From Research to Results: How Hani Developed a Portfolio with Over 300% Return

Hani Hamdan, algorithmic trader and entrepreneur, has achieved what many only aim for:
A real-money portfolio with over +300% return in less than 12 months—built on a fully automated strategy framework with risk tightly under control.

His performance has placed him #8 on the DarwinIA Gold, a benchmark reserved for the most consistent and profitable traders globally.

Below: Live equity curve from Hani’s portfolio

???? View verified results: https://quant-bot.com/trading/

In this in-depth interview, Hani explains:

• Why seven years of research and development were critical to his success
• How he uses StrategyQuantX to generate, validate, and manage a library of 1,000+ strategies
• His use of multi-layered risk protection, including automated stop-loss thresholds across strategy, weekly, and monthly levels
• How he filters strategies dynamically based on performance metrics and correlation analysis
• Why mindset, discipline, and a structured workflow are essential for long-term profitability

“We treat trading as a business. Automation and discipline are non-negotiable.”

If you are serious about algorithmic trading and looking for a real-world example of sustainable performance, this interview offers both strategic insights and practical inspiration.

???? Watch the full interview now:

Transcript:

I started trading back in 2002, really for me StrategyQuant made a huge impact.
I can say the most profitable strategies that are performing now were made on StrategyQuant.
It was not a gambling, it’s not a game.
The most thing to keep in mind while trading is managing risk.
So, hello traders, I’m glad to introduce next interview from our StrategyQuant series.
And today is with a very experienced trader, Hani from Lebanon, who recently achieved like
their account is now number 8 in DarwinX Gold Zero, which is a list of the very like list
of traders and ranked on the success and positive and profitable result with their portfolio.
So I was excited when Hani decided to join us and share his insight, what’s working,
what works and what is working for him in markets to share it with you.
So here’s the Hani.
Hello, Hani.
Hello.
Tomas Vanek, my colleague.
Hello, Tomas.
Hello, traders.
Let’s start.
Let’s start with the first question and simply Hani, please, could you introduce yourself?
What is your profession?
How did you start with algo trading?
Yeah.
Hello, everyone.
I would like first to thank you for this interview and the whole team of SQX for this
remarkable platform.
My name is Hani Hamdan.
I’m based in Lebanon, Middle East.
I’m a serial entrepreneur and business consultant and an algorithmic trader.
My academic professional background in information technology naturally shaped
my thinking to be systematic, analytical and automation oriented.
I started trading back in 2002.
I took several courses, but at that time, due to financial and time constraints while
launching my business, I stopped trading.
Then back in 2015, I had gained some freedom to fully commit to trading.
So I dedicated more than 15 hours a day and still to trading.
First, I was focusing on manual trading and preparing an environment to trade in.
I studied fundamental and technical analysis.
I didn’t like fundamentals because you need to stay alert and read the news and to be
always online.
But I like more technical analysis.
And I studied also harmonic patterns with Elliott waves and advanced patterns.
And I had a strategy in place, which I was trading for a year and a half.
And because the strategy was on a higher time frame, you know, I had the pleasure also of
time, the luxury of time.
So I said, why I don’t program the strategy to be an algorithm.
So I started working and training on MQL4 and MQL5, but also, you know, to create a
strategy that will take time.
So I was searching for another time for a platform to generate a strategy for me in
a much quicker time.
And I found StrategyQuant with other platforms.
But really, for me, StrategyQuant made a huge impact.
And I can say the most profitable strategies that are performing now were made on
StrategyQuant.
Now, at this stage, we have our own team.
We already developed our own automated filtering system and methodologies.
So, yeah.
Great.
Thank you.
Thank you for the introduction.
And I’d like to ask you, because there is a long career and every trader is at some
point, someone is starting, someone is just doing some research, someone is already trading
for a few years, someone just bought some courses.
And the path is quite similar, I might say.
So I would like to ask you how long it took you to be successful and what was the breaking
point, which is the most interesting, obviously, question everyone is interested in.
Yeah, let me tell you, from the beginning, my priority was learning and researching and
not rushing into live trading, because I know it was not a gambling, it’s not a game.
And I wanted to create a business from that.
So my goal was to build a track record, a solid track record before seeking any funds
or going to the next stage.
So at that time, I accepted breakeven during the early stages, no more than breakeven.
I don’t want to make money through trading, just want to enhance my trading.
But I can say after seven years, the success came when we developed a multi-layer automated
filtering system for our live strategies.
You can do manual filtering for the strategies that comes out from the process that you are
doing.
But why it took me seven years?
Because the whole time I was thinking of automation.
So I don’t want to even, I don’t want to filter manually the trades or the strategies that
are performing good.
So it took me time with the team to develop this.
So yeah, seven years, long seven years.
So long, several years, and then you simply finished all this and you were sure that it
makes sense.
And you started to work.
Yeah.
And what do you like the most on algo trading?
Where you thought you were aiming from the beginning to be algo trader, but there is
a lot of styles how to trade.
And some of them do not be necessarily like time consuming.
If you, for example, can trade place one, trade a month or whenever, or you can be option
trader and then can or spread trader or whatever.
What do you like the most?
Well, let me say algo trading combines my passion for coding in the first place.
Also my analytical problem solving and sitting in front of the computer.
I spent thousands of hours sitting in front of my computer.
But the issue, the thing is, the important thing is it allowed me to maintain the freedom
and flexibility of an entrepreneurial lifestyle.
And this is crucial to me, you know, because I didn’t fit to be an employee at my early
stages.
I was an employee for four or five years, but later on, I knew that I don’t want to
be an employee for all my life.
I was, I am an entrepreneur.
I have different businesses, but now I’m concentrating on algo trading.
So also it offers significant returns and the potential of returns are very high once
you are profitable.
If you are not profitable, it will be a disaster.
Yeah, it’s true that the algo trading or basically trading, it’s very easy to find out if you
are, if you are profitable or not, if your business is successful or not, comparing to
other businesses.
There is just one simple graph and that’s all.
Yeah, but I would say that every person is enemy of yourself because of course there
is advantage of the trader.
It is on your own, but if you are only, only you in your mind, it can be tricky.
So you need to be mind sharp and focused on the result and discipline.
Yes.
Yeah, it’s true.
This is true that with every trader we are having interview who is successful or basically
every trader we know, there is not just gaining the knowledge, but there is also a big personal
way and personal development behind.
Exactly.
