EA Studio & StrategyQuant - Real Systems & Live Experience

Welcome to this thread.

Before anything else, I want to make one thing very clear: this thread is not about hype, not about selling, and not about ego.
The purpose of this thread is simple. I will share my real experience using EA Studio and StrategyQuant. That includes the systems I build, the tools I use, my workflow, how I think, what I’ve done wrong, and what I’ve learned over time.
This is not theory.
This is not “perfect backtest” talk.
This is real process and real results.

What this thread is
This is a place where I will drop value based on actual experience:
Using EA Studio and StrategyQuant
System building and testing
Portfolio building and metrics
Equity curves and real account insights
Mistakes, lessons, and adjustments
Everything shared here comes from doing, not guessing.

What this thread is NOT
No grid systems
No martingale
No recovery tricks
No selling of robots
No signals
No affiliate links
No promotions

I am not affiliated with EA Studio or StrategyQuant in any way.
I am simply a user sharing what I do and what works for me.

About discussion:

Discussion is fine.
If you have real value, real experience, or a solid process, feel free to share it.
This thread is not meant for endless arguments, ego battles, or noise.
The goal is to keep it clean, useful, and focused.


The goal
If someone discovers EA Studio, StrategyQuant, or algo building in general and ends up on this thread, they should be able to learn something without confusion.
If someone is stuck, they should find clarity here.
That is the goal.

Final note
I do not care about likes, validation, or empty opinions.
That does not mean I think I know everything, because I do not. If someone brings a clear point, a solid workflow, real experience, or actual proof, I am always open to testing, learning, and improving.
Healthy discussion is always welcome.
What I do not care for is noise, vague opinions, or pointless arguments without structure, logic, or evidence.
If you find value here, take it.
If not, just move on.

To start with, who am I?

To be honest, nobody special.
Just like a lot of people here, I started because I wanted financial freedom. That journey started around 8 years ago for me, but I only became truly profitable from early 2025.

Like most people, I found out quite fast that the trading world is full of scammers, fake educators, rented knowledge, and people selling dreams. I got scammed too. Around 1000 euros gone. Expensive lesson, but at least it pushed me toward the right books, the right people, and a much better mindset and discipline.
I also spent time inside IM Academy, and in my opinion that was just another trap. You pay monthly, they do not really teach you how to trade, they teach you how to use their tools to follow their strategy. Stop paying and the whole thing disappears. I also went through binary options, bought useless robots, and wasted time on things that were never going to take me where I wanted to go.

Then I found EA Studio, and for the first time I had the feeling that this could actually become something real. But I underestimated the learning curve badly. Most of the help around these tools is basic. It explains features, but not really how to build strong systems properly. On top of that, you see the same thing again and again online: people selling workflows, selling private knowledge, selling their “method”. I do not do that, and that is also one of the reasons why I started this thread.

I started learning to build robots seriously around 5 years ago. The first years were on and off. A lot of mistakes, a lot of wrong turns, and life also gets in the way sometimes. But by the end of 2023, after making many mistakes myself, especially with over-optimization, I finally built a workflow that made sense to me. That changed everything.
From that point on things started to become real. In 2024 I built robots that are now live profitable for more than a year. Yes, one year is not everything, I agree. But when the behavior stays consistent across backtest, demo, and live, then you know you are not just looking at random luck anymore. You are looking at something that has structure behind it.
At the moment I have around 20 to 25 profitable robots, and around 10 of them are real standouts. Most of them were built with EA Studio, while a smaller number were built with StrategyQuant.


I also added 5 screenshots of my 5 live accounts.

I also want to make one thing clear. I am showing these live accounts once in the introduction, and after that the focus will shift to the individual robots themselves. This is not meant to become some flex thread or ego thread.

People who already build robots themselves will usually know quite quickly whether someone knows what he is talking about or not. But this thread is also for people who are newer, still discovering EA Studio, StrategyQuant, or algo building in general. And for those people, the psychology is simple: first they want to see whether you have actually built something real with it.
That is the reason I am showing the live accounts here in the intro. Not to impress anyone, but simply to make it clear from the start that I am not just talking for the sake of talking.
After this, I want the thread to be about the systems, the process, the workflow, the trades, and what is actually behind the results.

Live account 1 is my first live account. New robots that survive demo go there first. The best ones from live account 1 move to live account 2. Then the best from there move further into live account 3, 4, and 5.

Live account 1 has been live since early 2025. Live account 2 followed around 3 months later. Live account 3 started in November 2025, live account 4 in December 2025, and live account 5 in early February 2026. Live accounts 3, 4, and 5 are the more aggressive accounts in terms of lot sizes. Live account 3 currently runs 6 robots, live account 4 runs 3 robots, and live account 5 runs 5 robots. Live account 2 is also the account I use for some manual trading.

Later on I will also post the robots and talk more about what they do and how they work. But that does not belong in the intro.

You can also share your own results.
If you are building robots and have something real, feel free to show it.
Equity curves of individual systems, development progress over time, or specific robot behavior are all welcome.

If you are new, you can also introduce yourself once with your results or portfolio.

But this is not meant to become a thread where people constantly post full account overviews, portfolios, or “look at my account” updates every week.
It is also not meant to become a thread of daily trade-by-trade updates like “look, my robot is in this trade” or “look what this robot is doing right now.” That is not necessary.
The focus should stay on the systems, the process, the workflow, and the reasoning behind the results.
Be proud of your work, but keep it structured and meaningful.

LIVE1
LIVE2
LIVE3
LIVE4
LIVE5

XAUUSD and EA Studio: Why This Workflow Works

I want to explain why XAUUSD became my main market for building robots, and why it started producing much better results for me once I combined it with the right workflow.

I tested multiple pairs over time, but things only started to change once I stopped looking at random backtests and began forcing my robots through a much harder process before trusting them.
The workflow came first.
XAUUSD came after that.
Once both came together, I started finding multiple systems that were not only profitable on paper, but also much more robust in demo and live conditions.
The workflow is everything

The workflow is the foundation of everything
First, I set the Data Horizon like this:
Min start date: 01/01/2009
Max end date: 31/12/2022
This becomes the build period.

On top of that, I use 50% Out-of-Sample during the build process.

