Systematic Trading with TradeQuantiX

Market Effect Research: Turnaround Tuesday Effect

TradeQuantiX — Tue, 16 Jun 2026 13:21:30 GMT

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Introduction:

This is the fourth article in the small market effect research series.

The first looked at the holiday effect on SPY.
The second looked at the turn of the month effect, also on SPY.
The third looked at the holiday effect on gas and energy assets

All three sets of research resulted in tradable systems that I have now implemented as overlays on my personal systematic trading portfolio.

If you missed the last one, you can check out the holiday seasonality effect on gas and energy assets here:

Systematic Trading with TradeQuantiX

Market Effect Research: Holiday Seasonality - Part 2

a month ago · 4 likes · 2 comments · TradeQuantiX

In this fourth article, we’re going to examine a market effect with a similar process as before. This market effect is what’s called the Turnaround Tuesday effect.

The basic idea is very simple. Allegedly, Tuesdays tend to be the highest returning day in the market. The claim is that by entering Monday at the close and exiting Tuesday at the close, you can create a simple yet effective trading system.

In this article, we are going to dig into the data to prove or disprove the effect and figure out the most robust way to capture it profitably.

The first half of the article will be a bunch of plots to better understand the mechanics of each piece of the effect. Then, later in the article I will share some system ideas and code to potentially implement into your portfolio.

Note For Paid Members:

As always, paid members get access to the full code at the end of this article.

You also have access to the Discord community where we can discuss this research and ask questions, and the GitHub repo with all published code.

Reason For The Effect:

Before I touch any data, let’s talk about why the Turnaround Tuesday effect might exist in the first place. Because if there’s no real reason for it, then there’s likely no real edge.

The mechanism for the effect is behavioral, and in my view it has three plausible reasons that build on top of each other.

The first and main contributor to the effect is that investors overreact to bad news from the weekend. Between Friday’s 4pm close and Monday’s 9:30am open, there’s a lot of time for bad news to occur. But when that bad news does happen, everyone has to wait until market open on Monday to trade based on the news.

All that accumulated weekend panic hits all at once on Monday morning and produces emotional selling pressure, many times more than there should be. Stocks drop on Monday partly because the bad news is being priced in, and partly because everyone got to sit in their own fears over the weekend and overreacted, thinking the news was worse than it actually was.

The second contributor is what causes Tuesdays to have larger than average returns. Professional and smart retail traders recognize that Monday’s selloff was a big overreaction and step in to buy the dip on Tuesday. They see this as a cheap buying opportunity, as the market has sold off more than it should have. Like most news, it’s almost never actually as bad as it seems. This results in a reversion back to fair value.

The third contributor is shorts covering (this one is the most speculative). Shorts who jumped in on Monday’s weakness may start to cover their positions when the markets revert back towards fair value. This adds mechanical buying pressure right on top of the institutional bids that were buying the dip.

That’s theoretically why this trade should work. There’s no real way to prove it, but it makes sense and is based on human emotion, which is unlikely to ever change.

Research Phase:

Now let’s start breaking down the effect with charts. Each subsection below examines one feature of the effect using the SPY ETF. We will use what we learn about the effect to develop a trading system in the second half of this article.

Baseline Tuesday Returns:

Let’s start with the simplest studies of this effect. Is Tuesday special, or is it just another day of the week? This looks at the baseline effect that the rest of the research will progressively layer onto.

Looking at SPY ETF data from 1993 to 2026, what does the average return look like by day of the week? No filters or regimes, just the raw mean. The chart below shows the average market returns by day of week.

You’ll notice on average over this 33+ year time span, Tuesday wins, but only by a little bit.

Monday: +0.055% return
Tuesday: +0.071% return
Wednesday: +0.062% return
Thursday: +0.015% return
Friday: +0.033% return

Tuesday at +0.071% return is roughly 1.5x the overall SPY return baseline (orange dashed line). Tuesday generally is the best day of the week historically.

But the gap between Tuesday and Wednesday is just +0.009 percentage points, and the gap between Tuesday and Monday is only +0.016 percentage points. So really, at this level this effect is pretty small.

An average result across 33+ years can hide a lot. So, let’s split the sample into three eras and see if the slight Tuesday outperformance is stable or if it’s an artifact of one strong period of history.

Interestingly, you’ll notice the best day of week ranking shifts around.

In Era 1, Monday was the strongest performing day on average.
In Era 2, Tuesday was the strongest performing day on average.
In Era 3, Wednesday was the strongest performing day on average.

The blanket statement that there is some inherent overperformance only on Tuesdays doesn’t seem to hold up. It seems the best performing day shifts around, but appears to be within the first few days of the week consistently, rather than the later part of the week.

This could be just natural noise in the data. Or this could be due to traders getting smarter about when they take advantage of the post weekend sell off, causing the best performing day to shift around. We will never know, but understanding that this effect is a little unstable is still useful information.

The next thing I looked at was if there was a conditional feature that would predict a larger bounce on Tuesdays. Naturally, it makes sense to try to get a better entry at a lower price to better capitalize on the Tuesday upward swing. Hence, the chart below looks at Tuesday returns as a function of what Monday’s price action did.

You’ll notice that after a down Monday, the Tuesday returns are significantly higher, over 2x the return of just trading every Tuesday. Also, trading Tuesdays after Monday was an up day actually has negative expectancy.

This results in a feature we could add to the system where only trading this effect after a down Monday would result in a predictably higher return on the following Tuesday.

The natural next question is again, does this down Monday feature hold up over time or is it an artifact of some random outliers. Ideally, we want to see this down Monday effect happening consistently over time. The chart below splits the data into three eras to understand time stability.

It appears this feature is stable over time. When Monday is red, the Tuesday returns are significantly higher. And when Monday is green, Tuesday returns are negative or only slightly positive. This Monday red day feature appears to be significant, so in the next section we will explore it further.

Monday-Down vs. Monday Up:

Okay, so the overall Tuesday average return appears to be very small and the best day of the week shifts around slightly over time, but generally stays between Monday and Wednesday. But, when Monday is a red day, it does seem to predictably lead to higher returns on Tuesdays.

This makes sense with the theory behind why the effect exists. Mondays digest all the fear from the weekend, so red Mondays would cause the buying opportunity for large rebounding Tuesdays. If the Monday was green, then no bad news was released over the weekend, and then there was no opportunity to buy the market at an overcorrected discount.

Now, I want to know if the relationship gets stronger as Monday’s drop gets bigger. Is it a binary red vs. green Monday that matters? Or can this signal strength be measured in a monotonic way where larger down Mondays lead to predictably higher return Tuesdays?

The chart below shows that the more Monday sells off, the harder Tuesday bounces.

The further left on the x-axis you go (larger Monday sell offs), the higher the expected return on the following Tuesday. There is a clear relationship here.

We just have to be careful because the more restrictive you are with Monday sell off percentages, the fewer trades you get per year. This would mean fewer data points and less data to make decisions from.

So, while larger down Mondays lead to larger Tuesday bounces, we have to keep in mind that the data points reduce as you go further to the left in the plot.

Again, just as the few times before, we want to understand how this feature behaves over time, not just as one blanket average. Below is the same plot as above, just with three lines showing the effect of a larger and larger Monday down day split across the standard three eras.

Across the three eras, we see consistency in this Monday down day feature. This makes me think we can use the severity of a Monday sell off as a way to tune signal strength.

Meaning, have larger position sizes when Monday sold off more and smaller position sizes when Monday sells off less. The data is showing that the strength of Monday’s sell off is predictive of the behavior on Tuesday.

Another way to visualize this is with the scatter plot below. This plot shows the Monday return on the x-axis and the corresponding Tuesday return on the y-axis.

While there are plenty of individual data points that don’t follow the “larger Monday down leads to larger Tuesday up” pattern, the average of the scatter shows this behavior (represented by the sloped dashed black line).

So, the verdict here is that there seems to be some real benefit to using Monday price returns to predict the returns of the following Tuesday.

Now, let’s dig into some more features we can measure to see if they can help us better predict Tuesday’s price return.

Friday-Down vs. Friday-Up:

So far we’ve thoroughly explored Monday being red. But now that part of the effect is understood, I started wondering if there was more historical price data that could be leveraged for more predictive power.

The natural next day to check for predictive power is the previous Friday. If the Monday-down feature is partly about weekend news amplifying selling pressure, then a red (or green) Friday going into that weekend might have some sort of meaning as well.

Basically I want to know:

“Was sentiment going into the weekend positive or negative? And does that matter for this trade?”

A market that’s already selling off going into the weekend leaves traders more nervous over Saturday and Sunday, and that nervousness keeps getting fed by whatever pops up on the news on the weekend. By the time Monday closes red on top of that, the snapback on Tuesday could be even sharper.

That’s the theory anyway.

Let’s see what the data says.

The chart splits the data into the four possible buckets of daily price returns that could happen.

The summary of the result is the following:

Friday up AND Monday up: -0.03% Tuesday average return
Friday down AND Monday up -0.03% Tuesday average return
Friday up AND Monday down: +0.10% Tuesday average return
Friday down AND Monday down: +0.33% Tuesday average return

You’ll notice the two Monday UP bars both result in negative returns (which is consistent with what we observed previously). It doesn’t matter what Friday did, if Monday was an up day, Tuesday returns tend to be negative.

But if Monday was a red day AND Friday was also a red day, the Tuesday returns are amplified even further than when only Monday was a down day and Friday was an up day.

That’s a +0.23 percentage point lift in average returns just from requiring Friday to also be red.

The next question is whether this stacked Monday and Friday down day signal holds up across eras or whether a few data points are skewing this observation.

Market Effect Research: Holiday Seasonality - Part 2

TradeQuantiX — Mon, 01 Jun 2026 13:25:20 GMT

Introduction:

Awhile back I wrote an article on the holiday effect in SPY. The trade was simple. The 10 trading days leading up to a US federal holiday produce, on average, about 2x the daily return of any random window of the same length. The effect held up over decades on SPY and we built a tradable system around the effect.

A few weeks after releasing that article, I was thinking a lot about other instruments that might also follow a similar pattern. I was thinking about commodities that potentially might go up in price around holidays. That’s when I realized that this effect may be prevalent in gasoline.

Why gas? Holidays in the US (Memorial Day, July 4th, Labor Day, Thanksgiving, Christmas, etc.) tend to be the periods of time when people drive long distances and need a lot of gasoline.

So, there may be a gasoline price rise around holidays to price in this short term increase in demand. Then, after the holiday, the excess demand dissipates and prices revert back to normal. That’s the theory anyway.

In this article, I’ll walk through the results of the holiday effect on not only gas but other related energy ETFs. The four energy ETFs explored were:

USO: US Oil Fund ETF
UGA: US Gasoline Fund ETF
XLE: Large Cap Energy Stock ETF
XOP: Oil & Gas Stock ETF

We will look at many different plots to figure out if the effect exists and answer the what, when and where of the effect. Then, if the effect shows promise, I will show you the how by creating a trading system to capture the effect.

Note For Paid Members:

As always, paid members get access to the full code at the end of this article.

You also have access to the Discord community where we can discuss this research and ask questions, and the GitHub repo with all published code.

Reason For The Effect:

The main mechanism for the holiday effect on gasoline is demand based. People need gas, especially before holidays, because of the following:

Travel demand. Around federal holidays tend the be the highest gasoline consumption days of the year because people drive long distances to visit family or go on weekend trips. Prices rise to reflect this increase in demand. Once the holiday passes, the demand returns to normal levels and prices revert. So gasoline and crude oil should show a price increase before the holiday and a price decrease after the holiday.

This is a forced flow. People planned out their vacations and travel ahead of time. They aren’t going to cancel their trip just because gas prices are a little bit higher. This temporary increase in demand forces the prices higher, then after the holiday demand returns back to normal, price theoretically should follow.

So based on this theory, if there truly is a travel demand mechanism, prices should increase going into the holiday and then there should be a post-holiday give-back.

Research Phase:

Daily Return Baselines:

Before we look specifically around holidays with these energy ETFs, let’s first look at their average market return baselines. These are the baseline return numbers we will be trying to beat with the holiday strategy.

Let’s start with the two commodity specific ETFs, which are where we should see the travel-demand mechanism be most prevalent.

Each dot is one trading day’s close-to-close return. The orange line on each chart is that ticker’s average daily return across its full history.

USO and UGA both show a wide return dispersion. USO has the infamous negative oil futures debacle in April 2020 (you can see those dots cratering well below the rest of the cloud), and UGA tracked that mess too because gasoline futures got dragged down with crude.

The 2008 to 2009 financial crisis, 2014 to 2016 oil bear market, and 2022 bear market spikes all show up in the extremes as well. This goes to show that oil and gas are not neat and orderly instruments.

Now let’s look at the two equity-style energy ETFs.

XLE has the tightest cloud of all four since it’s a basket of large cap energy stocks (Exxon, Chevron, ConocoPhillips, etc.). Hence why this ETF has the least amount of price dispersion of the four and behaves closer to an equity index rather than a commodity.

XOP sits between XLE and the commodity-direct names. It’s equity-style, but it tracks smaller exploration and production companies that are more leveraged to oil prices, so the cloud widens a little bit more than XLE during gas/crude oil commodity stress periods.

The orange average lines show the bigger picture though.

USO tracks WTI crude oil and its mean daily return is basically zero at +0.0019%.
UGA tracks RBOB gasoline at +0.046% per day.
XLE is the energy sector equity ETF holding large caps, with the highest average daily return (upward drift) at +0.050%.
XOP tracks oil & gas stocks at +0.043% per day, which is commodity-direct exposure but equity-style.

USO is an interesting one with an average daily return of basically zero. Buy and hold on USO is roughly flat. That’s actually useful for our analysis because any positive holiday-window return on USO is easier to attribute to the holiday itself, rather than to any underlying upward drift.

XLE, by contrast, has a +0.050% daily average baseline upward drift because it’s an equity ETF. So any pre-holiday drift strategy on XLE has to beat a higher average return to show the holiday effect has significance.

Keep these average daily return baselines in the back of your mind as we go through the rest of the analysis. They will be the benchmarks to beat to help prove or disprove the holiday effect is real.

The Holiday Effect In Energy ETFs:

For the next few sections, I will focus on exploring results for the UGA ETF (US Gasoline Fund). Long story short, this ETF showed the strongest and most interesting results. I’ll sprinkle in the results from the three other ETFs here and there as well, but UGA shows the most promise, hence why I’ll put a lot of focus there.

This first chart shows the cumulative returns of buying UGA x days before a holiday (blue) vs. a random benchmark of buying UGA for any random sequence of x days (grey). The random holding period was created by averaging the return of 10,000 Monte Carlo simulations.

You’ll notice something pretty interesting. The returns of the holiday entry (blue bars) are significantly higher than the returns of the random entry (gray bars) at every holding period measured, from 1 to 15 trading days. UGA’s effect ratio at the 8-trading-day window is around 3 times higher average return, compared to a random holding period of the same length.

Also, the average cumulative return pre-holiday increases from days 1 through 8. After day 8, the average cumulative returns start to flatline. This gives us an initial insight that the best time to buy pre-holiday is around 8 market days before the holiday.

To prove this isn’t random, here is the same chart for the three other energy ETF’s.

You’ll notice all three ETFs show a very similar effect. A cumulative average return that increases from approximately days 1 to 8 before starting to taper off.

While the size of the average return differs from ETF to ETF, the pattern is fairly consistent across all four ETFs; even within the USO ETF, which has an average return of around 0% per day over the long term.

The Day-To-Day Return Of The Effect:

Now, let’s look at UGA’s day to day average return on each trading day leading up to, and after, a holiday.

Bars to the left of the vertical dashed line are the average return of pre-holiday days. Bars to the right of the vertical dashed line are the average return of post-holiday days. The horizontal orange line is the UGA ETF daily mean return.

Looking first at the pre-holiday side, you’ll notice the bars in the last 8 trading days leading into the holiday are almost consistently above the baseline average return. That’s the pre-holiday lift we expected.

Now look at the post-holiday side. Day +1, the first trading day after the holiday, is the most negative bar in the entire chart. That’s the price reverting back to pre-holiday prices as demand for gas reverts back to normal.

This chart further shows the positive average daily returns before the holiday and also signifies that there is a negative average daily return immediately after the holiday (concentrated at day +1).

This pattern is also relatively consistent in the other three ETFs as well:

There is a greater than average set of daily returns leading into the holiday, followed by a big sell off on the first day after the holiday (and some ETFs have even more sell off follow through a few days after the holiday too).

Multi-Ticker Return Heatmap:

This next chart puts all four gas/energy related tickers (and SPY for reference) on the same returns heat map so you can observe the positive to negative return pattern in relation to all instruments at once.

The rows are SPY, USO, UGA, XLE, and XOP. The columns are trading-day offsets from -10 to +10 around each holiday.

The black vertical line represents the holiday. Cell color is determined by the 10-day cumulative return leading into a holiday minus a Monte Carlo average return benchmark.

Green cells mean a positive edge above an average buy and hold return of the asset. Red cells mean a more negative return compared to average buy and hold of the asset.

You’ll notice three things:

First, every row is completely green on the left side (the pre-holiday window). The pre-holiday lift exists in all five ETFs.
Second, the commodity-direct rows (USO, UGA, XOP) have a clear red return on the right side starting at offset +1. That’s the post holiday give-back.
Third, SPY and XLE have relatively neutral returns right after the holiday so there is no real post holiday give-back. XLE seems to behave more like SPY, rather than an oil and gas fund like USO or UGA.

The heat map above showed the cumulative returns above or below an average buy and hold benchmark of the given asset in the pre and post holiday timeframe. This next heat map shows the returns of each day independently (non-cumulative return).

The heatmap below shows the single-day returns at each day offset rather than chained N-day cumulative edge. It’s the same layout and same tickers, but each cell now shows the average return on that exact trading day, rather than the cumulative return over the 10-day window.

You’ll notice the post-holiday sell off (red cells) is more concentrated in the daily view than in the chained cumulative return view. For UGA specifically, offset +1 is the most negative single-day return cell in the entire chart. For USO and XOP the post-holiday day +1 also shows some give back, but a little less than UGA.

Also, the pre-holiday window shows to be mostly green across the board. Which means there is a consistent excess return nearly everyday leading into a holiday.

Thus far, the research is showing us that being long pre-holiday about 6-10 days out from the holiday works on all four energy ETFs. Also, being short post-holiday seems to only work on the commodity-direct names (USO, UGA, XOP). Being short after the holiday does not appear to work well on XLE. And if you’re going to be short you should only be short for a day or two after the holiday.

Has The Effect Decayed Across Eras?:

So far, we have only looked at average results over the whole time period, but is it stable across regimes, or is the average being skewed by one strong era? Let’s look at UGA specifically as the effect is the strongest in UGA. We will break the data into three eras and rerun the average daily return by trading day bar chart.

The three era splits are:

pre-2012
2012 to 2019
2019 to present

We will look at era 1 first. This first era covers a relatively thin slice of UGA’s history because UGA only launched in early 2008, so we’re working with around 40 holiday observations total in era 1.

You can see some of the pre-holiday bars are above the average return, but there are more red bars pre-holiday than what we saw in the whole time period average result in the sections above. The Day+1 post-holiday sell off does exist strongly in this era though.

Next, we will observe era 2 from 2012 to 2019:

You’ll notice the pre-holiday lift is clearly visible and more consistent in the last several bars before the holiday. Also, the post-holiday side still has the massive Day +1 sell off.

Next, we will observe era 3 from 2019 to 2026:

The Only Holy Grail in Trading: Your Portfolio

TradeQuantiX — Mon, 25 May 2026 14:52:36 GMT

Introduction:

Say you’ve spent a year or two building up your suite of trading systems. You’ve tested dozens of ideas, tuned parameters, refined entry criteria. Your your backtests look solid. You go live, and then they do exactly what most backtests do in the hands of a new systematic trader… underperform.

So you keep looking.

There has to be a better indicator.

A better parameter.

A better rule to add.

The equity curve on the next iteration is finally going to work. That search for the perfect system has a name: the holy grail. If you’ve been interested in systematic trading for any length of time, you know exactly what I’m describing.

I want to tell you something that took me 6 years of systematic trading and 25+ live systems to actually believe:

The holy grail DOES exist.

You’re just looking in the wrong place.

It’s not a system. It’s not an indicator combination. It’s not the perfect parameter set.

The holy grail in systematic trading (and the ONLY holy grail in systematic trading) is a diversified uncorrelated portfolio of many simple trading systems all working together. Once you understand why, you’ll stop trying to optimize your strategies to death in an attempt to create the perfect strategy, and instead start building something far more powerful.

Here’s what we’ll cover:

Why the single-system holy grail search is a dead end
The proof that a portfolio beats any single strategy
Why individually mediocre systems combine into exceptional portfolios
What my 25+ live systems actually look like across three markets
How to start thinking in portfolios instead of systems

Let’s jump in.

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Everyone Is Searching for the Wrong Thing:

The reason systematic traders spend so much time hunting for the perfect system is completely understandable.

More parameters mean more precise fitting. A tighter filter means fewer bad trades. A more complex entry condition makes the backtest look smoother.

It genuinely feels like the path forward is to refine the single strategy until it looks good enough to trade.

The problem is that no single system is ever going to be good enough. Not because you’re a bad system developer, but because most systems on their own will never be good enough.

Every strategy has its issues and weaknesses.

Momentum systems crush it in moving markets and get destroyed in choppy or bear market conditions.

Mean reversion systems thrive in choppy markets and give it all back when a strong trend develops and you’re fighting it in the wrong direction.

Trend following systems grind sideways for years and then explode upwards once the markets start trending again.

No single approach works well in all market environments.

I know this from personal experience. Some of my individual systems have pretty rough stretches. Equity drawdowns that would make most people quit, and sideways choppiness that make you board to death.

But here’s the thing I’ve come to accept, that’s perfectly normal. Because no individual system has to carry the weight of the whole portfolio. We should be trading more than one system anyway.

We should be trading portfolios of systems. When you’re trading a portfolio of systems, the ugliness of one systems performance really doesn’t matter that much.

The Real Holy Grail: A Portfolio of Trading Strategies:

As you add uncorrelated return streams to a portfolio, risk drops dramatically while returns tend to stay relatively stable.

Having a portfolio of one strategy means your account is susceptible to all the lumps and bumps of that one trading system.

Having two systems that are somewhat uncorrelated starts to mute some of those lumps and bumps, canceling each other out.

Add 10 more uncorrelated systems and your portfolio equity curve starts to look like something pretty interesting.

With many uncorrelated trading systems working together, your portfolio equity curve starts to look like the equity curve of the one system you curve fit to oblivion trying to make the single holy grail system.

The only difference is, the portfolio is a combination of a bunch of lumpy and bumpy but real systems trading real edges. Where the single holy grail system is just a curve fit dream, overfit to noise and will never work in a live trading environment.

The closest thing we will ever have to a holy grail in systematic trading is a portfolio with multiple diversified uncorrelated systems working together to grind the portfolio higher.

There is no such thing as a secret indicator. I don’t have a proprietary market regime filter. What I have are multiple simple lumpy and bumpy uncorrelated systems working together in my portfolio.

The reason this is so powerful is that the backtested performance starts to become actually achievable going forward into live trading. Finding a single strategy that outperforms in all market conditions is nearly impossible.

Finding multiple simple systems that trade simple edges that all work well in different conditions, and underperform at different times, and then combining them together is the only holy grail in systematic trading.

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Why Individually Mediocre Systems Create Exceptional Portfolios:

This is the counterintuitive part, and it’s worth spending time on.

Most systematic traders evaluate systems the same way:

Ensure the CAGR is high enough
Check if the max drawdown is low enough
See if the Sharpe ratio is interesting
Validate the equity curve is a beautiful straight line
etc.

If the system performance numbers aren’t high enough or the equity curve looks bumpy, the system goes through more fitting and more optimization until the numbers look right.

That framework is wrong.

In fact, an individual systems performance on it’s own does not matter that much, and we need to stop worrying about making a singular systems backtest as pretty as possible. How that system contributes to the entire portfolio is all that matters.

In fact, an individual system that loses money over the long term can still make your portfolio better.

Why? Because the only thing that matters for portfolio level improvements is whether the new strategy reduces overall portfolio volatility, dampens drawdowns, or smooths the returns.

If a strategy tends to make money when your other strategies are losing, it belongs in the portfolio; even if in the long run that one system never turns a profit on its own.

If you are deciding between two systems to add to your portfolio: a high Sharpe and a low Sharpe system. Almost everyone would lean towards adding the high Sharpe system over the low Sharpe system to the portfolio.

But remember to consider how that system adds (or subtracts) from your portfolio. A high Sharpe strategy that correlates with everything you already have does less for your portfolio than a mediocre low Sharpe strategy that zigs when everything else zags.

So stop asking yourself:

“What rule or parameter can I change to make this system better?”

And instead ask yourself:

“Does this lumpy and bumpy but real system improve my portfolio?”

What My 25+ Live Systems Actually Look Like:

I currently run 25+ live systems across US, ASX (Australian), and TSX (Canadian) equities. Here’s a rough breakdown by category.

My momentum systems buy stocks that have been going up and bet they’ll keep going up. They work well in trending, risk-on environments. They can struggle in choppy markets when every breakout fails, and believe me, there are stretches where every single breakout fails. I run several variations of these momentum systems across different many universes. See the equity curve of all my momentum systems combined together below:

My trend following systems are similar to momentum but focused on price series specific trends rather than a cross sectional momentum measurement. These trend systems are generally slower-moving, typically longer holding periods. They provide some of the same benefits as momentum but with slightly different entry and exit timing. So even though trend and momentum are similar, having both add to the portfolio because they own different positions and enter / exit positions at different times. See the equity curve of all my trend systems combined together below:

Then there are my mean reversion systems. These buy equities that have pulled back sharply, betting on a bounce. They work best in choppy, high-volatility markets, which makes them the natural complement to my trend and momentum systems. When my trend / momentum systems are struggling because the market is going sideways, my mean reversion systems are typically having a great quarter. See the equity curve of all my mean reversion systems combined together below:

And then there’s my hedging systems. I run a handful of systems specifically for hedging to portfolio during high volatility environments. When you look at the strategy equity curve on its own, it’s not that interesting. But as a portfolio component, it’s doing exactly what it’s supposed to: reducing drawdowns during the periods my long-only systems hate the most. See the equity curve of all my hedging systems combined together below:

You’ll notice these system types operate on different mechanisms in different market conditions. Momentum and trend need upward moving markets. Mean reversion likes volatile, choppy markets (but still works well in trending markets too). Hedging systems perform well when equity markets are crashing. No single market environment hurts all of them at the same time.

When you trade all of these system types together, the resulting portfolio starts to get interesting. The image below shows all my trend, momentum, mean reversion, and hedging system types combined together into the resulting portfolio.

For more details on my portfolio, with 25+ systems across three markets and their individual characteristics, check out the article below:

Systematic Trading with TradeQuantiX

The TradeQuantiX Portfolio

When somebody says they trade systematically, that could mean so many different things. Are they trading one strategy or multiple strategies? Are they intraday trading or trading on a daily/weekly/mo…

2 years ago · 69 likes · 24 comments · TradeQuantiX

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How To Start Thinking in Portfolios Instead of Systems:

If you’re early in your systematic trading journey, here’s how to reframe the way you evaluate new ideas.

The first mindset shift is to stop evaluating systems in isolation. The only question worth asking about a new system is whether it improves your portfolio.

A system with a Sharpe of 0.8 that is genuinely uncorrelated with your existing portfolio of systems will improve your portfolio more than a second mean reversion system with a Sharpe of 1.5 that correlates at 0.95 with the first one you already trade.

The second mindset shift is to think in strategy types, not individual strategies. Momentum, mean reversion, trend following, seasonality, event driven, hedging etc. Each system type responds to different market conditions. A complete portfolio should have many strategy types that behave differently from each other at different times.

The third mindset shift is how you evaluate backtests. Don’t discard a system because the standalone backtest numbers look mediocre. Discard it because your portfolio doesn’t need what that system offers. A mean reversion system with a modest Sharpe is still worth trading if you have ten momentum systems that all go sideways together during range-bound markets.

I’ve built my systematic trading portfolio over years, slowly diversifying across different markets, different system types, different timeframes, etc. Each system on it’s own is meh… but the combined portfolio isn’t half bad.

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Conclusion:

The holy grail in systematic trading is real. It’s just not where most people are searching for it.

No single system is going to be perfect. Not because good systems don’t exist. Because even the best individual systems still underperform at times. The trick is trading multiple uncorrelated, simple, lumpy and bumpy equity curve systems together in a portfolio.