I can tell you that in many, many times I had the feeling that to drop off this thing
after many years of learning and building.
But every day I wake up in the morning and I am very enthusiastic to come to computer
and start again and start again.
So you have to keep in mind that discipline is a key for any success.
It’s not only in sports or business or anything else and everything.
Discipline is a key.
Yeah, but the problem is the time.
We don’t have the pleasure, you know, now I’m around 50 years old, so you don’t have
all the time in your life to be successful.
So that’s why a StrategyQuant made a huge impact on our lives, because now you can have
millions of strategies in one hour.
And in the traditional way, we need one week to code one strategy, which is a big difference.
Yeah, yeah, you are right that they don’t, all of them are basically not the profitable,
but you can know it very fast.
Yeah, yeah, we’ll talk about it.
Yeah, comparing to other ways, when you like spend two weeks, you just see that just this
way it doesn’t work and work and spending time with coding doesn’t make you money.
So that’s true.
Especially now with the AI.
Yeah, and maybe let’s go back to the more focus on the trading itself, algo trading
itself.
And so I would like to ask you if you can tell us more about your workflow, which you
are using for creating and selecting the best strategies.
Yeah, can you tell us more about this, let’s say your knowledge or if you can disclose
some knowledge?
Yeah, yeah, of course, of course.
We follow mainly two primary workflows.
The first one is academic driven, let’s say.
We review academic papers, we filter logical strategies that we want to focus on, then
we build and test them in SQX.
This is the first one.
The second one is data-driven mining.
We identify non-obvious patterns using SQX and testing their robustness.
We don’t look for indicators, we look for the average outcome of the whole portfolio.
So in both workflows, we use robustness testing that are somehow similar.
For example, out of sample testing, multi-time frame and multi-market validation, we use
four Monte Carlo simulations.
We concentrate on these, the slippage spread, historical and parameters randomization.
And sometimes we use work-forward optimization.
In a nutshell, let’s say if we take an example, for example, if we have 20 years of data for
one instrument, we take the first five years, we do the generation on those five years,
then we test them on a higher time period, let’s say for 10 years.
Because out of sample, it seems out of samples was the most validated thing in robustness
testing.
But this is not final.
Then we combine the first five years with the second 10 years and we do the whole test
slippage spread, etc.
Following somehow the course that SQX have done in the beginning.
So when we finish all the testing, then we do work-forward testing and we check if we
do the optimization for these strategies.
Are they going to go forward in the new data?
And we test them with the last out of sample data that we have.
If the strategies pass, then they go to a demo account.
And the demo account, we trade them not less than three months to see if they are performing
somehow, not exactly as they are mentioned or written to be trade.
So if yes, if the work-forward testing on a demo account is similar somehow to the back
testing that we have, then it might be a good strategy.
Then we go to another environment where automation is filtered and not manually interfered.
Yeah, it was a big package of information.
I’m glad that everyone can open a StrategyQuant and check this workflow and test it.
There’s a lot of wisdom inside how to combine because StrategyQuant have a lot of different
robustness tests.
But you need to, as you mentioned, you need to find the right path and the process which
makes sense.
Yeah, we have shared different workflows on the website, on the SQS website and also
in the Discord community.
Yeah, thank you.
And once you have the validated strategy, maybe can we discuss it from the point of
view of the portfolio?
What is the philosophy of creating an optimal portfolio?
Well, let me tell you something.
If you ask anyone, they would say an ideal portfolio should be uncorrelated across.
But this isn’t always sufficient because what we have seen in the market, even diversified
strategies like mean reversion and the trend following can fail simultaneously.
And when they fail simultaneously, the whole portfolio will be in a big drawdown.
So to overcome this, yeah, we do mainly two things.
We calculate the cumulative drawdowns across strategies and we take a dynamic average
threshold.
So it’s not the drawdown that we saw in a portfolio when we back tested.
OK, and then we define a portfolio wide drawdown threshold.
So this will stop the trading for a longer time of period.
So we have the first, let’s say, stop loss for each strategy.
OK, then a stop loss for a period of time, for a small period of time, for example, let’s
say a week.
Maybe there is a new cycle in the market and none of the strategies will work, even they
are mean reversion or breakout.
And then there is a protective stop loss on the whole portfolio for the whole month, for
example, so that we don’t cross a threshold that is specified previously.
I can say that we have studied the VAR value at risk that Darwinics have explained on
their website.
And we did something around this for our own.
Yeah.
And it has positive impact.
Yeah, so what I always say, the second layer of risk of protection is very important.
I can say the most thing to keep in mind while trading is managing risk.
Managing risk is more important than predicting the direction of the market.
Understand.
Yeah, I understand.
And it’s important.
It’s true, because when you have capital, then you cannot trade.
Yeah, it’s easy.
Yeah, you want to add some, Tomas?
Yeah, because we cannot plan the profits or, let’s say, estimate the movement of the
market.
But only what we can control is the risk.
That’s my point.
100%.
And can I ask you additional question to portfolio?
If you are selecting strategies into the portfolio, what kind of correlation analysis do you use
for picking of strategies into one portfolio?
We have two steps, if we can say.
The first step, the correlation that we take is at the earlier stages when we are generating
strategies.
So when we are generating strategies, we eliminate any correlation which is more than 0.3 with
all the strategies.
It doesn’t matter if these strategies will succeed or not succeed.
OK, so at the first level, we are eliminating this type of strategies or this, let’s say,
this value of correlation.
At the second layer, this is done automatically through our system.
So we select, for example, if we have 50 strategies of euro that are working on euro dollar, we
don’t take the 50 strategies, we take just four to five.
But those four to five are selected periodically by the system that we made and we don’t interfere
with it.
So that we don’t have, if you can, let’s say, if you can review the value at risk, how they
did it at DarwinX, it is somehow similar to what we have done, because sometimes if there
will be a correlation between euro dollar and pound dollar, OK, and they are not in
the past, they are correlated.
So when they are correlated in the market, you will see that strategies will not work,
although the strategies are totally different than each other that are working on euro dollar
and the selling dollar.
So by this, we reduce the size of each trade when we see a correlation in the market
following a specific factor.
Yeah.
OK, thank you.
And do you use correlation by profit and loss or just based on the loss?
Can you explain what you mean?
I mean, in the StrategyQuant, there are more options.
We can calculate the correlation based on the profit and loss, based on month or week.
OK, OK.
No, we take the period, we take it as a month, because we have, you know, we have hundreds
of strategies that are running.
