🔱Champion EA of a global competition on the TradingView platform

This project is being replicated based on content from TradingView, following the latest World AlgoTrading Championship.
The development team included 6 coding specialists in Pine Script and 3 financial experts, whose names, qualifications, and institutions where they studied, graduated, and were awarded in their careers are published in this week's official TradingView article.

Every year, TradingView launches a challenge to find the best-performing algorithm developed by its users. As an open platform, anyone — even a complete beginner — can submit their strategy and code to participate.
This year, over 1.2 million users participated by submitting their strategies and promising codes for initial review. After the first phase, around 480,000 users were granted demo accounts to run their algorithms.

The algorithms could be executed in two ways:

1. Via API using external personal platforms (acting like a regular user placing trades on the TradingView chart).
2. Using the Eithcap broker, where participants could choose any supported trading platform.

Our team chose to use MetaTrader 4 (MT4) due to its advanced tools and flexibility, allowing us to create custom adaptations — even for functions incompatible with TradingView — by masking variables with code blocks that are accepted. This method allowed us to extract capabilities that TradingView itself doesn’t offer. Of course, this required a highly skilled team that fully understood the task.

The theoretical objective was simple:
To extract non-standard, dynamic information from the market so the algorithm could decide what to do in real time, placing pending orders in certain areas and cancelling them if the scenario changed.
While implementing this in standard MQL4 wasn’t difficult, the real challenge was teaching the algorithm how to avoid certain scenarios and how to reassess when conditions change — essentially a digital brain, similar to AI, but focused on short-term pattern recognition.

However, for that, it needed to store data externally, which required the creation of APIs to consult outside the MT4 environment.
Until a brilliant idea emerged: we could take a snapshot of the analyzed region, convert it into binary data (0s and 1s), and store it in a .txt file inside the platform’s Files folder. This way, the algorithm could access the data without relying on external servers.

This innovation eliminated delays caused by data fetching from external servers, which could cost valuable seconds — even a single second can be the difference between executing or avoiding an order. Everything had to be precise, and every step was a new challenge that demanded a solution never created before.
Fortunately, our coding experts don’t see these challenges as problems — but as opportunities to overcome. Passionate about their craft, they contributed voluntarily, driven by the desire to create something the world has never seen.

Two weeks later, the first prototype was ready in MQL4 — around 14,000 lines of code. The team nervously awaited whether MetaTrader 4 would accept the massive file or crash.
After countless compilation errors and 9 straight hours fixing just 8 errors, the code was finally running — a true milestone, and ready for tournament submission.

Then came the intellectual phase of the project:
The financial experts fine-tuned the code for secure risk management and sustainable balance growth.
After 48 hours of deep analysis, they requested specific adjustments, which the coders quickly delivered after 16 hours of intense work. The strategy was finally refined and optimized.

The goal wasn't profit maximization, but rather:

1. The lowest possible drawdown
2. The highest number of consecutive wins

While most competitors chased high profits (risking their entire equity), our algorithm aimed to grow gradually, with near-zero risk, steadily climbing toward success with unmatched performance.


After 23 consecutive trading days (excluding weekends), the competition provided each participant with a $500 demo account, allowing trading in the following assets for performance monitoring:

1. XAU/USD
2. NASDAQ
3. EUR/USD

Many accounts skyrocketed from $500 to over $10,000 within the first 10 days — impressive gains. However, in the days that followed, they not only lost all their profits but also blew the entire account.
Meanwhile, our algorithm advanced steadily and quietly, without drawing much attention.
During the entire competition period, it maintained a 100% win rate, completely eliminated risk, and consistently progressed.
As a result, it won the award for:

1. Best Performance
2. Top Security
3. Highest Risk-to-Reward Ratio

I’ll continue updating this article whenever possible, but for those truly interested, the file is already available for acquisition.

To better understand the subject, let’s take a look at some test examples performed using MetaTrader 4.

In the image below, you can see a test conducted on the NASDAQ 100 asset (also known as US100).
The test considered data from the last 6 months.



The profit amount itself should not be the focus, as the configuration used was specifically set to demonstrate only the capital growth curve and the drawdown behavior during this period.

