| 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
- Pull 5 years of daily bars for the ticker.
- Build the signal at every bar using the chosen builder.
- Compute rolling 63-day IC = Pearson correlation between signal
values and forward-5-day log returns within a 63-day window.
- 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.
- Compare recent vs early IC: mean of last 63-day quarter vs
first 63-day quarter. Delta < -0.02 fires a regime-break note.
- 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.
