Link: https://trading.snagra.com?utm_source=hn (no signup required)
What you can try right now: - Watch live trades from GPT-4, Claude 3, and Gemini - Read each AI's full analysis and reasoning - Compare their different interpretations of the same market data - Track their real-time performance and win rates - View historical trades and performance metrics
Built this over the holidays to study how different AI models approach financial decisions. Each morning at 9:30 AM EST, the AIs analyze market data and make real trades with $5 stakes.
Technical Implementation: - Next.js frontend with real-time updates - Node.js/Lambda backend for AI processing - PostgreSQL for trade tracking - Alpaca API for automated trading - Consistent prompts for all models
Data Flow: 1. Daily market analysis (9:30 AM EST) 2. Each AI gets identical inputs: - Financial headlines - Market summaries - Technical indicators - Earnings reports 3. AIs provide: - Stock picks with reasoning - Entry/exit conditions - Risk assessment 4. Automated trade execution
Note: This is an experiment in AI behavior, not investment advice. The goal is to study how different LLMs interpret financial data and make decisions with real consequences.
I'll be around to answer questions about the implementation.
Or a monkey.
https://youtu.be/USKD3vPD6ZA?si=AGyGdPdSPpJezQJp
The scene towards the end where he pitches it to a bunch of hucksters is brilliant.
As is, this means absolutely nothing and not understanding the problem.
Adding a random walk to this would mean you have 4 random walks instead of 3.
There is also the problem that it is tough to make a prediction for tomorrow that is better than today's close.
or just a stocktrader haha
Many quant trading firms make 50%-100% annual returns, each year, over the past 15-20 years. The secret is leverage. And they do not accept outside investor money.
Many hedge funds outperform the market. However, the returns after fees, to the passive outside investor underperform S&P500.
But yes, publicly traded active ETFs generally underperform. But counter example is VGT or QQQ, both historically outperformed S&P500.
Maybe compare with this guy:
https://news.ycombinator.com/item?id=14713997 - Amazon engineer will let strangers manage his $50,000 stock portfolio 'forever' (2017-07-06, 172 comments)
Active controls (vs passive ones) are an important concept in experimental design.
I don't really buy this. If the goal was to study how different LLMs interpret financial data there would be no use for actual trades, since their interpretation cannot be influenced by the fact that the trading orders are passed for real.
I believe the goal is to see if AI can do better than rats [0]. There is no shame in that.
[0]: https://www.vice.com/en/article/rattraders-0000519-v21n12/
> their interpretation cannot be influenced by the fact that the trading orders are passed for real
It's not going to make much difference with $5 trades, but the impact on the market is non-zero.
Whenever I trade, I somehow always get an adverse price. I figure it's the "no fee" brokerage chiseling a bit off for themselves. I compensate by being a buy and hold hold hold investor, so paying very little in aggregate for that.
What I don't understand is how day traders avoid being eaten alive by this.
Technically every trade influences the stock, but I agree that it won't have any effect at all.
> I believe the goal is to see if AI can do better than rats [0]. There is no shame in that.
But even then you wouldn't have to perform real trades, you could still just calculate the profit as if trades would have happened.
I think the actual trading is just to make it more interesting.
Depending on the type of trades, the volume of the equities, etc.. it can be very difficult to simulate the ability to open/close positions with sufficient accuracy to evaluate the strategies.
Also, the reasoning is partially a hallucination - "The holding period of 9 months aligns with the expected completion of Grayscale's pivotal Phase 3 Bitcoin ETF trial, a major catalyst for unlocking investor demand and driving trust value realization."
There is no such thing as a "holding period", nor are they doing a "Phase 3 Bitcoin ETF trial". It's possible the "Phase 3" thing is picked up from news about a drug company.
The hallucinations are simply a mirror of a community that thrives on this nonsense. When nothing is real, you can’t blame the LLM for not figuring it out.
Me, I always regarded technical analysis as drawing pictures in clouds.
If any of those analysts were worth spit, they'd be working for a hedge fund, not the network.
Hopefully the LLM trainers didn't "accidentally" bias the model in weird ways that favor their employer or themselves... two of the three recommendations are a fund for investing in bitcoin and a company using blockchain to trace chemical supply chains.
I look forward to seeing if the AIs can beat an index fund, or if they'll just invest in a thousand blockchain, NFT, and AI companies. I suspect a LLM has a high opinion of a company making AI given how many press releases they're summarizing.
