Passing an FTMO Challenge is the dream of many traders. Doing it with an automated strategy built no-code with AlgoBuilderX? That’s proof that intelligent automation is now truly within everyone’s reach.

In this in-depth Q&A, we interview TraderS, an experienced trader who has chosen to remain anonymous but has decided to share his story with our community. With over 11 years on the markets, TraderS has managed to achieve as many as 30 prop firm payouts and to pass the FTMO Challenge thanks to a complex and refined system built entirely with AlgoBuilderX.

Let’s discover together his journey, the challenges he faced, his advice, and best practices for anyone who dreams of repeating his success.

---

Background and Motivation

1. Can you briefly tell us who you are and how you started in the world of trading?

I’m known in the trading community as TraderS. I don’t use my real name publicly for confidentiality reasons, as trading isn’t my full-time profession.
I began my trading journey 11 years ago, initially drawn in by lifestyle videos on YouTube from signal providers who portrayed trading as a path to financial freedom. Back then, I had no idea what I was really getting into.
Over the years, I’ve learned from a wide range of mentors and communities, adopting and refining countless strategies along the way. In the early days, I’d analyse as many as 23 currency pairs every Sunday and try to trade them all during the week. Unsurprisingly, that approach wasn’t sustainable. It took me eight years to become consistently profitable — but in that pivotal eighth year, I managed to recover the many thousands I had lost previously.
Since 2023, I’ve focused exclusively on XAUUSD (gold). Its volatility suits my trading style, and narrowing my attention to one pair has drastically improved my performance. As part of my analysis, I also monitor DXY, US30, and NAS100 for confluence. Gold has humbled me more than once, but in the past two years, I’ve achieved 30 prop firm payouts. At one point, I was funded up to $1.1 million, though I’ve since lost most of it due to firms shutting down or failing to pay out.

2. Do you have any previous experience with algorithmic trading?

Yes, I first ventured into algorithmic trading two years ago. Separate from my manual trading, I began experimenting with Expert Advisors (EAs) on MT4 and MT5. I purchased around 15 different EAs and tested them across both live and demo accounts. Unfortunately, I didn’t find success with any of them. About 90% were grid or martingale-based strategies — they’d generate small profits at first, but often sat in extended drawdown before eventually blowing the account.
After losing my last bit of funding in August 2024, I decided it was time to take a new approach. That’s when I began building automated strategies myself. I initially used ChatGPT to help code algos for cTrader. I built several bots with mixed results, but the process was often frustrating. The AI would frequently try to “improve” sections of code that were already working, which led to endless debugging and hours spent fixing build errors. The absence of a visual interface made the process even more challenging and time-consuming.

---

Discovery and First Impressions of AlgoBuilderX

3. How did you discover AlgoBuilderX and what convinced you to try it?

I discovered AlgoBuilderX during a particularly frustrating day of manual coding. I was scrolling through Instagram when I came across a post about the platform — it caught my attention because it mentioned a partnership with FTMO. That instantly gave it some credibility, so I decided to look into it.
Once I explored the platform, I was genuinely impressed. The interface, the visual logic builder, and the focus on helping traders bring their ideas to life — all of it felt like exactly what I’d been missing. It offered a much more intuitive way to build and test strategies without the constant headaches of debugging raw code. That’s what convinced me to give it a go.

4. What were your first impressions of the platform?

I was genuinely impressed by the clean and intuitive visual interface. At first, it took a bit of time to fully understand how the flow works across the different blocks — but I was determined to get to grips with it.
I started by watching all the tutorial videos and example walkthroughs — twice, in fact — to make sure I didn’t miss anything. After that, I explored a number of example projects to see how different methods were being applied in real-world strategies.
I dedicated the first 3–4 weeks to learning and experimenting. I began by creating basic algos and testing out individual blocks to see how they functioned. Over time, I’ve pushed myself further — building increasingly complex systems and really pushing the platform to its limits.

