Gautier Marti’s Post

View profile for Gautier Marti

Systematic Equities

I know… It’s not cool no more. But I felt I had to revisit “From Data to Trade” with ChatGPT-4, and see what could be trivially retrieved from its weights! The progress between the two versions is amazing! To a point, I had a near philosophical discussion with a former colleague on transformers, information theory, lossy vs. lossless compression, and the possibility that, at some point, all human knowledge could be stored in only a few hundred billions parameters. “Decoding the Quant Market” is definitely better than ChatGPT-3.5’s “From Data to Trade” essay produced last year, and while it won’t make you rich, this book contains lots of interesting entry points to relevant systematic and quant trading concepts that you can explore further with cornerstone books, papers, or even better, if you are in position to do so, by interacting with markets. #DecodingTheQuantMarket #QuantitativeTrading #MachineLearning #AI #ChatGPT4

Antoine F.

Cryptocurrency Derivatives

11mo

Can you pls re-explain how this was produced. Tks

I assume it also generated the references. That is incredibly cool, but I’m kind of lost with how they are directly connected to the text, e.g. I do remember the Sculley et al paper from SIGKDD ‘12, though iirc it’s about large scale classification with extreme imbalances 🤷♂️ maybe it sees something I don’t lol.

Romuald Elie

AI research scientist @ Google Deepmind // Mathematician

11mo

Out of curiosity, is it hallucinations cleaned up?

Sahand Haji Ali Ahmad, PhD

Systematic Trader (Quant-Algo)

11mo

What would it's output be if we ask "Summarize and reference all the papers applying NLP to financial market forecasting"?

Piotr Yordanov

Senior full-stack engineer, and algo trader

11mo

This doc offers a great overview of the broad concepts. However, it falls short of offering concrete real world use cases. Fro example, how would I use a moving average as a feature on a historical stock data with gaps, then run a deep learning to filter out weak signals. Or time series prediction on the equity curve to trade the strategy instead of the asset itself. Also, the audience is assumed to be either or both retail (can do just fine with TA or fundamentals, or portfolio optimisation) ani institutions (which would rather do high volume hft mm on order book imbalance for ex) Still, honestly a great read. Thanks chat gpt for it, Ani Gautier Marti for prompting it ☺️🙏

Like
Reply
Sawyer Oliphant

Founder @ Quant Farming | Quantitative Trading Systems Expert | Stock Portfolio Optimization | PM to Get Your Own Trading System

11mo

Gautier, as an algorithmic trader myself, this is an incredibly written document that can be comprehended by in my opinion, all backgrounds of finance. I will have to save this document for reference. Thanks for sharing!

Like
Reply
Dorine Bodson - Klopper

Empower AI with the purest financial exchange data. Colo/🌩️ Contributing to create better financial markets with more market participants.

11mo

Thanks so much for sharing. Thumbs up for Chapter 5, the source of the data is key, in particular for ML.

Like
Reply
Sahand Haji Ali Ahmad, PhD

Systematic Trader (Quant-Algo)

11mo

What prompt did you use Gautier? :)

Like
Reply
Like
Reply
See more comments

To view or add a comment, sign in

Explore topics