Adds generate_outcome_labels_d for the future aux supervision head
(Layer B of the anti-calibration plan, docs/superpowers/plans/2026-05-22-
horizon-rebase-n3-100-300-1000-and-aux-d-labels.md).
Per (snapshot t, horizon K, direction d ∈ {long, short}):
profit = signed_pnl(d) - cost
dd_against = max adverse excursion in [t, t+K] holding direction d
y[t, K, d] = profit - 1.5 × |dd_against|
The 1.5× drawdown penalty encodes loss-aversion per behavioral finance —
trades with deep drawdowns are penalized even if final outcome is positive.
Implementation: monotonic-deque sliding-window min/max per horizon,
O(N · N_HORIZONS) amortized. NaN at right-edge positions (t+K >= n).
Input validation rejects zero horizon and non-finite/negative cost.
13 tests pass: 5 hand-derived value checks (simple up, drawdown
penalty, NaN edge, sliding-window correctness, per-horizon independence),
3 additional edge tests (constant-series invariant, zero-horizon
validation, negative-cost validation), plus the 5 pre-existing tests
unchanged.
Not yet wired into the loader (Task B2) or used as supervision target
(Tasks B3-B5). New function only.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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