How this works

1bars_et = to_et(bars)
2rth = rth_session(bars_et)
3rth = rth.copy()
4rth['date'] = trading_date(rth)
5
6daily = rth.groupby('date').agg(open=('open','first'), high=('high','max'), low=('low','min'), close=('close','last'))
7daily = daily.sort_index()
8
9prev_close = daily['close'].shift(1)
10daily['prev_close'] = prev_close
11
12# gap up: today's open above prior day's close
13gap_up = daily[daily['open'] > daily['prev_close']].copy()
14
15# filled same day if the low of the day trades back down to/through prev_close
16gap_up['filled'] = gap_up['low'] <= gap_up['prev_close']
17
18n = len(gap_up)
19if n > 0:
20    fill_rate = gap_up['filled'].mean()
21    avg_gap_size = (gap_up['open'] - gap_up['prev_close']).mean()
22else:
23    fill_rate = None
24    avg_gap_size = None
25
26result = {
27    'sample_size': int(n),
28    'fill_rate': float(fill_rate) if fill_rate is not None else None,
29    'num_filled': int(gap_up['filled'].sum()) if n > 0 else 0,
30    'num_not_filled': int(n - gap_up['filled'].sum()) if n > 0 else 0,
31    'avg_gap_size_points': float(avg_gap_size) if avg_gap_size is not None else None,
32}