pairs-scanner

name

pairs-scanner

description

Screen every pair in a basket for cointegration on daily closes and rank the tradeable ones by spread z-score. Runs the Engle-Granger two-step on log prices, tests the residual with a Dickey-Fuller t-stat against MacKinnon 2010 critical values, estimates the Ornstein-Uhlenbeck half-life of mean reversion, and flags out-of-sample regime shifts via a 70/30 residual std ratio. Emits per-pair hedge ratio, ADF t-stat and p-value bucket, half-life, current z-score, and a stability label. Use when a PM or stat-arb desk has a sector basket and asks "which two names are statistically tethered right now, and how wide is the spread." Requires Stocks Starter.

pairs-scanner

You hand over a basket of tickers. The skill tests every pair for cointegration, estimates the historical tempo of mean reversion, and flags pairs where the spread is currently wide enough to trade.

This is a screen, not a strategy. It tells you which pairs are worth looking at, with the statistics that back the claim: hedge ratio, Engle-Granger t-stat, OU half-life, current z-score, and an out-of-sample stability read. The trader decides sizing, execution, and stop rules from there.

When to invoke

• A stat-arb desk with a 5-30 name sector basket asks "which pairs are cointegrated and currently wide"
• A PM building a pairs book wants to see the hedge ratio and mean-reversion tempo before committing
• The user says "run a pairs scan", "find cointegrated pairs", "test pair X-Y for mean reversion", "which of these are tethered"
• Follow-up work after sector-rotation-signal narrows to a single sector and you want the within-sector pairs read

Not for: universe-wide screens (this is O(n²); a 100-name basket is 4,950 pairs, too many to eyeball). Not for high-frequency pairs (this uses daily closes; intraday cointegration needs tick data).

What you need

• A basket of tickers (--basket, required, comma-separated)
• MASSIVE_API_KEY exported in the environment
• Stocks Starter plan minimum (unlimited REST for daily aggs)

Optional:

• --lookback-days (default 252): trading-day window for the fit
• --min-correlation (default 0.6): |Pearson rho| on log returns below which a pair is skipped without a cointegration test
• --min-pvalue (default 0.05): EG bucket ceiling for the tradeable flag
• --min-halflife (default 2) and --max-halflife (default 60): half-life bounds in trading days
• --z-entry (default 2.0): minimum |z_current| to flag tradeable

What you get back

Two output layers from one run.

Layer 1: canonical JSON matching output-schema.json. Per-pair hedge_ratio_beta, adf_tstat, cointegration_bucket, pvalue_upper_bound, half_life_days, z_current, os_std_ratio, stability_label, and a tradeable boolean with rejection reasons when false. Top-level tradeable[] is sorted by |z_current| descending; considered_but_rejected[] is sorted by ADF t-stat.

Layer 2: rendered table. Two sections: TRADEABLE (widest spreads first), then CONSIDERED BUT REJECTED with per-pair rejection reasons. See references/rendering.md.

How it works

1. Pull daily closes for every ticker over lookback_days * 1.6 + 21 calendar days via /v2/aggs/ticker/{T}/range/1/day/{from}/{to}?adjusted=true. Intersect dates so every ticker has a close on every sample date.
2. Prefilter on log-return correlation. |Pearson rho| below min_correlation means the pair doesn't co-move enough to bother testing. Skipped pairs land in skipped_correlation[].
3. Engle-Granger two-step in both directions. OLS log(A) on log(B) and log(B) on log(A). Pick the direction whose residual has the more negative ADF t-stat (the more stationary residual). The chosen dependent goes first in the pair label.
4. Dickey-Fuller t-stat on the residual, no constant, lag=1. Compared against MacKinnon 2010 critical values for N=2 cointegration with constant: -3.90 (1%), -3.34 (5%), -3.04 (10%).
5. OU half-life from OLS of Δresidual on lagged residual. half_life = ln(2) / θ, where θ = -slope. Returned as None when θ ≤ 0 (no mean reversion) or the estimate falls outside [0.5, 365] days.
6. Current z-score = (residual_last - mean) / std.
7. Out-of-sample stability. Split the sample 70/30. Fit beta on the in-sample chunk, apply it to the out-of-sample chunk, compare residual std ratio. stable when OS std < 1.5x IS std, else regime_shift.
8. Tradeable flag requires: p-value bucket ≤ min_pvalue, half-life in [min_halflife, max_halflife], |z_current| ≥ z_entry, and stability_label ≠ regime_shift. Any failing filter goes into tradeable_rejections[].

Methodology detail lives in references/methodology.md.

Foundations used

• massive-api-patterns for REST auth, retry, and the /v2/aggs daily endpoint conventions.

Output mode: table

Two-section table. TRADEABLE (widest spreads first) is the headline; CONSIDERED BUT REJECTED gives the transparency that says "here's what the scan looked at and why it didn't recommend those."

Endpoints used

• GET /v2/aggs/ticker/{ticker}/range/1/day/{from}/{to}?adjusted=true One call per ticker per run.

Doesn't handle (yet)

• Johansen test for higher-dimensional cointegration (triples, quads). Engle-Granger is a two-variable test. A basket-wide cointegration lens would need Johansen; queued.
• Augmented lag selection. The ADF uses lag=1 (Dickey-Fuller proper). AIC-selected augmenting lags are standard for daily equities and would sharpen edge cases; queued.
• Rolling-window stability past the single 70/30 split. A rolling 60-day residual std series would show when the regime shifted, not just that it did; queued.
• Half-life confidence interval. Reported as a point estimate. Bootstrap CI would let a caller filter on hl_upper_bound < 60 instead of the point value; queued.
• Cost model. No hedge ratio to notional, no borrow-cost or transaction-cost drag. Pair with slippage-cost or position-sizer when moving from screen to trade.
• Sector membership. --basket is caller-supplied. No auto-fill from a sector or industry; queued as --sector in v2.

These are clean PR extensions. The output schema is forward-compatible.