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

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

Welcome to the “Systematic Trading with TradeQuantiX” newsletter, your go-to resource for all things systematic trading. This publication will 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.

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.

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).

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

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 sys…

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.

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.

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:

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:

Portfolio Development Series - Part 11

TradeQuantiX — Mon, 03 Nov 2025 15:35:07 GMT

Introduction:

Let’s wrap up this portfolio development series with the final article, part 11. In this article, we’ll construct a U.S. equities only portfolio. Hopefully, you can leverage the systems and ideas presented here to enhance your own systematic trading portfolio.

There are multiple systems that will be in this portfolio.

There is a momentum system which targets U.S. large caps, specifically the S&P 100, NASDAQ 100, and Russell 1000.

There is a trend system which also focuses on U.S. large caps, particularly the NASDAQ 100 and S&P 100, incorporating trends from both a low volatility and momentum effect perspective.

There are two mean reversion systems. The first mean reversion system concentrates on NASDAQ 100 and S&P 100 stocks. This is complemented by a second mean reversion system on a diversified basket of leveraged ETFs spanning equities, gold, bonds, and Bitcoin.

There is also a tactical asset allocation system. This system allocates to a collection of diversified ETFs with the goal of achieving steady, smooth returns.

On the short side, there are three distinct systems: a breakout momentum type shorting system, an ETF mean reversion shorting system, and a long-volatility hedging system.

This portfolio is going to comprise of many different system types, and as I’ve preached many times before, a portfolio with multiple different system types is the key to exceptional portfolio performance.

System Summaries:

Let’s briefly summarize the equity curves and performance statistics of the systems that will be included in the U.S. only portfolio. This summary will refresh your understanding of each system’s individual performance.

None of these systems are amazing on their own, but once we combined them into the final U.S. only portfolio, you’ll see how the combined performance is greater than the sum of its parts.

System 1: The U.S. large cap mean reversion system:

System 2: The set of U.S. large cap momentum systems:

System 3: The set of U.S. hedging systems:

System 4: The U.S. ETF tactical asset allocation system:

System 5: The set of U.S. large cap trend following systems:

System 6: The U.S. ETF mean reversion system:

U.S. Only Portfolio System Allocations:

The system allocations for the U.S. only portfolio are summarized below:

System 1: U.S. large cap mean reversion: 30%
System 2: U.S. large cap momentum: 25%
System 3: U.S. hedging systems: 30% (each)
System 4: U.S. ETF tactical asset allocation: 30%
System 5: U.S. large cap trend following: 35%
System 6: U.S. ETF mean reversion: 35%

I tried to make all strategies contribute to the overall portfolio performance relatively equally. Meaning no one system is dominating the return profile, each system has a similar say in the returns of the overall portfolio equity curve. It is not perfect because different strategies start their backtests at different periods of time, but it’s close enough to show the power of this U.S. only portfolio.

You’ll notice these allocations sum to over 100%. That is intentional and we will discuss that shortly.

The combined portfolio is the dark blue line in the equity plot below. The other lines below the combined line are the contributions from each individual system.

This U.S. only portfolio is very intriguing. While I’m a strong advocate in trading multiple markets globally for diversification, this U.S. focused portfolio is highly tradeable.

This returns of this portfolio outperform buy and hold strategies by several percentage points, and the drawdowns are significantly lower than buy and hold. With a maximum drawdown just under 12%, compared to over 50% for a passive buy and hold approach. If I had a choice between trading this portfolio and passively investing in the S&P 500, I’d choose this portfolio any day.

One critical factor to consider is the portfolio’s exposure over time. The exposure plot for the combined portfolio shows that, for the first 10 years of the backtest, the portfolio is relatively underexposed, peaking at around 60–70% exposure.

This is because some systems lack historical data during this period and haven’t yet “activated.” But, by the early to mid-2000s, once all systems have data to work with, exposure ramps up to 100% and occasionally enters margin, reaching a maximum of 138% exposure.

While I’m comfortable with brief periods of margin use, as shown in the exposure plot, this is a key consideration for traders without margin accounts. If you’re trading in a cash only U.S. equities account, you’ll need to scale down system allocations to avoid margin usage.

As I mentioned above, the allocations to all the systems sum to over 100%, yet the exposure of this portfolio tends to be around 100% or lower; with only very occasional margin use. This shows the power of combining non-correlated systems.

Not all of the systems are activated of fully exposed at the same time. Some systems are on, others are off, others are partially allocated. It’s a constantly evolving dynamic of sharing capital back and forth. For example, the shorting systems only kick on during bear market events. During this time, all the long trend/momentum strategies are actively selling, freeing up capital for the shorting systems.

Portfolio Comparison To Benchmark:

The benchmarks to beat are the S&P 500 and the traditional 60/40 portfolio. Over the past 20 years, the S&P 500 has returned approximately 9.5% annually with a maximum drawdown of 55%, while the 60/40 portfolio has delivered around 8% returns with a maximum drawdown of 31%.

It’s astonishing to me that investors settle for such performance when, with a bit of effort, we can far surpass these metrics. By combining non-correlated systems that analyze the market from different perspectives, we can create a portfolio that’s greater than the sum of its parts.

Individually, the systems within the portfolio are volatile and imperfect, but together they produce remarkably smooth portfolio-level performance.

In the equity curve comparison, the U.S. only portfolio (blue line) significantly outperforms the 60/40 portfolio (green line) and the S&P 500 (pink line).

The U.S. only portfolio’s trajectory is nearly linear, contrasting sharply with the volatile paths of the two benchmarks. It shines particularly during turbulent market periods, where the U.S. only portfolio tends to move sideways while the S&P 500 and 60/40 portfolio suffer steep drawdowns.

This outperformance is further evident in a rolling volatility plot. The 10-day rolling volatility of the U.S. only portfolio (dark blue) is significantly lower and more stable than that of the S&P 500 or 60/40 portfolio. This again shows how the U.S. only portfolio experiences a significantly smoother return profile compared to traditional buy and hold approaches.

Capital Requirements Of The U.S. Only Portfolio:

You might be wondering:

“I have $25,000 to allocate, is that enough to manage this portfolio?”

That’s a great question, so let’s explore. Every system in a portfolio requires a minimum capital amount to operate efficiently due to real-world trading costs and constraints.

For example, if a system is allocated $10,000 and holds equal weight 20 positions simultaneously, each position is worth just $500. If the system needs to buy a stock priced above $500, it may skip the trade or select a cheaper stock, leading to inefficient and suboptimal capital use. The system said to buy a certain stock, but we didn’t because we didn’t have enough capital, that’s not ideal. This can harm performance.

Another issue is commission drag. With a small allocation, if a system earns $20 per trade on average, but pays a fixed $2 in commissions, that’s 10% of profits lost to fees. However, with a larger system allocation resulting in an average profit per trade of $1,000, that same $2 commission becomes just 0.2% of the profit, a much more manageable cost.

These two example situations illustrate why knowing the portfolio minimum capital threshold is essential for efficient trading.

To investigate the impact of portfolio size, I ran the portfolio with allocations of:

$10,000
$25,000
$50,000
$100,000
$250,000
$500,000
$1,000,000

The table shows the performance metrics begin to converge around a capital amount of $50,000. This is the capital amount where the portfolio overcomes commission drag and can position-size optimally. Beyond this point, additional capital still yields some performance improvements, but they are minor.

A common myth is you can start trading with $2,000 and grow your account to millions in a short period of time. This study shows that there is significantly more capital required to properly manage a well diversified portfolio of multiple systems; with $50,000 appearing to be the minimum for this specific setup. And even so, it’s going to take a long time before $1,000,000 is in the cards. All good things require patience.

Adding ASX and TSX Systems:

Although this is a U.S. only portfolio, let’s have some fun and experiment by adding the Australian (ASX) and Canadian (TSX) systems to see their impact. We’ve already made these systems in previous articles, so we may as well add them to the portfolio just to see what happens.

As a refresher, the ASX system is a trend following strategy on small caps, and the TSX system is a rotational momentum strategy, also on small caps.

I adjusted the portfolio to scale down the allocations to the U.S. systems, making room for the ASX and TSX systems to be added. I set the allocation values such that each system contributes roughly equally to the portfolio’s returns, aligning each systems equity curves more or less on top of each other.

The results of this expanded portfolio are very interesting. Nearly all metrics improved: returns increased from 14.3% to 16.8%, drawdowns decreased from 11.8% to 10.6%, and risk-adjusted metrics like the Calmar and Sharpe ratios rose (Calmar from 1.21 to 1.58, and Sharpe from 1.48 to 1.82).

This demonstrates the value of incorporating non-correlated markets/systems into a systematic trading portfolio. While this article focuses on U.S. only trading for those with limited market access, these results serve as a compelling call to action. If you’re currently trading only U.S. markets, please consider exploring non-correlated markets, like the ASX and TSX, to significantly enhance your portfolio’s risk-adjusted performance.

Conclusion:

This U.S. only portfolio illustrates the power of systematic trading, combining momentum, trend, mean reversion, and hedging systems to deliver superior returns with significantly lower drawdowns than traditional benchmarks like the S&P 500 or the 60/40 portfolio.

By carefully blending non-correlated strategies, we’ve created a portfolio that’s not only robust but also resilient, maintaining stability even during market turbulence.

This portfolio is a testament to the idea that thoughtful design and disciplined execution can transform your investment outcomes. Now it’s your turn to take these ideas and implement them into your portfolio in the best way you see fit.

Well this is officially the last article in the Portfolio Development Series. I hope you enjoyed these articles and now have a better understanding of how to create a systematic trading portfolio from scratch. As always, let me know if you have any questions.

Also, all code for every U.S. system is documented below. I also added the combined script that aggregates all U.S. systems together so you can run combined portfolio backtests. Enjoy!

Do you have ideas/suggestions for future articles? Let me know what you want to read about by clicking the button below and submitting your suggestions. Your suggestions are greatly appreciated as they help tailor this newsletter to what you want to read about.

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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.

Real Test Code:

Portfolio Development Series - Part 10

TradeQuantiX — Mon, 20 Oct 2025 15:32:26 GMT

Introduction:

I’ve received a request from several readers to convert the portfolio we developed is part of the Portfolio Development Series to focus solely on U.S. markets. While I’m a big believer in global diversification and trading as many markets as possible, there are reasons to create a U.S. only portfolio.

For one, some investors are restricted to trading only U.S. equities. Retirement accounts, for example, are often limited to U.S. only equities, and many major U.S. brokers also impose similar U.S. equities only trading restrictions. Given this, I can understand why there’s significant demand from U.S. investors for a U.S. only systematic trading portfolio.