Once I have enough strategies, I go back to the Data Horizon and extend the end date to the most recent data available, for example 2026.
At that point, robots built on 2009–2022 are now tested on unknown data from 2022–2026.
This is a very hard extra Out-of-Sample test inside EA Studio.
Selection process
Then I select based on the full 2009–2026 view.
What I look at:

1. equity curve
2. stability
3. behavior

If the robot still behaves well there, it moves to the next step.

Monte Carlo (mandatory)
After that, Monte Carlo is mandatory.
Main tests:

1. randomize history data
2. randomize starting bar
3. spread changes
4. slippage
5. confidence table

Fail = removed
Pass = candidate


Extra validation (optional but I use it)
I export the robot and backtest it in MetaTrader on:

1. 2003–2026
2. 100% tick data

This gives extra Out-of-Sample data and more realistic execution.


Demo phase
Robots go to a demo VPS first.
Minimum:

1. ideally 60 trades
2. 45–50 still acceptable

Below that = too little data
Even good demo robots can fail live because execution is different.

Live phase decision
I compare live behavior with:

1. EA Studio metrics
2. MetaTrader backtest
3. demo behavior

If it stays within expected behavior → OK
If not → reduce trust, pause, or move back to demo
Key principle
Robots are allowed to have bad months or even bad years.
What matters:

1. survival
2. consistency
3. long-term behavior

Why XAUUSD
For me, XAUUSD stands out because of:

1. strong directional moves
2. strong expansions
3. enough volatility

Even ranges still have movement inside them.
Compared to many forex pairs, gold often behaves cleaner and gives systems more room to perform.

Important note
I am not saying other pairs do not work.
Using the same workflow, I also built USDJPY robots that are performing cleanly in live trading.
But XAUUSD is where this workflow started producing multiple robust systems consistently.

Timeframes
I only build on M30 and higher.
Lower timeframes = more noise
More noise = less stability
Less stability = higher chance of failure in live
Higher timeframes give:

1. cleaner structure
2. more stability
3. better long-term consistency

Data matters
I always prefer broad historical data.
More data = more market regimes
More regimes = stronger test
Think of it like football.
You don’t judge a player on a few matches.
You judge him after many situations.
Same with strategies.
But data alone is not enough
You must separate:

1. build data
2. unseen data

That is why the extra Out-of-Sample step is critical.
Many people use a lot of data, but let the strategy see everything.
I do not.
I want the robot to prove itself on data it has never seen.

Simplicity over complexity
More indicators does NOT mean better systems.
More indicators = more conditions
More conditions = more fragility

Yes, I have one robot using 7 indicators that works.
But that is an exception.
In general:
simpler systems = more robust systems

Bigger perspective
It is the same in manual trading.
You can overload charts, or keep them clean.
In the end:
everyone reacts to price action and key levels.
Complexity is often overrated.

I am not saying XAUUSD is the only market.
I am saying that with this workflow:
it became one of the best markets for me
because it consistently produced:

1. robust systems
2. clean equity curves
3. stable behavior

Final message
It is possible to build good systems with EA Studio.
It is possible to do it on XAUUSD.
But only if:

1. workflow is correct
2. data is broad
3. unseen data is respected
4. timeframes are high enough
5. systems stay simple

This is not about finding one perfect backtest.
It is about forcing a robot to survive multiple layers of reality before it earns a place in demo or live trading.

ea 165 live1 equity curve

ea 165 live1 metrics overview
ea 628 live1 equity curve
ea 628 live1 metrics overview

ea 284 live1 equity curve
ea 284 live1 metrics overview
ea 712 live1 equity curve
ea 712 live1 metrics overview


I will not go into the internal logic of these systems here.
The goal of this post is to show that the workflow itself is what creates robustness.

Monte Carlo in EA Studio – How I Use It in Real Workflow
In this post I want to go deeper into one of the most important parts of my workflow: Monte Carlo testing.
This is not a theoretical explanation.
This is how I actually use it when building and filtering systems.

Monte Carlo confidence table
Monte Carlo simulations chart
What Monte Carlo is (in practice)
Monte Carlo is not a tool to improve a strategy.
It is a stress test.
It takes your existing system and runs multiple variations of it under slightly different conditions:
price sequence changes
spread variation
slippage
starting point shifts

The goal is simple:
to see whether the strategy is dependent on perfect conditions
or if it can survive realistic market imperfections

The settings I use
I keep my Monte Carlo settings simple and focused on realism.

Market variations
Randomize history data → ON
The market never moves exactly the same way twice.
Small differences in price sequencing are normal.
This test checks whether the strategy depends on perfect candle structure.
This is one of the most important tests.

Randomize spread → ON
Spread is never constant.
Broker conditions change continuously.
Especially on XAUUSD, spread has a major impact.
Without this, backtests are often too optimistic.

Execution problems
Randomize slippage → ON
Slippage is part of live trading.
Especially during volatility and fast moves.
This tests whether the system can handle execution imperfections.

Randomly skip position entry → OFF
This option simulates missed or skipped entries.
That can happen in specific situations, for example with news filters, spread filters, or execution restrictions.
But in my workflow, I do not use it as a standard Monte Carlo condition.
I use Monte Carlo mainly to test the broader structure of a system under realistic market stress, not to simulate every possible exceptional filter or execution scenario.
For that reason, I leave this option OFF.

Randomly skip position exit → OFF
This option simulates missed or skipped exits.
That is not something I consider a realistic standard condition for normal EA execution, so I leave it OFF.

Randomly close position → OFF
This simulates manual interference.


In my approach:
I do not interfere with robots.
That is a hard rule in my workflow.
The only time I broke that rule was earlier on, when I had a stack of XAUUSD robots all going against me at the same time with wide stop losses. That was an exposure mistake on my side, and I had to partial close twice because the total exposure was too high.
I learned from that. I later made adjustments to prevent that situation from happening again, and I will explain those adjustments in a future post.
But the key point is this: both times I intervened, I treated the robots as reset afterwards.
Since then, I have not broken that rule again, and I do not intend to break it again.

Backtest start
Randomize backtest starting bar → ON
This checks whether the starting point matters.
A robust system should not depend on a perfect start moment.
Together with history randomization, this is one of the strongest robustness checks.

Strategy variations
Randomize indicator parameters → OFF

Important clarification:
This is not re-optimization.
It slightly shifts parameters to test sensitivity.
In my workflow, parameter selection happens in the Reactor.
Monte Carlo is only used to test if those parameters hold.
Sequence:
Reactor → define parameters
Monte Carlo → validate stability
Not:
Reactor → Monte Carlo → search for new parameters
This is an important distinction.

