Investorsexchange.jl – parse trade-level stock market data in Julia
Backstory: I wanted to play with intraday stock data but couldn't find a free dataset anywhere. IEXCloud [1] offers API access to 1-minute granularity intraday historical price data, but I was worried that it could get expensive or unwieldy to build up a substantial dataset via API calls. Plus, IEX gives out their raw data for free. I probably should have just used the IEXTools python library [2] to parse IEX's raw data dumps, but I was working on a Julia project, so it felt more thematically appropriate to build a new tool from scratch. I haven't been actively using InvestorsExchange.jl a…
In plain words
Investorsexchange.jl is a Julia library for parsing intraday stock market data at one-minute granularity from IEX's free raw data dumps. It enables researchers and developers to build local datasets of historical trade data without relying on expensive API calls. The tool addresses the challenge of accessing and organizing substantial volumes of stock market information for analysis and experimentation within Julia projects.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Backstory: I wanted to play with intraday stock data but couldn't find a free dataset anywhere. IEXCloud [1] offers API access to 1-minute granularity intraday historical price data, but I was worried that it could get expensive or unwieldy to build up a substantial dataset via API calls. Plus, IEX gives out their raw data for free. I probably should have just used the IEXTools python library [2] to parse IEX's raw data dumps, but I was working on a Julia project, so it felt more thematically appropriate to build a new tool from scratch. I haven't been actively using InvestorsExchange.jl a lot lately, but it's made me the proud owner of a 50GB SQLite DB dump covering several years of trade data, and I think it would be awesome if I could help folks in the HN community more quickly build up this kind of dataset for their own curiosity or research. Feedback is also greatly appreciated! [1] https://www.iexcloud.io/docs/api/#historical-prices [2] https://github.com/lvfrazao/IEXTools
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