So we think that during a month there will be overlapping between different strategies
and different kinds of instruments.
So we will not have a huge drawdown, even though if we have a correlation somehow in
some instruments.
So we are covering our back, we can say, with the quantity of the strategies that we have.
Let’s say, for example, now we are running, we are having more than 1,000 strategies in
incubation phase, let’s say.
OK.
And we can point to another questions.
Are you using four strategies, parameters that are recommended by optimization?
And I’m pointing to work forward metrics, or you use different way?
I believe that optimization will lead to overfitting problems, although it can make a huge
impact on return, and for short period of times.
But we don’t do optimization.
We only do stress tests for parameters optimization and Monte Carlo simulation tests at the
beginning stage.
Once the strategy is live and running, we don’t care if the strategy is perfect or not.
Now or later on.
Once the strategy is not performing, it’s dropped automatically from the system.
So we don’t stick to one or ten strategies, let’s say.
OK, so you are leaving the parameters, just StrategyQuants suggested.
Exactly.
OK.
But we don’t delete the strategy, you know, at that stage, we don’t delete it.
We keep it running on a demo account, because sometimes there are cycles in the market.
We saw there are cycles in the market, and sometimes the strategies may not work for
one or two years.
But later on, it will start working as if it is a new algorithm in the market, and it
has a good record and performance in the market.
So then we use it.
We use it automatically by the system.
This is the importance of automation that we have made.
The automation that is done in the system without our interference.
We just monitor things.
We just ensure that the system is executing exactly as we want it, you know.
Yeah, perfect.
Perfect.
And you pointed to some system.
Can I imagine something like it is kind of database of strategies or can you tell us
more?
It’s a Python made, in-house made by our team just to do the filtering.
This is the first stage.
We saw everything on a dashboard.
We know how it is going.
Similar, if you want, to the program that you have in Quant Analyzer.
OK, similar to what you have, but it is our own.
And there is another system that is live running on all the servers that are hosting
strategies.
And there is a configuration inside that program that filters based on criteria that
we put.
OK.
Thank you.
We can go to another question.
And every portfolio sometimes suffers from drawdown.
What is your approach to overcome it and maintain the confidence in your robots?
Let’s say when you have losing streak.
What is your approach?
You know, this is what I was saying, because we use automated monitoring tools that
periodically assess performance and apply dynamic filters.
We don’t care if a strategy will fall or not.
All strategies goes through drawdowns.
But abnormal behavior prompt automatic removal from live deployment.
For example, let’s say a multiplier.
One of the factors, a multiplier of 1.5 that is mentioned in your courses for the whole
previous drawdown of each strategy is an indication that the strategy is not working as
before.
But as I said, we don’t delete the strategy.
We keep it running.
Maybe it will perform later on.
So we keep it.
So also we consistently compare historical traits with the forward performance.
So let’s say our philosophy, prepare for the worst always, always implement multiple
protective layers and accept losses.
Yeah, but it was pointed to single strategy.
But what about the whole portfolio?
And I’m pointing what helps you to overcome the drawdown psychologically, because, you
know, you can have started some doubts about if it’s working or if it stopped working.
Yes, yes, yes.
During those seven years that I was trading live on my own account, on my own money, I
passed through all of this.
And I always, when I see a drawdown, I go back to see the traits.
If they are exactly as executed on StrategyQuant, means the strategy is executing traits
as it should.
And the drawdown, you have to accept the drawdown.
So this is why it was normal to me.
And the layer, the protective layer that we made on, as I said before, on a strategy level,
on a portfolio level and the whole account for a periodic period, let’s say for one week,
for one day and for one month.
For example, if we cross seven percent drawdown in the whole portfolio on one index, we will
stop trading for the whole month.
For example.
Yeah, that’s interesting.
OK, we can go to another question.
And is there any source of knowledge which you would like to recommend to the other traders?
Nowadays, the online content is enormous and you can find trusted reviews on them,
which is different than what we had 20 years ago.
But I will mention new trading systems and methods by Perry Kaufman.
Also, evidence based technical analysis by David Aronson.
They have a valuable insights.
And if you need something out of the whole concept that we are talking about, you can
search for Michael S. Jenkins.
He talks about cycles in the market based on math, astrology and the most important
thing, William Ganz methods.
If you could combine what he is talking about and make something inside the StrategyQuant,
I think you will find the Holy Grail.
Also, Ali Qaisi, he has a YouTube channel.
His videos are highly recommended, especially when using SQX.
Also, the free courses on SQX website, as I said before, they really guide me through
the platform.
Let me say it took me more than one year to understand how the platform works back in
2017, I guess, if I’m not mistaken, or 18.
Okay, so I had to watch the course that you have on your website for maybe three or four
times.
Then I can say I had the knowledge how to work with the platform.
Because it is simple, yes, but it has hidden gems, you know.
It is sophisticated somehow.
Also, I would like to mention the Arabic course for Arabic users that we made in collaboration
with SQX, the Arabic course that I made.
And the valuable insights that you can find in SQX Discord community.
They are very supportive.
You will find traders there.
You will not find them in any Discord community.
Yeah, thank you.
Thank you for recommending.
I agree.
Ali Casey is a really good source of knowledge.
He has a really good YouTube videos explaining in depth.
So, do you have any tips about what to avoid or what to avoid?
Or what users should be aware of in algo trading?
Yes, mainly in algo trading.
Once you heard algo trading, you say overfitting.
This is the most critical pitfall in algorithmic development.
I remember when we first saw a long time ago, an expert advisor with remarkable historical
equity curve.
I said, that’s it.
This is the holy grail.
But this is all unreal.
Stress testing is a must for any algorithmic development stages.
The second big thing I can say is to always have different layers of protection.
As I was describing previously, a stop loss per strategy, a stop loss per portfolio.
Also, keep cyclic monitoring and testing for the strategies that you have.
You need to do weekly monitoring and monthly monitoring.
It depends on how you are working with your strategies.
What I’m saying is all from my own perspective.
Maybe other traders are doing something else.
But monitoring and testing what the strategies are doing.
This is essential so that you know what’s going on.
Also, I could say that they need to prepare the tools and the systems around them to set
up the environment before they start trading with bigger funds.
It’s not always easy.
Sometimes you can see yourself that now I’m profitable for one month or two months, let’s
say, but directly you get a drawdown.
Once you get this, you get panic.
It’s very important to have something steady for a longer time and you pass it through
a drawdown with your own money before you start trading for others.