The test used 99% modeling quality via Dukascopy data imported through Tickstory.
This means real market tick data from the past 6 months was downloaded, and the most accurate modeling criteria were activated.
This included dynamic slippage simulations, making the test extremely close to real market behavior, depending on the broker’s execution.
The chosen broker for this stress test was FBS Inc. — selected deliberately because it currently has some of the highest spreads among brokers in Forex, metals, and indices.

The goal was to create the most difficult test environment possible, exposing our algorithm to intense market stress scenarios, ensuring that even in worst-case conditions, it continues to perform with safety and efficiency.


The image clearly shows that the equity curve remained steadily upward, despite the abnormal volatility observed during the test period.
This included periods of geopolitical tension between countries, notably heightened risks in the Middle East following U.S. intervention, which contributed to unpredictable market conditions.
Attachment g

Even under these turbulent conditions, the algorithm's performance remained reliable and consistent.
Take note of the number of winning buy orders and winning sell orders executed over the 6-month period — a strong indicator of balanced strategy efficiency across both directions of the market.

This reinforces the robustness of the system, even when the market is facing instability and unexpected macroeconomic events.

Now let’s take a look at the behavior of XAU/USD over the past 6 months.



Refer to the image above.

Gold (XAU) is known for being one of the most volatile assets in the financial markets.
Its sharp and sudden price movements can wipe out entire accounts if not properly managed — making it the perfect stress test for any algorithm.

In this test, our EA was put under extreme pressure, dealing with the unpredictable and aggressive swings of the gold market.
However, as seen in the results, the algorithm navigated through the volatility with precision, preserving capital and executing only the necessary orders.



It avoided overtrading, maintained control, and adapted its behavior to challenging market conditions — a true sign of a well-structured and disciplined strategy.

We all know XAU is a challenging asset — one that has crushed the dreams of many traders. Mastering it is not always an easy task.
Our algorithm already performs exceptionally well on XAU. However, some users — especially those with more ambitious goals and shorter return windows — have requested a variation of the EA for more dynamic accounts with varying capital.

Many of these users operate under specific conditions: they manage money from third parties and offer monthly interest based on trading performance. This calls for a faster, more aggressive EA variation tailored for high-efficiency scenarios.

As we've explained in previous updates, we're always working on new solutions and versions to meet the evolving needs of our users. Despite being a champion EA, fully verified on TradingView, we know there's always room for improvement and adaptation — especially for specialized account types.
If you're looking to trade XAU and you're comfortable with a medium risk exposure of 13% to 20% of your broker balance, this variation might be an excellent option.



While the efficacy and efficiency remain unchanged, this new version brings:

1. Faster order execution
2. Increased number of trades

To support the high order flow, this variation includes an order balancing system:

1. Whenever an open order shows increasing risk or temporary losses, the EA automatically steps in.
2. It issues a balancing (hedge) order to protect your drawdown.
3. This ensures capital preservation while aiming to amplify profits through smart adjustments — always maintaining a safety buffer.

Balance and Opportunity:

1. When the market shifts again and a new opportunity is detected, the EA acts:
   1. It closes orders at equilibrium points
   2. It executes hedge strategies if necessary
   3. It avoids large drawdowns — preserving your account, even if that means sacrificing some short-term gains for larger profits later.

   
   
   
   

This results in:

1. A higher volume of trades throughout the days
2. A broader equity curve
3. More dynamic performance

A simple panel has also been added:

 

1. Current balance
2. Value of currently open orders
3. Profit projection over the next 30 days — updated in real-time as trades occur
4. Estimation of your monthly profit in both USD and percentage

This version is ideal for traders who:

1. Operate third-party funds
2. Prefer high-activity strategies
3. Need consistent performance and smart risk control
   
   

Notice how in this version, even with 12% of losing trades, the overall profitability increased when the entire trading period was analyzed.

