4bed8f2dbfb46387e263e06ef40a4c1948de35bf
246 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
jgrusewski
|
cc40780b80 |
chore(ml): file-level allow(unsafe_code) on 12 CUDA-launch files
CUDA kernel launches via cudarc::launch_builder and MappedF32::new (cuMemHostAlloc DEVICEMAP FFI) inherently require unsafe blocks. The workspace-wide '-W unsafe-code' lint produced ~80 warnings across these files, all structurally identical. Match the established pattern from ml-backtesting/src/harness.rs and ml-backtesting/src/sim/mod.rs: single file-level allow with a comment explaining the rationale. Files: alpha_dqn_h600_smoke, alpha_baseline, cublaslt_debug, gpu_walk_forward, cuda_pipeline/mod, hyperopt adapters (mamba2, ppo), DQN smoke_tests (helpers, td_propagation), trainers/ppo, and two ml/tests files. Documented in dqn-wire-up-audit.md. |
||
|
jgrusewski
|
78a9e08358 |
feat(loader): InstrumentFilter::FrontMonth for cross-quarter ES.FUT data
Replaces Option<u32> instrument_id_filter with InstrumentFilter enum {All,
Id(u32), FrontMonth}. FrontMonth runs a two-pass detect over the DBN
stream: pass 1 counts instrument_ids and collects SymbolMapping records,
picks the dominant id, validates it resolves to an ES contract via regex
ES[FGHJKMNQUVXZ]\d{1,2}; pass 2 streams the filtered records.
Motivated by alpha-perception-k54wd: a single-id filter on parent-symbol
ES.FUT data caught Q1 2024 (kept=73M) but kept=0 for Q2-Q9 because ES
front-month rolls quarterly (ESH4 -> ESM4 -> ESU4 -> ESZ4 ...). FrontMonth
self-tunes across the rolls without needing a per-file id table.
Sidecar keys distinguish modes: mbp10 / mbp10_instr<id> / mbp10_front_month.
CLI flag renamed --instrument-id -> --instrument-mode {all,id=N,front-month}
with matching parameter rename in argo-alpha-perception.sh + template.
|
||
|
jgrusewski
|
783297e002 | feat(loader): instrument_id filter + outdated test fix + sp18 fingerprint | ||
|
jgrusewski
|
045850e8f3 |
arch(crt-a): delete decision_stride field — greenfields atomic refactor
Per spec 2026-05-20-continuous-reasoning-trader-design.md §3.3 and §8:
decision_stride is REMOVED, not deprecated. No backwards-compat shim,
no fallback. Every consumer migrates in this commit per
feedback_no_partial_refactor.
Removed from:
- bin/fxt-backtest: RunArgs CLI flag, SweepBase field, SweepCell
override field, default_decision_stride() function, all three
BacktestHarnessConfig and RunArgs construction sites
- crates/ml-backtesting/src/harness.rs: BacktestHarnessConfig field,
MultiHorizonLoaderConfig decision_stride initializer, `let stride`
local, `if event_count % stride == 0` gate around
step_decision_with_latency; forward_step_into + step_decision now
share a single window-full guard (merged into one `if` block)
- crates/ml-alpha/src/data/loader.rs: MultiHorizonLoaderConfig field,
next_sequence stride logic simplified to stride=1 (consecutive
snapshots only)
- crates/ml-alpha/src/trainer/perception.rs: PerceptionTrainerConfig
field and Default impl; all four dt_s locals replaced with 1.0_f32
(training K-loop, graph-capture K-loop, forward_step_into CfC step,
eval K-loop)
- crates/ml-alpha/examples/alpha_train.rs: CLI flag, trainer_cfg and
both loader configs
- crates/ml/examples/alpha_baseline.rs: CLI flag, train + eval stride
gates replaced with unconditional read_all()
- config/ml/*.yaml: decision_stride: lines removed from
sweep_smoke, sweep_threshold_tuning, sweep_deployability,
sweep_decision_stride_example (file repurposed as generic example)
- tests: forward_step_golden, perception_overfit (×7 structs including
the stride=4 smoke repurposed as a second convergence check),
multi_horizon_loader (stride=4 spacing test repurposed as
ts_ns monotonicity check), ring3_replay, trainer_parity
Harness loop now invokes BOTH forward_step_into AND
step_decision_with_latency on every event whenever the snapshot window
is full. forward_step_into advances SSM state and writes alpha_probs_d;
step_decision_with_latency reads alpha_probs_d immediately after —
no CPU roundtrip, no stride gate.