This is not necessarily a poor value trading strategy.
I've been working on the same concept for the past 2y now and have our performance results here: https://trend.fi/performance
It conducts millions of simulations daily for each asset, then provides a snapshot of the top-performing results to GPT-4o for final selection.
I'm really pushing the limits of GPT-4o currently. I started testing with o1 just last week and it performs better. It's just so much more expensive.
It would be neat to also see another experiment of a MAS doing this and coordinating to gamble together. Perhaps even different system/arch/expert configs.
It currently makes up to recommendations, since not all stocks support fractional shares (I'm only doing $5 per trade). As part of the buy recommendation, a holding period is suggested as well.
Once the holding date is reached, that is when the sell order happens.
Would love to answer any other questions you may have.
1. Role + Goal Setting: The AI acts as a creative market analyst focused on discovering overlooked opportunities and emerging trends.
2. Structured Analysis Framework: - Detailed evaluation criteria (innovation, moat, management, growth potential) - Sector diversity requirements - Focus on finding hidden gems vs obvious mega-cap tech stocks
3. Time-Bound Precision: Instead of vague "3-6 months" holding periods, I require exact hour calculations tied to specific catalysts like: - FDA approval dates - Earnings releases - Product launches - Conference presentations
4. Quality Controls: - Must be valid NYSE/NASDAQ symbols - Diverse across sectors/market caps - Conviction level scoring (1-10) - Each pick needs unique thesis + catalyst - JSON output format for consistency
The key is combining structured analysis with creative discovery - pushing the AI to look beyond obvious choices while maintaining some analytical rigor.
I just recall Navinder Singh Sarao "$1T Flash Crash" as a notable addition to a long list of algorithmic trading strategies going sideways ( https://marketrealist.com/who-is-navinder-singh-sarao-the-ma... .)
The stock market was built on information asymmetry, unfair positions, and ambitious gamblers... statistically it is rarely a reasonable investment for amateurs.
Good luck, =3
Did a different email, it accepted it, I got the email, but got this error message when trying to confirm it: {"error":"Invalid verification token"} and a pretty-print checkbox that did nothing.
EDIT: disregard…I saw in another comment you mentioned you were using mailgun. Thanks.
More simply what i mean to ask is -> the moment market knows about your advantage, shouldn't you lose it because everyone else will use that information to balance the market?
URL looks like that: http://undefined/api/verify-email?token=.....
Assume the experiment runs ~250 trading days in a year, consider the worst case they lose all their invested money=$3750.
A little more than $5 :)
> Best Performer: AIs are tied
> Total Profit: $0.00Some things to watch out for:
- LLMs, by default, don't follow the best practices for trading or investing. Without careful constraints, they can ignore fundamental investment best practices. This is something I learned while building https://decodeinvesting.com/chat.
- I see Claude bought a penny stock SMX. This could be volatile, and the price could change significantly in 24 hours before the next execution at 9:30 am.
- The LLMs are day trading on some volatile securities; while LLMs could be good at day trading, unlike humans (we will find out), this setup has the disadvantage of only trading once a day.
from a study in Brazil: "97% of all individuals who persisted for more than 300 days lost money. Only 1.1% earned more than the Brazilian minimum wage and only 0.5% earned more than the initial salary of a bank teller — all with great risk."
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3423101
If you don't want your bot to be a day trader, then just get low cost index funds.
So props on doing proper double opt-in for newsletters.
> AIs are tied
Sounds about right
> At 6:00 AM PST, trades are automatically executed based on AI recommendations, investing $5 per trade
The best trading decision most days is to not trade. Outliers and diversions from the mean don't happen every day. This is trading just for the sake of it.
I predict a slow crawl down into zero eaten up by fees.
It would be great to see this tested with more commercial LLMs (O1 / Amazon Nova, / Llama 3.2 / etc.). If you're open to it, I’d be happy to contribute support for these models via LiteLLM - https://docs.litellm.ai/docs/providers
Really cool project and subscribed to follow along.
- does the AI perform the same trades given the same input?
- does the AI perform the same trades given slightly different inputs? (E.g. same data, but re-ordered)
And also pure randomness of picking the one trade from list of trades
Time to add some side wagers and bet on different models.
Is this AWS? Why did you pick lambda over say Python code, say in Flask to perform actions?
> BOUGHT TLRY
Thanks and Happy New Year