Point-in-Time Macro Join

Point-in-Time Macro Joins - The Most Dangerous Join in Finance

Scene: your first quant research review. You proudly show a strategy that goes risk-off whenever inflation is hot, and the backtest is gorgeous - a smooth equity curve, a Sharpe your PM would kill for. The senior researcher asks one question: "On the day you traded January 31st, what CPI number did your model actually see?" You check. Your join handed it the January CPI print - which the Bureau of Labor Statistics did not publish until February 14th. Your model traded on a number that did not exist yet. The Sharpe was a fiction. This mistake has a name, and killing it is what today's lesson is about.

🧠 The mental model: read tomorrow's newspaper

Look-ahead bias is like backtesting a horse-racing system while secretly reading tomorrow's results. Of course you win. Point-in-time discipline forces every simulated decision to use only what a real trader could have known at that instant - the newspapers already printed, never the one still at the presses.

Macro data is where this bites hardest, because macro releases describe the past but arrive weeks late:

• US CPI (Consumer Price Index) is published by the BLS roughly two weeks after the month it measures.
• The January 2023 CPI figure of 6.4% was released on February 14, 2023.
• Join "January 2023 CPI" onto a trade dated January 31, 2023 and your backtest believes it knew inflation two weeks early. It did not.

Why this is a billion-dollar problem, not a footnote: in the 1990s D.E. Shaw and Citadel staffed entire data-infrastructure teams whose whole job was maintaining point-in-time databases - stores that record not just what a value is, but when it was first published. WRDS (Wharton Research Data Services), the academic standard used by nearly every finance PhD program, keeps two dates on every fundamental: a datadate (the period the data covers) and an rdq (the release date it was announced). Getting the join to key on the release date is one of the first things a finance PhD is taught.

The concept: a correlated subquery that only looks backward

For each trade date you want the most recently RELEASED macro value - not the value for the matching period. That is a tiny per-row lookup: filter to releases on or before the trade date, sort newest-first, take one.

-- ✅ POINT-IN-TIME: only data already released on the trade date
(SELECT value
 FROM   macro_indicators m
 WHERE  m.indicator_name = 'CPI_YoY'
   AND  m.release_date  <= e.trade_date   -- the key line
 ORDER  BY m.release_date DESC            -- newest first
 LIMIT  1) AS cpi_pit

Flip that one operator and you invite the ghost of look-ahead in:

Regime Classification

Once each month carries its point-in-time CPI and Fed Funds rate, a simple CASE labels the macro regime:

Your Task

The starter is complete except the CPI lookup, where a naive analyst wrote a look-ahead filter (release_date >= first_date, newest-last). The Fed Funds lookup right below it is already correct - use it as your template. Fix the one 👉 marked spot so the CPI join becomes point-in-time.

Predict before you run: the earliest CPI print is dated 2022-01-28. With the buggy filter, which month becomes the first row of the result, and what CPI does it wrongly show? After your fix, that phantom month should vanish - the first surviving row should be 2022-02 carrying CPI 7.5 (the Jan-28 print, the only one a January-2022 trader could actually have read).