Before we look at some intraday trading strategies in detail, I have to admit I’m biased. I don’t believe in day-trading for several reasons:
- No basic edge: studies point to merely 5% of day-traders being consistently profitable. In contrast to long-term investing, where an unskilled advantage exists for everyone by participating in economic growth, trading is a Zero Sum Game. The average of all traders must lose by the amount of fees they pay and these are considerable for short-term strategies.
- High cost of doing business: Commissions and slippage play a large role when the trade frequency is high and they take a larger percentage out of your profits, because smaller movements are traded. Overall cost can easily add up to a double digit percentage drag on return and few day-traders ever overcome this headwind. Add to that the cost for the necessary technology: access to data and the computing power to process it in real-time.
- Less sustainable edges: in a zero sum environment trading opportunities evolve and go through cycles quickly, so edges become harder to maintain. Long-term risk premia are much more stable, but don’t play a role in an intraday time-frame.
- Noise overshadows signal: advantages that are quite pronounced at longer time horizons will become more random the shorter the time frame becomes.
- Too simple: Patterns that are clear and simple have been known for ages, they should have been arbitraged away a long time ago. Can there really exist such strong, constant behavioral market forces, that profitable intraday patterns persist even after the advent of algorithmic trading?
- Not robust: on the other hand complex pattern and indicator combinations often rely on over-fitted backtests – optimized parameter sets are unlikely to perform as well in the future as they did in the past.
- No performance advantage: most realistic strategy statistics show no advantage over mid- and long-term strategies which are less time consuming and less error prone. There is a limit to sustainable returns set by the risk one needs to take to get there. These risks can be controlled, but that prevents leveraging up beyond a certain point. Overtrading will lead to extreme volatility and diminishing growth rates. Sustainable compounding at 50%, 100% or more annually would lead to unimaginable wealth within a few years. There is no evidence such wealth creators exist – the visible super-rich investors posted returns much closer to normal market returns over long periods of time (15% to 25% per year).
- Industry of selling secret strategies: almost everyone in the short-term space tries to sell you stuff, often backed up by very little reliable statistical data. This, to me, is another sign of the difficulty of making money purely from trading. If I had such a great money making machine, I would just procure as much capital as possible to trade it and not bother with selling its signals. If I were convinced of its sustainability, I would give back by making education freely available – as I do through this blog just like many others who invest over a longer time horizon.
- Enhances psychological difficulties: Intense screen time is an attention grabber which compounds behavioral mistakes and distracts from productive research and development.
- Limited capacity: limits to liquidity become more pronounced in the short-term space. Slippage quickly becomes a decisive cost factor which is hard to incorporate realistically in backtests. This low capacity may exclude many institutional players from using these strategies and give an advantage to smaller retail traders.
- The advantage of a reliable short-term strategy lies in the possibility to bet on your edge more frequently, which could lead to a higher Sharpe ratio (higher returns at lower volatility) – as you can see in the hypothetical example below. If this return stream is uncorrelated to longer-term strategies and doesn’t tie up a lot of capital it might make sense to add it to a portfolio.
Perhaps this skepticism serves as healthy protection when looking at the short-term trading space. It prevents me from doing too many dumb things and to take things at face value too easily.
But I’m also curious and I keep my eyes open in my research – the dynamics of the space are quite fascinating.
So when I come across empirical evidence, supported by my own tests, that something seems to work overwhelmingly well, I feel compelled to dig deeper and even to put some money on the line to figure out what may be going on there. Maybe, in the face of empirical evidence, I will even change my mind.
Fading the Opening Gap
One strategy that intrigued me is fading the S&P opening gap using cost-efficient, ultra-liquid futures. It buys / sells at the market open against the morning gap direction and closes the position when price retraces back to yesterday´s close. Its simplicity and pre-defined time window mitigates some of the problems inherent in intraday strategies.
Very good performance statistics boil down to profits of equal size to losses with a win probability of about 60% to 70% with little optimization (see rules below). This holds up in long-term independent backtests from several sources (eg. here – with lots of strategy detail
– and here
) as well as in my own tests on recent data.
This simple strategy is extremely easy to execute manually, with no discretionary judgement necessary and takes just 15 minutes a day with no additional screen time after the open. Betting opportunities arise almost once every trading day, adding up to an estimated number of 100 to 150 trades per year. The strategy seems to be uncorrelated to other portfolio assets and strategies and could easily be added to a combination of investments and strategies requiring only temporary margin.
Edit after several months of live testing: Correlation seems to be an issue in real trading and the strategy had severe losing streaks in concert with drawdowns in other strategies – more details at the end of this post.