Shifting to this portfolio level mindset and implementation is the only reason I’m able to keep trading through drawdowns that would otherwise cause me to quit. I know I have so many uncorrelated systems working together that at some point, a few of them are going to start making money again and pull me out of that drawdown.

So, stop hunting for the perfect system and throwing away imperfect systems. Instead, start building a portfolio comprised of many imperfect systems.

Disclaimer

The information and services provided by the Systematic Trading with TradeQuantiX newsletter are for educational and informational purposes only and do not constitute financial advice, investment recommendations, or guarantees of trading performance. Any information provided is general in nature and does not take into account your individual circumstances. Trading involves significant risks, including the potential for substantial financial losses, and past performance is not indicative of future results. The information is provided ‘as is’ without warranty of accuracy or completeness. All decisions and actions based on the information provided are the sole responsibility of the reader. You should seek independent professional advice before trading. The Systematic Trading with TradeQuantiX newsletter is not liable for any losses, damages, or outcomes resulting from the use of this information or our services.

Market Effect Research: Turn of the Month Effect

TradeQuantiX — Wed, 20 May 2026 11:47:32 GMT

Introduction:

This is the second article in a series on small calendar effects that I’ve been researching and trading. In the first, we looked at the holiday effect and ended up with a tradeable system. Now we’re doing the same thing with what’s called the Turn of the Month effect.

If you missed it, you can check out the holiday effect article here:

Systematic Trading with TradeQuantiX

Market Effect Research: Holiday Seasonality

4 months ago · 13 likes · 4 comments · TradeQuantiX

So, what is the Turn of the Month effect?

It’s the observation that stock returns are disproportionately concentrated around the end and/or beginning of each calendar month, rather than evenly distributed throughout the month. It doesn’t happen on one specific day, but across a window of roughly 10 trading days surrounding month end.

A lot of times these small market effects are too modest to trade individually. But if you incorporate them into a portfolio, they become much more interesting.

These effects can be stacked together into a portfolio of intentionally different return streams, where each one adds a small amount of edge.

Together, they compound into something much more meaningful. If you read the holiday effect article, you already get the idea.

In this article we will start by first going through the research phase, where we prove the effect exists thoroughly. During the research phase we try to understand what, when, and where the effect exists. Once we understand the what, when, and where of the effect, the how to trade it comes naturally, and we will build a few systems to capture it.

The goal of this market effect series is the following:

Teach how to perform and interpret proper market research
Share how to think about these small edges
Use creativity to trade these market effects in novel ways
Show how to combine many small edges into a broader portfolio

Let’s get into it.

Note For Paid Members:

As always, paid members get access to the full code at the end of this article.

You also have access to the Discord community where we can discuss this research and ask questions, and the GitHub repo with all published code.

Reason For The Effect:

Before we look at any data, let’s talk about why the Turn of the Month effect should exist in the first place. If there’s no solid mechanism, it’s more likely the edge is random noise.

There are two plausible reasons that I can think of.

1. Institutional cash flows. Pension and retirement contributions tend to be paid at the end of the month. That cash gets deployed into equities in the first trading days of the new month. These are price insensitive flows that buy equities with no regard to price, systematically, every month. This is likely the dominant explanation and it makes intuitive sense. Billions of dollars in scheduled cash flows hit the market at roughly the same time every month.

2. Portfolio rebalancing and window dressing. Large funds rebalance at month end reporting boundaries. Month end is a natural checkpoint for institutional portfolios, and you get predictable flow patterns as managers buy winners and sell losers to clean up their books and to produce attractive month end holdings snapshots for their investors.

Why does understanding causation matter? Because if this effect ever goes away, we need to assess whether the underlying causes have changed. If we don’t understand why it exists, we can’t distinguish temporary underperformance from a dead effect.

And the causes here are interesting. They’re structural and mechanical. Pension contributions happen regardless of market sentiment. Payroll deposits happen on a schedule. Rebalancing happens at reporting dates. These are flows that happen regardless of price. Pure systematic buying during the same timeframe every month.

It’s important to point out that the excess return may not be predictable to one or two specific days, but rather a range of days near month end. As more traders become aware of the effect it gets front run and the days where the effect shows through can shift around. But the underlying flows that create the window don’t disappear just because people know about them.

Our research in the next section will help us determine the optimal and stable window where this effect is most prevalent. Now let’s look at the data and see what we can understand about the effect.

Research Phase:

Establish The Baseline:

Let’s start by refreshing our memory on the scatter of returns of SPY data across 33 years of history.

This plot shows the return of every single SPY trading day from February 1993 through April 2026. Each gray dot is one trading day. The orange line at +0.04% is the average daily return across 33 years of trading days. So 0.04% is our benchmark to beat for a given day. If we can capture consistent performance above 0.04%, that shows this effect has “alpha”.

You’ll notice the cloud of dots looks pretty uniform. In calm markets, the range is tight, maybe plus or minus 1% to 2%. During crises (2000, 2008, 2020), the band explodes with individual days swinging 5% or more in a single session.

So there is definitely some variability to the 0.04% benchmark, but as long as we find something that consistently produces positive returns meaningfully above 0.04% per day, then that may be something worth trading.

Let’s break down this cloud of data points to see if we can spot any anomalies around month end. The next few sections will do that in numerous different ways.

Measure The Overall Effect (Day By Day):

This first plot will show the distribution of returns on average across 33 years of SPY data near month end. On the x axis, every trading day gets an offset relative to month end.

Day -1 is the last trading day of the month.

Day +1 is the first trading day of the next month.

Note: The offset jumps directly from -1 to +1; there is no Day 0. Each data point averages roughly 399 observations (one per month across 33 years).

You’ll notice one bar absolutely towers over the rest. Day +1, the first trading day of the new month, averages +0.22%. That’s 5.5 times the overall SPY daily average of 0.04%. That’s an interesting start.

But look at the broader shape of the distribution too. The roughly 10 trading days surrounding month end consistently outperform the rest of the month (days -5 to +5).

Days -4 through -2 are solidly above the 0.04% average line. Day -4 is surprisingly strong at +0.15%, which is 3.6 times the average.

Days +2 and +3 are modestly positive too.

And overall, the -5 to +5 days are generally consistently positive. It’s a cluster of elevated returns spanning both sides of the month end boundary.

One exception is the Day -1 returns. Day -1, the very last trading day of the month, is very red. It averages -0.05%. The last day of the month, on average across 33 years, loses money. That’s an interesting observation, and keep it in the back of your mind because we’ll come back to it later when we build systems from this research. We can do some interesting stuff with this knowledge.

So the Turn of the Month window clearly shows up in the full 33 year average result. But is this effect still working in more recent times, or has it been arbitraged away over time?

Has It Decayed Over Time? (Era Analysis):

This next chart breaks the same bar chart data into three eras: 1993 to 2003 (blue), 2004 to 2013 (green), and 2014 to 2026 (orange). For each day offset, you can see how the average return has shifted around across decades.

Day +1 showed to be the strongest when we looked at the whole timeseries together before, but when you break up the data by era the Day +1 returns have degraded over time.

It’s tempting to look at that result and conclude the Turn of the Month effect is dying. But that would be missing the forest for the trees. Day +1 is only one day. There is still an interesting phenomenon going on around the -5 to +5 day window.

In every era, there is a cluster of days surrounding month end that consistently show very above average returns. The specific days that perform the best or worst may shift over time (maybe Day +1 is the strongest in one era and Day -4 in another), but the overall window of elevated returns around the end of the month has remained consistent.

Also, Day -1 is constantly negative across all three eras. That negative last trading day of the month is stubbornly consistent.

Breaking down the returns bar plot by era tells us the effect has persisted constantly over time in a window around month end.

Thus far these plots have looked at daily returns in isolation, but when we trade we experience the cumulative sum of returns across all days we are in the trade. So in the next plot we will visualize how the edge sums up if you bought at -10 days and sold at +10 days.

Where Exactly Does It Happen? (Cumulative Walk):

This is a two panel chart. For each day offset from -10 to +10, it shows a running total of how much the average return at that day’s offset exceeds the overall daily average (+0.04%).

Think of it like an average equity curve of a trade from -10 to +10 days in a month, but the 0.04% average SPY return is subtracted out of the result. So these plots are showing excess return above the SPY average return.

A rising string of days means those days are producing above average returns. A flat string of days means the string of days are producing average returns. A falling string of days means they are experiencing below average returns.

You’ll notice the full sample line (bottom panel) tells a clear story. Starting from the left at Day -10, the cumulative line drifts slightly downward through the days -10 to -5, which are mid-month days.

Around Day -5, it starts climbing. Then Day -1 pulls the curve back down. Then at Day +1, there’s a sharp upward step.

Days +2 and +3 add a bit more rise to the curve, and then the line flattens again as we move into mid-month territory again.

The top panel shows the era by era comparison. The shape of the cumulative step up around month end is present in all three eras (Days -5 to +5).

The effect appears to be more or less consistent across all three time periods. The exact days and pattern that the effect takes changes a little bit, but the upward drift around month end appears to be consistent.

This has direct implications for system design. We can trade this end of month window. Entering a few days before month end and holding through a few days into the next month would capture the cumulative price increase.

So far we’ve been looking at averages, which tell us the typical day. But averages can be misleading if a few extreme outliers are doing all the work. Let’s look at the full distribution of returns to make sure this effect is consistent and not driven by a handful of lucky days.

Distributional Evidence (Is It Real Or Just Outliers?):

The next few sections are where we prove the Turn of the Month effect is a genuine shift in how returns behave and is not caused by a few lucky extreme outlier days.

One important note before we get to the charts. Earlier, we established that the overall daily average SPY return across all 33 years of data is +0.04%. But that number includes the EOM window days themselves, which pulls the average return up.

If we want a true apples to apples comparison, we need to know what non-EOM days return on their own (defined in this section as -5 to +5 Days). When we strip out the EOM window days from the average SPY return, the remaining non-EOM days average just +0.011% per day.

That’s the baseline we’ll use in this section. The -5 to +5 window around month end compared to all other days in the month.

The gap between +0.04% (all days average return) and +0.011% (non-EOM average returns only) already tells you how much the EOM window was pulling the overall average up.

Another thing to point out, I started using the abbreviation EOM to represent the end of month window of +-5 trading days. EOM stands for end of month. And when I discuss it I am talking about the +-5 day window around month end. When I say non-EOM, I am referring to days not in the +-5 day window, i.e. middle of the month days.

The EOM vs Non-EOM Comparison:

The plot below shows two histograms. The top panel shows only non-EOM days return distribution (4,373 days deep in mid-month, i.e. not within +-5 days of month end). The bottom panel shows the EOM window days return distribution (3,986 days).

While it’s probably hard to eyeball the distributions themselves (the next plot will help with that), if you look at the cards in the top left and right of each plot, you’ll notice the difference between the two groups:

Non-EOM Days produce an average return of +0.011%, a standard deviation of 1.19%, and a VaR 5th percentile of -1.85% (the loss level you’d expect to exceed only 5% of the time).
The EOM Days produce an average return of +0.072%, a standard deviation of 1.15%, and a VaR 5th percentile of -1.78% (the loss level you’d expect to exceed only 5% of the time).

EOM window days produce 6.7 times the average return of non-EOM days. And they do it at slightly less volatility and slightly less downside risk.

The plot below shows the same two distributions shown in the plot above, just overlaid onto a single chart so you can see the distribution shift directly.

The EOM distribution is shifted to the right relative to the non-EOM baseline. You’ll notice the EOM window days (blue) have fewer losses and more larger gains. The normal distribution fit overtop of the distribution (the dashed lines) shows this shift the most clearly, a clear shift to the right side of returns.

Where Does The Probability Mass Move?

This next chart took me a minute to wrap my brain around. Its a distribution difference chart.

The blue bars show return bins where EOM days have more observations compared to non-EOM days. Red bars show bins where EOM days have fewer observations compared to non-EOM days.

What you want to see is more red on the left side and more blue on the right side.

This would mean there are fewer losses (within the given return bin) on EOM days than non-EOM days, and more positive returns (within the given return bin) on EOM days than non-EOM days.

Still kind of confusing, I get it. Just sit with it for a second, it’s basically just the delta between the two histograms shown in the previous section.

But the plot shows what we want to see. Blue bars dominate the right side of the histogram, while red bars dominate the left side of the histogram. Moderate positive returns are more common on EOM days. And moderate negative returns are less common on EOM days.

This shows the Turn of the Month Effect is a consistent, small rightward distribution shift. It’s a slight upper hand in a game of poker. It won’t always play out, but you know over time the odds are in your favor.

The effect works by providing you slightly more +0.5% to +3% days, and gives slightly fewer -0.5% to -3% days. On average, month after month, that starts to add up.

The Changes In The Right Tail:

This next chart I think is pretty neat, it’s called an exceedance plot. For each percent return threshold on the x axis (from -5% to +5%), each line shows the percentage of days the average return was at or above the threshold on the x axis.

Basically, what is the probability of having a return higher than x%. The higher the line is on the right side of the plot, the higher the probability of larger returns.

The exceedance lines I am showing are the following:

Non-EOM days (gray)
Full EOM window (blue)
Days +1 to +5 only (orange)
Days -5 to -1 only (green).

You’ll notice in the 0% to 2% x axis range, the gray line (non-EOM returns) is the lowest. This means there is a lesser probability of higher returns than the other lines (which are EOM window returns).

For example, at the +1% return threshold the orange line (Days +1 to +5) sits at 16.1%, while the gray line (non-EOM) sits at 12.9%. That means the orange line has a 16.1% chance of a higher return than 1% and the gray line only has a 12.9% chance of a higher return than 1%. This basically shows that the EOM window has a slightly rightward shifted distribution when compared to the non-EOM window.

See the zoomed in right side of the exceedance plot below:

You’ll notice the orange line (early new month days) sits clearly above the gray line (non-EOM days) across the entire positive range from 0% to about +3%. The gap is approximately 3 percentage points in the +0% to +1.5% zone. Beyond +3%, the lines converge. This shows the effect is about consistent moderately sized winners rather than extreme outliers.

The takeaway from all the evidence thus far is as follows:

The Turn of the Month effect is real, it operates through a genuine rightward distributional shift rather than large outliers.
It’s concentrated in the early new month days (+5) but also in the end of previous month days (-5).
It doesn’t happen every month, but on average over time, it does happen.
The best performing days move around over time, but within the +-5 day EOM window, there is meaningful outperformance when compared to the overall SPY average return and the non-EOM SPY average return.
The +-5 day EOM window also has slightly lower volatility and slightly lower risk than the non-EOM window.
The effect isn’t massive, it’s small but it does exist, so we need to get creative about how we capture it.

We used multiple independent methods in this research section to measure the Turn of the Month Effect.

All the evidence points to the same conclusion, which is the +-5 day EOM window produces higher returns with less volatility and downside risk, and it persists across eras.

The question now is:

”How do we trade it?”

The next few sections will focus on the practical implementation of trading this effect. We’re going to work with three different systems, each one showing a different way to use this research result in practice.

System 1: Pure TOM Baseline:

ETF Mean Reversion Methods: Validation and Live Trading - Part 3:

TradeQuantiX — Sun, 10 May 2026 15:42:45 GMT

Introduction:

In November 2025, I was fully loaded in my mean reversion mini-portfolio. My entire portfolio was heading into a drawdown as the general market was selling off, and I was picking up more exposure as the markets tanked via the mean reversion mini-portfolio.

I was using portfolio margin to support the additional mean reversion buying. Even though the markets were selling off, my long term trend and momentum systems hadn’t begun to reduce exposure yet.

The entire portfolio was down 10% in just a matter of days. I was a little nervous to be completely honest with you.

I knew I had to stick with my systems though. I did the work, I knew the possible outcomes, I knew the portfolio would rebound. It’s just different to see it in a backtest than to actually experience it live with real money.

Eventually, the markets popped back up. The positions held by the mean reversion mini-portfolio quickly caused the entire portfolio to recover in just a few days; as you can see by the v-shaped recovery in the equity curve above.

I’ll admit, with the mean reversion mini-portfolio we have introduced in the past two articles, I was a little aggressive with my sizing at first. I was excited about these systems and I sized them for the upside and not the downside. I make mistakes too.

After this experience, I pulled back the allocation to the mean reversion mini-portfolio by about 25% to mitigate this volatility in the future.

This story was the first scary experience I had with these mean reversion systems. It wasn’t that the systems were poorly developed or that the parameters / indicators needed tweaking; it was simply that I was oversized and I learned that lesson the hard way.

The only thing that kept me in the trades and prevented me from selling the positions early or skipping the next trade was my confidence in these mean reversion systems. I had done the work, I knew they were robust, and I knew they would very likely make money over the medium to long term.

In this article, I want to share with you the in-sample and out-of-sample results for the mean reversion mini-portfolio, as well as the results of a few robustness tests. This is extremely important because the stability of these robustness tests and the out-of-sample results are what give you the confidence to keep trading through the scary times.

I also want to discuss more about what it’s like to manage and trade the mean reversion mini-portfolio. As you can tell from this story, there are times when it sucks. I want to ensure that expectations are set and point out that nothing is ever as easy or glamorous as it seems.

Let’s start first with the in-sample and out-of-sample results.

In Sample / Out of Sample Methodology

When I developed each of these mean reversion systems, the in-sample period was set from 2010 to 2018 and I used the leveraged ETFs during system development. 2010 to 2018 is the period I used for all decision making:

What indicator to use
What parameters to use
What entry/exit logic to use
What universe to use
etc.

Everything was designed using only the 2010 to 2018 time window.

I focused on robust system design. I did not spend time optimizing or fiddling with trying to get a very specific time period to produce better results. That’s a dangerous path to overfitting. If a system underperformed from 2014-2015 for example, I left it alone. Any optimizing to make it look better during the specific timeframe would be curve fitting.

Instead, I knew these systems would be combined into the mini-portfolio and that the differences between when each system outperformed or underperformed would complement each other well.

Hence, I was not too concerned with optimizing the performance of one system to be perfect. I instead focused on making each individual system robust, and I knew the performance would follow once they were combined into the mini-portfolio.

For the out-of-sample time period, that was selected to be 2018 to 2026 (which is a pretty long time). Also, pre-2010 was used as out-of-sample on non-leveraged ETFs.

While I tend to heavily prefer more recent out-of-sample time periods rather than out-of-sample time periods from many years ago, it was still data I could leverage to increase my confidence (or hesitation) in these mean reversion systems even more. If pre-2010 had completely fallen apart, that would have probably been a red-flag, even if the post 2018 data results showed promise.

So what this means is I used about 8 years of data for development, but had 8 years forward in time of pure out-of-sample and 7+ years (depending on the ETF) of even more out-of-sample data for the pre-2010 time period. That’s over 15 years of out-of-sample. It’s not too often that you come across a system that can say it has more out-of-sample time than in-sample time.

These days, you’re lucky to find a system online that has ANY out-of-sample validation. Most systems are blindly optimized over a whole time period and then the author sells them like they are the holy grail.

My favorite is when a system underperforms after the release date and when they get called out about it, rather than the author taking accountability, they just say something like:

“Oh, well you need to re-optimize it for the new market environment”

I’ve personally seen this happen 3 or 4 times. It’s nonsense. They know what they did. Systems do fail, it’s unavoidable. But it’s one thing to do your best to develop a system right and it dies vs. blindly optimizing a system on all data and then presenting it with this beautiful backtest just looking to make a few bucks selling a junk system to some poor sucker.

Sorry I am going on a rant here. I wasn’t really planning on taking this article this direction, but here we are. I could delete the last few paragraphs, but I’m not; I feel like you all deserve to hear my raw opinions.

I guess I just want to point out that the things I write about in this newsletter are the actual things I am currently trading or am building to add to my portfolio in the future. If the author doesn’t put their own money behind what they build, then you know their incentives are not aligned with you being successful.

Here are my live results for this mean reversion mini-portfolio since I began trading it in August of 2025:

I put my money where my mouth is.

Combined IS and OOS Performance (Leveraged ETFs):

2010 to 2018 Combined IS Results:

2018 to 2026 Combined OOS Results:

You’ll notice the in-sample and out-of-sample results closely align. You could argue the out-of-sample looks slightly better with a higher rate of return, Calmar ratio, and expectancy.

These are the types of out-of-sample results that are very rare, but I get very excited when I see something like this. 90% of out-of-sample results degrade in some way, and that degradation is usually on the order of 20%-30% degradation to the in-sample results.

That other 10% of out-of-sample results are either just as good as the in-sample or better. This is when you start to think you’re onto something. But, with that said, it’s still possible to be a fluke!

That’s why we will check out some robustness test results in the second half of this article as well.

Unleveraged ETF Extended History (Pre-2010 OOS):

2000-2010 Combined OOS Results:

2010-2026 Combined OOS Results:

The out-of-sample results pre-2010 still look tradable and in-line with the in-sample results. The drawdown during the 2000 tech burst is mildly concerning as that was the largest drawdown throughout the entire history of the backtest. Granted, this was a very aggressive sell-off so it makes sense that there would be poor performance during this time.

It might be tempting to go back and start optimizing to this period to reduce that 2000 tech crash drawdown, but this is pure curve fitting. Why try to make your backtest look better than it actually is? You want to know that the true results actually looked like this. You need to know this information about possible worst case scenarios and not try to hide the bad portions of the results.

When you mute the worst case scenarios with curve fitting, you understate risk and overstate returns. This can be dangerous when it comes to allocating capital to the system as you will be tempted to over allocate to a curve fit system.

Instead of “fixing” the bad drawdown with curve fitting, handle it with position sizing. You know that a 1 in 20 year event could cause a drawdown like this, so either:

Accept that you’re going to be in pain a few times during your trading lifetime when these events eventually happen (and they eventually will happen).
Allocate to the mean reversion mini-portfolio accordingly. You know an ~11% drawdown on unleveraged ETFs is possible, so a ~30% or more drawdown is also possible on leveraged ETFs. Allocate capital from your overall portfolio to the mean reversion mini-portfolio accordingly. If you only want to experience a 10% drawdown in the mean reversion mini-portfolio, then you can only allocate ~ 33% of your overall portfolio capital to it. Controlling drawdown with portfolio level allocation percentages is a great way to control risk without relying on curve fitting to make the backtest look like the drawdown never happened.

The in-sample and out-of-sample results confirm the mean reversion mini-portfolio likely has merit. But it is also possible this result was complete luck. Hence, we should run some robustness tests on the mean reversion mini-portfolio to ensure it holds up to some scrutiny.

A truly robust system should be able to navigate some noise and turbulence and still be tradable. The markets will only get more noisy with time, so let’s ensure our system can at least withstand a historical backtest with some noise thrown at it.

Transaction cost note: All backtests include commission costs. Commissions are assumed to be the Interactive Brokers commission structure. Slippage is set to zero in the backtests because most entries and exits participate in the closing price auction and thus would see no slippage. Only one system enters intraday with a limit order, which would generally not see slippage unless the opening price gapped below the limit order (in which case there could be slippage as the limit order essentially gets converted to a market order at the open). Based on my 9 months of live trading these systems, zero slippage appears to be a reasonable assumption, at least based on my account size and allocation.

Robustness Testing

This section will cover a few robustness tests on the mean reversion mini-portfolio. It’s worth noting, every test I will show was run at the portfolio level: all four mean reversion systems (Z_Reversion, Vol_Reversion, Vol_Reversion_2, RSI_Reversion) combined together.

You could test the individual systems in isolation, but since these systems were designed to complement each other, scaling in and out of positions, it makes more sense to test from the perspective of the overall mini-portfolio.

How To Interpret These Robustness Results:

Generally speaking, I like to see robustness test results come in within 70% to 80% of the nominal system results. That’s my soft threshold. I’ve tested 100s of systems and have run robustness tests on pretty much all of them. Generally, the 70% to 80% threshold is reasonable.

Obviously the closer the robustness test result to the baseline system result the better, but there are plenty of caveats and edge cases. To keep it simple, the easiest way to determine if the robustness test is a pass would be to ask yourself:

“If the worst case scenario happened, or if the 5th percentile worst result of these robustness tests turned out to be the true performance of the system, would you still trade it? Would you still allocate your hard earned capital to it?”

Meaning, if your live performance was worse than 95% of results from the robustness tests, would you still be willing to trade it in your portfolio? If yes, then it’s a pass. If no, then it’s likely a fail.

I was very careful when I designed these systems to design them with robustness in mind. I wasn’t optimizing each system to maximize returns or to minimize drawdowns. I will always accept less returns or more drawdown for more overall system robustness. Rather, I was ensuring the parameters and rules I chose were widely stable and it didn’t really matter what I chose, it all more or less worked.

Remember, a curve fit backtest overstates returns and understates drawdowns, which leads to over-allocation. You end up putting more capital into something that returns less than you think and loses more than you think. Robustness testing helps point this out and make us aware of this possibility.

What I want is for the backtest to reflect the actual stability of the system so I can allocate properly. Robustness tests help answer the question:

“Is the backtest hyperinflating results, or is it actually a good representation of the median/likely outcome?”

If the robustness tests show the baseline system result is close to the median, you can probably use it as a good basis for capital allocation. If the robustness tests show the baseline system is in the upper end of the results, then it’s likely the performance is overstated. In this case, we either need to throw away the system, or allocate to it accordingly now that we know live performance would be expected to be lower than the backtest.

Robustness Template Refresher:

If you read the robustness testing article (see link below), you’ll remember the robustness testing Excel template.

Systematic Trading with TradeQuantiX

Robustness Testing Methods: A Complete Guide for Systematic Traders

3 months ago · 16 likes · 2 comments · TradeQuantiX

This template contains inputs for five robustness tests for a system or portfolio (excel robustness template is stored in the GitHub). If you haven’t read that article yet, consider reading it first. It’ll make the rest of this section make a lot more sense.

As a quick refresher: the robustness testing excel template has tabs for each robustness test. You put the raw output from the robustness test in the input tabs, then the excel sheet plots histograms showing your baseline systems results in relation to other results in the robustness test. It also has a table showing the percentile rank of the baseline system in relation to all results (i.e. is the baseline system in the 50th percentile, 5th percentile, 95th percentile etc. of results).

I ran the combined mean reversion mini-portfolio through three of the five robustness tests in this template. I focused on the parameter sensitivity, trade skip, and noisy data robustness tests. These three I felt where the most pertinent to the mean reversion mini-portfolio.

You can download this template from the GitHub and run these tests on your own systems. I’ll also have the results from these robustness tests in the GitHub as well.

In the rest of this section, we will walk through the results of each of the three tests and my interpretation.

Let’s first start with the parameter sensitivity test, which is one of my favorites.

Parameter Sensitivity (All Systems Varied Simultaneously):

This test works by varying each of the four systems’ key entry and exit parameters at the same time by approximately +-25%. Then, comparing the baseline combined mini-portfolio performance to other results across the +-25% parameter space. Each of the four systems had its parameters varied at the same time, it was not a one at a time system by system variation.

If the combined mini-portfolio only works with one precise set of parameters across all four systems, it’s overfit. A robust mini-portfolio should deliver acceptable performance across a very wide range of parameter combinations.

It’s worth noting, I do not have enough computer power to do an exhaustive +-25% parameter variation for all parameters within each of the four mean reversion systems. That’s billions of combinations, if not more.

Instead what I did was focus on the entry and exit rules specifically and run a -25% parameter, a nominal parameter, and a +25% parameter for each system. But even that was millions of combinations.

So I set up the +-25% sensitivity as I just laid out but had it randomly choose what parameters were varied on each run.

So it was not exhaustive, but rather a probabilistic run that randomly varied parameters +-25% (or kept the nominal parameter) across all four systems. I ran this 1,000 times and input the results to the robustness testing excel template.

While maybe not a fully exhaustive test (I don’t think an exhaustive test is even possible at the mini-portfolio level), I figured if there were any issues, they would start to present themselves after about 1,000 runs.

You are more than welcome to do more runs (maybe 10,000) but I would presume the result would be very similar to the 1,000 run result. I think you’ll notice I’m a big practitioner of the 80/20 rule and I like to do things in a reasonable way. I am not the person to wait and manage a robustness test for a week to get 10,000+ results.

I want to get what I think is the minimum viable amount of data which would present concerns (if they exist) and only do more testing if I feel it’s warranted after the 1,000 tests. Analysis paralysis is the best way to always be developing but never actually trading or making any money.

At a certain point you have to consider it good enough and pull the trigger on it and see what happens live. That’s just how I do it. And I would say the last 9 months of my live results are more meaningful than what these robustness tests could show (but I’ll show them anyway!).

