Show HN: TrendFi – I built AI trading signals that self-optimize

Hey HN,

For the past few years, I've been obsessed with trying to get AI to produce accurate trading signals. The main challenges I found with AI trading models are lack of consistency, context window bottlenecks, hard to backtest, and high cost.

Asking ChatGPT "Should I buy Bitcoin today?" doesn't work well because the LLM doesnt have a set trading strategy to opperate from. In addition, the small context window makes it challenging to fit enough historical data into. Not to mention it gets very expensive as well.

My solution is a hybrid approach. Instead of having the LLM make direct predictions, I use AI as the "conductor" in the system that has the ability to run a series of backtesting simulations on high thread count AMD EPYC servers.

The AI then processes the results, identifies optimal parameter changes, and can self-optimize after every trade (as needed) to adapt to changing market conditions. This setup uses LLMs for high-level reasoning and CPU cores for what they do best.

I should mention that TrendFi is designed as a trend based model. It's not for day trading or catching every small market fluctuation. Its goal is to accurately identify major trend changes and provide clear alerts for those key moments.

Check it out: https://trend.fi

– Michael

Show HN: TrendFi – I built AI trading signals that self-optimize
trend.fi

The market is a battlefield of adversaries. For someone to cash out on a signal profitably, the others have to get less. A signal system is then easily abused by the signal maker who takes a position before hustling a bunch of people to take it. Or it is abused by a hedge fund or market maker who does the same and raises spreads on you when you are trying to make trades, etc.

Usually, the reason is you lack capital to trade on your signals yourself.

That's a fair concern.

Because TrendFi is a trend based model, the signals are not for HFT or day trading. Instead, we identify major market shifts that play out over weeks or months. This long timeframe means the signals are less vulnerable to the immediate front running or spread manipulation that plagues short-term signals.

Also, the AI's ability to self optimize after trades means the underlying parameters are not static. If external players were to try and consistently game the signals, the system is designed to adapt to those new dynamics.

This doesn't make as much sense as you think it does. If you could predictably trade a flip from bearish to bullish (for example, of course there are other trend-based signals), you would not share that signal because others would overcrowd your trade (by buying/shorting and moving the price more quickly towards the trending direction than you).

A potential argument is that these signals are only applicable to a certain bracket of portfolio sizes (e.g. larger AUM funds would not be able to trade this strategy) -- but you are sharing this with folks presumably in your range of portfolio size.