Taking into account fees and slippage a minimum win probability of about 52% is needed to reach a profitable strategy with a reward to risk ratio of 1 : 1, when using cost-efficient index futures. To reach a decent annual return a win probability above 55% would make the strategy worth trading – so 60% to 70% is very good for an intraday strategy. Let’s say that there are 8-10 valid setups a month, then returns would add up to an average of 2% to 3% monthly risking 1% per trade (a fairly conservative risk level with a big margin of safety) – easily beating a buy and hold allocation to the S&P as well as most longer-term strategies.
If this edge is normally distributed over time at a 66% win rate and the average profit equals the average loss including cost, then we can use the Kelly Criterion to calculate an optimal bet size. This results in risking 8% per trade at a quarter Kelly – the full Kelly bet would risk a whopping 32% of your capital per trade.
Optimal bet sizes in percent of capital calculated with the Kelly Criterion in Excel: =((A1/A2)*A3-(1-A3))/(A1/A2). More on bet sizing here
Say, for example, you want to make an average of $5.000 before taxes monthly – with around 9 trades per month, $22.000 of capital would be sufficient. Which, I think, is utterly ridiculous as it would imply a consistent return of 24% monthly – „that’s simply impossible“ immediately comes to my mind.
It is easy to see how such a direct calculation (9 trades per month with 6x 8% profit – 3x 8% loss = 24% return on average) will lead to serious overtrading and a blow up as soon as the edge is not totally consistent. Things like a non-normal distribution of returns or a serial correlation between losses leading to long trends of underperformance would derail it quickly and volatility would be huge, even if your statistics were spot on.
Add to that some imperfections and mistakes in the execution of the strategy (which always happen in real trading) as well as large fluctuations in actual realized returns over time (all strategies go through long periods of underperformance at times) and we quickly come back to more conservative bet sizes.
Unconstrained bet sizing is the source of many of the myths surrounding day-trading as a means to make a living off a very small capital base. It might work for a while, but it is unlikely to be sustainable over any period of time.
A simple rule of thumb is often more sensible. For example, to risk 1% to 2% of capital per trade (a very basic position sizing technique proposed by scores of trading and investment books) will make most phases of underperformance survivable as long as a long-term edge persists. Given the performance stats above this would lead to 2% to 4% monthly profits. Capital of $125.000 to $250.000 would be sufficient to support a $5000 a month lifestyle. That certainly seems achievable.
When using futures to trade it, the strategy can even be run alongside a fully invested portfolio of strategies as it only uses margin temporarily.
These numbers clash with the often quoted statistic: „only 5% of day-traders make profits consistently“. Why doesn’t everyone simply exploit such a clear edge, spending just 10 minutes a day? It is certainly not a very difficult strategy.
As it is easily programmable, hordes of algos should take this obvious advantage out of the market in no time, or not?
There must be an overwhelming amount of dumb money pushing prices away from the previous close at the opening, only to be faded back to the closing price 70% of the time. Does that make sense, if the tendency of the gap can usually be seen hours before the actual open in the continuous futures market?
Where lies the fallacy or why could it continue to work?
- Robustness may be an issue. There is no real reason why the edge may not suddenly vanish or cycle on to a different strategy.
- Doesn’t work across all markets. The best concepts tend to work everywhere eg. value and momentum, but the gap fade doesn´t.
- No basic edge. While about 70% of overnight gaps are filled during the day, the basic return for this strategy is still zero. It needs extra rules to make money. This is quite typical for a mean reversion strategy: the size of profitable trades is defined by the size of the gaps while losses are unconstrained: rare, but large, losing trades will bring down the profitability of the strategy.
- A more nuanced return profile. In live trading many trades likely will not hit the profit target nor the stop loss, resulting in a lower average profit and loss than the initial risk that was taken on each trade and therefore a lower overall return.
- Popularity. The strategy is well known, but other ideas trading the opening, that often trade in the opposite direction from the same entry point, may be more popular.
- The tendency for opening gaps in the S&P to close with a probability of 65% to 70% has been around for decades.
The universe of strategies using the market open
It may be a good idea to take a close look at different popular strategies that are used by day-traders around the time of the S&P 500 market open. Some currently show an edge, others don’t – it is possible that an edge cycles between these strategies. Some strategies overlap, others enter opposing positions and each uses variations leading to different outcomes. I concentrate on one additional strategy concept in particular and list some of the other techniques I came across.