How I use the Confidence Table
The Confidence Table is where most of the real information is.
Each row represents a confidence level:
20% → optimistic scenario
50% → median
85–95% → stress zone
100% → worst case
These are not new backtests.
They are variations of the same system under stress.

What I look at
I do not read the table row by row.
I read it vertically:
How fast does performance degrade
How stable is Return / DD
How does drawdown evolve
Does SQN collapse or remain meaningful

My key metric

For me, Return / DD is one of the most important signals.

Example:
If the original Return / DD is around 12, then ideally I want to see at least around half of that still remaining at the 80–85% confidence zone.
So in that example, a value around 6 would be very strong. That means the system still holds a significant part of its structure.
That is a strong sign of robustness.

That said, I am not overly strict with it. If the rest of the system still looks good in terms of equity curve, overall metrics, and general behavior, I can still accept a Return / DD around 4–5 in the 80–85% confidence zone.
If I were too strict on Monte Carlo alone, I would end up throwing away too many systems that are still good enough on paper and often still worth testing further.

What I want to see
A good system:
Profit decreases gradually
Drawdown increases in a controlled way
SQN declines but stays relevant
Behavior remains consistent

A bad system:
Profit collapses quickly
Drawdown spikes aggressively
SQN breaks down
Equity structure changes completely

Number of simulations
I usually run 50 simulations as default.
Sometimes I repeat with 100 or more.
The exact number is not the most important factor.
Consistency of behavior is.

Important note
Monte Carlo is not a guarantee.
Passing Monte Carlo does not mean a system is ready for live trading.
It only means:
the system has survived a realistic stress test

It still needs:
Out-of-Sample validation

demo phase
live validation

Final perspective
Monte Carlo is one of the core filters in my workflow.
If a system fails here clearly,
and the rest of the metrics are not convincing,
I discard it immediately.
There is no reason to continue with a weak structure.
Over time, your eye becomes trained.
You start to recognize stability patterns very quickly.
But that intuition always starts with a solid Monte Carlo foundation.
This is not about finding perfect systems.
It is about filtering out fragile ones before they ever reach demo or live trading.

Monte Carlo example (real system)
Below you can see two Monte Carlo tests of one of my systems (EA 628):
50 simulations (my standard test)
100 simulations (extra confirmation)

Monte Carlo confidence table 50 runs
Monte carlo confidence table 100 runs
Monte Carlo simulation chart 50 runs
monte carlo simulation chart 100 runs
expert advisor 628 equitycurve main account


This is not a random system.

This robot has already been:
backtested (tick data MT4)
tested in EA Studio
validated on demo
and traded live
So what you are seeing here is not theory
this is a system that has already proven itself, now being stress-tested.
What stands out immediately

If you look at the simulation charts, you can see:
All curves follow the same general structure
No chaotic divergence
No random collapse scenarios
The system keeps its shape under stress
That is exactly what you want to see.
A fragile system would show:

wide spread between curves
inconsistent behavior
completely different equity structures
That is not the case here.
Confidence table interpretation

Now the most important part: the confidence table
What matters is not the exact numbers, but the structure.
You can clearly see:

Profit decreases gradually
Drawdown increases in a controlled way
Return / DD stays relatively stable
SQN declines slightly but remains meaningful
This is what I call a stable degradation curve.
50 runs vs 100 runs

This is where it becomes interesting.
When moving from 50 → 100 simulations:
The structure remains the same
No new weaknesses appear
No sudden breakdown in performance
The behavior is consistent
That confirms that the 50-run result was not random noise.
This is exactly why I sometimes run 100 simulations:
not to make the test stricter,
but to confirm that the structure holds.
Key takeaway

Monte Carlo is not about perfect numbers.
It is about structure.
This system shows:
consistency
controlled degradation
stability under stress
That is what robustness looks like in practice.
Also keep in mind:

This is a system that already proved itself in:
backtest
demo
live trading
Monte Carlo is not used here to “find” something.
It is used to confirm that the structure remains intact under variation.

EA Studio vs StrategyQuant – A Real User Perspective
I want to share my honest perspective as someone who uses both EA Studio and StrategyQuant extensively in real building, testing, demo trading, and live trading.
This is not about saying one is “better” in a simplistic way.

Why I am making this comparison
The reason I am making this comparison is not to convince anyone to use one tool over the other.
I genuinely do not care whether someone chooses EA Studio, StrategyQuant, or decides not to use either.
That is not the goal.

The real reason is this:
There are still many people who are interested in algorithmic trading, but believe they need to know programming to get started.
What I want to show is that this is not necessarily the case.
There are tools available that allow you to build your own Expert Advisors, generate as many strategies as you want, and test them properly without needing to code everything from scratch.
That is the purpose of this comparison.
Not to sell anything.
Not to push people in a direction.
But simply to show what is possible.

Both tools are powerful.
But they are not the same, and they come with different strengths, weaknesses, and practical trade-offs.

EA Studio is, in my opinion, one of the best tools available when it comes to:
- simplicity
- speed
- usability
- efficiency
It is extremely user-friendly and allows you to generate strategies quickly and consistently.
That is exactly why many traders, including myself, have built strong and profitable robots with it.
It removes a lot of unnecessary complexity and lets you focus on what matters.
And that is a major advantage.

Another practical advantage is that EA Studio is web-based.
That makes it very accessible.
It also does not require the same kind of hardware power as heavier desktop software.
So from a usability and accessibility standpoint, EA Studio is very strong.


It also lacks some practical flexibility in certain areas.

For example:
- no native option for risk-based position sizing (like risk % per trade)
- no support for adding custom indicators
- strategy building is largely limited to the standard built-in indicators available in MT4 and MT5

That naturally limits how far you can push strategy logic beyond the built-in components.

But the weakness is this:
when too much is automated, you lose the ability to properly inspect the visual results of those tests.
And in my opinion, that matters.
Because it is not enough that a strategy simply “passes” Monte Carlo or Walk Forward inside an automated process.