n_decisions ≈ events_processed - seq_len + 1 after this commit
(vs ~9999 at stride=200 in the S2 baseline).
cargo check --workspace: clean
cargo test -p ml-backtesting --lib: 33 passed
cargo test -p ml-alpha --lib: 33 passed (6 ignored)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
|
||
|
jgrusewski
|
10f4bcc15b |
feat(alpha): decision-stride + cluster 9-fold CV workflow
Two complementary additions to validate the minute-horizon alpha hypothesis at IBKR-realistic costs: 1. `alpha_baseline --decision-stride N`: emits a new action every N steps; between decisions force action=0 (wait) so an open position is held rather than re-decided per bar. Cuts per-bar trade counts ~stride× and removes the coin-flip overtrading. Local 2Q sweep showed stride=200 + scaled training (8K episodes × 25 envs × H=1200) flipped Sharpe at ¼-tick from -4.29 (per-bar, 3-fold mean) to +1.78, with std collapsing from ±8.8 to ±1.15. Break-even cost moved from <¼-tick to ~1-tick — for the first time positive at IBKR-realistic passive-execution frictions. 2. `alpha_train_stacker --max-rows N`: optional cap on bars consumed from the fxcache. Used during local 2Q smoke (--max-rows 4M against the 17.8M-row 9Q fxcache) to fit Mamba2 training on a 4 GB consumer GPU; on the cluster (--no-cap) it sees all 9Q. 3. New Argo workflow `alpha-cv`: standalone template that compiles alpha_train_stacker + alpha_baseline + alpha_fill_coeffs.json, trains the stacker on the 9Q fxcache, then runs 9 sequential walk-forward folds of alpha_baseline on disjoint 1.9M-bar windows (one per quarter). Launcher script `scripts/argo-alpha-cv.sh` mirrors argo-train.sh conventions. The local 2Q test that motivated this commit is summarised inline in the alpha-cv template comments; the verdict was "framing was the bug — once decision cadence matches the multi-minute alpha horizon, the strategy is positive at IBKR commission". Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
e7ce4395e8 |
perf(precompute): parallel trades load + predecoded sidecar cache
Flamegraph of precompute_features on 1Q ES showed 62% of CPU time in zstd decompression, 6% in DBN FSM parsing, and only 2% in the actual feature math — single-threaded zstd was the bottleneck, not compute. Two fixes: 1. Per-quarter parallelism on the volume-bar trades loop (was sequential `for file in &trade_files`); brings it in line with the OFI path that already used par_iter. 2. Predecoded sidecar cache in `crates/ml-features/src/predecoded.rs`: first call to a `.dbn.zst` writes a bincode'd Vec<Mbp10Snapshot> or Vec<DbnTrade> under `<output_dir>/predecoded/`. Subsequent calls deserialize the sidecar and skip zstd entirely. An mtime+size header self-invalidates the sidecar when the source changes — no manual flush needed when a quarter is re-downloaded. Local 1Q ES results: - cold (writes sidecar): 40.7s (was 39.3s; +1.4s for write) - warm (HIT): 4.7s (8.7× faster) - zstd in flat perf: 62% → 0% of CPU samples - sidecar disk per Q: ~150MB The sidecar layer also auto-dedupes within a single run: the OFI section re-loads trades, but the second call hits the sidecar that the volume-bar section wrote moments earlier. CLI: `--rebuild-predecoded` purges sidecars for cold-path testing or after a wire-format change to Mbp10Snapshot / DbnTrade. Sidecars also self-invalidate on format-version mismatch so old caches are skipped silently rather than mis-deserializing. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
623ebfcf71 |
fix(precompute): in-place z-score + drop feature_vectors early
Previous workflow train-4qwtc hit memory-pressure thrash (~56Gi
cgroup.current sitting at the 56Gi pod limit, kernel reclaim
hammering page cache) right after OFI completed on the 9-quarter
17.8M-bar dataset. Two refactors reduce peak by ~12GB:
(1) walk_forward.rs: new `normalize_batch_in_place(&mut features)`
that rewrites the slice in place. The previous `normalize_batch`
`.collect()`s a new Vec — at this dataset size that's a
transient ~6GB peak while both pre- and post-normalised arrays
are alive.
(2) precompute_features.rs:
- call `normalize_batch_in_place` instead of the rebinding form.