Once you are there, you will see that family members and your friends, they will come to
you and they want to invest with you.
You don’t want to ruin your life doing this and the relation between you and your friends.
So you need to be sure that you are doing the right things.
Also, I can say you need to use reliable brokers with a consistent live and demo trading execution.
What I mean by this?
For example, start trading with one or two maximum brokers and ensure that to open an
account, a real account and a demo account with them and compare the same exact strategies
if they are executing trades on the demo account exactly as on the live account.
If they are doing this execution correctly, then you can say you can trade with them.
Because in my previous days when I started doing this, I had five brokers.
I have hundreds of strategies on each broker.
I get messed with all the results.
So I wasted around more than one year just trying to match what’s going on, etc.
So this is why I’m saying only stick to one reliable broker.
It will be more than enough.
And the important thing, save the historical data that you have throughout the strategies.
Because too many brokers on demo accounts, they will wipe your historical data and you
don’t have access to them.
And to us, the system that we created is based on what it can read from the system directly,
from the MT4 and MT5 historical record.
So we had also a problem with them.
DarwinX, let’s say, they don’t delete your track record, even on the demo account.
So sorry to mention DarwinX always, always, but this is the truth.
I’m not trying to promote them.
Yeah, they are really supportive.
And I also really like their approach because they don’t push the traders on DarwinX
zero or DarwinX gold to do some unusual returns like the other prop firms.
But yeah, they are quite good.
They have different business model than prop firms.
May I ask you, honey, because for now, from until now, it was about how you trade.
May I ask you something about the results you are achieving
and with your group of traders, with your team?
Well, now we are on the 8th position on DarwinX gold.
This index is only gold based.
The real account made more than 280% in less than a year.
But, you know, because DarwinX has their own VAR system, the value at risk system,
they reduce the lot size, they reduce the exposure of the account.
But we are on the 8th position and we have another indexes also on DarwinX that
they are in the DarwinX silver stage, but hopefully they will be on the gold index also.
A big congratulation to that, because it’s really not easy to achieve such a good position
in this global ranking.
The important is to keep that record, to keep that position.
This is what we are trying to do, to be always on the first, let’s say top 10 or top 50 in that level.
That’s really interesting.
Interesting and congratulations for that.
I would like to ask you, do you sometimes remove strategy even if
the strategy performed well in some period?
Or if it performs well, do you keep it run or do you remove
also this kind of strategy after some time?
No, no.
As I said before, we don’t interfere directly with each strategy.
We have different criteria for dropping off the strategy.
For example, let’s say the minimum, the last 20 trades, they should be, for example, with
a profit factor more than 1.1, for example.
Also, in addition to this, the last 10 trades, they should have a profit factor more than 1.1%.
Also, for example, return to drawdown should be no more less than 2 for the whole
number of trades, the last 10 trades or the last 20 trades.
So the system, we can select what we want from the system and the system automatically do this.
If I understand what you are trying to aim to, if a strategy has a streak of winners,
maybe it will come to later on with a streak of losers.
We don’t interfere with this.
We leave the strategy as it is.
The system will drop it off once it doesn’t meet the criteria that we put.
So that portfolio that made over 200%, you just set it up and keep it run for the whole year?
Or there were some changes?
Yes, there had some changes in the beginning.
If I’m not mistaken, the third month, we had a drawdown because one of the strategies,
I don’t know what happened, but one of the strategies didn’t perform as it should.
We drop it off because that exact portfolio, we were treating it, we were aiming it for
another thing.
But when we saw the return of it, we kept it as it is and we removed everything that
might disrupt the results.
But yes, the first three months, we had a huge drawdown around and on the real account,
we have around 35% drawdown.
But later on, we didn’t touch that percentage.
I guess the maximum we reached around 15% or 20% maximum on the real account.
Yeah, that’s a really good result.
Yeah, sometimes the thing is, you need to be dynamic.
You need to accept the changes.
It’s not something that’s strict and this is it.
This strategy is working and keep it live.
The methodology you are building your strategies on or how you are treating your strategies
should not be like the Bible.
You need to accept the changes.
Everything is changing around us.
Technology is going so fast, so we need to adapt any changes to the strategies themselves
or to how we are developing our strategies.
Yeah, perfect.
That’s a really good perspective.
And approach.
And maybe we are pointing to the last question.
Would you like to share some recommendation to the algo developers?
What focus on, what mindset and etc?
Yeah, before diving in, I would say for any trader, ask yourself.
Do you have the time?
Do you have the mindset?
Do you have the emotional resilience to handle trading?
If yes for all of these, then you go to the next step.
The next step is start with a live account.
I’m in contrary to anyone would say start with a demo account.
Don’t start with a demo account.
Demo account will make you think that you are unbeatable and you can make money.
No, start with your real account.
Maybe $100, maybe $1,000, maybe $10,000.
It doesn’t matter.
Just invest what you can afford to lose.
And start trading.
If you accept the losses that will hit you while trading, then it’s fine to move to the
other steps.
But these two things are essential for me.
Because I have personally passed through them.
And in my back brain, I know that I will lose money, you know, but I put a limit for the
loss and I thought in it in a different way.
What I mean by this, all the investment that I put in trading was only to learn and to
develop, not only to trade and make money on trading.
So, once you say yes for the two questions, you want to have it as a career, you should
focus on four phases.
The first phase is to learn, learn, learn, learn, then try to build your methodology,
build your systems, build whatever you want, your strategy.
If you want to do it manually, I don’t know.
And then test, test what you have learned, test what you have built.
If OK, then go live trading on your money.
And later on, everything is very easy.
And as a final note, also, I mentioned it before, you need to remain flexible, curious
and critical, flexible to adapt to any change.
Curious to search.
There are too many things around us now online that we can, that we can, that they can support
us in our thinking methodologies and to be critical, not to take anything for granted
that this is it and that’s it.
Mm hmm.
And maybe what time frame you would recommend to the new traders or new algo traders?
What time frame should focus on?
The higher the time frame is better.
Why?
Because the smaller time frame, the small, the more will put stress on the trader.
So the higher the time frame is better.
And our own strategies also, we don’t have less than one hour time frame.
Most of the time, most of them are one hour and four hours time frame.
And some of them daily.
Some of them daily.
But we have in parallel, let’s say at this stage, we have in parallel what we are doing
in manual coding, using AlgoWizard and other platforms also, and our knowledge in programming.
We are doing systems for our own, not to trade
funds for others, no, for our own money.
