Two-part fix for a class of bugs causing un-normalised features to
silently flow into training.
(1) data_source mismatch between precompute writer and trainer reader.
precompute_features.rs:214,633 hardcoded "ohlcv"
train_baseline_rl.rs:582 hardcoded "ohlcv"
config/training/dqn-production.toml: data_source = "mbp10"
Production runs the trainer with the production profile (data_source
= mbp10), but the actual cache lookup hardcoded "ohlcv". Smoke worked
by accident (smoke profile is also "ohlcv"). Any future profile with
a different data_source silently mismatches → cache MISS → DBN
fallback path.
Both call sites now hardcode "mbp10" (the canonical production data
source per CLAUDE.md). precompute_features adds a `--data-source`
CLI override for the rare case a smoke flow needs to regenerate
the local "ohlcv" fxcache; default is "mbp10".
(2) DBN-fallback path didn't normalise features.
precompute_features.rs:629 applies NormStats::normalize_batch on the
canonical fxcache write path. The fallback in train_baseline_rl.rs
(cache-miss → load DBN files → extract features → upload to GPU) did
NOT normalise. Any cache miss (data_source drift, schema-hash mismatch,
missing file) silently uploaded RAW features. Raw close prices
(~$5180 ES futures) flowed into next_states[:, 0]; the aux next-bar
head's label_scale EMA latched onto raw-price magnitude (~5443 vs
expected ~1.0 z-score); the shared trunk learned to predict next-bar
prices; the policy effectively traded with future-price knowledge →
train-h5gxb epoch-0 Sharpe = 141 with 0.32% max-drawdown over 214k
bars (impossibly good = oracle leak).
DBN fallback now applies the same z-score normalisation unconditionally
as defence-in-depth, so a future cache-miss cannot reintroduce raw
values into training.
Audit entry updated.
db9936b9ff