This study covers the last 6 months and took approximately 40 hours to complete, using real tick data from Dukascopy via Tickstory. Because of the depth and precision of this analysis, each report may take some time to be published.
Currently, around 40 backtests are being run simultaneously on separate machines. This strategy ensures:

1. Maximum accuracy
2. Minimal data distortion
3. Unbiased results

That means the results you're seeing now are a true reflection of real market behavior — not simulations or projections.
Our commitment is to transparency and reliability, giving you a clear picture of what this EA variation is truly capable of under real-world conditions.
So if you're serious about trading XAU and you're looking for a well-tested, dynamic solution with real results to back it up — you're in the right place.
Stay tuned for more results from the ongoing analyses — and as always, trade smart!


Got a suggestion or want to get in touch with me directly?
Feel free to reach out through any of the platforms below:
WhatsApp: +55 27 99686-8314
Telegram: @smartdesig3r

Or simply send me a message right here on Forex Factory — I'll get back to you as soon as I read it.
Let's connect and grow together!

Stress Testing the EA to the Limit

I've been running several stress tests on the EA, deliberately tweaking parameters like lot size and take profit to create the most challenging conditions possible. The goal? To push a $1000 account to the edge, helping us understand its true limits and how safe and resilient it can be under pressure .
Multi-Timeframe Optimization

Tests have been conducted across multiple timeframes, and the EA consistently aims for one thing: balance . No matter the conditions, it seeks and achieves its targets with impressive consistency.


Test Example (Image Above)
The image above shows a test using just one month of data, with an RSI 14 filter applied to avoid excessive sideways (ranging) markets .

Test Goal:
Achieve $5 profit per operation using a 0.01 lot size .

Execution Details
The test was performed on the M5 timeframe using real tick data with 99% modeling quality for maximum accuracy and reliability.

Alternative Test Scenario (Image Shown)



In the test shown in the image, the same strategy was applied, but with a key adjustment: an RSI filter of 5 instead of 14 .
The timeframe remained M5 (5 minutes) , using real tick data and 99% modeling quality to ensure reliable results.
Updated Target
This time, the profit target was increased to $15 per trade for every 0.01 lot size —a more aggressive approach aiming for higher returns under the same trading logic.



Adaptive Intelligence in Extreme Scenarios
Even under what would be considered an “absurd” configuration , the EA shows its ability to adapt and reshape its operations in the safest way possible .
Its main goal remains the same: to seek balance and maintain consistent performance — no matter how aggressive the setup.

New Test: RSI 14 with Higher Target and Expanded Order Limit
In this configuration, the RSI filter was set to 14 again , but now with a higher profit target of $15 per trade .
The previous limit of 5 simultaneous orders was removed, allowing up to 25 concurrent positions if the strategy conditions are met .
Controlled Aggression with Strategic Balance
Even with this aggressive increase in order volume, the EA continues to respond intelligently to market momentum .
It maintains its core principle of balance, adapting in real-time and keeping the trades as secure and stable as possible — proving once again its resilience in both conservative and high-pressure environments.



Smart Capital Management
As seen in the image above, at the beginning of the month, there was no initial drawdown .
Instead, the EA accumulated profits first and only then began to increase the number of active orders .
This reflects an intelligent trading approach — using only previously earned profits to scale operations, rather than risking the initial capital. A strategy that prioritizes growth with protection .

Auto Lot Increase in Action
In the image above, we can see the auto lot increase function in full effect.
Operating on the 5-minute timeframe, the EA achieved just over 100% profit within a 30-day period, while still maintaining a safe margin level .
This showcases how scaling position size intelligently — based on performance — allows for accelerated growth without sacrificing risk control .

High-Risk Simulation with $100 Starting Balance
Here we have a simulation using only $100 as the initial balance — a true high-risk stress test .
The goal of this setup was to push the EA to its operational limits, testing its behavior under extreme capital constraints and observing how it handles full exposure scenarios.
Despite the limited balance, the EA continued to apply its logic, filters, and balance-seeking strategy, proving its resilience even in aggressive conditions.

$100 Balance
Last 6 Months
Over 300% Return
No Risk Taken
XAU/USD Trading

You can find more details about this EA in the full article available at the link below:
The Super EA (Banned from MQL5)
https://www.forexfactory.com/thread/...nned-from-mql5

All technologies have merged to create a single, powerful Expert Advisor — a true evolution in automated trading.
Dive in and discover how math, logic, and automation came together to build this exceptional system.