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zackmorris 5 minutes ago [-]
I helped someone day trade 25 years ago and we made 40% in 4 months, mostly on AAPL, and it was easy. Way easier than working the 2 years moving furniture that it would have taken to earn that money, which I was foolishly doing to make rent for my defunct shareware business.
I've heard that high frequency trading eats market opportunities within milliseconds of spotting them now though, basically making the market even more random. The catch being that human nature isn't random, it's stochastic, so there will always be more money to be made on trades (or else trading firms wouldn't exist).
I'm thinking about getting back into trading because AI represents the end of buying software and we'll all be out of work soon, even if we're in denial about it. But everything I invest my time and energy into turns to crap. In a very real sense, as soon as I start trading, then karmically that could trigger recession, market reforms which ban what I'm trying, or even the end of money. I'm kind of joking and kind of not.
I've worked on a lot of really hard stuff over my brief but stupid career, and am basically used up mentally and physically, at least for now. Quant stuff is easy with AI. Should I try it?
not_a_bot_4sho 11 minutes ago [-]
It's always been a joke that stock prediction is a rite of passage for software engineers introduced to ML training. I see the same is true for commodity LLMs.
_pdp_ 6 hours ago [-]
If this worked it wouldn't have been open source?
Anyway, I have been running my own trading experiment and so far it has lost a bit of money. That being said I have not tried to optimise anything - just let it do whatever it wants. The losses are small and it might be able to recover later this year. Who knows.
I am thinking to output all the chat logs to HF as well for research.
You can run your own trading agents that communicate over a message buss in your own terms by downloading the CBK platform and running it locally with your own models. I have also shared my trading blueprint if you want to give it a go. https://chatbotkit.com/hub/blueprints/trader
dominotw 4 minutes ago [-]
just seems to have trump tweets and war. surely those are nt the only market movers?
NichoPaolucci 1 hours ago [-]
Am I reading these logs correctly? You gave an agent 100k!? Very bold, hopefully it does turn things around.
I had done a small experiment with 100$, but would be hesitant on more than that. I suppose if it's money you're willing to lose.
ludvigk 50 minutes ago [-]
The title is 'Paper trades' so it's very likely fictional money.
Aperocky 35 minutes ago [-]
Paper trades are more lenient afaik? You won't run into actual transaction problems particularly when market fluctuates by the milliseconds.
ppalata 3 hours ago [-]
The "blog" says that it's just holding cash or am I misunderstanding something? Judging by the "buying power" number, you allow it to use 4x leverage against the cash it has? That seems dangerous unless you are fine with losing 380k+.
csomar 2 hours ago [-]
[dead]
whazor 6 hours ago [-]
It would be more interesting to compare trading agents with index tracking ETFs. The better version of an ETF could maybe be a model where you zoom in on the companies and add/remove to your portfolio on the company related news, but keeping a broader portfolio.
Maybe agentic trading still performs worse than ETFs. But alternatively, if it were meaningfully better then it would be okay to opensource, similarly how ETFs are publishing their portfolios.
harrouet 3 hours ago [-]
The issue at stake is that financial markets are order-2 chaotic system, i.e. acting on them can change their outcome.
Put simply, if you open source your magic recipe, the behavioral change will affect the prices and you recipe will not work anymore.
_pdp_ 6 hours ago [-]
I think this might work.
When I started working no the trading agent I mentioned above I wanted to see if it can be just a better investor over the long run. The intention was not to do high-frequency trading. As you can see most of the days it is not taking any actions. The losses where down to mistakenly setting the stop losses too close to the top. If it wasn't so careful it might have made some money tbf.
My gut feeling is that AI agents will be able to manage a long-term portfolio much better than a human. Though it is just a gut feeling.
dakolli 6 hours ago [-]
Lmao, you llm people have some crazy delusions. You realize markets are zero sum, and if you're using a public model that everyone else also has access too, you llm psychos will destory eachothers "agentic" edge (not that there ever was one). Not to mention all the other obvious flaws with llms, lime having an effective memory of ~200k words and no ability to judge whats actually going on in the real world.
whazor 6 hours ago [-]
The 'edge' is holding the investments over long periods of times. Agents are merely automating the portfolio managing part for lower costs.
XenophileJKO 6 hours ago [-]
You really have no idea what you are talking about.
