from pandas import Series, Timedelta, date_range, DatetimeIndex

hloc = kwargs["processed"]
config = kwargs["config"]
timeframe = config["timeframe"]
timedelta = Timedelta(timeframe)

date_index: DatetimeIndex
date_index = date_range(start=min_date, end=max_date, freq=timeframe, normalize=True)
balance = {pair: Series(0.0, index=date_index) for pair in hloc}
balance_total = Series(0.0, index=date_index)
close_time = results["close_time"].values
open_time = results["open_time"].values
for pair in hloc:
    hloc[pair].set_index("date", inplace=True)
for date in date_index.values:
    # set the balance for all the opens that are not instantly closed
    where = (open_time == date) & (close_time != date)
    for _, tr in results.loc[where].iterrows():
        pair = tr["pair"]
        close = hloc[pair].at[date, "close"]
        # balance at date
        bal_at_date = 1 - tr["open_rate"] / close - slippage
        balance[pair][date] += bal_at_date
    # set the balance for all the closes
    for _, tr in results.loc[close_time == date].iterrows():
        pair = tr["pair"]
        # balance at date, since it's the close we take the profits minus slippage
        bal_at_date = tr["profit_percent"] - slippage
        balance[tr["pair"]][date] += bal_at_date
        # since we are closing, update all the balances since open time
        # ranges are inclusive:[):exclusive
        for open_date in (
            balance[pair]
            .loc[tr["open_time"] + timedelta : tr["close_time"]]
            .index
        ).values:
            close = hloc[pair].at[open_date, "close"]
            balance[pair][open_date] += 1 - tr["open_rate"] / close - slippage
    # settle the balance for all the pairs at current date
    for pair in balance:
        balance_total[date] += balance[pair][date]