Improving Our Database Service
Previously, we shared works in creating a database for our quantitative data in MongoDB with Python, and created a database service class to seamlessly work with dataframes and read/write to our database.
With a few tricks, we managed to achieve a fair performance. However, can we make it more performant?
This week, we publish a new paper that improves upon our data service classes to accomplish the same benchmark in just 1 minute without using a multi-core approach! Using asyncio, we share tips and tricks to ensure our CPU is used efficiently, bringing us closer to a efficient quant toolset.
Additionally, we improve the code structure to include different data engines, bringing us closer to a production worthy setup.
The paper & code files are available here:
https://hangukquant.thinkific.com/courses/retrieval-of-financial-data-and-implementation-of-quant-db
For those who have already obtained this course, no further action is required - the lessons and code files are added as a new chapter. Note that some technical depth is required to understand the code presented here.
For paid readers, here is your full report (34 pages):