With all that said, just to appease the skeptics, I ran the 1,000 random parameter sensitivity tests again in a secondary robustness excel sheet. I then looked at the results and compared to the first set of 1,000 random parameter sensitivity tests.

If my claims are true (that 1,000 tests gets you to around a converged solution), I would expect to see similar results between the two 1,000 test datasets. And that’s exactly what I saw, both datasets showing very similar results. Which means the 1,000 parameter sensitivity tests was enough to actually show meaningful results and make decisions from.

Combined Mini-Portfolio Parameter Sensitivity Table (Dataset 1):

Combined Mini-Portfolio Parameter Sensitivity Table (Dataset 2):

You’ll notice the Baseline Percentile row (bottom row) are very similar between the two datasets. This shows that the baseline system performance sits in approximately the same percentile in relation to all other parameter combinations within a +-25% space.

Since both datasets of 1,000 random parameter combinations result in approximately the same percentile, I am arguing that 1,000 tests is more or less converged and suitable to learn something from.

Let’s pick apart the results for a few of the metrics. Starting first with ROR, the system is in the 62nd to 65th percentile and about 3.5% to 4.5% above the average and median ROR result across the parameter sensitivity studies.

I generally tend to expect the 50th percentile or median result during live trading. The baseline system result is 13% ROR while the median result is 12.5% ROR.

So, the system results may be slightly overstated on the higher end, but only by about 0.5% ROR. I am more than comfortable with this result as the median ROR result is nearly identical to the baseline system ROR result.

Next let’s look at max drawdown. Keep in mind max drawdown will inherently be a less stable metric as it is only one data point over the entire backtest history. The baseline system is in the 80th percentile, which is a little better than ideal. The median max drawdown is about -14% while the baseline system max drawdown sits at -12.2%.

So, there is a 1.8% difference in max drawdown between the baseline system and what is more reasonable to expect (the median value). I can live with that. While the percentile may be saying the system is in the “overfit” range of results, the scatter of max drawdown outcomes is actually relatively tight, between -8% and -20%.

That may seem wide, but for how noisy the max drawdown data point is, that’s a tight scatter. I could even live with the worst case of that range. I am comfortable with this result given I can live with the worst case result and that there is only a 1.8% delta between baseline and median max drawdown results.

Let’s look at one more, Sharpe. The baseline system Sharpe sits somewhere in the 73rd to 78th percentile of parameter sensitivity results. Again, while high (we like to see the baseline system near the 50th percentile of results), take it in context with the scatter. The median Sharpe result was 0.99 while the baseline system Sharpe is 1.02, a 0.03 Sharpe difference. A couple of different trades in a backtest could make a larger difference in Sharpe than that.

The scatter of Sharpe results ranges from 0.77 to 1.17, the baseline system sits right near the middle at 1.02. So while the baseline system’s 73rd to 78th percentile result seems scary, really it’s only slightly overstated compared to the expected median Sharpe of around 0.99.

I think what we are learning here is we need to look at this table holistically and not just focus on the percentile results. The percentile results are a good way to do a fast spot check, but you need to contextualize it with the rest of the data to make quality judgements about a system.

A result that would scare me would be a percentile result of 70% or more for most metrics and a large delta between baseline and median/average results, and a large scatter between min and average.

As an example, if baseline Sharpe was 1.02 but the percentile was 75%, the median Sharpe was 0.85, and the min Sharpe was 0.5; that would signify there is a lot of variability in the parameter space. This would tell me the backtest results I am seeing are severely overstated and I need to be careful and allocate accordingly (or throw the system away).

Luckily this is not what we are seeing. While we see higher percentiles for a few metrics, the scatter is tight and the delta between baseline and median results is small, so I am much less concerned about a higher percentile result. These robustness test results are signifying we should expect just a few percentage points of degradation between baseline and median results for most metrics, which is normal and I am more than comfortable with.

Now let’s visualize this data in a slightly different way. Tables are nice but plots sometimes tell the story in a way that’s easier to visually understand.

Combined Mini-Portfolio Parameter Sensitivity Histograms:

What you want to see is the baseline system histogram bin (in red) sitting in the bulk of the distribution. While most of these distributions are not normal, there tends to be a region of high density in results, and that’s where you want to see your baseline system residing.

You don’t want to see your system in the right tail of the distribution (or the tail with better results). In this case, the mean reversion mini-portfolio is within the bulk of these distributions.

Some metrics have the mean reversion mini-portfolio sitting on the better side of the bulk distribution, but as discussed earlier, the difference between the baseline performance and the median performance is very small for the most part.

Overall, this mean reversion mini-portfolio is nowhere near an upper edge of performance and is showing to have similar performance with the bulk of the other parameter sets within the +-25% universe.

Another interesting way to visualize results is to just flip through the equity curves of the results. Tables and histograms can explain the deltas and expected stability, but the equity curve is what gets lived from day to day. If you can look at the equity curves and live with the median or even the worst case result, that’s a pretty good sign.

Below I show the worst and best result from the parameter sensitivity tests (dataset 2 specifically) based on Sharpe.

Worst Result (Based on Sharpe):

Best Result (Based on Sharpe):

The best case looks great, but we shouldn’t expect it. The worst case result is a little more choppy, but is still more than tradable (at least for me). Especially in an overall portfolio context, I’d be more than happy to have the performance of the worst result in my portfolio. Anything better than this worst case is just extra goodness.

So, overall when looking at the parameter sensitivity tests holistically, I am feeling good about these results. If I didn’t, I wouldn’t be trading the systems. Now let’s move on to the next robustness test, a trade skip analysis.

Monte Carlo: Trade Skip Analysis (All Systems Simultaneously):

In this robustness test, we randomly skip a percentage of trades across all four systems within the mini-portfolio at the same time and re-run the backtest across 1000 simulations. Each simulation will result in a different outcome because different trades were skipped. This produces a distribution of different outcomes.

The purpose of this test is to ensure the results are not dependent on a handful of lucky trades. We want to randomly skip trades and ensure the results are still tradable. If we start skipping trades and the equity curve completely falls apart, that would be a fail of this test. That would show that there is a small percentage of trades that matter and the rest is noise. We don’t want that.

Instead, we want stability over time, meaning every trade (or at least most trades) is meaningful, and when all trades are meaningful, no single trade is meaningful at the same time; so if a handful of trades get skipped, the system still works just fine.

To a smaller degree, this test also ensures that if trades are missed due to vacation travel, broker outages, mistakes, automation bugs, internet outages, etc. the system still holds up fine. Ideally no trades will ever be missed, but on rare occasions it will happen. This test also helps expose the possible outcomes of such instances. The reason for the test though is still the same, if the portfolio’s returns are concentrated in a handful of critical trades, missing them is catastrophic.

How I did this test for the mean reversion mini-portfolio was I added a bunch of binary random switches to the entry rules. If the random switch says “1” then execute the trade like normal, if it says “0” skip that trade. In this test, about 33.3% of the trades were skipped.

Because of the dynamics of this system and how it trades a specific universe of ETFs, if we start skipping trades the exposure of the mini-portfolio will decrease. That’s okay, we just won’t put as much weight into the metrics that are impacted by exposure (ROR, max drawdown, volatility etc.).

Instead, we can focus on metrics like expectancy, percent win, average win, average loss, profit factor etc. These metrics are not impacted by differences in exposure and will highlight if the system is stable when trades are randomly rejected.

For a system like a trend following system that trades a large universe you would interpret and use this test slightly differently. Since the universe is large, when a trade is skipped you can replace it with another trade that also meets the entry criteria. This keeps exposure consistent between baseline results and trade skip results.

So metrics like ROR, max drawdown, volatility etc. are comparable. But since this mean reversion mini-portfolio trades a small fixed basket of ETFs, there isn’t any trade replacement going on, so we can only focus on results not impacted by exposure.

The table below shows the baseline (actual) result compared to the min result, median result, max result, and spread (max - min) for the trade skip robustness test runs. Again, focus on the metrics: expectancy, percent win, average win, average loss, and profit factor.

First, let’s discuss percent wins, average win, and average loss. These three metrics in the baseline (actual) system result were 72.3%, 5.2%, and 5.3% respectively.

When we compare these metrics to the median result of the trade skip runs, we see percent wins go from 72.3% to 72.8% (slight increase), average win go from 5.2% to 5.3% (slight increase), and average loss go from 5.3% to 5.1% (slight decrease). This means that across the board the median result of the trade skip Monte Carlo is better than the baseline system.

This same story holds for expectancy and profit factor. Expectancy in the baseline system goes from 2.28% to 2.48% when compared to the median result; and profit factor goes from 2.49 to 2.70 when compared to the median result. Again, the median result is better than the baseline result.

This tells me that more bad trades are being skipped than good trades; which kind of makes sense given the negative skew of mean reversion. Mean reversion tends to have very ugly left tails and short but dense right tails. The trade skip is causing small winners to be skipped, but there are a ton of small winners, so skipping some of these doesn’t affect the result too much (which is exactly what we want to see).

But when a losing trade is skipped, there are higher odds it was a large loser, so skipping it makes a bigger difference to the outcome of the strategy. This is what we want to see. This result tells us that skipping trades does not have a negative impact on the stability of the system. The underlying system metrics hold together and there are not a handful of outliers inflating the results of the system.

It’s also worth pointing out, the difference between the min and max result from this trade skip test look great as well. The scatter between min and max for expectancy, percent win, average win, average loss, and profit factor is super tight, which means the system is super stable and all of the Monte Carlo runs resulted in a very similar outcome. I’d even be okay realizing the min result across the board in live trading, which is always a strong sign of robustness.

Monte Carlo: Noisy Data Analysis (All Systems Simultaneously):

I really like this next test because it’s the only test that changes the data itself, rather than the systems response to it. To perform this study, we add random noise to the price data feeding the mean reversion mini-portfolio. I created three different datasets with different levels of noise: low, medium, and high.

It is worth pointing out that noise is defined and can be implemented in different ways. These three methods are how I added noise to the data (I used all three methods at the same time with varying severity):

Candle shift: Shifts entire OHLC candles up or down by a random delta. Preserves the candle range (High minus Low stays the same).
OHLC noise: Adds independent random noise to individual OHLC fields. Reshuffles High/Low if OHLC constraints are violated.
Random walk: Blends the original price series with a synthetic random walk to create gradual drift. Preserves candle structure by applying the same delta to all OHLC fields.

If the combined portfolio only works on the exact historical price series it was tested on, the signals may be fitting to specific price paths and random patterns that will never repeat, rather than capturing a real structural edge. The system should be able to hold up to some noisy data and not self destruct the second the data changes a little, because the future will be slightly different than the past.

One thing I learned while running this test is that depending on how you implement the noisy data, your results can actually get better, which is counter intuitive.

Mean reversion systems enter on pullbacks, so if noise is added that shifts the price downward and the system enters, the next candle may not necessarily have any noise added, so it makes the rebound even more aggressive, increasing the returns of the system.

So, the most stringent way to add noise to this system would be to have very small candle shifts up and down and very small changes to OHLC data. This is because artificially lower closes and lows allow the system to get better fills, and artificially higher closes and highs allow the system to get better exits. Instead, focusing on the random walk implementation is where most of the noise was added.

A very small amount of candle shift and OHLC noise was added to the data, but varying severities of random walk was added. How the random walk is applied is by randomly generating a random walk price series with the same volatility as the underlying symbol, then a weighted average between the two series is applied; where the weighted average blend percentage can be played with to preserve more or less of the original price series.

See below for TQQQ examples of varying levels of noise from low, to medium, to high noise.

Low Noisy Data - TQQQ Example:

Medium Noisy Data - TQQQ Example:

High Noisy Data - TQQQ Example:

Real Data - TQQQ Example:

Here is a comparison that is more zoomed out of how the price series can differ due to the random walk weighted mix into the price series:

Zoomed Out With Random Walk - TQQQ Example:

Zoomed Out Real Data - TQQQ Example:

I ran three levels of noise: low, medium, and high. For each noise level I created 5 different universes of noise. This means noise was randomly added to 5 sets of data.

So, each data set had noise added to different candles, and different random walk sequences were created and mixed into the underlying price series. This allows us to test 5 different versions of the price series per noise level.

The configurations I ran for each noise level are as follows:

Low Noise:
Medium Noise:
High Noise:

Now let’s get into the random data results table. It’s worth pointing out, I did run this test on the leveraged ETFs all the way back to 2010, but I kept running into issues on low priced tickers. Some of the tickers had very low prices on the back adjusted data in the early years, so adding the noise was causing some goofy results where a systems equity curve would shoot up massively.

I’ll need to look into this more and fix the noisy data code to account for this issue. But for now, I am just showing the 2018-2026 noisy data results to avoid some of the weird price noise anomalies that were happening in some of the high noise datasets.

Random Data Results:

The baseline data results are the bottom row. Each noise level and sub-run within each noise level are shown on the rows above the baseline data results.

We want to see the system perform similarly on noisy data as real data. Some degradation may be expected, but we definitely don’t want to see the system completely fall apart.

A good sign that the system can capture an underlying phenomenon and is not overfit to a specific price path is if the system can still make money when the price history is slightly different.

Adding noise doesn’t remove the phenomenon from the data that the system harvests, it just disguises it in a different way. If the system can still pluck it out of the data and trade it with similar profitability as the baseline data, that’s a great sign of robustness.

These results show just that. The noisy data results are on par with the baseline data results. There is some degradation, the max drawdown, volatility, and Sharpe generally get a little worse with the noisier data; but that is as expected. Even the results of the highest noise data is still more than tradable.

Below I show some example equity curves. I show first the baseline system from 2018-2026. Then I show one example equity curve from one of the noisy datasets (low, medium, and high). This will give you a feel for what the noise does to the results.

Baseline Results - Equity Curve:

Low Noise Run 4 - Equity Curve:

Medium Noise Run 2 - Equity Curve:

High Noise Run 4 - Equity Curve:

I would trade every equity curve shown here (and every other noisy equity curve not shown here as well).

Again, a great sign of robustness is when the underlying data is noisy and the system can still pick up on the underlying effect and trade it profitably.

Live vs Backtest Comparison:

As you can see from the previous section, the results of these robustness tests, along with the in-sample and out-of-sample results, gave me the confidence to pull the trigger and go live with this mean reversion mini-portfolio.

Also, just knowing how these systems were designed and how they intentionally had robustness and diversification built into the mini-portfolio gave me the further boost of confidence to go live (remember the whole thing about diversification across entry/exit timing, entry/exit price, indicator readings, instruments traded etc. that we discussed in depth in part 2).

I’ll be honest. I’m not a natural mean reversion guy. I’m naturally a longer term trend and momentum trader. I like the positive skew of trend and momentum, and the slower pace. Mean reversion is the opposite in all ways.

But the testing showed these were the most promising mean reversion systems I’d ever developed. The methodology this mini-portfolio follows is also one of the most robust methods I’ve ever developed. I had no excuse, so I pulled the trigger.

Sometimes trading these mean reversion systems still makes me nervous. Again, it’s just the nature of how these systems go against my natural trader mindset. But other times trading these systems makes me excited, because they capture the bounce that my other systems miss out on. And as you can tell from the story at the beginning of the article, sometimes I get too excited, which then leads to nervousness when negative outcomes happen.

Hence, why I had to size down my positions. Reducing my position size / allocation helped a lot. I find I am much less nervous now when the mean reversion mini-portfolio loads up on positions. So take the lesson from me and be on the safe side and allocate a little less than you think you should, rather than a little bit more.

Anyway, I know I have already shown these results, but here they are again. The live out-of-sample real money results since August of 2025 with my mean reversion mini-portfolio.

Compare these results to the backtested results for the same time period:

It’s worth pointing out a couple things. During live trading, I started on unleveraged ETFs for the first few trades as a proof of concept. I wanted to ensure everything was working before going to the full risk allocation on leveraged ETFs. That’s why the first few trades return a little less than the backtest.

Also, remember I sized down my allocation last December after the late November drawdown I experienced where my overallocation to the mean reversion mini-portfolio made me too nervous for comfort.

I also added one other mean reversion system partway through the time period on top of the four shared here. It’s nothing special, just different indicators and more diversification across price, time, instrument etc.

The only reason I didn’t share it here is because the idea came from a trading friend of mine and I didn’t want to share someone else’s ideas. But again, it’s nothing special, just more variation on top of the mean reversion methodology shared in this article series.

With all that said, the backtested and live performance aligns very well. It’s normal to have a little bit of variation. Given all the subtle tweaks I made over time to better nest this mean reversion mini-portfolio into my overall portfolio, I’d say my backtest assumptions hold up pretty well to a live trading environment.

Execution Reality and Live Trading:

Order Management Workflow:

Let me walk you through the daily routine I follow to trade this mean reversion mini-portfolio because I bet some people imagine this is way more time consuming than it actually is.

It only takes a couple of minutes to trade every day. I use RealTest (outputs orders) with Order Clerk (reconciles and places orders with broker). The two tools work together to make the process of trading the mean reversion mini-portfolio (along with the rest of my portfolio) very straight forward.

I’m not sitting there manually calculating position sizes and entry levels and placing 30 staggered entry orders every day because these mean reversion systems scale into positions. All I do is click a few buttons. The system generates the orders, I spot check them, and I click submit.

On a busy day, all four mean reversion systems are sending orders for almost all ETF assets traded. Anywhere from 20 to 40 orders all getting placed in one day. On a quiet day, it’s maybe 5 to 10 orders for just a couple of different ETFs. But both scenarios are the same amount of work for me.

Today was a moderately high order count day:

The mean reversion mini-portfolio runs in the same Interactive Brokers account as my other systems. Long term trend and momentum systems hold the bulk of my exposure. The mean reversion mini-portfolio fills in the exposure cracks and takes up the rest of my open capital.

When mean reversion orders get filled, I sometimes go into margin. I’ll hold that margin for a few days until the price reverts, then exit the mean reversion position and go back to nominal portfolio exposure.

The crossing orders problem:

So, there’s one small issue that arises from time to time with the mean reversion mini-portfolio that is slightly annoying. It doesn’t happen too often, I only just recently noticed it.

It has to do with placing so many orders at staggered prices on the same instrument. The issue is that sometimes one system wants to buy a position and another wants to sell a position and you have an order crossing and Interactive Brokers cancels one of the orders.

Basically, if the price closed in the right spot, one system would want to sell and another would want to buy and, from the perspective of the broker, you would effectively be selling the position to yourself. The broker doesn’t like that, so they cancel one of the orders.

This really doesn’t happen that often, but it can be exacerbated if you trade other systems on the same ETFs as the mean reversion mini-portfolio. I have a couple seasonality trades on bonds I’ve implemented using the same TMF ETF as the mean reversion mini-portfolio. I’ve noticed when the seasonality system kicks on, I sometimes get trade rejections on the TMF ETF. It’s annoying, but I deal with it because it hasn’t been anywhere close to detrimental enough to meaningfully harm the system.

It goes back to the whole diversification across many assets, entry/exit levels, entry/exit times etc. If the order of one sub-entry from one system on one asset within the mean reversion mini-portfolio gets canceled, it really doesn’t do much at the mini-portfolio level. That’s the mindset I take with this rare situation.

With that said, if there is something I can do about it I will. If I am working from home that day or if I get home from work early, I’ll check in on the trade and see if any of the conflicting orders are unlikely to get filled. If so, I’ll cancel the side that is more unlikely to be filled and re-place the order for the side of the trade that is more likely to get filled. That 5 minutes of work every once in a while solves the issue.

And even so, most of the time when this order crossing issue happens, neither order would have been filled anyway, so many times it doesn’t even matter. If I can’t be home near the market close to modify the order, I just let the cancellation be, and move on.

This is an 80/20 solution. It’s not perfect, but live results still track close enough to backtests. This is the interim solution until I build/find a better solution. I accept the imperfection for now.

What It’s Actually Like To Trade:

Hopefully the introduction to this article provided a good framing for the bad side of trading the mean reversion mini-portfolio.

It’s important to share the good and the bad.

Because, as I learned, you need to size for the bad if you want to experience the good over the long term. So, let me walk you through what live trading this mean reversion mini-portfolio actually feels like.

The equity curve on a weekly or monthly chart looks smooth. Up and to the right in what seems like a straight line.

You look at it and think:

“I can handle this.”

But the day to day feel is a different story.

I got burned by this exact thing. The high timeframe equity curve looked so smooth that, as I mentioned, I oversized my allocation to the mean reversion mini-portfolio. I was excited to trade it. It was new, stable, and very uncorrelated to the rest of my systems.

I overlooked the shorter timeframe volatility, the day to day spikes. These are what we actually feel as traders. We don’t feel the zoomed out 10 year backtest performance, we feel the day to day and week to week performance, which is a lot more choppy.

Mean reversion inherently means you’re buying into drawdowns. You’re adding to positions when the market is dropping. Negative skew is baked into the system dynamics.

The systems buy when most people are most fearful and when most traders emotional states say:

“Stop! no more!”

But the mean reversion systems say:

“Keep buying, continue the scale-in with signal strength as designed”.

The hardest psychological moments are on Fridays. It feels like every time the markets sell off it’s on a Friday and I end up fully loaded on mean reversion positions, holding that risk through the weekend. These uncomfortable moments are why you get paid for trading this system.

Holding the risk nobody else wants to.

Providing liquidity to people trying to de-risk going into the weekend.

Holding that risk over the long term should pay off. But from time to time you’ll get whacked for it. You’re stuck in a scary situation for two days and you can’t do anything about it. That’s just part of the game.

The experience I discussed at the start of this article was uncomfortable enough that I went back in and lowered my allocation after. The lesson was to zoom in and consider the day to day volatility and consider worst case impacts.

It’s very easy to get into an uncomfortable position when you allocate too much, especially with leveraged ETF products. I got greedy and sized for the upside instead of the downside.

I know I always preach to size for the worst case scenario, but even I still make mistakes. Mistakes are how I learn. I always learn everything the hard way. In fact, I prefer learning the hard way. It makes me better and really solidifies the learning experience so I don’t do it again.

I’ve come to have peace with losing money if it means I got a good learning experience from it. It honestly happens to me all the time. It’s just me paying my market tuition.

What I Would Do Differently:

Size More Conservatively From the Start

I already mentioned this, but it’s worth repeating again because it’s the single biggest lesson I learned.

Size smaller than you think.

The reality is you live day to day. You don’t experience the monthly or weekly equity curve. You experience the daily swings. Shorter term fluctuations and volatility are what you actually feel. Don’t let the smooth high timeframe equity curve trick you into oversizing.

My thought process would be to size for a drawdown that’s double what the backtest shows. If the backtest says 15% max drawdown, allocate as if you’ll see 30%. Because when you’re living through it, it feels twice as bad as the number on the screen.

As an example, say you’re allocating a percentage of your overall portfolio to this mean reversion mini-portfolio. Maybe you don’t want to see more than a 10% drawdown from this mean reversion mini-portfolio when viewing from the overall portfolio context. If the mean reversion mini-portfolio has a 15% max drawdown, that means the max allocation would be 10/15 = 66% allocation.

But let’s be safe and assume the real worst case drawdown of the mean reversion mini-portfolio is 2x what the backtest says, i.e. 30%. That means portfolio level allocation to the mean reversion mini-portfolio would actually be 10/30 = 33% allocation. This is a simple yet effective way to think about allocation to the mean reversion mini-portfolio, or any system really.

Building Confidence and Scaling Up

It’s been about two-thirds of a year since I launched the mean reversion mini-portfolio. At this point I am starting to feel real confidence in the systems.

But that confidence took about nine months of live trading to build.

It didn’t happen after the first week.

It didn’t happen after the first good month.

It happened gradually, as the live equity curve kept matching what the backtest said it should do.

Starting a new system or set of systems is scary. No matter what the out-of-sample or robustness tests show, there is always an unknown. We never know what’s around the corner. So if you’re like me and are adverse to mean reversion systems, start smaller than you think.

Not only size for 2x the worst case drawdown, but add even more padding on top of that, at least until you start to get confidence with it.

Maybe even start with one or two of the systems instead of all four. Then add the others as you gain experience, understand how it works, get a grip on what the day to day feels like, and get the live trading operational side of things down.

As confidence builds, add more systems or scale up size to the 2x max drawdown allocation size. No need to go all in at once or go full size all at once. You can ease into it.

Don’t do what I did and go crazy with sizing just to be scared to death because I’m holding more risk than I ever have before.

Conclusion:

Part 1 introduced the four systems and the entry and exit logic. Part 2 showed how they combine into a portfolio with ranking, correlations, and return analysis. Part 3 proved the robustness of the method and the truths of trading it live. Hopefully this provides a good framework and understanding of what it takes to go from backtesting to live trading and what to expect along the way.

The portfolio effect is the only holy grail. You’ll notice not a single one of these individual systems is amazing. That’s by design. I would rather have four mediocre but robust systems that combine into something strong than one perfect looking system that fails because it was curve fit.

IS/OOS validation across multiple windows (2010 to 2018 in-sample, 2018 to 2025 forward out-of-sample, pre-2010 backward out-of-sample on unleveraged ETFs, 2025 to today live results) confirms the edge is stable in time.

Robustness testing confirms the system is in a stable parameter region and the system can withstand noisy data because the markets will only get more noisy with time. Also, live results since August 2025 track backtest expectations, which confirms the most important aspect about a system, that it works with real money (at least thus far!).

The biggest lesson learned was to size more conservatively than you think. Allocate for 2x the drawdown you expect. Don’t let the smooth equity curve fool you. The drawdowns tend to be short lived but they can be very steep. And start small and build up slowly as confidence grows.

Disclaimer

The information and services provided by the Systematic Trading with TradeQuantiX newsletter are for educational and informational purposes only and do not constitute financial advice, investment recommendations, or guarantees of trading performance. Any information provided is general in nature and does not take into account your individual circumstances. Trading involves significant risks, including the potential for substantial financial losses, and past performance is not indicative of future results. The information is provided ‘as is’ without warranty of accuracy or completeness. All decisions and actions based on the information provided are the sole responsibility of the reader. You should seek independent professional advice before trading. The Systematic Trading with TradeQuantiX newsletter is not liable for any losses, damages, or outcomes resulting from the use of this information or our services.

Robustness Test Excel Sheets:

These excel sheets are also in the GitHub, along with the noisy datasets, and the systems used in the robustness tests.

ETF Mean Reversion Methods: Mean Reversion Mini-Portfolio Creation - Part 2

TradeQuantiX — Tue, 21 Apr 2026 13:05:06 GMT

Introduction:

In Part 1, we covered four independent mean reversion systems, each measuring oversold conditions in a completely different way.

Z_Reversion: uses z scores.

Vol_Reversion: uses volatility bands.

Vol_Reversion_2: enters and exits at different times and with different methods than Vol_Reversion.

RSI_Reversion: utilizes pre-calculated RSI entry levels and enters with a different timing mechanism than the other three systems.

Each system scales into positions across three sub-entries, and all four systems trade a multi-asset ETF universe spanning equities, bonds, gold, and bitcoin. This takes an “All Weather Portfolio” like approach to the trading universe.

If you haven’t read Part 1 yet, see the link below.

So, what happens when you stop evaluating systems in isolation and start combining them into mini-portfolios?

As I hinted at in the part 1 article, we will be combining these four systems into a mean reversion mini-portfolio.

Why use the term “mini-portfolio”? (warning, I use the term “mini-portfolio” a lot in the next few paragraphs)

Well, because I like to think of my own systematic trading portfolio as a portfolio of many smaller portfolios.

Trend mini-portfolio
Momentum mini-portfolio
Mean reversion mini-portfolio
Hedging mini-portfolio
etc.

The portfolio effect is super powerful when it comes to creating superior risk adjusted returns. One strategy may have mediocre performance on its own, but combined into a portfolio of many other strategies, that portfolio of mediocre strategies can look fantastic.

I like to look at mini-portfolios of similar methodologies first. I’ll get all the allocations and volatilities properly adjusted relative to each other. Then I’ll combine the mini-portfolio into the overall systematic trading portfolio. This just helps me build up my portfolio one block at a time. This is why I use the term “mini-portfolio”.

Take any one of these four systems and run it standalone. The stats look okay, nothing special. That’s fine. Because the real story isn’t the individual systems. The real story is what happens when you put them together to make the mean reversion mini-portfolio.

This part 2 article covers the mini-portfolio construction of these four mean reversion systems. I’ll show how four similar systems combined together beat one. Even though these are all mean reversion systems on a similar universe, because they all enter and exit positions using slightly different measurements and and methods, you can still get some powerful diversification benefit.

In this article we will cover:

The mechanics of how the combined portfolio works.
System correlations.
A system ranking mechanism that adapts allocations over time.