- explicit `drop(feature_vectors)` after copying the slice into
`features` — `feature_vectors` would otherwise stay alive
until end-of-main shadowing the ~6GB allocation through
every downstream step.
Combined with the prior `t.into_iter()` refactor (
|
||
|
jgrusewski
|
110d3b4125 |
chore(ml): delete dead imports, parens, and the unused MappedI32::read
Cleanup of compiler warnings flagged by both local cargo check and the cluster ensure-binary log. Per `feedback_no_hiding`, every site is either deleted or wired up — no #[allow] suppressions. Lib (5 sites): - gpu_backtest_evaluator.rs:34 — drop unused DevicePtrMut. - gpu_dqn_trainer.rs:49 — drop unused DevicePtrMut (8 device_ptr_mut calls don't need the trait import in current cudarc). Line 19852: drop unnecessary parens around `b * sh2`. - training_loop.rs:20 — drop unused DevicePtrMut; unbrace single- symbol use at 5766. - state_reset_registry.rs:4 — delete the 10-symbol use-block of slot constants. Names appear in description strings (documentation only), symbols are never referenced. Examples (3 sites): - alpha_dqn_h600_smoke.rs:181, 186 — drop COL_RAW_CLOSE, FEAT_DIM, FillCoeffs, FillModel imports. - alpha_baseline.rs:79 — delete unused MappedI32::read. Batched path uses read_all for N-element action readback; the single-element method was leftover from the pre-batched legacy path. Lib + examples now have zero removable warnings. The remaining unsafe_block lints (each cudarc kernel launch needs unsafe) are structural and not actionable under the project's -W unsafe-code policy. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
a27cb40a9a |
fix(precompute): consume DbnTrade Vec during Mbp10Trade conversion
The 9-quarter precompute_features run OOM-killed at ~56Gi on the ci-compile-cpu pool (POP2-HC-32C-64G) on 2026-05-16. Root cause: lines 672-684's `t.iter().map(...).collect()` borrows the source DbnTrade Vec while building the Mbp10Trade Vec — both alive simultaneously, transient peak ~25GB just from this transformation for the 199M-trade dataset. `.into_iter()` consumes the source element-by-element so the allocation drops as the destination grows, capping peak at the larger of the two Vecs (~15GB) rather than their sum. Should let the 9-quarter precompute fit comfortably on the existing 64GB ci-compile-cpu pool without provisioning a high-memory node. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
d49003de6d |
feat(regime): vol_ref floor controller + cross-invocation disk persist
Closes the TrainingPersist loop for the regime defense per the KELLY_F_SMOOTH precedent (pearl_kelly_cap_signal_driven_floors). Three layers: (1) Per-step: alpha_regime_vol_update_kernel tracks min(vol_obs > 1e-12) in new slot 553 (REGIME_VOL_OBS_MIN_INDEX). Filtered against artifact- zero observations from stationary snapshots where curr == prev. (2) Per-cell: stacker_threshold_controller_update gains a fifth branch that reads vol_ref (slot 550) at cell-end, computes `target = floor_target_ratio × vol_ref`, slow-EMAs the floor anchor (slot 552) toward the target with rate `floor_update_rate` (0.1 per cell). Subfloor 1e-12 inside the kernel guards against the anchor collapsing to zero. (3) Per-invocation: alpha_baseline reads `config/ml/alpha_baseline_state.json` at startup and seeds slot 552 from the `regime_vol_ref_floor` field. At end of main(), the learned floor is written back via tmp+rename atomic write so concurrent walk-forward invocations see a consistent file. Matches the cross-fold-persistent shape of KELLY_F_SMOOTH. Block extended to 15 slots (539..=553). Smoke + kernel unit test pass -1/-1 for the new floor-controller indices (backward compat). Walk-forward CV verdict (Q1 fxcache, 3 sequential folds): iteration fold-A fold-B fold-C mean ± SD pre-defense (no regime) +91.52 -21.44 +46.74 +38.94 ± 56.88 hardcoded 1e-9 floor -19.77 +65.04 +6.45 +17.24 ± 43.42 learned floor (0.5 × cell_min) +74.78 -12.72 +8.80 +23.62 ± 45.59 learned floor (0.1 × vol_ref) -19.53 -26.51 +15.46 -10.20 ± 22.49 Controller infrastructure is structurally correct (loop closes, floor persists across invocations, kernel + disk + ISV all roundtrip). The TUNING is data-dependent — single-quarter CV doesn't have enough regime diversity to anchor the floor against. Multi-quarter fxcache validation is the next step (built cluster-side on the 9-quarter 2024-Q1..2026-Q1 ES futures dataset, downloaded as a single artifact for local CV). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
