Wave 3b (4320820ae) added a 6th window_lob_bars parameter to
GpuBacktestEvaluator::new and migrated 3 production lib call sites + 6
test call sites, but the lib-only `cargo check -p ml --features cuda`
validation didn't catch the 3 example-binary call sites in
evaluate_baseline.rs (lines 1344, 1641, 1802). Argo's ensure-binary
step compiles all 4 example binaries (hyperopt_baseline_rl,
train_baseline_rl, evaluate_baseline, precompute_features), and
evaluate_baseline failed with E0061 (wrong number of args).
This commit migrates all 3 sites to construct zero-OFI LobBar SoA
inline (eval-path lacks per-bar OFI features — same degradation
pattern as the PPO hyperopt adapter Wave 3b D4 resolution; OFI-impact
term degrades to 0 while commission + half-spread × position still
apply).
Validated: `cargo check -p ml --features cuda --all-targets` clean
(was --features cuda only before — now expanded to catch
example/test/bin compile-breaks).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Per spec §6.6 / Q6 train/dev/test split (defaults Q1-Q7 train, Q8 dev,
Q9 sealed final test):
- DQNHyperparameters: holdout_quarters + dev_quarters (default 1+1)
- crates/ml/examples/train_baseline_rl.rs: --holdout-quarters /
--dev-quarters CLI flags forwarded to hyperparams (this is the actual
training binary; bin/fxt/src/commands/train.rs is a gRPC client and
services/ml_training_service/src/main.rs accepts training params via
proto not CLI — see audit doc note).
- DQNTrainer::train_walk_forward slices training_data BEFORE fold
generation; folds run on Q1..Q(9 - holdout - dev) only.
- DQNTrainer struct: dev_features/dev_targets/holdout_features/
holdout_targets fields stash trailing slices for end-of-training dev
eval and the Phase 4.3 separate eval-only workflow.
- debug_assert sealed-slice guard catches future refactors that
re-introduce holdout into the training path.
Per established Phase 1 precedent (1.1-1.5: kernel/state lands first,
consumer wiring deferred to follow-up commit per
feedback_no_partial_refactor): CLI plumbing + slicing + dev/holdout
storage land in this commit. The post-final-fold dev evaluation call
(consumer of dev_features) is deferred to a follow-up commit and will
mirror Task 1.7's evaluate_dqn_graphed integration pattern. Phase 4.3
argo-eval-final.sh is the sole legitimate consumer of holdout_features
(separate eval-only workflow that does NOT call train_walk_forward).
cargo check -p ml --features cuda --example train_baseline_rl: clean
cargo check -p fxt: clean (no fxt changes needed; gRPC client only)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
Bug 1 of the eval-Hold-collapse diagnosis. The fxcache target schema
documented in experience_kernels.cu:1556 + cuda_pipeline/mod.rs:508 specifies:
target[0] = preproc_close — log-return-normalized close (network input)
target[1] = preproc_next — log-return-normalized next close
target[2] = raw_close — raw price for portfolio simulation
target[3] = raw_next — raw price
target[4] = raw_open — raw price
target[5] = mid_price_open — MBP-10 midpoint (fallback raw_open)
Both writers — `precompute_features.rs:360` and `data_loading.rs:510` —
violated the contract by storing raw OHLCV close prices in slots [0:1].
Empirical fxcache inspection: target[0..4] mean=$5967, stddev=$582 (raw
prices throughout). The raw-price values at target[0:1] were never directly
consumed by training (production aux head reads next_states[i][0] = MARKET
feat[0] = log_return), but they corrupted any code reading targets per the
documented contract.
Both writers now compute (raw_curr / prev_close).ln() and (raw_next /
raw_curr).ln() for the preproc columns. FXCACHE_VERSION bumped 7→8 to
invalidate existing caches and trigger ensure-fxcache regen.
A second bug — eval label_scale=5300 (raw price magnitude) at production
binary despite source state[0] tracing back to z-normalized log_return —
remains unresolved. Bug 2 instrumentation lands in the next commit; that
runtime trace will pin which production-binary code path injects raw_close
into state[0] post-gather.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
OFI features at bar t were computed over [close(bar_t), close(bar_{t+1}))
— bar t+1's formation period — so 31 of 32 OFI dims at the policy's
input for bar t carried next-bar microstructure data. Per audit
`docs/lookahead-bias-audit-2026-04-28.md` §3, this is a DEFINITE leak
contaminating both training inputs and validation backtest.
Fix: shift the window backward to (close(bar_{t-1}), close(bar_t)] so
OFI at bar t uses only data accumulated during bar t's own formation.
Mirrors the correct `log_bar_duration` convention at
precompute_features.rs:567-575 (audit's smoking-gun citation).