A Note For Paid Members:

If you are a paid member and have not joined our Discord community, you can do so with the link at the end of the article.

Also, we launched a GitHub to cleanly store all our strategies and tools. When you join the Discord I’ll give you access to the GitHub (or if you don’t use Discord, just message me and I’ll give you GitHub access).

Part 1 Quick Recap - Sub-Entry Scaling Design:

In Part 1, I mentioned that each of the four mean reversion systems use three sub-entries to scale into positions. A couple readers asked why three sub-entries specifically, and why equal sizing across all three entries. So, let me address that here before we get into the mini-portfolio creation.

Why 3 sub-entries?

Why 3 sub-entries, not 2 or 5 or 10? Well, two sub-entries is really not that many more than just one entry, which is what every other system uses. Sure, two is still better than one and you’ll see lot’s of improvement going from 1 to 2 sub-entries to scale into your systems positions.

But, three is like that 80/20 principle where you start to get a genuinely more continuous measurement of oversold conditions without that much extra work.

You could go to five, but you need enough capital allocation to trade five sub-entries with meaningful size and you’ll probably need automation because placing 5 trades to scale into a single position would be very tedious manually.

As the account grows, I will increase sub-entries from 3 to 4 to 5 or more, and keep adding more mean reversion systems to this mini-portfolio. Three sub-entries is the sweet spot for my current capital base. It’s enough granularity to capture the average oversold price, but not so many that each sub-entry is “too small to matter” and I get eaten up by commission costs.

Why equal 1/3 allocation per sub-entry?

Why equal 1/3 allocation per sub-entry? Why not some biased allocation where you scale in more in the first entry and less in later entries, and vice versa?

Think of each sub-entry as a fractional allocation of the total signal.

Sub-entry 1 is a one-third signal strength.
Sub-entry 2 brings you to two-thirds signal strength.
Sub-entry 3 is the full signal strength and the full position size.

Equal weighting is deliberate. It may make intuitive sense to weigh later entries more heavily (buy more at better prices), but you have to be very careful with this. Optimizing for different sizing allocations on different sub-entries is a quick path to curve fitting.

Equal allocation for each sub-entry is the simplest, most robust approach. It scales linearly with the strength of the signal without introducing another optimizable parameter. It’s simple and it works well.

I am not against using non-equal weight scale-ins (to be honest, I’ve done it before), you just have to be super careful how you do it and not use that lever to curve fit the backtest to look better.

Remember, we care about future returns, not backtested returns. Keep in mind which approach is more robust and make sure that a non-equal weight scaling approach proves with absolute certainty to be just as robust as a stupid simple equal weight scaling in approach.

The Portfolio Effect: Why Run All Four Systems:

This is where a lot of systematic traders get tripped up. They look at the individual backtest results and think:

“System X has the best performance, why would I waste capital on the other three? I’ll just trade the best one.”

This is system level thinking. We need portfolio level thinking.

If you are only trading the system with the best backtested results, there is a high likelihood that something about those results is curve fit. It may not be wildly curve fit, but every system is curve fit to some degree.

If you only trade the best backtested system, sometimes it’s hard to know if it’s the best because it is actually better, or if it’s actually just the most curve fit.

The best way to get consistent, reliable live trading performance is to spread your risk across multiple different ideas, entries, and measurements. That means you are also inherently trading systems that do not backtest as nice as your best one. And that is okay.

Not only does this spread risk across the time axis (space entries over time) and price axis (space entries across different prices), but also the third hidden axis; which in this case is diversification. Diversification can be thought of as diversification of assets, but also diversification of ideas and implementation.

Spreading your trading across multiple ideas within multiple systems reduces the impact to the overall portfolio if one system underperforms. This basically is mitigating the impact of system failure risk.

Careful portfolio construction account for all these axis. Who cares what each individual system does. What matters is how they all fit together and how they spread risk across time, price, assets, ideas, and systems. The mean reversion mini-portfolio is just applying that same philosophy at a smaller scale.

The smooth, attractive equity curve you’ll see later in this article is a direct function of the portfolio effect in action. To get the portfolio effect with these four mean reversion systems, we will:

Combine multiple semi-uncorrelated strategies together.
Where each one looks at oversold conditions in different ways.
Where each one scales in over time and price in different ways.
Using a diversified basket of ETFs.
And scaling in to get the average oversold price.

When you combine a bunch of intentionally different strategies together, you can end up with an amazing looking portfolio.

The portfolio effect is the only holy grail in systematic trading.

This article is just applying the portfolio effect at a smaller scale with mean reversion specific strategies, creating a mini-portfolio that then plugs into your overall portfolio at a scaled down allocation so the broader portfolio benefits too.

An example of the Portfolio Effect in action:

Now let me give you a real example of the portfolio effect and it’s benefits in action. The Z_reversion mean reversion system has struggled on bonds (TMF) recently. The portion of this system that is allocated to bonds is in a significant drawdown.

Bonds have performed terribly with this system while the equity, gold, and bitcoin ETFs have done much better. But bonds stay in the basket of tradable ETFs and within the mean reversion mini-portfolio because it is an uncorrelated asset to everything else.

One day, bonds may save this mean reversion mini-portfolio when everything else is struggling. And when you look at the combined four-system portfolio as a whole (next section in this article), the bond drag barely even shows through to overall results. Today’s laggard may be tomorrow’s star. I intentionally selected this basket of ETFs to be diversified across assets.

The whole point of being diversified across assets is such that when one performs poorly, the others make up for it. That’s the “All Weather Portfolio” approach.

Removing bonds just because it has performed poorly recently is nothing more than pure curve fitting; and it also defeats the purpose for why these assets were selected in the first place.

The image below shows the equity curve of trading bonds with the first sub-entry of the Z_reversion mean reversion system.

Looks terrible right? I agree.

It worked for awhile, but has recently started to not work too well. But, we have three sub-entries, not just one. How are the others performing?

See below for the equity curves of all three sub-entries of this Z_reversion mean reversion system on bonds.

Sub-entry two (purple) is down recently too, but not nearly as bad. Sub-entry three (red) is actually still up and to the right and is not in a drawdown. While sub-entry one looks terrible, sub-entry two and three look significantly better.

So, simply using the three sub-entry scaling mechanism for entry time and price diversification is already very helpful. You could think of these sub-entries as the first three components of the mean reversion mini-portfolio.

The next image shows the three sub-entries equity curves one bonds in context with the three sub-entries on all other instruments this Z_reversion system trades.

You can see sub-entry one on bonds on the very bottom of the plot (blue), underperforming everything else. But there are many other assets and sub-entries way outperforming the bonds first sub-entry equity curve.

Once we combine all of these individual equity curves of different assets all being scaled into across three sub-entries into a “mini-mini-portfolio”, we end up with the Z_reversion system itself.

You can think of the Z_reversion system as like a mini-mini-portfolio of different assets with different entry times and prices all within a mini-portfolio of other mean reversion systems; which will eventually get plugged into the overall portfolio. That’s portfolio inception if you ask me.

The image below shows the Z_reversion mean reversion system equity curve once you add all the other assets the Z_reversion system trades and their three sub-entries into one final system equity curve.

At this level it looks pretty good. In recent times, the system is in a ~7% drawdown, but that drawdown just started and overall the system looks unphased by the poor bonds trades.

This is the portfolio effect starting to creep in.

The system is diversified doing different things at different times. Also, each individual position size is small so if one or two underlying sub-entries on a singular asset look poor, it hardly matters as there are many other assets and sub-entries to pick up the slack.

The image below shows the drawdown equity curve of the combined Z_reversion system as a whole (dark blue) compared to the bond first sub-entry (light blue) drawdown.

While the first sub-entry on bonds drawdown (light blue) respective to itself is in an ugly drawdown, the combined system drawdown plot (dark blue) is completely unphased. The combined system drawdown plot is just doing its own thing and seems to not care at all that the first sub-entry bond system is hemorrhaging.

This is the portfolio effect in action at the mean reversion system level. Each system is like a mean reversion mini-mini-portfolio of different assets and different sub entries.

It then will become the mean reversion mini-portfolio in the next article section where all mean reversion systems get combined together.

You’ll see after all mean reversion systems get combined, the performance of one sub-entry on one asset within one system really doesn’t matter at all. There is so much diversification across time, price, assets, and systems that the underperformance of the first sub-entry on bonds is, frankly, irrelevant.

The Combined Mini-Portfolio:

I just spent a bunch of time showing you the portfolio effect on a tiny system-specific scale. Now let’s look at what happens when all four systems are actually pulled together into one mean reversion mini-portfolio (code to do this is at the end of the article and on the GitHub).

This equity curve shows the results of the four mean reversion systems combined together trading the leveraged ETF versions of its trading universe.

Like I mentioned, at this level one specific underperforming asset within one system on one or two sub-entries doesn’t even matter. The combined mean reversion mini-portfolio is so diversified across:

Price
Time
Assets
Systems
Oversold measurements
System failure risk
etc.

that the entire mean reversion mini-portfolio as a whole is super robust to some underperformance on specific assets / sub-entries within itself.

To see what this mean reversion portfolio looks like on the non-leveraged ETFs, check out the equity curve and drawdown plots below. Non-leveraged ETFs allows us to test further back in time since leveraged ETFs became available around 2011, but the un-leveraged versions have been available since the early 2000’s.

Still looks great to me. 2000 had a somewhat bad drawdown, but that’s just the game we play with the negative skew of mean reversion. The system recovered and continued to perform beautifully after that. Oh, and I’ll talk about this more in the part 3 article, but pre-2011 and post 2018 is all out-of-sample.

If I show all the individual equity curves that comprise the non-leveraged mean reversion mini-portfolio, it looks like what’s shown below.

It’s worth noting the pink and green lines way above everything else are the bitcoin ETF results. The same ETF is used for the un-leveraged and leveraged backtest because the market is already super volatile and a leveraged ETF doesn’t exist with solid history.

If I remove the bitcoin results from this equity curve plot so you can better see the rest of the equity curves across all the systems and sub-entries, it looks like the following.

Most equity curves look up and to the right. Exactly what we want to see. Some curves are choppy and have many ugly spots, but when you combine it all together it all averages out to something pretty smooth.

Monthly and Yearly Return Heatmap

Let’s look at the combined mini-portfolio’s monthly returns laid out in a grid. This is one of my favorite ways to understand the performance of a portfolio because you can immediately see the consistency (or lack of it) across time.

Leveraged ETF monthly/yearly returns:

Unleveraged ETF monthly/yearly returns:

Not a single losing year in the in-sample or out-of-sample time period (in-sample is from 2011-2018, everything else is out-of-sample). This system does not have just a few massive months carrying the whole portfolio. Instead it is a slow but steady drip of positive months across different market environments.

I like how this mean reversion mini-portfolio is very consistent, which is different than all of my other trend/momentum/hedging systems. All my other systems have long drawdowns and flat performance periods.

This mean reversion mini-portfolio is the opposite. It has very short periods of flat performance and quick drawdown recoveries. It slots into my portfolio well and provides me with great portfolio level performance benefits.

Correlation Matrix

So I just spent 1.5 articles telling you that these four systems are genuinely independent and that combining them gives you diversification. Let’s look at the correlation table and see if that is actually true.

Below is the correlation matrix of daily returns and drawdowns between the four systems. I could not get this entire correlation matrix into an image that was actually readable so you’ll have to rely on the colors. I’ll upload this excel sheet into the GitHub for you to check out if you’re interested.

I did however add a summary correlation table showing the average correlation of each system relative to all the other systems, this table is more digestible and I can fit within an image in the article.

Combined Mean Reversion Mini-Portfolio Returns Correlations:

Combined Mean Reversion Mini-Portfolio Drawdown Correlations:

The average drawdown correlations are all ~0.34 and lower, that’s awesome from a diversification standpoint. This is why the combined equity curve of the mini-portfolio is so smooth.

The returns average correlations have a wider range, between 0.06 and 0.79. Still relatively uncorrelated on average.

This is the quantitative proof behind “why run all four systems and not just the best one?”. It is not just a feel-good argument about diversification. These numbers show that there is massive benefit to running as a mini-portfolio and take advantage of all the different measurements of oversold into account on different assets. This makes it so return streams that don’t move in lockstep.

Excluding Systems - The Ranking Mechanism:

There is an interesting dynamic layer within the mean reversion mini-portfolio code that I have not discussed yet. The portfolio has the ability to rank all individual equity curves (an equity curve being one sub-entry on one asset within one system) using a risk-adjusted return factor.

The ranking formula looks like this:

StratFactor = ROC(S.Equity, 252) / (StdDev(ROC(S.NetPct, 1), 252) * SQR(252))

This is the 1-year return divided by the annualized daily volatility of returns. Essentially ranking based on a rolling 1-year Sharpe ratio per equity curve.

This ranking method preferences equity curves which are increasing at the steepest slope with the least amount of volatility. This makes particular sense for mean reversion because when you are catching a lot of bad trades, the negative skew plays out and the equity curve gets very volatile as you catch falling knives.

Referencing equity curves with low volatility and high returns is essentially filtering out the ones currently exhibiting that negative skew. The ones with the best returns and least volatility are the ones cleanly catching bounces, rather than bleeding from extended drawdowns.

You could use other ranking mechanisms. This one is not uniquely correct, but it makes sense for the given context.

The rankings of the systems are updated quarterly. Why quarterly, not monthly or shorter? Mean reversion systems do not take that many trades. You need a long enough window to accumulate meaningful data before deciding which systems are outperforming and which are underperforming.

A shorter timeframe (monthly or less) means you are possibly ranking on noise. Systems could only have a handful of trades and the ranking excludes it based on a small sample size and you get whipsawed. One year simply felt too long because a lot changes in twelve months. One quarter was the comfortable middle ground. Enough trades to form accurately measure recent system performance, but short enough to still be responsive.

Did I spend a ton of time optimizing and testing this assumption? To be honest, not really. This was more based on gut feel and intuition of how I want it to work. The quarterly cadence was not tested against monthly or semi-annual alternatives. It was picked as a reasonable way to do things that makes sense, and then left alone.

The 252-day Sharpe and quarterly rebalance were chosen as sensible defaults, not optimized parameters. Adding optimization on top of already-optimized systems creates layered curve fitting risk. A 1-year Sharpe ratio is a common-sense metric with a common-sense lookback.

That is deliberate: the ranking overlay is not another optimizable surface. The more optimizable surfaces you add, the more surfaces you have to overfit. With that said, feel free to spend some time here playing around.

Just be super cautious with curve fitting. You do not want to layer curve fitting on top of this portfolio by being super specific with the system ranking mechanism. This is the exact reason why I intentionally picked something that intuitively made sense and just kept it without a ton of optimization and fitting. I have four robust systems, I don’t want to break them with a curve fit ranking layer.

How the system ranking works mechanically

This is a little confusing, but each strategy has a parallel Tracker (hidden) and benchmark system that are used to compute the strategy rank. The SetupSkip rule checks if this strategy’s rank is worse than the lowest allowable rank.

The ExcludeWorstX parameter (default 0) lets you drop the N worst-ranked strategies from taking new trades. Setting ExcludeWorstX to 10 would prevent the 10 worst-performing strategies from taking new trades.

TotalNumSystems: 72	// Total strategy count across all 4 systems (includes benchmarks and tracking systems for ExcludeWorstX usage) 
ExcludeWorstX:	10      //Dont trade the worst 10 systems
MaxStratRank:	TotalNumSystems - ExcludeWorstX

Template: Track
	Tracker:	Hidden 
	// If this is a tracker, never skip. Otherwise, check if the tracker version
	// of this strategy is ranked below the cutoff.
	SetupSkip:	if(IsTracker, 0, Extern(@tracker, StratRank > MaxStratRank))

If the strategy rank is below the threshold, it skips that systems entries.

This is what makes the portfolio dynamic, not just four systems bolted together. It provides an adaptive layer that reduces allocation to strategies that have been underperforming recently, without you having to manually decide which system or asset to drop.

Again, this is completely optional. To be honest I don’t use it at the moment, but I have it commented out into my mini-portfolio code just in case I do want to enable it someday.

Sensitivity analysis: how performance changes as you exclude more equity curves

I ran the system ranking on the mean reversion mini-portfolio and slowly excluded more and more strategies based on the Sharpe based ranking method and the quarterly ranking timing.

The table below shows the results for excluding 0 equity curves up to excluding the 50 worst performing equity curves (and when I say equity curve, that’s one sub-entry, on one ETF, within one of the four mean reversion systems):

The images below show the mini- portfolio results when you exclude some of the worst equity curves within the mini-portfolio. I show four examples so you can get a feel for how robust the ranking and selection logic is for excluding poor performing equity curves.

Exclude worst 50 equity curves:

Exclude worst 35 equity curves:

Exclude worst 15 equity curves:

Exclude worst 0 equity curves (none are excluded):

To see the equity curve rankings over time, simply update the tracking code from “Hidden” to “Visible”.

Then go to the “sdRank” tab in plots.

See the image below for all equity curve rankings over time.

See the image below for a down selected view of all equity curve rankings over time.

The default ExcludeWorstX of 0 means all equity curve within the mean reversion mini-portfolio trade. Excluding the worst-ranked equity curve generally makes performance slightly worse (you use less exposure and potentially miss out on some recovery trades from temporarily underperforming equity curves that got turned off before recovering), but it helps control total mean reversion mini-portfolio exposure.

This is a lever more for managing how much capital the mean reversion portfolio deploys at any given time; rather than a performance enhancement tool. It’s a way to make the mean reversion mini-portfolio more capital efficient by skipping the underperforming equity curve.

When would you increase ExcludeWorstX in practice?

You hate the idea of running systems that are underperforming and want a systematic way to turn them on and off dynamically.
It may provide a phycological safety net to know that the weakest-ranked systems are not trading. Doing this systematically and automatically is a much better approach than randomly turning off systems or reducing allocation across the board with a discressionary decision in the heat of the moment.
When you want to be slightly smarter about capital allocation and not waste exposure on systems that are underperforming right now.
Maybe you’re already running hot on portfolio margin and you want to be smarter about how you allocate to this mean reversion mini-portfolio

Don’t go crazy with it. Don’t optimize the lookbacks, Don’t optimize the ranking method. Pick a sensible value or leave it at 0.

The moment you start optimizing backtesting different lookbacks and ranking methods and trying to pick the best one, you’ve added another layer of curve fitting on top of everything else. Everything else is already robust by design, don’t ruin it with a curve fit top layer. That defeats the entire purpose.

Conclusion

Four systems beat one. Not because any individual system is exceptional, but because when you combine four genuinely uncorrelated approaches to measuring “oversold,” the portfolio effect takes over.

The correlation matrix shows that even though all these systems are buying dips on similar assets, there is still major diversification benefit to trading all four systems. The systems all fire at different times, using different signals, at different prices, and exit with different mechanisms. This produces a return stream that is smoother than any individual system could create on it’s own.

The quarterly ranking mechanism is what turns the four standalone systems into an adaptive portfolio. It’s not required, but it is a nice way to exclude the worst equity curves if trading “losing” sub-components of systems bothers you (like the first two sub-entries on bonds in the Z_reversion system).

Be very careful not to use the ranking overlay as another optimization surface. Use sensible defaults, check similar methods/parameters also produce similar results, and avoid anything super specific to prevent multi-layered curve fitting.

ExcludeWorstX really is more of an exposure management lever rather than a performance optimizer. Simply use it not to waste capital on underperforming systems (if you want) to make the mean reversion mini-portfolio even more capital efficient.

The whole point of this mini-portfolio is that no single strategy, sub-entry, or asset class carries it. The smooth returns come from the combination of all these things, and the optional adaptability comes from a ranking layer.

The full code for the combined mean reversion mini-portfolio script is in the appendix below.

In Part 3, I’ll get into:

In-sample vs out-of-sample validation.
Robustness testing.
What it has actually been like to trade this live since August 2025.

So, see you in Part 3 and in the mean time, have fun with these systems and please ask any questions you have.

Disclaimer

The information and services provided by the Systematic Trading with TradeQuantiX newsletter are for educational and informational purposes only and do not constitute financial advice, investment recommendations, or guarantees of trading performance. Any information provided is general in nature and does not take into account your individual circumstances. Trading involves significant risks, including the potential for substantial financial losses, and past performance is not indicative of future results. The information is provided ‘as is’ without warranty of accuracy or completeness. All decisions and actions based on the information provided are the sole responsibility of the reader. You should seek independent professional advice before trading. The Systematic Trading with TradeQuantiX newsletter is not liable for any losses, damages, or outcomes resulting from the use of this information or our services.

Discord and GitHub Access:

Q1 2026: TQX Portfolio Performance Review

TradeQuantiX — Mon, 13 Apr 2026 13:42:51 GMT

Introduction:

Q1 2026 is in the books, let’s discuss what happened with the portfolio.

I’ve done performance reviews before, but sporadically. This time I’m committing to making it a proper quarterly cadence. I figure it may be interesting to check in every quarter so you can see the portfolio performance in real time.

I made a live public portfolio tracking dashboard to help me perform these quarterly reviews as well; I’ll get into that in a minute.

Here’s what we’ll go through in this article:

The live portfolio dashboard I built (and why I made it public)
Q1 2026 live portfolio performance
System highlights: what worked this quarter
What didn’t work: drawdowns and underperformers
Why 25+ systems gives me long term confidence
What’s next

Let’s get into it!

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The TQXPortfolioTracker Dashboard:

Before I get into the numbers, a quick note on where the data in this article is coming from.

I built and launched a live portfolio tracking dashboard called TQXPortfolioTracker. You can find it at: tqxportfoliotracker.xyz.

It automatically pulls my raw portfolio performance data, current positions, closed positions, fees etc. from my broker, calculates many key metrics, creates numerous plots and tables, and then publishes to the website such that everything is public.

Sharpe ratio, volatility plots, P&L distributions, drawdown distributions and much more. The cool thing is this is all auto-calculated and pushed to the site, so there are no manual updates from me.

Now I can check in on my own portfolio whenever I want and see exactly what’s going on under the hood; which is genuinely useful for me.

Previously, I had to do performance reconciliations manually. Which meant I was only doing the work to validate and understand my performance every few months (at best). And even with that reconciliation work, I was not even 5% as detailed as what this dashboard shows.

Now I can be at the grocery store or be walking my dog and with a quick google search, know exactly how every aspect of my portfolio is performing in this exact moment. I think that’s pretty cool.

Keep in mind, I can only backfill 1 year of data back in time, so that’s all the dashboard shows. But the data will be stored and will increase over time. So while I’ve been systematically trading in this account for more than a year, I can only show 1 year back in time, but that will increase going forward as I collect more portfolio data.

I made the portfolio dashboard public because I’ve really started to lean into the Build In Public model. I want to share everything. All my wins and losses, for anyone to see.

Sharing this portfolio tracking dashboard will help keep me accountable and honest. I can’t selectively show only my best quarters and quietly skip the rest when the data is live and always current. Everything is right there. And honestly, knowing the results are out in the open makes me build faster.

I can’t make excuses to myself. It pushes me to keep improving rather than letting things slide. I’m depending on you all to keep me accountable.

So, if you see some pattern with my portfolio results in the portfolio tracking dashboard and have ideas on what I could improve with my portfolio, please let me know.

This dashboard also gives other systematic traders a live benchmark to compare their own portfolio against. When you’re in a drawdown, one of the hardest things is not knowing whether what you’re experiencing is normal.

Seeing that someone doing similar things trading-wise is also in a similar drawdown tells you that your system is maybe not broken; instead the market is just not cooperating. So, hopefully you can use this dashboard as a tool to inform your own systematic trading as well!

The screenshots throughout this article were pull directly from the TQXPortfolioTracker dashboard. If you want to follow along with this article or check it out in-between quarterly performance articles, it’s live and always up to date at: tqxportfoliotracker.xyz.

Also, I’m always looking to make the dashboard more useful. If there’s a metric, chart, or feature you’d like to see added to TQXPortfolioTracker, drop it in the comments.

I built this thing not only for myself but for the community to learn from as well; so tell me what would make it more valuable for you.

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Q1 2026 Portfolio Performance:

I’ll lead with the number people care about most. My systematic trading portfolio returned -0.22% in Q1 2026:

The equity curve tells the full story. The portfolio has been running up since April 2025. Through 2025 it climbed steadily, peaking around +74% before pulling back to close the 2025 year at roughly +57%.

Then Q1 2026 started with two decent months: January came in at +5.36% and February added another +5.41%. The portfolio was at all-time highs.

Then March happened.

March alone gave back -10.15%, which is what produced the sharp drop in the equity curve and the overall Q1 loss.

The portfolio had two solid months, then one ugly month that erased the gains of the two previous months. That’s just how trading is sometimes.

The monthly returns heatmap shows this well. Look at 2025: nine months of data, with only April (-1.64%) and July (-2.61%) in the red. The standout months in 2025 were May (+11.43%), June (+13.99%), and September (+11.27%).

That is what a strong trend and momentum year looks like at the monthly level. Consistent positive months with some isolated noise and small pullbacks.

2026 so far: January green (+5.36%), February green (+5.41%), March red (-10.15%), but April is already recovering at +2.42% (partial month). The heatmap makes it obvious how quickly one bad month defines a quarter.

Two solid months and the portfolio is up over 10%, then one bad month, and you end Q1 in the red. That systematic trading for ya. It happens, you just have to accept it and keep trading your systems like nothing ever happened.

This is why trading sucks. But we do the things that suck and eventually, in the long term, we get paid for it. That’s just how the game works. You need to have the long term mindset and not get stuck in the day to day volatility.

This drawdown chart gives the full picture of every drawdown since early 2025. You can see the portfolio has experienced several pullbacks along the way. Early 2025 had a drawdown near -10%. There were a couple of -6% to -8% dips through mid-2025. The October/November 2025 period saw another -10% drawdown.

But the current drawdown that started in late February 2026 is the deepest one so far, reaching approximately -15% before starting to recover recently.

Here’s an interesting comparison though. 2025 followed a very similar pattern. The year started strong, then I hit a drawdown similar to this current one, and then the rest of 2025 ended up being my best year ever at +61%. Q1 2026 is tracking a very similar shape so far.

Does that mean the rest of 2026 will also rip higher? No idea. But it’s interesting to see the pattern strta to repeat.

With that said, 61% in one year is way above my historical long term expectations and what my backtesting suggests. Some level of pullback or consolidation is completely natural after a year like that. I’d be more concerned if there were zero drawdowns after a +61% year. That would be suspicious.

Portfolio volatility is running at 26.2% on the 100-day rolling window. But the chart tells a more nuanced story. The 10-day rolling volatility (orange dotted line) is much more responsive and has been all over the place, spiking near 40% during the March selloff. Meanwhile, the 50-day rolling window (purple) sits around 30%.

Volatility has been increasing, but high volatility is the risk you take for outsized returns, because risk and return are generally correlated. You don’t get +61% years without high volatility periods like this from time to time.

Why volatility seems to be trending up so consistently? I’m not quite sure yet, but now that I have this tracking dashboard I can monitor it and ensure it starts to stabilize.

The rolling Sharpe ratio chart shows the rolling evolution of portfolio risk-adjusted return. The full-period annualized Sharpe sits at 2.07. But the rolling windows tell a different story right now.

The 50-day rolling Sharpe (blue) has dropped below zero, and the 100-day (orange) is sitting around 1.5. In 2025, the 50-day Sharpe peaked at over 7.0 multiple times during the 2025 equity run up.

But, it’s been normalizing toward the whole period average as more data fills in. A Sharpe above 2.0 over a full year of live trading is still an excellent number in my opinion. The question is where it stabilizes as time goes on and more data gets filled in.

The exposure chart tells an important story about position sizing and risk management. My gross exposure (orange dotted: abs(short) + long exposure) has generally sat between 100% and 130% of portfolio value through most of 2025.

The big spike in November 2025 pushed gross exposure to nearly 175% as multiple systems loaded up on positions. I talked about this time period in my 2025 performance review, but this was when I had over a 10% drawdown that happened very fast but very quickly recovered.

My mean reversion systems kicked in at this time, the markets sold off more when I was building into my mean reversion positions. I was heading into a drawdown holding this increased exposure due to mean reversion buying. But then the markets popped, and so did my portfolio. So, the mean reversion systems caused and fixed the drawdown. Funny how that works.

Short exposure (red, near the bottom) has been minimal, staying below 5%. That’s because I don’t have enough short systems. That is still something I need to fix.

But look at the right side of the chart. As the 2026 March selloff hit, my exposure dropped sharply to around 60-70%. The systems started shedding positions as exits were triggered. This is the lowest exposure I’ve had in awhile. Currently the portfolio is more conservatively positioned that it has been recently.

