Comment by thisisit
16 days ago
LLMs are very good at NLP/classification tasks and weak at calculations and numbers. So, I doubt feeding it numerical data is a good idea.
And if you feeding or harnessing as the blog post puts it in a way that where it reasons things like:
> RSI 7-period: 62.5 (neutral-bullish)
Then it is no better than normal automated trading where the program logic is something along the lines if RSI > 80 then exit. And looking at the reasoning trace that is what the model is doing.
> BTC breaking above consolidation zone with strong momentum. RSI at 62.5 shows room to run, MACD positive at 116.5, price well above EMA20. 4H timeframe showing recovery from oversold (RSI 45.4). Targeting retest of $110k-111k zone. Stop below $106,361 protects against false breakout.
My understanding is that technical trading using EMA/timeframes/RSI/MACD etc is big in crypto community. But to automate it you can simply write python code.
I don't know if this is a good use of LLMs. Seems like an overkill. Better use case might have been to see if it can read sentiments from Twitter or something.
>>But to automate it you can simply write python code.
haha, if it would be that easy, most of them would do this? :-D
The thing is - its fucking complicated and most people will give up far before they enter any level of operational capability.
I've developed such a system for myself and Im running it in production (though, not with crypto): And whilte most people will see the complexity in "whatever trading magic you apply", its QUITE the opposite:
- the trading logic itself is simple, its ~ 300 lines
- whats not simple is the part of everything else in the context of "asset management", you need position tracking, state management (orders and positions and account etc.), you need to be able to pour in whatever new quotedata for whatever new assete you identify, the system needs to be stable to work in "mass mode" and be super robust as data provider quality is volatile; you need some type of accounting logic on your side; you need a very capable reporting engine (imagine managing 200 positions simultaneously), I could enlength this list more or less unlimited.
There is MUCH MORE in such an application than the question of "when and how do I trade" - my systems raw source is around 2 MB by today, 3rd party libs and OSS libs not included.
You seem to be debating a point which was never made by holding on to one word - simple. I didn't say trading code is simple neither I did say that your trading code setup is simple.
Still let me clarify - the trading logic as you say is simple and just 300 lines. That is what LLMs seem to be doing in part in the post. The point I made is that doesn't seem to be a good use case for LLMs given that everything costs token. IMO, you could run this in your complex application without spending that much money on tokens.
If you can explain why original opinion of wasting tokens on something which can "simply" be done in python is wrong, I am all ears.
You wouldnt feed it numerical data, but you would allow it to make certain calculations (via tools of a harness) as it relates to your portfolio.