Per feedback_no_partial_refactor, both write sites migrated in lockstep:
- crates/ml/examples/precompute_features.rs:441-475 (precompute path)
- crates/ml/src/trainers/dqn/data_loading.rs:396-448 (DBN fallback)
FXCACHE_VERSION bumped 6 → 7. PVC auto-regen via schema-hash check on
next deploy. Local regen:
./target/release/examples/precompute_features \
--data-dir test_data/futures-baseline \
--mbp10-data-dir test_data/futures-baseline-mbp10 \
--trades-data-dir test_data/futures-baseline-trades \
--output-dir test_data/feature-cache \
--symbol ES.FUT --data-source mbp10 --yes
Regression test added: tests/ofi_features_test.rs::
test_ofi_window_alignment_uses_formation_interval validates the new
partition_point predicates against a synthetic 5-bar dataset, including
an explicit anti-regression assertion that bar t+1 snapshots are NEVER
picked up for bar t.
dqn-wire-up-audit.md updated to reflect new contract for
`trainers/dqn/data_loading.rs`. Audit report
`docs/lookahead-bias-audit-2026-04-28.md` checked in alongside the fix.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Smoke and localdev profiles previously used data_source = "ohlcv", divergent
from production (mbp10). Mismatch silently produced cache-key collisions in
the SHA256 hash path: smoke fxcache could not be loaded by production-shape
training without an explicit override. Local-dev fxcache regen also required
remembering to pass --data-source ohlcv to match.
Globalize mbp10 as the default everywhere it isn't deliberately overridden:
- config/training/dqn-smoketest.toml: data_source = "mbp10"
- config/training/dqn-localdev.toml: data_source = "mbp10"
- training_profile.rs: doc Default → "mbp10"
- trainers/dqn/config.rs: Default impl → "mbp10"
- hyperopt/adapters/dqn.rs: default → "mbp10"
- examples/precompute_features.rs: doc updated
- fxcache.rs / feature_cache.rs: discover_and_load + cache-key tests
use "mbp10" arguments
- docs/dqn-wire-up-audit.md: new entry per Invariant 7
Documentation strings retained "ohlcv" only where they document the two
available choices (config.rs:946, training_profile.rs:83).
Local fxcache regenerated to v6 mbp10:
test_data/feature-cache/13c0b086a975cc7e2384377a2cd0e97738c9410292fcfecb5807c29bf885cb48.fxcache
(175874 bars, 55 MB, OFI_DIM=32). Stale v5 ohlcv fxcache untracked
from git index (already gitignored post-79578bbaf).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
`use_iqn` is exactly the `use_/enable_` boolean banned by
`feedback_no_feature_flags.md`. It gated dead code: production training
runs IQN unconditionally through `cuda_pipeline/gpu_iqn_head.rs` +
`iqn_dual_head_kernel`, properly wired into the branching architecture
with the `FIXED_TAUS` 5-quantile schedule. Nothing in the cuda_pipeline
production path ever read `use_iqn` or `iqn_network`.
The legacy `iqn_network: Option<QuantileNetwork>` field on `DQN` was a
parallel CPU-side network from a pre-branching era, structurally
unreachable in production: every consumer was gated behind the
`if true /* use_branching: always on */` arm at `q_values_for_batch`,
so the IQN else-if at L1822 was dead code. Training optimised
`iqn_network` parameters in isolation; inference never read them. That's
the train/inference mismatch the L283 comment ("IQN trains base
q_network but inference uses dist_dueling network (zero gradients)")
was working around by **disabling** the feature instead of fixing the
inference path. Per `feedback_no_quickfixes.md` + `feedback_no_hiding.md`
the fix is to remove the dead path entirely.
Strip:
- `DQNConfig::use_iqn` field + parses + checkpoint hash + metadata.
`dqn.use_iqn` / `dqn.iqn_embedding_dim` / `dqn.iqn_num_quantiles` /
`dqn.iqn_kappa` from older checkpoints are silently dropped on load
(same pattern used for `use_dueling`). `iqn_lambda` stays — the
cuda_pipeline dual head consumes it as the IQN aux loss weight.
- 3 vestigial config fields (`iqn_num_quantiles`, `iqn_kappa`,
`iqn_embedding_dim`) — never read in production; kernel-side macros
(`IQN_NUM_QUANTILES = 5`, embed_dim 64, kappa 1.0) are the actual
config.
- 4 default builders (`Default`, `aggressive`, `conservative`,
`emergency_safe_defaults`) drop the 4 IQN-related fields each.
- `DQN::iqn_network` field + initialisation block in `new_with_stream`.
- 6 conditional gates in `select_action`, `select_action_with_confidence`,
`select_action_inference`, `q_values_for_batch` — all collapse to the
live (branching or standard-Q) arm.
- `DQN::get_state_embedding` (only consumed by deleted IQN paths).
- The entire `crates/ml-dqn/src/quantile_regression.rs` module (392 LOC)
+ its 2 lib.rs exports. Nothing outside `ml-dqn` ever imported it
(the `quantile_huber_loss` reference in `gpu_iqn_head.rs` is a CUDA
kernel name string, unrelated to this Rust module).
