How to only make trades during the open?

[A7DC]

I'm struggling with this: how do I limit the entries to only 930-1030? I know there's the discussion here (#156) and the Jupyter notebook here (https://nbviewer.org/github/polakowo/vectorbt/blob/master/examples/TradingSessions.ipynb) but I'm lost to how this applies to my use case and I get different errors depending on what I try. I've tried to simplify the code as much as possible. Here it is:

import pandas as pd
import numpy as np
import vectorbt as vbt
import glob
from pathlib import Path
from IPython.display import display

df_gapper_list = []
df_intraday_analysis = []

def GET_CHARTS(date, ticker):
    folder = "intraday_bars_gapped_new/{}*.csv".format(ticker)
    take_profit = 1
    for fname in glob.glob(folder)[:25]:
        ticker = Path(fname).stem
        df = pd.read_csv(fname, index_col='datetime', usecols=["open", "high", "low", "close", "volume", "datetime", "date", "time"], parse_dates=True)
        # get the current day bars
        df_current_day = df[(df['date'] == date)]

        # get the market hours
        df_market_hours_idx = df_current_day.between_time('9:30', '16:00', include_end=True).index
        df_market_hours = df_current_day.loc[df_market_hours_idx]
        # get the first hour of the open
        df_open_hours_idx = df_current_day.between_time('9:30', '10:30', include_end=True).index
        df_open_hours = df_current_day.loc[df_open_hours_idx]

        # only enter during open bars
        df_open_hours['bull_c'] = np.where(((df_open_hours['close'] < df_open_hours['open'])), True, False)
        # just a placeholder so it runs
        df_current_day['bear_c'] = np.where((df_current_day['close'] >= (df_current_day['open'] + take_profit)), True, False)

        entries = df_open_hours['bull_c']
        exits = df_current_day['bear_c']
        # return the entries / exits which are inside the open and market hours
        entries = entries[df_open_hours_idx]
        exits = exits[df_market_hours_idx]

        if not df_market_hours.empty:
            pf = vbt.Portfolio.from_signals(df_market_hours['close'], entries, exits, fees=0.005, sl_stop=0.05)
            print(pf.stats())
            pf.trades.plot().show_svg()
            # print(df_current_day.[0], 'df_current_day')
            fig = df_market_hours.vbt.ohlcv.plot()
            fig.show_svg()


df = pd.read_csv('mikeys-spreadsheet.csv', usecols=['date', 'ticker', 'PM 200 ema hit'])
# return only the days where the 200 ema was not hit during pre market
df_filtered =  df[df['PM 200 ema hit'] == False]

for index, row in df_filtered.iterrows():
    date, ticker = row['date'], row['ticker']
    GET_CHARTS(date, ticker)

And here's the error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
c:\Users\audac\Documents\trading\backtesting\vectorbt.ipynb Cell 2' in <cell line: 44>()
     44 for index, row in df_filtered.iterrows():
     45     date, ticker = row['date'], row['ticker']
---> 46     GET_CHARTS(date, ticker)

c:\Users\audac\Documents\trading\backtesting\vectorbt.ipynb Cell 2' in GET_CHARTS(date, ticker)
     29 exits = exits[df_market_hours_idx]
     31 if not df_market_hours.empty:
---> 32     pf = vbt.Portfolio.from_signals(df_market_hours['close'], entries, exits, fees=0.005, sl_stop=0.05)
     33     print(pf.stats())
     34     pf.trades.plot().show_svg()

File c:\Users\audac\anaconda3\envs\vectorbt\lib\site-packages\vectorbt\portfolio\base.py:2898, in Portfolio.from_signals(cls, close, entries, exits, short_entries, short_exits, signal_func_nb, signal_args, size, size_type, price, fees, fixed_fees, slippage, min_size, max_size, size_granularity, reject_prob, lock_cash, allow_partial, raise_reject, log, accumulate, upon_long_conflict, upon_short_conflict, upon_dir_conflict, upon_opposite_entry, direction, val_price, open, high, low, sl_stop, sl_trail, tp_stop, stop_entry_price, stop_exit_price, upon_stop_exit, upon_stop_update, adjust_sl_func_nb, adjust_sl_args, adjust_tp_func_nb, adjust_tp_args, use_stops, init_cash, cash_sharing, call_seq, ffill_val_price, update_value, max_orders, max_logs, seed, group_by, broadcast_named_args, broadcast_kwargs, template_mapping, wrapper_kwargs, freq, attach_call_seq, **kwargs)
   2893 keep_raw[close_idx] = False
   2894 broadcast_kwargs = merge_dicts(dict(
   2895     keep_raw=keep_raw,
   2896     require_kwargs=dict(requirements='W')
   2897 ), broadcast_kwargs)
-> 2898 broadcasted_args = broadcast(*broadcastable_args.values(), **broadcast_kwargs)
   2899 broadcasted_args = dict(zip(broadcastable_args.keys(), broadcasted_args))
   2900 close = broadcasted_args['close']

File c:\Users\audac\anaconda3\envs\vectorbt\lib\site-packages\vectorbt\base\reshape_fns.py:567, in broadcast(to_shape, to_pd, to_frame, align_index, align_columns, index_from, columns_from, require_kwargs, keep_raw, return_meta, *args, **kwargs)
    565 # Get final shape
    566 if to_shape is None:
--> 567     to_shape = _broadcast_shape(*map(np.asarray, arr_args_2d))
    569 # Perform broadcasting
    570 new_args = []

File c:\Users\audac\anaconda3\envs\vectorbt\lib\site-packages\numpy\lib\stride_tricks.py:421, in _broadcast_shape(*args)
    416 """Returns the shape of the arrays that would result from broadcasting the
    417 supplied arrays against each other.
    418 """
    419 # use the old-iterator because np.nditer does not handle size 0 arrays
    420 # consistently
--> 421 b = np.broadcast(*args[:32])
    422 # unfortunately, it cannot handle 32 or more arguments directly
    423 for pos in range(32, len(args), 31):
    424     # ironically, np.broadcast does not properly handle np.broadcast
    425     # objects (it treats them as scalars)
    426     # use broadcasting to avoid allocating the full array

ValueError: shape mismatch: objects cannot be broadcast to a single shape.  Mismatch is between arg 23 with shape (185,) and arg 31 with shape (4,).

Can someone please point me in the right direction please?