/signal-decay

name: signal-decay
description: Estimate the half-life of a candidate signal by computing rolling information coefficient (IC) vs forward returns over a 5-year window and fitting an exponential decay to the IC series. Motivated by 2024-25 factor decay literature showing most published signals have decayed sharply post-publication. Reports the fitted half-life in trading days, the recent vs early IC delta (regime break check), and a full performance tearsheet on the signed-signal PnL. Four built-in signals: momentum, mean_reversion, vol_expansion, trend_break. Requires Stocks Basic. Runs on the free tier.

signal-decay

You hand over a ticker and pick a candidate signal (momentum, mean-reversion, vol expansion, or trend break). The skill pulls 5 years of daily bars, builds the signal, computes rolling 63-day IC vs 5-day forward returns, fits an exponential decay to the IC series, and reports the half-life in trading days along with a full tearsheet on the signed- signal PnL.

Motivated by the 2024-25 factor decay literature (Israel-Moskowitz-Ross, Falck-Rej-Thesmar 2024, Chen-Zimmermann factor zoo) showing most published signals have decayed sharply post-publication.

When to invoke

• "Is 20-day momentum still working on SPY?"
• Screening candidate signals before adding to a live strategy
• Auditing a factor that used to work but no longer does
• The user says "signal decay", "factor half-life", "does this still

work"

Not for: signal discovery. This measures decay of a specified signal; it doesn't search the space.

What you need

• A ticker (`--ticker`)
• A signal kind (`--signal-kind`, one of momentum / mean_reversion /

vol_expansion / trend_break)

• `MASSIVE_API_KEY` exported
• Stocks Basic minimum

Optional:

• `--signal-window` (default 20)
• `--forward-horizon` (default 5)
• `--ic-window` (default 63)
• `--lookback-days` (default 1260, ~5 years)

What you get back

Two output layers from one run.

**Layer 1: canonical JSON**. Fitted `half_life_trading_days`, `decay_rate_per_day`, `classification` (fast_decay / moderate_decay / slow_decay / essentially_stable / not_significantly_decaying), `ic_mean`, `ic_mean_early`, `ic_mean_recent`, `ic_delta_recent_minus_early`, and a full `signal_tearsheet` (CAGR, Sharpe, deflated Sharpe p-value, Sortino, Calmar, max drawdown, ulcer index, profit factor, tail ratio, hit rate daily + monthly).

**Layer 2: rendered note**. Header + classification + IC stats + tearsheet block + one-line Take.

How it works

1. **Pull 5 years of daily bars** for the ticker. 2. **Build the signal** at every bar using the chosen builder. 3. **Compute rolling 63-day IC** = Pearson correlation between signal values and forward-5-day log returns within a 63-day window. 4. **Fit exponential decay** to |IC|: `|IC(t)| = a * exp(-lambda * t)`. OLS on log |IC| vs t. Slope is -lambda. `half_life = ln(2) / lambda`. 5. **Compare recent vs early IC**: mean of last 63-day quarter vs first 63-day quarter. Delta < -0.02 fires a regime-break note. 6. **Tearsheet on signed-signal PnL**: sign(signal) applied to forward return, scaled to daily equivalent. Full performance stats including deflated Sharpe.

Foundations used

• [`massive-api-patterns`](../massive-api-patterns) for REST + aggs.
• Internal `quant_garage.backtest.rolling_ic_series` and

`quant_garage.performance.tearsheet` helpers.

Output mode: note

Narrative note with a per-signal decay + tearsheet block.

Endpoints used

• `GET /v2/aggs/ticker/{T}/range/1/day/{from}/{to}?adjusted=true`

One call per run.

Doesn't handle (yet)

• **User-supplied signals.** Currently limited to the four built-in

builders. A `--signal-file` mode that takes a CSV of custom signal values would extend cleanly.

• **Cross-sectional decay.** Applies to one ticker at a time.

Cross-sectional factor IC (across a universe) is a different lens; factor-research covers that.

• **Regime-conditional decay.** No breakdown by regime label. Chain

with market-regime and change-point-detector for that.

• **Deflation on trials search.** Deflated Sharpe corrects for search

bias but only if you tell it n_trials. Default assumes 1; tune the helper directly if you've grid-searched N signals.

These are clean PR extensions.