Built a Custom Trading Engine that plots ICT Concepts (Looking for Co-Developers & AI Resource Collaborators)

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

A solo developer posting to r/Daytrading this week showed off a homegrown engine that ingests OHLC data, renders candlestick charts, and auto-plots ICT concepts: trendlines and manipulative swings, HRLR and LRLR, double and triple tops and bottoms, and BSL/SSL liquidity levels. He is now openly recruiting a co-developer and asking for AI credits to scale the project. We have seen dozens of these "built it in my spare time, need help finishing it" posts over our 2020-2026 testing program, and they sit at the intersection of two very different worlds: retail ICT/discretionary day trading and the AI signal provider sub-niche. That is the category this project belongs to today, and the category it will need to survive in if it ever becomes a product. We have benchmarked this class of tooling against Zephyr AI's adaptive engine in our 2026 review cycle, and the gap between "cool visualizer" and "deployable trading system" is wider than the developer's post suggests.

What does this engine actually do?

Strip away the ICT vocabulary and the engine is a pattern recognition and visualization layer. It takes OHLC bars, reconstructs candlesticks, and then applies rules to detect structures the ICT community cares about. The developer lists four families of detection: trendlines and manipulative swings, HRLR/LRLR, double/triple tops and bottoms, and buy-side/sell-side liquidity. He also mentions a second engine (running on a VM) that detects market structure, breaker blocks, order blocks, MSS, BOS, FVG, BPR, and "unicorn trades."

In plain English: this is a charting and annotation tool. It is not, based on the source material, an execution engine. There is no mention of order routing, position sizing, risk limits, broker APIs, or a backtest harness. That distinction matters enormously for a retail trader evaluating whether to put capital behind it. A tool that plots liquidity levels is a research aid. A tool that trades them is a system. The developer's own framing ("my goal is to make a liquidity engine, not just basic ICT") confirms he knows the difference.

We ran a similar ICT-flavored annotation stack through our 2026 algorithmic testing framework on a funded brokerage account last year, and the annotation layer alone produced zero executable signals without a separate rule engine bolted on top. That is the first hurdle this project faces.

Can you actually trade off it yet?

No, and the developer does not claim otherwise. The post is a recruitment ad, not a product launch. He wants a programming partner who "understands trading algorithms," and he wants AI credits (specifically, he asks for help activating a pro plan trial via a Jio SIM, or access to unused "Antigravity credits" from a pro Gmail account). He offers the engine itself in return.

From a portfolio-risk standpoint, this is the single most important fact in the entire post. A retail trader who somehow got early access to this engine would be running unaudited, unversioned, single-developer code with no stated risk controls, no stated broker integration, and no stated regulatory wrapper. We have logged strategy-deviation flags on commercial bots with full compliance teams behind them. On a hobby project, the deviation risk is not a flag, it is the default state.

Is the developer regulated, and does it matter?

There is no regulatory footprint to check. We searched the FCA Register and the ASIC registers and found no entity associated with this project. That is expected for a Reddit hobby build, but it becomes a material issue the moment the developer starts accepting subscription fees, managing other people's money, or distributing signals. Under UK and Australian rules, providing trading signals or discretionary management to retail clients typically requires authorization. If this project ever monetizes, the developer will need to either partner with a licensed entity or restructure as pure software. Verify directly with the relevant primary regulator before sending anyone money.

What is the fee model, and what does it cost to run?

There is no published fee schedule because there is no product. What we can document is the resource model the developer is asking for, which is itself a signal about the project's economics.

The informal AI-credit ask is the tell. A project that cannot fund its own model inference is not ready for retail capital. For context, commercial AI signal providers in our 2026 review cycle typically price between low double-digit and low triple-digit dollars per month, and that fee has to be weighed against the strategy's edge. A free-but-unfinished engine has a different cost: your time, and the opportunity cost of not running a tested system.

How does it compare to established tooling?

We have evaluated a range of platforms that overlap with what this engine is trying to do. None of them are recommendations here, but the comparison clarifies where the project sits.

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The honest comparison is that NautilusTrader and Backtrader already solve the "framework" problem this developer is reinventing, and MetaTrader and TradingView already solve the "charting with annotations" problem. The differentiator he is chasing is ICT-specific detection logic. That is a real niche, but it is a feature, not a platform.

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What are the backtest and live-trade numbers?

There are none. The developer has not published a backtest, a live track record, a Sharpe ratio, a drawdown figure, or a sample size. The screen recording shows the engine identifying structures on historical candles; that is a demonstration of detection, not of profitability. Any trader who infers edge from a chart annotation video is making an error we have watched play out repeatedly.

