TQTradingQuery

Session behavior · MNQ

How often does a 5-minute opening range breakout on MNQ continue in the same direction for the rest of the session

48.5%

continuation rate

n=1302026-03-04 to 2026-09-06

continuation rate

48.5%

num continued

63

num failed

67

Methodology

Analysis code was generated by an AI model up to 25 times, run in a sandbox against real MNQ 1-minute bars from 2026-03-04 to 2026-09-06, and reviewed automatically. Attempts that computed the same set of measures were grouped, and the figures shown are the median of the largest group. Agreement between attempts lowers the chance of a one-off coding slip; it does not prove the code matches the question, so the exact code is shown below.

Show the code

et = to_et(bars)
rth = rth_session(et)
rth = rth.copy()
rth['date'] = trading_date(rth)

or_end = minutes_after_open_cutoff(5)

results = []
for date, day in rth.groupby('date'):
    day = day.sort_index()
    orb = day[day.index.time <= or_end]
    if orb.empty:
        continue
    or_high = orb['high'].max()
    or_low = orb['low'].min()
    rest = day[day.index.time > or_end]
    if rest.empty:
        continue
    session_close = day['close'].iloc[-1]

    # find first breakout direction after opening range
    breakout_dir = None
    breakout_idx = None
    for ts, row in rest.iterrows():
        if row['high'] > or_high:
            breakout_dir = 'up'
            breakout_idx = ts
            break
        elif row['low'] < or_low:
            breakout_dir = 'down'
            breakout_idx = ts
            break
    if breakout_dir is None:
        continue

    if breakout_dir == 'up':
        continued = session_close > or_high
    else:
        continued = session_close < or_low

    results.append(continued)

n = len(results)
if n > 0:
    rate = sum(results) / n
else:
    rate = None

result = {
    'sample_size': n,
    'continuation_rate': rate,
    'num_continued': int(sum(results)) if n > 0 else 0,
    'num_failed': int(n - sum(results)) if n > 0 else 0,
}
  • Generated by an AI model up to 25 times, run in a sandbox, and reviewed automatically; the figures above are the median of the largest group of attempts that computed the same measures. Agreement between attempts lowers the chance of a one-off coding slip -- it does not prove the code matches the question, so check the definition and code. A one-off calculation, not a permanent fixture.

This is historical statistical information only. It is not investment advice, and past performance does not indicate future results. Trading involves risk of loss.