The Opening Range Breakout (ORB) is probably the most well known opening strategy due to its cult status since the 1990´s, when it delivered outstanding returns. It follows an opposite concept to the gap fade as it tries to capture a trend that develops when price breaks out of the opening trading range, while the gap trade tries to capitalize on mean reversion occurring right after the open. ORB often enters in the direction of the opening gap right around the price at which the gap fade stops out at a loss. It can also be a continuation of the gap fade, when the closing of the gap occurs right after the open and the move continues in a trend counter to the gap opening triggering a breakout signal.
ORB recently has had a very low win percentage around 35% (low win rates are a hallmark of trend strategies and not a real problem as long as average wins are much higher than average losses – but I would feel more comfortable with at least a 40% win rate).
From my own observations, I found an extraordinarily high number of false breakouts occurring at several time periods (15 minute, 30 minute and 1 hour opening ranges) – often you will see a false breakout in one direction in the 15 to 30 minute window only to witness another false breakout in the opposite direction using the 1 hour opening range.
Additionally all intraday trend strategies face a serious conceptual problem: At the heart of trend-following lies the concept of letting your winners run while cutting your losses short. Exiting your positions at the daily close (or earlier) curtails the chance for extraordinary winners that often make up the bulk of a trend strategy’s profits.
Several recent discussions and strategy statistics, I came across, point to the classic ORB as having been an unprofitable strategy after cost in recent years
. The strategy is widely available as a commercial strategy package, which is never a good thing.
In fact the opposite approach – fading the ORB – might give an edge. Veteran trader Linda Raschke has said that markets in the mornings tend to be mean reverting and in the afternoons trending in nature.
ORB´s popularity may divert attention away from mean reversion approaches like the gap fade.
Additionally many other gap strategies from the 80´s and 90´s
trade after price breaks yesterday´s close against the gap direction betting on a continuing reversal after the gap closed. This entry point and direction exactly coincide with the exit of the gap fade strategy. (Eg. Connors/Wiliams 10% Oops which recently tests negative
Gap continuation strategies, similar in concept to ORB, trade in the direction of the gap rather then against the gap – stop runs on these strategies will essentially result in a closing of the gap (a successful gap fade) as stops are commonly placed on the other side of the gap.
Other variations include:
- Return to open after 5 / 15 / 30 or 60 minutes: essentially an anti-ORB approach that looks quite promising, because of the many failed ORB breakouts. I integrated this idea to some extent in my gap fade setup by trying to get a better execution price a couple of minutes after the open, but hold the position for a complete gap close (see rules below).
- ORB that enters at a predetermined distance from the open (for example calculated as a percentage of the previous day´s range) at any time. Other variations add momentum and trend filters etc..
- Single Stock strategies that often screen for unusually large gaps.
In general studies on single stocks
over a wide universe and length of time point to a full gap being more of a mean reversion then a continuation signal. Especially up-gaps led to short term (over the next 1-3 days) underperformance historically.
Other markets: opening strategies work across many futures markets, but in others they fail (which is a red flag). In general these strategies work better on a basket of securities (eg. equity indices) than on single stocks – random volatility equals out between all securities in an index and the signal is clearer. This is found to be true at longer time frames by different authors (eg. Antonacci, Clenow) and should matter even more at lower time frames when random noise increases.
Bringing it all together
An explanation for the validity of a gap fade strategy could be, that it has a smaller following than an ORB or other trend trading strategies around the opening range and therefore takes advantage of anti-ORB arbitrage. If so, it can be expected to be an unsustainable advantage as popularity shifts towards strategy variations that work and cause them to work less well in the future. Much like the ebb and flow we can witness between different long-term return factor models, for example between value and momentum strategies or short- versus long- volatility ideas.
Regimes may change between those that favor a trend approach and those that work better for mean reversion.
We could find a mechanism to determine where we are at and / or combine different concepts for trading around the opening for a more robust strategy. Using a Google search, for example, may indicate a popularity shift in progress as many more hits can be found for “fading the opening gap” than for “opening range breakout”…
A pure gap fading strategy seems to be too simplistic to be sustainable over the long run. On the other hand the effect of gaps closing with a probability of about 70% has been around for decades without any visible deterioration.
Real money test
As the downside is small and the statistics are enticing with little additional effort involved (I run a daily check of my portfolio around the US open anyway), I have decided to run a real money test with a small risk allocation to find out what is behind this. I will keep track of the performance in this blog entry (look for updates here every month or so). When the statistics drop below break even for a period of more than a month, I will switch to observation only.
