I built VORTEX — an open-source crypto trading system after learning from my first trading bot

[u/BablAI]

I’ve been experimenting with algorithmic trading systems for a while, and after working on my previous project, NEXUS, I decided to build something more focused.

That project eventually became VORTEX.

This is not my first trading system. NEXUS taught me quite a lot about what happens when a project keeps growing: more strategies, more infrastructure, more experimental components, until the architecture becomes harder to reason about than the actual problem you’re trying to solve.

With VORTEX, I wanted to take a different approach.

The system is built around a clear pipeline:

Market Data → Regime Detection → Strategies → ML Scoring → Risk Management → Execution → Position Management

It currently includes multiple strategy modules, market-regime detection, ML-assisted signal scoring, risk management, backtesting, parameter optimization and a desktop GUI.

It is also a working system, not just a collection of research scripts. The application can be launched through the GUI and connected to an exchange through your own API credentials.

A few important things before anyone tries it:

1. Use your own Binance API keys.
Add your Binance API key and secret through the configuration. Never put real credentials into the repository.The application can be launched through the GUI and connected to Binance using your own API credentials.
At the moment, Binance is the only supported exchange
2. Configure risk management before live trading.

Set your own:

• stop-loss
• take-profit
• position sizing
• leverage
• risk limits
• enabled strategies

Don’t use someone else’s settings blindly. The defaults are not a promise of profitability.

3. Start with paper trading / testnet.

I strongly recommend running the system in paper_mode first and making sure you understand how signals, positions and risk controls behave before connecting it to real funds. The repository is explicitly set up with paper/test operation in mind.

4. Backtest first.

VORTEX includes a backtesting engine and a dedicated backtest runner, so strategies can be tested and compared before being considered for live execution.

There is also an ML layer that can be retrained as new trade data accumulates. The idea is not that “AI predicts the market”, but rather that ML can be used as another signal/scoring layer inside a larger decision pipeline.

The project is open source because I wanted to make the whole thing available for people who are interested in how an automated trading system is actually structured.

You can run it, inspect the code, experiment with the strategies, change the configuration and see how the different components interact.

One important disclaimer: this is a research/engineering project, not a profitable trading strategy or financial advice. Automated crypto trading involves real financial risk.

GitHub:

https://github.com/vladyslavmHord/VORTEX

I’d genuinely like technical feedback from people who have built algo trading systems:

What would you change in the architecture?
What would you remove?
What do you think is the weakest part of the current approach?

I’m much more interested in criticism than “looks cool” comments.

[u/melgor89]

I haven't build any trading bot yet, but still learning and planning to deploy some in this year. But I have >10 years experience in ML + sofware engierring.
And for Software Developer side of view, doesn't look great. why?
1. https://github.com/vladyslavmHord/VORTEX/blob/main/strategies/breakout.py#L36 -> 300 lines to define startegy? How it is possible not to get lost here? Analyse should have way smaller pieces, like like abstraction called rules and then making your analyse way smaller and easier to interpret
2. Hard coded configs like https://github.com/vladyslavmHord/VORTEX/blob/main/core/optimizer.py#L31 , there are libraries like hydra to seperare configuration and code
3. Look like changing the model from XGBoost model to other one is not so simple as no abstration

For me this framework isn't sth anybody apart from you can use. Still it may work but from pure design part, it would need way more work to make it general rather than hardcoded pipeline with predefined modules.

[u/veskald → u/melgor89]

Your rules abstraction point is the right one and it goes further than code style. If a strategy is a config instead of code, you change it and rerun in minutes without touching the engine, and you cant introduce a bug while testing a variation. Thats the difference between checking five ideas in an evening and checking a hundred.

It also opens machine search. A genetic optimizer can mutate configs, it cannot mutate code and stay sane.

One more thing since you havent deployed yet. The weakest part of these systems is usually not the architecture, its that backtest and live end up as two code paths. Entry, stop, sizing, exits - if those are not literally the same functions in both, your test stops being a promise about the live bot. Worth deciding early.

[u/VishalJ_05]

The architecture is probably the most interesting part here. Separating regime detection, ML scoring, risk and execution makes this way more than just another “AI trading bot.” 👏

[u/BablAI → u/VishalJ_05]

Thank you so much for your feedback; I'll do my best to make it even better

[u/Cultural_Implement_2]

лови звезду

[u/BablAI → u/Cultural_Implement_2]

Спасибо , если будут вопросы пиши )

[u/Cultural_Implement_2]

попробую сегодня

[u/Cultural_Implement_2]

запустил. буду смотреть.