """ Loging function for this Strategy"""
        dt = dt or self.datas[0].datetime.date(0)
        print("%s, %s" % (dt.isoformat(), txt))

    def __init__(self):
        self.up_down = ThreeDaysBreakoutIndicator(self.data)
        self.buy_signal = CrossOver(self.data.close,self.up_down.up)
        self.sell_signal = CrossDown(self.data.close,self.up_down.down)

    def next(self):
        # buy_price = self.data.close[0] * (1+0.002)
        # sell_price = self.data.close[0] *(1-0.002)
        # cash = self.broker.get_cash()
        # size = cash/buy_price
        # size = 1
        # print("cash = %f , will buy price = %f size = %f" %(cash,buy_price,size))

        if not self.position and self.buy_signal == 1:
            self.order = self.buy()

        if self.getposition().size > 0 and self.sell_signal == 1:
            self.order = self.sell()

    def notify_order(self,order):
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log("Buy executed = %f, volume = %f , position = %f" % (order.executed.price, order.executed.size,self.getposition().size))
            elif order.issell():
                self.log("Sell executed = %f, volume = %f, position = %f" % (order.executed.price, order.executed.size,self.getposition().size))



if __name__ == "__main__":
    cerebro = bt.Cerebro(cheat_on_open=True)
    cerebro.broker.set_cash(100000)
    df = resample(rule_type='1D')
    df = df[df.index > pd.to_datetime('20180601')]
    data = bt.feeds.PandasData(dataname=df)
    cerebro.broker.setcommission(commission=0.002)
    # cerebro.broker.set_slippage_perc(perc=0.005)
    # cerebro.broker.set_slippage_perc()
    cerebro.adddata(data)
    cerebro.addobserver(bt.observers.TimeReturn)
    cerebro.addstrategy(ThreeDaysBreakoutStrategy)

    # cerebro.addwriter(bt.WriterFile,csv=True,out='ThreeDaysBreakoutStrategy.csv')

    print("The initial Cash = %.2f" %cerebro.broker.get_cash())
    cerebro.run()
    print("The final Cash = %.2f" % cerebro.broker.get_cash())

    cerebro.plot(style='candle')
    # here is the log for executing the code 
    """
      The initial Cash = 100000.00
      2018-06-19, Buy executed = 6711.390000, volume = 1.000000 , position = 1.000000
      2018-06-23, Sell executed = 6045.920000, volume = -1.000000, position = 0.000000
      2018-07-01, Buy executed = 6391.080000, volume = 1.000000 , position = 1.000000
      2018-07-11, Sell executed = 6296.910000, volume = -1.000000, position = 0.000000
      2018-07-17, Buy executed = 6723.330000, volume = 1.000000 , position = 1.000000
      2018-08-01, Sell executed = 7735.670000, volume = -1.000000, position = 0.000000
      2018-08-17, Buy executed = 6316.000000, volume = 1.000000 , position = 1.000000
      2018-08-21, Sell executed = 6251.000000, volume = -1.000000, position = 0.000000
      2018-08-24, Buy executed = 6525.000000, volume = 1.000000 , position = 1.000000
      2018-09-06, Sell executed = 6399.040000, volume = -1.000000, position = 0.000000
      The final Cash = 99930.95
   """

You can see from the log that even if I have $10000, and the executed price = 6711.390000, my strategy only buy 1 BTC , how can I buy 1.49 ( 10000/6711.39 = 1.49BTC) . Thanks and best regards.

@ramoslin02

in the next() method, my logic is like this: if I don't have any position , and my buy_signal is equal 1, I will buy the BTC , with the the set price and calculate the size I can buy , but doesn't work, how can I fixt this ? thanks .

@backtrader ok , I can send you the code.

class ThreeDaysBreakoutIndicator(bt.Indicator):
lines=('up','down',)

def __init__(self):
    self.addminperiod(4)
    self.plotinfo.plotmaster = self.data
def next(self):
    self.up[0] = max(max(self.data.open.get(ago=-1,size=3)), max(self.data.close.get(ago=-1,size=3)))
    self.down[0] = min(min(self.data.open.get(ago=-1,size=3)),min(self.data.close.get(ago=-1,size=3)))

class ThreeDaysBreakoutStrategy(bt.Strategy):

def log(self,txt,dt=None):
    """ Loging function for this Strategy"""
    dt = dt or self.datas[0].datetime.date(0)
    print("%s, %s" % (dt.isoformat(), txt))

def __init__(self):
    self.up_down = ThreeDaysBreakoutIndicator(self.data)
    self.buy_signal = CrossOver(self.data.close,self.up_down.up)
    self.sell_signal = CrossDown(self.data.close,self.up_down.down)

def next(self):
    buy_price = self.data.close[0] * (1+0.002)
    sell_price = self.data.close[0] *(1-0.002) 
    cash = self.broker.get_cash()
    size = cash/buy_price
    # size = 1
    # print("cash = %f , will buy price = %f size = %f" %(cash,buy_price,size))

    if not self.position and self.buy_signal == 1:
        self.order = self.buy(size=size,price=buy_price)

    if self.getposition().size > 0 and self.sell_signal == 1:
        self.order = self.sell(size=self.getposition().size,price=sell_price)

def notify_order(self,order):
    if order.status in [order.Completed]:
        if order.isbuy():
            self.log("Buy executed = %f, volume = %f , position = %f" % (order.executed.price, order.executed.size,self.getposition().size))
        elif order.issell():
            self.log("Sell executed = %f, volume = %f, position = %f" % (order.executed.price, order.executed.size,self.getposition().size))

it seems that the buy method does work , how can I make sure that my order will be completed with the set price and size. thanks

@ed-bartosh said in Anyone use backtrader to do live trading on Bitcoin exchange?:

guide

Do you ever experience the problem of self.order = self.buy(size=1.5) or in the buy method ,the param size can not set to a float value ?

For example , I have $10000 or 10000USDT ,and the price of BTC is 6500, so I can buy 1.53 BTC. So for the backtesting , I will buy 1.53 BTC , so in backtrader's buy method, I will set self.order = self.buy(size=1.53) , but doesn't work, the param size only accept the int value , float value doesn't work in backtrader.

We use the buy method with the params size = 1.5 ,but it does work. I use backtrader in cryptocurrency in btc/usdt market, but does not work, what should I do or how to modify the code so the backtrader support in cryptocurrency market ? thanks