Rules for fading the gap
To reach the performance described above it is necessary to use screens to identify the gaps that are most likely to close and use stop losses to protect against large trending moves as well as define the initial risk. These ideas are based largely on the excellent book “Understanding Gaps” by Scott Andrews
. I only use the two most essential screens to avoid over-optimization as much as possible and add some details of my own around entries and exits.
- Gap size < 40% ATR(20) (ATR = Average True Range over the last 20 days; average distance between daily highs and lows). Small gaps have a higher probability to close than large gaps. In May 2018 around 10 points in the S&P is optimal – this will change with different index and volatility levels which is why I use the ATR to automatically adjust for these changes. If the gap is very small (< 20% ATR) I look for a better price (more then 20% ATR – 8 points currently – from the close) in the first minutes after the open, otherwise the stop loss will be too close to the entry given the current volatility.
- Inside gap: the opening price should be within the previous day´s range. The key is to avoid violent reversals where price gaps beyond the previous candle on the opposite side of the close (above yesterday’s high after down days or below yesterday’s low after up days).
- Entry at open. Often the entry price an hour or two before or right after the open is better than the actual opening price and I put in limit orders to catch a good execution, usually in two lots. A better entry price results in a wider stop loss, which often makes the difference between success and failure as the price runs in the direction of the gap quite regularly before reversing (this would essentially be a failed ORB or gap continuation breakout).
- Position size: my maximum risk is 1% of capital (NAV). The distance between entry price and yesterday’s close determines the number of contacts traded: ES (S&P mini future) contracts = (NAV/(gap size in points*50))/100
- A Stop Loss is placed at the same distance as the profit exit: entry price minus yesterday’s close.
- Exit to take profits at yesterday’s close; this gives a reward to risk ratio of 1 : 1. I usually split my order in two lots and close the second part 2 or 3 points beyond yesterday’s close as many gap closes overshoot their target a little bit. I put in an automatic OCO order for stop and exit and don’t monitor the price after the entry.
- Time exit: I close the trade after 1 PM as I think the dynamic of the gap close is over by then and we essentially face an equal probability which direction the afternoon price action will take. An exit at the daily close probably yields similar results, but I prefer to have the position open for a limited time only.
Even though the strategy is as easy as can be, in real life trades are sometimes missed and discretionary elements creep in. Take responsibility, if you use these rules and expect results to differ from mine sometimes.
Real money performance
Performance after 4 weeks (4/12/18 to 5/9/18):
The left column shows the actual average profit and loss including fees and slippage (as a percentage of the capital at risk = 1% NAV) and the win probability as well the resulting Kelly-optimal bet size. On the right is a strategy benchmark derived from different backtest sources for comparison.
After running live for one month I extrapolated an expected annual return of 18% (not compounded as many traders routinely take profits out of their account) and concluded:
Pretty good – just about „warrenbuffetty“ – for the relatively safe level of risk taken, but a fair bit lower than the results I extrapolated from external sources in the beginning of the article. For now it is a worthwhile strategy to include in a portfolio of strategies using few extra resources.
Final update: Performance after 3 months (4/12/18 to 7/16/18):
The picture radically changed after 3 months as the strategy quickly turned unprofitable in live trading – I would expect the performance figures above to add up to a loss of -7% per year at a risk level of 1% NAV per trade.
This is mostly due to my own mistakes. I’m not really suited to short-term trading: it distracts me from looking at the bigger picture and my overall portfolio by capturing a lot of my mental energy and attention.
Behavioral mistakes (eg. doubling down instead of honoring my stops) creep in, because of the overconfident illusion to be able to predict short term movements and thus overriding the strategy. This especially happens when I’m distracted from the trade e.g. on holiday. As I don’t want to be a slave to such a strategy, I decided to stop it as long as losses are still moderate.
These are the statistics without major mistakes:
There is still some edge there, but it is so slight it will add up to no more than a return of 5% per year – the initial edge contained in a high win probability completely disappeared over the test period. A single mistake per month (which should be expected realistically) would ruin the strategy and that is not worth the emotional energy to me.
The Gap Fade Strategy in a portfolio context
As usual I look at all strategies in the context of the overall portfolio to determine my allocation and to avoid duplication by using strategies that have a very similar return profile.
Even if the actual return of the strategy should turn out to be zero, if it is uncorrelated or negatively correlated to the rest of the portfolio, it will still boost the overall risk-adjusted return by dampening portfolio volatility.
Unfortunately in June 2018 I observed the opposite effect in live trading: a drawdown across strategies in my portfolio deepend through a string of losses generated by the “fading the gap” strategy simultaneously.