These systems are very fast, not HFT, but they are fast in executing.
They execute too many trades.
But this is something out of what we are saying.
But if you add at this level, you can do what you want.
You can test anything.
Okay, thank you.
So I guess, Korney, do you have some additional questions?
I think everything was greatly covered.
There is a lot of knowledge inside.
So anyone who is interested, start to listen to it again and again, make notes,
and get some inspiration for his trading.
So thank you.
Thank you again, Hani.
And thank you for organizing this event.
And I hope we are not seeing each other the last time.
And so see you next time.
Bye.
Bye-bye.
See you.I started trading back in 2002, really for me StrategyQuant made a huge impact.
I can say the most profitable strategies that are performing now were made on StrategyQuant.
It was not a gambling, it’s not a game.
The most thing to keep in mind while trading is managing risk.
So, hello traders, I’m glad to introduce next interview from our StrategyQuant series.
And today is with a very experienced trader, Hani from Lebanon, who recently achieved like
their account is now number 8 in DarwinX Gold Zero, which is a list of the very like list
of traders and ranked on the success and positive and profitable result with their portfolio.
So I was excited when Hani decided to join us and share his insight, what’s working,
what works and what is working for him in markets to share it with you.
So here’s the Hani.
Hello, Hani.
Hello.
Tomas Vanek, my colleague.
Hello, Tomas.
Hello, traders.
Let’s start.
Let’s start with the first question and simply Hani, please, could you introduce yourself?
What is your profession?
How did you start with algo trading?
Yeah.
Hello, everyone.
I would like first to thank you for this interview and the whole team of SQX for this
remarkable platform.
My name is Hani Hamdan.
I’m based in Lebanon, Middle East.
I’m a serial entrepreneur and business consultant and an algorithmic trader.
My academic professional background in information technology naturally shaped
my thinking to be systematic, analytical and automation oriented.
I started trading back in 2002.
I took several courses, but at that time, due to financial and time constraints while
launching my business, I stopped trading.
Then back in 2015, I had gained some freedom to fully commit to trading.
So I dedicated more than 15 hours a day and still to trading.
First, I was focusing on manual trading and preparing an environment to trade in.
I studied fundamental and technical analysis.
I didn’t like fundamentals because you need to stay alert and read the news and to be
always online.
But I like more technical analysis.
And I studied also harmonic patterns with Elliott waves and advanced patterns.
And I had a strategy in place, which I was trading for a year and a half.
And because the strategy was on a higher time frame, you know, I had the pleasure also of
time, the luxury of time.
So I said, why I don’t program the strategy to be an algorithm.
So I started working and training on MQL4 and MQL5, but also, you know, to create a
strategy that will take time.
So I was searching for another time for a platform to generate a strategy for me in
a much quicker time.
And I found StrategyQuant with other platforms.
But really, for me, StrategyQuant made a huge impact.
And I can say the most profitable strategies that are performing now were made on
StrategyQuant.
Now, at this stage, we have our own team.
We already developed our own automated filtering system and methodologies.
So, yeah.
Great.
Thank you.
Thank you for the introduction.
And I’d like to ask you, because there is a long career and every trader is at some
point, someone is starting, someone is just doing some research, someone is already trading
for a few years, someone just bought some courses.
And the path is quite similar, I might say.
So I would like to ask you how long it took you to be successful and what was the breaking
point, which is the most interesting, obviously, question everyone is interested in.
Yeah, let me tell you, from the beginning, my priority was learning and researching and
not rushing into live trading, because I know it was not a gambling, it’s not a game.
And I wanted to create a business from that.
So my goal was to build a track record, a solid track record before seeking any funds
or going to the next stage.
So at that time, I accepted breakeven during the early stages, no more than breakeven.
I don’t want to make money through trading, just want to enhance my trading.
But I can say after seven years, the success came when we developed a multi-layer automated
filtering system for our live strategies.
You can do manual filtering for the strategies that comes out from the process that you are
doing.
But why it took me seven years?
Because the whole time I was thinking of automation.
So I don’t want to even, I don’t want to filter manually the trades or the strategies that
are performing good.
So it took me time with the team to develop this.
So yeah, seven years, long seven years.
So long, several years, and then you simply finished all this and you were sure that it
makes sense.
And you started to work.
Yeah.
And what do you like the most on algo trading?
Where you thought you were aiming from the beginning to be algo trader, but there is
a lot of styles how to trade.
And some of them do not be necessarily like time consuming.
If you, for example, can trade place one, trade a month or whenever, or you can be option
trader and then can or spread trader or whatever.
What do you like the most?
Well, let me say algo trading combines my passion for coding in the first place.
Also my analytical problem solving and sitting in front of the computer.
I spent thousands of hours sitting in front of my computer.
But the issue, the thing is, the important thing is it allowed me to maintain the freedom
and flexibility of an entrepreneurial lifestyle.
And this is crucial to me, you know, because I didn’t fit to be an employee at my early
stages.
I was an employee for four or five years, but later on, I knew that I don’t want to
be an employee for all my life.
I was, I am an entrepreneur.
I have different businesses, but now I’m concentrating on algo trading.
So also it offers significant returns and the potential of returns are very high once
you are profitable.
If you are not profitable, it will be a disaster.
Yeah, it’s true that the algo trading or basically trading, it’s very easy to find out if you
are, if you are profitable or not, if your business is successful or not, comparing to
other businesses.
There is just one simple graph and that’s all.
Yeah, but I would say that every person is enemy of yourself because of course there
is advantage of the trader.
It is on your own, but if you are only, only you in your mind, it can be tricky.
So you need to be mind sharp and focused on the result and discipline.
Yes.
Yeah, it’s true.
This is true that with every trader we are having interview who is successful or basically
every trader we know, there is not just gaining the knowledge, but there is also a big personal
way and personal development behind.
Exactly.
I can tell you that in many, many times I had the feeling that to drop off this thing
after many years of learning and building.
But every day I wake up in the morning and I am very enthusiastic to come to computer
and start again and start again.
So you have to keep in mind that discipline is a key for any success.
It’s not only in sports or business or anything else and everything.
Discipline is a key.
Yeah, but the problem is the time.
We don’t have the pleasure, you know, now I’m around 50 years old, so you don’t have
all the time in your life to be successful.
So that’s why a StrategyQuant made a huge impact on our lives, because now you can have
millions of strategies in one hour.
And in the traditional way, we need one week to code one strategy, which is a big difference.