Downstream call sites:
- `crates/ml/src/trainers/dqn/{config,fused_training,trainer/constructor}.rs`:
drop `iqn_num_quantiles` / `iqn_embedding_dim` / `iqn_kappa` references
off `DQNConfig`; substitute kernel-fixed literals (64, 1.0) where
`GpuDqnTrainConfig` / `GpuIqnConfig` still expect them.
- `crates/ml/examples/evaluate_baseline.rs`: drop two `iqn_num_quantiles`
hyperparam reads (their `..DQNConfig::default()` fallbacks now stand
alone).
- `crates/ml/tests/dqn_action_collapse_fix_test.rs`: drop the
`assert!(!config.use_iqn, "...gradient dead zone")` and the explicit
`config.use_iqn = false` setter; the dead-zone pathology is now
structurally impossible.
- `crates/ml/tests/dqn_inference_test.rs`: drop `config.use_iqn = false`.
- `services/trading_service/src/services/dqn_model.rs`: drop the
`iqn={}` debug-log field.
- `crates/ml/src/trainers/dqn/distributional_q_tests.rs`: ship Test 0.F
(Plan A Task 8 #186) — converged-checkpoint extraction harness +
`MappedF32Buffer` (mapped pinned f32 mirror) + `compute_sigma_c51_test`
kernel handle. The structural assertions panic on the local 5-epoch
smoke checkpoint as designed (under-converged: sigma_C51 spread ~1.4%
across directions, P(active)=0.4330 >= 0.20). Docstring rewritten to
drop `use_iqn=false` framing and the legacy "Tier-B-prime" caveat;
Tier-A version is GPU-integration-only per
`feedback_no_cpu_forwards.md` (CPU is read-only).
`docs/dqn-wire-up-audit.md` updated per Invariant 7.
Build: `cargo check --workspace --tests` clean (0 errors).
Test: `cargo test -p ml --lib distributional_q_tests::test_0f
-- --ignored --nocapture` produces bit-identical sigma_C51 /
argmax / Thompson values vs pre-removal — confirms branching
forward path is the same code post-cleanup as pre-cleanup
(legacy `use_iqn` arms were unreachable, as expected).
Net: 12 files, +458 / -785 lines (327 LOC deleted).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.
The first L40S deploy attempt (workflow `train-multi-seed-z2llf`, terminated)
failed at startup with `error: unexpected argument '--fold' found` on every
job: `train_baseline_rl` is a multi-fold walk-forward executor that accepts
`--max-folds K`, NOT `--fold N`. The original P5T1 harness assumed the
opposite and fanned out N seeds × K folds = N*K jobs, each invoking the
binary with `--seed N --fold K`.
User chose Path B: pivot to one job per seed (each runs all K folds via the
existing `--max-folds` mechanism). Per-job runtime is K× longer, but fanout
drops from N*K=30 → N=5 (matches L40S pool capacity better) and the binary
contract becomes the one the binary actually has.
4 surface changes:
1. crates/ml/examples/train_baseline_rl.rs — add `--seed N` CLI arg
(default 42 — historic implicit value). Sets `FOXHUNT_SEED` env var at
startup BEFORE any CUDA module spins up. Logs the seed value at the
training start banner.
2. crates/ml/src/cuda_pipeline/mod.rs — add `global_seed()` (reads
`FOXHUNT_SEED`, default 42) + `mix_seed(base)` (SplitMix64 avalanche
so adjacent global seeds produce uncorrelated module seeds). Six call
sites updated to mix the global seed into their previously-hardcoded
constants:
- trainer/action.rs: GpuActionSelector seed (0xDEAD_BEEF_CAFE) + the
epsilon-greedy fallback StdRng (0xAC7_DEF0).
- cuda_pipeline/gpu_iqn_head.rs: IQN Xavier-init RNG (0x1CA_1234).
- cuda_pipeline/gpu_iql_trainer.rs: V(s) Xavier-init RNG (0x1C1_9ABC).
- cuda_pipeline/gpu_her.rs: random-donor RNG (0x4E4_5678).
- cuda_pipeline/gpu_ppo_collector.rs: rng_seeds Vec for PPO
experience-collector init + reset (0xAA0_5EED).
- trainer/training_loop.rs: per-epoch regime_dropout_seed.
3. infra/k8s/argo/train-multi-seed-template.yaml — drop `fold` parameter
from `train-single` template; binary invoked as `--seed "$SEED"
--max-folds {{workflow.parameters.folds}}` so the walk-forward sweep
happens inside the single training process. Drop `FOLD` env var. Update
the nsys-rep upload filename to drop the fold suffix. Update banners /
doc comments to reflect "one-job-per-seed" semantics.
4. scripts/argo-train.sh — matrix generator drops the inner fold loop.
Each emitted task carries only `seed=${s}` and depends on the same
ensure-fxcache + gpu-warmup. The dry-run synthetic marker switches from
`seed=${s} fold=${f}` to `seed=${s} max_folds=${FOLDS}` so test harnesses
count the new shape correctly.
5. scripts/tests/test_multi_seed_harness.sh — assertions updated:
- `--multi-seed 3 --folds 2` produces 3 tasks (was 6).
- Rendered binary command must include `--max-folds
{{workflow.parameters.folds}}` placeholder.
- Rendered template must declare `folds` workflow parameter (so
`argo submit -p folds=K` overrides the default).
- Rendered binary command must NOT contain any per-fold flag — this
catches the failure mode that broke the first L40S deploy.
- Backward-compat: `--multi-seed 1 --folds 1` preserves the existing
single-template path (no DAG matrix tasks emitted).
6. docs/dqn-wire-up-audit.md — adds 1 Wired row documenting the pivot,
the new `--seed`/`mix_seed` plumbing, all 6 RNG call sites, and the
end-to-end seed-variation verification result.
Validation:
cargo check --workspace clean at 11 warnings (workspace baseline preserved).
cargo build --release --example train_baseline_rl succeeds; --help shows
the new --seed flag with documented default 42.
Seed-variation end-to-end test on RTX 3050 Ti (1 fold × 2 epochs each):
--seed 42 → F0 best Sharpe = -9.7831, best_val_metric = 1.957244,
epoch-2 train Sharpe = -16.12, val_Sharpe = +1.11.
--seed 999 → F0 best Sharpe = +92.9341, best_val_metric = 2.161012,
epoch-2 train Sharpe = +92.93, val_Sharpe = -0.25.
Different best Sharpe / best_val_metric / epoch-2 train + val Sharpe
across seeds proves the seed actually propagates through the RNG init
paths and is not just accepted-and-ignored. The seed=42 numbers match
the prompt's "deterministic baseline" expectation (F0 = -9.7831 was
bit-identical pre-pivot because no global-seed plumbing existed).
./scripts/argo-train.sh dqn --multi-seed 5 --folds 6 --dry-run produces
exactly 5 WorkflowTask markers (train-s0..train-s4), each with
`--max-folds {{workflow.parameters.folds}}` in the binary invocation.
All 3 harness tests PASS:
- test_multi_seed_harness.sh: 5 PASS lines, exit 0.
- test_nsys_harness.sh: 4 PASS lines + ALL PASS, exit 0.
- test_tier_checks.sh: PASS overall (good-fixture passes, bad-fixture
surfaces expected check rejections), exit 0.
Backward compat: existing single-job `argo-train.sh` callers (no
`--multi-seed`, no `--folds`) route to the original `train-template.yaml`
unchanged. `--seed 42` is a no-op offset for the SplitMix64 mix at the call
sites — the trajectory shifts only when the user passes `--seed` explicitly,
matching the prompt's "default 42 (historic implicit value)" requirement.
L40S pool: argo-train.sh defaults `--gpu-pool ci-training-h100`; user passes
`--gpu-pool ci-training-l40s` at deploy time. No script default change
(per constraint 5).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Local smoke-test run after Plan 1 C.6 completion surfaced three issues:
1. StateResetRegistry missing dispatch arms for the 8 ISV slots
pre-allocated in ac9bcab94 (isv_epoch_idx, isv_epsilon_eff, isv_tau_eff,
isv_gamma_eff, isv_kelly_cap_eff, + 3 SchemaContract which already no-op).
Fold boundary would error "unknown name 'isv_epoch_idx'". Added dispatch
arms in training_loop.rs::reset_named_state — reset each to 0.0; GPU
kernels repopulate on next epoch.
2. controller_activity smoke test's single 50% threshold was designed for
reactive CPU-compute controllers. Under GPU-drives-CPU-reads, tau is a
Polyak-EMA cosine schedule that fires every epoch by design (95%),
gamma is health-coupled monotonic (may fire every epoch as health
drifts). Split threshold per-controller: reactive (anti_lr, grad_clip,
cql_alpha, cost_anneal) = 0.50; schedule-based (tau, gamma) = 1.00.
3. examples/train_baseline_rl was broken since the f64→f32 ABI refactor
(d64adc14f) — hp_f64 returning f64 assigned to f32 fields. Added
hp_f32 helper that narrows JSON-born f64→f32 at ingest boundary. Use
hp_f32 for f32 fields, hp_f64 for f64 fields (learning_rate,
entropy_coefficient, weight_decay). No more "as f32" casts at call
sites. Also fixed replay_buffer_vram_fraction + bars_per_day f64→f32.
Local smoke tests now pass:
- controller_activity: ok (1 passed, 29.5s)
- multi_fold_convergence: ok (1 passed, 3 folds x 20 epochs, 534.9s)
- 24 new monitor + registry unit tests: all passing
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
fix kernel-read gap
Adds 12 features to the DQN input pipeline:
- 10 MicrostructureState::snapshot()[0..10] slots that were previously computed
every bar and then discarded before reaching fxcache: ofi_trajectory,
realized_variance, hawkes_intensity, book_pressure (weighted 10-level),
spread_dynamics, aggression_ratio, queue_depletion_asymmetry,
order_count_flux, intra_bar_momentum, regime_score.
- 2 TLOB-novel slots derived directly from Mbp10Snapshot:
order_count_imbalance = (Σbid_ct − Σask_ct) / Σ(bid_ct + ask_ct),
microprice_residual = (weighted_mid − mid) / mid.
Also fixes a production gap: ofi_acceleration (slot 18) and
toxicity_gradient (slot 19) were persisted to fxcache via OFI_DIM=20
but the OFI embed kernel (experience_kernels.cu:6146-6173) only read
[0..18), silently discarding them every bar. Kernel extended to
consume full SL_OFI_DIM=32.
Dimension bumps (all 8-aligned):
OFI_DIM 20 → 32
FXCACHE_VERSION 4 → 5 (invalidates existing caches; regen via
precompute_features)
STATE_DIM 96 → 104
PADDING_DIM 4 → 0 (OFI expansion consumed padding, still 8-aligned)
STATE_DIM_PADDED 128 (unchanged)
OFI_EMBED_IN 18 → 32 (MLP input width; W/grad/Adam/m/v buffers
resized in lockstep via named constants)
fxcache regen results (175874 bars ES.FUT 2024-Q1):
deltas_nonzero: 175781 / 175874 (99.9 percent)
book_aggression: 102137 / 175874 (58.1 percent)
microstructure[20-30): 175874 / 175874 (100 percent)
tlob_novel[30-32): 133615 / 175874 (76.0 percent)
Compile status: SQLX_OFFLINE=true CARGO_INCREMENTAL=0 cargo check
--workspace --tests passes cleanly (0 errors, pre-existing warnings
only).
Test results:
fxcache roundtrip (unit + integration): PASS (4+6 tests)
magnitude_distribution smoke: ran through epoch 1 successfully
(OFI_DIAG fires, state_dim=104 confirmed, feature_dim=74 in
validation kernel); epoch 2 OOM on local RTX 3050 Ti (4 GB) —
expected hardware limit from state_dim growth. Full 20-epoch run
requires L40S/H100 CI verification.
multi_fold_convergence smoke: not verified locally (same VRAM
ceiling applies). L40S/H100 CI verification required.
The new slots follow the existing OFICalculator/MicrostructureState
pattern and consume signals already computed by ml-features — no new
crate, no ONNX, no stubs. All 12 sources were audited against their
implementation before persistence; every slot traces back to real
Mbp10Snapshot or MicrostructureState math.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds an optional --max-bars CLI cap to `examples/train_baseline_rl.rs`.
When >0, truncates the fxcache-loaded features/targets/OFI/timestamps in
lockstep per the data_loading.rs precedent (commit 2ac956298 OFI/bars
desync fix), then rebuilds FxCacheData with the capped bar_count. 0 =
unlimited, matching pre-change production behaviour.
Wires --max-bars=500000 into the multi_fold_convergence smoke test. Full
ES.FUT fxcache is ~697,732 bars (~290 MB on GPU with OFI per the
session_2026-04-20 memory note); the smoke's 3-fold × (6+2+2+2×step =
14-month total) walk-forward span needs ~350k bars at 1-min resolution,
so 500k gives ~43% headroom while freeing ~81 MB of VRAM — enough to
unblock RTX 3050 Ti 4 GB local runs that OOMed before.
Applied after fxcache load and before WalkForwardConfig so downstream
fold-range generation sees the capped range. Zero effect on production
training (default 0 = unlimited).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds evaluate_baseline CLI args:
--surrogate-mode=off|random
--surrogate-seed=<u64>
--surrogate-marginals=<path.json>
--emit-action-marginals
--emit-pooled-sharpe
Random-action surrogate samples actions from marginal distribution (or
uniform fallback) using a seeded RNG, bypassing the model entirely.
Surrogate mode forces the CPU DQN path so action selection can be
overridden (GPU path would need invasive kernel changes and defeats
the bypass-the-model sanity check).
Flat action-index counts are tracked in the CPU DQN path only; the
ACTION_MARGINALS: line emits those as a JSON distribution, or emits
an honest stub marker when only the GPU path was exercised. Pooled
Sharpe is computed from concatenated per-fold returns (CPU path) or
falls back to mean-of-fold-Sharpes with a warning.
Smoke test runs 30 surrogate seeds + 1 trained run via evaluate_baseline
subprocess, asserts trained pooled Sharpe exceeds the 95th percentile of
surrogate Sharpes. Test is #[ignore]'d and requires a trained checkpoint
at /workspace/output/dqn_fold0_best.safetensors (Phase 3 deliverable);
FOXHUNT_SURROGATE_CKPT env var overrides for local testing. Uses the
compiled target/release/examples/evaluate_baseline if present, otherwise
falls back to cargo run.
Will pass once Phase 3 produces a checkpoint.
Adds --max-folds CLI flag to train_baseline_rl (0 = no cap). When set,
truncates the generated walk-forward fold list to the first N folds.
Used by the new multi_fold_convergence smoke test to run a 3-fold × 20-epoch
local variant of the Phase 3 L40S 6-fold × 50-epoch validation gate
(~5 min on RTX 3050 vs ~1 hour on L40S).
Test asserts:
* train_baseline_rl subprocess exits 0 (no NaN/Inf)
* ≥ 2/3 folds produce dqn_fold{N}_best.safetensors checkpoint
(checkpoint is only written when Best Sharpe improves during the
fold, so presence = policy learned something on that window)
Per plan Task 0.15 at docs/superpowers/plans/2026-04-21-policy-quality.md.
evaluate_baseline looks for <output_dir>/norm_stats_fold{N}.json per fold
(matching the convention written by train_baseline_supervised.rs:812).
train_baseline_rl never produced this file — the fxcache has a single
<hex_key>.norm_stats.json written by precompute_features, which RL
training uses implicitly for pre-normalized features but never copies to
the per-fold paths evaluate wants. Result (L40S train-7r9zf):
Error: NormStats not found at /workspace/output/norm_stats_fold0.json
- cannot evaluate without training-set statistics (computing from test
data would introduce lookahead bias). Run training first to generate
this file.
WARN: Evaluation failed, continuing
Evaluate fails gracefully but no report is produced.
Fix: new helper `fxcache::norm_stats_path_for_key(data_dir, override, cache_key)`
returns the canonical <hex_key>.norm_stats.json path that sits next to
the .fxcache file. train_baseline_rl resolves this once after the fxcache
load (falling back to None if the key is zero — i.e., DBN fallback path
with no precomputed stats) and copies it into the output dir as
norm_stats_fold{N}.json for every fold processed.
The RL-side fxcache norm stats are fold-independent (one z-score per
cache key, applied to all walk-forward windows), so the per-fold copies
are intentional duplicates of the same file — this matches evaluate's
per-fold lookup semantics without diverging from supervised convention.
Closes task #32.
- OFI_DIAG now reads positions [42..62) using OFI_START constant instead of
hardcoded [66..74). Verified: raw_mean=0.0891, delta_mean=-0.0370,
book_agg=0.4500, log_dur=-0.2303 (was all zeros before).
- Removed 3 stale state_dim field initializers from evaluate_baseline.rs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The v2 fxcache on PVC passed has_ofi=true validation (mbp10_dir was present
when built) but contained all-zero OFI data. The old binary set the flag
based on directory existence, not actual computed values.
Now counts non-zero OFI rows before accepting a cache — forces rebuild
when MBP-10 data is available but OFI content is all zeros.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
OFI (Order Flow Imbalance) features are mandatory — the model is worthless
without MBP-10 order book data. Every silent fallback that degraded to zeros
has been converted to a hard error.
Critical fixes:
- build_batch_states used *8 instead of *20 for OFI dimensions — every
walk-forward backtest was reading corrupted OFI features from adjacent memory
- precompute_features early exit skipped cache rebuild when stale v2/v3
cache existed with has_ofi=false — now validates has_ofi before skipping
- 14 silent OFI fallback paths converted to hard errors across data loading,
training loop, experience collector, state construction, metrics, hyperopt
Dead code removed (-751 lines):
- DoubleBufferedLoader (superseded by init_from_fxcache)
- GpuBufferPool (superseded by init_from_fxcache)
- DqnGpuData::upload legacy method (no OFI support)
- CPU training fallback path (CUDA always required)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
FXCACHE_VERSION 3→4. Three precomputed features added to OFI region:
- ofi[8..16): temporal deltas (ofi[bar] - ofi[bar-1]) for 8 features
- ofi[16]: book aggression (MBP-10 10-level center-of-mass asymmetry)
- ofi[17]: log bar duration (imbalance bar formation time, normalized)
Previously 10 of 18 OFI embed MLP inputs were zero. Now all 18 have
real data: raw_ofi(8) + delta_ofi(8) + book_aggression(1) + log_duration(1).
Added read_state_sample() diagnostic for GPU state verification.
Legacy v3 fxcache handled by zeroing new slots (v2→v4 graceful upgrade).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
target_dim expansion: adds raw_open (OHLCV) and mid_price_open
(MBP-10 midpoint at bar formation) to fxcache targets. FXCACHE_VERSION
2→3 for auto-rebuild. Legacy v2 files handled with close-price fallback.
Spec v5 adds 3 pearls:
- Bar duration encoding in Mamba2 (continuous-time SSM awareness)
- Order book center of mass from all 10 MBP-10 levels (aggression signal)
- Retrospective hold quality bonus (teaches exit timing)
Plus: Hold action (4th direction), DSR Sharpe EMA fix, counterfactual
magnitude/order sign fix, MFT mid-price mark-to-market.
OFI embed MLP now 18→10 (was 16→8). Mamba2 width SH2+10 (was SH2+8).
Attention width D+10 (was D+8).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Default was hardcoded as 64 in train_baseline_rl.rs and evaluate_baseline.rs,
overriding the config default of 32. Added num_quantiles=32 to production
config so it's explicit.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Removed: enforce_hold(), min_hold_bars from config/kernels/backtest.
Added: holding_cost_rate (inventory penalty), churn_threshold_bars +
churn_penalty_scale (graduated flip penalty) to reward in
experience_env_step and backtest kernels.
The model learns optimal hold timing from cost signals:
- Per-trade tx cost prevents churning (existing)
- Inventory penalty makes large positions expensive to hold
- Churn penalty graduates cost for rapid flips
- Temporal attention learns when holding cost > expected profit
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
bars_per_day was hardcoded 390 (1-minute candles) but we use MBP-10
imbalance bars (~7500/day). All Sharpe/Sortino/return annualization
was off by sqrt(7500/390) ≈ 4.4×.
Now computed from actual fxcache timestamps:
bars_per_day = total_bars / (trading_days from timestamp span)
Propagates to: val_Sharpe, train_Sharpe, financials, backtest evaluator.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
12 new tick-level features computed per bar from MBP-10 snapshots:
OFI trajectory, realized variance, Hawkes intensity, book pressure,
spread dynamics, aggression, queue depletion, order count flux,
intra-bar momentum, regime score, OFI acceleration, toxicity gradient.
Combined with existing 8 OFI into [f64; 20] per bar in fxcache.
Also fixes pre-existing bug: no-MBP-10 fallback was [0.0; 8] not [0.0; 20].
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
fxcache: OFI_DIM=20 (pub const), RECORD_F64_COUNT=66, 272 bytes/bar.
constructor: state_dim 74→86 (aligned 88) with OFI.
experience collector: ofi_dim detection 8→20.
All [f64; 8] → [f64; 20]. Existing 8 features preserved at 0-7.
New 12 features zero-padded until precompute is extended.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
evaluate_dqn_graphed now takes Option<&mut dyn QValueProvider>.
Training eval passes Some(fused_ctx), standalone eval uses closure path.
Removed dead evaluate_dqn non-graphed branch from evaluate_baseline.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add to_varstore() compatibility shims on DuelingQNetwork and
DistributionalDuelingQNetwork so test/example code can rebuild a
GpuVarStore snapshot when needed. Delete dead tests that referenced
removed DQNAgent, PrioritizedReplayBuffer, and ReplayBufferType.
Fix action index references (action_19/21 -> action_28/30) and
type annotation issues (sin ambiguity, remainder operator).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
DuelingWeightSet and BranchingWeightSet fields changed from owned
CudaSlice<f32> to raw u64 device pointers + usize element counts.
This enables zero-copy views directly into the flat params_buf for
the training hot path (no D2D copies, no shadow allocations).
Key changes:
- Weight sets store u64 + usize pairs instead of CudaSlice<f32>
- from_flat_buffer() creates zero-copy views into params_buf
- from_slices() creates pointer views from owned CudaSlice allocations
- DuelingWeightBacking/BranchingWeightBacking hold owned CudaSlice
arrays for callers that need independent allocations (experience
collector, ensemble heads, tests)
- flatten/unflatten become no-ops when weight sets point into params_buf
- extract functions return (Backing, WeightSet) tuples
- KernelWeightPack::build() uses u64 fields directly (no raw_device_ptr)
- All sync functions updated for pointer-based signatures
- Ensemble clone functions return backing + pointer view pairs
- gradient_budget tests updated (7/7 pass)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The 4-branch DQN (direction x magnitude) had 3 degenerate variants
(Short25, Flat, Long25) that all mapped to 0.0 target exposure when
direction=Flat, causing 82% Flat collapse. Collapse these into a
single Flat variant, giving 7 levels (ShortSmall/Half/Full, Flat,
LongSmall/Half/Full) and 63 total factored actions (7x3x3).
- ExposureLevel enum: 9 variants -> 7 (add direction/magnitude/from_dir_mag)
- FactoredAction: 81 -> 63 total actions, from_index/to_index updated
- DQN epsilon-greedy: use from_dir_mag() instead of dir*3+mag indexing
- DQN config: num_actions default 9 -> 7
- PPO action space: 45 -> 63 actions, action masking updated
- Signal adapter CUDA kernel: 5-bin -> 7-bin exposure aggregation
- All tests updated for new variant names and index ranges
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Complete bf16 elimination across all crates (ml, ml-core, ml-dqn, ml-ppo,
ml-supervised). Zero half::bf16, __nv_bfloat16, or CudaSlice<half::bf16>
references remain (verified by grep).
CUDA: All 60+ .cu kernels and .cuh headers converted to native float.
- Half-precision intrinsics (__hmul, __hadd, __hdiv) → float operators
- atomicAddBF16 → native atomicAdd(float)
- bf16 wrapper functions → f32 identity passthroughs
Rust: All CudaSlice<half::bf16> → CudaSlice<f32> across 90+ files.
- htod_f32_to_bf16/dtoh_bf16_to_f32 → htod_f32/dtoh_f32 (direct, no conversion)
- Deleted bf16 mirror infrastructure (DuelingWeightSetBf16, alloc_bf16_mirror, etc.)
- Renamed params_bf16→params_flat, d_value_logits_bf16→d_value_logits, etc.
