StrategyQuant CEO Reveals BIG AI Plans for 2025!

Ever wondered how one idea could revolutionize the way traders build algorithmic strategies?

In our brand-new exclusive video interview, we sit down with Mark Fric, the founder and CEO of StrategyQuant, to uncover:

• The aha moment that sparked StrategyQuant’s creation

• How the software evolved from a simple builder to a powerhouse used in over 120 countries

• The real truth about “black box” trading (and how to avoid common mistakes)

• Why Algo Trading isn’t just for hedge funds anymore

• A first look at AlgoCloud, a unique no-code platform for stock picking strategies

• And an exciting sneak peek at AI-powered trading tools coming in 2025!

Whether you’re a pro trader or just getting started, this interview is packed with insights you won’t find anywhere else.

???? Watch now and discover the future of algo trading.

Transcript:

So, hello everyone and welcome to today’s interview with Mark Fric, the founder and
CEO of StrategyQuant.
And for those of you who don’t know StrategyQuant yet, it’s a very unique software for building,
testing, and optimizing algo strategies.
Thank you, Mark, for joining me today.
I know how busy you are, so I really do appreciate it.
Thank you for having me here.
Great.
So, just for our followers, our viewers, today you will peek behind the curtains not only
of StrategyQuant’s history, present, but also I hope that Mark will share with us some of
his future plans.
So, Mark, I’ve prepared more than just a few questions, so whenever you are ready, we can
start.
Sure.
Let’s start.
Okay.
The first very basic and common question is what actually sparked the idea of StrategyQuant?
I mean, was there some kind of specific moment that set everything in motion?
Oh, yeah.
It was a very long time ago.
Yes, there was this kind of a moment.
At that time, I was a normal trader trading various instruments, mainly forex and futures,
but I have a background as a programmer.
I was always interested in automatizing trading, so I was converting my ideas to algos or ERO
bots, both to test them on our historical data and to trade them live.
I was at that time active on multiple forums, always looking for new trading ideas, and
on one of those forums, somebody posted an idea of generating the strategy rules using
machine learning, and this was my aha moment.
I realized that this should be possible, and I had an idea how to do it, and I decided
to try it.
Okay.
So, from then on, I started working on this idea, and the very first version of a program
that was then named Genetic Builder was born.
Okay.
And how long ago it was, actually?
Can you tell?
If you remember, 12 or 13 years, maybe?
13, 14 years, I think, yeah, maybe more, maybe 15 years.
That’s quite a time.
I have to check the history.
I don’t know.
Okay.
Okay.
That’s quite a time.
And do you have any, like, some kind of untold story you would like to share from these very
early days?
Perhaps, I guess, there was a major breakthrough or unexpected challenge that shaped strategic
want we know today.
Yeah, when you develop a program to be used by many users, there are many challenges.
For example, how to make the user interface understandable and easy to use.
There are really different challenges you don’t expect when somebody else is using your
program.
One of the major breakthroughs for me was when I realized that just generating and backtesting
strategies will be not enough, that by nature of data mining and machine learning, this
kind of strategies will be very prone to overfitting to historical data.
So I had to look for ways to ensure that the strategies have a real edge.
And the whole idea of robustness checks in SQS came from this realization.
Yeah.
Yeah, having edge is definitely one of the most important stuff when it comes to algo
trading.
Yeah.
And do you remember what was perhaps, like, the biggest, the hardest, toughest obstacle
when you were developing strategic want and how did you overcome it?
Well, the biggest obstacle was probably not technical.
I think you can do anything, you can program almost anything you want.
But the biggest obstacle was also more related to business because the program is quite
complex. It requires some time to learn.
And honestly, it wasn’t selling that well in the beginning.
And I was dependent on the sales to continue with the development.
So fortunately, I was able to find very good partners in the beginning who developed first
courses and tutorials on how to use it.
And it started growing and attracting more users.
Yeah, that sounds great.
I mean, you said that anything can be coded or programmed.
But I mean, you can do it.
You can do it definitely.
But I don’t think everyone could do that.
Like for me, it’s like a different universe.
Well, my approach is that I can do anything I want.
That’s great.
And have you ever thought about if StrategyQuant did not exist today, what do you think the
algo trading would look like?
Because from what I know, StrategyQuant is used worldwide in more than 120 countries
and it’s used by universities.
So have you ever thought about that?
Well, I’m not sure if we have that extreme impact on the world.
Yes, it would still be algorithmic trading also without SQX, but I think it would be more
difficult to create an algorithmic strategy without us.
So SQX opened this world also to non-programmers.
And also, I have to mention that we made some inventions of our own, like robustness
test, implementations of WorkForward, of custom project workflow, of generating strategies
using strategy templates.
And many of them are still pretty unique only to StrategyQuant.
Yeah, that sounds amazing.
So many features that are original, there must have been a lot of work.
But the thing is, I was talking with a lot of people about algo trading and there is
this thing that it’s often seen as some kind of black box and mainly by the traditional
discretionary traders.