You also want to see the structural behavior:
- the simulation chart
- the confidence table
- the way the equity changes under stress
- whether the degradation is controlled or ugly

To be clear:
this limitation mainly applies when using automation inside EA Studio.
The visual analysis is still available, but you need to run Monte Carlo, Walk Forward, and other tests separately to properly inspect things like the simulation charts and confidence tables.
If you cannot properly inspect that within the workflow itself, then a pass/fail result alone is not enough for a trained eye.
That is one of the areas where EA Studio still falls short for me.

StrategyQuant
At the same time, we also have to be honest about something:
StrategyQuant operates on a different level in terms of depth and technical capability.
It is:
- more complex
- more advanced
- more configurable
- more detailed at every stage of the process
From building to testing to portfolio handling, StrategyQuant simply offers more control.

Another advantage of StrategyQuant is flexibility in strategy design.

It provides:
- more trade management options
- a larger set of built-in indicators and signals
- support for custom indicators

This allows for much deeper customization compared to EA Studio.

However, there is a trade-off.

Adding custom indicators in StrategyQuant is quite advanced and not straightforward.

It requires going through a more complex setup process, which can be a barrier for many users.

So while the flexibility is higher, it also comes with additional complexity.

One of the biggest differences is automation.
With StrategyQuant, you can build a much more automated workflow:
- strategy generation
- filtering
- robustness testing
- portfolio creation
- export directly to your MetaTrader folders

A lot of this can run without you even being at your computer.
That is a completely different level of workflow compared to a more manual or semi-manual process.
Another major strength is robustness testing.
This is one of the areas where StrategyQuant really stands out.

It offers:
- more types of tests
- more configuration options
- deeper stress testing
- clearer visual feedback on what is happening

You can see much more clearly how strategies behave under different conditions.
EA Studio also has robustness testing, and it is good.
But StrategyQuant simply goes further in this area.
That is just the reality.
That said, StrategyQuant is not perfect.
It has more moving parts, more complexity, and in my experience also more bugs.
To be fair, they do fix bugs regularly and release updates often.

So it is not that the software is abandoned or ignored.
But the reality is still that more complexity usually means more room for technical issues.
Another practical downside is hardware demand.
StrategyQuant is desktop software, and to really use it properly at scale, you need a strong PC.
It consumes much more processing power than EA Studio.
That is a real consideration.
With EA Studio, this is much less of an issue.

So from a practical standpoint:
EA Studio is lighter, easier, and more accessible.
StrategyQuant is heavier, deeper, and more demanding.

Another practical difference is platform coverage.
EA Studio is limited to MT4 and MT5.

StrategyQuant also supports MT4 and MT5, but goes beyond that with support for platforms such as TradeStation and MultiCharts.
That is a real advantage, because not everyone builds for MetaTrader only.

The real difference
The way I see it:

EA Studio excels in:
- simplicity
- speed
- clean workflow
- accessibility

StrategyQuant excels in:
- depth
- control
- automation
- advanced testing
But those extra strengths also come with extra complexity, higher hardware demands, and more technical friction.

Conclusion
Both tools are valuable.
Both can produce profitable strategies.
But they are built for different types of users and different stages of development.
In my experience:
EA Studio is one of the most efficient tools for generating solid strategies in a clean and accessible way.
StrategyQuant is one of the most powerful tools for going deeper, testing harder, and automating more of the full process, but it also demands more from both the user and the machine.
That is my honest view as someone who uses both.
Not marketing.
Not theory.
Just experience.

About the screenshots below

Just to keep things clear:

The results and equity curves shown below are from StrategyQuant-built strategies.

This is not to prove anything or to suggest that one tool is “better”.

It is simply because in this part of the thread I am focusing on the StrategyQuant side of the workflow.

If you want to see my EA Studio results and robots, they are already shared earlier in this thread.

I prefer to keep them separated so everything stays structured and easy to follow.

Again:

This is not about showing off Eas or results.

It is about showing real examples from both environments, in a clean and honest way.

strategyquant ea 139 overview main account
strategyquant ea 139 equity curve main account
Strategyquant 12396 overview main account
strategyquant 12396 equity curve main account
sq ea 122 overview main account

The Psychology Behind Grid & Martingale Strategies
This is my personal opinion on grid robots.
I have seen the grid function being added to EA Studio, and on an experimental level I understand it. In that sense, I think it can be an interesting addition.

But I think we should ask ourselves a more important question first:
Why would we want to use a grid robot in the first place?
For me, the answer is not technical first. It is psychological.
Most people want money fast.
And not just money fast, but a lot of money fast.
At the same time, most people do not handle losses well. Loss creates pain. It affects the mind and the nervous system.


What do grid and martingale systems try to do?
They try to reduce that pain through recovery.
Instead of accepting the loss, they try to fix it by increasing exposure and recovering the position.

And yes, this can work.
You can build softer grid systems.
You can build softer martingale systems.

But the principle stays the same.
The structure is still based on recovery.
And because of that, I do not believe you can ever run those systems with complete peace of mind.
You always need to watch them.
You need to monitor them closely.

Sometimes you even need to stop them when the market is no longer behaving the way the system needs it to behave.
And in my eyes, that already says a lot.

If a robot constantly needs to be watched because it could damage the account, or because it has already almost damaged the account before, then for me that is not a truly robust system.
If you need to stop grid robots between phases to protect the account, then in my opinion that is already proof that the structure itself cannot be trusted in a fully robust way.
That is why, in my eyes, the mental comfort is only temporary.

Yes, recovery can feel better in the short term because the system is “doing something” about the loss.
But underneath that, the account becomes a ticking time bomb.

To be honest, I personally do not believe grid or martingale systems can ever truly be called robust in the same way as non-recovery systems.
Can they work for years? Yes, of course.
A grid system can work for 3 years, 5 years, maybe even longer.
But that is not the point.

The point is that one bad phase can undo everything.
A system that survives for years but can still destroy the account in one later phase is, in my view, not robust. It is simply delayed fragility.
And yes, even robust non-grid systems can stop working at some point. That is true.

But there is still a major difference.
When a normal non-recovery system stops working, the account is usually not being structurally eaten alive in the same way. The losses are visible, realistic, and limited by the design of the system.
That difference matters.

For me, the goal with robots is long-term survival.
Long-term survival of the strategy.
Long-term survival of the account.

That is why I would always prefer slower, more stable account growth with a realistic curve, realistic losses, and realistic drawdown over fast money built on ticking bombs.

