Around 65 to 70% of opening gaps fill the same day they are created. That single statistical reality turned Scott Andrews from a failed options trader into one of the most recognised names in systematic gap trading. Known as “The Gap Guy,” Scott launched masterthegap.com in 2008 and has since published over 1,500 daily gap analysis videos covering exactly how to identify, filter, and execute gap trades before the market opens each morning.
This episode covers the full framework: which markets and gap sizes work, the three conditions Scott evaluates on every trade, gap zones, when to fade versus follow, how to set stops and targets, and a concept called ensemble systems that can improve any style of systematic trading.
Watch the full episode below, then read on for the complete breakdown.
Why opening gaps have a built-in edge
The core logic is simple. When a market gaps up at the open, large institutional players, the Goldman Sachs types in Scott’s words, are not going to chase retail prices. They want wholesale. So they push the market back down toward the prior close to get positioned at a better price before riding the trend in the original direction.
That institutional pressure is why two-thirds of opening gaps revert to the prior day’s close on the same day. It is not random. It is the natural result of how big money actually operates.
Scott’s research has tested this across S&P, NASDAQ, Dow, Russell, DAX, FTSE, gold, oil, corn, and most major futures markets. The phenomenon shows up everywhere. The base rate in equity indices is 68 to 70% gap fill. That is your starting point before applying any filters.
Markets where gap trading works best
Scott avoids gaps in momentum stocks: biotech and high-momentum technology companies. These have their own dynamic that consistently defies the historical mean reversion tendency. They gap and keep going.
Indices, on the other hand, are what he calls “heavy.” A single news event, even significant economic data, has a much harder time sustaining a directional move in a broad index through the full day session. That makes indices the best candidates for fading gaps.
For individual stocks, Scott looks for sectors averaging gap fill rates of 75 to 80% in a given year (well above the 68% base rate) while avoiding sectors sitting at 50%, which signals too much trending momentum for a fade strategy to work reliably.
The three conditions Scott evaluates on every gap
Scott does not trade gaps in isolation. Every gap is evaluated in the context of three things:
- Market conditions. Is the market in a raging bull, a raging bear, or somewhere in between? Scott categorises this several different ways and notes that any reasonable definition works, the key is having a framework. Over time, identifying and quantifying market conditions has become the part of his analysis he spends most of his time on.
- Short-term price patterns. What has price been doing over the past two to five days? Has the market been rallying, selling off, or alternating? Scott typically uses a three-day lookback to categorise recent price action.
- Calendar considerations. Day of week matters. Fridays, for example, tend to have more predictable tendencies because futures traders who do not want weekend exposure close out positions, creating a more consistent flow pattern. Mondays tend to be more difficult. Specific dates, like the first day of a month, also register in historical data.
These three conditions are evaluated separately for gap direction (up or down) and for gap size.
Gap size: the 40% ATR threshold
Gap size is one of the most important filters Scott applies. The relationship between gap size and fill probability is roughly as follows for the S&P (using SPY as a reference):
| Gap size | Approximate fill probability |
|---|---|
| 10 cents (tiny gap) | ~90% |
| 20 cents | ~80% |
| 30 cents | ~60–70% |
| 40–60 cents | ~40% |
The dividing line Scott has found across virtually every market he has tested is 40% of the five-day average true range. Gaps larger than 40% of the 5-day ATR see fill probability drop sharply, sometimes to near coin-flip territory. For fading, those large gaps need careful scrutiny. For following the gap, they are often the best setups.
Gap zones: where the gap opens matters as much as how big it is
Scott’s original insight, the one that made money from day one when he started testing in Excel, was that the open, high, low, and close of the prior day create distinct zones, and each zone has a significantly different historical fill probability.
The best-performing zones tend to be those where the gap opens within the body of the prior candlestick. If the prior day closed up (green candle) and the market opens between the high and close, it is a smaller gap with short-term price direction still intact. Anyone long the prior day is likely to take some profits by selling at the open, which adds natural pressure back toward the prior close.