3c035ce1ae |
feat(regime): vol_ref bootstrap window + ISV-driven permanent floor
Two coupled fixes to the vol-EMA regime detector exposed by walk-forward CV after all features were promoted to always-on: (1) Bootstrap window for vol_ref (slot 551 = REGIME_VOL_REF_SAMPLES) - Replace the Pearl-A "first observation replaces directly" bootstrap with a running mean over the first N=100 vol_ema observations, then switch to β-tracking. For IID observations the running-mean estimator has variance σ²/N — a 100-sample mean is 10× less noisy than the single-shot replace. (2) Permanent floor on vol_ref (slot 552 = REGIME_VOL_REF_FLOOR) - The bootstrap alone exposed the asymmetric deadband-deadlock: if vol_ref converged to a tiny value during a calm initial stretch, vol_ref / vol_ema fired the moment any realistic vol resumed and Kelly stayed trapped at regime_scale_floor=0.25 forever. Floor lives in ISV slot (TrainingPersist) with a hardcoded 1e-12 sub-floor inside the kernel as numerical-underflow guard. - Host seeds slot 552 with 1e-9. Future controller kernel will refine this from observed cell-level vol minima with cross-fold persistence. Walk-forward CV on Q1 fxcache (3 folds, window=700K, train_frac=0.6): cost fold-A fold-B fold-C mean ± SD 0.00 -19.77 +65.04 (100%) +6.45 +17.24 ± 43.42 Fold B turnaround is the headline: -47.64 (bootstrap-only) → +65.04 (bootstrap + floor) confirms the floor is the load-bearing fix. Cross-fold std-dev compressed 24% at cost=0; mean dropped from +38.94 (pre-defense) to +17.24 (with defense). Classic mean/variance trade. Block extended to 14 slots (539..=552). Kernel sig: vol_ref_floor moved from f32 scalar to vol_ref_floor_index i32, so the anchor is named/addressable in ISV. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
34586dad68 |
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> |
||
|
jgrusewski
|
d090685ca9 |
feat(phase-e-4-a-T16): vol-regime detection + cost-aware training
Adds two coupled interventions on the regime fragility exposed by
walk-forward CV (mean Sharpe +27 ± 56 at half-tick across 3 folds —
std-dev ≈ mean means the strategy is regime-dependent).
(1) Vol-EMA regime detector (new ISV slots 549/550)
- New alpha_regime_vol_update.cu kernel: per inference step, reads B
parallel-env mid prices, computes cross-env mean squared log-return,
and maintains ISV[549]=REGIME_VOL_EMA (Wiener-α with 0.4 floor +
Pearl A bootstrap) and ISV[550]=REGIME_VOL_REF (slow tracker β=0.005,
≈200-step horizon).
- Block-tree-reduce (no atomicAdd), guards against zero/non-finite mids.
(2) Pre-emptive Kelly attenuation (modified stacker controller)
- stacker_threshold_controller.cu takes 3 new args: regime_vol_ema_idx,
regime_vol_ref_idx, regime_scale_floor.
- Multiplies its reactive Sharpe-error Kelly output by
regime_scale = clamp(vol_ref / vol_ema, 0.25, 1.0)
- Disabled when indices = -1 (backward-compatible smoke + kernel test).
(3) Cost-aware training (--train-cost-hi)
- alpha_compose_backtest --train-cost-hi: when > --train-cost, each
training epoch samples cost ~ U[lo, hi] so the Q-network learns
cost-conservative behaviour across the realistic ES range.
(4) Wiring
- alpha_compose_backtest --regime-scale enables both per-step regime
kernel firing during eval AND the regime hookup in the per-episode
controller call. Mapped-pinned mids buffers, all compute device-side.
- ExecutionEnv exposes current_mid() so the host gather reads the
active snapshot mid per env without leaking the private cursor field.
Smoke + test sites pass -1/-1 for regime indices (backward compat).
Doc: docs/isv-slots.md ledger for slots 549/550.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|
jgrusewski
|
3aef276255 |
feat(phase-e-4-a): walk-forward CV via --data-start-offset
Adds a sliding-window walk-forward harness for the T10 backtest:
- New load_snapshots_from_fxcache_at(start_offset, ...) loader variant
reads bars [start_offset..start_offset+max_snapshots) from the fxcache.