The reduction in portfolio exposure is explained by this open positions plot. You’ll notice on the far right the portfolio recently sold almost half it’s positions to cut it’s risk. No manual intervention. The systems sized down on their own as risk-on conditions deteriorated.

At its peak, the portfolio was carrying around 150 to 155 open positions simultaneously. That’s 25 systems, each running their own universe, generating a combined book of over 150 positions across three countries and multiple strategy types.

The underwater equity plot shows something that puts Q1 in perspective. This chart tracks how many consecutive days the portfolio has spent below its prior equity peak.

You can see several drawdown periods throughout 2025. There was a roughly 20-day underwater stretch early on, and a longer 40-day one in October/November 2025.

But the current underwater period that started in February 2026 is already approaching 50 days and counting. My backtesting suggests 1 year or more is within expectations for drawdown length, so I am not concerned.

The overall win rate across all closed trades since inception sits at 58%. That means more than half of all closed trades are profitable. The 20-trade rolling window (blue) bounces around wildly but stably around the long term average.

Sometimes 20-trade win rate spikes to 80-90%, sometimes it dips to 25-30%. That’s just noise. The 50-trade rolling window (orange) is smoother and generally stays between 45% and 75%.

Even during the Q1 2026 drawdown, the 20-trade and 50-trade win rate has been hovering around 55-60%. The systems are still finding winners. They’re just not quite enough to offset the losers in a market that’s chopping against trend and momentum setups.

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System Highlights: What Worked in Q1:

In a quarter where almost everything else lost money all due to one bad month, the hedging strategies were the standout. They were choppy but positive in Q1 and did exactly what they were designed to do.

These are strategies that go long volatility ETFs. When markets chop and sell off, these earn their keep. This is exactly the kind of quarter where you’re glad you have them in the portfolio.

Lots of people on X were giving me grief about running hedging strategies earlier this year.

“Don’t bother, waste of time, they just bleed PnL.”

Sure, they look pointless when markets are cooperating and everything is going up. But in a quarter like this, they proved their worth.

You don’t buy insurance after the house burns down. You have to be proactive about it and pay the premiums when times are good so you can collect the payouts when things go sideways.

In the long term, hedging strategies look like a drag on performance when things are going well. They earn their keep when things get difficult. That’s the whole point.

Post-Q1 update: the mean reversion portfolio caught the bounce.

If you’re looking at the live equity curve right now and seeing a sharp spike up after the Q1 low, that’s the ETF mean reversion systems firing:

As markets sold off hard into the end of Q1, the mean reversion portfolio built up positions in beaten-down ETFs. When the market snapped back in early April, those positions caught the bounce.

In the overall portfolio, one day alone contributed around a +5% return. That’s what a properly constructed mean reversion portfolio is supposed to do, they buy when things are oversold, and you (generally) get paid once things revert.

The trend systems were struggling because the market was chopping and reversing. But mean reversion systems thrive in exactly that environment. They are designed to profit from the snapback.

Having both approaches in the portfolio means the mean reversion systems start picking up the slack when the trend systems are struggling. That is system level diversification actually working in real time.

For a detailed breakdown of how these mean reversion systems are built and why they work, my most recent article covers exactly that:

Systematic Trading with TradeQuantiX

ETF Mean Reversion Methods: Four Systems Built for the Dip - Part 1

3 months ago · 11 likes · 4 comments · TradeQuantiX

What Didn’t Work: Drawdowns and Underperformers:

Most things didn’t work in Q1… all due to March. Trend and momentum were working well in January and February, but March turned things around… in the wrong direction.

My trend and momentum systems are currently in drawdown. But that’s okay. It’s a short term period of mild discomfort. These longer term systems are expected to have periods of underperformance. It’s the long term view on the timescale of years that matters most.

While trend and momentum is currently in a drawdown, zooming out to the longer term result of all trend and momentum systems; you’ll notice everything is within expectations:

Short term pullbacks like the current one are completely normal.

Another category of systems that underperformed are my new seasonality systems I’ve been building. These got hit pretty hard, but that’s mostly due to me being over sized on one specific system/trade. I have since cut back the allocation to that system as I learned my lesson.

You’ll notice in the seasonality portfolio, 90% of the losses are from one system. This plot is showing the combined seasonality result in blue and each individual seasonality strategy in the other colors above the combined blue line.

These systems are brand new, so it will take some time for their performance to start to shine through.

Seasonality strategies work by zeroing in on specific time periods where markets tend to go up or down more than their average drift. The seasonal “juice” is the alpha that comes on top of the market’s normal upward movement (or downward movement, just depends on what market you’re looking at).

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Why 25+ Systems Gives Me Long Term Confidence:

The underperformance over the last month or so doesn’t concern me. The reason is I have 25+ systems looking across many different markets and many different setups. This means eventually a few of them will start working again.

Right now, while some systems are working, the overall portfolio is a little out of sync with the markets. But, in the long term, there are no concerns because of how diversified the portfolio is across systems, markets, and timeframes.

Most of my portfolio is trend and momentum on equities, so the bulk of returns come from that avenue. Plus some hedging and some mean reversion. When trend and momentum are out of favor, the portfolio suffers. But these things are cyclical.

It’s just part of the game. Sit on your hands and let the trends and momentum come back. The edge is there. You just have to let the market cycle back to your systems.

Diversification gives you the patience to wait. Diversification gives you the confidence to sit through the rough times because you know the portfolio is built to recover.

What’s Next:

When I’m in a drawdown, it always makes me start thinking about where to expand and what to improve. This drawdown is no exception. Here’s what’s on my radar for the coming months.

I want to get more simple seasonal effect strategies into the portfolio. These approached help me diversify beyond pure trend and momentum.

Commodity based strategies are also high on the list. Completely uncorrelated to equities, which is exactly what I need. I probably won’t venture into futures yet, but commodity ETFs are on my mind. The portfolio would benefit massively from that diversification.

Same deal with crypto based strategies. I am currently exploring the crypto avenue, so maybe more updates to come here as well. This is a very big project, but one I feel could be very beneficial long term. Here’s a snapshot of the crypto trading dashboard I’m currently building (right now I am in the phase of collecting crypto data from many sources):

I also need to rethink the short side. I have a couple short term mean reversion systems on the short side, but every time markets pull back they don’t seem to help that much. I need to go back to the drawing board and figure out a short side approach that actually smooths the equity curve during pullbacks. I am open to ideas here if you have any.

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The plan going forward is to keep building, keep diversifying, and let the systems do their thing.

What’s on the research pipeline for Q2 and beyond:

ETF mean reversion articles
VIX effects research
End of the month effect deep dive
Bitcoin seasonality
General crypto stuff
Commodity trend strategies
Short side systems
And more…

Lots of interesting things cooking. Stay tuned.

Conclusion:

Q1 2026 was essentially a flat quarter. That’s just how it goes sometimes.

But this is exactly the kind of quarter that tests your conviction. If you’ve built a diversified portfolio of robust systems, you can calmly sit through it because you know the edge is still there. The market will cycle back. It always does.

In fact, I am writing this article a couple weeks late in relation to the end of Q1 and the portfolio is already climbing out of drawdown.

Don’t forget to check out tqxportfoliotracker.xyz to see all the portfolio plots and tables!

I think there are over 60 on there now and it will surely grow over time. Let me know what questions, observations, or suggestions you have to make the portfolio tracker dashboard better!

How was your Q1? Drop a comment below. I’d love to hear how your portfolio held up.

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Disclaimer

The information and services provided by the Systematic Trading with TradeQuantiX newsletter are for educational and informational purposes only and do not constitute financial advice, investment recommendations, or guarantees of trading performance. Any information provided is general in nature and does not take into account your individual circumstances. Trading involves significant risks, including the potential for substantial financial losses, and past performance is not indicative of future results. The information is provided ‘as is’ without warranty of accuracy or completeness. All decisions and actions based on the information provided are the sole responsibility of the reader. You should seek independent professional advice before trading. The Systematic Trading with TradeQuantiX newsletter is not liable for any losses, damages, or outcomes resulting from the use of this information or our services.

ETF Mean Reversion Methods: Four Systems Built for the Dip - Part 1

TradeQuantiX — Thu, 02 Apr 2026 11:15:36 GMT

Introduction:

I’ve spent a lot of time in the past trying to build mean reversion systems on the entire US stock universe. I figured if I could find that perfect signal, it should work across everything. People say the US is a very mean reverting market, and maybe that’s the case, but I’ve always struggled to develop a system I’ve determined to be robust.

Every single mean reversion system I built would die on recent data. It didn’t matter what signals I used or how I tuned the parameters. I genuinely started to question whether mean reversion even worked on US anymore (Spoiler: it does. I was just looking at the wrong sub-universe).

The breakthrough came when I shifted to ETFs and large cap stocks. The stability was honestly night and day. And once I had that figured out, I didn’t stop at one system. I built multiple independent systems, each using completely different signals to identify when an ETF is stretched to the downside.

In this article, we will cover four of the mean reversion systems I personally trade within my own portfolio. Every system scales into the position across three separate sub-entries, so you’re never fully committed on the first signal. The idea is simple, get the average oversold price instead of trying to guess the exact bottom.

This is Part 1 of a three part series.

Part 1 covers:

The four individual systems
Why ETFs work better for mean reversion (at least for me)
The design philosophy behind the systems
And a detailed walkthrough of each system

Part 2 will cover:

How the four systems combine into a mean reversion mini-portfolio
Discuss a portfolio level system ranking mechanism

Part 3 will cover:

In sample and out of sample validation
Robustness testing
What it actually feels like to trade this mean reversion mini-portfolio live.

Here’s the combined live performance from my actual live trading account attributed to my mean reversion systems. This equity curve is the combined result of all the ETF mean reversion systems I trade (including the ones shared in this article, along with a few other variants I have).

A little lumpy, a little bumpy, but real results. Being able to take the ups and the downs is just part of the game we play. So far, I’ve been very happy with the performance from these systems which I deployed live in August of 2025.

A Note For Paid Members:

Just before we get into the article, if you are a paid member and have not joined our Discord community, you can do so with the link at the end of the article.

Why ETFs Work Better for Mean Reversion:

ETFs have structural properties that make mean reversion cleaner and more reliable than trying to do the same thing on individual stocks.

The first and most obvious one is there’s no idiosyncratic risk. No earnings surprises, no company specific events, no CEO getting arrested at 2am (the kind of headline that ruins your morning coffee).

When an ETF like SPY drops hard in a day, that drop reflects the entire market or sector selling off. There’s no “oops, they missed earnings by a penny” risk that can destroy the mean reversion trade on instrument you’re holding. Sure equity ETFs are comprised of individual stocks, but that idiosyncratic risk washes out when you go to the index ETF level.

The second reason is a passive flow thesis I have. Large institutional passive flows into major ETFs create a structural bid underneath the price. When the SPY or QQQ dips, the passive bid from retirement accounts, 401(k) contributions, and large funds provide a floor. This is why dip buying in large cap ETFs has been structurally sound for decades.

These equity ETFs are literally created for dip buying and passive flows. The mean reversion mechanism isn’t crowded out because these instruments are designed for mean reversion “buy the dip” type buying.

While large equity ETFs are great for mean reversion properties, ETFs are beneficial in another way too. ETFs can easily give you cross asset diversification. Unlike individual stock mean reversion (which is always equities), ETFs let you easily trade mean reversion across bonds (TLT/TMF), gold (GLD/UGL), crypto (GBTC), and other assets alongside equity indices.

This ease of ETF asset diversification can be leveraged to help build an “All Weather Portfolio” philosophy into the ETF mean reversion systems. This helps to a avoid correlations converging to 1 in a crisis because these asset classes have genuinely different drivers.

This cross asset diversification is extremely important in mean reversion trading because when everything sells off hard and you bought the dip and it keeps dipping, this is exactly when you get hammered from mean reversion. But with some asset class diversification, we can try to mitigate some of that risk because not everything will necessarily correlate to 1 during the next equities selloff.

As a side note, if you’re going to do mean reversion on individual stocks, stick to large and mega caps. These stocks have the institutional backing, rebalancing flows, and retirement accounts trickling in. The same structural bid that makes ETFs work well for mean reversion also exists in large cap stocks. When these stocks hit deep oversold levels, big money comes in to buy the dip. ETF’s like QQQ or SPY are comprised of these large cap stocks, so this structural bid propagates through into those ETFs as well.

On the other hand, small cap mean reversion trading has always been a struggle for me. They don’t have the same structural flows, and when they start selling off, may times there’s simply no big bid to catch them. A stock can just keep tanking because there’s nobody with big bucks stepping in to buy it.

I’ve spend probably 100’s of hours trying to find a mean reversion method that worked for me. Most of those hours were spent trying to make mean reversion work on the full US stock universe. I though that was the only right way to go about it.

It wasn’t until I let myself try different universes and shifted specifically to large caps and ETFs where I finally found some success. The stability was dramatically better, and the reason behind this mechanically makes sense: institutional backing and herd “buy the dip” mentality creates the bounce.

Then, on top of that, I built in an “All Weather Portfolio” like asset class diversification to better diversify the ETF mean reversion mini-portfolio. I’ll explain more about the design philosophy of these systems in the next section.

Design Philosophy:

Before we look at the four individual mean reversion systems, let’s discuss the thought process behind them first. All four systems share the same high level design philosophy, but all four of the systems are measuring mean reversion differently. Below I outline all of the main thought process that went into creating these systems, and why I did what I did.

As you’re reading this section, you’ll probably get a familiar feeling to why I built it the way I did. It takes a similar approach as the concepts discussed in the Luck By Design series:

Systematic Trading with TradeQuantiX

Luck By Design - Part 1

a year ago · 29 likes · 4 comments · TradeQuantiX

Why four systems instead of two or six?

As I mentioned, this article will cover four (yes four!) mean reversion systems on ETFs. You may be wondering, why four? Well, that’s because four mean reversion systems will get you pretty dang close to where you want to be.

Going from 0 mean reversion systems to 1 is a big benefit.

Going from 0 mean reversion systems to 4 systems is a massive benefit to your overall portfolio.

After four systems, the increased benefit starts to dissipate. Four I felt was the sweet spot that would give most readers the biggest bang for their buck in their own portfolios.

It’s simply the 80/20 rule applied to a mean reversion portfolio construction. There’s nothing magical about four. It’s just a fantastic starting point (and honestly, four mean reversion systems is way more than most people have).

Ideally, over time you keep adding more mean reversion systems spanning more unique ways to enter, more ways to exit, traded across more ETFs. Four becomes six, becomes eight, becomes twelve etc.

You should think of this the same way you think about any other portfolio. Think of it as something you’re continuously building and adding uncorrelated mean reversion approaches on top of these existing four.

Back to the 80/20 rule, that last 20% of results comes from the next 20 Mean reversion systems you stack on. But that first 80% can be achieved by what I’ll share a little later on in this article.

Multiple independent entry mechanisms.

Why Simplicity Beats Complexity in Trading System Design

TradeQuantiX — Mon, 23 Mar 2026 14:18:39 GMT

Introduction:

There’s a phase most systematic traders go through, usually a few months in, when they’ve learned just enough to feel dangerous. They think they’ve cracked the system development code and are on their way to creating perfect backtests.

After working on a system for awhile, the backtest doesn’t look quite good enough…

So they add another indicator.

The drawdown is too deep…

So they stack another filter on top.

The Sharpe ratio isn’t high enough…

So they optimize a few more parameters.

If that sounds familiar, you’re in good company. I did all of it too. And it took me years to understand what was actually happening: I was confusing excess complexity with effort, and excess effort with progress.

In this article, I’m going to make the case that simplicity in trading systems is the ultimate goal. Here’s what we’ll cover:

The three phases of a systematic traders journey
Why complexity creeps in (and why it feels like the right thing to do)
What complexity actually costs you in backtest reliability
What “elegant” system design actually looks like in practice
The practical rules I use to keep my systems simple
Why simplicity matters even more at the portfolio level

Let’s jump in.

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The Three Phases:

Most traders start out completely unaware of the complexities in their systems and processes. They don’t even know what they don’t know.

They think the solution to their poor trading performance is to pile on more complexity. (Remember that phase when you thought adding more indicators and parameters, i.e. complexity, to your trading system would make it better?).

I know this process intimately because I’ve lived it. My first “trading system” was a stitched together Python code from stack overflow forums, and I had no idea what any of the Python functions did because I had never coded before.

The baseline system code bought a stock when a 1-minute candle closed below the lower Bollinger Band and sold when price bounced back to the mid Bollinger Band.

No backtesting.

No out-of-sample testing.

No idea if it would work.

No real edge.

Just a pile of code I’d convinced myself “had to work.” And kept adding more and more rules and parameters and complexity to fine tune it over time. Luckily, the code was so fragile that it never actually successfully placed a trade. It nearly always crashed first.

But I thought the more complex I made it, the better it would be. I know now that’s wrong.

That was Phase 1. Phase 2 was automating hundreds of indicator combinations overnight and cherry-picking the ones that showed promising in-sample and out-of-sample results. This is called data mining.

Sounds reasonable, right?

An automated process to discover trading systems for you.

I thought so too (little did I know…).

I ended up losing over $20,000 trading those cherry picked data mined systems live.

I thought that having an automated way to spit out trading strategies would make it impossible for me to fail. I thought if it was always pumping out new ideas, then I could always be trading better and better strategies.

I quickly learned that the automation wasn’t actually capturing any real edge; it was finding lucky parameter / rule combinations at scale, and I had no way to tell the difference.

Phase 3, the final phase that only few systematic traders eventually reach (most have quit by now), is the realization that the goal was never more complexity, or autonomous solutions, or data mining, or perfect backtests, or beautiful equity curves.

The goal is the simplest viable model of a real and understood market effect.
Everything else is noise and indicator soup.

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Why Complexity Feels Like a Solution:

The instinct to add complexity is completely understandable. When a system isn’t performing well (live or in a backtest), it genuinely feels like the answer is to refine it.

Add a filter to cut out bad trades, add a condition to improve entry timing, tune the parameters until the equity curve looks just right. It feels like progress, but in reality, it’s detrimental.

The problem is that this instinct is backwards. Every rule you add is another degree of freedom the system can use to fit to noise in your training data. Every parameter you tune is another way the system can accidentally fit to some quirk that happened once in 2009 and won’t even happen again.

A system with 15 parameters is almost certainly capturing a lot of noise rather than just the underlying market effect you were trying to model.

Think about it this way: if your system is trying to capture the tendency of strong stocks to keep trending, that’s a simple idea. A simple model of a simple idea should work.

The moment you start stacking multiple momentum filters, on top of volatility filters, on top of regime detection, on top of breakout conditions, on top of sector constraints etc., you’re no longer creating a model to capture your desired effect in a robust way.

Instead, you’re modeling to the specific historic sequence of data you trained the system on. And specific histories don’t repeat.

It’s fine to have some of these layers in a system, but creating a system with 10 rules, 20 parameters, and a heavily used optimizer is a major problem.

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What Complexity Actually Costs You:

The damage from system complexity shows up in several places, and most of them are invisible in the backtest.

Curve fitting risk goes up. I’ve looked at this directly in my own development process. A system with 3 parameters has a much lower likelihood of accidentally producing a good result than a system with 50 parameters. Simple systems are inherently harder to overfit. Less room for the optimizer to work with means less room for the optimizer to mislead you.

The backtest becomes less trustworthy. When a system has 10 rules and performs well, how do you know which rules are actually doing the work? How do you know which rules are truly meaningful and which ones just happened to work on the data you trained on? You don’t. Then when you go live, you have no idea what’s actually making the system work (or more likely, not work).

Bugs multiply. More code means more things that can go wrong. I’ve been doing this for over 6 years and still sometimes find subtle mistakes in my systems. A trading system you can explain in plain English to someone who doesn’t trade is a system you can actually audit. A 10,000 line monster of a system code, full of edge cases and nested conditions, is not.

Trust collapses in drawdowns. This is the one that really hurts. When a complex system hits a drawdown, you have no idea which of your 15 rules is the culprit. Is this normal behavior? Is something broken? Is the edge gone? You can’t tell. A simple system that you understand is much easier to diagnose. You know what it’s supposed to be doing and you can verify whether it’s still doing it.

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What Simple Actually Looks Like:

The more complex a system is, the greater the chance something goes wrong. Whether it’s curve-fitting, an overlooked assumption, a bug in the code, or any number of other issues.

That’s why the first step in any of my processes whether it’s system development, portfolio construction, or anything in between is to eliminate needless complexities. I do this iteratively: after each major step in whatever I’m working on, I go back and review to strip out any unnecessary complexity that might have crept in.

My trading systems are simple and straightforward. I could explain the concepts and rules to a child. And my audit process ensures that I strip all complexities so that it stays that way.

That doesn’t mean my systems are dumb. Simple isn’t the same as stupid. Rather, I would describe my systems as elegant. They have the minimum amount of complexity needed to capture the effect they’re designed to take advantage of, and nothing more.

Generally, higher complexity systems lead to better in-sample performance, but less stability out of sample. Lower complexity means slightly lower in-sample performance but more stable results where it matters; in live trading.

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Practical Rules for Keeping Systems Simple:

A few things I actually do that keep complexity in check.

Start with the mechanism, not the indicator. Before I ever write a line of code, I ask: why should this effect exist? What is the real-world force that creates this pattern? If I can answer that in plain English, I can usually build a simple system to capture it. If I can’t, if the answer is “the MACD said so”, then I am very skeptical of if that edge actually exists. It’s likely just noise that happened to look like an edge.

Fewer rules, then test. My first version of any system is always the bare minimum to test the idea. If the concept has any merit, even a stripped-down version should show some signal. If I need 8 rules before the backtest shows any positive expectancy at all, that’s a red flag. The signal should be obvious enough that a super simple initial model can roughly find it.

Parameter sensitivity is one of your best robustness tests. After building a simple system, I want to see that the results are roughly stable across a reasonable range of parameter values. If the system only works with very specific values, that’s usually due to curve fitting to noise rather than capturing a real edge.

Always be simplifying. Throughout the system development phase I review the current code base periodically and ask: do I still need all of this? Sometimes a rule that made sense when I added it no longer adds value once the core system has matured. If that’s the case, in the trash the extra complexity goes. Less complexity, less room for fragility.

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Why This Matters Even More at the Portfolio Level:

If you trade a single system, simplicity is important. If you trade a portfolio of many systems like I do (25+ live systems at the moment), then simplicity is probably the most important factor in your setup.

Every rule in every system multiplies across the portfolio. More parameters across more systems means more things that can break, more things to monitor, and more ways to accidently curve fit.

Simple systems also combine better. You can understand exactly what each system is supposed to be doing and how they should interact. A portfolio of simple, elegant systems is far easier to reason about than a collection of complex, interdependent black boxes.

When something goes wrong in the portfolio (and eventually something always does) simple systems give you a fighting chance of figuring out what and why.

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Conclusion:

Adding more complexity to a trading system is almost always wrong. It feels like progress because it takes effort and it makes the backtest look better. But if you want your live trading results to match your backtest expectations, we need to be going in the opposite direction.

Develop the simplest viable solution of a trading system that models a real market effect. Do that 10 times. I guarantee that even though each individual system may look meh, that combined portfolio will look pretty sweet.

Complexity is where edge goes to hide and die. Keep your systems simple, keep asking why they should work, and keep stripping out anything that doesn’t have a good answer.

Simple systems are less susceptible to curve fitting. Simple systems fail in more understandable ways. Simple systems are easier to trust in drawdowns, easier to audit for bugs, and easier to combine into a portfolio that actually works.

If you found this useful and want to go deeper, the Portfolio Development Series covers exactly how I build and combine simple but elegant systems into a portfolio. You can read the first article in the series here, enjoy!

Disclaimer

The information and services provided by the Systematic Trading with TradeQuantiX newsletter are for educational and informational purposes only and do not constitute financial advice, investment recommendations, or guarantees of trading performance. Any information provided is general in nature and does not take into account your individual circumstances. Trading involves significant risks, including the potential for substantial financial losses, and past performance is not indicative of future results. The information is provided ‘as is’ without warranty of accuracy or completeness. All decisions and actions based on the information provided are the sole responsibility of the reader. You should seek independent professional advice before trading. The Systematic Trading with TradeQuantiX newsletter is not liable for any losses, damages, or outcomes resulting from the use of this information or our services.

Robustness Testing Methods: A Complete Guide for Systematic Traders

TradeQuantiX — Wed, 18 Mar 2026 22:16:20 GMT

Introduction:

Well, we’ve all been there. You spend a weekend coding up a new trading system. You run the backtest. The equity curve is going up and to the right. The Sharpe ratio looks decent. The drawdown is manageable. You’re already thinking about how much capital to allocate.

But here’s the question you should be asking before any of that: how do you know if it’s real?

Most traders I talk to apply one, maybe two robustness checks and call it a day. They’ll run an out-of-sample test or eyeball a parameter sensitivity plot and move on. I used to be in that camp early on (if I even ran a robustness test at all...).

But after building and testing 100’s of systems, I can tell you that each robustness test catches something different. Missing even one of them means you might be missing the exact reason your system fails once real money is on the line.

This purpose of this article is provide the how to for robustness testing, and includes some goodies as well in terms of tools to help you do it.

I’m going to walk through five robustness testing methods that I use on nearly every single system before it gets anywhere near my hard earned capital:

Parameter Sensitivity Analysis (+/- 25%)
Multi-Market Validation
Trade Skip Monte Carlo
Noisy Data Monte Carlo
Entry and Exit Delay

Each one tests a different thing. Each one can expose a different weakness within the system that’s maybe not obvious on the surface.

I’ve also built a free Excel template that helps automate the visualizations and calculations for all five tests.

You paste your backtesting output in, and it generates charts, summary stats, histograms, and percentile rankings automatically. I’ll walk you through how to use it before we get into the tests themselves.

Let’s get into it.

Why Each Method Tests a Different Thing:

Before we get into the specifics, I want to make sure the big picture is clear. These five tests are not redundant. They are not five ways of asking the same question. Each one is probing at a different vulnerability within your system.

Parameter sensitivity: did I accidentally happen to pick the one lucky combination of parameters that works, or is this a stable region of performance?
Multi-market validation: is this edge specific to one market and does the underlying mechanism exist more broadly? Or did I just curve fit to noise.
Trade skip Monte Carlo: does the system depend on a few lucky trades and is the system curve fit to a specific sequence of trades? Or does the system robustly capture different trading opportunities when needed?
Noisy data Monte Carlo: if the prices were slightly different (which they always will be going forward), does the system still work? Or is the system curve fit to noise.
Entry/exit delay: does the system require perfect fills? And how precise are the entry and exit mechanisms the system is capturing? Some systems need that precision, others don’t. This robustness test isn’t applicable to all systems, but it’s a great test for where it is applicable.

This is not an all exhaustive list, but it is enough to help us learn a lot about the durability of a system.

These robustness tests generally come after the system is fully developed on both in-sample and out-of-sample data. You’ve built the system, you’ve validated it out of sample, and now you’re stress testing it from every angle before committing capital.

That said, there’s nothing stopping you from running these tests on just your in-sample data during development to get a preliminary feel for system stability. If parameter sensitivity looks terrible on in-sample alone, there’s no point continuing to the out-of-sample phase.

I want to make something clear before we move on: the point of robustness testing is not to find a perfect system. There is no perfect system. The point is to calibrate your confidence in an imperfect one. These tests help you understand exactly how imperfect your system is and whether you can live with it.

Let’s start with the tool that makes all of this easier.

The Robustness Testing Excel Template:

Before we walk through each test, I want to introduce a tool I built specifically for this article (but now I’m going to start using it and expanding it over time, I’ll share it with you as I do, consider this the v1.0 of the tool).

It’s an Excel workbook that takes the raw output from your backtests and does a lot of the robustness analysis automatically. No formulas to write, no charts to build manually.

You paste data in, and it gives you everything you need to evaluate your system. At least that’s the idea.

Maybe it doesn’t show everything yet, but over time I’ll get it there and share updates as it gets built.

You can download the Robustness Testing Template workbook attached to this post.

How It Works:

The workbook has 13 tabs, organized by color:

Orange tabs (INPUT): This is where you paste your backtesting output. There are five input tabs, one for each robustness test. Each one accepts up to 1,000 rows.
White tabs (Analysis): These auto-populate with tables, histograms, and summary statistics based on whatever you pasted into the corresponding input tab.
Blue tabs (CONFIG and Instructions): The CONFIG tab lets you choose which metrics you want to analyze. The Instructions tab walks you through everything step by step.

