Nifty Gap Up History: Do They Actually Fill? 10 Years of Real Data

What you're about to read will contradict something most Indian retail traders believe. We ran the numbers across 10 years, 2,532 sessions, and 85 gap up events. The findings are not what the trading YouTube universe told you.

What Is a Gap Up Opening?

A gap up occurs when a stock or index opens significantly higher than its previous day's closing price. In the context of Nifty 50, a gap up means the market begins the trading session at a price above where it closed the night before — with no trades having occurred in between.

This happens because of overnight developments: global market movements, news announcements, FII activity, economic data releases, or geopolitical events that shift sentiment before Indian markets open at 9:15 AM.

The question every trader asks when they see a gap up is simple: will it hold, or will it fill?

This study answers that question with 10 years of real Nifty data — not opinion, not theory, not YouTube strategies. Numbers.

Our Data and Methodology

We used daily OHLC data for Nifty 50 from January 2016 to March 2026 — a total of 2,532 trading sessions sourced via institutional-grade broker API access.

How we define a gap up: The opening price of a session is at least 1% above the previous session's closing price.

How we measure fill: A gap is considered filled if, during the same trading session, Nifty's low touches or crosses below the previous day's closing price.

Why 1% threshold? Gaps smaller than 1% are routine noise — price adjusting for overnight sentiment. At 1%+, a gap represents a meaningful directional signal that traders actively respond to. This threshold gives us 85 clean events to analyze.

Here is the complete gap size distribution across all 2,532 sessions — this shows just how rare meaningful gaps actually are:

How Often Do Gap Ups Occur?

Across 10 years, Nifty produced 85 gap up sessions of 1% or more. That is roughly 8 to 9 per year on average — but the distribution is far from uniform.

The year-wise breakdown reveals something important:

• 2020 alone contributed 36.5% of all gap ups — 31 events in a single year driven by COVID-19 volatility
• 2022 contributed 18.8% — the Russia-Ukraine period and global rate hike cycle
• 2018 and 2023 had just 2 gap ups each — calm trending markets produce almost none

This distribution matters for interpreting any gap up strategy. A strategy that "worked in 2020" was largely a product of extreme volatility, not repeatable edge.

If we exclude 2020, the average drops to 5.4 gap ups per year — confirming that large gap ups are genuinely infrequent in normal market conditions.

The Core Question: Do Gap Ups Fill?

This is what traders most want to know. If Nifty gaps up 1% at open, does it come back down to fill the gap during the same session?

The answer: Only 29.4% of the time.

Out of 85 gap up events, just 25 gaps were filled during the same trading session. The remaining 70.6% — 60 events — did not fill on the day of the gap.

This single finding contradicts one of the most repeated beliefs in Indian retail trading: "gaps always fill."

They do not. At least not on the same day.

Breakdown by gap size:

The larger the gap, the less likely it fills the same day. Gaps above 2% filled in only 25% of cases — and some of the largest gaps in our data (the 4.86% gap on February 3, 2026 and the 4.48% on April 7, 2020) showed no signs of filling at all.

Gap Size Changes Everything

The aggregate fill rate of 29.4% hides an important nuance. Gap behavior changes significantly depending on how large the opening move is.

Small gap ups (1% to 1.5%) — 56 events: The most common category. These gaps closed lower than the open 55.4% of the time, with an average intraday return of -0.32%. Weak conviction in either direction — the gap creates initial excitement but fades through the session.

Medium gap ups (1.5% to 2%) — 13 events: The only category with a bullish edge. 53.8% of these sessions closed higher than the open, with an average return of +0.08%. Small sample size means this should not be traded mechanically, but the data directionally suggests medium gaps carry more genuine buying conviction.

Large gap ups (above 2%) — 16 events: The strongest reversal tendency. Only 37.5% closed higher than open — meaning nearly two-thirds reversed during the day. The average gap size for this category was 3.05%, but the market gave most of it back intraday. Large gaps are frequently news-driven — results, policy decisions, global events — and once the initial reaction fades, profit booking takes over.

The Monday Effect

One of the most interesting findings from this dataset concerns the day of the week.

Monday accounts for 28.2% of all gap ups — significantly above the expected 20% if gaps were randomly distributed across five trading days. Nifty gaps up on Monday more than any other day.

The reason is structural: Nifty has two full days of global market activity to absorb over the weekend — US markets on Friday, global events Saturday and Sunday — before Indian markets open Monday morning. Two days of unprocessed sentiment creates a larger opening adjustment.

But here is the counterintuitive finding: Monday gap ups are also the most bullish.

Of the 24 Monday gap ups in our data, 58.3% closed higher than the open — the highest bullish rate of any day. This is despite Monday being the most frequent gap day.