In this article, let’s take a look and see what we can develop in terms of a U.S. specific systematic trading portfolio.

What Systems To Add To The US Only Portfolio:

Let’s start by reviewing what we’ve built so far in the Portfolio Development series, specifically for U.S. markets. Thus far, we’ve created the following U.S. specific systems:

A U.S. large cap mean reversion system:
A set of U.S. large cap momentum systems:
And a set of U.S. hedging systems (three total):

These three systems provide a solid foundation for a mildly diversified U.S. only systematic trading portfolio.

But let’s expand further.

In previous articles (before the Portfolio Development series), I’ve explored other systems for the U.S. market that may be worth adding to this U.S. only portfolio, including the following:

A U.S. ETF tactical asset allocation system:
And a set of U.S. large cap trend following systems:

If we start by taking the three U.S. strategies developed as part of the Portfolio Development series and combined those with the two systems from previous articles, we’re working toward a robust five-system portfolio.

And remember, some of these systems contain sub-systems on different markets, or sub-systems looking for different market conditions to be met, so really its like an 11 system portfolio.

But let’s pause and assess what system types we have and what system types we might be missing.

We’re pretty well-covered on the hedging side, with three different shorting/hedging systems to protect us during adverse market events.

On the trend and momentum side, we’re in good shape with two trend following systems on two different large cap universes; and one momentum system across three different U.S. large-cap universes.

However, when it comes to long side mean reversion, we only have one U.S. mean reversion system. All other major system categories (trend following, momentum, hedging/shorting) we have at least two different systems; but for long side mean reversion, we only have one system currently.

This suggests that long side mean reversion is a category where our portfolio may be lacking. So, let’s develop another mean reversion system to enhance and diversify this portfolio further.

Planning The Mean Reversion System:

The existing mean reversion system buys oversold stocks in the Nasdaq 100 and S&P 100. Personally, I’ve never found much success trading mean reversion on small caps, especially in recent years. From my experience, mean reversion systems on small-cap equities have significantly degraded in recent times, with many systems failing outright, particularly during 2022 and 2023.

It could just be me, maybe my ideas are not good enough, but I can’t seem to create a stable system on small caps. I know many other systematic traders who have experienced the same as well. Hence, why up until now I have focused my mean reversion development on on large cap stocks.

I have a theory that the passive flows from large funds find their way into large cap U.S. equities, which tends to make them more mean reverting. With flows buying nearly every dip. There’s always a large institutional bid, hence why they tend to always rebound after a sell off.

Leveraging the same theory, large cap equity ETFs should follow a similar phenomena. Hence, I have just recently started to explore and develop mean reversion systems on U.S. ETFs. I have found a lot of success here and have been able to develop multiple systems with very stable results.

When developing ETF trading systems, there are a few traps to avoid. One issue I see with many ETF mean reversion systems is that they’re often built around only a single ETF, like an S&P 500 mean reversion system, a NASDAQ 100 mean reversion system, or a Bitcoin mean reversion system, etc. I take issue with this approach because it’s easy to overfit a system to a single time series.

If the behavior of the ETF shifts in the future, then the system could suffer significant losses. It’s also extremely easy to overfit to the limited data in just one ETF time series. Hence, why single ETF systems make me nervous.

To help avoid this risk, I prefer to develop ETF mean reversion systems that trade a basket of ETFs. This ensures some level of diversification and reduces the risk of curve-fitting as I have more data points to develop the system with.

After I’ve chosen the basket of ETFs I’d like the system to trade, I like to develop the system using just a few of the ETFs, from say 2000 to 2018. Then, I’ll validate the system on the unseen ETFs and on the unseen timeframe. Then I’ll trade the final system across the entire basket of ETFs. This provides more out-of-sample data for validation and adds diversification within the system.

I also avoid choosing ETFs that all belong to the same asset class. For example, I don’t want to trade only large-cap stock ETFs like the S&P 500 or NASDAQ 100, as these are highly correlated (though they do mean revert well, likely due to the passive flows theory I mentioned a few paragraphs ago).

Instead, I take more of an all-weather portfolio or tactical asset allocation approach. This ensures the ETF basket covers a broad range of asset classes. I include ETFs for assets like bonds and gold for example, to avoid overexposure to a single asset class.

The danger of focusing on one asset class is that during crises, correlations within equities tend to converge to one. Going all in on equities during the COVID crash, for instance, could be catastrophic if the systems timing is off.

By including other asset classes, which behave differently during adverse market moves, the system is more diversified and will likely not be “all in” during a major market crash. For example, equities may be crashing, while gold may be bouncing to the upside, resulting in a winning gold trade and losing equities trade, smoothing the equity curve.

Portfolio Development Series - Part 9

TradeQuantiX — Sun, 12 Oct 2025 18:42:49 GMT

Introduction:

Up until this point in the Portfolio Development series, the following broad high level topics have been explored:

Planning out what types of systems will be included in the final portfolio
Developing those systems with high quality development techniques
Combining those systems into the final portfolio and determining allocations.

These are the general concepts that one needs to understand to create a systematic trading portfolio.

But there are other very specific small details that one needs to consider to really get the most out of their systematic trading portfolio.

In this article, I'm going to go into the nitty-gritty considerations. These are the considerations that should be carefully mapped out to ensure your portfolio performs at its best. Thinking through these topics now will help reduce the number of issues that occur after you launch your newly developed systematic trading portfolio.

This article is not necessarily going to be the most entertaining or action-packed, but it is going to be one of the most important.

I will discuss multiple different high-level concepts that you’ll need to understand and consider. These concepts are:

Practical portfolio design considerations
Non-quantifiable portfolio allocation considerations
Quantifiable portfolio allocation considerations
Portfolio external capital considerations
Portfolio expansion over time
Unforeseen portfolio risks
Portfolio live execution considerations

If you do not consider and find solutions for the topics I am going to discuss, then your portfolio will likely suffer.

Practical Portfolio Design Considerations:

The first genre of aspects to consider is practicality. This includes things like time zone considerations, currency conversions, regulatory items, and mental capacity.

Time Zones:

Let’s start with time zones. If you decide to trade a market different from your home market, realize that there will be a time zone difference. This stock market will open and close at a different time than your home stock market.

It's possible that the market may be in a time zone that does not align with your schedule, making it difficult to run a trading system on said market. For example, if you live in Australia and want to trade the US markets, the US market opens around midnight Australia time and closes around 6 am Australia time.

I don’t know about you, but I don’t prefer the night shift. Depending on how the trading system operates, it could be an absolute nightmare to trade.

There are ways around this, though. What I do is place all of my orders during times when the market is closed. The orders sit with my broker and get executed market-on-open when the other country’s market opens. This allows me to place orders when I have the free time to do so; rather than staying up late to place orders exactly when the market opens.

This method works if you trade the daily timeframe, but if you’re trading intraday, then you either need to stay up all night to babysit your system trading the foreign market, or you need to develop very robust automation.

Remember, while you are developing trading systems, the backtest may look great, but if it’s a nightmare to trade, you may not be able to stick with it.

Personally, I trade the USA, Canadian, and Australian markets. I have found a time that works for me when all of these time zones align such that all of the markets are closed. This allows me to process and place the orders for the next market session. I would recommend you open a time zone converter online and look at the time differences across all the markets you plan to trade; then plan out how you intent to account for the time zone mismatches.

The more markets you add across multiple different countries, the more difficult it's going to be to find off-market times. You may find you have to process and place your trades multiple times a day across all the different markets. That could be pretty annoying to do everyday.

Currency Conversions:

Another thing to consider is currency conversions. If you trade a market other than your home market, it is likely there will be a necessary currency conversion to place that trade. Whatever position size your system spits out must be multiplied by the current currency conversion rate to ensure you end up with the correct exposure.

While a currency conversion isn't difficult, it can be an annoying nuance to do every day if not automated. It's also an aspect that is commonly overlooked, leading traders to be either massively under or overexposed in their position sizes compared to what they should be. So, be sure not to forget about currency conversions.

Below is a plot of USD/CAD. Assuming your base currency is USD, when the price goes up, that means you can buy more CAD with your USD; meaning the value of CAD has dropped. I also have a 1-year rate of change plotted at the bottom as well.

Imagine holding a trade between 2014 and 2016, unhedged from currency fluctuations. CAD dropped nearly 50% in value in relation to USD! If you were holding a trade on the Canadian market, the value of that trade would have also dropped 50% in relation to USD.

Regulatory Items:

Another practical nuance to look out for is regulatory items. These are another minor headache to tackle. Be sure that whatever market you plan to trade, you are legally able to trade.

For example, I am from the United States, and therefore, legally, I cannot trade the Indian markets, even though I would like to. If I had not known this ahead of time, I may have potentially bought data for Indian markets and spent a lot of time developing systems just to be frustrated when I found out that these systems were unusable.

Another example would be developing a shorting system on a market that doesn’t allow you to short. You can't short 95% of the stocks on the Australian market. You're lucky if you can get shorts for the top 50 stocks. So, spending lots of time creating an ASX short system is likely going to result in subpar live results and frustration.

There could also be special tax considerations applicable to trading other markets. For example, the London Stock Market charges a tax on all trades of about 0.5%. If I had created a trading system that had low expectancy on the London markets and I did not consider this tax being taken out of my P&L, this could transform the system from being wildly profitable to a system that loses money. So, spend time upfront figuring out if there is a special tax situation to account for before you spend a bunch of time developing your portfolio.

Here’s an example system where I assume no taxes are applied to every trade (but slippage and commissions are applied):

And here is that same system with a 0.5% tax on every trade per the London Stock Exchange’s rules:

Imagine not knowing that the market you plan to trade has this tax, and your system can’t overcome these extra fees, then its performance is severely degraded. Never assume fees are negligible.

Psychological Considerations:

The last set of practical considerations are psychological ones. You could develop a killer system that has amazing performance, but the only downside is it's extremely difficult to execute.

If you have a busy life and a demanding day job, adding a very demanding trading system into that mix could result in disaster. This is why I personally focus on the edges that are relatively easy to exploit so that I do not have to spend a lot of time worrying about execution.

I'm very grateful for this on busy mornings when I wake up late and need to rush out the door to get to work for my first meeting. The fact that I can crack open my trading systems, generate the orders, and place them with my broker in less than 5 minutes makes my morning trading routine a breeze. This is because I don't use convoluted systems to trade.

I designed the systems such that they work synergistically with my life and routine. I don’t bend my life to fit around how a system needs to be traded. So, consider how you’re actually going to be required to trade your portfolio every day and plan the system design accordingly.