Yeah, yeah, you are right that they don’t, all of them are basically not the profitable,
but you can know it very fast.
Yeah, yeah, we’ll talk about it.
Yeah, comparing to other ways, when you like spend two weeks, you just see that just this
way it doesn’t work and work and spending time with coding doesn’t make you money.
So that’s true.
Especially now with the AI.
Yeah, and maybe let’s go back to the more focus on the trading itself, algo trading
itself.
And so I would like to ask you if you can tell us more about your workflow, which you
are using for creating and selecting the best strategies.
Yeah, can you tell us more about this, let’s say your knowledge or if you can disclose
some knowledge?
Yeah, yeah, of course, of course.
We follow mainly two primary workflows.
The first one is academic driven, let’s say.
We review academic papers, we filter logical strategies that we want to focus on, then
we build and test them in SQX.
This is the first one.
The second one is data-driven mining.
We identify non-obvious patterns using SQX and testing their robustness.
We don’t look for indicators, we look for the average outcome of the whole portfolio.
So in both workflows, we use robustness testing that are somehow similar.
For example, out of sample testing, multi-time frame and multi-market validation, we use
four Monte Carlo simulations.
We concentrate on these, the slippage spread, historical and parameters randomization.
And sometimes we use work-forward optimization.
In a nutshell, let’s say if we take an example, for example, if we have 20 years of data for
one instrument, we take the first five years, we do the generation on those five years,
then we test them on a higher time period, let’s say for 10 years.
Because out of sample, it seems out of samples was the most validated thing in robustness
testing.
But this is not final.
Then we combine the first five years with the second 10 years and we do the whole test
slippage spread, etc.
Following somehow the course that SQX have done in the beginning.
So when we finish all the testing, then we do work-forward testing and we check if we
do the optimization for these strategies.
Are they going to go forward in the new data?
And we test them with the last out of sample data that we have.
If the strategies pass, then they go to a demo account.
And the demo account, we trade them not less than three months to see if they are performing
somehow, not exactly as they are mentioned or written to be trade.
So if yes, if the work-forward testing on a demo account is similar somehow to the back
testing that we have, then it might be a good strategy.
Then we go to another environment where automation is filtered and not manually interfered.
Yeah, it was a big package of information.
I’m glad that everyone can open a StrategyQuant and check this workflow and test it.
There’s a lot of wisdom inside how to combine because StrategyQuant have a lot of different
robustness tests.
But you need to, as you mentioned, you need to find the right path and the process which
makes sense.
Yeah, we have shared different workflows on the website, on the SQS website and also
in the Discord community.
Yeah, thank you.
And once you have the validated strategy, maybe can we discuss it from the point of
view of the portfolio?
What is the philosophy of creating an optimal portfolio?
Well, let me tell you something.
If you ask anyone, they would say an ideal portfolio should be uncorrelated across.
But this isn’t always sufficient because what we have seen in the market, even diversified
strategies like mean reversion and the trend following can fail simultaneously.
And when they fail simultaneously, the whole portfolio will be in a big drawdown.
So to overcome this, yeah, we do mainly two things.
We calculate the cumulative drawdowns across strategies and we take a dynamic average
threshold.
So it’s not the drawdown that we saw in a portfolio when we back tested.
OK, and then we define a portfolio wide drawdown threshold.
So this will stop the trading for a longer time of period.
So we have the first, let’s say, stop loss for each strategy.
OK, then a stop loss for a period of time, for a small period of time, for example, let’s
say a week.
Maybe there is a new cycle in the market and none of the strategies will work, even they
are mean reversion or breakout.
And then there is a protective stop loss on the whole portfolio for the whole month, for
example, so that we don’t cross a threshold that is specified previously.
I can say that we have studied the VAR value at risk that Darwinics have explained on
their website.
And we did something around this for our own.
Yeah.
And it has positive impact.
Yeah, so what I always say, the second layer of risk of protection is very important.
I can say the most thing to keep in mind while trading is managing risk.
Managing risk is more important than predicting the direction of the market.
Understand.
Yeah, I understand.
And it’s important.
It’s true, because when you have capital, then you cannot trade.
Yeah, it’s easy.
Yeah, you want to add some, Tomas?
Yeah, because we cannot plan the profits or, let’s say, estimate the movement of the
market.
But only what we can control is the risk.
That’s my point.
100%.
And can I ask you additional question to portfolio?
If you are selecting strategies into the portfolio, what kind of correlation analysis do you use
for picking of strategies into one portfolio?
We have two steps, if we can say.
The first step, the correlation that we take is at the earlier stages when we are generating
strategies.
So when we are generating strategies, we eliminate any correlation which is more than 0.3 with
all the strategies.
It doesn’t matter if these strategies will succeed or not succeed.
OK, so at the first level, we are eliminating this type of strategies or this, let’s say,
this value of correlation.
At the second layer, this is done automatically through our system.
So we select, for example, if we have 50 strategies of euro that are working on euro dollar, we
don’t take the 50 strategies, we take just four to five.
But those four to five are selected periodically by the system that we made and we don’t interfere
with it.
So that we don’t have, if you can, let’s say, if you can review the value at risk, how they
did it at DarwinX, it is somehow similar to what we have done, because sometimes if there
will be a correlation between euro dollar and pound dollar, OK, and they are not in
the past, they are correlated.
So when they are correlated in the market, you will see that strategies will not work,
although the strategies are totally different than each other that are working on euro dollar
and the selling dollar.
So by this, we reduce the size of each trade when we see a correlation in the market
following a specific factor.
Yeah.
OK, thank you.
And do you use correlation by profit and loss or just based on the loss?
Can you explain what you mean?
I mean, in the StrategyQuant, there are more options.
We can calculate the correlation based on the profit and loss, based on month or week.
OK, OK.
No, we take the period, we take it as a month, because we have, you know, we have hundreds
of strategies that are running.
So we think that during a month there will be overlapping between different strategies
and different kinds of instruments.
So we will not have a huge drawdown, even though if we have a correlation somehow in
some instruments.
So we are covering our back, we can say, with the quantity of the strategies that we have.
Let’s say, for example, now we are running, we are having more than 1,000 strategies in
incubation phase, let’s say.
OK.
And we can point to another questions.
Are you using four strategies, parameters that are recommended by optimization?
And I’m pointing to work forward metrics, or you use different way?
I believe that optimization will lead to overfitting problems, although it can make a huge
impact on return, and for short period of times.
But we don’t do optimization.