The worst-performing zone Scott calls the “blood zone”: below the low of an up day. When a green-candle day is followed by a gap opening below that prior day’s low, two forces collide. Longs are exiting and covering positions. Shorts are entering, seeing a potential trend reversal. Both sides are active at the same time, creating messy, low-predictability conditions for fading in either direction.
When to follow the gap instead of fading it
About one-third of gaps do not fill. Scott calls these “follow-the-gap” scenarios. The conditions that shift the odds toward following rather than fading are:
- Gap size above 40% of the 5-day ATR
- Market in a long-term uptrend (above 200-day moving average) but pulling back in the short term (below a 10 or 15-day moving average)
- Gap direction matches the long-term trend
In that setup, short-sellers from the prior pullback are forced to buy to cover, and speculative buyers step in expecting a resumption of the long-term uptrend. Those two flows together push the market away from the prior close rather than back toward it. Fading that gap is dangerous. Following it is the better trade.
Stops, targets, and trade management
Scott’s mechanical system uses a stop of approximately 30% of the five-day ATR. He is explicit that this is not obsessively optimised. Over-fitting stops and targets to the nearest hundredth of a percent is one of the most common mistakes he sees from new systematic traders. You end up curve-fitting to past data and building in unrealistic expectations.
Targets for fade trades are typically at the prior day’s close, just short of it, or occasionally just beyond it depending on the market condition. Extended targets beyond the prior close are reserved for specific setups: markets that have been selling off for four or five consecutive days, hitting multi-day lows, and then gap down. In those conditions, if the gap fills, Scott finds a roughly 90% probability of price continuing through the prior close and closing above it. Those are the trades worth holding past gap fill.
In raging bull markets, he typically exits at or just before the prior close. With big money on the other side waiting to buy back, trying to squeeze out extended targets exposes you to getting run over by institutional buying.
Trades that have not hit their target by end of day are closed out at the NYSE close (4:00 to 4:15 Eastern). Scott never converts a gap trade into a different trade by holding overnight. He wants to measure each decision independently, without letting one trade bleed into the next.
Ensemble systems: the concept that changes how you think about strategies
The second major topic in this episode is ensemble systems, and it applies well beyond gap trading.
Scott hit a natural limit trying to fit every variable into a single system. Adding each new variable (gap size categories, market condition filters, moving average conditions) cut his sample size in half. Start with 10 gap categories across 2,000 trading days, you get 200 trades per category. Split each category again, you are down to 125. Add a moving average filter, you are at 60. At that point, you cannot trust the statistics.
His solution: build separate systems that each focus on one conceptual theme (market conditions, short-term price patterns, seasonality). Each system generates its own signal. He only trades when the average signal across all three systems clears a minimum threshold, typically a 60% historical win rate and at least 15% more profit than loss across the ensemble.
The result, over 410 live trades tracked publicly over one year: a 65% win rate with profits per contract at the best level he has seen. The systems are not complicated individually. What makes them work is that they are conceptually different enough that they are lowly correlated. They do not fire at the same time. When they do all agree, the quality of the setup is higher.
It is the same principle behind how the National Weather Service models work: run many different model variants, average the outputs, use the consensus as your forecast. The individual models have errors. The ensemble smooths them out.
How markets have changed and how Scott adapted
Quantitative easing, starting in 2009, changed the gap trading landscape materially. Shorting up gaps became significantly less profitable in a persistently bullish, Fed-supported environment. Buying down gaps also became harder as extra liquidity in the market created stop-out volatility before the eventual fill.
Scott’s response was not to rebuild his systems. He made one set of changes to his mechanical system (Pearl) in four years of live trading. Instead, he diversified across more markets. Adding correlated but not identical markets smoothed the equity curve and spread capital more effectively. That diversification, not constant system overhauls, has been his main adaptation over the past three years of his then-12-year trading history.
Related episodes
- New volatility based trading techniques with Rob Hanna
- How to find statistical edges in trading (Scott Andrews, part 2)
- How this simple weather prediction technique can improve trading strategies
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