Alpha-cache lookups use absolute bar indices, so the same
alpha_logits_cache.bin works across folds.
- New --data-start-offset CLI flag on alpha_compose_backtest.
- scripts/walk_forward_cv.sh runs 3 folds (window=700K, train_frac=0.6)
at offsets 0 / 600K / 1.2M, producing /tmp/cv_fold_{A,B,C}.json plus
an aggregated mean±stddev Sharpe table across folds.
Walk-forward result (alpha_logits_cache trained on bars 0..1.57M, so
fold C eval is fully past the stacker cut):
cost fold-A fold-B fold-C mean ± stddev
0.0000 +91.52 -21.44 +46.74 +38.94 ± 56.88
0.0625 +84.94 -27.97 +38.42 +31.79 ± 56.74
0.1250 +79.91 -31.22 +33.51 +27.40 ± 55.82
0.2500 +72.77 -45.41 +15.16 +14.17 ± 59.09
0.5000 +50.52 -59.82 -12.75 -7.35 ± 55.37
Fold B (mid-quarter, bars 600K..1.3M) is a disaster — win rate
collapses to 0-22% across all costs. Folds A and C succeed strongly.
Cross-fold SD ≈ mean, so the policy is regime-dependent and cannot
be reliably deployed without regime detection.
Mean Sharpe at half-tick (+27.40) is still ~7× the stateless
Phase 1d.4 baseline (-4.0), so the temporal encoder adds real value
on average — but the single-window +62 OOS celebrated earlier was
a cherry-picked favorable regime, not a deployment-ready result.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|
jgrusewski
|
3dc022b843 |
feat(phase-e-4-a): T10 Mamba2 backward chain + KC calibration
T10 wires the C51 → Mamba2 backward chain in both binaries:
- New alpha_train_window_store_batched_kernel captures per-step windows
for end-of-epoch re-forward (Mamba2 cache required for backward).
- Mamba2Block::backward_from_h_enriched lets the C51 grad-input feed
directly into Mamba2 backward, skipping the unused W_out projection.
- alpha_c51_grad_input → backward_from_h_enriched → Mamba2AdamW.step
closes the loop in alpha_dqn_h600_smoke and alpha_compose_backtest.
Smoke (--temporal --c51): all 4 KCs PASS. R_mean -6.3 → +4.2 vs
Phase E.3 close R_mean -4.7 (no-temporal). EARLY_Q_MOVEMENT
calibration (mamba2_snapshot + mamba2_weight_distance) lifts the
diagnostic from 0.0023 (head-only) to 0.0590 (head + encoder),
giving an honest learning signal when the encoder absorbs gradient.
Backtest (--c51 --temporal --window-k 16 --isv-continual):
cost=0.0000 best τ=0.250 Sharpe_ann=+34.56 (was +10.41 head-only,
-22.54 frozen-Mamba2)
cost=0.0625 best τ=0.250 Sharpe_ann=+33.22
cost=0.1250 best τ=0.250 Sharpe_ann=+30.85 (Phase 1d.4 baseline: -4.0)
cost=0.2500 best τ=0.250 Sharpe_ann=+27.73
cost=0.5000 best τ=0.250 Sharpe_ann=+15.68
Caveat: in-sample results; OOS gate next.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|
jgrusewski
|
0ab54dc8ef |
feat(phase-e-4-a): batched parallel-env TRAINING (greenfield)
T15: training rewrite mirroring T14 eval. N_par parallel envs lockstep H steps per epoch; ONE batched C51 update at B = N_par * H. Expected ~15× speedup vs sequential. - NEW kernel alpha_h_enriched_store_batched_kernel for batched h_enriched slot writes - Training section greenfielded: legacy sequential loop deleted - CLI flag --n-train-par (default 50) - Terminal next-state slot zeroed; done=1 at horizon masks Q_next contribution in Bellman projection — no terminal Mamba2 forward - docs/isv-slots.md updated per kernel-audit-doc hook Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
90c9d54454 |
feat(phase-e-4-a): batched parallel-env eval rewrite (greenfield)
The Phase E.4.A T14 backtest at B=1 with per-step stream.synchronize()
was running ~150μs/step × 9M steps = ~22 min — dominated by sync
overhead, not GPU compute. RTX 3050 Ti to L40S swap wouldn't help
(launch overhead is the bottleneck, not FLOPS).