We only do stress tests for parameters optimization and Monte Carlo simulation tests at the
beginning stage.
Once the strategy is live and running, we don’t care if the strategy is perfect or not.
Now or later on.
Once the strategy is not performing, it’s dropped automatically from the system.
So we don’t stick to one or ten strategies, let’s say.
OK, so you are leaving the parameters, just StrategyQuants suggested.
Exactly.
OK.
But we don’t delete the strategy, you know, at that stage, we don’t delete it.
We keep it running on a demo account, because sometimes there are cycles in the market.
We saw there are cycles in the market, and sometimes the strategies may not work for
one or two years.
But later on, it will start working as if it is a new algorithm in the market, and it
has a good record and performance in the market.
So then we use it.
We use it automatically by the system.
This is the importance of automation that we have made.
The automation that is done in the system without our interference.
We just monitor things.
We just ensure that the system is executing exactly as we want it, you know.
Yeah, perfect.
Perfect.
And you pointed to some system.
Can I imagine something like it is kind of database of strategies or can you tell us
more?
It’s a Python made, in-house made by our team just to do the filtering.
This is the first stage.
We saw everything on a dashboard.
We know how it is going.
Similar, if you want, to the program that you have in Quant Analyzer.
OK, similar to what you have, but it is our own.
And there is another system that is live running on all the servers that are hosting
strategies.
And there is a configuration inside that program that filters based on criteria that
we put.
OK.
Thank you.
We can go to another question.
And every portfolio sometimes suffers from drawdown.
What is your approach to overcome it and maintain the confidence in your robots?
Let’s say when you have losing streak.
What is your approach?
You know, this is what I was saying, because we use automated monitoring tools that
periodically assess performance and apply dynamic filters.
We don’t care if a strategy will fall or not.
All strategies goes through drawdowns.
But abnormal behavior prompt automatic removal from live deployment.
For example, let’s say a multiplier.
One of the factors, a multiplier of 1.5 that is mentioned in your courses for the whole
previous drawdown of each strategy is an indication that the strategy is not working as
before.
But as I said, we don’t delete the strategy.
We keep it running.
Maybe it will perform later on.
So we keep it.
So also we consistently compare historical traits with the forward performance.
So let’s say our philosophy, prepare for the worst always, always implement multiple
protective layers and accept losses.
Yeah, but it was pointed to single strategy.
But what about the whole portfolio?
And I’m pointing what helps you to overcome the drawdown psychologically, because, you
know, you can have started some doubts about if it’s working or if it stopped working.
Yes, yes, yes.
During those seven years that I was trading live on my own account, on my own money, I
passed through all of this.
And I always, when I see a drawdown, I go back to see the traits.
If they are exactly as executed on StrategyQuant, means the strategy is executing traits
as it should.
And the drawdown, you have to accept the drawdown.
So this is why it was normal to me.
And the layer, the protective layer that we made on, as I said before, on a strategy level,
on a portfolio level and the whole account for a periodic period, let’s say for one week,
for one day and for one month.
For example, if we cross seven percent drawdown in the whole portfolio on one index, we will
stop trading for the whole month.
For example.
Yeah, that’s interesting.
OK, we can go to another question.
And is there any source of knowledge which you would like to recommend to the other traders?
Nowadays, the online content is enormous and you can find trusted reviews on them,
which is different than what we had 20 years ago.
But I will mention new trading systems and methods by Perry Kaufman.
Also, evidence based technical analysis by David Aronson.
They have a valuable insights.
And if you need something out of the whole concept that we are talking about, you can
search for Michael S. Jenkins.
He talks about cycles in the market based on math, astrology and the most important
thing, William Ganz methods.
If you could combine what he is talking about and make something inside the StrategyQuant,
I think you will find the Holy Grail.
Also, Ali Qaisi, he has a YouTube channel.
His videos are highly recommended, especially when using SQX.
Also, the free courses on SQX website, as I said before, they really guide me through
the platform.
Let me say it took me more than one year to understand how the platform works back in
2017, I guess, if I’m not mistaken, or 18.
Okay, so I had to watch the course that you have on your website for maybe three or four
times.
Then I can say I had the knowledge how to work with the platform.
Because it is simple, yes, but it has hidden gems, you know.
It is sophisticated somehow.
Also, I would like to mention the Arabic course for Arabic users that we made in collaboration
with SQX, the Arabic course that I made.
And the valuable insights that you can find in SQX Discord community.
They are very supportive.
You will find traders there.
You will not find them in any Discord community.
Yeah, thank you.
Thank you for recommending.
I agree.
Ali Casey is a really good source of knowledge.
He has a really good YouTube videos explaining in depth.
So, do you have any tips about what to avoid or what to avoid?
Or what users should be aware of in algo trading?
Yes, mainly in algo trading.
Once you heard algo trading, you say overfitting.
This is the most critical pitfall in algorithmic development.
I remember when we first saw a long time ago, an expert advisor with remarkable historical
equity curve.
I said, that’s it.
This is the holy grail.
But this is all unreal.
Stress testing is a must for any algorithmic development stages.
The second big thing I can say is to always have different layers of protection.
As I was describing previously, a stop loss per strategy, a stop loss per portfolio.
Also, keep cyclic monitoring and testing for the strategies that you have.
You need to do weekly monitoring and monthly monitoring.
It depends on how you are working with your strategies.
What I’m saying is all from my own perspective.
Maybe other traders are doing something else.
But monitoring and testing what the strategies are doing.
This is essential so that you know what’s going on.
Also, I could say that they need to prepare the tools and the systems around them to set
up the environment before they start trading with bigger funds.
It’s not always easy.
Sometimes you can see yourself that now I’m profitable for one month or two months, let’s
say, but directly you get a drawdown.
Once you get this, you get panic.
It’s very important to have something steady for a longer time and you pass it through
a drawdown with your own money before you start trading for others.
Once you are there, you will see that family members and your friends, they will come to
you and they want to invest with you.
You don’t want to ruin your life doing this and the relation between you and your friends.
So you need to be sure that you are doing the right things.
Also, I can say you need to use reliable brokers with a consistent live and demo trading execution.
What I mean by this?
For example, start trading with one or two maximum brokers and ensure that to open an
account, a real account and a demo account with them and compare the same exact strategies
if they are executing trades on the demo account exactly as on the live account.
If they are doing this execution correctly, then you can say you can trade with them.
Because in my previous days when I started doing this, I had five brokers.