---

Strategy Development

5. Can you tell us how you structured your winning strategy with AlgoBuilderX?

Absolutely. This particular algo is a highly advanced build — it currently spans over 7,000 lines of code and includes hundreds of blocks spread across the OnBar, OnTick, and OnPosition events.
It’s designed to trade XAUUSD and US30, and includes four independent entry methods that can execute individually:

1. Breakouts: This is the most complex entry method. It combines fractals with custom-built price action logic to detect market structure shifts. Two candle closes are required to confirm a new high, and a new low is calculated using a lookback mechanism to find a specific price pattern.
   A breakout is only confirmed if:
   • A specific candle closes above or below a breakout level,
   • A retest occurs within a defined timeframe,
   • A re-break of the confirmation candle follows.
     Entry validation includes custom Boolean switches, trigger lines, and invalidator logic to filter out false signals.
2. Continuations: This setup is simpler but still multi-layered. It typically starts with a significant candle on a higher timeframe like H4. Once the high and low of that candle are marked, I then require multi-timeframe structure shifts (e.g. on M1, M5, and M15) to occur within the H4 candle window — such as higher highs and higher lows for a buy setup.
3. Wickfills: When a large wick appears on a higher timeframe candle, the bot looks for a pattern that suggests the wick will be “filled” — meaning price is likely to retrace and cover the wick area, often continuing beyond it.
4. Fakeouts: I’ve developed custom logic to detect fakeouts above or below key structure levels. These include signs like:
   • A large wick on a candle that closes back inside a range, or
   • Multiple bullish candles closing below a support break level, hinting at a false breakout.
   • Execution Logic
     All entry signals are detected within OnBar.
     The confluences and confirmations occur OnTick using lines as triggers and counters, which reset if the setup fails to play out.
     I make heavy use of Formula blocks to calculate distances between key levels, candles, and values — essential for filtering quality entries.
     The algo supports all four strategies running independently or together, but with smart restrictions:
   • Max trades per day
   • Daily equity loss limits
   • Prop firm risk parameters
   • Dynamic trade management, including optional scaling out or stop-loss reduction if the trade shows signs of reversal.
     I’ve also built in logic that isn’t fully deployed yet — for instance, if I’m in a buy trade and bearish intent starts forming (e.g., candle closes below key lows), the bot can optionally reduce position size, move stop loss, or close part/all of the trade.

6. Did you use any specific blocks or indicators? Was there any in particular that impressed you for its usefulness or versatility?

7. How long did it take you to optimize your strategy before you felt ready for the challenge?

It was extremely challenging and incredibly time-consuming. If I had tried to run the full optimization in one go, my system estimated it would take over 90 days to complete!
At the time, I was working with a MacBook Air, a VPS, and a dedicated server with 64GB RAM and 6 cores — even with that setup, I had to break the optimization down into phases.
I’m very conscious of overfitting and curve fitting, so I optimized each strategy component independently, running all session filters enabled to ensure robustness. Once I had strong individual results, I then fine-tuned risk management, session controls, and stop loss settings at a global level.
In total, the optimization process took around four full weeks running 24/7.
But it’s important to note: even before optimization began, it took me months to build the full strategy. I often stayed up late into the night — or worked through entire weekends — just to piece it all together. I manually reviewed hundreds of trades using the visual backtester to validate logic and fix errors. Every single entry was scrutinized to make sure the strategy made sense not just on paper, but in price action terms too.

8. Did you test your strategy in demo before attempting the real challenge? If so, what results did you see?

Yes — I tested the strategy on a demo account for one month before attempting the live challenge. During that demo phase in live market conditions, the bot delivered a 13% return, which gave me the confidence to proceed.
I then started a $100,000 FTMO challenge. The strategy hit the 10% target for Phase 1 and the 5% target for Phase 2 — both stages were completed successfully within 24 days.

---

User Experience with AlgoBuilderX

9. Is there any feature of AlgoBuilderX that made your work particularly easier?

Absolutely — the visual interface is brilliant. For me, the drag-and-drop functionality is a complete game changer. It makes the process of building complex logic far more intuitive and accessible, especially compared to writing raw code.
I also really appreciate the ability to work across separate visual workspaces — like OnBar, OnTick, and OnPosition. I regularly switch between them during development, and having those clearly defined areas helps keep everything organised and manageable, even in large-scale builds.

10. Did you find any limitations in the platform? If so, how did you overcome them?

The biggest challenge I’ve faced is troubleshooting. Sometimes, you’re convinced a setup is correct — but when it doesn’t work as expected, it can take a while to track down the issue. That said, I don’t see this as a flaw in the platform itself. It’s more about the complexity of building advanced logic. I’ve found the best way to overcome this is by building in clear stages, validating each part before moving on to the next.
Looking ahead, I’m really excited for Version 2. I’d love to see more price action-related blocks to simplify builds even further, and perhaps some clever logic for working with support and resistance levels.
That said, I haven’t come across many limitations that I couldn’t manually build myself in ABX — it just takes time, patience, and a bit of creative problem-solving to figure things out.

