Signal Decay

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 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.