And this is also what I keep seeing online.
A lot of people selling robots focus on grid systems because they produce smooth and attractive equity curves.
That is what people like to see.
But in my opinion, that is exactly where the danger is.
The curve looks good, but the structure behind it is not something I personally trust.
And if I cannot trust the structure, I will not put it on a live account.
It is as simple as that.
Again, this is just my personal perspective and opinion.
But for me, the priority is not how good the curve looks.
The priority is whether the system can be trusted over time.

Why Buying Robots Online Is a Trap (Hard Reality)
And this brings me to the next important topic: buying robots online.
This is not theory.
This is a warning based on real experience.
Because the reality is simple:
there is almost nothing online that is truly robust long-term.

The first problem: the type of robots being sold
Around 95% of all robots sold online fall into one category:
grid systems
martingale systems
recovery-based logic
Even when sellers claim:
“no martingale”
“no grid”
In most cases, it is still there just hidden differently.

Sometimes through:
position stacking
lot scaling
averaging behavior
The structure is the same.

Why these systems are sold
These types of systems produce:
smooth equity curves
frequent wins
visually attractive backtests
And that is exactly what most buyers look for.


But what they don’t understand is:
these systems are ticking time bombs.
They can work for:
months
sometimes even years
Until one moment:
the account is gone.

The biggest question people never ask
If a system is truly robust and profitable:
why would someone sell it to the public?

Think about it logically.
As a builder, if I have a strong system:
I can run it on multiple MT4 accounts
I can scale it
I can compound it
So why would I sell it to:
anyone on the internet
for a fixed price
while giving away the edge
That does not make sense.


The reality behind most sellers
The common explanation is:
“I sell it as an extra income stream.”
But in reality, in many cases:
the system is not strong enough
the edge is not stable
the real profit comes from selling, not trading

So they sell:
something that looks good
something that performs short-term
something that eventually fails
Because the curve sells.
Not the robustness.


The second problem: broker dependency
This is something almost nobody talks about.
Every serious builder knows:
a strategy is built on specific conditions.
That includes:
historical data
spread
slippage
swaps
execution behavior

When I build a system, I align everything with my broker.
That means the strategy is built and tested around that specific environment.
In EA Studio, this is also important when loading data and preparing symbol settings.
If you want realistic results, you need to adjust the symbol settings to match your broker as closely as possible.
That includes things like:
spread
swap
digits
contract specifications
general trading conditions

The same logic applies to builders who code their own systems.
Even when someone programs strategies manually, serious backtesting is still usually done in the environment of the broker they actually want to trade with.
Because in the end, a system is never being tested in a vacuum.
It is always being tested inside a specific broker reality.

What people don’t understand
Not all brokers are the same.
Differences include:
spread variations
candle data differences
execution speed
slippage behavior
liquidity conditions

So a system that works on:
Broker A
can easily fail on:
Broker B

The biggest lie in robot selling
You often see:
“works on any broker”
That is simply not true.
It is one of the biggest misconceptions in this space.

Another major problem is how these robots are presented.
Most sellers do not show real validation.
What they usually show is:

1. a backtest
2. a smooth curve in backtest
3. High win rate in backtest

But a backtest is still just a backtest.
It is not demo testing.
It is not forward testing.
And it is definitely not long-term live trading.
That is one of the biggest tricks in this space.

People see a beautiful backtest and assume the system has been proven.
In reality, most of these robots have not been tested deeply enough by the seller himself.
Not for years.
Not through real demo validation.
Not through real live conditions.

And the reason is simple:
that takes time.
Selling is faster.
That is why many of these people are not really traders or serious algo builders.
They are sellers.

Because if a system is truly robust, well-tested, and making money, then again the same question comes back:
why sell it to everyone instead of running it yourself?

The conclusion
Most robots sold online:
are not built for long-term robustness
rely on recovery logic
are optimized for specific conditions
are sold because selling is the real business

That does not mean:
nothing works

But it does mean:
you need to understand what you are buying.

Final message
If you are serious about algorithmic trading:
Do not focus on:
buying robots
copying systems
chasing curves
Focus on:
understanding structure
building your own systems
testing properly
validating across conditions
Because in the end:
robustness is built, not bought.

Is there an EA somewhere without expiration and other restrictions? Or is it just an advertisement?

No advertisement.
And I think you need to read the thread more carefully, because your question does not really match what I have been explaining here.
I do not sell robots.
I do not sell courses.
I do not sell anything.
The whole purpose of this thread is to show how I build systems, how I test them, and what actually matters in a real workflow, so people who are interested in algo trading can understand the process and build their own systems themselves.
As for your actual question: yes, of course there are EAs without expiration or restrictions. But that is not the point of this thread.
The point is that whether a robot has restrictions or not tells you nothing about whether it is actually robust, well-tested, or worth using long term.
And in my opinion, the best robots are usually not the ones being sold online anyway.
The highest chance of ending up with good EAs is by building them yourself, testing them properly, and using them in your own environment not by buying them from someone else.
Everything shared here is free, based on real experience, and meant to give clarity, not to promote or sell anything.

This is something I don’t see many people talk about, but for me this was one of the biggest shifts in my whole process. And you can literally see it in the screenshots.

What I did here is actually very simple, but the impact is huge.

In the first screenshot below, you see the strategy built on data from 2009 until 2020.

This is close to how I was actually doing it back then in early 2024.
Back then I was using slightly different starting ranges as well, sometimes more like 2007–2019 and then extending forward, but the idea itself was exactly the same.
That is the important part.
EA 165 build proces and optimization


The principle never changed:

build first, then push the same strategy into extra unseen data.
This is the normal build phase. This is where the strategy is generated and optimized. At that stage, the strategy already looks solid. The equity curve is good, the net profit is there, the trade count is decent, and the SQN is already 3.45.
For many people, that would already be enough to say the strategy looks good.

But this is where I started thinking differently.
Because for me, it is not enough that a strategy looks good only on the period where it was built and optimized.

That is exactly where many people stop too early.
They build, they check the normal out of sample inside EA Studio, and then they move on.
But I wanted to push the strategy further.
I wanted to see what really happens when you expose it to data it has never seen.
So what I started doing back then was this:


after generating and optimizing the strategy, I do not touch the strategy itself anymore.
I do not change the rules
I do not reoptimize
I do not tweak anything
I only change one thing:
the data end.
So the strategy stays exactly the same, but now I extend the end date forward.