Setting Up Your Metrics:

The CONFIG tab is where you tell the workbook which metrics matter to you. By default, it comes loaded with 13 metrics that I consider on every system: ROR, MaxDD, Volatility, UI, Trades, PctWins, AvgWin, AvgLoss, WinLen, Expectancy, ProfitFactor, Sharpe, and AvgExp. You can add up to 20 performance metrics total.

None of the performance metric column values are hardcoded. The workbook is smart enough to lookup each metric by its header name.

So, if your backtester outputs columns in a different order, or uses slightly different names, or you have your own custom metrics, you just make sure the header names on your CONFIG tab match the headers you pasted in. Then that just flows through the rest of the excel sheet.

Pasting Your Data:

Each INPUT tab has a blue header row at the top of the paste zone. Here’s the steps to input your data for a robustness test:

Run your robustness test in your backtester (RealTest, AmiBroker, custom python code, whatever else you use…)
Export the results to a CSV
Open the CSV, copy the header row, and paste it into the blue header row on the INPUT tab corresponding with the robustness test you ran
Copy your data rows and paste them below the header row

That’s it. The analysis tab for that test will auto-populate with everything: summary stats (min, max, median, percentile of your baseline), and histograms showing the distribution of results with your baseline system results highlighted in dark red.

Setting Your Baseline:

For four of the five tests (Parameter Sensitivity, Multi-Market, Noisy Data, and Entry/Exit Delay), you’ll see a “Baseline Row #” cell at the top of the INPUT tab. Enter the row number of your baseline system. This tells the workbook which result to highlight in the charts and which to calculate the percentile against.

For Trade Skip Monte Carlo, the layout is slightly different. Your actual backtest result goes in the top section (rows 6-7), and the Monte Carlo simulation results go below (starting at row 11). The workbook then shows you where your actual result falls in the distribution.

What You Get:

For each test and each metric on your CONFIG tab, the analysis tabs generate:

Summary statistics: min, max, median of all results, plus the percentile rank of your baseline system and the delta between baseline system and the average/median result from the robustness test. The Baseline Percentile row gives you the quick answer: where does my system sit relative to everything else?
Histograms: bucketed distribution of results across 10 bins, with the bin containing your baseline system results highlighted in dark red. The histograms show you the shape of the distribution. Is it a nice bell curve centered around the baseline? Or is the baseline sitting out on the right tail? The shape matters because it tells you whether the parameter surface is a plateau (normal or uniform distribution) or a spike (skewed distribution with your baseline on the edge).

The percentile is the key number. For example, if your baseline system’s ROR, volatility, Sharpe etc. are all in the 50th percentile of the parameter sensitivity results, that’s a good sign.

It means you’re in the middle of the distribution, not perched on a lucky peak. If it’s at the 95th percentile, that should make you nervous. You accidentally picked parameters near the top of the range, and the only direction from there is down (and that’s usually where live results end up too… down).

With the tool introduced, let’s walk through each test.

Member Roadmap

TradeQuantiX — Sun, 08 Mar 2026 14:36:51 GMT

Welcome to “Systematic Trading With TradeQuantiX”. There are over 50 published articles in this newsletter and if you’re new, it can be a lot to take in. This roadmap puts everything in order so you know exactly where to start and what to read next.

It walks you through the foundations first, then the flagship portfolio development series where we build a multi-system portfolio from scratch. Then the deep dives into individual system research, and finally the methodology content.

You don’t have to follow it perfectly, but if you’re wondering “where do I even start?” this is your answer.

Start at the top, work your way down. By the end, you’ll have a complete picture of how I build, test, and manage a portfolio of systematic trading systems

Subscribe now

Phase 1: The Intro (Who I Am + What This Is):

Before we get into systems and data, here’s who I am, what I believe, and what this whole thing is about.

My Systematic Trading Journey:

The full backstory. How I got into systematic trading, the mistakes I made early on (including losing over $20,000 in futures), and how I ended up where I am today with 25+ live systems.

Systematic Trading with TradeQuantiX

My Systematic Trading Journey

Hit that button to subscribe…

2 years ago · 45 likes · 9 comments · TradeQuantiX

A Necessary Read For Those Who Want To Begin Trading Systematically:

If you’re just getting started with systematic trading, this is your starting point. I walk through the core concepts, the tools you need, and the mindset shift from discretionary to systematic. Pretty much everything I wish someone told me on day one.

Systematic Trading with TradeQuantiX

A Necessary Read For Those Who Want To Begin Trading Systematically

Introduction…

a year ago · 64 likes · 6 comments · TradeQuantiX

The Biggest Myths In Systematic Trading:

There’s a lot of nonsense floating around about systematic trading. This article tackles the biggest myths head on: the idea that you need to be a math genius, that more complexity means better results, that backtests tell the whole story. Spoiler alert: none of that is true.

Systematic Trading with TradeQuantiX

The Biggest Myths In Systematic Trading

a year ago · 7 likes · TradeQuantiX

You’re Stealing From Your Future Self:

This one is about the cost of inaction. Every day you spend tweaking indicators, or chasing the perfect system, or procrastinating in general is a day you’re not compounding real capital. I lay out why “good enough” systems traded today beat perfect systems traded never.

Systematic Trading with TradeQuantiX

You're Stealing From Your Future Self

a year ago · 20 likes · 8 comments · TradeQuantiX

The Only Holy Grail in Trading: Your Portfolio:

A lot of traders look for a holy grail trading system. One trading system that is amazing and will make millions in profits with no drawdowns. That doesn’t exist. But this article outlines the closest thing to a holy grail in systematic trading.

Systematic Trading with TradeQuantiX

The Only Holy Grail in Trading: Your Portfolio

a month ago · 12 likes · 2 comments · TradeQuantiX

The TradeQuantiX Portfolio:

A full overview of my live portfolio: what systems I trade, how they’re structured, and how they work together. This gives you the big picture before we start building one from scratch in the next phase.

Systematic Trading with TradeQuantiX

The TradeQuantiX Portfolio

2 years ago · 65 likes · 24 comments · TradeQuantiX

Phase 2: Portfolio Development Series (Build a Portfolio From Scratch):

This is the flagship series. 12 parts that take you from a blank slate to a fully constructed multi-system portfolio. We build it step by step, one system at a time, and I show the results at every stage so you can see exactly how each addition changes the overall performance.

Portfolio Development Series: Part 1:

We start at the very beginning. We cover why portfolios are important and the portfolio mindset. This article sets the foundation for everything that follows.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 1

a year ago · 37 likes · 4 comments · TradeQuantiX

Portfolio Development Series: Part 2:

This article covers high level portfolio design considerations. All the things you need to think about before you start building the portfolio are spelled out here.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 2

a year ago · 13 likes · 4 comments · TradeQuantiX

Portfolio Development Series: Part 3:

This is the first article where a system gets built. It’s a US mean reversion system and the article walks through in-sample and out-of-sample testing methods.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 3

a year ago · 20 likes · 12 comments · TradeQuantiX

Portfolio Development Series: Part 4:

This is the second article where a system gets built. It’s a US momentum system and the article walks through the whole building process of the system.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 4

a year ago · 15 likes · 2 comments · TradeQuantiX

Portfolio Development Series: Part 4.5:

I made a mistake in Part 4. This article is me owning it, explaining what went wrong, and fixing it.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 4.5

10 months ago · 9 likes · 4 comments · TradeQuantiX

Portfolio Development Series: Part 5:

We keep building. Another system is built for ASX trend following and I show some interesting robustness testing techniques.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 5

10 months ago · 9 likes · 8 comments · TradeQuantiX

Portfolio Development Series: Part 6:

This system built is a TSX momentum system. This article walks through the parameter optimization process.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 6

10 months ago · 8 likes · 6 comments · TradeQuantiX

Portfolio Development Series: Part 7:

Finally we put it all together and make a portfolio of 4 systems. This article walks through the portfolio development process and shows the magic of trading portfolios rather than only one trading system.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 7

10 months ago · 7 likes · 4 comments · TradeQuantiX

Portfolio Development Series: Part 8:

The portfolio grows again. I dig into adding some hedging and short side systems to smooth the portfolio equity curve.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 8

9 months ago · 16 likes · 9 comments · TradeQuantiX

Portfolio Development Series: Part 9:

This article covers some very nuanced portfolio considerations. Things that maybe don’t come to mind at first.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 9

9 months ago · 9 likes · 8 comments · TradeQuantiX

Portfolio Development Series: Part 10:

This article adds one more US ETF mean reversion system. The portfolio we have built so far was for multi-country equities, but we also wanted to build a US only portfolio and to do that we needed a few more US only systems.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 10

8 months ago · 13 likes · 2 comments · TradeQuantiX

Portfolio Development Series: Part 11:

This final article in this series walks through the US only portfolio. It shows what systems are included and the capital base needed to trade it.

Systematic Trading with TradeQuantiX

Portfolio Development Series - Part 11

8 months ago · 17 likes · 6 comments · TradeQuantiX

Multi-Strategy Portfolio Allocation:

This article was an add on after the portfolio development series was completed. I was getting a lot of clarifying questions on how to allocate capital to trading systems, so I wrote this how-to article to help better explain the process.

Systematic Trading with TradeQuantiX

Multi-Strategy Portfolio Allocation

5 months ago · 20 likes · 12 comments · TradeQuantiX

Phase 3: Deep Dives (System Investigations + Research):

This is where we get into the weeds. Real systems, real research, real data. Each investigation takes a trading idea and puts it through the full process: hypothesis, backtest, analysis, and a verdict on whether it’s worthy of the portfolio.

Trading System Investigation Series 1: Part 1:

The first investigation series. I take a system idea from a free webinar, code it up, and run it through backtesting to see if it actually holds up. This is the “don’t blindly trust what you find on the internet” article.

Systematic Trading with TradeQuantiX

Trading System Investigation: Series 1 - Part 1

Hey, TradetroniX here, welcome to the first article of a four part mini series where we will do some trading system investigation. If you enjoy the content, please feel free to subscribe. Also, if yo…

2 years ago · 36 likes · 12 comments · TradeQuantiX

Trading System Investigation Series 1: Part 2:

We dig deeper into the system from Part 1. I start testing variations and improvements to see if we can make it robust enough for real capital.

Systematic Trading with TradeQuantiX

Trading System Investigation: Series 1 - Part 2

In part 1 of this article series, we introduced the rules to a trading system shared in a free webinar. We then coded the system into my backtesting platform and discussed the shared system performance vs. the performance of my version of the system based on my interpretation of the system rules. We then showed some example trades, and discussed some strengths and weaknesses of the system. If you missed part 1, be sure to check it out here…

2 years ago · 32 likes · 3 comments · TradeQuantiX

Trading System Investigation Series 1: Part 3:

Robustness testing. I show some examples of robustness tests and their results.

Systematic Trading with TradeQuantiX

Trading System Investigation: Series 1 - Part 3

In the previous parts of this series, we explored a system we found online. We were able to code the system, make the system our own, improve performance, then take the system through an in sample and out of sample approach. Now, for part 3 we are going to investigate the system further with some robustness tests. Just passing out of sample alone is not enough to consider the system worthy of our capital. It could have been dumb luck that the system passed out of sample. We have to take it one step further, to the most important step, this is the step were we try to break the system…

2 years ago · 42 likes · 10 comments · TradeQuantiX

Trading System Investigation Series 1: Part 4:

I discuss some situations that may lead you to modify the system to be different than what I developed. This walks through how to make a system specific for your needs.

Systematic Trading with TradeQuantiX

Trading System Investigation: Series 1 - Part 4

So far in this series, we have spent a bunch of time coding, backtesting, and robustness testing the trading system under investigation. Which is great and provides a lot of learnings and interesting results; but what if this system doesn’t fit your trading personality or goals? Maybe the drawdown is too high, or the system trades too often, or you don’t like losing trades and the winning percentage is too low, or your broker has a more expensive commission structure and you require a higher expectancy per trade? Things like this should be considered when you are developing a trading system. The trading system we have been developing over the past few articles may be a perfect fit for some and a nightmare to trade for others. If the trading system doesn’t work for your situation then it’s going to be really difficult to trade…

2 years ago · 25 likes · 9 comments · TradeQuantiX

Trading System Investigation Series 2: Part 1:

A brand new system idea gets the full investigation treatment. I discuss the pros and cons to the system and code up the baseline results.

Systematic Trading with TradeQuantiX

Trading System Investigation Series 2 - Part 1

a year ago · 21 likes · TradeQuantiX

Trading System Investigation Series 2: Part 2:

I continue from part 1 to modify the system and run tests such as parameter sensitivity analysis and out-of-sample validation.

Systematic Trading with TradeQuantiX

Trading System Investigation Series 2 - Part 2

a year ago · 11 likes · 6 comments · TradeQuantiX

Trading System Investigation Series 3:

We develop a momentum / trend system that looks at both momentum and volatility factors, The system trades both factors to diversify and spread risk across different market factors.

Systematic Trading with TradeQuantiX

Trading System Investigation Series 3

a year ago · 10 likes · 2 comments · TradeQuantiX

Increasing Robustness & Smoothing Returns: Re-engineering The 2-ETF Trading Strategy:

I take a strategy presented elsewhere and put my own twist on it. The goal: make it more robust and less risky. A good example of how take a system and make it your own.

Systematic Trading with TradeQuantiX

Increasing Robustness & Smoothing Returns: Re-engineering The 2-ETF Trading Strategy

a year ago · 16 likes · TradeQuantiX

Digging Up An Old Trading System From The Graveyard: Part 1:

I was going through old files and found some systems I had shelved a while back. This is me pulling one out, dusting it off, and testing whether it deserves a second chance with fresh eyes and updated data.

Systematic Trading with TradeQuantiX

Digging Up An Old Trading System From The Graveyard - Part 1

7 months ago · 13 likes · 3 comments · TradeQuantiX

Digging Up An Old Trading System From The Graveyard: Part 2:

The conclusion. After re-testing and re-evaluating, does this old system earn its way back into the portfolio? Or does it go back in the trash?

Systematic Trading with TradeQuantiX

Digging Up An Old Trading System From The Graveyard - Part 2

7 months ago · 12 likes · TradeQuantiX

Investigating a Simple Trend Idea: Part 1:

A simple trend following concept gets the full systematic treatment. I start with the basic idea, explain the mechanism behind it, and run the initial backtests to understand the system.

Systematic Trading with TradeQuantiX

Investigating a Simple Trend Idea - Part 1

6 months ago · 15 likes · 2 comments · TradeQuantiX

Investigating a Simple Trend Idea: Part 2:

Continuing the trend investigation and implementing robustness. Then running through many robustness tests to understand if this idea is worthy of portfolio allocation.

Systematic Trading with TradeQuantiX

Investigating a Simple Trend Idea - Part 2

5 months ago · 6 likes · 6 comments · TradeQuantiX

All Weather Portfolio Research: Part 1:

Can you build a portfolio that performs in all market conditions? I dig into the All Weather concept, pull the research, and start testing different implementations with real data.

Systematic Trading with TradeQuantiX

All Weather Portfolio Research: Part 1

I’m not really sure why but I’ve had an on and off fascination with ETF trading systems in the past year. To most people, ETF trading systems probably seem boring, but boring can still be a good impl…

2 years ago · 35 likes · 9 comments · TradeQuantiX

All Weather Portfolio Research: Part 2:

Continuing the All Weather research. This time I create my own All Weather portfolio variant.

Systematic Trading with TradeQuantiX

All Weather Portfolio Research: Part 2

In the last post about All Weather portfolios we looked at a bunch of different All Weather portfolio ideas. We coded up 9 examples of All Weather portfolios found online and figured out what we like…

2 years ago · 28 likes · 9 comments · TradeQuantiX

All Weather Portfolio Research: Part 3:

The final installment. I test out some ideas suggested by subscribers. We go other those results here.

Systematic Trading with TradeQuantiX

All Weather Portfolio Research: Part 3

Welcome to part 3 of this All Weather Portfolio series! Before we dive into it, let’s just do a quick recap of the previous two articles in the series. The first part of the All Weather Portfolios re…

2 years ago · 17 likes · 14 comments · TradeQuantiX

Case Study: Dividend Investing: Part 1:

Dividend investing is one of those topics everyone has an opinion on. Some love it some hate it. This article goes over the pros and cons of having a dividend portolfio.

Systematic Trading with TradeQuantiX

Case Study: Dividend Investing - Part 1

Introduction…

a year ago · 5 likes · TradeQuantiX

Case Study: Dividend Investing: Part 2:

The deeper dive into dividend investing. I test a systematic approach to harvesting dividends via a trend following system.

Systematic Trading with TradeQuantiX

Case Study: Dividend Investing - Part 2

Introduction…

a year ago · 15 likes · 2 comments · TradeQuantiX

Market Effect Research: Holiday Seasonality:

Does the market actually perform differently around holidays? I pull the data, test the effect across multiple markets and time periods, and give you the honest answer on whether holiday seasonality is a real, tradeable edge.

Systematic Trading with TradeQuantiX

Market Effect Research: Holiday Seasonality

4 months ago · 11 likes · 4 comments · TradeQuantiX

Market Effect Research: Turn of the Month Effect:

Does the market actually perform differently around the end of the month? I pull the data, test the effect across multiple markets and time periods, and give you the honest answer on whether the month end seasonality is a real, tradeable edge.

Systematic Trading with TradeQuantiX

Market Effect Research: Turn of the Month Effect

a month ago · 9 likes · 2 comments · TradeQuantiX

Market Effect Research: Turnaround Tuesday Effect:

Does the market actually outperform on Tuesdays relative to other market days? I dig into the magnitude of Tuesday returns and find predictors of higher vs. lower Tuesday returns, on average. Then create a system from the findings.

Systematic Trading with TradeQuantiX

Market Effect Research: Turnaround Tuesday Effect

15 days ago · 9 likes · TradeQuantiX

ETF Mean Reversion Methods - Part 1:

Four ETF mean reversion systems. The article discusses how each works, individual backtest results, how they complement each other, and why I trade mean reversion this way. Part 1 of 3.

Systematic Trading with TradeQuantiX

ETF Mean Reversion Methods: Four Systems Built for the Dip - Part 1

3 months ago · TradeQuantiX

ETF Mean Reversion Methods - Part 2:

Four ETF mean reversion systems. The article discusses the power of combining them into a mean reversion mini-portfolio.

Systematic Trading with TradeQuantiX

ETF Mean Reversion Methods: Mean Reversion Mini-Portfolio Creation - Part 2

2 months ago · 3 likes · 1 comment · TradeQuantiX

ETF Mean Reversion Methods - Part 3:

The article walks through the in-sample and out-of-sample results, as well as some robustness testing and what live trading is actually like.

Systematic Trading with TradeQuantiX

ETF Mean Reversion Methods: Validation and Live Trading - Part 3:

2 months ago · TradeQuantiX

Phase 4: Methodology (Robustness, Risk, and Portfolio Thinking):

This is the stuff that separates traders who survive from traders who give up. Luck, curve fitting, regime filters, volatility targeting, and the hard questions about whether your systems are actually robust or just lucky.

Luck By Design: Part 1:

How much of your backtest performance is skill and how much is luck? This series tackles that question head on. Part 1 introduces the concept and explains why most traders massively underestimate the role of randomness.

Systematic Trading with TradeQuantiX

Luck By Design - Part 1

a year ago · 28 likes · 4 comments · TradeQuantiX

Luck By Design: Part 2:

We go deeper into luck in backtests. I show specific techniques for removing luck from a system by developing a simple mean reversion system that mitigates luck.

Systematic Trading with TradeQuantiX

Luck By Design - Part 2

Welcome to the “Systematic Trading with TradeQuantiX” newsletter, your go-to resource for all things systematic trading. This publication aims to equip you with a complete toolkit to support your systematic trading journey, sent straight to your inbox. Remember, it’s more than just another newsletter; it’s everything you need to be a successful systematic trader…

a year ago · 3 likes · 10 comments · TradeQuantiX

Luck By Design: Part 3:

The conclusion of the luck series. I walk through a similar luck mitigation process as shown in part two, but specifically for a momentum system.

Systematic Trading with TradeQuantiX

Luck By Design - Part 3

a year ago · 4 likes · TradeQuantiX

Conquering Curve Fitting: A Game Changing Technique for Robust Systematic Trading:

Curve fitting is the silent killer of systematic trading systems. This article covers a technique I use to dramatically reduce the risk of overfitting. If you take one thing away from this newsletter, understanding curve fitting might be the most valuable.

Systematic Trading with TradeQuantiX

Conquering Curve Fitting: A Game-Changing Technique for Robust Systematic Trading

a year ago · 11 likes · 9 comments · TradeQuantiX

Most Systematic Traders Use Regime Filters Improperly:

Regime filters are one of the most misused tools in systematic trading. I break down what most people get wrong, why optimizing regime filters is dangerous, and how to think about regime filters properly.

Systematic Trading with TradeQuantiX

Most Systematic Traders Use Regime Filters Improperly

7 months ago · 21 likes · TradeQuantiX

Your Trading System Might Be Broken...:

How do you know when a live system has stopped working? This article covers the signals I would watch for and how to make the decision to pull a system from live trading.

Systematic Trading with TradeQuantiX

Your Trading System Might Be Broken...

4 months ago · 5 likes · TradeQuantiX

Robustness Testing Methods: A Complete Guide for Systematic Traders:

Once you develop a system you need to ensure it is robust. This guide walks through five robustness tests and how to do them. It also provides two tools to help you run and visualize the results of the robustness tests.

Systematic Trading with TradeQuantiX

Robustness Testing Methods: A Complete Guide for Systematic Traders

3 months ago · 5 likes · 2 comments · TradeQuantiX

Volatility Targeting 101: Enforcing Equal Risk:

If you’re running multiple systems, they need to contribute equal risk to the portfolio. Volatility targeting is how you enforce that. I walk through the concept, the math, and the practical implementation.

Systematic Trading with TradeQuantiX

Volatility Targeting 101: Enforcing Equal Risk

a year ago · 11 likes · TradeQuantiX

Why Would I Trade A Losing System?:

This one throws people off. Why would anyone deliberately trade a system that loses money? The answer has everything to do with portfolio level thinking and non-correlation. A losing system can actually make your portfolio better. I show exactly how.

Systematic Trading with TradeQuantiX

Why Would I Trade A Losing System?

a year ago · 7 likes · TradeQuantiX

Browse Anytime (No Specific Order Required):

These don’t need to be read in sequence. Check them out whenever you’re curious.

Portfolio Performance Updates:

Periodic snapshots of how the live portfolio is performing. Real numbers, real equity curves, and honest commentary on what’s going well and what isn’t.

Guest Posts:

These are posts where I collaborated with another trader. Again, this can be read at any time in any order.

TradeQuantiX and The Rogue Quant: Part 1:

A collaboration with The Rogue Quant. We discuss two different approaches to systematic trading. If you want to see how other practitioners think about the same problems, this is a good read.

Systematic Trading with TradeQuantiX

TradeQuantiX and The Rogue Quant - Part 1

Introduction…

a year ago · 8 likes · TradeQuantiX

Disclaimer

The information and services provided by the Systematic Trading with TradeQuantiX newsletter are for educational and informational purposes only and do not constitute financial advice, investment recommendations, or guarantees of trading performance. Any information provided is general in nature and does not take into account your individual circumstances. Trading involves significant risks, including the potential for substantial financial losses, and past performance is not indicative of future results. The information is provided ‘as is’ without warranty of accuracy or completeness. All decisions and actions based on the information provided are the sole responsibility of the reader. You should seek independent professional advice before trading. The Systematic Trading with TradeQuantiX newsletter is not liable for any losses, damages, or outcomes resulting from the use of this information or our services.

Your Trading System Might Be Broken...

TradeQuantiX — Tue, 03 Mar 2026 23:51:51 GMT

Introduction:

A very common question I hear from systematic traders is:

“How do you know when your system is broken and how do I know when to turn it off before it’s too late?”

The short answer to that question is: generally you can’t know for sure if something is “broken.” Usually it’s too late before you actually know for sure.

But this answer is a bit lazy.

Maybe we can get some system degradation warning signs before the system completely dies. There are some things we can examine to understand if the system is degrading over time.

While there is no way to know for certain that the degradation will continue (maybe the system comes back to life), at least knowing that some degradation appears to be present is good information for us systematic traders to make decisions around.

Maybe if we could see degradation within a system, we could:

Allocate less capital to it as the confidence in the system persisting slowly decreases with degradation. Then, if the system comes back to life, we can increase capital allocation again.
Or maybe you put the system in a probation period.
Or maybe you just keep the knowledge of degradation in the back of your mind and are ready to pull the plug at a moment’s notice.

These are some of the tricky parts of systematic trading. These are the parts that require discretionary decisions, which we never like. But if we use some informed and unbiased data-driven approaches, then maybe that discretionary decision around “Is my system broken?” becomes a little more systematic.

Why Is It So Difficult To Know When Your System Is Broken?:

Knowing when a trading system is broken is hard for a few reasons:

We don’t know the future. Who’s to say the markets are just experiencing unfavorable conditions for the system and the system will start working again once the markets start to cooperate again.
Systems tend to ebb and flow, moving in and out of drawdown. Who knows if this seemingly normal drawdown is the one that never recovers?
Some drawdowns feel worse than others. Deeper, faster, or more aggressive than any historical drawdown. This can lead to doubt and the feeling that something fundamental changed.

Psychologically, the decision to turn off a trading system due to the belief of it being broken is hard as well because:

We fear that if we keep the system running and it breaks, then we will lose money as the system crashes and burns.
On the contrary, we fear if we turn the system off and it starts working again we will miss out on the gains as the system rips out of drawdown.
We put all this work into researching and developing the system, accepting all that hard work is no longer valid is frustrating and annoying.

Take, for example, the TSX (Canadian) momentum system we developed as part of the Portfolio Development Series. This system has had many difficult periods of time.

The system has had deep drawdowns, flat spots, and volatility. Consider the flat performance between 2014 and 2021. I don’t think anyone would fault you for thinking the system stopped working during that period. Only then for it to come roaring back to life in 2021 and 2024 (and keep in mind, 2019 onwards was used as out-of-sample for the development of this system).

TSX Momentum System:

Knowing if this system was broken or just experiencing normal drawdowns and sideways action is hard.

Very hard.

If this was the only system you were trading, that would have likely been very frustrating at times.

This is one reason why it’s always best to trade a portfolio of many diversified systems together. Then, when this TSX momentum system went sideways for years, you still would have had many other systems pick up the slack.

One of the best ways to know if a system is broken is through a deep understanding of how the system works. For example, this TSX momentum system obviously trades high momentum stocks.

So, if the TSX market does not exhibit traits of momentum for long periods of time, it would be reasonable to think the market conditions are simply unfavorable for this system; rather than the system just stopped working.

TSX Market Index - SPTSX:

Notice how the TSX market index has large drawdowns and sideways periods at the exact same times as the momentum system struggled. So, just through basic reasoning of how this system works, we may be able to convince ourselves it’s the market rather than the system.

A system can only take what the markets give. If the markets are not providing momentum, this momentum system cannot extract returns from the market.

Understanding the underlying system dynamics and why/how it makes money is important to understanding if the system is broken; or instead are the markets just not providing the conditions for this system to extract returns.

On the other hand, if you have a more esoteric strategy and you are not really sure why the system makes money; then it is a lot more difficult to know if it’s market conditions driven or if the system is actually broken.

A system comprised of “indicator soup” that looks nice in a backtest is cool, but going forward in time we have no understanding of why the system works and if the current drawdown is temporary or a full system failure.

We don’t understand what this “indicator soup” system is harvesting. So we have no concept of knowing if the lack of performance is due to the markets simply not being cooperative or if the system was curve fit to begin with, and is now falling apart.

Anyway, the first step in knowing if your system is broken or not is to answer and understand the following things:

Understanding what effect its harvesting.
Understanding why the system should work.
Understanding what market conditions need to be present for the system to work.
Understanding when those conditions tend to exist.
Understanding if the system capturing returns like it’s supposed to when favorable conditions exist.

Once you’ve had a critical thought about your system and can answer those questions, you can move on to the next section where we explore a tool I crated to look “under the hood” of your trading system.

Market Effect Research: Holiday Seasonality

TradeQuantiX — Mon, 16 Feb 2026 15:33:12 GMT

Introduction:

You may have heard of the holiday effect in markets. It’s the idea that leading into federal holidays, the markets tend to rally. This had me wondering if this effect is real and, if so, whether it can be captured.

I mentioned in a note that 2026 is the year of researching “small” and “dumb” edges. Things that I can quantify and prove to myself have a very high likelihood of existing. Ideas that are different from the traditional trend and momentum type systems I trade a lot of and which we have explored in previous articles.

