Information horizon

Every alpha is perishable: its forecasting power (IC) decays over time at a rate that is an intrinsic property of the signal. tradeflow/alphas/horizon.py measures that decay and turns it into two free wins — a principled rebalance cadence and a lagged blend that raises the information ratio.

Research clock only

A diagnostic over an existing alpha's decay structure; it places no orders.

Decay and half-life

IC measured at increasing lags n (the alpha at t vs the residual return realized n periods later) follows:

IC(n) = IC₀ · δ^n            half-life HL = −ln 2 / ln δ

fit_decay fits δ by log-linear regression on the reliable, positive-IC lags and reports the fit R² — a low R² means the decay isn't a clean exponential, so don't over-trust the half-life. Because value added scales with IR², the value-added half-life is half the IC half-life — stale alphas are more damaging than they look.

The fit also reports a confidence band on the half-life (half_life_lower / half_life_upper, from the regression's own slope standard error) — short histories give wide bands, and a point estimate alone invites over-trusting a noisy fit. The multi-period trading policy uses the upper (slower-decay) bound when discounting alphas, so a shaky short-history estimate doesn't prematurely kill a signal that's actually persistent.

Cadence and the breadth↔frequency tradeoff

IR(Δt) = IC(Δt) · √(1/Δt)

Rebalancing faster raises √BR but acts on less-confirmed information (lower IC) and costs more. The optimum is interior: recommended_cadence returns the Δt that maximizes this IR proxy, so the cadence is chosen, not assumed. The half-life is also the holding period transaction cost should be amortized over.

The optimal current/lagged blend

Given decay γ (= δ) and the signal's autocorrelation ρ, the IR-maximizing weight on the current signal is:

w_now = (1 − γ·ρ) / (1 + γ² − 2·γ·ρ)        w_lag = 1 − w_now

• δ > ρ → diversify: w_lag > 0 — the lag carries independent information.
• δ < ρ → hedge: w_lag < 0 — it mostly cancels current noise.
• δ = ρ → the latest signal alone is sufficient.

Superseded by the multi-period trading policy

This two-point blend was the first, ad-hoc answer to "should I slow down trading into a decaying signal?" The multi-period trading policy (allocate --policy aim) generalizes it into a continuous decay discount composed with the optimizer's own no-trade band — the blend here is the degenerate two-point special case. Prefer --policy aim for new work; this report's recommendation stays accurate on its own terms either way.

Matching signal to return horizon

A signal of half-life HL predicts returns best at a horizon ≈ 1.26·HL (where the signal-return correlation peaks). Surfaced as peak_return_horizon so a short-horizon signal isn't scored against a long-horizon return — a common way to understate real skill.

Caveats it surfaces

• Decay should be measured out-of-sample — fitting it on the selection data overstates persistence.
• The blend adds turnover — its lagged leg is priced through the cost model and blend_recommended is only true when the blend diversifies and its annual turnover cost is modest; a high-turnover blend that would lose net is flagged.
• Regime-dependent decay — the half-life isn't constant; re-estimate on a rolling window rather than freezing one number.

Where it runs

services.compute_horizon measures the IC-vs-lag profile (reusing the information-analysis forward-residual machinery), fits the decay, and recommends the cadence + blend. The CLI (python main.py horizon) and the read-only MCP tool compute_horizon route through it.