Solution: batched parallel envs. N=cli.n_eval_episodes environments
run in LOCKSTEP per cell — ONE sync per step (instead of N syncs).
Expected ~30× speedup at N=500.
Changes:
1. ExecutionEnv snapshots → Arc<Vec<SnapshotRow>>
- new() wraps Vec into Arc internally (backward compat)
- new_arc() takes pre-existing Arc (for parallel envs)
- snapshots_arc() accessor for snapshot sharing
- 50MB × N memory duplication avoided
2. alpha_window_push_batched_kernel (NEW CUDA)
- Same chronological shift+insert semantics as single-env kernel
- Grid (state_dim_blocks, B, 1): one thread per (batch, feature)
- launcher: launch_alpha_window_push_batched
3. MappedI32 (per-binary) gains len param + read_all()
- smoke & backtest pass len=1 for existing single-int use
- backtest passes len=N for batched action readback
4. backtest binary eval loop GREENFIELDED
- Legacy sequential 'for ep in 0..N { for step in ... }' loop
body deleted entirely
- New: 'for step in 0..horizon' outer, lockstep over N envs
- Build N envs sharing snapshots_arc at cell start
- Per step: gather N states (CPU loop, <100μs for N=500) →
write to mapped-pinned [N, STATE_DIM] → push kernel B=N →
Mamba2 batched forward → C51 batched forward → Thompson
batched → ONE sync → read N actions → step N envs on CPU
- ISV-continual moved from per-episode to per-cell (single fire
with aggregate stats)
5. docs/isv-slots.md updated per kernel-audit hook
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|
jgrusewski
|
2feeeda8bb |
fix(phase-e-4-a): GPU kernel for h_enriched slot copy — eliminate per-step CPU roundtrip
The Phase E.4.A T8 wiring stored Mamba2's per-step cache.h_enriched
into h_enriched_buf_dev via a dtoh+htod sequence:
let h_host = stream.clone_dtoh(cache.h_enriched.cuda_data())?;
let mut buf_host = stream.clone_dtoh(&h_enriched_buf_dev)?; // <- whole buffer
for j in 0..hidden_dim { buf_host[slot_offset + j] = h_host[j]; }
stream.memcpy_htod(&buf_host, &mut h_enriched_buf_dev)?; // <- whole buffer
This violates feedback_cpu_is_read_only AND
feedback_no_htod_htoh_only_mapped_pinned. Worse, the buffer-wide
dtoh+htod every step is ~20K floats × 600 steps × 500 eps × 30 cells
= ~9M roundtrips totaling significant PCIe latency in the backtest.
Fix: new tiny CUDA kernel alpha_h_enriched_store_kernel in
alpha_window_push.cu (one thread per hidden-dim feature, writes
src[j] → buf[slot_offset + j]). Replaces the dtoh/htod sequence
in both smoke and backtest binaries.
Estimated speed-up at backtest scale: 3-6× on the temporal eval
path. Pure-GPU per-step inference restored — no synchronisation
points on the hot path.
docs/isv-slots.md updated per kernel-audit-doc hook.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|
jgrusewski
|
4d65ace625 |
feat(phase-e-4-a): mirror --temporal in backtest binary + T11 ISV-continual
Phase E.4.A Tasks 11+12: backtest binary gains the same
--temporal Mamba2 forward chain as the smoke binary, plus the
--isv-continual flag that fires the stacker-threshold controller
at the end of each eval episode (Pillar B).
Changes:
- Imports: ml_alpha::mamba2_block::{Mamba2Block, Mamba2BlockConfig},
ml_core::cuda_autograd::gpu_tensor::GpuTensor
- CLI flags: --temporal, --window-k, --mamba2-hidden-dim,
--mamba2-state-dim, --isv-continual
- Cubin loading: alpha_window_push + stacker_threshold_controller
- Q-net sizing: c51_input_dim = mamba2_hidden_dim when --c51 --temporal
- Buffers: window_tensor GpuTensor, h_enriched_buf_dev, isv_dev,
ctl_wiener_dev
- Training inference path: push + Mamba2 forward + h_enriched →
C51 forward (mirrors smoke binary)
- Training batched compute: terminal Mamba2 forward, h_enriched_buf
for current/next, c51_input_dim threading
- Eval inference path: push + Mamba2 forward + h_enriched → C51
forward + Thompson select (with scoped borrow guard)
- T11 ISV-continual: stacker-threshold controller fires at end of
each eval episode; ISV slot 543 (threshold), 545 (observed-rate),
546 (Kelly atten) update with realized rollout stats. Co-exists
with the τ-grid sweep (τ-grid still gates; ISV updates parallel
observation of "live deployment" behaviour).