Another consideration is if you do not have any sort of automation, but you decide to trade 10+ trading systems, this could lead to overwhelm and mistakes. With every new market you add, you are essentially adding that much more work to your daily trading process. After awhile, there will come a tipping point where the amount of work required to run your systematic trading setup will be so much that you’ll start becoming prone to errors.

This is why some sort of automation is key. Having a batch script to run all your systems and generate your orders is a relatively simplistic thing to create, but it can have a massive impact on your systematic trading every day. Creating some sort of automation will generally save you time, stress, and reduce silly errors. I have a handful of small utility scripts that I use every day to make my life 10x easier and less prone to error.

Non-Quantifiable Portfolio Allocation Considerations:

In this portfolio development series, we have spent a lot of time discussing how to combine all your systems into a final portfolio. We quantified our allocation decisions, but sometimes there are allocation decisions that are non-quantifiable.

Portfolio Allocation (Over)Optimizing:

Once you have a set of trading systems and you go to combine them into a portfolio, it's very easy to think that simply optimizing the allocations to each system and picking the best result is the way to go. Why would you not want to trade that best set of allocation parameters, right?

I'm gonna tell you this is the wrong way to go about it.

Similar to when we develop trading systems, we don't just pick the best parameter for a certain trading rule. Portfolio construction should be treated the same way.

If you were to simply run an optimizer to find the best allocations to each system, what that would result in is an overallocation to the best backtest and an under allocation to the worst backtest.

On the surface, this sounds fine, but this method of blindly finding the best system allocation percentages has a lot of hidden dangers that need to be considered.

Consider: is your best backtest the result of a genuinely well-designed trading system that exploits an edge extremely efficiently? Or has overfitting found its way into the backtest of this system?

Say I built a small portfolio of three strategies:

Strategy 1: amazing performance, likely an overfit strategy
Strategy 2: okay performance, likely not an overfit strategy
Strategy 3: okay performance, likely not an overfit strategy

When I ran an optimization of allocation parameters, the best results based on this optimization were between a 75%-100% allocation to Strategy 1, and a 0%-25% allocation to Strategies 2 and 3. But that’s not ideal; that removes all diversification.

Also, if Strategy 1 fails in the future, and it probably will because it’s likely curve-fit in this example, that’s going to really hurt because we are overexposed to it.

Whereas if we traded all three strategies with a more equal allocation, we would have a much more robust portfolio. In this instance, if one strategy failed, yea, it might suck, but the entire portfolio isn’t going to zero, because there are two other strategies picking up the slack.

Now, when I say overfitting, that doesn't necessarily mean that the system will completely crash and burn during live out-of-sample. It could, but really what I mean is the backtested performance is overstated.

If your backtest says the system should have a 20% return with a 20% drawdown, but in reality, it's slightly curve-fit, and going forward, the return is more like a 15% return with a 30% drawdown, then you're going to be overstating the returns and underappreciating the risk involved in that system.

Assuming this system had the best overall performance in relation to the other systems being considered in the portfolio, then the results of your portfolio optimizer would heavily over allocate to this slightly curve-fit system. Then, during real-time trading, your live results would suffer due to the degradation from curve fitting.

Not only do you get worse results because the system was slightly curve-fit, but it’s being exacerbated by the fact it was also heavily overallocated to. This is why we can't blindly optimize allocations. Sure, optimizing allocations can be a decent starting point to get an idea what allocation to start with, but we need to add another layer of critical thought on top of this.

Geopolitical Risk:

Portfolio Development Series - Part 8

TradeQuantiX — Tue, 30 Sep 2025 22:25:33 GMT

Introduction:

In the previous article in this portfolio development series, we finally combined the four systems we created and evaluated the portfolio’s performance. Even with just four systems, the portfolio performance was very solid, yielding a a fantastic starting portfolio to build from.

While the portfolio didn’t fully meet all the objectives we originally outlined, it came extremely close. Considering the portfolio consists of only four systems, I am not concerned, there is still plenty of room to build on top of the four system portfolio. To further improve the portfolios performance, it was decided that the next step is to add a hedging system, to benefit from adverse market events.

Why Adding Short Side Exposure Is Important:

Currently, the portfolio is long-only, so it’s expected to outperform during bull markets, but go flat or enter drawdown during bear markets. Consider a time period such as the 2008 bear market: many systems within the portfolio went into drawdown, and remained in a drawdown for years.

When looking at a backtest over 30 years, we loose perspective of how long the systems were in a drawdown. Going forward in time into the future, we do not have the luxury of knowing when we will hit new equity highs. We have to remember we are going to live that equity curve everyday, in real time. Would you be able to continue to execute the orders of your system for years while your system remains in drawdown?

Therefore, creating the smoothest equity curve possible is desired. Having short side systems to profit during adverse market events will help with that smoothing effect; as shorting systems are typically uncorrelated to long systems.

So, what kinds of systems could we develop to benefit from adverse market events? There are likely many solutions, but I categorize them into a few different types of approaches.

The first is a system that shorts the market during a downtrend. This could involve shorting via breakouts to the downside or mean-reversion strategies which short local relief rallies within a larger downtrend.

Another approach is to use what I call long-volatility strategies. During adverse market events, volatility typically spikes. A system that goes long volatility can be highly profitable during volatile bear markets. However, these systems are like insurance policies. You pay a premium every month, slowly bleeding cash, until a crisis hits, only then do you receive a payout.

Long-volatility systems work similar to buying insurance. During normal market conditions, you may lose money gradually, but when a crash inevitably occurs, the system spikes with profits, capitalizing on the surge in volatility.

In this article, I’ll explore all three of these concepts. We’ll implement a long volatility system, a system that shorts breakouts to the downside during bear markets, and a system that shorts relief rallies in bear markets using mean reversion.

I can almost guarantee these systems won’t look pretty on their own, but that’s not the point. The goal is to smooth portfolio equity curve during periods of time when the long side systems underperform.

Short Side Mean Reversion:

The first system we’ll cover is a short-side mean-reversion system on equity and bond ETFs. This system trades the SPY, QQQ, and TLT ETFs on the short side.

If you recall the mean-reversion system from Part 3 of this series, we used the concept of scaling in to the position to obtain an average price during oversold conditions. For this short mean reversion system, we’ll follow a similar approach but scale into an overbought position.

The system works by measuring the RSI and placing limit orders at various overbought RSI levels, scaling into the position over a few days. This technique provides more stable results and is less prone to curve fitting, as it doesn’t rely on a single set of parameters that we hope will remain optimal into the future. Instead, it diversifies across multiple parameter sets.

You can also think of this as scaling in by signal strength. As the overbought signal strengthens via the RSI increasing, the system scales in more aggressively, increasing position size with the signal’s intensity.

As a simple way to emulate this scaling in, I developed four versions of this RSI mean reversion shorting system. Each uses a different RSI value for entry and exit. See the end of this article for the code.

You can see the equity curve for this system below. Note the four equity curves on the bottom of the plot are the four different variants of this system using different RSI values. The resulting equity curve of these four variants combined together is the dark blue line.

Short Side Mean Reversion System:

You’ll notice the returns of this system are low, but the time periods where it makes returns is what’s important. The system makes money during 2000, 2008, and 2022 which are all bear markets where the current portfolio struggles. This system is also relatively low risk, as shown by the drawdown, with a max drawdown of only 4.4%.

Because this system is comprised of four sub-systems, the exposure can scale up and down based on signal strength, as discussed above. As the ETF shorting signal increases, the position size increases and thus the exposure.

You’ll notice in the exposure plot below that a large majority of the time, this system is off, yielding zero exposure to the markets. When the system is in a position, generally it is only at 25% to 50% allocation. Only a few times is this system at 75% to 100% allocation. See the exposure plot below.

If you were okay with taking on a bit of leverage from time to time, you could bump up the allocation to this system such that more equity is used more often. Just keep in mind there will be a day where this system asks for leverage if you do that. Personally, I use a small amount of leverage in my portfolio, so that doesn’t bother me but it’s dependent on every individuals risk profile and goals.

Portfolio Development Series - Part 7

TradeQuantiX — Mon, 15 Sep 2025 14:37:24 GMT

At this point in the portfolio development series, we have created four unique trading systems across three global equity markets. In this Part 7 article, I will start assembling a portfolio of systems so we can see the portfolio effect in action. What we will find when we build the portfolio is that, even with just four trading systems, phenomenal performance can be achieved at the portfolio level.

In this article, I will cover a handful of topics, including:

Recap the performances of the four original trading systems.
Review the portfolio level goals laid out in the first article in this series.
Combine the 4 trading systems into a portfolio.
Understand the portfolio's strengths and weaknesses.
Outline next steps.

Ideally, this article will show you why having multiple systems working together within a systematic trading portfolio is the only true Holy Grail. There is no such thing as the Holy Grail at the trading system level.

The only Holy Grail that exists is the result of combining multiple different and uncorrelated trading systems into one unified systematic trading portfolio. This article will show you how to do just that.

As a sneak peek, here is where we end up: a portfolio of four trading systems, with allocations set such that each system contributes similarly to the overall equity curve. This shows the power of developing a systematic trading portfolio. Just four uncorrelated trading systems can create a magical result.

A Quick Recap:

First, let's start by recapping where we stand up to this point. We have developed four unique trading systems across three different equity markets. Each trading system has its own unique performance profile.

No individual system is amazing on its own. The good news is that no system has to be amazing on its own; what matters is the effect on the portfolio as a whole.

Below are the backtested equity curves and performance metrics for each of the four systems we have developed thus far:

System 1 - US Mega Cap Mean Reversion:

System 2 - US Mega Cap Momentum:

System 3 - ASX Small Cap Trend Following:

System 4 - TSX Momentum:

Next, let’s quickly outline the performance goals of the systematic trading portfolio. These goals were laid out in the second article in this series. We will see if the four-system portfolio is sufficient to meet these goals. If it does, great! If not, we will discuss methods to meet these goals in the Next Steps section.

If you missed the article that outlined these performance goals, you can check it out here:

Portfolio Performance Goals:

Portfolio CAGR Goal: >20%
Portfolio Max Drawdown Goal: <25%
Portfolio Max Drawdown Length Goal: <1 year
Portfolio Average Drawdown Goal: <10%
Portfolio Volatility Goal: <10%
Portfolio Sharpe Goal: >2.0

If the portfolio falls short of these goals, we will simply have to keep working toward them over time. We don’t have to be perfect from the beginning. A systematic trading portfolio is a never-ending improvement project anyway. You should always be striving to make the portfolio better.

