I forgot to mention that there is also a trading fee for opening and closing a trade of 0.04%

Hi all,

I read the docs about custom commission schemes and credit interest but not sure I understand it correctly.

Some context: I'm trading on crypto exchange Binance on BTCUSDT futures market, my strategy use 4h data to compute long and short signal. Binance has a funding fee that has to be paid if a position is open at 0-8-16h00 and is approximatly 0.03% daily (0.01% per funding interval) + a premium (that I will ignore)

"Binance uses a flat interest rate component, with the assumption that holding cash equivalent returns a higher interest than BTC equivalent. The difference is stipulated to be 0.03% per day by default (0.01% per funding interval since funding occurs every 8 hours)"

import backtrader as bt

class MyCommissionInfo(bt.CommInfo):
    def _get_credit_interest(self, size, price, days, dt0, dt1):
        '''
        This method returns  the cost in terms of credit interest charged by
        the broker.

        In the case of ``size > 0`` this method will only be called if the
        parameter to the class ``interest_long`` is ``True``

        The formulat for the calculation of the credit interest rate is:

          The formula: ``days * price * abs(size) * (interest / 365)``


        Params:
          - ``data``: data feed for which interest is charged

          - ``size``: current position size. > 0 for long positions and < 0 for
            short positions (this parameter will not be ``0``)

          - ``price``: current position price

          - ``days``: number of days elapsed since last credit calculation
            (this is (dt0 - dt1).days)

          - ``dt0``: (datetime.datetime) current datetime

          - ``dt1``: (datetime.datetime) datetime of previous calculation

        ``dt0`` and ``dt1`` are not used in the default implementation and are
        provided as extra input for overridden methods
        '''

    return days * abs(size) * price * (self.p.interest / 365.0)

Not sure how to modify this to have what I want.

Thanks for your help and let me know if more informations are needed!

@mudassar031
Hello,

Thanks for your indicator!

How do you use it in order to compute a backtest?
Do you use it with cerebro.addstrategy or cerebro.add_signal?

I tried both but can't manage to make it work...
Thanks for your help!

Hi guys,

I'm coding on a t2.micro instance on AWS ec2 running on Amazon Linux 2 os. I built a strategy with 2 data feeds and everything works well but when I try to plot it, my jupyter notebook kernel stay busy forever... I cant even stop jupyter, I have to close the cmd and reboot my instance in order to use it again.

Anyone faced the same issue or have a solution?

Thanks,
Antoine

@kaber-mobile Hi, I may have a few question as I am trying to develop an algo trading system for crypto based on ML. How can I contact you?

Maybe this will help someone... I had the same issue and solved it by using a percentage of my portfolio

class Antoine_sizer(bt.Sizer):
    params = (('prop', 0.1),)

def _getsizing(self, comminfo, cash, data, isbuy):
    """Returns the proper sizing"""

    if isbuy:    # Buying
        target = self.broker.getvalue() * self.params.prop    # Ideal total value of the position
        price = data.close[0]
        size_net = target / price    # How many shares are needed to get target
        size = size_net * 0.99

        if size * price > cash:
            return 0    # Not enough money for this trade
        else:
            return size

    else:    # Selling
        return self.broker.getposition(data).size    # Clear the position