refactor(alpha_baseline): rename, drop conditionals, strip dead paths

Rename binary alpha_compose_backtest → alpha_baseline and remove the
boolean flags whose features are now mandatory:

  --c51            (always C51 distributional Q)
  --temporal       (always Mamba2 temporal encoder)
  --isv-continual  (controller always fires per eval episode)
  --regime-scale   (vol-EMA regime defense always on)
  --pruned-actions (FALSIFIED 2026-05-15 per pearl_action_pruning_falsified)

Every dependent code path was stripped, not just gated:

- Linear-Q kernels (lq_fwd, lq_grad, munch_kernel) and their cubin loads
  are gone — C51 is the only Q-network.
- Single-env push_kernel / h_store_kernel loads removed; the backtest
  has been batched-parallel-env since T14 and only the _batched
  variants are called here. (The smoke binary still uses single-env
  variants because one env per episode is its job.)
- Dead transition buffers removed: states_dev, next_states_dev,
  actions_dev, rewards_dev, dones_dev, q_current_dev, q_next_dev,
  target_dev, single_state_dev, single_q_dev, probs_current_dev,
  probs_next_dev, m_dev, single_probs_dev, single-env state_pinned,
  action_pinned, window_tensor, h_enriched_buf_dev.
- Dead constants and helpers: PRUNED_ACTIONS, N_WEIGHTS, N_BIASES,
  epsilon_greedy, epsilon_greedy_gated.

End-to-end verification on the existing Q1 fxcache (rebuild was OOM
locally; full multi-quarter validation is the next phase):

  cost=0.0000  best τ=0.250  Sharpe_ann=+36.83  win=0.984  trades/ep=83.3
  cost=0.0625  best τ=0.250  Sharpe_ann=+38.53  win=0.996  trades/ep=83.2
  cost=0.1250  best τ=0.250  Sharpe_ann=+38.37  win=0.994  trades/ep=83.3
  cost=0.2500  best τ=0.250  Sharpe_ann=+34.24  win=0.990  trades/ep=85.6
  cost=0.5000  best τ=0.250  Sharpe_ann=+31.83  win=0.946  trades/ep=84.8

Numbers track the prior T16-flag config (within stochastic noise),
confirming the conditional-stripping was a pure simplification — no
behavioral change, just a smaller, honester binary.

Also updated:
- scripts/alpha_pipeline.sh — A/B conditions collapse to fixed-cost
  vs cost-randomized training (the only opt-in left).
- scripts/walk_forward_cv.sh — drop legacy flags, pass --window-k only.
- crates/ml/src/env/loaders.rs — module doc-comment updated.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

scripts/alpha_pipeline.sh

@@ -18,7 +18,7 @@ set -euo pipefail
 REPO=/home/jgrusewski/Work/foxhunt/.worktrees/sp19-20-wr-first
 CACHE_DIR=/home/jgrusewski/Work/foxhunt/test_data/feature-cache
 FILL=config/ml/alpha_fill_coeffs.json
-BIN_BT=./target/release/examples/alpha_compose_backtest
+BIN_BT=./target/release/examples/alpha_baseline
 BIN_STACK=./target/release/examples/alpha_train_stacker
 
 cd "$REPO"
@@ -55,7 +55,7 @@ fi
 echo "Alpha cache: $ALPHA_OUT"
 
 # ----- 2. Build backtest binary (in case of code changes) -----
-SQLX_OFFLINE=true cargo build --release --example alpha_compose_backtest -p ml >/dev/null
+SQLX_OFFLINE=true cargo build --release --example alpha_baseline -p ml >/dev/null
 
 # ----- 3. Walk-forward CV across 4 conditions × 3 folds -----
 # Fold A: offset=0,      window=1.2M → train 0..720K, eval 720K..1.2M
@@ -63,11 +63,13 @@ SQLX_OFFLINE=true cargo build --release --example alpha_compose_backtest -p ml >
 # Fold C: offset=1.8M,   window=1.2M → train 1.8M..2.52M, eval 2.52M..3.0M
 # (Assumes Q1+Q2 fxcache has ~3-6M bars total at MBP-10 resolution.)
 
+# Cost randomization is the only opt-in left: `--train-cost-hi 0.5` enables
+# uniform[0.0625, 0.5] per-epoch cost sampling. C51 / temporal / ISV-continual
+# / regime-scale are now always-on properties of alpha_baseline; the A/B
+# split is now just fixed-cost vs cost-randomised training.
 declare -a CONDS=(
-  "A_baseline:--c51 --temporal --window-k 16 --isv-continual"
-  "B_costrand:--c51 --temporal --window-k 16 --isv-continual --train-cost-hi 0.5"
-  "C_regime:--c51 --temporal --window-k 16 --isv-continual --regime-scale"
-  "D_both:--c51 --temporal --window-k 16 --isv-continual --train-cost-hi 0.5 --regime-scale"
+  "A_fixed:--window-k 16"
+  "B_costrand:--window-k 16 --train-cost-hi 0.5"
 )
 
 declare -a FOLDS=(
@@ -108,7 +110,7 @@ echo "Alpha pipeline cross-quarter walk-forward summary"
 echo "===================================================================="
 python3 - <<'PY'
 import json, os, statistics
-conds = ["A_baseline", "B_costrand", "C_regime", "D_both"]
+conds = ["A_fixed", "B_costrand"]
 folds = ["A", "B", "C"]
 
 # For each (cond, cost): list of best Sharpe across folds

scripts/walk_forward_cv.sh

@@ -13,7 +13,7 @@ set -euo pipefail
 FXCACHE="${FXCACHE:-/home/jgrusewski/Work/foxhunt/test_data/feature-cache/9297017b6db6795f75e57be8aefb03e45e6427513f42c08e22521e58fa025d5e.fxcache}"
 ALPHA="${ALPHA:-config/ml/alpha_logits_cache.bin}"
 FILL="${FILL:-config/ml/alpha_fill_coeffs.json}"
-BIN="${BIN:-./target/release/examples/alpha_compose_backtest}"
+BIN="${BIN:-./target/release/examples/alpha_baseline}"
 
 WINDOW=700000
 TRAIN_FRAC=0.6      # 420K train, 280K eval per fold
@@ -38,7 +38,7 @@ for fold_spec in "${FOLDS[@]}"; do
     --data-start-offset "$offset" \
     --max-snapshots "$WINDOW" \
     --train-frac "$TRAIN_FRAC" \
-    --c51 --temporal --window-k 16 --isv-continual \
+    --window-k 16 \
     --out-path "$out"
 done