feat(hpo): env-var knob overrides + sweep script (18 configs)

- train.py knobs all readable from JEPA_* env vars (JEPA_WINDOW, JEPA_D_MODEL,
  JEPA_DEPTH, etc.) so hpo_sweep.py can override without touching source
- scripts/hpo_sweep.py: 3×2×3 grid over D_MODEL × DEPTH × WINDOW,
  logs to results/hpo/hpo_results.jsonl with leaderboard at end
- 3 new tests: env override correctness, configs() schema validation
- 19/19 tests pass

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

train.py

@@ -17,21 +17,22 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-# --- agent-tunable knobs ---
-USE_HOURLY  = True   # prefer eurusd_hourly.parquet when available
-WINDOW      = 240    # hourly: 10 trading days; if USE_HOURLY=False reset to 60
-PATCH_LEN   = 24     # hourly: 1-day patches (10 tokens); if USE_HOURLY=False reset to 10
-D_MODEL     = 128
-DEPTH       = 2
-N_HEADS     = 4
-ALPHA       = 0.1   # VICReg mixing weight (fixed at 0.1 in HEPA paper)
-DELTA_T_MAX = 3     # max prediction horizon in patches (1..min(DELTA_T_MAX, N-1-c))
-BATCH_SIZE    = 512   # mini-batch per step (hourly dataset is too large for full-batch)
-EPOCHS        = 300
-LR            = 3e-4
-PHASE1_EPOCHS = 200   # supervised head epochs (encoder frozen)
-PHASE1_LR     = 1e-3
-SEED          = 0
+# --- agent-tunable knobs (all overridable via JEPA_* env vars for HPO) ---
+import os as _os
+USE_HOURLY    = True
+WINDOW        = int(_os.environ.get("JEPA_WINDOW",        240))
+PATCH_LEN     = int(_os.environ.get("JEPA_PATCH_LEN",     24))
+D_MODEL       = int(_os.environ.get("JEPA_D_MODEL",       128))
+DEPTH         = int(_os.environ.get("JEPA_DEPTH",         2))
+N_HEADS       = int(_os.environ.get("JEPA_N_HEADS",       4))
+ALPHA         = float(_os.environ.get("JEPA_ALPHA",       0.1))
+DELTA_T_MAX   = int(_os.environ.get("JEPA_DELTA_T_MAX",   3))
+BATCH_SIZE    = int(_os.environ.get("JEPA_BATCH_SIZE",    512))
+EPOCHS        = int(_os.environ.get("JEPA_EPOCHS",        300))
+LR            = float(_os.environ.get("JEPA_LR",          3e-4))
+PHASE1_EPOCHS = int(_os.environ.get("JEPA_PHASE1_EPOCHS", 200))
+PHASE1_LR     = float(_os.environ.get("JEPA_PHASE1_LR",   1e-3))
+SEED          = int(_os.environ.get("JEPA_SEED",          0))
 # ---------------------------
 
 torch.manual_seed(SEED)