The likely explanation: when global sentiment is strong enough to gap Nifty up significantly after a full weekend, that sentiment often has enough conviction to hold through the session.

The 2020 Effect — Why Context Matters

2020 distorts this entire dataset significantly. Removing the COVID year changes the picture considerably:

In non-crisis years, gap ups are roughly balanced — 48% bullish close, near-zero average return. The strongly negative average return in the full dataset is primarily a product of 2020's extreme volatility, where gaps occurred during a falling market and frequently reversed sharply.

This matters for strategy development. A trader building a gap up framework on 2016-2019 or 2021-2024 data would find a very different risk profile than one using the full 10-year window. Market regime is not a secondary consideration — it is central.

What Happens the Next Day?

One finding that surprised us: the day after a gap up, Nifty closes higher 60% of the time, with an average next-day return of +0.10%.

This is counterintuitive. If gap ups are bearish intraday (which they are on average), why do the following sessions tend to be positive?

The most likely explanation: the gap itself signals directional market intent. Even when intraday profit booking brings the close below the open, the underlying directional pressure often persists into the next session. Traders who act on gaps the following morning — rather than at the open — capture better risk-reward.

7 Data-Backed Findings

1. Gap ups are bearish intraday more often than not. Nifty closes below the gap-up opening in 55.3% of events. The average intraday return across all 85 gap ups is -0.25%.

2. The fill rate is far lower than most traders assume. Only 29.4% of gap ups fill the same day. The popular belief that "gaps always fill" does not hold — at least not within the same session.

3. Gap size is the most important variable. Small gaps (1-1.5%) and large gaps (>2%) both tend to reverse. Only medium gaps (1.5-2%) show a slight bullish lean. Treating all gaps the same is the most common analytical error.

4. Large gaps rarely fill — and often reverse sharply. The 16 events above 2% filled only 25% of the time, and their average intraday return was -0.29% despite the large gap. News-driven gaps are the worst to trade in the direction of the gap.

5. Risk is asymmetric against gap buyers. On bearish close days: average loss of -1.16%. On bullish close days: average gain of +0.86%. The downside is materially larger than the upside when the trade goes wrong.

6. 2020 is not representative of normal gap behavior. 36.5% of all gap ups in 10 years occurred in a single year. Strategies calibrated on this data are partially calibrated on a crisis period that may not repeat in the same form.

7. Monday is both the most frequent and the most bullish gap day. 28.2% of gap ups happen on Mondays — structural, not random. And Monday gaps close higher 58.3% of the time — the best record of any day.

Gap Calculation — The Code

The core gap calculation used in this analysis:

import pandas as pd

# Load your OHLC data
df = pd.read_csv("nifty_data.csv")
df['Date'] = pd.to_datetime(df['Date'])
df = df.sort_values('Date').reset_index(drop=True)

# Previous day's close
df['Prev_Close'] = df['Close'].shift(1)

# Gap percentage
df['Gap_Pct'] = ((df['Open'] - df['Prev_Close']) / df['Prev_Close']) * 100

# Intraday return (open to close)
df['Intraday_Return'] = ((df['Close'] - df['Open']) / df['Open']) * 100

# Gap filled? (for gap ups: did Low touch previous close?)
df['Gap_Filled'] = (df['Low'] <= df['Prev_Close']) & (df['Gap_Pct'] > 0)

# Filter gap up events
gap_ups = df[df['Gap_Pct'] >= 1.0]

print(f"Total gap ups: {len(gap_ups)}")
print(f"Fill rate: {gap_ups['Gap_Filled'].mean()*100:.1f}%")
print(f"Bullish close: {(gap_ups['Close'] > gap_ups['Open']).mean()*100:.1f}%")

The complete analysis with year-wise breakdown, day-of-week analysis, and all charts is available as a downloadable PDF for our Telegram community members.

Trader Cheat Sheet

This table is a reference, not a signal system. Sample sizes — particularly for medium and large gaps — are small. Use this as context for reading price action, not as a standalone entry trigger.

Frequently Asked Questions

Conclusion

Ten years of Nifty data produces one clear conclusion about gap ups: they are not the bullish signal most traders assume.

The average gap up of 1% or more closes below the opening price 55.3% of the time. The gap fills on the same day only 29.4% of the time. And when it does not fill, the loss on bearish days (-1.16%) exceeds the gain on bullish days (+0.86%) — an asymmetric risk profile that favors caution over aggression.

The exceptions matter: medium gaps of 1.5% to 2% show a slight bullish edge, Monday gaps are historically the most reliable, and removing 2020 shifts the picture toward balance. Context and gap size matter more than the gap itself.

All data used in this study is sourced from NSE historical records via institutional-grade market data access. The analysis covers 2,532 trading sessions from January 2016 to March 2026. This is a data study, not trading advice. Past market behavior does not guarantee future results.