And what do you think is like the biggest misconception about it, about algo trading?
Well, yes, partly they are right.
It can be a black box if you just use some algorithms, algorithm or robots you find on
the web or on a forum without understanding how exactly it works, why it works and what’s
the performance and the volatility in performance over time.
This is one of the most important points, because would you trust the algo that you
found somewhere without the knowledge of it when you are in a 10%, 20%, 30% drawdown?
It might seem simple, but traders underestimate how they feel, how they will feel at the
time. So I think in this sense, it shouldn’t be a black box for you.
You should know your strategies and you should know why they work or should know the
performance you can expect from them.
So anyone who would like to get like a robot like algo strategy should focus on its logic
to be sure that they understand it and that they can see it all, if I’m right.
It can be either a logic or you have to test it, you can see the result.
You can, when you use quantified approach, you don’t really need to care about what the
logic is, but you should care about what the results tell you.
If the strategy works on like the markets that you want to trade, if it works on
additional markets, if it works with Monte Carlo test, for example.
And what is the performance characteristics?
Yes.
Because the strategy can have 10% or 20% drawdown and you have to, if you know that it
can happen, it happened in the past and it can happen in the future as well.
And you have to be, you have to know it, know it.
Yeah, yeah, understood.
And I guess, you know, already throughout all these 15 years, many traders who kind of
switched from discretionary trading to algorithmic one.
And what you would say is the biggest mistake they are doing while transitioning into
algo and how, if anyone would be interested to this transition, how would you say people
can avoid these mistakes?
I think it is quite challenging, but quite interesting, the transition from discretionary
to algorithmic.
You can tell by yourself.
Yes, I was also discretionary trading in the beginning.
And one of the surprises that I had during the transition was that many ideas that I
thought are working in manual trading really don’t work when you really test them on
historical data.
They seem to work for like a few weeks, but when you backtest it on a few years, you will
find out that the idea is not that good probably.
So when transitioning to algo, you have to learn how to think in a quantified way.
You should be able to understand the results of the backtesting.
You should learn to use the tools like Monte Carlo, WorkForward, Tests that can test the
strategy robustness and edge.
And those things are by their nature, they cannot be used in manual trading.
So there are some things that you should learn.
Yeah, so it’s a lot of studying and getting new information about the whole world.
On the other hand, it’s not that difficult.
The concepts are simple and even the usage of those tools, for example, are not that
difficult to use. Just click on it, set, you can use the default setting and it works
pretty well for many cases.
So yeah, I can tell by myself, of course, I’m working with strategy quant software and
it’s really, you know, it can fit to traders of all levels.
So for pro and for beginners too, absolutely.
And when it comes to beginners, you know, newbies on algorithmic trading field, there’s
like this common belief that algo trading in general trading, let’s say, is only like
for hedge funds or highly technical traders or, you know, only for people who have like
bunch, bunch of money.
But what is the reality?
What would you say?
I’d say in today’s world, algo trading is accessible also for people with very low
capital, thanks to trading.
Also for people with zero coding experience.
And I think it might be even more suitable for them than manual trading.
With tools like strategy quant or algo clouds and various wizards, you are able to create
the ideas or transform the ideas of your strategy into a working strategy and then trade
them on a broker without need to know the programming and the exact source code.
And you mentioned algo cloud, maybe not everyone already knows, but currently you are
running a very new project is a trading platform.
It’s called algo cloud.
And let’s say it’s a future of stock picking strategies.
It’s a very new platform.
You don’t need any coding knowledge or you don’t need VPS.
So what inspired the creation of algo cloud?
Because in my eyes, it’s very unique.
I don’t really think I did some research on the web and I haven’t found anything like
it. So it’s pretty unique.
Yeah, some things that are quite unique and as was the case of strategy quant, for the
big part, it was our own needs and recognizing that if we have this need for this kind of
tool, then others might have also interest in this.
Because we wanted to use algo trading also for stocks and specifically for stock picking.
We already have working strategies, but there was no way of simple trading them without
using things like Python scripting.
And so even I mean, I had strategies myself that I wanted to trade, but I didn’t want to
run Python scripts and VPS and so on.
So algo cloud was created to help us and other people like us who want to trade robust
stocks and stock picking strategies, but they didn’t have a platform for it.
And algo cloud is still the only platform that allows you to trade this kind of strategies.
That’s fantastic, even though the stock picking is like a famous trading method for many
years. From what I know, this is the app of the very first kind, I would say.
And can you just very, very briefly say what is stock picking?
Because maybe there are people who are starting with algo trading and they are not sure
about it. Can you please briefly describe it?