I have been running an intermittent experiment with a multi agent "investment firm" for over a year now across model releases.
They certainly can beat indexes, BUT.. the model families have some biases that you have to design around. The stop loss that bit the parent is certainly one. The models like to create rules. Often rules, one of those is making all kinds of exit conditions.
Another big one from my experience is the bias to inaction in a scenario with risk. This means a model without structure around it will bias to keeping too much cash.
ppalata 6 hours ago [-]
This would be nice if it was supported by some hard data. But even if it was, one year is too short of a timeline to make any kind of reasonable conclusions about its efficacy.
Buying stock based on coin flips can beat indexes short term too, that does not mean it is a better strategy or that it works over the long term.
kakacik 1 hours ago [-]
There must be some room for some anti-llm agent that can profit from specific behaviors of these models when deployed against actual markets.
The idea that somebody here came up with idea that all professional algo traders didn't explore to the last penny a year if not more ahead of others is funny... but its not my money adding liquidity to the markets.
irthomasthomas 5 hours ago [-]
Is it opensource?
fg137 3 hours ago [-]
"just trust me bro"
dakolli 5 hours ago [-]
Come to me when you have 500 trades and can beat Vangaurd's-VOO over a multi year time frame. Ill bet my entire networth and all future earnings for the rest of my life that your bot doesnt beat it. Your llm induced Dunning Kruger is going to get you in trouble one of these days I promise.
rienbdj 6 hours ago [-]
If you like building but have no capital you might release something like this that works.
hacker_9 7 hours ago [-]
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.
empath75 8 minutes ago [-]
The obvious and dumb theory for trading on LLM recommendations is that lots of people are doing it so the price of stocks LLM recommend should go up. (ie: following the herd).
XenophileJKO 6 hours ago [-]
Actually there is a really good reason, it has to do generally with making sure that all the aspects that you want evaluated are actually evaluated.
Now it may be possible with models like Astra that you no longer need to do this, but in earlier models it was beneficial.
So I might want a macro economic read which leads to a market thesis. Then I would hunt for exposure, then evaluate the candidates across different aspects. Breaking the process up at least made sure no steps were missed and the different aspects considered.
looofooo0 7 hours ago [-]
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.
lordnacho 3 hours ago [-]
I'd say it was more to do with how incentives lined up among different actors.
There's always risk somewhere in a financial system.
hacker_9 6 hours ago [-]
The problem wasn't the analysis, given it was found out before it happened. The problem was politics, and as usual pushing the system to its limits and beyond.
elzbardico 6 hours ago [-]
Probably not. On the contrary, it would probably just amplify the mood.
audinalexandre 7 hours ago [-]
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
dsl 6 hours ago [-]
I spent about an hour looking at the code and found some glaring issues that should be fixed before trusting it with real money.
- Yahoo News is introduced twice (sentiment and news analysis) which double weights it
- Sentiment analysis prompt primes the model to be bullish on Nvidia.
- In the self learning loop there is a complex parsing bug that results in hallucinated memories when agents return truncated responses
- You can completely control sentiment analysis of a subreddit by simply maintaining a majority of the 5 most recently posted messages, regardless of any quality metric
- The reflection prompt states the agent must cite alpha, which in a market wide downturn causes it to think correctly placed calls were losses
throw93947309 5 hours ago [-]
There is also problem with underlying models. There was a study, where they always repest the same investing/management strategy: trust strangers, be open minded/adopt to new unproven ideas, prefer cooperation... Basically they were trained on disney-boomer bull(shit) market of last 15 years.
They have zero guards against market manioulations, and will get wiped without bull market!
embedding-shape 6 hours ago [-]
> Sentiment analysis prompt primes the model to be bullish on
Eeh, yeah? At that point I'd stop reading the code and just leave the project behind. How exactly is the prompt doing this right now?
autorunfun 4 hours ago [-]
[flagged]
politelemon 8 hours ago [-]
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.
testaccount121 7 hours ago [-]
103K stars in a few months, smells of purchased stars IMO.