And this is exactly what you see in the second screenshot below.
The same strategy is now extended from 2009–2020 to 2009–2024.
And that is the key point.
EA 165 after build proces extr oos


That extra period was never optimized.

So what you are looking at there is pure unseen data.
Real out of sample.
No fitting
no tweaking
no hidden optimization
Just the strategy being pushed forward into new market conditions.
And this is exactly where weak strategies usually break.
That is why this became such an important step in my workflow.


Because if a strategy looks good until 2020 but then falls apart when I extend it to 2024, then I already know enough.

I don’t care how nice it looked before.
It is not stable.


But if I extend it and it keeps going, stays stable, and even improves, then now we are talking about something completely different.

And that is exactly what you see here.
The strategy holds.
The equity curve keeps moving.
The structure stays intact.
That is what you want to see.
For me, this is one of the clearest ways to separate:


strategies that look good
from strategies that are actually stronger
Because now you are not looking at in-sample anymore.
You are forcing the strategy through a completely new period it has never seen.
That is real testing.
That is why I always say:

strategies break or survive on out of sample.
And this is just one more layer of that idea.
Simple concept.
Huge difference.
One important thing I want to add here about the build process itself.


When I build strategies, I use full data optimization with 40 steps.
And there is a reason for that.
A lot of people are afraid of heavy optimization because of overfitting.

But that is exactly where Monte Carlo comes in.
Monte Carlo is not there to replace optimization.
It is there to test it.
So I actually push the optimization harder on purpose.

I give the strategy room to explore the data.
And then I use Monte Carlo to check if that structure is still stable under randomness.
This strategy you see here was built with 40 optimization steps.
And then you can clearly see what happens when it goes through heavy Monte Carlo testing.
That is the balance.
Strong optimization → then strong validation.

And on top of that, the extra out of sample period is not optimized at all.
So that is pure OOS.
Also during the build itself, I already use 50% out of sample in EA Studio.

So from the start there is already a separation between in-sample and out-of-sample data.
But the extra OOS trick is what really pushes it further.
That is where things get exposed.
After that, I move to the next step.

Monte Carlo.

Normally, like I explained before, I use:
50 runs
then 100 runs as confirmation


But here I pushed it much further.

I ran 1500 simulations.
And this is where things become very clear.
Because now you are not looking at one equity curve anymore.
You are stress testing the same strategy across a massive number of variations.

Randomized history
randomized spread
randomized slippage
different starting points
Everything gets pushed.
And if a strategy is weak, this is where it shows immediately.
EA 165 MC 1500 runs

Now look at the confidence table.


Even under heavy randomization, the structure holds.
The performance stays consistent
the SQN remains solid
the degradation is controlled
Even at higher confidence levels, the strategy is still standing.
That is not normal for weak strategies.


EA165 MC 1500 Confidence

And if you look at the editor, you can see the magic number.

This is EA 165.
So again, this is not a random example.

This is a real strategy.
And this strategy has been running live for around a year.
That is also why I was confident enough to push it to 1500 Monte Carlo simulations.

Because by that point, I already had real live data behind it.


But the important part is this:

back then, when I first built it, I was not doing 1500 runs.
I was doing 50 → then 100 runs as confirmation.
Exactly like I explained before.
The 1500 runs you see here are just an extra layer on top of something that was already proven step by step.
So what you are really seeing here is this:


a strategy built on historical data
then extended into real unseen data (extra OOS)
then stress tested through massive Monte Carlo
And it still holds.
That is what I call confirmation.
Not one good backtest.
Not one lucky curve.
But multiple layers, all pointing in the same direction.
That is the difference.

That is also why for me this is not optional.
This is a filter.
If a strategy fails here → it is out.
If it survives → it earns the right to move forward.
That is the whole idea.

Data
out of sample
extra out of sample
Monte Carlo
Stack those layers.
And you will see very quickly what is real and what is not.
Simple idea.
Huge difference.
For the live results of EA 165, you can check at the beginning of the thread

One last thing to make this even more clear.


After everything you just saw, I extended the same strategy even further.
From 2009 all the way to 2026.


EA 165 FULL DATA 2026

Again:

no changes
no reoptimization
same logic
Just more unseen data.
And this is what matters.


Because at this point, you are not just looking at a strategy that survived a small extension.

You are looking at a strategy that continues to hold its structure over an even longer period.
The equity curve keeps moving
the behavior stays consistent
the structure remains intact
That is not something you fake.


And if you compare this with the previous topic where I shared the live performance, you can start connecting the dots.

This is exactly the point.
Not one test.
Not one phase.



build
extra out of sample
extended data
Monte Carlo
demo
and live performance
That is where real confidence comes from.
Everything has to align.
And when it does, this is the result.

That is how you separate
robust systems vs curve-fit systems.

Would you run someone elses EA through this process?

Good question actually.

It depends on how the EA is built.

If the EA comes from EA Studio or StrategyQuant, then yes, because I can re-import the strategy and run the same process I showed here.
That means:

1. extending the data into extra unseen periods (extra out-of-sample)
2. not changing or reoptimizing anything
3. pushing it through Monte Carlo
4. and checking if the structure still holds

If it is just a compiled EA, then no, because I cannot access the internal logic or test it in the same way.

The only exception would be if you know the full strategy logic yourself meaning the exact rules, indicators, parameters, pair, timeframe, and everything needed to rebuild it properly. In that case, I could try to recreate it inside EA Studio or StrategyQuant and then test it from there.


But at that point, we are already talking about a much deeper and more complex process than simply testing someone else’s EA.

So my question to you is: did you build the EA yourself?
And if so, was it built in something like EA Studio / StrategyQuant, or fully coded manually?

This was just a general inquiry to see if it was possible and if you would spend the time and effort to do so.
I do have an EA that is currently running on the Gold 1M chart made by another member on FF using AI. The rules are mine and it could run on higher TFs i suppose. I was intrigued by the comprehensiveness of your process and how it would give a broader insight into an EAs perfomance. Right now all we have is a few tests back to Oct 2025, which is alot of trades but nothing like you are doing. I do understand the work involved and would let you decide as it would be your time and effort.

Thanks, Chris.

I understand what you mean, Chris, and I appreciate the way you explained it.

First thing I would be curious about:
are you running that EA on demo right now, or is it already live?

