Quantpylib access (2025 Discount!)
In the last post, we added tick-data archival services into the hft feed for quantylib modules. This allows us to do efficient and seamless archival of book depth and trade information into efficient parquet data-store in disk.
All with one line of code.
In the next post, we will show you my code script that I use to continually backup this archival data into a backblaze bucket (or you can do it to an aws s3 cloud storage), as well as retrieval for hft simulations.
We want to give the readers an opportunity to join us in learning, contributing and using the repo for trading and quant dev development.
For 5 DAYS, we are running a 50% haircut on the quantpylib access pass:
https://hangukquant.thinkific.com/courses/quantpylib
quantpylib is our community github repo intended for learning and trading:
