Direct inspection of ES.FUT_2024-Q1.dbn.zst (databento python client, offline
kubectl-cp from PVC) pinpoints the source of the 8,799 corrupt bars that
sanitize_bars (Fix 25) caught at the bar-level gate.
Q1 file content:
Outrights (correct): ESH4/ESM4/ESU4/ESZ4/ESH5 — 43,353 records (76.87%)
price range $5,063–$5,478
Spreads (poison): ESH4-ESM4/ESM4-ESU4/... — 13,048 records (23.13%)
price range $47.30–$219.70
Calendar spreads trade at the price DIFFERENCE between adjacent contracts
(~$60-150 cost-of-carry roll basis). Databento's stype_in=parent resolution
for ES.FUT returns BOTH outrights AND every spread combination in the same
DBN stream. The legacy decoder keyed only by ts_event + dedup-by-volume;
during low-volume overnight windows + active rollover periods, spread bars
beat the outright on volume and survived the dedup. 780 spread bars
survived in Q1 alone; ~8,800 across 2024-2026.
Fix: build dbn::TsSymbolMap from metadata once, resolve each record's
instrument_id → symbol, skip any symbol containing `-` (spread separator).
Same-ts dedup-by-volume continues to handle legitimate front/back-month
overlap among outrights.
Adds `time = "0.3"` to ml/Cargo.toml (dbn::TsSymbolMap uses time::Date).
Why complementary to the Fix 25 sanitize_bars gate:
- Spread filter (this commit) catches the cause precisely by symbol pattern,
but only for instruments where parent-symbol resolution is the source.
- sanitize_bars catches the symptom universally by close-ratio bound, but
can't distinguish a low-basis spread from a fast-moving outright in
extreme cases.
Both layers active: spread filter dispatches at parser level, sanitize as
defense-in-depth backstop for unknown future contamination shapes.
Validation: `cargo check -p ml --example train_baseline_rl` clean.
Refs: Bug 2 chain (#191, #194, label_scale=5443 leaks), today's
sanitize_bars find (Fix 25). Closes the contamination-source investigation.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stops chasing one-off corruption bugs. Three+ historical fixes patched
specific writers (#191 fxcache column-0, Bug 1 target schema,
label_scale=5443 leaks, today's state[0] heavy-tail). Each new
corruption shape found the next hole. This installs a structural
defense so corruption is REJECTED at the data-loading boundary
regardless of source.
Five independent layers, each mandatory:
1. Bar-level sanity (baseline_common::sanitize_bars):
drop bars with non-positive OHLC, high<low, non-finite values, or
close/prev_close outside [0.5, 2.0]. Catches DBN parse glitches,
broker tick errors, near-zero-open bars at source.
2. safe_log_return result clamp (extraction.rs:1246):
ratio.ln().clamp(±0.1). Real-market 1-bar log returns rarely
exceed ±0.05; ±0.1 traps every legitimate move while rejecting
the corruption shape (corrupt bar with bar.open ≈ 0 → ln = -30
→ previously normalized to -30000-magnitude state[0] outliers).
3. validate_features pre-norm bound (extraction.rs:538):
|val| ≤ 5.0 post-extraction. Pre-norm features come from
safe_normalize ([0,1]/[-1,1]), safe_clip (max ±3), or clamped
log-returns (±0.1); ±5 catches extractor invariant breaks.
4. NormStats::normalize post-norm clamp (walk_forward.rs:688):
((val - mean) / std).clamp(±20.0). Even if upstream produces
outliers, every value uploaded to GPU is bounded.
5. Shared validate_normalized_features gate (walk_forward.rs):
single source-of-truth invariant enforced at THREE sites:
- fxcache fast path (after discover_and_load)
- DBN fallback (after normalize_batch)
- precompute writer (before fxcache write — never persist
a poisoned cache)
Removed: DIAG_BUG2 + DIAG_BUG2_v2 one-shot diagnostics
(~125 lines of host-side download + outlier scan in
training_loop.rs). Replaced by structural defense — instrumentation
isn't needed when corruption can't reach state[0].
FEATURE_SCHEMA_HASH auto-bumps via build.rs FNV-1a hash over
SCHEMA_FILES (extraction.rs included). All pre-fix .fxcache files
on PVC are invalidated at load time; ensure-fxcache regen produces
clean cache with new clamps applied.
Why this finally closes the chapter: per-writer fixes are reactive
(land after corruption hits prod). Boundary validation is
proactive — every future regression to extraction or normalization
trips the gate at load, not at epoch 5 of a 50-epoch run. The 5
layers are independent: a bug in any one leaves the others as
backstop.
Validation: cargo check -p ml --all-targets --offline clean.
NormStats unit tests (walk_forward.rs:706+) still pass — clamp +
validate are additive; existing test inputs are well within bounds.
Refs: SP5 Bug 2 (state[0] std=570 outliers on smoke-test-xb78r),
historical #191#210#214#193#195 chains.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Previously train_baseline_rl.rs only passed commission (tx_cost_bps) to
DQN/PPO trainers, ignoring bid-ask spread slippage. Now computes per-fold
average spread via spread_cost_bps() (same as evaluate_baseline) and passes
total cost (commission + spread) to both trainers.
Removes #[allow(dead_code)] — function is now used by all 4 example binaries.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add dimension validation in DQN, PPO, Mamba2, TGGN, TLOB, Liquid,
KAN, xLSTM, Diffusion constructors (fail-fast on zero-dim inputs
that would cause CUDA_ERROR_INVALID_VALUE at runtime)
- Add num_unknown_features > 0 guard to TFT (temporal input required)
- Fix 12 dead-code/unused warnings in test compilation
- Remove opt-level=3 and codegen-units=1 from target rustflags
(was forcing O3 + single-thread codegen on dev/test builds)
- Remove hardcoded jobs=16 cap (cargo now auto-detects CPU count)
- Switch linker to clang+lld (2-5x faster linking)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Extend ml_training_service to dispatch GPU training jobs as K8s batch/v1
Jobs, collect results via a Rust sidecar uploader, and support model
promotion with operator approval via fxt CLI.
- K8s dispatcher creates Jobs on gpu-training pool with native sidecar
- training_uploader crate: watches DONE/FAILED marker, uploads to S3,
reports completion via ReportJobCompletion gRPC
- PromotionManager compares metrics, queues better models for approval
- 4 new proto RPCs: ReportJobCompletion, ListPendingPromotions,
ApprovePromotion, RejectPromotion
- fxt commands: train start, model list/approve/reject
- Training binaries write DONE/FAILED markers + metrics.json
- Dockerfile, K8s job template, and CI pipeline updated
- StartTraining gracefully falls back to in-process when outside K8s
- 27 new tests (16 service + 11 promotion), 141 total service tests pass
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a parent/child GitLab CI pipeline for ML model training:
- Generator script produces per-model hyperopt/train/evaluate jobs
- Parent pipeline (.gitlab-ci-training.yml) with manual trigger
- NFS-backed ReadWriteMany PVC for shared training outputs
- Hyperopt params wired into training binaries (DQN, PPO, TFT, Mamba2)
- Shared DBN loader eliminates duplicate code across hyperopt adapters
- Supervised hyperopt unified to DBN data (was parquet-only)
Pipeline: hyperopt (4 models) → train (10 models) → evaluate ensemble
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 17 library crates into crates/, CLI binary into bin/fxt,
consolidate 10 test crates into testing/, split config crate
from deployment config files.
Root directory reduced from 38+ to ~17 directories.
All Cargo.toml paths and build.rs proto refs updated.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