These performance metrics were chosen loosely based on my own personal situation. I'm in my late 20s, looking for strong absolute portfolio performance to quickly compound my capital. I do not want to work in the corporate world my entire life, so I need a high CAGR to compound my capital quickly.

While I am willing to take on some risk to achieve a high CAGR, I do not want to take on so much risk that I am constantly stressed and can’t sleep at night. Hence, the drawdown tolerance at the portfolio level is moderate, at 25%, but not absurdly high.

Risk and reward are proportional. If I want more returns, I generally have to take on more risk. Therefore, I’ll accept downside risk up to 25% to achieve a CAGR of 20%.

Equal Weight Portfolio Allocation:

Now, let's start digging into the assembly of the systematic trading portfolio. But, before we start, let’s discuss some of the assumptions surrounding how this portfolio will work:

All systems will share capital from one account.
- The systems do not operate in silos; they all work together and share the same capital from one account. If System 1 makes money, that capital is equally distributed across Systems 2, 3, and 4.
System allocation and position sizing are based on the net liquidation value of the account. This value is updated every day.
- Every day, the allocations to each system are recalculated based on the net liquidation value of the account. These allocations are then used to position-size new trades within the system, but existing trades are not updated based on the day-to-day allocation fluctuations.
- Open trades are not rebalanced based on changes in the account's net liquidation value; unless there is a specific rule in the system to rebalance periodically (some of the systems have a rule to rebalance monthly or if the trade size is significantly different from the current ideal size).
The starting account balance of the portfolio backtest will be $100,000.
- There will be no deposits or withdrawals from the account over the entire portfolio backtest period. Only compounding of the original $100,000 will be shown.

Think of this portfolio as a team. If one member of the team is performing well, the entire team benefits. The dollar allocation to each system builds or reduces based on the performance of every other system, but the percentage allocation remains static.

There are many unique ways we can allocate capital to each trading system. I want to pick a few allocation methods and experiment to see which capital allocation method is the best to achieve our portfolio goals. The capital allocation methods I want to test are:

Equal weight allocation
Equal volatility weighted allocation (risk parity allocation)
Equal return weighted allocation (return parity allocation)

Each allocation technique has its own pros and cons. For example, equal weight allocation may be naively simple, but it ensures equal exposure to each system and/or market, which may be desired.

Let’s dive into each of these allocation methods, see how they behave, and decide which is best for this portfolio. We may even decide we don’t like any of these allocation techniques and instead go a hybrid route. There’s no correct answer; it’s all about experimentation and picking something that suits your goals.

One of the most important things is to pick an allocation style you can stick to without constantly changing allocations or quitting trading the portfolio altogether.

You may find that one of the allocation methods has better results on paper (however you define “better”), but it may not be something you would actually be comfortable trading live. For example, maybe the allocation method has amazing returns but too much risk for your liking. Thus, you modify the allocations to allocate less to some of the more volatile systems to create more of a hybrid allocation approach.

Portfolio allocation is more of an art than a science. You’re going to be the one who has to ride the equity curve of this portfolio every day, so allocate in a way that makes it a ride you want to take.

Equal Weight Portfolio Allocation:

We will first start with the simplest allocation method: an equal allocation percentage given to each system. Since the portfolio has four systems, each system receives 25% of the total portfolio capital.

Portfolio Development Series - Part 6

TradeQuantiX — Mon, 01 Sep 2025 15:03:29 GMT

Introduction:

Welcome to the part 6 article of the Portfolio Development series. In this article we are going to head up north to the Canadian market. Thus far, we've developed a couple of systems on the US market, a system on the ASX market, and now we are going to add the TSX market.

Having diversification across multiple world equity markets can provide massive benefits to a systematic trading portfolio. Think about it, different economies perform better during different times. The US market may be going sideways, the Australian market may be in decline, and the Canadian market may be growing. You never know what the economic situation will be, and so having exposure to multiple different economic environments can be extremely powerful.

On the Canadian market, I'm going to develop a momentum trading system. To be honest, I'm pretty nervous about developing this system. I have attempted to develop momentum systems on the Canadian markets a couple of times in the past, and every attempt has failed out-of-sample or robustness testing.

I'm actually starting to write this article before I even develop the system. That way I fall into the sunk cost fallacy and work really hard to develop a system that actually passes out-of-sample testing and is robust. So wish me luck.

In this article, I'm going to focus on parameter optimization during the in-sample development phase. I'll share the final system results, but the majority of the article will focus on robust optimization practices and setting yourself up for success in out-of-sample.

The worst feeling is when you spend hours developing using in-sample data just to check out-of-sample and the system completely falls apart. It's demoralizing and feels like you've wasted an entire day, and then the rest of your day you just feel defeated.

This is why I want to focus on robust parameter optimization techniques so that you set yourself up for success for the out-of-sample test. There's no guarantee, obviously, that the out-of-sample will pass, but it will give you a better probability of passing when you implement these techniques, as opposed to not.

Common Optimization Pitfalls:

It's very common for systematic traders to throw a bunch of indicators on a chart, they then say:

“That looks good, all it needs is some optimization!”

Then they run a brute force exhaustive optimization on a million parameter combinations and pick the very best result. This is a faulty practice and is nearly guaranteed to fail in out-of-sample.

A better practice than picking the best parameter is to pick the most stable parameter. This is why I think optimization confuses people. The word "optimizing" is the wrong word. It should be called stability searching, or something of that nature.

Generally we need to find a parameter with a stable performance surface, meaning all parameters surrounding the chosen parameter have similar performance. This allows for the markets to shift a little bit and the system still work into the future.

Now, it’s not always this easy. Sometimes parameter optimization surfaces are bumpy, sloped, and noisy. There is a lot of nuance that goes into how I pick parameters during these scenarios. Those examples will be outlined in this article.

Picking the best parameter (the performance peak) allows no wiggle room for future market shifts, and thus is more likely to fall apart in the future. Performance peaks generally are not real, instead, they are noise within the data.

The reason why systematic traders are prone to pick performance peaks and overfit rather than create robust systems is because robust systems aren't as pretty. Overfitting results in beautiful backtests. It's very easy to fall in love with how your backtest looks. But a backtest is just that, there's no guarantee of future performance, and the more fragile and overfit your backtest is, the less likely you are to have any positive performance in the future.

Instead, this is why we need to focus on robust backtests, which are uglier backtests, but they're real. These backtests are much more likely to have continued positive performance into the future. Ask yourself, would you rather:

Trade a system with a lumpy and bumpy equity curve with drawdowns and periods of underperformance that is robust and continues to make money over the long term for years into the future?
Or trade a system that has a perfectly straight equity curve and is prone to catastrophic failure during the next market shift due to overfitting?

I know what I would pick, the uglier but more robust system. Robustness is like the 2005 Toyota you've had in your garage for 20 years. Yeah, it's ugly, it's old, and it's not going to turn any heads, but it's going to run forever.

Let’s go over a silly, but real example of overfitting. I say real because I have seen people do this kind of thing before. Say, for example, you're optimizing a system to only trade one day a week. You run an optimization to see which day the system performs the best. You find Monday, Tuesday, Thursday, and Friday all have about the same performance, but for some reason Wednesdays have 20% better performance.

Now the question is, why? Is there something special about Wednesdays? If you were running a system on crude oil, then yeah, Wednesday is when the weekly crude oil report comes out, so it would make sense that Wednesdays may have more profit opportunities. But if there's no fundamental reason as to why Wednesdays are the best, then it's likely just a random anomaly within the data.

If you were to really dig in and dissect the data, you may find that there just happened to be a couple of random Wednesdays in the backtest that were really big winners-no rhyme or reason to it; it just happened to be on a Wednesday. Picking Wednesday in this scenario, when there's no fundamental reason why Wednesday should be better, is just curve fitting to noise. This is just a performance peak that will likely not persist into the future.

A better way to actually prove if Wednesdays are better would be to run a rolling optimization like a walk-forward analysis. This would help determine if the Wednesday anomaly persists over time or if it's just due to a few random Wednesdays that had a big winner in the past.

This was an example of what not to do. Don’t just start digging through the data and plucking out random anomalies, they’re likely noise. Instead, think through fundamentally why that anomaly should or shouldn’t exist. If there is no reason to think Wednesdays are better than any other day, then it’s likely not. I'll digress from this example as I think you probably get the point.

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TSX Momentum System Rules Outline:

Portfolio Development Series - Part 5

TradeQuantiX — Mon, 25 Aug 2025 14:46:27 GMT

Introduction:

This is the first article in the Portfolio Development series focusing on a market outside the US market. While most traders and investors concentrate on the US markets due to its liquidity and maturity, the US market is not the only viable market to trade. The Australian stock market, known as the ASX, offers diversification opportunities for a systematic stock trading portfolio.

As I’ve mentioned in all my articles, each piece in this series focuses on a specific aspect of the system development process to keep things fresh and engaging. In this article, while I’ll briefly cover how the system works and its in-sample and out-of-sample results, the main focus will be on robustness testing.

In this article, I will develop a long term trend following strategy for the ASX market. This trend following strategy will use moving averages in a way that reduces the dependency on just one single parameter combination.

As I always preach, the more we can leverage and be exposed to the entire parameter space, the better. Hence, this strategy will use a technique similar (but a little different) to what was outlined in the article below, to determine the entry and exit signals.

This is generally a sign of unintentional curve fitting. We don’t try to curve fit, but it’s a lot easier to do accidentally than I wish it was.

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As a sneak peak, this is the system I will develop and then robustness test in this article. Not too bad for a long term trend following system!

Why The ASX?

The ASX has distinct advantages over the US market. The US market is relatively efficient, with many large institutional players extracting alpha everyday, leaving little for retail traders, like us. The ASX, on the other hand, is less institutionalized, providing more opportunities for retail traders to find an edge.

This is a double-edged sword, however. A mature, institutionalized market tends to be highly liquid, whereas a less mature, less institutionalized market like the ASX is typically less liquid. While smaller markets like the ASX may offer greater edges to exploit, the reduced liquidity can be a challenge.

Depending on your trading style, this lower liquidity may or may not be an issue. If you trade systems with long-term time horizons (holding trades for weeks, to months, to even years) in a less liquid markets, you’ll likely be fine. This is because the increased expectancy due to holding positions for long periods of time can overcome the extra slippage caused by lower liquidity.

However, if you’re trading a fast mean-reversion system on the ASX, like the one we developed for US markets in Part 3 of this Portfolio Development series, your profits could be eroded by excessive slippage due to illiquidity. This isn’t to say there are not stocks in the ASX which are liquid, there are quite a few very liquid stocks on the ASX, there are just fewer than in a more mature market, like the US market.

