Having worked in hedge funds for the last decade, this seems to miss the mark. Firstly we often reward skillstacking ie a technical person later becoming a trader. The more one person knows the better. These people are rare though hence the reason there is still many seperate job functions, so a person can specialize. But an AI agent? They all have the same brain, so why nerf them by specialising.
Secondly, browsing reddit for sentiment and doing technical analysis is not even a feature in the trading world. At the most basic level, these are lagging indicators. Something on options IV and premiums would have been closer to the mark.
Hedge funds are akin to the maintenance crew for markets, we keep them efficient and liquid. The process is quite scientific, you come up with a theory and validate with real data. Or you go from data to theory.
Given what a know about the 2008 financial crisis, wouldn't an AI analysis in the years before that crisis of the real state funds helped to understand the risk of them better and avoid the big exposure.
As local contexts/skills get better at depicting what's to be expected and what an agent can work with and work to get better at, having more and more little specialized agents working as a swarm get you, with a field-skilled human as a supervisor, really good results even in highly niche and technical fields
103K stars, so clearly it's popular. Has anyone here used it, and what are the outcomes like, and importantly, who is the target audience for this?
I can see the intention behind crawling social media and news feeds to determine some 'evidence', but am not sure if that's the best approach or even if an LLM is the best way to get an assessment, or whether having so many input sources is a good idea.
>Isn't a single agent with a good harness better than any multi agent env?
Multiple agents can each have their own isolated LLM context that is focused on their respective narrow scoped tasks. That way, the accumulated "memories", "instructions", etc do not stomp on each other. E.g. the agent loop that is gathering "signals" from the market has a context memory full of instructions related to scraping websites and using REST APIs and it doesn't interfere with the other agent loop that does sentiment analysis that has context memory dedicated to text of news transcripts correlated with historical price signals.
You can have a single agent acting as a top-level "orchestrator" but complex tasks can't have a single agent that does "everything" with high quality. The drawback is the more agents you spawn for different tasks to take advantage of isolating contexts, the more you multiply the tokens consumption and costs.
Another other driver for multi-agents is scaling out cpu usage, especially across different machine boundaries.
I think multi-agent (eg _different_ underlying LLMs) everything is really the future. Code produced via multi-agent workflows and reviews seems noticeably better. I've been experimenting with a multi-agent message board recently: https://github.com/pjlsergeant/dogpark
Experimented with multi-llm analysis for problem solving over summer, combined with multi-agent approaches it can tease out interesting angles to problems that I never considered. Expensive but use only for my high value problems.
Having worked in hedge funds for the last decade, this seems to miss the mark. Firstly we often reward skillstacking ie a technical person later becoming a trader. The more one person knows the better. These people are rare though hence the reason there is still many seperate job functions, so a person can specialize. But an AI agent? They all have the same brain, so why nerf them by specialising.
Secondly, browsing reddit for sentiment and doing technical analysis is not even a feature in the trading world. At the most basic level, these are lagging indicators. Something on options IV and premiums would have been closer to the mark.
Hedge funds are akin to the maintenance crew for markets, we keep them efficient and liquid. The process is quite scientific, you come up with a theory and validate with real data. Or you go from data to theory.
Given what a know about the 2008 financial crisis, wouldn't an AI analysis in the years before that crisis of the real state funds helped to understand the risk of them better and avoid the big exposure.
As local contexts/skills get better at depicting what's to be expected and what an agent can work with and work to get better at, having more and more little specialized agents working as a swarm get you, with a field-skilled human as a supervisor, really good results even in highly niche and technical fields
103K stars, so clearly it's popular. Has anyone here used it, and what are the outcomes like, and importantly, who is the target audience for this?
I can see the intention behind crawling social media and news feeds to determine some 'evidence', but am not sure if that's the best approach or even if an LLM is the best way to get an assessment, or whether having so many input sources is a good idea.
I feel like a lot of the stars for repos like this are from people who are riding the AI hype train and not actually interested in using the software
I do not understand the value of multi agent approach? Isn't a single agent with a good harness better than any multi agent env?
>Isn't a single agent with a good harness better than any multi agent env?
Multiple agents can each have their own isolated LLM context that is focused on their respective narrow scoped tasks. That way, the accumulated "memories", "instructions", etc do not stomp on each other. E.g. the agent loop that is gathering "signals" from the market has a context memory full of instructions related to scraping websites and using REST APIs and it doesn't interfere with the other agent loop that does sentiment analysis that has context memory dedicated to text of news transcripts correlated with historical price signals.
You can have a single agent acting as a top-level "orchestrator" but complex tasks can't have a single agent that does "everything" with high quality. The drawback is the more agents you spawn for different tasks to take advantage of isolating contexts, the more you multiply the tokens consumption and costs.
Another other driver for multi-agents is scaling out cpu usage, especially across different machine boundaries.
It helps you spend more token, is more expensive, and thus is obviously more AI. Also novelty and more complexity means less scrutiny of the approach.
These are necessary and perfectly sufficient for an investment firm thesis I believe.
If it's a single agent then people will just call it a chatgpt wrapper, can't have that can we
In case there is interest, I've got a fork with some custom improvements. See section "What this fork adds" in README.md
https://github.com/skanga/TradingAgents
1) costs? 2) profits/costs?
Which will make you bankrupt faster, this framework or the its token consumption?
I think multi-agent (eg _different_ underlying LLMs) everything is really the future. Code produced via multi-agent workflows and reviews seems noticeably better. I've been experimenting with a multi-agent message board recently: https://github.com/pjlsergeant/dogpark
Experimented with multi-llm analysis for problem solving over summer, combined with multi-agent approaches it can tease out interesting angles to problems that I never considered. Expensive but use only for my high value problems.
https://github.com/monkeydust/rightmind
nightmare horseshit, don't waste your tokens