- JSON output: new fields temporal, window_k, mamba2_hidden_dim,
mamba2_state_dim, isv_continual.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|
jgrusewski
|
ed6f5588e1 |
feat(phase-e-4-a): wire Mamba2 forward in smoke --temporal path
Phase E.4.A Task 8: wire ml_alpha::Mamba2Block as the temporal
encoder before the C51 head when --temporal is set.
Architecture (--temporal):
state_pinned ──push─▶ window_tensor[1, K=16, in_dim=10]
│
▼ Mamba2Block::forward_train
h_enriched[1, hidden_dim=32]
│
▼ launch_alpha_c51_forward (input dim=32)
probs[1, 9, 51] ──▶ Thompson selector
Implementation:
- Mamba2Block constructed at startup with config (in_dim=10,
hidden_dim=32, state_dim=16, seq_len=K=16). Loaded from ml-alpha's
precompiled cubin.
- Per-step: window push (shift+insert), then forward_train returns
(logit, cache). We discard logit (ml-alpha's binary classifier head)
and use cache.h_enriched as the C51 input.
- Per-step h_enriched cached into h_enriched_buf_dev[(t)..t+hidden_dim].
- Batched training (end-of-episode): the C51 forward + grad use
h_enriched_buf_dev[0..ep_len*hidden] for the current state and
[hidden..(ep_len+1)*hidden] for next-state (1-step offset). Runs
one extra Mamba2 forward on the terminal window to populate slot
ep_len.
- C51 input dim (W shape) becomes mamba2_hidden_dim when --temporal,
STATE_DIM otherwise.
100-episode smoke verdict (vs C51-flat baseline):
R_mean ep 50: C51-flat -8.2 → --temporal +0.4 (+8.6)
R_mean ep 100: C51-flat +0.5 → --temporal +10.0 (+9.5)
rvr: +1.045 → +1.046 (unchanged)
Q_SPREAD: 23.9 → 12.6 (sharper distributions)
ACTION_ENTROPY: 1.42 → 1.49 (now PASSES 0.5×ln(9) threshold)
Note: Mamba2 weights are FROZEN at random Xavier init in this
commit — T10 (backward + AdamW step) lands next. The R_mean lift
above is from C51 learning over RANDOM temporal projections of the
window — random SSM acts as a feature-engineering reservoir.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|
jgrusewski
|
35dcb87709 |
refactor(phase-e-4-a): window push to shift+insert (chronological layout)
Switch alpha_window_push from circular-buffer-with-head_idx layout to shift+insert layout matching production mamba2_update_history. Slot 0 = oldest, slot K-1 = newest after each push, matching Mamba2Block's [B, K, in_dim] input contract directly (no reorder). Cost: O(K-1) shifts per state_dim feature per push. For K=16, state_dim=10: 10 threads × ~15 ops each = trivial. Kernel signature: drops head_idx, adds K. Test updated to verify chronological shift across 3 pushes into 4-slot buffer. docs/isv-slots.md updated per kernel-audit-doc hook. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
70df697328 |
feat(phase-e-4-a): wire sliding-window buffer in smoke (buffer-only)
Phase E.4.A Task 7: maintain a GPU-resident circular window buffer in the smoke binary's --temporal path. Per-step: 1. mapped-pinned state_pinned write (existing) 2. alpha_window_push_kernel writes state into window[head_idx] 3. head_idx = (head_idx + 1) % window_k 4. C51 forward proceeds against state_pinned (consumer of window wires in T8 — Mamba2 over the window) On episode reset: zero the buffer and reset head_idx so Mamba2 sees clean zero-context for the first window_k-1 steps. CLI: --temporal flag + --window-k (default 16, kernel max 32 per mamba2_alpha_kernel constraint). Validation: 100-episode smoke with --temporal produced bit-identical R_mean / rvr / kill-criteria values to the C51-flat baseline run — confirms buffer maintenance has zero side effect on the existing C51 path. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
5d7d4fa3c6 |
feat(phase-e-4-a): add mbp10_dir param to fxcache loader (signature-only)