Also, you mentioned Gold on the M1 timeframe, and that is something important.
Lower timeframes like M1 and M5 naturally come with more noise, faster movements, and a lot more randomness in price behavior. Yes, they generate many trades, which can help build statistics faster, but they are also much harder to make robust over the long term.

In my experience, the higher the timeframe, the more stable the structure tends to be. That applies in both algo trading and manual trading. That is also why most of my systems are built on M30 and higher.

It does not mean M1 systems cannot work, but finding ones that remain stable over many years and different market conditions is significantly harder.
You also mentioned testing back to October 2025. Do you know roughly what the full backtest period is? That would already tell a lot about how much the system has actually been exposed to different market phases.

I gave that part a bit more thought as well.
To be honest, I would still rather have people learn to run this process themselves.
Not because I do not want to help, but because the process takes real time and attention, and if I start doing it for others, the thread can quickly turn into a service instead of a learning resource.

What I can do, and what I think is more valuable long term, is help you think through how to test it properly yourself.
But I should be clear: the process I described is much easier to apply when the EA comes from EA Studio or StrategyQuant.
If it doesn’t, then we are no longer working inside the same environment, and the process becomes much more limited unless you use other tools, custom code, or something like Python to recreate parts of it.

MT5 does have some robustness features, but it does not provide the same type of structured Monte Carlo analysis with simulation distributions and confidence tables like EA Studio or StrategyQuant. That is why I prefer using those tools for this specific step.

You mentioned the EA is built using AI, and that is actually a good thing in one sense.
Because no historical data is used during the creation of the logic itself.
The code is generated first, and only after compiling it gets backtested.
But that is only the starting point.
After that, you still need to see a strong backtest.
For me personally, I want to see at least around 20 years of data.
To be a bit less strict, 15+ years can already be acceptable, especially on lower timeframes like M1.
The problem is that very few M1 and M5 EAs can actually survive that kind of long-term testing.
And even when they do, many of them still fail very quickly when moved to demo or live conditions.
That is unfortunately one of the biggest issues in this space.

Hi,
Thank you for the information. I have been following Chris, and I can see that he has put a lot of hard work into his EA and the system he has built.
I am sure your system can do big improvements
Best regards

Hello Shama369,
Thank you for the reply.
And yes, I agree with you.
You can clearly see Chris has put serious work into that system.
But to be honest, not only Chris. The whole thread has clearly helped shape and improve it over time.
That is also why, in my eyes, his thread is a high value thread.
So respect to Chris, and respect to everyone in that thread who helped build it further.
And thank you as well, because your reply clearly shows respect for the work behind it.
Respect.

This is a direct follow-up to post #11.
Because this EA has already been running live for about a year, I decided to push the validation much further.
So this is not a random test.
This is the same proven strategy from post #11, now being pushed much harder to see how far the structure really holds.

Step 1: Increasing the stress
I deliberately increased the stress:

1. heavily randomized history data (80% changed bars)
2. ATR variation increased to 40%
3. slippage increased up to 40 points
4. and 1500 Monte Carlo simulations

Not because I needed to “prove” it.
But because I already knew this strategy could handle it.

hard stress settings MC


Why?
Because this is not a fresh build.

This is an EA that has already:

1. passed the full build process
2. survived extended out-of-sample
3. and proven itself in live conditions

So at this stage, the objective is different.

Not discovery.
Confirmation under pressure.

Step 2: What happens under stress
Now instead of looking at one equity curve, you are looking at the same strategy under hundreds of variations.
Different conditions.
Different executions.
Different paths.

chart MC

And this is where weak strategies usually break.
But here, you can clearly see something else:

1. the structure holds
2. the curves stay grouped
3. no chaotic breakdown
4. no complete collapse

That is what strength looks like under pressure.



Step 3: The numbers behind it
Now look at the confidence table.
This is where the real validation is.

confidencetable MC


Even under heavy stress:

1. performance remains consistent
2. degradation stays controlled
3. SQN remains solid across confidence levels

This is not what weak strategies look like.

Key point
No changes were made to the strategy.
No reoptimization.
No adjustments.
Just pure stress testing.
At this level, you are no longer looking at a backtest.
You are looking at behavior under extreme conditions.
And it still holds.

This is where most strategies fail.
This one doesn’t.

Building Strategies: Broad Data vs Short Data (A Practical, Real-World Comparison)

There are generally two different approaches I see when it comes to building strategies:
1) Building on broader datasets
2) Building on shorter, more recent datasets

I want to break both down in a realistic way, because both approaches exist, and both can work depending on how they are used.

First of all, I want to be clear that this is not about saying one is “wrong”. There are definitely skilled builders who can make shorter datasets work in live trading. But the behavior and implications of both approaches are very different.

Building on broader datasets
When you build on a longer historical range, the strategy is exposed to more market regimes, more conditions, and more variation.
Every candle represents past behavior of the market, including different trends, ranges, volatility phases, and reactions to events.
Because of that, the strategy is forced to adapt to a wider range of conditions during the build process.
In my experience, this tends to produce strategies that are more stable and more consistent over time.
Another advantage is that you usually don’t need to constantly replace them. They are built with more “experience”, so to speak.
The downside is that you might get fewer strategies passing your filters, and the process can feel slower.

You can also look at it from a different perspective.
Imagine you are choosing a trader to manage your capital.
Would you choose someone who has been trading for 2–3 years, or someone who has been consistently profitable for 15–20 years?
Most people would naturally lean towards the trader with more experience, because they have already gone through more market conditions and proved themselves over time.

It’s a similar idea when building strategies.
A strategy that is built on a broader dataset has effectively “seen” more of the market before being tested further.
Of course, just like with traders, past performance is never a guarantee. Time will always be the final test.
But more exposure during the build process generally gives a stronger foundation.

Building on shorter datasets
When you build on a shorter range (for example a few years), the strategy is exposed to much less variation.
That means fewer market regimes, fewer structural changes, and less overall information during the build process.
Even if you use OOS inside that range, the total amount of information is still limited.
Because of that, strategies can look very good in backtests, but they are often more sensitive when new conditions appear, especially when the market behaves in ways that were not present in the build data.
This approach can work, but it usually requires more rotation, more monitoring, and more frequent replacement of strategies.

