Posts made by jaezilla

Definitely not efficient, but I manually do it by saving all the trades and then iterating through them in post processing:

 def notify_trade(self,trade):
        self.trades.setdefault(self.data._name, [] ).append(trade)

trades_dict:dict = {}
for trade in result.trades.get(symbol, []):

    if trade.status == 1:

        trades_dict[trade.baropen] = {'open': {'size': trade.size, 'price': trade.price, 'value': trade.value } }

    elif trade.status == 2:
        size = trades_dict.get(trade.baropen).get('open').get('size')
        open_price = trades_dict.get(trade.baropen).get('open').get('price')
        open_value = trades_dict.get(trade.baropen).get('open').get('value')
        close_value = open_value + trade.pnl
        close_price = close_value / size

        open_commission =  min ( max (min_commision, (size * commission_rate_per_share) ), ( max_commision_pct *  size * open_price  ) ) 
        close_commission = min ( max (min_commision, (size * commission_rate_per_share) ), ( max_commision_pct *  close_value  ) ) 
        close_fee = close_value * trns_fee_pct
        close_fee += min ( (size * finra_sell_fee_per_share) , finra_max_fee_amount)

Interactive Brokers has a size based commission with a min and max limit. There is also a value based fee assessed on closing a position. This only works with opening and closing a position in its entirety.

Here is an example of what I did:

Generating the lines and params tuples by passing in list(df.columns)

def get_extra_df_columns(self, df_columns: list[str] = []) -> list[tuple]:
    lines: tuple = ()
    params: tuple(tuple) = ()
    for column in df_columns:
        if column not in ['symbol', 'open', 'high', 'low', 'close', 'volume']:
            lines = lines + (column,)
            params = params + ( (column, -1), )

    return [lines, params]

Creating the subclass by parsing the df to be parsed then returning it with the subclass attributes set dynamically:

def _create_data_feeder(self, database: Database, df: pd.DataFrame ):

        lines, params = database.get_extra_df_columns(df_columns=list(df.columns) )
        return type('PandasDataFeed', (bt.feeds.PandasData, ), {'lines':lines, 'params':params} )

Adding the dataframe to cerebro via the created data_feeder subclass

self.data_feeder = self._create_data_feeder(database=database, df=df)
self.cerebro.adddata(self.data_feeder(dataname=df, name=symbol ))

The extra columns in the df are vwap and supertrend and are accessible without hardcoding the lines and params

class TestStrategy(bt.Strategy):

    def __init__(self):

        for i, d in enumerate(self.datas):
 
            bt.ind.SMA(d.supertrend, period=1, subplot=False, plotname='supertrend')
            bt.ind.SMA(d.vwap, period=1, subplot=False, plotname='vwap')