Based on my AI research experience, agents excel at reasoning and classification when given sufficient data, but they struggle with accurate predictions, such as in weather forecasts or traffic. This limitation likely extends to algorithmic trading as well (also not taking hallucination into account). Another issue is that commercial LLMs, while good for general purposes, often fail at domain specific task especially those involving precise numerical predictions. However, LLMs can perform very well in sentiment analysis. By scraping news and analyzing sentiment, LLMs can provide valuable insights. When these insights are combined with metrics from statistical or shallow ML models, they may enhance the overall analysis. This approach be a fun and informative experiment, which I will be trying at some point in coming months, but I’m not sure if it can be better than just using the statistical approaches. More on reddit.com

They are already here (kind of). If you are a systematic trader with a cleanly defined trading plan write it into TRADE_PLAN.md. Wake it up, have it implement the plan. When it trades have it log to TRADE_JOURNAL.md (also have it log its notes / learnings on the trade). On a given cadence have it analyze the journal and make improvements to the plan and its own skills (you have to define guard rails for statistical significance tests, how stringent to be in terms of its gate for updates/edits to the system etc). You now have a self-improving trading agent. If you already have a well defined system / trading plan then agents can actually help a lot in taking out the human emotions (if your system isn’t 100% algorithmic / automated with 0 human intervention). Another application of trading agents that are ready now is sourcing trades for you. Define scans / your setups well, and agents are perfectly capable of reasoning over them (based on your instructions on how to reason) and surfacing trade setups that fit your preferences). Is this perfect? 100% no. Will they fail to surface viable setups every once in a while? Sure. But agents can filter through 500 of your setup candidates while you make coffee and it will surface you the top 5 to 10 that you can evaluate for entry. Have an algorithmic trading system? Throw it into an analytics pipeline, have the llm analyze the results of the system and produce a report on its findings where it surfaces purposed weaknesses / gaps / blind spots. If you know what you’re doing as a practitioner then your instructions to the agent should be powerful enough to produce viable insights / you’ll be able to tell right away if it’s garbage or not. Much more to say but too lazy to type. More on reddit.com

It might be a cool project, but I would imagine its potentially un-backtestable as you cannot avoid data leakage in the years that it is trained. Ex: you give it sentiment and fundamentals for NVDA in 2023 Q2- its obviously going to say BUY because it already knows it generated like a 500% return. LLMS can be thought of as a highly compressed recall system with good success in generating useful output from latent space between training points. But you can't pick and choose the timeframe to recall from- not exactly ideal for backtesting. More on reddit.com

Not personally, but the CEO at my old startup did. Lost everything including his house. More on reddit.com