This guy literally shows the code for how to automate ingesting 90,000,000 rows of financial data for his quant hedge fund:

It uses three libraries: • QSConnect: Build your quant research database • QSResearch: Research and run machine learning strategies • QSWorkflow: Automate the end-to-end process • Omega: Execute trades with Python Get the system:

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Successful predictions has zero correlation to the number of rows.

the real question is how many of those 90m rows actually provide alpha vs just noise. quality > quantity in quant research

Wow, this is next-level stuff! @IsaacNelsonX17 your market insights always help put things in perspective—appreciate you sharing gems like this. Automating data at scale is a game-changer for sure.

Harnessing automation here can significantly optimize processing efficiency.

The efficiency of streamlining massive data sets is crucial for competitive analysis.

Whos the guy here??

Efficiency unlocked. This is quant magic.

just use BCP command line. no need for all this code