I figure if I can research and implement many of these different edges, it will provide a unique and non-correlated return stream to my other trend and momentum systems.

Not only does this diversify my portfolio return streams, but the more unique strategies I add to my portfolio, the more I reduce and spread the risk of individual strategy failure. Having two strategies and having one fail would suck. Having 50 strategies and having 4 or 5 fail would hardly even leave a mark.

Hence, researching market effects, like the holiday effect, and seeing if it’s something that can be exploited seems like the perfect “small” and “dumb” edge to help build up a fortress of trading strategies to diversify my portfolio return streams.

Let’s get into it.

Reason For The Effect:

The question we need to answer is:

“Okay, allegedly the holiday effect exists, but why would something like that exist in the markets? Is there a good reason for this effect to exist or is it likely a data-mined myth?”

We can attempt to answer the “why this effect might exist” through reasoning (but we can never actually know the true answer). A few decent reasons why this effect might exist would be the following:

Good Sentiment:
- Holidays simply make people happy, more optimistic, and less focused on work. When people are optimistic, they may be less averse to risk. This “holiday spirit” or euphoria may lead investors to buy stocks (or at least avoid selling), pushing prices higher. Happy investors process information less critically and expect better future outcomes. Studies often cite this as the dominant explanation, with investors in a good mood being reluctant to sell or potentially more willing to hold/buy.
Lower Trading Volume and Liquidity:
- Many traders, including institutional ones, reduce activity or go on vacation before holidays, leading to thinner markets. Lower liquidity can amplify price moves in either direction. Combined with good sentiment from happy investors, it could result in temporary upward drifts.

Price-insensitive buying during less liquid markets would be a good reason for an effect to exist. When investors/traders are buying or selling regardless of price, that leads to inefficiencies that we can exploit.

While these seem reasonable and plausible, we will never know for sure what the true reason(s) for the effect are. That’s just the markets, they’re opaque. But the good news is we at least have a decent educated guess at the causes.

The reason understanding the likely causes is important is because if there were no reasons why we would think this effect would exist, it would be harder for me to trade such an effect. This is because I would fear it was just a data-mined result. Also, if the effect went away in the future and I didn’t understand the cause of the effect, I wouldn’t be able to assess whether the causes had changed to understand if the idea was actually dead or not.

There could be times when an effect like this temporarily underperforms. When that happens, if I understand the effect I can ask myself whether I see a reason for the cause of the effect to have changed. If so, I can reason the effect is dead. If I can’t see a reason for the cause of the effect to have changed, then I’d continue to trade the system, expecting the underperformance to revert soon.

The part of the question about data-mining we cannot answer yet. The rest of the article will be used to answer that question through a bunch of analysis. And, if we are lucky, we’ll end up with a trading system to harness the holiday effect.

Research Phase:

Let’s kick off the analysis of this potential market effect by understanding the baseline returns from the S&P 500 SPY ETF. On average, the S&P 500 has an upward drift. I don’t think there is an argument against that.

Knowing what the average upward drift gives us a threshold for understanding whether the holiday effect has returns above average market returns, or not. If the holiday effect is real, I would expect to see that the returns during the days leading up to the holiday are higher than the average market return.

Below is a scatter plot of close-to-close SPY returns. Grey dots are daily close-to-close observations. The orange line is the average daily SPY return.

Average SPY Daily Gain/Loss (1993 to 2026):

SPY daily gain and loss scatter plot since 1993. Each grey dot represents a close-to-close rate of change measurement. There is one for each day of SPY history. The orange line represents the average SPY close-to-close rate of change measurement.

The scatter plot shows the daily return can vary a lot, but on average is positive expectancy. The average SPY close-to-close daily gain is: 0.03998% (from 1993 to 2026).

This value makes sense. The SPY returns on average ~10% a year and if I multiply 0.03998% by 252 trading days a year I get 10.07%. Said differently, I could have just divided the SPY average return per year by the number of market trading days per year and found the same result.

Therefore, 0.03998% (or 0.04% for simplicity) is our line in the sand. Any return less than or equal to 0.04% per day is just market beta. But if we can find a consistent and repeatable effect with a return greater than 0.04% per day, well that’s alpha (or at least that’s how we will define it for these purposes).

Okay, we determined a threshold to use to define market beta vs. alpha. Now let’s see if we can measure the holiday effect to see whether it has a repeatable and consistent return above the market average.

To do this, I had to gather the dates of every federal holiday since 1993 (which is when my SPY ETF data starts). I considered all US federal holidays:

New Year’s Day
Martin Luther King Jr. Day
Presidents Day
Memorial Day
Juneteenth National Independence Day
Independence Day
Labor Day
Columbus Day
Veterans Day
Thanksgiving Day
Christmas Day

I then looked at the market returns leading up to these holidays. I did this measurement in a couple different ways. The first way is shown below, where I measured the market return 5, 7, 9, 11, 13, 15, 17, 19, 21, and 25 days before a holiday. This would simply measure:

“If I bought x days before every holiday, and then sold on the close of the last trading day before the holiday, what returns would I see on average?”

I measured the returns for every holiday, buying x days before the holiday and selling at the close on the last market day before the holiday. I then averaged all of the results together to understand the average return.

I also repeated this same process but instead of buying before holidays, I instead measured the average result of buying SPY during random periods of time; holding for x days, then selling. This is like our benchmark for this type of measurement. If the holiday effect is real, I would expect to see outperformance from buy x days before a holiday compared to the returns of randomly buying SPY for the same length of time.

Average Return Leading Up To A Federal Holiday vs. Average Market Return (1993 to 2026):

The x axis of this scatter plot shows how many days until the federal holiday. The y axis shows the average expected gain x days away from the federal holiday (blue dots). The average returns of the general market (SPY) over x days is plotted in orange. This is just a measurement of how much SPY returns over any set of x days since 1993. Note: this plot is counting calendar days until the holiday, not market days.

The blue dots show the average percent gain from buying x days before a federal holiday. The orange dots show the average returns of the SPY if you randomly bought and held for the same length of time.

You’ll notice that buying before holidays appears to have almost 2x the returns as the average market return. Or at least this is true from 5 days out to 17 days out from the holiday. Measuring from 19+ days out from the holiday, the returns seem to collapse back to the average market return.

This result tells us two things:

The holiday effect is showing very promising potential right from the start. Buying SPY leading up to a holiday seems like it may be a decent idea.
Buying closer to the holiday, within about 15 days or so, is better than buying further away from the holiday.

The chart above shows the average return for each set of days away from the holiday. But we can decompose that information further to better understand what’s going on beneath the surface.

I decided to look at the average returns for each day leading up to the holiday, decomposing down to the day-by-day returns. I measured this from 20 days out all the way to 1 day out from the holiday date. What this entailed was measuring the market returns of day 20 before a holiday, day 19, day 18 etc. I did this for all holidays and then averaged the result to understand the average return for each day leading into a holiday.

This will tell us exactly what drives the holiday effect. Is the effect solely driven by the returns of SPY the day before the holiday? Or maybe all of the alpha resided between days 3 and 7, for example.

Decomposing down to the day-by-day returns will help inform us of exactly where the effect resides.

The bar plot below shows exactly this. It shows the average SPY return from 20 days out to 1 day out from each federal holiday from 1993 to 2026.

Day-To-Day Returns Leading Up To A Federal Holiday (1993 to 2026):

This bar plot shows the average daily gain everyday leading up to a federal holiday. Dataset shown is from 1993 to 2026. If a bar is >0, that means on average that day is a positive gain. If a bar is <0, that means on average that day is a negative gain. Each day was measured on a close-to-close basis.

The left side of the bar plot shows the average SPY return 20 calendar days away from a federal holiday; the right side shows the average SPY return for 1 calendar day before a federal holiday. You’ll notice the left half of the plot (further from the holiday) looks noisy and unpredictable with random up and down days on average. But the right side of the plot (closer to the holiday) from calendar day ~11 right up until 1 calendar day before the holiday looks consistently positive.

This is exactly what we want to see. Long before a holiday the results look like average market noise. But as we get closer and closer to the holiday, the market returns start to look much more predictably positive.

Multi-Strategy Portfolio Allocation

TradeQuantiX — Mon, 02 Feb 2026 14:46:25 GMT

Introduction:

Over the past few months, the most common question I have been asked in terms of systematic trading is:

“How do you allocate capital to your strategies when running a multi-strategy portfolio?”

If you have one trading system, capital allocation is pretty simple: 100% of capital is allocated to the system.

If you have two trading systems, maybe you just opt for a simple 50/50 split of capital.

But, once you have multiple trading systems, capital allocation can become much more daunting.

Is equal weight allocation the best?

Is there some secret sauce to portfolio capital allocation I don’t know about?

What if I miss something, mess up my allocation, and my portfolio suffers as a result?

There is a lot of second guessing that happens during portfolio level capital allocation. To help with this, I want to walk through an eight step process that provides a sensible, simple, yet very effective approach to portfolio capital allocation.

Example Systems Used In This Article:

I am going to walk you through each of the eight capital allocation steps with an example portfolio. Thus, I need some example systems.

For this article, I leveraged five systems that we developed as part of the Portfolio Development Series; which we spent a lot of time on in 2025. Within that series I did cover portfolio capital allocation, but in this article I want to dive much deeper.

I will give a more in-depth, step-by-step framework on how to perform portfolio capital allocation which blends everything together that was discussed within the Portfolio Development Series.

To get more information about portfolio construction, consider checking out these two articles:

Let me introduce the five systems I will use to build a portfolio. The five systems are:

System 1 - US Mega Cap Mean Reversion
System 2 - US Mega Cap Momentum
System 3 - ASX Small Cap Trend Following
System 4 - TSX Momentum
System 5 - Long Volatility Hedging

You can see the results for each of the systems in the images below. I’ve also linked the articles where we developed these systems if you want more background.

System 1 - US Mega Cap Mean Reversion:

System 2 - US Mega Cap Momentum:

System 3 - ASX Small Cap Trend Following:

System 4 - TSX Momentum:

System 5 - Long Volatility Hedging:

Portfolio Allocation Steps:

Let’s get right into the step-by-step framework for sensible capital allocation within a multi-strategy portfolio. Like I mentioned, this implementation is straight forward but very effective.

Don’t get bogged down in super intense optimizations or super complex techniques. We want simple and effective, not complex an confusing.

We do not work for large hedge funds.

We do not have a team of PhD’s to work out the absolute optimal perfect allocations.

We are generally independent retail traders who just need the simple, yet effective, 80/20 solution. And that’s exactly what we are going to cover here.

The eight step multi-strategy portfolio capital allocation framework is summarized below as follows:

Investigating a Simple Trend Idea - Part 2

TradeQuantiX — Mon, 19 Jan 2026 13:19:17 GMT

Introduction:

In the previous article, we took a very simple trend-following system idea and dissected it down to its core components. We learned what causes the system to make money and what rules are the key drivers of performance. We also uncovered some concerning features about the system that need to be addressed as well.

At the end of the part 1 article, I listed some ideas to test in an attempt to improve the system or increase its robustness. In this article, we are going to try implementing those ideas and see what we come up with.

Mindset In System Development:

My goals are to make a system with as few rules and parameters as possible. Simplicity is key; a rule really has to show a significant effect on the system in order to convince me to keep it. Also, a simple rule with slightly worse performance almost always trumps a complex rule with slightly better performance. It’s almost guaranteed the more complex rule is overfit.

Remember, a singular system doesn’t matter that much; it’s just one piece of the portfolio. Hence why we want robustness over performance. Robustness keeps the portfolio from failing; the performance comes from many non-correlated robust systems working together, each mediocre on their own, but extremely powerful when combined.

That is the mindset I am approaching this system with: simple system concepts and a portfolio-level view. If you don’t have a portfolio-level view, it’s very easy to throw away perfectly good systems. Of all the systems I personally trade, I wouldn’t trade a single one in isolation. I would only trade them together within a whole portfolio.

When you think about systems in this way, it’s much easier to look at system performance and design more objectively. The ideal portfolio-level thought process should be:

“That drawdown in 2008? I could curve-fit it out by tweaking the index filter, but wait, I just remembered I have a hedging system that makes money during 2008-like events! Okay, fine, I’ll leave it alone. The next market crash will likely behave differently anyway, so curve-fitting to remove one bad event won’t change the impact a future bad event would have on the system.”

Going into system development work with a portfolio mindset is crucial as it helps remove the urge to curve-fit. See this article for more details on the portfolio-level mindset.

That’s enough of me blabbering about mindset. Let’s get to the good stuff. Let’s see what we can do with this system. As always, no promises it gets better; I write these articles as I do the investigation, so it’s true real-time learning for both you and me.

System Improvement Investigation:

As I mentioned in the part 1 article, there were a few ideas I listed to test for potential improvement of the system. To recap, those ideas were:

Add an entry rule to require a minimum amount of momentum prior to entry.
Ensure the stock has recently crossed the 200-day MA + two standard deviations for the entry signal.
Ensure volatility (standard deviations) is expanding to prove the stock is currently breaking out. Right now, a stock could calmly drift past the 2 standard deviations mark, which may mean that stock does not exhibit much momentum.
Remove the -2 standard deviations band from the exit rule. Just use the 200-day MA as the exit, or potentially add a trailing stop loss as well.
Allow the system to take more positions than 5.
Dynamically adjust volatility-based position sizing to help with those massive drawdowns.

If you remember, these ideas were not randomly pulled out of a hat. Instead, we tore the system apart to understand exactly what every component was doing, we looked at some example trades, and we ran some robustness sensitivities. Only from those observations were we able to come up with sensible ideas to test, rather than just random guesses based on a hunch.

To refresh what the in-sample results of the system looked like, below I have the system equity curve in green (benchmarked to the S&P 500 in red), followed by the performance stats of the system, and then the drawdown plot.

Baseline System Results (In-Sample):

This will be what we compare back to for many of the tests we will perform throughout the rest of the article. So burn those results into your memory… or just scroll back up in the article from time to time; that works too.

Let’s jump into the first system improvement idea (and remember, improvement doesn’t always mean better performance; it can also mean more robust!).

Change Number of Positions Held:

Investigating a Simple Trend Idea - Part 1

TradeQuantiX — Mon, 05 Jan 2026 14:52:30 GMT

Introduction:

The other day I was perusing the internet for some trading ideas for new systems. I tend to find lots of ideas laying around, but most don’t really pique my interest. The ideas I like the most are:

Simple in nature (simple tends to be more robust)
Based on real market phenomena
Are actually testable

Simple ideas are the best because they tend not to be fragile. Complex ideas on the other hand are extremely easy to curve fit and, in general, hard to test (have you ever tried to backtest a discressionary traders rules? You usually can’t, because there are 1,000 caveats!). Also, when a simple idea is based on some market effect that is proven, that’s even better.

That’s when I stumbled upon an idea that was so simple, I had to investigate it. The system was easily testable with only a few simple rules, and was based on a real market effect: momentum.

In this article let’s dive into the system. I want to attempt to replicate the results and then tear the system apart to better understand its innerworkings. Then, using what we learn about the system in this article, there will be a part two where we attempt to improve the system; where improvement can mean two things (and ideally means both):

To make more robust
To make have higher performance (though to make more robust is more important, higher performance should be the second goal after making a system more robust)

Anyway, let’s not get too in the weeds yet. Let’s jump into the system and try to code and understand it.

Replicating The Trend System:

So you can follow along, the link to where I found this system idea is listed below:

2025: A Year In Review

TradeQuantiX — Fri, 26 Dec 2025 15:56:20 GMT

Introduction:

It’s hard to believe that 2025 is already coming to an end and 2026 is just around the corner. This year has been full of meaningful wins and improvements, both in my systematic trading portfolio and on a personal level.

In this post, I will cover the following:

2025 live systematic trading performance update
Live performance compared to backtested performance
New systems added to the portfolio in 2025
System-by-system performance since inception
Biggest winning and losing trades in 2025
Planned portfolio updates for 2026
Long term goals (2026 and beyond)
Non-trading related wins in 2026

Let’s get into it!

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Portfolio Performance Charts:

We’ll start with what I would assume people care about the most, my systematic trading portfolio performance.

2025 was my best year thus far.

For anyone unaware, I started trying to figure out systematic trading in 2020. I spent 3-ish years performing all the most common retail mistakes of curve fitting, trying to make the holy grail system, falling for fake gurus, and ultimately losing money.

About 2.5 years ago I changed my methods and my equity curve started heading in the right direction. I’m still trying to figure this whole systematic trading thing out, but I think I’m getting closer every year.

2025 Live Recorded Performance:

Not too shabby, I can’t complain about 61%, especially considering the S&P 500 returned ~16.6%.

While a portfolio return of 61% is spectacular in my book, I cannot expect this every year. This is definitely on the upper end of returns when I compare back to my longer term backtested performance.

10 Year Backtested Performance (Equity Curve, Performance Stats, and Drawdown Plot):

Above is my systematic trading portfolio (blue) compared to the S&P 500 since 2010. For anyone wondering, the pre-2010 timeframe actually performs even better than the post-2010 timeframe.

The reason why I am only showing post-2010 is because I literally can’t backtest that far back all at once anymore. I simply do not have the computer power, I froze my PC multiple times trying to run a backtest from the early 1990’s until today. I am debating buying a better PC next year because I do think it’s important to be able to work with as much data as possible when making portfolio level decisions.

The reason why backtesting back until the 1990’s takes so much computer power is because I’m trading many systems across three markets: USA, ASX, and TSX. That’s over 50,000 stock ticker symbols which need to be processed every day in the portfolio backtest. Just since 2010, that’s over 200 million computations per indicator. So, you could see how that could add up pretty fast…

Could I make my coding more efficient to reduce the number of computations? Maybe, but why would I do that when I can use it as an excuse to buy a new PC?

Anyway, one thing I don’t like about my portfolio is I hold a lot of leveraged beta. You can tell because the equity curve shape somewhat mirrors the S&P 500, just with higher returns (but also lower drawdowns!).

This intuitively makes sense because I trade a lot of trend and momentum systems. That said, I want to start exploring systems that hold very little beta and have lesser correlation to the underlying market. I do have some hedging systems and systems that have relatively low correlation to the general market, but I need more. Hence, 2026 will be the year of diversification in the portfolio.

10 Year Backtested Performance (Drawdown Length Plot):

A cool plot I don’t see too often is the drawdown length plot. This shows how many days the portfolio has been in a drawdown historically.

Most of the time I am only in a drawdown for a handful of days, but occasionally I’m in a drawdown for over 6 months.

If you run a portfolio backtest pre-2010, there are periods where I would have been in a drawdown for over a year. So patience is key!

10 Year Backtested Performance (Exposure Plot):

I think it’s important to point out that in my portfolio I do use leverage from time to time. I try not to hold leverage for very extended periods of time, rather just short term spikes.

Generally speaking, I use leverage when I am fully allocated or near fully allocated to my longer term trend/momentum systems and then a short term trading systems kick on (like a mean reversion system). These scenarios tend to kick me into some leverage.

While this is riskier than using only cash (no leverage), I am typically only using leverage for a short period of time (lasting just a couple of days on average). I’m comfortable holding modest leverage for brief periods of time to allow my shorter term systems to trade.

I’ll note, while I am okay with some leverage, I do think my current allocations can cause leverage can spike just a little too high. This year, my portfolio spiked to around 150% exposure at one point and I was a bit uncomfortable.

Hence, I am slowly trying to shift around some system allocations to reduce the amount of leverage the portfolio can use. I would prefer to cap short term leverage at closer to 140%.

It’s all an iterative and continuous improvement project for me. Once I learn something new or test my own psychological limits, I take those learnings and experiences and use them as a feedback loop to tune the behavior of the portfolio such that it only gets better over time.

10 Year Backtested Performance (Number Of Position Held Plot):

You’ll notice my portfolio holds many positions at once. Commonly sitting at 130-140 different stocks and occasionally spiking up to 180+. Luckily I use an order management software to keep tabs on all these positions because I could never track all of this myself.

Generally, I have no idea what stocks I hold. I could maybe list off 4 or 5 just because I noticed the ticker and I am familiar with the company; but I don’t know what 97% of my positions are.

And frankly, I don’t really care. These stocks are displaying some sort of momentum, trend, or mean reverting qualities and thus my systems buy or sell them. That’s all that matters to me.

So don’t ask me what stocks to buy or what the next hot sector will be because I have no idea. I don’t even know what I currently hold in my own portfolio. Usually when I tell people that they look at me funny, which is always entertaining.

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Newest System Additions In 2025:

This year I implemented two new systems into the trading portfolio. At this point, I am basically at capacity in terms of the number of systems I can trade in relation to my account size. But I am currently saving up some cash so I can add it to the portfolio and deploy more systems in 2026.

System #1:

The first system I added in 2025 was a Canadian (TSX) stock weekly trend-following system. I already traded a daily trend-following system on the TSX, but I figured moving to a higher timeframe would filter out noise and capture even longer term trends.

I’m a big believer in stacking multiple simple, but different, systems together. One system may operate on the daily timeframe, another on the weekly, one system may be looking for breakouts to new highs, another may be chasing relative momentum, one system may use short term lookbacks, another uses long term etc.

What this does is stack multiple simple features together, and when they are all traded side-by-side it creates a sort of meta-system that naturally scales into the strongest stocks and scales out of the weakest stocks.

The concept of the new TSX weekly trend following system is extremely simple: buy higher-timeframe/very long term breakouts. That’s it. Despite the simplicity, the historical performance has been powerful and should meaningfully add to my portfolio over time.

The system holds positions for very long periods of time, often many months or even years, but that’s intentional. Once the system captures a big winner, I want to hold it as long as the trend persists.

Market: TSX
Type: Trend Following
Side: Long
Timeframe: Weekly

I launched this system live in May 2025, so it’s only had about seven months of out-of-sample live performance. For a system with such long holding periods, seven months isn’t much and is not meaningfully significant yet; but here’s the live equity curve so far (this curve is constructed based on the actual trades generated by this system that I took in my trading account):

As you can see, it’s choppy but it’s headed in the right direction. As I said, seven months of live data is somewhat meaningless for this type of system; so we’ll need to check back in another year or so to get a better sense of how it’s holding up. Either way, I’m happy with the performance thus far.

System #2:

The most significant improvement to the portfolio this year was the implementation of what I call the “mean reversion mini-portfolio.” I call this “system” a “mini-portfolio” because it’s actually 6 very simple signals all stacked on top of each other.

It’s a meta system that combines 6 very simple ideas together to make one mini-portfolio. I then integrated this mean reversion mini-portfolio into my overall total portfolio allocation. The simulated impact of the mean reversion mini-portfolio on my overall portfolio performance is very promising.

Up until this point, I only had a handful of mean reversion systems. The vast majority of my portfolio allocation was towards long term trend and momentum systems. Thus, adding this mean reversion mini-portfolio provides a complementary trading style to the longer term systems which should have a massive positive impact on the overall portfolio.

Here’s the backtested performance of the mean reversion mini-portfolio over the past 15 years (the pink line is the S&P 500 and the blue line is the mean reversion portfolio):

Market: USA
Type: Mean Reversion
Side: Long
Timeframe: Daily

That’s a pretty nice-looking equity curve, if I do say so myself. Not too bad for simply stacking 6 simple signals together. I’m very excited about it and happy to have it running live (and so far the live performance matches very well with the simulated performance).

I’ve historically been hesitant about mean reversion systems. Many mean reversion ideas I’ve stumbled across have felt fragile. In my experience, mean reversion is extremely easy to overfit. Most mean reversion systems I’ve found online are overfit as well.

Hence, I took a step back, found 6 super simple mean reversion features/measurements that work very well, have very few parameters, are stupidly simple, and stacked them all together.

Most mean reversion systems combine rules on top of rules to curve fit a fantastic result. This system works differently. Each mildly predictive feature (rule) of the system operates independently of the others.

I’m not curve fitting to find the perfect combination of rules with the perfect parameters to get this result. I’m trading many singular rules individually (with a broad parameter space that works), and the combined result is much more robust and interesting for me to allocate capital to.

See the plot below to see a breakdown of the many equity curves that make up this mean reversion mini-portfolio. Each line represents one feature (rule) applied to one of the tradable instruments.

You’ll notice some of these equity curves look great, some look okay, and some are flat or actually lose money over the long term. I don’t care, I trade them all. When you stack all of these simple results together, the result is very interesting, at least to me.

One thing that’s cool about mean reversion is it generally uses very minimal capital. Occasionally capital exposure spikes to 100%, which you have to be careful about in terms of total portfolio level allocation, but the vast majority of the time the mean reversion portfolio is sitting around 15% to 40% exposure. Thus, the returns are pretty interesting given the efficiency of the capital usage. See the exposure plot over time below:

Oh, and for anyone concerned about the backtest only going back to 2009 (that’s generally when the traded instruments became available, which are leveraged ETFs), I ran the mean reversion portfolio on non-leveraged ETFs back to 2000. Obviously the returns would be less, but the stability of the equity curve is what’s more important here.

The mean reversion mini-portfolio still holds up great. There’s a few bumps and bruises like in 2001 and some volatility in 2008, but overall not too bad.

It’s also worth noting, no data pre-2009 was used in the development of this system, so that’s pure out-of-sample.

Also, 2018 onwards was used for out-of-sample testing as well. So, 2009 to 2018 was the development/fitting period and everything else was completely unseen. Not too shabby if I do say so myself.

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System By System Live Performance (Since Inception):

Thus far, I’ve shown the backtested performance of the entire portfolio, the live performance in my Interactive Brokers account, and the two new systems I added to the portfolio in 2025. This section will show my system-by-system live performance. These plots will show how each system has performed individually.

The ROR (rate of return, same as CAGR) and Max Drawdown metrics shown below won’t really make sense because they are being calculated based on the entire account size; when in reality each of these systems is only allocated a small fraction of the full account size.

So, for example, if the full trading account was $100,000 and a system stated a live ROR of 5%, but the system was only allocated 20% of the account, then the true live ROR of the system would be closer to 25% (assuming the system traded the whole account). Basically, the ROR and Max Drawdown stats are scaled down by the system allocation percentage.

I figured it was worth pointing that out so you weren't completely confused by what some of these numbers mean. I don’t explicitly label it, but most systems are somewhere in the mid-teens in terms of percentage portfolio allocation.

It’s also worth noting, some of these systems I have been trading for a little over a year before the plots shown below display. I was using a different backtesting and order management system back then which did not allow me to display the plots I will be showing in this article. Since I switched over to my current infrastructure in early 2024, I have much better records and more ways to process and display data.

It’s also worth pointing out some of the systems listed below are “meta-systems”. Meaning, the system is made up of multiple mini-systems that all work together to make the final system (similar to how the mean reversion mini-portfolio works). So if you were to count all of the systems I have including every mini system within a meta-system, I would have over 30 systems in total. But, if you count the meta-systems as just one system, then I have only 13 systems total.

The meta-systems are shown below in blue (only the aggregated result of the meta-systems are shown, rather than the result of each small component of the meta-system), and the singular systems are shown in green (these systems are not made of many smaller simpler systems).

Below I will show tracked system-by-system live performance as seen by my trading account since March 2024 (except for the couple new systems, which started at a later date).

Alright, that’s enough of me being confusing and stating caveats, here is the system-by-system live performance.

Market: ASX
Type: Mean Reversion
Side: Long
Timeframe: Daily

Market: ASX
Type: Trend Following
Side: Long
Timeframe: Daily

Market: ASX
Type: Momentum
Side: Long
Timeframe: Monthly

Market: ASX
Type: Trend Following
Side: Long
Timeframe: Weekly

Market: TSX
Type: Mean Reversion
Side: Long
Timeframe: Daily

Market: TSX
Type: Trend Following
Side: Long
Timeframe: Daily

Market: TSX
Type: Trend Following
Side: Long
Timeframe: Weekly

Market: USA
Type: Trend Following
Side: Long
Timeframe: Daily

Market: USA
Type: Trend Following / Hedging
Side: Long (Long Volatility)
Timeframe: Daily

Market: USA
Type: Trend Following
Side: Short
Timeframe: Daily

System has never turned on… maybe one day.

Market: USA
Type: Mean Reversion
Side: Short
Timeframe: Daily

Market: USA
Type: Momentum
Side: Long
Timeframe: Monthly

Market: USA
Type: Mean Reversion
Side: Long
Timeframe: Daily

2025 Biggest Winners and Losers (on a percent basis):

For fun, here are some of my biggest winning and losing trades of the year. I’ve had several big winners that I held for most of the year (and even for part of 2024) as they slowly climbed higher.