MikeNotThePope 1 hours ago [-]
It should be sponsored by a model provider.
mechazawa 7 hours ago [-]
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
jeanmichelselli 3 hours ago [-]
This would make sense only if the LLMs would be constantly updated with a new data set and training phase every day. In that case, I'd could see this approach as having some sense. But, otherwise, these are just stochastic machines trained on static (outdated) data and I don't see how their predictions should be better than any other method around or even better than a human guessing.
mshafir 2 hours ago [-]
Instead of going after money printing schemes like this (which I feel are unlikely to actually work and carry a ton of risk). I (thoughtfully) vibed up tooling and skills to just help LLMs manage my investments long-term. While not flashy, tax loss harvesting and direct indexing are areas where LLMs can reliably save you on fees that you'd otherwise have to pay. It's been very helpful, the LLM automatically deploys new cash based on my priorities, implements tax harvesting for me, and provides customized exposure guidance while keeping my goals in mind (ESG/sustainability focus, no FF rules) . Way better than any financial planner has ever done for me. https://github.com/mshafir/investbot
skanga 8 hours ago [-]
In case there is interest, I've got a fork with some custom improvements. See section "What this fork adds" in README.md
I vibe coded a little stock market sim game then i wrote an agent who’s job is to win the game. A couple friends and family members play the game too. If it works I’ll just follow along and hold the same portfolio the bot does. The bot, named stonker, makes its first trades in about an hour actually. Assuming it works I mean hah
edit: i just talked to my little sister ("Ginger" on the leaderboad) who is doing a good job beating the sp500. She said she just asks her ai what she should invest in and then executes those trades.
shaolinspirit 8 hours ago [-]
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?
BenoitP 8 hours ago [-]
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.
7 hours ago [-]
podocarp 7 hours ago [-]
If it's a single agent then people will just call it a chatgpt wrapper, can't have that can we
elzbardico 6 hours ago [-]
Modularity maybe. Remember, agents are basically workflows that call LLMs in certain nodes.
Which will make you bankrupt faster, this framework or the its token consumption?
adyavanapalli 6 hours ago [-]
As the saying goes, those who know don't say and those who say don't know.
alastairr 3 hours ago [-]
I'd be curious as to how correlated development on these frameworks (ai or otherwise) is correlated with the market cycle. It seems during bull runs would be traders think they have some edge - whereas they're probably all just buying the trend.
Using a token slot machine to beat a financial slot machine, what could go wrong?
walrus01 3 hours ago [-]
I am sure it won't be long until some rando from /r/wallstreetbets/ turns something like this loose without considering the ramifications and loses $250k.
oinoom 3 hours ago [-]
Losing that amount of money would be very uncharacteristic of wsb
htrp 2 hours ago [-]
serious traders working on this type of project would have run extensive back testing on both real as well as synthetic analogues
jatins 4 hours ago [-]
Anytime I see an investment framework I look for a “Performance” section or at least a backtest
fg137 3 hours ago [-]
There is a section in their paper.
Although I don't think it even matters. They could easily cherry pick a period that is favorable for them. Wasting time and money on short term trading, rather than long term investment, using LLM or not, is never a good strategy for most people.
jatins 1 hours ago [-]
They backtested with public models, wouldn't the model weights already have the data? I double checked with chatgpt and looks like agents also had web search tool available so they could just lookup the past.
What is the purpose of this repo? Is it to simulate the market so you can reliably backtest trading strategies?
Whatever the stated purpose is, where can I read the test results to show it accurately fulfills that purpose.
Anyone can make a markets simulation that models interactions between market participants. Making a simulation that is accurate enough to be useful for anything is hard.
fedeb95 7 hours ago [-]
1) costs?
2) profits/costs?
m3kw9 45 minutes ago [-]
give them 1 dollar each run, and maybe you will make 2 bucks on the 100,000th run.
petesergeant 8 hours ago [-]
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
alansaber 2 hours ago [-]
There are good arguments for subagents/multi-agents. But the added overhead is usually massive.
monkeydust 7 hours ago [-]
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.
I've built agents that call different LLMs and keep separated memories. Remember, agents are just long-running workflows with some nodes calling LLMs and that sometimes can be started as tool from other "agent".
There are times when I wonder if couldn't just draw then in a BPMN designer that allowed me to write custom code for nodes. Is BPMN still a thing?
I've heard that high frequency trading eats market opportunities within milliseconds of spotting them now though, basically making the market even more random. The catch being that human nature isn't random, it's stochastic, so there will always be more money to be made on trades (or else trading firms wouldn't exist).
