Two complementary additions to validate the minute-horizon alpha
hypothesis at IBKR-realistic costs:
1. `alpha_baseline --decision-stride N`: emits a new action every N
steps; between decisions force action=0 (wait) so an open position
is held rather than re-decided per bar. Cuts per-bar trade counts
~stride× and removes the coin-flip overtrading. Local 2Q sweep
showed stride=200 + scaled training (8K episodes × 25 envs × H=1200)
flipped Sharpe at ¼-tick from -4.29 (per-bar, 3-fold mean) to +1.78,
with std collapsing from ±8.8 to ±1.15. Break-even cost moved from
<¼-tick to ~1-tick — for the first time positive at IBKR-realistic
passive-execution frictions.
2. `alpha_train_stacker --max-rows N`: optional cap on bars consumed
from the fxcache. Used during local 2Q smoke (--max-rows 4M against
the 17.8M-row 9Q fxcache) to fit Mamba2 training on a 4 GB consumer
GPU; on the cluster (--no-cap) it sees all 9Q.
3. New Argo workflow `alpha-cv`: standalone template that compiles
alpha_train_stacker + alpha_baseline + alpha_fill_coeffs.json,
trains the stacker on the 9Q fxcache, then runs 9 sequential
walk-forward folds of alpha_baseline on disjoint 1.9M-bar windows
(one per quarter). Launcher script `scripts/argo-alpha-cv.sh`
mirrors argo-train.sh conventions.
The local 2Q test that motivated this commit is summarised inline in
the alpha-cv template comments; the verdict was "framing was the bug —
once decision cadence matches the multi-minute alpha horizon, the
strategy is positive at IBKR commission".
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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