Sure. In short, stock picking means that you are trading a group of stocks, for example,
all 500 stocks that are in the S&P 500 index and using the simple strategy.
In case of algo clouds, when you have a strategy running on algo cloud on this S&P 500
group of stocks, the algo cloud interprets the trading rules for each stock in the group.
It collects trading signals for all of them and then chooses the best stocks to trade from
this group. The advantage of algo cloud, I’m sorry, the advantage of stock picking
approach to trading is that it can be much more robust and you can have a much better
performance than just by buying S&P index ETF.
So in short, you can have a better performance and a lower drawdown by trading this way.
OK, and from what I know from other traders, one major challenge in algo trading is
deploying and maintaining these strategies.
I’m very curious, how does algo cloud solve this?
This is like a critical thing, so.
Yes, well, exactly.
This was my own struggle when I wanted to trade those kind of strategies, to maintain a
VPS, to maintain Python script and maintain all of it was a headache, even though I am a
programmer. So this is something that algo cloud does for users.
When you deploy your strategy on algo cloud, you need to care if your VPS or trading
platform is running, if there is some technical or internet outage, everything is handled
by algo cloud. And the backend of algo cloud runs on reliable Amazon AWS infrastructure
and is made to be robust and scalable.
OK, OK, right.
And now a big question into the future, like very near future, but we are entering the
second quarter of 2025.
So can you tell us where do you see algo cloud this year?
Like, are there any, let’s say, game changing features coming that you can tease for us a
little bit?
Well, algo cloud is just starting and we have many features.
Many ideas for features that we want to implement and some of them are some kind of
usage of AI in various parts of algo cloud, including creating strategies using AI.
Another is a better support for managing portfolios because you usually trade not just
one strategy, but a portfolio of strategies.
OK, but as I said, we are starting and we’ll also listen to active users and any good
suggestions they could lead us to things we cannot imagine right now.
OK, OK, great.
And now when I get back to strategy quant and trading, what are your biggest goals for
strategy quant for this year?
And does algo cloud somehow fit into that vision?
Because I think it could be very interesting to somehow like come join these two projects.
I mean, I can imagine that it would be just great.
So is there something like that?
Sure, well, we have big plans for SQX in 2025.
There will be a few major features added to SQX this year.
And also to algo cloud.
I don’t know if you know, but strategy quant and algo cloud already are in some way
integrated together. You can create stock picking strategy in strategy quant and then
deploy it on algo cloud.
And one big feature is adding more AI to both SQX and algo cloud, as I already said.
We are already working on AI features.
OK, and we’ll show the very first version probably in the next major build, which will
be in the next few months.
OK, this AI usage is rapidly growing everywhere and we in SQX don’t want to be an
exception. We plan to use AI across all the SQX to help you create new strategies, to
help you configure a custom project, explain whatever you don’t know in SQX.
And many other things, as I said, we already started working on it.
We’ll show you what we have done and we’ll see where it will lead us.
Another major feature we are researching for SQX is order flow, incorporating order
flow into algo strategies.
For those who are familiar with order flow, it gives a new and different look to the
market and it could give an additional edge to your strategies.
OK, OK, the more edge there are, the better.
So that’s definitely great news.
When you’re talking about AI, it’s definitely everywhere.
It’s progressing very, very fast anywhere you look on every field.
And do you see AI and machine learning, revolutionizing stock picking strategies any
further? I mean, what’s next?
What do you think is next for automation in trading?
It is hard to say for now because nobody knows what AI will bring.
Its capabilities are increasing in multiple areas.
What I think is pretty sure even now is that AI will be able to greatly help users
create new strategies, help them with the kind of communication with the programs like
SQX in a natural language, because this is something that is possible even now.
It could help users or traders also in stock picking kind of strategies to manage the
groups of stocks that they will be trading.
They don’t need to be trading just S&P 500 stocks, but they can create their own custom
groups, possibly created with the help of the AI.
I think the future of using AI in trading is very interesting and very exciting.
And we in StrategyQuant want to be a part of this.
OK, Mark, so that was quite a lot of questions, so I’m not going to keep you any
further. Just one last question or is there something you would like to tell to our
followers, to our users and viewers?
So I hope you will be as excited as we are about the new features in AlgoCloud and
StrategyQuant and especially the AI that will lead us to things that we cannot imagine
even cannot imagine right now.
Yeah, well, let’s hope so.
The future is bright.
So, Mark, thank you very much for your time.
I appreciate it again.
I know how busy you are.
And like at the end, I would like to tell to our followers and viewers, thank you very
much for watching this interview today.
And if you would have any questions, you can write down in the comments and don’t forget
to give us like and follow so you won’t miss any biggest news from the world of Algo
Trading. OK, Mark, thank you very much again and have a great rest of the day.
Thank you as well.
Thank you. Bye.