Inside My Python Hedge Fund: The Data Pipeline (Most Backtests Fail Here) [Part 3]

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In part 3 of " The Building a Quant Hedge Fund in Python" series, I dive deep into the critical data layer of our quant hedge fund platform, emphasizing that a strong data foundation is essential for successful trading strategies.

We explored the types of data we ingest, including price data, fundamental data, and the importance of data validation checks to ensure reproducibility. I also demonstrated our data pipeline, which includes raw data ingestion, cleaning, and storage in a SQL database optimized for backtesting.

I invite you to join our one-hour live deep dive workshop, where Jason and I will showcase our hedge fund code and answer your questions in real-time. Please comment with your interests—whether it's data sources, validation processes, or backtesting—so I can tailor future content to your needs.

TABLE OF CONTENTS
00:00 Bad data will blow up your strategy (why data matters)
00:25 What you’ll learn in this video (agenda)
01:35 Disclaimer (not financial advice)
02:05 Series context + why the data layer is the foundation (Part 5)
02:32 What a “data layer” actually means in a hedge fund
03:32 Data types we ingest (prices, corporate actions, fundamentals)
05:33 Reproducibility: the #1 rule for reliable backtests
06:12 The real pipeline flow: raw → clean → database → research-ready
08:20 Data validation checklist (preventing fake backtests)
10:43 Live demo: hedge fund platform, nightly refresh, and database tables
21:07 Recap + live workshop invite + Part 6 teaser