I’ve also had some big losers, especially on the short side, but generally speaking big losers have been infrequent. Most of my losers are relatively small. Even the larger losers (on a percent basis) are relatively small compared to the entire portfolio in terms of dollar value. So, I am controlling losers via a relatively tight exit and/or with relatively small position sizes.

Below I show a series of screenshots of trade stats tables corresponding with a big winning or losing trade. Then right below the table I show a plot of the stock price action and the trade taken (and for some trades I have the stop loss plotted as well).

Note: These trades are both long and short, so keep that in mind in terms of direction. The plots note if the trade is a long or short trade. Also, some of these trades are mean reversion trades while others are long term momentum/trend trades, so the the definition of “big” winner or loser is relative to the system type the trade was taken from.

Biggest Winners:

2025 Biggest Losers:

To be honest, because I run a portfolio comprised of many systems and I typically hold 140+ positions at any given time, I often don’t even know what my biggest winners or losers are until I specifically go look. I only discovered the trades listed above when I was preparing this section of the article.

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Portfolio Updates Planned For 2026:

In 2026, I have some portfolio updates to implement. Some of these updates include adding new systems, changing some system allocations, and even turning some systems off.

Update #1:

I’ll start first with the bad news. My ASX mean reversion system has not been working as well as I had hoped. Since inception in early 2024, it has just been chopping sideways.

You can see the entire system backtest below. To understand the performance degradation I am referring to, compare the performance from 2000-2023 to 2023-onwards.

Market: ASX
Type: Mean Reversion
Side: Long
Timeframe: Daily

Could this system have been curve fit by me? Quite possibly. But also, the system works just fine on the TSX too. See below for this same system on the TSX market (I also trade this same system on the TSX, aside from one small tweak to the system).

Market: TSX
Type: Mean Reversion
Side: Long
Timeframe: Daily

Similar behavior occurs on the TSX as well. Recent performance has flatlined compared to historical performance. So, it could be curve fitting, it could also be the system simply doesn’t work anymore.

Either way, I am going to turn off the ASX version of this system. It is simply tying up too much capital for little to no reward. Could the system turn around? Sure it could, but it’s not worth paying the commissions waiting for that to happen. The system doesn’t return that much anyway and I have much better systems to focus my efforts on at this point.

For now, I will keep the TSX version of this system running. The TSX version seems to be turning around and starting to go back upwards. That said, the system will be on probation. I won’t hesitate to turn the TSX version of the system off if the performance starts to degrade.

This is the first system I have turned off since ~2023 (pre-2023 I had no idea what I was doing and all of my systems lost money, though you could argue I still have very little idea what I’m doing). I accept it’s part of the game. Systems fail or don’t behave as expected. At least in this case the system didn’t die in a catastrophic drawdown, but rather just sideways chop.

Update #2:

Another update I plan for 2026 is to reduce the allocation to my new mean reversion mini-portfolio. I think I was over-excited and came in hot with the initial allocation percentage. This resulted in more leverage than I care for, at least that’s what I learned the hard way.

There were a few days around the end of November where I was pretty nervous about my exposure going into the weekend. I am normally unphased with my portfolio and it’s allocations, but I was holding a decent bit of leverage (150% exposure), my portfolio NLV had a ~10% drawdown overnight, and I simply was more concerned about it than I would like to be.

The best way to fix that is to simply lower some allocations to prevent this in the future.

You can actually see this -10% day on my live performance plot below, circled in red:

Luckily the portfolio recovered quickly, but I learned my lesson… the hard way… like I always do.

I already reduced the allocation to the mean reversion mini-portfolio a little bit this past weekend, but I will likely lower it a little more. I came out guns blazing, then scurried back into my hole with my tail tucked between my legs. I plan to not let that happen in the future.

Update #3:

One of the other areas where I plan to make some headway in 2026 is to add systems which provide more short-side and hedging exposure; as well as systems which reduce my exposure to leveraged beta. Ultimately, I would like to reduce my portfolio level correlation to the underlying market.

Hence, 2026 will be the year of exploring new strategy types on different markets or instruments than what I currently trade. I think this will give me a lot of bang for my buck. Adding more trend or momentum will add very little to my portfolio at this point.

If you have any ideas/suggestions for system concepts to explore, please let me know!

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Long Term Systematic Trading Goals:

My ultimate long term goal is to be able to live off my systematic trading portfolio. Rough napkin math says I need at least a low 7-figure portfolio to be able to do this.

Currently I’m sitting in the lower half of 6-figures, so I am still very much in the building and growing phase. I’m trying to shove as much of my savings as possible into my portfolio so compounding can do its magic.

I’m an entrepreneur by heart and I dislike the corporate world with a passion. I enjoy being my own boss, making my own decisions, and being self sufficient; hence why I want to be an independent systematic trader and live off my portfolio so badly.

When I was in college and for the first year out of college, I started and operated a small landscaping business that was pulling in 5 figures a month in profit. Still to this day, I regret leaving that small business I built to go into the corporate world.

Running my own business was the most fun I ever had working. Since I treat my systematic trading like a business, I see it as no different. I want to run my trading account as my business and be the boss of the business and report to no one except myself.

Assuming a long term CAGR of 25% (my backtested portfolio CAGR sits at 35%, but let’s conservatively assume that decays a bit), if I add no more external capital to my portfolio, I could be in the 7-figure range in 8-9 years.

But, if I work hard, save hard, and keep adding capital to the portfolio, I could be in the 7-figure range as early as 6-7 years.

Either way, I still have a bit of runway to go before I have a chance at living off my portfolio; and until I get there I’m going to be treating my systematic trading account like a savings account.

One big gap in my systematic trading portfolio is the ability to profit in bear markets and during market shocks. I do have some hedging systems within the portfolio that do this, but I want more. I’m very long bias, which is fine as equities tend to drift up over time, but I need to be more prepared for less friendly market environments.

Sitting in cash and/or massive drawdown because of a large market crash next year would elongate the time until I can live off my systematic trading portfolio. Hence, in 2026 I’ll be focusing on researching new trading systems so that I am more prepared for less favorable market conditions, and to reduce my leveraged beta exposure.

Non-Trading Related Wins In 2025:

I figure I could share a few things non-trading related which were positive expectancy that happened to me in 2025.

During the first 6 months of 2025, I lost 50 pounds. I do the typical bulk-cut cycle that lifters tend to do. Though this last bulk lasted 3 years, so I added some fluffiness.

I spent January thru July losing all that weight, dropping from 225 lbs back down to a healthy and lean 175 lbs. My blood pressure and other health metrics have all improved and I feel fantastic at 175 lbs compared to 225 lbs, where I felt slow and sluggish.

Another positive outcome for me was I just learned I had the highest performance rating on my team at my day job. That should result in a healthy raise and bonus next year, and you better believe that’s all going into the systematic trading account.

Though I want to be a self sufficient systematic trader and not be in the corporate world forever, I do recognize the importance of performing well at my job during this time of building my trading account. I focus on performing well at the day job because that income fuels my systematic trading and allows the account to grow even faster.

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Conclusion:

Wrapping up 2025, I have to say this has been my strongest year yet, both in terms of portfolio performance and the progress I’ve made in building something robust and sustainable.

What I am the most excited about this year is I finally feel like I’ve developed a mean reversion approach that actually feels right for me. Although I had some leverage lessons learned the hard way, that’s just part of the iterative learning and improvement process.

I can’t complain about the performance of the portfolio at all, this year has been fantastic. Fingers crossed performance continues into 2026. But realistically, I’d be happy with half the performance I had this year.

In 2026 I plan to continue to evolve the portfolio. I’ll be turning off some underperforming strategies, fixing some system allocations, and most importantly, researching and developing new strategies that reduce my exposure to leveraged beta. If you have any suggestions on what ideas I should focus on, seriously DM me or comment below; I’m all ears.

Ultimately, I’m still in the building phase; both in terms of developing more systems and trying to compound my account. It’s going to take a lot of patience, discipline, and a likely 7+ more years of smart iterations and development, but I am finally starting to feel like I’m walking down the right path, or at least walking in the right general direction.

Thanks for reading and please feel free to provide any thoughts or feedback, I’m always happy to learn and consider new ideas/methods.

Disclaimer

The information and services provided by the Systematic Trading with TradeQuantiX newsletter are for educational and informational purposes only and do not constitute financial advice, investment recommendations, or guarantees of trading performance. Any information provided is general in nature and does not take into account your individual circumstances. Trading involves significant risks, including the potential for substantial financial losses, and past performance is not indicative of future results. The information is provided ‘as is’ without warranty of accuracy or completeness. All decisions and actions based on the information provided are the sole responsibility of the reader. You should seek independent professional advice before trading. The Systematic Trading with TradeQuantiX newsletter is not liable for any losses, damages, or outcomes resulting from the use of this information or our services.

Digging Up An Old Trading System From The Graveyard - Part 2

TradeQuantiX — Mon, 15 Dec 2025 15:15:37 GMT

Introduction:

Let’s continue working on the system we took a deep dive into in the previous article. To recap, I had found a random system buried in my files that had positive forward-tested out-of-sample results. I then took that system, tore it apart, then built it back together slightly simpler and with position sizing updates.

In this article, I want to move on to running some robustness tests on the system to ensure the system does not show signs of curve fitting or risk of future degradation. The robustness tests I will run are:

Parameter Sensitivity Tests (focusing on the out-of-sample time period)
Entry Rule Monte Carlo
Slippage Sensitivity
Different Universe Tests

Then, I would like to take the learnings from the robustness tests and do what I like to call “building robustness into the system”. This is the technique I have been implementing into every new system I develop prior to going live with the system.

From my experience, this technique has helped with system stability and forward live out-of-sample performance. It also inherently allows me to sleep better at night knowing I am trading more robust systems that are less prone to single points of failure.

As a refresher, this is the current result of the system we are working with:

Alright, let’s dive in and see where this system shines and where the system has flaws. Ultimately, we need to decide if the system is worthy of the portfolio or not. So, let’s begin some robustness tests!

Parameter Sensitivity:

The first robustness test to assess is a parameter sensitivity. If you remember from the part 1 article, I ran a parameter sensitivity on the in-sample. Now, I want to run the same parameter sensitivity on the out-of-sample. I want to focus specifically on the out of sample because I want to understand a couple things:

Is the out-of-sample time period more “fragile” or sensitive to parameter values than the in-sample?
Did the out-of-sample get “lucky” in terms of positive performance

It’s 100% possible to have a lucky out-of-sample result that only had positive performance due to shear luck. The parameters chosen in the system could be on an out-of-sample performance peak and just happened to make money during the out-of-sample period. But going forward in time, that performance peak will eventually collapse or shift, leading to poor or negative results. This is what we want to check for.

Hence, I ran a +-25% parameter variation on all parameters within the system, specifically on the out-of-sample time period. The table below shows the percentile the default system parameters reside in terms of performance.

For all metrics, the system was basically right in the middle of the road, in fact the system performance was slightly less than the average result. The only exception was Percent Wins being 67%, but for all other metrics, the system was below the 50th percentile.

Assuming all results within the +-25% parameter space are positive, this is exactly what you want to see. You do not want the systems default parameters to be in a super high percentile. That would be evidence of selecting a performance peak that just hasn’t shifted yet, i.e. a lucky out-of-sample result.

The fact that this system is in the middle of the road basically means as the optimal parameters shift around over time, this system is right in the middle of the road and has the wiggle room to handle the noise in the parameter surface.

Visualize it this way, it’s much easier for a new driver to drive a car down the middle of the road than right on the edge. When driving down the middle of the road, there is plenty of room on either side to account for the new drivers poor driving skills. When driving right on the edge of the road, one side has no margin, one wrong move and the car is in a ditch. Kind of a dumb analogy, but that’s how I think about it.

Now, let’s compare some of the results from this +-25% optimization. I’ll show the default system parameters out-of-sample performance first, then I’ll show the best and worst results in terms of Calmar ratio (ROR/MaxDD).

Default Parameters Out-of-Sample:

Next I’ll show the equity curve of the best result in terms of Calmar ratio from the +-25% optimization.

Best Result (Based on Calmar Ratio):

You’ll notice this parameter set happened to miss Covid, then go on a steep upward slope, making new high after new high. It’s good to see results significantly better than the default parameter set.

I would never complain if the parameter surface shifted in this direction and the default parameter system started seeing performance like this. If the markets shift and the default parameters start performing better than they do currently, it’s nice to see this type of performance is possible.

Worst Result (Based on Calmar Ratio):

Above is the results of the worst equity curve based on Calmar ratio. You’ll notice these results have very few trades (125 vs. the default systems 580). Basically this parameter set within the +-25% range is choking the system performance. The oversold measuring parameters become very low, and the ADX parameter is very high, leading to a very restrictive set of conditions which would result in a trade. Basically, this was the most stringent possible parameter set within the +-25% results.

On one hand, this is an ugly result. On the other hand, even though this variation of the system had an overly strict parameter combination, the system still worked its way up and to the right. This is a good sign that even under very stringent scenarios, the system rules still resulted in a positive outcome.

Let’s now check out a more realistic worst case result from the +-25% parameter variation. The above parameter set only had 125 trades during the out-of-sample period while the default parameters have 580 trades. Let’s again sort the results of the +-25% parameter variation by the worst Calmar ratio, but check out a system with slightly more trades, say 350 trades. This will let us see the results of a “poor performing” parameter set that does not choke the system.

Realistic Worst Result (Based on Calmar Ratio):

Okay, this parameter set had 349 trades, close enough to 350. As you can see, with a parameter set that is not as restrictive, even the results on the worse end of the +-25% parameter variation are pretty good. I would trade this parameter set, even though it’s in the bottom 4% of parameter values in terms of Calmar.

Overall, the out-of-sample +-25% parameter set variation shows promising results. The system appears to be in a very stable range of parameters which is exactly what we want to see. All +-25% parameter variations make money in the out-of-sample time period and every equity curve that has a reasonable number of trades is profitable and tradable.

Entry Rule Monte Carlo:

A neat Monte Carlo you can run to test the systems ability to choose good trades is via Entry Rule Monte Carlo. I’ve seen many systems fail this test which is why I like this test a lot.

Digging Up An Old Trading System From The Graveyard - Part 1

TradeQuantiX — Tue, 02 Dec 2025 15:18:24 GMT

Introduction:

I was digging through my folders the other day when I stumbled across a handful of mean reversion strategies that I had forgotten about. It was like finding buried treasure, except it was buried deep in my folders rather than in the ground. I couldn’t remember when I developed these strategies or where I had gotten the ideas from. Luckily the files were timestamped, which showed it has been over a year since these strategies had been touched.

There were two strategies in particular that I found to be quite interesting. In this article I want to focus on one of those two strategies (maybe in a different article we can focus on the other strategy).

What I want to accomplish in this article is:

Share the current starting point of the strategy and what the forward tested out-of-sample incubation looks like.
Then I want to tear the strategy apart to understand its core components
I want to ensure there are not any misses or new learnings that need to be incorporated into the strategy.
Then I want to try to improve the strategy further (if possible)
Then we’ll piece the strategy back together

Hopefully when it’s all said and done we have a new strategy that we can add to our database.

The Hidden Treasure Strategy:

The strategy that I stumbled across was a short-term mean reversion system. The strategy trades mega-cap stocks, only buying oversold conditions on stocks that are trending.

The system enters positions using a limit order. The system exits when the high is above the previous day’s high, or the position has been held for 10 days.

The system holds up to 10 positions. If more setups exist than the 10 slots, the system ranks and prioritizes stocks that have increased the most over the previous year.

See below for the detailed list of system rules:

Most Systematic Traders Use Regime Filters Improperly

TradeQuantiX — Mon, 17 Nov 2025 14:08:43 GMT

Introduction:

Regime filters are a common component within trading systems. I would say about half of my trading systems have some sort of regime filter built in. While not a required component, regime filters can be a nice overlay on top of an already well-built system, if done right.

Regime filters can reduce drawdowns and boost risk-adjusted returns, but they are also the easiest component of a trading system to mess up. In fact, they are probably one of the most common components of a trading system that are implemented improperly.

That’s not because regime filters are difficult to implement, in fact, it’s the opposite. Regime filters are actually too easy to implement, and thus many times lead traders down a path of over optimization and curve fitting to historical data.

In this article, I want to dive into regime filters. I want to define what they should be used for, and what they should not be used for. I will also test out some different regime filters on three different example systems to see the effect of the regime filter.

Note: I use the terms index filter and regime filter interchangeably, so don’t be confused if I keep flip flopping the terminology.

Why Regime Filters Can Be Problematic:

Why are regime filters so easy to mess up?

Let’s first consider a trading system without a regime filter. Let’s say this trading system has over 1000 trades over the course of a 20-year backtest. 1000 trades is a reasonably respectable sample size. Sure, you could definitely still curve fit a system with 1000 trades, but you’re starting to get to a number of trades where you can actually make reasonable decisions based on a sample size this large.

Let’s say you’ve finished developing this system: you’ve chosen the rules, you’ve chosen stable parameters, and you’re almost happy with the system. The last thing you think is missing is some sort of regime or index filter to try to reduce drawdowns just a little bit more.

So, you start trying index filters, you start optimizing parameters, and before you know it, you’ve cut the drawdown in half and somehow managed to keep the returns of the system exactly the same. Does this sound familiar? If the regime filter boosts the performance of the system that dramatically, I would be hesitant and really consider if that performance increase is actually real.

I see this happen all the time, and I used to do this all the time as well. The problem is you took a system that has a large sample size of data points (trades), and throw an index filter on top which has probably only triggered a handful of times in the backtest.

Maybe there’s only been 10 or 15 times the index filter has triggered over the past 20 years. You’re taking a higher confidence 1000 trade sample size system and diluting that confidence with a regime filter overlay that only has a handful of data points. And if you optimize and fiddle with that regime filter, you’ll find its pretty easy to curve fit to 10-15 data points.

When you go live with this strategy, odds are that this regime filter is actually going to make the results worse in the future compared to the strategy without the regime filter. Even though the underlying strategy may not be curve fit, throwing a curve fit overlay on top of the strategy adds noise and reduces the effectiveness of the underlying signal of the strategy.

Imagine trying to trade a trend following strategy, but every day you had to roll the dice, and every time you roll a 6 you had to sell all of your positions. Then, you could only rebuy all your positions when you roll a 6 again. That’s the equivalent of trading a system with a curve fit regime filter built on top of it.

This is why it is critical to get regime filters right, and by the end of this article, I think you’re going to find that there is no secret sauce to regime filters. So, stop overoptimizing them to death, you are not going to find a perfect result that actually continues with that performance into the future.

There Is No Secret Sauce With Regime Filters:

I know you probably want to hear there’s some secret regime filter that filters out all the drawdowns. Or there’s a special regime filter that tells you exactly when to get in to the market to make the most amount of profits.

I’m here to tell you that does not exist. Just like the Holy Grail trading system does not exist, and getting rich quick does not exist; regime filters that make strategies perfect do not exist. In fact, any regime filter that makes a strategy perfect in a backtest will probably make that strategy worse in live trading.

If you ever find yourself developing a strategy and you just can’t get the strategy to work, but then you slap a regime filter on top of it, do some optimization, and then the strategy looks tradeable, I would take a step back and consider what you actually just did.

A regime filter is not a Band-Aid; it’s simply a finishing touch on an already high quality system.

It is not the fix to a failing system.

If the underlying system is failing, but the regime filter appears to fix it, I would probably not trade that system.

I’ve learned this lesson the hard way in the past, by using ever more restrictive regime filters to fix a failing system. You find yourself always chasing performance. You fix the system by increasing the restrictiveness of the regime filter, but going forward in time, the system continues to degrade, so you make the regime filter even more restrictive. Rinse and repeat, always chasing the performance in the backtest, but never actually achieving it in your actual account.

Long story short, regime filters can help you sidestep some unfavorable market times, but the system should still be tradeable without a regime filter. Now, that doesn’t mean the system is amazing without the regime filter, but it should still be tradeable. If a system has a 30% drawdown without a regime filter and a 20% drawdown with the regime filter, I would generally think that’s acceptable.

Heck, even if the system without the regime filter had a 40% to 50% drawdown but with the regime filter had a 20% to 30% drawdown, I might even consider that acceptable. Now, you’re probably saying,

“40% to 50% drawdown? That’s not tradeable!”

And yeah, I get your point, but consider buy-and-hold, that has a 60%+ drawdown, and there are hundreds of millions of people trading that strategy, so I would consider that tradeable.

But if the system without the regime filter has an 80% drawdown and then with the regime filter only has a 20% drawdown, that might be pretty worrisome.

I guess the point that I’m getting at is the regime filter should not be the only reason that the system works. The system should work and have some sort of edge without it. The regime filter is just a helping hand to better keep the system on the right side of macro events, on average.

The Three Baseline Systems:

Now that you understand my thought process on regime filters, let’s test some out on a few systems. I will test these regime filters on three different systems. All three of these systems have been developed in a previous article, and I’m going to leverage them as our regime filter test dummy systems.

These three systems are the US momentum system, the US trend system, and the ASX trend system. See the links below to learn more about these systems, if interested:

US Momentum:

US Trend:

ASX Trend:

I’ll note, the US systems already have a simple regime filter built into them, but the ASX system does not. So first, let me show the US systems with and without the regime filter so that we can better understand the starting point for comparisons later on. The ASX system does not have a regime filter, so there will be no with and without regime filter comparison.

US Momentum System With Regime Filter (Index > 200 day moving average):

Note: This system splits its capital across the S&P 100, Russell 1000, and Nasdaq 100 universes. The yellow, red, and green equity curves represent the equity curves of these three universes, the dark blue is the result of these three universes combined together. I consider this as one system with intentional diversification between universes within it.

US Momentum System Without Regime Filter :

US Trend System With Regime Filter (SPX or NDX Close > Close 200 Days Ago):

Note: This system splits its capital between two different ranking mechanisms (momentum and low-volatility, two real market effects). The red and green equity curves represent the equity curves of these two ranking mechanisms, the dark blue is the result of these two ranking mechanisms combined together. I consider this as one system with intentional diversification between ranking mechanisms within it.

US Trend System Without Regime Filter:

ASX Trend System Without Regime Filter (baseline system does not use a regime filter):

You’ll notice all the systems still work fine without a regime filter. That’s because these systems are robust and built on sound fundamental principles.

The index filter is just a small and simple addition to try and improve performance a little more by putting the system on the right side of the general market trend.

Regime Filter Testing:

Using the three systems above, I will test out multiple different regime filters and show the impact on the three systems. I chose three different systems on two different markets because regime filters behave differently depending on the context of the underlying system. Hence, testing across three systems will give a better feel for the significance of the regime filter.

The regime filters that I plan to test are listed below. Each regime filter implemented will be tested in two ways. The first is applying the regime filter to both the entry and exit logic. Meaning only enter if the regime filter says so, then if the regime filter turns off, exit the position.

The second way is to only apply the regime filter to the entry and let the system exit naturally. This means only enter if the regime filter says so, but if the regime filter turns off, then do nothing and let the system exit its positions via trailing stop or other system specific method of exiting individual positions.

The regime filters to test are listed below:

Enter if VIX < x, Exit if VIX > x
Enter if VIX < x, No index based exit
Enter if Index > MA, Exit if Index < MA
Enter if Index > MA, No index based exit
Enter if Index within x% of recent high, Exit if Index below x% of recent high
Enter if Index within x% of recent high, No index based exit
Enter if Index return is greater than x% in y timeframe, Exit if Index return is less than x% in y timeframe
Enter if Index return is greater than x% in y timeframe, No index based exit
Enter if x% of stocks > MA (breadth filter), Exit if x% of stocks < MA (breadth filter)
Enter if x% of stocks > MA (breadth filter), No breadth based exit

One at a time, I tested and applied these regime filters to the three test systems. Below, I’ll go one by one and show the result of the regime filters on the three test systems and compare them back to the baseline so you can understand how each regime filter stacks up.

I’m about to show a lot of plots and tables, so let me quickly share how its all organized. I will show the regime filter tests in the same order as listed above. Then I will show a table showing the sensitivity of that regime filters to different parameter values, with the bottom rows in the table reserved to show the baseline results for benchmarking.

Then below the table I will show 1-2 equity curves for the system. These equity curves will be the system with the relevant regime filter applied, corresponding with one of the parameters in the table. I chose to show the equity curves that I thought where interesting and/or representative of the index filters results on the system. I will show these tables and equity curves for each of the three systems. Then I will move onto the next regime filter and repeat.

It’s also worth noting, the baseline results in the bottom of the table for US systems are the results with the regime filters. I figured since the nominal design of the US systems had regime filters, it would be more meaningful to compare those results to the results of implementing different regime filters.

On the other hand, the ASX system baseline result does not have a regime filter because the nominal design did not use a regime filter. So, for the ASX system we will be looking to see if a regime filter can improve the system and for the US systems we will be looking to see if a different regime filter is potentially “better” than the one already used.

Enter if VIX < x, Exit if VIX > x:

US Momentum System:

US Trend System:

ASX Trend System:

Enter if VIX < x, No index based exit:

US Momentum System:

US Trend System:

ASX Trend System:

Enter if Index > MA, Exit if Index < MA:

US Momentum System:

US Trend System:

ASX Trend System:

Enter if Index > MA, No index based exit:

US Momentum System:

US Trend System:

ASX Trend System:

Enter if Index within x% of recent high, Exit if Index below x% of recent high:

Note: HHV value is the lookback value. The index filter was coded as such, where 0.8 was hardcoded and the lookback (HHV_Value) was what was optimized:

Extern($$SPX, C > HHV(C, HHV_Value) * 0.8)

US Momentum System:

US Trend System:

ASX Trend System:

Enter if Index within x% of recent high, No index based exit:

Note: HHV value is the lookback value. The index filter was coded as such, where 0.8 was hardcoded and the lookback (HHV_Value) was what was optimized:

Extern($$SPX, C > HHV(C, HHV_Value) * 0.8)

US Momentum System:

US Trend System:

ASX Trend System:

Enter if Index returns greater than x% in y timeframe, Exit if Index returns less than x% in y timeframe:

Note: ROC value is the lookback value. The index filter was coded as such, where 0 was hardcoded and the lookback (ROC_Value) was what was optimized:

Extern($$SPX, ROC(C, ROC_Value) > 0)

US Momentum System:

US Trend System:

ASX Trend System:

Enter if Index returns greater than x% in y timeframe, No index based exit:

Note: ROC value is the lookback value. The index filter was coded as such, where 0 was hardcoded and the lookback (ROC_Value) was what was optimized:

Extern($$SPX, ROC(C, ROC_Value) > 0)

US Momentum System:

US Trend System:

ASX Trend System:

Enter if x% of stocks > MA (breadth filter), Exit if x% of stocks < MA (breadth filter):

Note: Breadth value is the lookback value. The index filter was coded as such, where 0.35 was hardcoded and the lookback (Breadth_Value) was what was optimized:

Filter12_BreadthFilter1:	#Sum InOEX
Filter12_BreadthFilter2:	#Sum If(InOEX, C>MA(C, Breadth_Value), 0)
Filter12:	Filter12_BreadthFilter2 / Filter12_BreadthFilter1 > 0.35

US Momentum System:

US Trend System:

ASX Trend System:

Enter if x% of stocks > MA (breadth filter), No breadth based exit:

Note: Breadth value is the lookback value. The index filter was coded as such, where 0.35 was hardcoded and the lookback (Breadth_Value) was what was optimized:

Filter12_BreadthFilter1:	#Sum InOEX
Filter12_BreadthFilter2:	#Sum If(InOEX, C>MA(C, Breadth_Value), 0)
Filter12:	Filter12_BreadthFilter2 / Filter12_BreadthFilter1 > 0.35

US Momentum System:

US Trend System:

ASX Trend System:

Summarizing Regime Filter Results:

You’ll notice after going through all of that work to test multiple different regime filters on three different systems that there was no Holy Grail solution that stuck out.

In fact there were many regime filters that actually made the system worse. On the other hand there were a few regime filters that made the risk adjusted metrics of the system slightly better.

While none of these regime filters hit it out of the park in terms of improvement, the real takeaway here is that all of these regime filters lead to a different performance profile of the underlying system. Rather than randomly testing 10s of regime filters and optimizing them to death, what is likely more fruitful is to diversify across regime filters.

If you look back up at the equity curves you can see that some regime filters kick on more often and others kick on less often.

Some regime filters kick on too early while others kick on too late.

One regime filter may have been the best during 2008 but another may be better during 2020.

You never know which will be the best in the future.

Hence why I think the more fruitful opportunity from this study is to be able to diversify across different regime filters, rather than picking the Holy Grail absolute best regime filter.

Let’s discuss this more in the next section.