Another consequence of this approach is that constant rotation can sometimes lead to throwing away strategies that were not actually broken, but simply going through a normal drawdown or stagnation phase.
When the build-and-replace cycle becomes too frequent, it becomes harder to distinguish between a genuinely weak strategy and one that just needs more time.


You can take the idea even one step further.
Even when people think they are using “enough data”, in reality the effective build data is often much smaller than they realize.

For example:
If you build a strategy on 8–10 years of data while using 50% out-of-sample, then only half of that data is actually used for building and optimization.
So in practice, you are not building on 10 years.
You are building on 4–5 years of effective in-sample data.
That is not a lot when you think about how complex and changing the market really is.

It means the strategy is trained on a relatively limited window, and then validated on another limited window within the same overall period.
And while that can still work, it also means the strategy has not been exposed to a truly wide range of market conditions yet.
That is exactly why I prefer working with broader datasets.

Because even after applying out-of-sample, there is still enough depth and variation left in both the build phase and the validation phase.
It simply gives more information, more exposure, and a stronger foundation before the strategy ever reaches demo or live trading.


The goal of building strategies is always the same:
To extract something that survives outside the data it was built on.
And from that perspective, the amount of data matters a lot.
More data means more exposure.
More exposure means more information.
More information generally leads to more robust behavior.

The same principle applies later in the process as well.
You wouldn’t judge a strategy after just a few trades or a couple of weeks. You want enough data and enough observations before trusting it.
So it makes sense to apply that same logic during the build phase.


Both approaches can work, but they lead to very different styles.
One focuses more on long-term stability and consistency.

The other relies more on adaptation, rotation, and active management.

From my experience, the more data a strategy is exposed to during the build process, the more robust it tends to be when facing new conditions later on.
That does not guarantee success.
But it significantly increases the probability of building something that can actually survive real market conditions.

Most people think they are building robust systems.

In reality, many are just building on limited information without realizing it.

Consistency Across Data, Platforms, and Time (EA 165 Example)
What I want to show here is something I personally find very important when judging robustness.

This is still the same EA: EA 165
That is exactly why I included multiple screenshots.
The point is not to throw random charts together.
The point is to show the same system from different angles, across different environments, and over a much wider stretch of time.

1. EA Studio – Original Structure

2007-2026 equity Curve EA studio

This is the original balance curve from EA Studio.
This is where the strategy was first built and where the structure first showed itself.

2. MT4 Tick Data Backtest (2003–2026)

MT4 equity curve 2003-2026

Now here you see the same EA pushed through a much deeper test.
This backtest was run in MT4 on tick data from 2003 to 2026, with 99.90% modeling quality and a fixed spread.
So this is not some light extra check.
This is a serious historical stress view of the same strategy.

3. MT4 Backtest Metrics

MT4 report EA 165

And here are the numbers behind that backtest.
This matters, because a curve by itself is never enough.
You also want to see the statistics behind it:

1. profit factor
2. drawdown
3. total trades
4. how many wins and losses happen in a row

That gives the curve actual context.

4. Live Performance

live equity EA 165

And this is where it becomes even more meaningful.
This is the same EA running in live conditions.

5. Live Metrics

live stats EA 165

Here you can see the live metrics behind that performance:

1. profit factor
2. drawdown
3. return / DD
4. expectancy

This is no longer historical simulation.
This is real execution.

The metrics should stay roughly consistent across everything:
from EA Studio
to MT4
to demo
and then to live.

I’m talking about things like:

1. winrate
2. profit factor
3. drawdown behavior
4. and also how many wins and losses happen in a row

That sequence matters more than people think.
Because that is part of the structure.

If those metrics stay aligned across all stages, it tells you something simple:
the behavior is real.
The structure is the same everywhere.

And that becomes very important later on.

Because once the EA is running live, you already know what “normal behavior” looks like.
So if after some time, for example a year, you suddenly see:

1. completely different metrics
2. worse performance
3. different win/loss sequences
4. behavior that no longer matches what you tested

then you know something changed.
At that point, you don’t guess.
You act.

1. reduce lot size
2. pause the system
3. let it run on demo again
4. or cut it completely

Because once the structure breaks, you are no longer trading the same system you validated.
And that is the real risk.



6. Trade Analysis

trade analysis EA 165

And this part matters more than many people think.
Because this gives more insight into how the EA actually behaves in practice:

1. profit distribution
2. behavior across days
3. behavior across hours
4. overall execution consistency

What this actually shows
This EA was originally built on a window roughly around:
2007–2019
That means that, at the time it was built, everything that came after was still unseen.
And that is the key point.
Back then, the strategy had not yet “seen”:

1. the market phases after 2019
2. later volatility changes
3. the behavior of 2024–2026

Of course, today I already know more.
I now know that 2024 behaved well from demo observation, and I know that 2025 into 2026 also behaved well in live trading.

But that is knowledge I have now.

It was not knowledge available at the moment the EA was originally built.
That is exactly why this kind of comparison matters to me.

What I actually care about
I do not care about:

1. one nice backtest
2. one strong year
3. one optimized curve

What I care about is this:

1. does it survive unseen data?
2. does it keep its structure?
3. does the same behavior appear again when you push it deeper?

Look at the curve carefully
I marked a few sections on purpose.

Not to highlight difficult periods, but to make something else very clear.
The curve structure in EA Studio and the curve structure in the MT4 tick-data backtest follow the same overall behavior.

That matters a lot to me.

Because it shows I am not comparing two random examples.
It is the same EA.
The same structure.
The same kind of movement.
Just viewed through a much deeper historical test.

That kind of consistency gives me more confidence that I am looking at something real, not something that only looked good in one isolated environment.
And when I then also see the live performance moving in the same general spirit, that becomes even more meaningful.

This is the real point

The point is not:
“look how much money this made”

The point is:
look how consistent the behavior is across environments.
That is a very different thing.

Full validation stack
This EA went through:

1. the build phase with 50% OOS
2. forward unseen data
3. Monte Carlo
4. confidence table review
5. cross-platform validation between EA Studio and MT4
6. deep historical testing
7. demo
8. and live trading

That is where confidence starts to come from.
Not from one test.
Not from one period.
But from alignment across multiple layers.


When I see a system that:

1. keeps its structure over time
2. behaves similarly across platforms
3. survives deeper testing
4. and then also continues in demo and live

then I am no longer looking at something fragile.
I am looking at something with real structural strength.