In summary, smaller markets provide larger edges for retail traders to exploit, but these edges are generally best captured on longer timeframes to mitigate the impact of slippage due to lower liquidity. It’s also worth noting that commissions on the ASX are higher, likely because it’s a smaller market with fewer brokers, thus there is less competition to drive the commission prices down. Higher commissions and lesser liquidity leading to more slippage is a disaster for short term trading systems, hence we are looking longer term.

This is why I’m developing a long-term trend-following system for the ASX market. Being long-term, the system’s expectancy should be high enough to remain robust to significant slippage and commissions. The system will focus on small-cap stocks, as smaller stocks on smaller markets tend to offer larger edges for us retail traders to exploit.

Institutional investors often can’t easily trade smaller stocks due to liquidity constraints, making it easier for us retail traders to compete. Running a long-term trend-following system on ASX small-cap stocks is like playing poker against your drunk friends instead of a professional poker player.

The ASX Trend System Rules:

I personally run multiple trend-following systems in my systematic trading portfolio. Rather than leveraging an existing system I already have, for this article, I wanted to create something original. I went a completely different direction than I would normally go for trend following. While the indicators and filters I used are not original, I think the way I used them is original and also adds robustness.

I’m a big believer in always pushing yourself to coming up with new ideas, no matter how goofy they may seem. You never know, one of those ideas may work. This is exactly what I did here, I forced myself to go outside the normal way I define trend following, and the results were pretty interesting.

I built the system on an in-sample dataset from 2000 to 2018, with out-of-sample testing ranging from 2018 to mid-2025 (today). The rules I finalized for the system are as follows:

Portfolio Development Series - Part 4.5

TradeQuantiX — Tue, 19 Aug 2025 14:22:26 GMT

Introduction:

This is an article I was not expecting to write as part of this series; hence it being called part 4.5. I made a mistake in the previous part 4 article of this Portfolio Development series.

After I posted the previous article about creating a US momentum trading system, a subscriber was kind enough to point out to me a very subtle survivorship bias within the system. Now, don’t freak out, I have fixed the system and it still works just fine, the updated code will be provided at the end of this article.

Since I am in this awkward position, I figured I should make the most of it and use it as an opportunity to discuss mistakes in system development. In this article, I want to:

Discuss the mistake in the system
Discuss why it matters
Discuss how it could have been avoided

It’s important not only as a learning opportunity but to also point out that even someone who has been doing this for years can still make mistakes!

If you missed the previous article, check it out here. There is still a lot of informative content aside from the mistake!

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The Survivorship Bias Mistake:

The coding mistake within the momentum system was within the breadth filter. The current breadth filter logic is as shown below:

BreadthFilterThreshold: 0.5	
BreadthFilter1:	#Sum InOEX
BreadthFilter2:	#Sum C>MA(C, LongTermLookback)
BreadthFilter:	BreadthFilter2 / BreadthFilter1 > BreadthFilterThreshold

In English, this code was meant to:

Sum the number of stocks currently in the S&P 100 index
Sum the number of stocks in the S&P 100 currently above the 200 day moving average
Only take a trade if 50% of stocks in the S&P 100 are above the 200 day moving average

The survivorship bias issue occurs in step 2. I missed a conditional to only sum the number of stocks above the 200 day moving average if they are a constituent of the S&P 100 at that point in time in the backtest. Accurate historical constituency is key.

Instead, this rule was checking for 200 day MA uptrends in all stocks currently in the S&P 100, previously in the S&P 100, and to be added in the future to the S&P 100.

This issue is a mix between what's called survivorship bias and a future leak. The breadth rule was using data in the calculation that would not have been available at that point in time.

This is obviously no good, hence why I need to fix it. This can severely skew results, and survivorship biases and future leaks are generally some of the worst biases in backtesting. Luckily, the impact to the system is minor, but more on that in a bit.

It is why it is extremely important to have an accurate index constituency time series. This makes it so a strategy can know what stocks were in the S&P 100 every single day of the backtest. In this case that is what the InOEX function does in the RealTest code.

Survivorship bias and future leaks can wreak havoc on live trading results. A backtest with survivorship bias/future leak may look fantastic, but once you go live your results are slowly going to degrade over time.

This is because during live trading we don't know what the future holds, so the system is only going to be using the current data. But as time goes on, the system will be subtly adjusting it's historical performance as new future information is known. The history of the backtest will be “repainted” as some call it.

You’ll notice that some of your live trades inexplicably change. You may also notice that your live results look a lot worse than the backtested results over the same period. It’s a slow and subtle degradation in between live and backtested performance.

For example, let’s say a handful of new stocks were added to the index in 2021. With a future leak/survivorship bias, the breadth filter calculation is going to include the stocks that were added to the index in 2021 for all years prior to 2021 as well. But before 2021 we would not have known those stocks were going to be added to the index yet. The system was making decisions based on unknown future data.

The equity curve will essentially be constantly changing with every stock that is added to the index. The back test will not be static, it will be a constantly moving target that is impossible to hit because you don't know the future; but the backtest does.

Luckily The fix is relatively straightforward. All I need to do is add the conditional to check index constituency in the rule that calculates if the close is above the moving average.

BreadthFilterThreshold: 0.5	
BreadthFilter1:	#Sum InOEX
BreadthFilter2:	#Sum If(InOEX, C>MA(C, LongTermLookback), 0)
BreadthFilter:	BreadthFilter2 / BreadthFilter1 > BreadthFilterThreshold

These are the subtleties in back testing that are really easy to mess up. I've been doing this for over 5.5 years now and I still sometimes make subtle mistakes like this, everyone does.

Luckily the mistakes have reduced over time but obviously from time to time they still creep in. This is why it is extremely important to double check your work and if you have a second pair of eyes to look over your system code, that never hurts as well. Especially when the mistakes are not obvious, such as this one.

Removed Survivorship Bias Results:

After implementing the fix to remove the survivorship bias/future leak and ensure a proper historical index constituency, I re-ran the backtest. Shown below are two backtests comparing the original results with the survivorship bias free results.

Original US Momentum System - Survivorship Bias In Breadth Filter:

US Momentum System - Fixed Breadth Filter, Survivorship Bias Free:

Luckily, the results actually got better after removing survivorship bias. This is not common, I’ll reiterate that we got lucky. Returns increased by 1% and drawdown decreased by 5%.

The results got better likely because he breadth filter was being flooded with the price action of irrelevant stocks. A stock that joined the index in 2015 was being used to calculate breadth in 2000. Basically there was a lot of non-useful information being fed into the breadth filter, making its result more noisy.

Yes, future data was being used, but in this case that future data wasn’t actually that helpful to the system. It’s like cheating on a test by getting a hold of the answer key, but the answer key was for an outdated version of the test.

I decided to check and see if the breadth filter was even needed. I ran a quick optimization on the breadth filter threshold. Breadth threshold being defined as the percentage of stocks above the long term moving average within that index. The results are shown below:

Sharpe vs. Breadth Threshold Value:

ROR vs. Breadth Threshold Value:

Max Drawdown vs. Breadth Threshold Value:

Number of Trades vs. Breadth Threshold Value:

The Sharpe and rate of return are basically unchanged until the breadth threshold becomes 0.5 or so. Then the Sharpe and ROR drop off in terms of performance. Max Drawdown also follows a similar trend. Also, if you look at the number of trades, that drops off a cliff at the same rate.

This tells me the breadth filter is essentially just choking the system. It doesn’t do anything until after a threshold of about 0.5. Then for values higher than 0.5, it’s just reducing the number of trades, decreasing ROR and Max Drawdown proportionally. Thus, I am inclined to remove the breadth filter all together, it doesn’t appear to be adding to the system.

Avoiding This Issue In The Future:

There's actually another step that could have been taken that would have prevented this mistake from happening in the first place. In my system development process, I have a step where I ensure the significance of each rule in the system.

After I’ve finished developing the system, I go rule by rule and ensure that its contribution to the system performance actually matters and that the rule deserves a place in the system. For whatever reason, with this particular system, I forgot to do this step. I apologize for that.

What I’ve realized is that this breadth filter rule actually doesn't contribute that much to the system performance. The index filter and the breadth filter more or less perform the exact same function, and really I only need one within the system.

So, I could completely remove the breadth filter and the system would still work fine. In my eyes, this would be a great solution because it would remove needless complexity from the system.

As I always say, a system should be the simplest viable solution to capture the market effect you're aiming for. In this case I can get more or less the same results without the breadth filter. Removing it makes the system simpler and less complexity leads to less chance of error; and this whole article has outlined how error has already snuck into the system.

So, removing the breadth filter from the system results in the equity curve performance as shown below.

US Momentum System - No Breadth Filter, Survivorship Bias Free:

As you can see the system still performs just fine without this filter. I personally would just trade this version of the system without the breadth filter.

But, in case you want to explore the breadth filters impact more, I have both versions of the code below, with and without the fixed breadth filter. Maybe you’ll be able to find a better way to model it such that it has a more significantly positive impact on the system.

Conclusion:

This article is a great lesson in not only double checking your work but having a second pair of eyes to examine your systems. Luckily, in this case the survivorship bias/future leak in the system actually didn't have too much of an impact, but generally we don't get this lucky.

After fixing the survivorship bias/future leak and then examining the results with and without the breadth filter, it was determined that the breadth filter is needless complexity that doesn't add too much to the overall system performance.

Now that the system is fixed we can move on to the next article in the series which will focus on an ASX trend following system. Special thanks to the subscriber that pointed out this survivorship bias issue and working with me to resolve it.

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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.

Corrected Real Test Code With Breadth Filter:

Portfolio Development Series - Part 4

TradeQuantiX — Mon, 04 Aug 2025 14:09:32 GMT

Introduction:

In this fourth installment of the portfolio development series, I will create a long-side momentum system focused on US mega-cap stocks. In my opinion, this is the bread and butter, the meat and potatoes, of a well-rounded systematic trading portfolio.

Momentum is my favorite system type. You jump into the strongest stocks and sell the weakest ones—a simple yet robust concept. Momentum systems allow you to gain exposure to the market's upward bias, creating a solid return stream for your portfolio.

Momentum systems maintain exposure to the markets when they are hot and avoid them when they are cold. This allows our portfolios to experience the best of the good times while avoiding the bad times.

As mentioned in the previous article, each article in this portfolio development series focuses on a different part of the system development process. This keeps the articles engaging and allows me to take deep dives into specific aspects of trading system design and validation. In this article, I will focus on:

The initial planning phase of the system.
Thinking through why and how the system will operate fundamentally.
Building the system from the ground up, one piece at a time.

