Tutorials

Nine tutorials, in two parallel tracks, teaching the same nine analytical steps twice: once with no code, once with Python. Work through either track start to finish, or move between them as you like, since both tracks build the same skills on the same dataset.

Access reality, read this first

  • Students use the sandbox. Register at sandbox.altfndata.com with a work or school email; approval is automatic and no API key is required. The No-code track below runs entirely inside the sandbox's browser SQL editor and chart panels.
  • API keys are issued manually, not self-serve. The Code track uses the production API at api.altfndata.com, which requires an X-API-Key header. If your class wants to work in Python, have your instructor request a shared class API key from the ALT/FNDATA team ahead of time. Do not request a key on your own for a self-study session; use the sandbox instead.
  • Documentation for both surfaces lives at docs.altfndata.com, including a quickstart, the downloadable Python client (altfndata_client.py), and an Evaluation Guide.
  • A companion runnable notebook, altfndata_tutorials_student.ipynb, sits in this same 03_Tutorials folder and mirrors every Code track tutorial as executable cells. Open it once you have a class API key.

No-code track (sandbox, zero setup, no key)

Browser-based SQL and charts. Nothing to install. Start here if you do not yet have an API key, or if your course is not code-focused.

  1. NC 01: Discovery, tables and schema
  2. NC 02: Your first query
  3. NC 03: Filter by brand and date range
  4. NC 04: Pricing power
  5. NC 05: Sell-through rate
  6. NC 06: Demand over time
  7. NC 07: Brand to ticker, linking auction results to public companies
  8. NC 08: Text search across titles
  9. NC 09: Export to CSV

Code track (Python client and raw REST)

Uses the reusable altfndata_client.py client, with the equivalent raw requests.post call shown alongside it so you understand the underlying REST shape (POST /v1/tables/{name}/query, header X-API-Key, response envelope {"table", "result_count", "data"}). Requires a class API key from your instructor. Each tutorial below mirrors a section of the companion notebook, altfndata_tutorials_student.ipynb, in this same folder.

  1. PY 01: Discovery, tables and schema
  2. PY 02: Your first query
  3. PY 03: Filter by brand and date range
  4. PY 04: Pricing power
  5. PY 05: Sell-through rate
  6. PY 06: Demand over time
  7. PY 07: Brand to ticker, linking auction results to public companies
  8. PY 08: Text search across titles
  9. PY 09: Load into pandas

What both tracks cover

The two tracks mirror each other tutorial for tutorial, so a class can split into a no-code group and a code group and still compare notes at the end of each session:

  • Finding tables and reading a schema
  • Running a first filtered query
  • Combining a brand filter with a date range
  • Pricing power: the median ratio of realized price to pre-sale high estimate, where a value above 1.0 means buyers paid over the auction house's own estimate
  • Sell-through: the share of offered lots that actually sold
  • Demand over time: a quarterly count of sold lots for a brand, built as a learning exercise in time bucketing, not a market-appreciation claim (recent quarters are typically under-ingested and should be treated as provisional)
  • Brand-to-ticker: pulling everything a listed parent company sold across its brand portfolio, using stock_ticker
  • Text search across item titles
  • Getting results out: CSV export in the sandbox, or a pandas DataFrame in code

A note on interpretation

Every metric in this track is built on stable ratios and counts (pricing power, sell-through, medians, row counts) rather than year-over-year cleared-price change, because recent quarters in the dataset are still being ingested and a naive year-over-year comparison can read as a market move when it is really a data-recency effect. The demand-over-time tutorials (NC 06 and PY 06) call this out explicitly and should be treated as a lesson in building a time series, not as investment or market commentary.

Questions

For access issues, class API keys, or anything else, contact info@altfndata.com.