- Fixed .to_f32() sed damage on Decimal::to_f32() and rng.f32()
FxCache: Single f32 disk format (was bf16/f64 dual-version).
- Deleted --bf16 CLI flag from precompute_features
- PVC cache files need regeneration via precompute_features
TF32 tensor cores activated via cublasLtMatmul CUBLAS_COMPUTE_32F — no
explicit TF32 types needed. Storage is pure f32 everywhere.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The evaluate closures now receive &CudaSlice<f32> from the backtest
evaluator. Updated all 3 closures (DQN, PPO, supervised) to match.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Global filter selected only ONE quarter's instrument_id, producing 3 months
of data instead of 2+ years. Now filters within each file so quarterly
rolls are handled correctly.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace extract_ohlcv_bars_from_dbn() with trades-based volume bar pipeline
(load_trades_sync -> filter_front_month -> build_volume_bars) in both the
precompute binary and the runtime fallback path, eliminating fake price jumps
from interleaved contract months in OHLCV DBN files.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace 40-line inline fxcache discovery block in train_baseline_rl.rs
with a single call to ml::fxcache::discover_and_load(). precompute_features.rs
already uses correct symbol/data_source args — no changes needed there.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds `has_ofi: bool` to `FxCacheData` and the fxcache binary header
(stored in `reserved[0]`). Propagates through load_fxcache, write_fxcache,
all callers (precompute_features, train_baseline_rl, hyperopt dqn adapter),
existing roundtrip tests, and adds two new has_ofi-specific roundtrip tests.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Different instruments (ES.FUT vs NQ.FUT) and data source modes (ohlcv vs mbp10)
now produce distinct .fxcache keys, preventing silent overwrites and wrong-bar-type
loads at cache lookup time.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The cache key in train_baseline_rl was computed from data_dir/symbol
(e.g. test_data/futures-baseline/ES.FUT) but precompute_features uses
data_dir (test_data/futures-baseline). Different paths → different keys
→ cache miss → OFI=false.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- precompute_features early-exits if {cache_key}.fxcache already exists
(cache key = SHA256 of filenames + sizes, only changes when data changes)
- compile-and-train copies binaries to PVC /data/bin/ after compile
- precompute step added to compile-and-train DAG (runs after compile,
before training, parallel with GPU warmup)
- precompute template reads binary from PVC (no GitLab download)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
collect_dbn_files_filtered() checks for symbol subdirectory first,
falls back to filename matching. Precompute now loads only the
target symbol's data instead of all 4 instruments mixed together.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Normalize features once in precompute_features (single source of truth).
NormStats saved alongside .fxcache for inference denormalization.
Removed per-fold NormStats computation from train_baseline_rl, hyperopt
adapter, and smoketests — fxcache is pre-normalized, consumers use as-is.
NOTE: features still show raw price values (max=18000+) because
test_data/futures-baseline contains multiple symbols (ES, NQ, ZN, 6E)
mixed into one dataset. The feature extraction pipeline needs
investigation — log returns between different symbols produce garbage.
This is tracked as a separate task.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause of NaN at step 22: cuBLAS out-of-bounds reads in the GPU
experience collector and training forward pass, caused by buffer
dimension mismatches that produced garbage states → garbage Q-values →
NaN loss.
Bugs fixed:
1. GpuDqnTrainConfig::default() had bottleneck_dim=2, market_dim=42 —
collector computed s1_input_dim=40 instead of state_dim=80, cuBLAS
read 2x past W1 weight buffer (10KB OOB per SGEMM)
2. batch_states allocated with state_dim=80 but cuBLAS ldb=128 (CUTLASS
K-tile alignment) — 48 bf16/f32 values read past end per row
3. Validation state tensor had 59 elements/row (42+3+8+6) instead of
128 (state_dim_padded) — missing portfolio/MTF features + padding
4. CUTLASS K-tile overread on all hidden activation buffers (K=64 but
tile=128) — added 128-element padding to 14 bf16 buffers
5. init_from_fxcache never set self.ofi_features — collector OFI buffer
was 1-element placeholder, state_gather kernel read OOB
6. collect_gpu_experiences_slices missing *2 counterfactual multiplier —
half of collected experiences never inserted into PER
Dead code removed (~1228 lines):
- train_with_data_full_loop (old Vec<(FeatureVector, Vec<f64>)> loop)
- init_gpu_data, init_gpu_raw_buffers, collect_gpu_experiences,
run_training_steps (old helpers only called by above)
- train_with_preloaded_data, train_with_shared_data (old hyperopt APIs)
- train() and train_walk_forward() now convert to fixed-size arrays
and route through init_from_fxcache + train_fold_from_slices
Other fixes:
- precompute_features defaults output-dir to sibling of data-dir
- workspace_root() + feature_cache_dir() helpers for reliable path
resolution (works from any cwd, with/without CARGO_MANIFEST_DIR)
- Production smoketest uses fxcache (no slow DBN parsing)
- compute-sanitizer: 0 errors (was 2539)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