Here is a sneak peak of where we will end up in this article: a momentum system across three different US market universes.

Let’s get started!

Why Momentum Systems Are Great:

As I mentioned, momentum systems are my favorite.

But why is that?

First, they make fundamental sense, at least to me. If I were a discretionary stock trader, I would buy the strongest stocks during their peak performance periods and sell into strength, capitalizing on the upward move while avoiding the downturn. This is exactly what momentum systems do: they buy the strongest stocks during their strongest periods and then sell them as the momentum starts to cool off.

This trading method aligns with my personality and how I think about the markets, making momentum systems effortless for me to trade. I rarely hesitate to place orders for my momentum systems because I know they seek to get my portfolio involved in the best performing stocks in the market at any given time.

With mean reversion systems, however, I sometimes feel nervous placing trades because mean reversion buys the losers. Mean reversion systems go against how my brain operates. There’s definitely a trader personality type, and my personality type is momentum.

Second, momentum systems, at least the way I develop them, are fairly straightforward to trade.

Identify a small basket of the strongest performing stocks
Buy that basket of stocks
Wait for a week to a couple of months
Re-evaluate the basket of stocks currently held
Sell the laggards
Replace the laggards with the most current strongest performing stocks

It’s a simple yet effective concept.

This means that, day-to-day, there isn’t much work to do with this type of system. Once you establish the portfolio of potential outperformers, there’s generally a quiet period where you let these outperformers do their thing.

After a week to a few months, you re-evaluate and adjust the momentum portfolio as needed; ensuring you’re holding only the best and strongest stocks again.

Check out the example momentum trade in the image above. That trade just feels good. It entered during a moment of low volatility and then followed the explosive trend until it started to settle down. These are my favorite types of trades.

A Tangent On Periodicity and Trading Frequency:

All the momentum systems in my personal systematic trading portfolio use a monthly periodicity. This means I re-evaluate the momentum portfolio at the beginning of every month, buying the winners and selling the laggards, then do nothing for the rest of the month. My momentum systems place trades only 12 times a year.

The relatively slow trading pace of momentum systems makes them ideal for someone with a busy lifestyle; or someone who prefers less action in their systematic trading portfolio. Some people may not want to place trades every day, which I understand, as it can be a lot of work especially if you manage multiple trading systems.

A person with this preference might opt for a portfolio of systems operating on weekly or monthly periodicities; where trading activity occurs only on weekends or at the end of the month.

You don’t need a highly active systematic trading portfolio that places trades daily. You can adjust the trading frequency by developing systems that operate on higher periodicities.

Don’t want to trade every day?

Develop systems that trade weekly, monthly, quarterly, or even yearly.

Additionally, higher timeframes often have less noise in the data, which can lead to stronger signals.

That said, if you have order management software, as I do, periodicity becomes less relevant. Hypothetically, I could place hundreds of orders daily and it would be no different in terms of workload than placing one order a day.

This is because my systematic trading portfolio is 80% automated, and I use order management software to place and track all my orders. The computer does all the annoying management and tracking work, I just do a quick review to ensure everything looks reasonable. This makes my portfolio theoretically infinitely scalable and allows me to trade various periodicities without issues.

I’ll digress from this tangent about periodicity, trade frequency, and my portfolio. I just wanted to highlight that a trading portfolio doesn’t have to be highly active. You can use different system types on various periodicities to make the portfolio slower and less frequent in its day-to-day activity.

Don’t throw a system away because it trades too infrequently, sometimes infrequent trading has a real edge.

Defining Momentum And Other Market Effects:

Momentum is a well-documented market effect that has been studied extensively. The momentum effect has persisted in markets for centuries and continues to do so today. Because momentum is a real market effect, retail traders like us can capture it.

But what does momentum mean?

Sure, buy stocks when they’re rising and strong, and sell them when they’re no longer strong; but how do you convert that description into quantifiable rules or equations?

Saying "momentum" is vague and can have many interpretations. We need to quantify it, and that’s the tricky part. Thus, the first step in developing this system is figuring out how to measure and quantify momentum.

So, we need to define momentum.

But what else other than momentum do we want to incorporate into this system that also needs defined?

Can we get a two-for-one?

Yes, I think we can. I also want to incorporate the low volatility effect into this system. If you’re unfamiliar, the low volatility effect is another well-studied market phenomenon that persists over time.

Research shows that stocks with lower volatility outperform those with higher volatility on a risk-adjusted basis, meaning low volatility stocks offer more return for a given amount of risk. You can read more about the low volatility effect in the article below where I developed a system exploiting this effect:

Thus, trading momentum with a bias toward a basket of lower-volatility stocks should improve risk-adjusted returns. Stacking multiple market effects is powerful because it builds proven, profitable methodologies into the trading system.

But, as with momentum, we must ask:

How do we define low volatility?

Qualitatively, low volatility means stock A fluctuates less than stock B. We need to quantify the magnitude of this fluctuation. Once we do, we can leverage the low volatility effect by biasing the system toward stocks that offer better risk-adjusted returns.

Universe Selection And Regime Filtering:

Two other major components of this system that need to be defined before we begin development:

The universe the system will trade
The regime filtering, if any, to avoid unfavorable market periods.

Universe Selection:

Starting with the universe, large and mega-cap stocks tend to exhibit momentum with more stability and reliably than smaller-cap stocks. This isn’t to say smaller-cap stocks lack momentum, but their momentum is often more erratic, noisy, and less predictable, at least in my experience. I’m sure there are systematic traders out there with killer momentum systems on small cap stocks, but I personally have struggled here.

Mega-cap stocks exhibit momentum for several reasons. First, they represent businesses which are considered the best of the best, consistently growing with strong fundamentals. Stocks with strong fundamentals are the ones that large funds focus on. These stocks also have high liquidity, allowing large billion-dollar funds to scale into positions over time. This scaling in of large positions gradually increases the stock price over time, and creates an upward trend.

There’s also a herding effect, where stocks performing well continue to do so. As people learn about a stock’s performance, they want to buy in to join the action. Mega-cap stocks, often household names covered by the mainstream media, are familiar and comfortable for everyday investors. Investors see these big name stocks they’re familiar with exhibiting a lot of momentum, so they jump on the train. Like with large funds, this herd-like behavior drives more money into the stock, boosting its price and momentum over time.

Mega-cap stocks are also included in major indices like the S&P 500, NASDAQ 100, or Russell 1000. ETFs and mutual funds representing these indices make up a significant portion of investors’ retirement and buy-and-hold portfolios. These accounts often deposit regular sums of money into these ETFs and mutual funds, which flow into the mega-cap stocks, fueling further their consistent growth.

Due to these fundamental effects, momentum persists consistently and cleanly in mega-cap stocks. Thus, this momentum system will be designed to trade a mega-cap stock universe.

The universes I’m considering are the NASDAQ 100, S&P 100, and Russell 1000. But, rather than picking one universe, I’m considering letting the system trade all three universes to diversify risk across sectors.

Many systematic traders focus on developing trading systems only on the NASDAQ 100. The NASDAQ 100 consists of the top 100 technology-oriented stocks, resulting in significant tech exposure. The tech sector has historically shown strong growth, and I don’t foresee it slowing down. That said, I want exposure to other sectors too, in case tech underperforms in the future.

The reason why many systematic traders focus on developing trading systems only on the NASDAQ 100 is because of the very good historical performance. Only trading the best universe is a selection bias. To get around this bias, we will force the system to trade multiple universes.

The S&P 100 overlaps some with the NASDAQ 100 but is much less tech-heavy, with a more even distribution across US sectors. This allows exposure to momentum in other areas of the market. Momentum shifts; sometimes it’s strong in tech, sometimes in healthcare or energy etc. Trading the S&P 100 ensures access to the top 100 US stocks across many sectors, positioning the system to capture the hottest sector at any given time.

The Russell 1000 provides exposure to other large stocks just outside the top 100. It includes the top 1000 stocks by market capitalization, expanding our breadth even further than the S&P 100.

In short, we will focus on momentum within mega-cap stocks across multiple US index universes, providing access to various market sectors. This ensures the system has a diversified basket to choose from, allowing it to dynamically capture momentum in the hottest sector.

This also helps prevent selection bias, avoiding only trading the system on what has historically been the strongest universe; because we don’t know which universe will be the best performing in the future.

Regime Filtering:

The other component that needs to be defined for the system is regime filtering, a much debated topic. Some systematic traders oppose regime filtering; others don’t. I personally use regime filtering, but am cautious about implementation.

It’s easy to overfit regime filters. Many traders apply indicators to a market index and brute force optimize for the best result, which I strongly oppose. Using a regime filter on a market index representative of your trading universe is fine, but excessive optimization is risky due to limited data points.

Optimizing an indicator on one time series can yield great results quickly, but they are likely not robust. The limited data points in a single index time series don’t justify such precise fitting. A simple, blunt, unoptimized regime filter is much better than a complex, precise one; at least from a robustness point of view.

From the point of view of having the most beautiful looking backtest, a very optimized regime filter will look much better, but these results will certainly not hold up in future live out-of-sample.

I’m a big fan of intentionally diversifying regime filters. If one system uses a specific regime filter, I’ll purposely use a different regime filter in another system. Or I’ll use the same regime filter but with an intentional selection of different parameters.

This spreads my risk across regime filters, allowing gradual scaling in and out as markets shift from risk-on to risk-off environments. This is better than being fully in or out of the markets at once, just because your one regime filter in all your systems triggered.

To me, being risk on or risk off is a gradient, therefore I treat it as such. This ensures some systems turn off quicker and at different times than other systems.

For this momentum system, I’ll need to determine what regime filter to use. I’m considering two types of regime filters:

A pure price action-based regime filter → An indicator on the index timeseries
Or a market breadth regime filter → A cross sectional measurement of all stocks within the index

I’m unsure which will be sufficiently robust and give good results, so I’ll investigate both during system design. I may even end up using both regime filter types, who knows.

I really like breadth filters because they tend to be more robust as they use more data points, analyzing the timeseries of all stocks within the universe. This allows the filter to have many more data points in the regime identification.

System Design and Implementation:

There are many other important aspects within a trading system, but the components above-market effects (momentum and low volatility), trading universe, and regime filtering-make up about 80% of the system operation. These components allow the system to pick and choose what stocks to buy and sell.

Another super important aspect of system development that has not been addressed yet is position sizing. Position sizing I see almost as a second layer. We need a way to quantify and measure a trading signal first so we know what to buy and sell, but from there it’s all position sizing.

For many years I was in the “position sizing is overrated” camp. After many many hours of research, I have since proved myself wrong. Position sizing is the most important part of system design after the definition of the entry signal.

