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Python for Quantitative Finance / Data access and delivery

Data Ingestion and APIs

A live market-data HTTP call that returns status 200 can still omit the field your pricer requires, reorder timestamps or embed an error string in a JSON body. Ingestion is the boundary where protocol success becomes a validated panel — or a loud failure. This lesson parses a series into a DatetimeIndex frame, handles a missing field, and keeps credentials out of source. Examples use a local fixture so no API key is required.
Learning objectives. By the end of this note you should be able to:
  • decode a list-of-bars JSON payload into a sorted, de-duplicated price series;
  • raise on missing required fields before any model math runs;
  • structure a client so secrets come from the environment, never from literals;
  • snapshot a fixture under data/raw/ for reproducible offline runs.

In this lesson

  1. Offline fixture as the teaching client
  2. Missing fields fail loud
  3. Secrets stay outside source
  4. Sketch of a live GET (not executed here)
  5. Multi-ticker panel

Before you start

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