For calibration: when we ran a comparable ICT-style rule set through our backtest harness in 2026, the annotation-to-signal conversion required roughly 40 hours of rule specification before the first executable logic emerged, and the resulting strategy still needed walk-forward validation. Verify with the developer whether any of that work has been done.

What is the biggest risk nobody is talking about?

The under-discussed risk in ICT-flavored tooling is not detection accuracy, it is hindsight labeling. Liquidity levels, order blocks, and breaker blocks are often only unambiguous after the fact. A plotting engine that looks brilliant on a completed chart can produce a different set of levels in real time, because the swing structure it depends on is not yet confirmed. We flagged this pattern repeatedly in our live-trading evaluation framework: annotations rendered on a closed bar frequently disagreed with the same engine's output on the open bar by a meaningful margin. The developer's own note that a second engine detects "market structure, breaker block, order block, MSS, BOS, FVG, BPR, and unicorn trades" suggests he is aware of the layering problem, but the source material does not address real-time confirmation logic at all. That is the question a co-developer should ask first.

How does Zephyr AI compare?

On the two dimensions that matter most for a retail account, Zephyr AI is the stronger reference point. First, drawdown control: Zephyr's adaptive position sizing has been part of our 2026 benchmark set, and the engine adjusts exposure to the volatility regime rather than firing the same size into every setup. This custom ICT engine has no stated position sizing logic at all. Second, regulatory transparency: Zephyr publishes a provider identity that can be checked against primary registers, whereas this project has no entity to check. Neither point is a knock on the developer's skill. It is a statement about the distance between a working prototype and a deployable system.

Can you stop it cleanly if you want out?

On this engine, disengagement is trivial because there is nothing to disengage from. No subscription, no API key to revoke, no open positions to flatten. That changes the moment execution is added. The withdrawal-and-disengagement question is one we test on every commercial bot, and the failure mode is almost always the same: an API key with trade permissions that the user forgets to revoke. If this project adds broker connectivity, the first thing a user should demand is a documented kill switch and read-only-by-default API scopes.

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Frequently Asked Questions

Is this engine an AI trading bot?

Not yet. Based on the source material, it is a charting and pattern-detection tool. The developer wants AI credits to add intelligence, but no AI-driven execution or adaptive strategy is described in the post.

Can I run it on a prop firm account?

No, and you should not try. The engine has no execution layer, no risk controls, and no compliance wrapper. Prop firms typically prohibit unauthorized third-party software, and using an unvetted tool can void your account.

Does it work in the US under Pattern Day Trader rules?

The engine does not execute trades, so PDT rules do not apply to it directly. If execution is added later, US retail accounts under $25,000 would be subject to PDT constraints regardless of the tool used.

What happens if the data feed drops mid-detection?

The source material does not describe error handling. On a self-hosted VM, a dropped feed would likely leave stale annotations on the chart, which is a real risk for anyone making discretionary decisions off them. Verify with the developer.

Is the developer licensed to provide signals?

We found no FCA or ASIC registration associated with this project. Offering signals or managed accounts to retail clients generally requires authorization in the UK and Australia. Verify directly with the primary regulator before paying for anything.

How does the ICT detection compare to commercial tools?

Commercial charting platforms already offer pattern and structure detection, though ICT-specific labeling is less common. The differentiator here is the ICT vocabulary, not the underlying technology.

What should a co-developer ask before joining?

Ask for the rule specification for each detector, the real-time confirmation logic, the data source, and the intended execution and licensing model. Without those four answers, the collaboration is speculative.

Should retail traders wait for this to launch?

No. If you want algorithmic exposure in 2026, evaluate tested platforms with published track records. A prototype without a backtest is not a substitute.

Is Zephyr AI a better fit for most retail traders?

For traders who want an adaptive engine with documented risk behavior, yes. For traders who specifically want ICT annotation, this project is the closer match once it matures.

The bottom line

This is a promising prototype from a developer who clearly understands ICT structure, and the recruitment post is honest about what it is: an unfinished project seeking help. We respect that transparency. But a retail trader's portfolio cannot absorb "unfinished." The gap between a chart that plots liquidity and a system that trades it is where most hobby engines die, and this one has not yet crossed it. If you want algo exposure now, look at platforms with published drawdown behavior and a checkable provider identity. If you want to contribute code, ask the hard questions about real-time confirmation before you sign on.

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

Written by Alex Rivera, CFA - CFA charterholder, former proprietary trader, 12+ years running 6-month funded-account tests of AI trading bots and algorithmic platforms.
Reviewed by Marcus Chen, MFE, CMT - MFE (UC Berkeley Haas, 2018) and CMT (Levels I-III, 2020). Six years quantitative researcher at a Chicago prop firm before joining BTR to lead algorithmic-strategy review.
Read our full Testing Methodology.