---

Results and Analysis

11. What were the main challenges you faced during the FTMO challenge?

To be honest, the process was fairly straightforward overall. The biggest challenge wasn’t technical — it was resisting the urge to interfere.
There were moments where I’d see the bot preparing to take a trade and instinctively think, “That’s not going to work.” For example, if XAUUSD had been selling off all day and we were approaching a key support zone during the New York session, my algo might still take another sell setup — because technically, it meets the criteria.
The difficulty lies in the fact that it’s incredibly hard to code nuanced discretion like, “Don’t take a sell here because price is extended and nearing support.” That’s the kind of judgment a human trader makes — and replicating it in code is one of the most complex aspects of algorithmic trading.

12. Can you share any interesting numbers from your challenge (drawdown, win rate, risk management, etc.)?

I use 1% risk, see below for the metrics. See below for the verified performance data.

13. Was there a key moment when you realized you were going to pass the challenge?

During the verification stage, I came close to hitting the target — but then I hit a period of drawdown. At that point, I made the decision to step back, stay patient, and stop watching the trades so closely. That made all the difference.
Fortunately, I had already built logic into the bot to automatically close all trades and stop the algorithm once the profit target was reached. That gave me confidence to trust the system and let it run without interference — and eventually, it did exactly what it was designed to do.

14. What was the most important lesson you learned throughout this journey?

The biggest lesson was simple but powerful: trust the process and don’t interfere.
In the past, I’ve intervened — closing trades early, overriding the system — and more often than not, it hurt the results. Almost every time, it would have been better to leave the bot alone and let it do what it was built to do. Learning to step back, trust the logic, and stay disciplined has been a game changer.

---

Advice and Future Plans

15. What advice would you give to someone who wants to use AlgoBuilderX for an FTMO challenge?

Go for it — but go in with a plan. Before anything else, make sure you’ve built a robust strategy and tested it thoroughly on a demo account in live market conditions.
One thing I often see is traders looking for a quick fix — building a simple RSI or Bollinger Band strategy and expecting instant success. If it were that easy, everyone would be profitable.
Instead, my advice is to:

• Find a strategy that works manually
• Mark up 10–20 trades in TradingView to understand the setup clearly
• Write out a structured plan to turn that logic into an algo
• Build in stages, checking that your bot executes exactly like your manual examples
  And if you fail a challenge? Don’t worry — I still fail them too. The key is to take a long-term view. A single payout can easily fund multiple future attempts, so keep improving, stay consistent, and don’t give up too soon.

16. If you could go back, would you change anything in your approach?

I don’t think so, I would like to improve my algo further but am now busy working on many more but it’s on my list to do!.

17. What are your future plans, both as a trader and as a user of AlgoBuilderX?

My focus moving forward is to continue developing automated strategies, because I truly believe this is the future of trading. Manual trading — especially price action-based — can be stressful and time-consuming. Watching every candle close and managing trades live isn’t sustainable long-term, particularly when you’re juggling other responsibilities. My goal is to step back from manual execution entirely while still embedding my knowledge and experience into well-structured, intelligent algos.
It’s not a perfect science yet — automated systems still struggle with contextual observation and human discretion. But I’m working to bridge that gap. I’ve already built in dynamic risk management, allowing my bots to respond to signs of reversal and exit trades early when conditions change. I plan to keep expanding on this so my algos can make smarter decisions in real time.
Right now, I’m actively developing two NAS100 algos focused on price action during the NYSE open. I’m also refining my market structure shift engine to make it even more precise. Beyond that, I have several other ideas I’m eager to explore once those builds are complete.
As AlgoBuilderX evolves and adds more features, I’m confident it will only get easier to bring complex strategies to life and push the limits of what’s possible in retail algo trading.

---

Feedback on AlgoBuilderX

18. Would you recommend the platform to other traders? Why?

Absolutely — it’s a brilliant platform.
But to get the most out of AlgoBuilderX, you need to put in the time. It’s not a plug-and-play solution — it’s a powerful tool that rewards those who are willing to learn and build with intention.
If you’re serious about algorithmic trading, I highly recommend diving into the more advanced features — things like Boolean switches, formula blocks, custom triggers, and multi-layered counters. Mastering these elements is what allows you to bring truly complex, high-performance strategies to life.
With patience, structure, and creativity, there’s almost no limit to what you can build with ABX.

---

TraderS’s story is proof that determination, study, and a methodical approach can lead to great results — especially when supported by the right tools like AlgoBuilderX.
Do you also want to build your strategy and take on the FTMO Challenge professionally? Start today with AlgoBuilderX.

Do you have questions or want to share your experience? Join our Discord community!