I have hundreds of strategies on each broker.
I get messed with all the results.
So I wasted around more than one year just trying to match what’s going on, etc.
So this is why I’m saying only stick to one reliable broker.
It will be more than enough.
And the important thing, save the historical data that you have throughout the strategies.
Because too many brokers on demo accounts, they will wipe your historical data and you
don’t have access to them.
And to us, the system that we created is based on what it can read from the system directly,
from the MT4 and MT5 historical record.
So we had also a problem with them.
DarwinX, let’s say, they don’t delete your track record, even on the demo account.
So sorry to mention DarwinX always, always, but this is the truth.
I’m not trying to promote them.
Yeah, they are really supportive.
And I also really like their approach because they don’t push the traders on DarwinX
zero or DarwinX gold to do some unusual returns like the other prop firms.
But yeah, they are quite good.
They have different business model than prop firms.
May I ask you, honey, because for now, from until now, it was about how you trade.
May I ask you something about the results you are achieving
and with your group of traders, with your team?
Well, now we are on the 8th position on DarwinX gold.
This index is only gold based.
The real account made more than 280% in less than a year.
But, you know, because DarwinX has their own VAR system, the value at risk system,
they reduce the lot size, they reduce the exposure of the account.
But we are on the 8th position and we have another indexes also on DarwinX that
they are in the DarwinX silver stage, but hopefully they will be on the gold index also.
A big congratulation to that, because it’s really not easy to achieve such a good position
in this global ranking.
The important is to keep that record, to keep that position.
This is what we are trying to do, to be always on the first, let’s say top 10 or top 50 in that level.
That’s really interesting.
Interesting and congratulations for that.
I would like to ask you, do you sometimes remove strategy even if
the strategy performed well in some period?
Or if it performs well, do you keep it run or do you remove
also this kind of strategy after some time?
No, no.
As I said before, we don’t interfere directly with each strategy.
We have different criteria for dropping off the strategy.
For example, let’s say the minimum, the last 20 trades, they should be, for example, with
a profit factor more than 1.1, for example.
Also, in addition to this, the last 10 trades, they should have a profit factor more than 1.1%.
Also, for example, return to drawdown should be no more less than 2 for the whole
number of trades, the last 10 trades or the last 20 trades.
So the system, we can select what we want from the system and the system automatically do this.
If I understand what you are trying to aim to, if a strategy has a streak of winners,
maybe it will come to later on with a streak of losers.
We don’t interfere with this.
We leave the strategy as it is.
The system will drop it off once it doesn’t meet the criteria that we put.
So that portfolio that made over 200%, you just set it up and keep it run for the whole year?
Or there were some changes?
Yes, there had some changes in the beginning.
If I’m not mistaken, the third month, we had a drawdown because one of the strategies,
I don’t know what happened, but one of the strategies didn’t perform as it should.
We drop it off because that exact portfolio, we were treating it, we were aiming it for
another thing.
But when we saw the return of it, we kept it as it is and we removed everything that
might disrupt the results.
But yes, the first three months, we had a huge drawdown around and on the real account,
we have around 35% drawdown.
But later on, we didn’t touch that percentage.
I guess the maximum we reached around 15% or 20% maximum on the real account.
Yeah, that’s a really good result.
Yeah, sometimes the thing is, you need to be dynamic.
You need to accept the changes.
It’s not something that’s strict and this is it.
This strategy is working and keep it live.
The methodology you are building your strategies on or how you are treating your strategies
should not be like the Bible.
You need to accept the changes.
Everything is changing around us.
Technology is going so fast, so we need to adapt any changes to the strategies themselves
or to how we are developing our strategies.
Yeah, perfect.
That’s a really good perspective.
And approach.
And maybe we are pointing to the last question.
Would you like to share some recommendation to the algo developers?
What focus on, what mindset and etc?
Yeah, before diving in, I would say for any trader, ask yourself.
Do you have the time?
Do you have the mindset?
Do you have the emotional resilience to handle trading?
If yes for all of these, then you go to the next step.
The next step is start with a live account.
I’m in contrary to anyone would say start with a demo account.
Don’t start with a demo account.
Demo account will make you think that you are unbeatable and you can make money.
No, start with your real account.
Maybe $100, maybe $1,000, maybe $10,000.
It doesn’t matter.
Just invest what you can afford to lose.
And start trading.
If you accept the losses that will hit you while trading, then it’s fine to move to the
other steps.
But these two things are essential for me.
Because I have personally passed through them.
And in my back brain, I know that I will lose money, you know, but I put a limit for the
loss and I thought in it in a different way.
What I mean by this, all the investment that I put in trading was only to learn and to
develop, not only to trade and make money on trading.
So, once you say yes for the two questions, you want to have it as a career, you should
focus on four phases.
The first phase is to learn, learn, learn, learn, then try to build your methodology,
build your systems, build whatever you want, your strategy.
If you want to do it manually, I don’t know.
And then test, test what you have learned, test what you have built.
If OK, then go live trading on your money.
And later on, everything is very easy.
And as a final note, also, I mentioned it before, you need to remain flexible, curious
and critical, flexible to adapt to any change.
Curious to search.
There are too many things around us now online that we can, that we can, that they can support
us in our thinking methodologies and to be critical, not to take anything for granted
that this is it and that’s it.
Mm hmm.
And maybe what time frame you would recommend to the new traders or new algo traders?
What time frame should focus on?
The higher the time frame is better.
Why?
Because the smaller time frame, the small, the more will put stress on the trader.
So the higher the time frame is better.
And our own strategies also, we don’t have less than one hour time frame.
Most of the time, most of them are one hour and four hours time frame.
And some of them daily.
Some of them daily.
But we have in parallel, let’s say at this stage, we have in parallel what we are doing
in manual coding, using AlgoWizard and other platforms also, and our knowledge in programming.
We are doing systems for our own, not to trade
funds for others, no, for our own money.
These systems are very fast, not HFT, but they are fast in executing.
They execute too many trades.
But this is something out of what we are saying.
But if you add at this level, you can do what you want.
You can test anything.
Okay, thank you.
So I guess, Korney, do you have some additional questions?
I think everything was greatly covered.
There is a lot of knowledge inside.
So anyone who is interested, start to listen to it again and again, make notes,
and get some inspiration for his trading.
So thank you.
Thank you again, Hani.
And thank you for organizing this event.
And I hope we are not seeing each other the last time.
And so see you next time.
Bye.
Bye-bye.
See you.