You need a signal to warrant a position, but as soon as the system says be risk on, the next question is: By how much? How aggressive should we be risk on? Are we scaling in or out? Are we using leverage? So many questions to answer on position sizing.

Rather than trying to dive super deep into position sizing theory here, I’ll let the results show for themselves a little bit later in the article. I’ll show how I built up the system, piece by piece, and you’ll see how position size layering really enhances the systems risk adjusted performance.

If you want to learn more about position sizing, you can check out this article linked below. I also plan to explore position sizing more in future articles.

After spending about five hours on system design, I developed a system I was satisfied with. I worked hard to incorporate original ideas, as I’ve built many momentum systems before, and they can become redundant. The rules I finalized can be read from the code at the end of the article. But we will also walk through, rule by rule, and see the impact on the system performance in just a bit.

I built this system from the ground up, starting with the most basic concept and iterating from there. The foundation and starting point for the system was a momentum ranking factor that incorporates volatility. The system ranks all stocks in the universe, and the top 10 are selected for investment. The ranking factor used is shown below:

0.4 * ROC(C, 63)/HVOL(63) + 0.2 * ROC(C, 126)/HVOL(126) + 0.2 * ROC(C, 189)/HVOL(189) + 0.2 * ROC(C, 252)/HVOL(252)

Where ROC is the rate of change over x days and HVOL is the volatility of the stock averaged over x days.

This ranking factor considers both momentum and volatility, using different quarterly lookbacks. This approach, inspired by Investor’s Business Daily, is a stable way to measure historical momentum. My only twist is I normalized momentum by volatility. This ensures both the momentum and volatility effects are being built into the ranking factor.

I decided to use the S&P 100 universe as my in-sample data, using the date range from 1991 to 2018. 2018 to mid 2025 (today) was used for out-of-sample.

Below I’ll show a series of plots. Each plot will show the results of a new addition incorporated into the system, showing how I built the system from the ground up.

The first plot will show the S&P 100 in-sample dataset with only the volatility normalized momentum ranking factor applied, equal position sizing, and a monthly rotation (all plots will use a monthly rotation).

From there, I add in one new component to the system until the system is finished. The new component will be specified as we go.

See the first plot below for the starting point with only the volatility normalized momentum ranking factor, equal position sizing, and a monthly rotation. We are only looking at the in-sample data for now.

Current Momentum System Rules:

Volatility Normalized Momentum Ranking Factor
Equal Weighted Position Size
Monthly Rotation

The starting returns are 13.5%, better than market average, but drawdowns are massive, on par with the market at 52%. The volatility normalized momentum ranking captures the momentum effect, but there’s nothing controlling downside risk yet. It’s worth noting, the drawdown is actually even worse when the momentum ranking factor is not normalized by volatility.

To make this system more tradable we need to focus on reducing the downside risk. One way to manage downside risk is through position sizing. Remember I mentioned position sizing can have a sizable impact on system performance?

Portfolio Development Series - Part 3

TradeQuantiX — Mon, 21 Jul 2025 13:37:01 GMT

Introduction:

This is the first article in this portfolio development series where I will focus on developing a trading strategy for the portfolio. Up until this point, we have focused on portfolio design considerations and understanding portfolio level thinking. Now it’s time to get to the fun stuff: the actual system development work.

This article will focus on a US mega-cap long-side mean reversion system. To be honest, I’m not much of a mean reversion guy, but I understand the importance of having mean reversion system types in the portfolio.

My preference is with momentum and trend systems, but for you guys, I will step outside my comfort zone and develop a mean reversion system. Heck, if I like what I develop, maybe I’ll implement it into my own personal portfolio as well as the portfolio we develop throughout this series.

I’ll note that in each of these system design articles, I’m going to focus on a different part of the system development process to keep these articles from becoming stale. In some articles, I may focus on robustness testing; in others, the actual development of the system or how to brainstorm system ideas, etc.

In this article, I want to focus on the in-sample and out-of-sample testing. I’ll give a brief background on the development and why I made certain design decisions, but that won’t be the focus.

The focus will be the comparison between in-sample and out-of-sample performance, including my thoughts on the results, what concerns me, what I like, and what my next steps would be after seeing the out-of-sample results.

I think one of the things you’ll pick up on is in-sample and out-of-sample testing is just as much of an art as it is a science. We can have metrics to hit for the out-of-sample, but there are a lot of caveats and other things to consider that are harder to quantify. This is where some of the discretion comes in with systematic trading.

As a sneak peek, the images below show the equity curve, drawdown, and stats for the US mega-cap long-side mean reversion system that is discussed in this article. Not too bad, if I do say so myself!

Let’s get into it!

Mean Reversion And Capital Efficiency:

Portfolio Development Series - Part 2

TradeQuantiX — Mon, 14 Jul 2025 14:28:15 GMT

Introduction:

In the first article in this portfolio development series, we discussed the concept of portfolio-level thinking. I took a simple momentum system and applied portfolio-level thinking to show how the performance of one individual trading system does not matter as much as many believe. What really matters is how that one trading system contributes to a portfolio of many trading systems.

That introductory article was written because understanding portfolio-level thinking is critical to developing a portfolio of trading systems. Without this understanding, we could potentially throw away perfectly good systems that look ugly on their own but would otherwise make a great addition to a portfolio of many trading systems.

In this article, I want to start planning out what the final systematic trading portfolio might look like. I will lay out a general plan and direction with some portfolio-level performance goals. We don’t want to confine ourselves to a design space that’s too specific, but knowing the general direction will really help in figuring out what needs to be developed to create this systematic trading portfolio. This plan and set of goals will help us develop a well-rounded equities portfolio. So, let’s dive in and start planning.

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High Level Portfolio Design Items To Consider:

Before diving into specific trading system ideas or portfolio goals, we need to define the high-level direction the portfolio will take. We can’t just start developing a portfolio without considering the things I will discuss below.

We need some sort of direction in terms of market type/asset class, market region, timeframe, and side (long/short/both) for which this portfolio should be designed.

So, first things first, what market type/asset class will this portfolio be designed for?

Market Type/Asset Class:

Equites
Crypto
Futures
Forex
Options or Shares
etc.

You could create an entire portfolio for each market type/asset class listed above (and I’m sure there are others too, which I failed to list). You could also trade each market type/asset class through the lens of shares or options.

Each market type/asset class has its own pros and cons, but as equities traded with shares are my specialty, we will focus there. Trading equities with shares is also likely the most familiar to the majority of people as well.

One thing to note in case you are not interested in trading equities via shares, these portfolio development principles apply to any market type. So, everything discussed throughout this Portfolio Development series will be applicable to whatever sandbox you want to play in. You could even develop a portfolio for each market type/asset class, where each portfolio contains multiple trading systems, essentially a portfolio of portfolios concept.

Example:

A portfolio of 5 shares trading systems on equities
A portfolio of 3 spot trading systems on Crypto
An options strategy portfolio on equity ETFs
setc.

For those who are not sure which market type/asset class they want to trade, here are a couple of questions consider. Under each question I added some examples of things to think about:

How stable is the market type/asset class?

As an example, equities exchanges are very reliable, but crypto exchanges may not be. However, there is likely more edge in crypto than equities.
Crypto has significantly more volatility and downside risk than equities. More care needs to be taken with crypto to ensure risk is accounted for.
With that said, with more risk there is generally more reward. The less mature or stable the market type/asset class the more alpha there will be laying around.

How well do you understand the market type/asset class and its dynamics?

You may be very comfortable with trading equities shares and understand how they work, but futures and options are a completely different ballgame for you.
In that case, it may make more sense to start with what you know and are familiar with, rather than spending a lot of extra time trying to learn a new market type/asset class.
You can always transition to more sophisticated methods on more complex or less familiar markets later, but start with what makes the most sense and has the lesser barrier to entry.

How sophisticated are the market players?

Futures, forex, and large-cap equities tend to be dominated by massive hedge funds and banks. These are sophisticated players with access to much more data, tools, and resources than you.
Starting in a less sophisticated market like crypto, small-cap stocks, or smaller countries' stock markets may be a better option as a starting point.
Think of this like sitting at a poker table full of professional players versus sitting at a table full of all your drunk friends. The more sophisticated the market players, the harder it will be to extract an edge.

How easily can you access the market?

For a while, certain crypto exchanges were banned in the region I live in. This made it extremely difficult for me to trade the crypto market. I could only access exchanges with maybe 25–50 coins listed, whereas in reality there were tens of thousands of coins across the entire crypto market; I just didn’t have access to them.
These restrictions made is so I either had a very limited trading universe or had to take massive risks to access a larger universe, such as signing up for an exchange with a VPN to a different country and risking having my funds locked If the exchange found out.
On the other hand, I have access to several countries' equities markets, and the barrier to entry is nothing more than a few button clicks with my broker to request access to the market. I wanted to trade crypto, but it made much more sense for me to focus on equities in this case.

As outlined above, there is a bit more to it than just randomly picking a market to trade. There are practical considerations that account for ease of access, legality, the size of the edge that exists, and overall market risk. Give those points some real thought before randomly picking a market type to trade.

Next comes the specific market regions to trade. This category will be slightly different depending on what market type/asset class you choose. Since this article series is directed toward equities, we will focus there:

Equities Market Regions:

North America
- USA Stock Market
  - Nasdaq 100
  - Russell 1000
  - S&P 500
  - Etc.
- Canadian Stock Market
Australian Stock Market
European Stock Markets
- France
- Germany
- Norway
- Sweden
- United Kingdom
- Etc.
Asian Stock Markets
- Hong Kong
- Japan
- Singapore
- Etc.
South American Stock Markets
- Brazil
- Etc.
Etc.

There are so many options to choose from in terms of country stock markets available to trade. A pro tip: don’t just go to the USA stock market just because it is the biggest and most liquid. The liquidity and numerous stocks to choose from is great, but the US stock market also has the most sophisticated players.

You can extract more edge from smaller stock markets (or smaller cap stocks within the USA stock market). This is not to say don’t trade the USA stock markets—I definitely do, and there are real effects/inefficiencies to extract there—I’m just saying do not box yourself into only the USA stock market.

The next set of items to consider are the timeframes you want your trading systems to use. There are some real considerations here. For example, trading an intraday system on 5-minute bars may not make sense for people who can’t monitor the system all day. This is because if something goes wrong (e.g., internet goes out, bugs in the system, API data feeds drop, orphaned positions, etc.) you need to be in front of your computer at all times to fix the issue. Also, very short timeframes are much more susceptible to the impacts of slippage and commissions, so your fills have to be near perfect.

