rallies

Software engineer, trying to get into fintech. This is an open source tool I built to test out a bunch of portfolios I'm building with AI.

Unfortunately we already have a product we're building. Probably can't find any extra time.

this is such a great answer, thanks! printing them out and reading them today

Not difficult at all.

Nof1 has actually been doing that.

Cruel. Was having a good day! I know it's my own thing, but it's free, and it was a lot of work.

Two goals.

- First is to actually evaluate whether these LLMs have any intelligence around investing. If you actually give them all the data, can they do well? Can they beat the market? I'm not sure, we're testing that.

- My thesis is that they will actually beat the market (I know a lot of you will disagree). If that's the case, how can we invest a lot of resources in building the best harness, tool calling, etc to enable these models to invest.

I agree. A couple of things.

- We've built a local vector database with every SEC filing over the last few years. And we've built a tool call on top of that to allow these LLMs to read and query sec filings. - Have done the same for a lot of other data sources, just giving the LLM access to them and allowing it to spend some time to actually research.

correct.

haha lol.

I actually think it's doing better now. It was just too stubborn to exit its position for the first few months. It did that, and put some money into MSFT/JPM recently.

In case it's not clear.

This is an experiment to see how well can LLMs invest in the market through a lot of research. We give them tool calls to access every financial dataset that exists online, and also some money to manage. And we then see how well they do.

The experiment started in November 2024.

The AI models are. Somewhat.

I'm fixing it. Give me a minute, apologies.

Edit (reply on your edit lol): you're right.

Second edit: fixed now. No more walls anywhere.

there is no human in the loop, there is no high frequency trading. We're trying to have AI mimic what fund managers do:

- lots of research - longer time horizons - zero humans in the loop, but explain every single thing you do.

ML driven is. LLM driven is still nascent, especially the idea that as large language models get more advanced, can they research and invest like a fund manager.

Founder here: YC actually had an "AI hedge fund" idea in one of their recent "request for startups" post. We've been working on evaluating the capabilities of frontier models in investing money in the stock market. Results are encouraging and we're not doubling down on it.

Happy to answer any questions.

https://rallies.ai

Working on building an investment assistant backed by real time data. ChatGPT and Perplexity finance are amazing, but all of them are based on web search data only, which is a big limitation in finance since realtime data is important.

We have an agent that has access to almost every data point you can think of in the stock market (as much as we can get), which gets leveraged before answering.

And we also figured out ways to build amazing charts in between answer snippets, which looks very cool. Investors are usually very visual.

Here's a nice parsing of all the important financials from an SEC report. This used to be really hard a few years ago.

https://docs.google.com/spreadsheets/d/1DVh5p3MnNvL4KqzEH0ME...

I work at the intersection of AI and investing, and I'm really amazed at the ability of this model to build spreadsheets.

I gave it a few tools to access sec filings (and a small local vector database), and it's generating full fledged spreadsheets with valid, real time data. Analysts in wallstreet are going to get really empowered, but for the first time, I'm really glad that retail investors are also getting these models.

Just put out the tool: https://github.com/ralliesai/tenk

We're running some live experiments these days, for both stocks and options. https://rallies.ai/arena

I think the big limitation of nof1 is that they're not using a lot of data that an actual investor would use when researching companies.

We're trying to fix some of those limitations and run a similar live competition at https://rallies.ai/arena

This is pretty cool.

We're also running a live experiment on both stocks and options. One difference with our experiment is a lot more tools being available to the models (anything you can think of, sec filings, fundamentals, live pricing, options data).

We think backtests are meaningless given LLMs have mostly memorized every single thing that happened so it's not a good test. So we're running a forward test. Not enough data for now but pretty interesting initial results

https://rallies.ai/arena

Developer here: We've only started this last week so not enough data right now, but it's a really interesting experiment

Working on building a chatgpt wrapper with real time stock market data. More than 70% investors are using Chatgpt for their investment analysis these days, but the data is quite dated since it's all based on web search. Trying to fix it.

https://rallies.ai

Correction: made a mistake in assuming equal contribution for all holdings. Just fixed it to account for the actual value. The correct returns are 90%.

Came here to say this. Although the UI is clean, it's in no way a great user experience using NotebookLM. It's just such a great product so I go back to it, but the user interface is not my favorite part.

So that's 10% of their latest series?