I'm thinking about getting back into trading because AI represents the end of buying software and we'll all be out of work soon, even if we're in denial about it. But everything I invest my time and energy into turns to crap. In a very real sense, as soon as I start trading, then karmically that could trigger recession, market reforms which ban what I'm trying, or even the end of money. I'm kind of joking and kind of not.
I've worked on a lot of really hard stuff over my brief but stupid career, and am basically used up mentally and physically, at least for now. Quant stuff is easy with AI. Should I try it?
Anyway, I have been running my own trading experiment and so far it has lost a bit of money. That being said I have not tried to optimise anything - just let it do whatever it wants. The losses are small and it might be able to recover later this year. Who knows.
The agent writes a blog about its progress here https://trades.chatbotkit.space/
I am thinking to output all the chat logs to HF as well for research.
You can run your own trading agents that communicate over a message buss in your own terms by downloading the CBK platform and running it locally with your own models. I have also shared my trading blueprint if you want to give it a go. https://chatbotkit.com/hub/blueprints/trader
I had done a small experiment with 100$, but would be hesitant on more than that. I suppose if it's money you're willing to lose.
Maybe agentic trading still performs worse than ETFs. But alternatively, if it were meaningfully better then it would be okay to opensource, similarly how ETFs are publishing their portfolios.
Put simply, if you open source your magic recipe, the behavioral change will affect the prices and you recipe will not work anymore.
When I started working no the trading agent I mentioned above I wanted to see if it can be just a better investor over the long run. The intention was not to do high-frequency trading. As you can see most of the days it is not taking any actions. The losses where down to mistakenly setting the stop losses too close to the top. If it wasn't so careful it might have made some money tbf.
My gut feeling is that AI agents will be able to manage a long-term portfolio much better than a human. Though it is just a gut feeling.
I have been running an intermittent experiment with a multi agent "investment firm" for over a year now across model releases.
They certainly can beat indexes, BUT.. the model families have some biases that you have to design around. The stop loss that bit the parent is certainly one. The models like to create rules. Often rules, one of those is making all kinds of exit conditions.
Another big one from my experience is the bias to inaction in a scenario with risk. This means a model without structure around it will bias to keeping too much cash.
Buying stock based on coin flips can beat indexes short term too, that does not mean it is a better strategy or that it works over the long term.
The idea that somebody here came up with idea that all professional algo traders didn't explore to the last penny a year if not more ahead of others is funny... but its not my money adding liquidity to the markets.
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.
Now it may be possible with models like Astra that you no longer need to do this, but in earlier models it was beneficial.
So I might want a macro economic read which leads to a market thesis. Then I would hunt for exposure, then evaluate the candidates across different aspects. Breaking the process up at least made sure no steps were missed and the different aspects considered.
There's always risk somewhere in a financial system.
- Yahoo News is introduced twice (sentiment and news analysis) which double weights it
- Sentiment analysis prompt primes the model to be bullish on Nvidia.
- In the self learning loop there is a complex parsing bug that results in hallucinated memories when agents return truncated responses
- You can completely control sentiment analysis of a subreddit by simply maintaining a majority of the 5 most recently posted messages, regardless of any quality metric
- The reflection prompt states the agent must cite alpha, which in a market wide downturn causes it to think correctly placed calls were losses
They have zero guards against market manioulations, and will get wiped without bull market!
Eeh, yeah? At that point I'd stop reading the code and just leave the project behind. How exactly is the prompt doing this right now?
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.
https://github.com/skanga/TradingAgents
https://stonks.jettdigital.app
edit: i just talked to my little sister ("Ginger" on the leaderboad) who is doing a good job beating the sp500. She said she just asks her ai what she should invest in and then executes those trades.
These are necessary and perfectly sufficient for an investment firm thesis I believe.
Although I don't think it even matters. They could easily cherry pick a period that is favorable for them. Wasting time and money on short term trading, rather than long term investment, using LLM or not, is never a good strategy for most people.
https://chatgpt.com/share/6aa00d5e-6920-83ee-8e3e-9cbf23f7bd...
Whatever the stated purpose is, where can I read the test results to show it accurately fulfills that purpose.
Anyone can make a markets simulation that models interactions between market participants. Making a simulation that is accurate enough to be useful for anything is hard.
https://github.com/monkeydust/rightmind
There are times when I wonder if couldn't just draw then in a BPMN designer that allowed me to write custom code for nodes. Is BPMN still a thing?