I have a day job, so intraday strategies do not make sense for me. Believe me, I tried, and I failed at it. My internet went out, systems crashed, by broker platform crashed etc. all while I was at work; not a great situation. Having a day job and trading intraday is extremely difficult.

I now focus on the daily timeframe and higher, as the pace of the trades is something I can handle with a day job. I can also better account for things like internet outages and orphaned trades because I have a lot of time between market close and market open the next day to reconcile any issues.

Timeframes:

Intraday
Daily
Weekly
Monthly
Etc.

A timeframe can really be whatever you want. I personally trade systems on many different timeframes. You can also take a hybrid approach and have a system that calculates indicators on daily bars but only executes orders weekly or monthly; I do this as well.

Another thing to consider is which direction you plan to trade: long, short, or both? This is important because certain markets may not let you go short. For example, most stocks on the ASX exchange will not let you short the shares, but you could buy put options. On the TSX exchange, maybe 75% of stocks are shortable. Whereas most stocks in the USA stock market will let you go short.

With an equities portfolio, you may be able to get away with a long-only set of trading systems, considering equities tend to go up over time; though having some sort of short side hedge can be very helpful during bear markets. But with other market types, like forex or futures, the short side is more crucial because the fundamental drivers of price are completely different, and instruments can trend down just as easily as they can trend up.

Side:

Long
Short
Both

Only once you have given serious thought to the items listed above should you move on to the next steps. This will give you direction in terms of what to focus on and prevent you from getting lost during the portfolio development process.

Some of these portfolio design options may be obvious; maybe you already know you want to trade equities long and short, but you have no idea what timeframe to focus on or what stock market(s). Maybe you thought trading intraday on the USA stock market was a good idea, but after reading this, you’re starting to rethink that decision.

Really do some research and think hard about the direction you want your portfolio to go, and remember you can always add a different set of market types, market regions, timeframes, and sides later on down the road.

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The Portfolio Development Series Design Space:

For the portfolio that will be built in this portfolio development article series, we will focus on the following design space:

Market Type:

Equities

Market Regions:

USA Stock Market
Canadian Stock Market (TSX)
Australian Stock Market (ASX)

Timeframes:

Daily
Hybrid Weekly and/or Monthly
- Look at daily data but make decisions weekly and monthly

Side:

Long
- USA, Australian, and Canada
Short
- USA stock market only as the Australian and Canadian are more difficult to short

Notice with this list, I am not completely boxed in. I have wiggle room to develop whatever systems I want within the bounds outlined above. I’m not boxing myself into USA large-caps or small-caps only, but rather just the USA stock market as a whole. I do not have any systems made yet, and I do not know where I will be able to develop the best system, so I am giving myself some creative freedom to look at a specific market region from many different angles.

Also, with the timeframes I have defined, I want daily, weekly, and/or monthly timeframes. But again, I do not know what timeframe will work best for the particular market, so I need some creative freedom. What is important is that I know not to look at intraday at all; those systems just won’t work for me. This helps me focus on one set of timeframes without getting distracted by others once I start the system development process.

This first step of defining the direction of portfolio design does not have to be overly specific; in fact, it is better if there is some wiggle room. What matters is that we took a massive design space and reduced it to a manageable region to focus on and gain expertise in.

Portfolio Performance Goals:

Now that the high-level direction of our portfolio has been determined, we know exactly what we need to focus on when it comes time to start building trading systems. But before we start brainstorming trading systems to develop, there is one more thing to consider, and it’s an important one: we need to define what our portfolio performance goals are.

Knowing these performance goals will help us figure out what we need to develop to get there. If the goal was to design a portfolio that had S&P 500-like returns with less volatility, that would be a significantly different portfolio of trading systems than a portfolio with the goal of the highest possible outright returns.

So first, here are some example questions to ask yourself to help you define the purpose of your systematic trading portfolio and what you want the outcome to look like. Thinking through questions like this will help you define your portfolio performance goals:

Why are you creating a systematic trading portfolio?

Is it for income?
Is it to support retirement later in life?
Is it so you can quit your job?

What size drawdown makes you uncomfortable?

If you woke up tomorrow and your portfolio was down 10%, how would you feel?
What about 20%?
What about 30%?

What amount of returns feel boring to you?

If your portfolio only made 5% a year, would all this work be worth it to you?
Where is the threshold in terms of returns that makes all this portfolio development work worth it?
Remember, a portfolio is not set-and-forget once you develop all the systems. You’ll have to place the trades every day, and there will be ongoing maintenance and upkeep.

What length of time of being in drawdown makes you uncomfortable?

If you were in a drawdown for 6 months, how would you feel?
What about 1 year?
What about 2 years?

What types of equity swings in your account value make you uncomfortable?

Do you want slow and steady growth, or are you okay with fast but more erratic growth?
If your portfolio was down 10% on Monday but up 15% on Friday, could you stomach this volatility?
Or would you prefer being down 2% on Monday and up 3% on Friday?

These questions will help you get a general qualitative idea of what you wish to expect from your portfolio’s performance. Your portfolio equity curve is like a roller coaster with many ups and downs. Some roller coasters are small; they don’t go too high in the air, but they also don’t drop down as far. Others are huge; they quickly go up hundreds of feet and plummet down even faster.

So, the question is: how big of a roller coaster do you want to ride?

Once you understand why you want to develop a systematic trading portfolio and what its purpose is, we need to quantify some goals for the portfolio. We need some numerical metrics to compare our portfolio to so that we can ensure it falls within the bounds of our performance goals. Some great quantitative metrics to use are:

Compound Annual Growth Rate (CAGR): The average growth of the account per year.
Max Drawdown: The most the portfolio will be down at any given time.
Average or Median Drawdown: The average/median amount the portfolio will be down at any given time.
Max Drawdown Length: The maximum amount of time the portfolio will be in a drawdown.
Ulcer Index: A measure of both the depth and length of the portfolio drawdown.
Volatility: How much the portfolio equity wiggles up or down from day to day.
Sharpe Ratio: A measure of portfolio returns in relation to the variability of those returns.

There are many other metrics, but these are some of the more common quantitative metrics used to evaluate portfolio performance.

An Example Of Converting Qualitative To Quantitative:

As an example, I’ll discuss my own qualitative situation and how I want my portfolio to behave and then convert those to quantitative metrics for the portfolio for measuring purposes. It will also show how I have chosen to structure my portfolio to suite my own goals. Use this example to help you plan your own portfolio metrics and design structure.

I trade systematically for the end goal of financial freedom one day. I do not want to work in the corporate world my entire life. Therefore, I need a portfolio that makes high returns so I can accumulate enough capital to quit my job or go to reduced hours sooner rather than later.

I want high returns but I don’t want to be pulling my hair out and constantly stressed to get there. I don’t want to feel the pit in my stomach of massive drawdowns or volatility. High and stable growth with reasonable volatility is the goal for me.

So, a CAGR of 20% or more is desired. This CAGR is reasonable for a systematic trading portfolio but also will help me compound my capital very fast. I am okay enduring some drawdowns along the way to hit that CAGR goal. I know from experience I can endure 10%–15% drawdowns with no problem, so if that was the average, that would be perfect. The drawdowns would start to get uncomfortable if they exceeded 25%, though.

So keeping worst case drawdown at 25% or lower, but closer to 10-15% on average would be ideal. As shown from part 1 in this portfolio development series, stacking a few momentum strategies on different markets can achieve this CAGR and drawdown goal. If you missed part 1, check it out here:

I’m also fine sitting in a drawdown for a decent period of time. A year-long drawdown would be manageable for me, but any longer would make me worried my portfolio is broken.

I know from experience momentum and trend following systems can have drawdowns longer than 1 year. So, to meet this goal, I’d need some mean reversion strategies in the portfolio as they tend to have shorter drawdown lengths. I could also implement some hedging strategies, which would reduce the drawdown depth, making them quicker to climb out of when the bull market comes back.

Finally, as far as equity curve volatility goes, 10% seems like a good goal to aim for. Statistically, a 10% portfolio volatility with a CAGR of 20% would result in a max drawdown of 20%–30% and an average drawdown of 10%–15%; which is right in line with my goals.

Finally, a Sharpe Ratio of 2 or greater I think would be pretty impressive, so let’s shoot for that. Sharpe is a good metric for risk adjusted returns. A lot of people like to use Calmar ratio (CAGR divided by max drawdown), and while it can be helpful, the denominator is only 1 data point. Sharpe is a better metric in my mind for risk adjusted returns as both the numerator and denominator take into account all equity curve data points.

I think these goals will help me accelerate portfolio growth while keeping drawdowns and volatility within a reasonable range. If you want more returns, you have to take more risk, so if I desired a higher CAGR, that would generally come with a larger drawdown and more volatility.

While a CAGR higher than 20% sounds nice, I don’t want to take on the extra risk required to get there. I don’t want to be drowning in stress because my portfolio is in a 35% drawdown. If you can handle the extra risk then no problem, take a little more risk and boost your returns. It’s all a give and take. So, to sum that up:

Portfolio Performance Goals:

Portfolio CAGR Goal: >20%
Portfolio Max Drawdown Goal: <25%
Portfolio Max Drawdown Length Goal: <1 year
Portfolio Average Drawdown Goal: <10%
Portfolio Volatility Goal: <10%
Portfolio Sharpe Goal: >2.0

After quitting my job in the future, I am okay living a modest life and living on the returns from my trading portfolio. My portfolio goals may shift when this happens. I will likely be less aggressive and want lower volatility and drawdown. I’ll adjust my portfolio when that time comes, but for now, I need the higher returns and will accept a little more volatility and drawdown as a side effect.

For this portfolio development series, the goals listed above will be used for the portfolio I am going to attempt to develop. We shall see if I can achieve them.

This portfolio development series is going to be a “develop in public” set of articles. I’m writing all these articles as I go. This article will be released before all the systems are developed and much before the final portfolio is assembled. This will give me opportunity to discuss any failures and talk about any required pivots as I go.

There is no guarantee the final portfolio will hit all these performance goals going forward. The best we can do is implement robust system development practices and do our best to be as diversified with non-correlated systems as possible. Outside of that, we can only extract what the markets give us.

The assumption here is that our backtested performance will be somewhat representative of the future, with some slight variation. Whether or not that is a good assumption, I don’t know—I cannot see the future. But we will position ourselves to achieve our goals in the best way we possibly can by developing a robust portfolio with multiple diversified trading systems on multiple markets and timeframes. That’s about the best we can do.

Benchmark Considerations:

While this isn’t the most important item to consider for portfolio development, it can help you determine if your final portfolio is worth trading. That item to consider is: