Posts made by konichuvak

@toinou222 instead of modifying the commission scheme you can simply increment the value of the account with every funding.

    dt = self.datetime.datetime()

    if not self.position.size:
        # Entry logic
        ...
    else:
        # Collect funding
        rate = self.data._dataname.loc[dt].funding
        if not np.isnan(rate):
           funding = -self.position.size * rate
           self.broker.add_cash(funding)
           
       # Exit logic
       ...

Here I am assuming that you are using Pandas DataFeed and there exists a funding column in it with NaNs everywhere except at 0-8-16h. You can pull the historical data using Binance's API endpoint.

Anyone figured a clean way to attach individual analyzer per datafeed? The purpose is to compare performance of the strategy using build-in analyzers (return, sharpe, drawdown, etc) across multiple venues. I am aware that analyzers are meant to operate on strategies, however I was wondering whether anyone came up with a reusable pattern to write analyzers that operate on datafeeds.

For now, I am simply running an individual backtest per datafeed in a separate process, which allows for such parallelism but introduces some overhead.