Phase E.4.A Task 4: extend load_snapshots_from_fxcache with `mbp10_dir: Option<&Path>`. When provided, the loader will peek MBP-10 by timestamp and populate SnapshotRow.bid_l[1..10]/ask_l[1..10] from real LOB depth — but the real-peek implementation lands in Task 5 follow-on. This commit: - introduces the parameter (callers pass None) - warns at runtime if mbp10_dir Some until T5 lands - enables downstream wiring of --use-real-depth + --mbp10-dir CLI flags in the smoke / backtest binaries T5 deferred: on ES futures the --real-spread experiment showed 76% of fxcache bars hit the 1-tick floor, so depth-from-MBP-10 likely won't move the needle for ES. Higher-leverage work (Mamba2 wiring) prioritised. T5 implementation reopens as a follow-on if E.4.A gates pass with synthesised depth. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
eb49e2a0f7 |
feat(alpha): Phase E.3 follow-up — C51 distributional Q + Thompson + L1-L10 depth + falsifications
C51 distributional Q-network with GPU Thompson selection borrowed
minimally from production (alpha_c51.cu: forward, project, grad,
expected_q, thompson_select kernels; ~260 lines). Uses Huber
negative-tail compression in projection per production
block_bellman_project_f. Action selection 100% GPU via mapped-pinned
i32 output + __threadfence_system + host volatile read (matches
gpu_training_guard MappedBuffer pattern).
Backtest result (2D sweep, 500 episodes per cell, 30 cells):
cost=0 C51 +10.41 vs linear-Q -15.72 (+26pt, BEATS Phase 1d.4
no-RL baseline +4.4 by 6pt)
cost=0.125 C51 -13.81 vs -29.17 (+15pt closes half-tick gap)
Win rate at cost=0 best τ: linear-Q 0.008 → C51 0.552.
Calibration hypothesis vindicated; documented in
memory/pearl_c51_thompson_closed_phase_e3_gap.md.
Also in this commit (Phase E.3 follow-up cleanup):
- --pruned-actions falsified (2.4× worse Sharpe). Documented in
memory/pearl_action_pruning_falsified.md.
- --real-spread falsified for ES futures (76% of bars at 1-tick floor).
- SnapshotRow bid_l/ask_l extended from [f32; 3] to [f32; 10].
L4-L10 synthesized in this commit; real MBP-10 peek lands in E.4.A T5.
- docs/isv-slots.md updated per kernel-audit-doc hook requirement.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|
jgrusewski
|
771936b768 |
feat(alpha): --train-threshold for backtest + Phase E.3 honest verdict
Phase E.3 follow-up. Adds --train-threshold to alpha_compose_backtest so
the Q-network can be trained against a FIXED gate (instead of just
applying the gate at eval). Default 0.39 = the equilibrium the smoke's
controller stabilized to at ep 200+ (alpha_dqn_h600_smoke gated run).
Smoke result (gated training, controller running):
ep 100: thresh=0.32 obs=0.226 R_mean=-5.5 atten=0.75
ep 200: thresh=0.38 obs=0.082 R_mean=-3.0 atten=0.50
ep 300: thresh=0.39 obs=0.081 R_mean=-3.2 atten=0.25
ep 1000: thresh=0.39 obs=0.039 R_mean=-4.7 atten=0.10
The controller CONVERGES cleanly to threshold ≈ 0.39 with observed
trade rate at/below the 0.08 target. rollout_R_mean drops from -19
(no-gate training) to -4.7 (gated training): 4× less loss per episode.
rvr stays at +1.045σ (unchanged). The closed-loop architecture works
end to end.
(Note: smoke verdict FAILs on ACTION_ENTROPY (0.68 < threshold 1.10).
This is the policy correctly Waiting 95%+ of the time — the kill
criterion was designed to catch "collapse to one bad action," but
collapse-to-Wait under a strong gate is the RIGHT behavior. Verdict
threshold is misaligned with the gated paradigm; not a regression.)
Backtest result with --train-threshold 0.39:
cost eval-gate only train+eval gated Δ
------ -------------- ---------------- ----
0.0000 -15.72 -17.06 -1.3
0.0625 -21.30 -22.91 -1.6
0.1250 -29.17 -31.26 -2.1
0.2500 -42.12 -36.68 +5.4
0.5000 -54.86 -53.83 +1.0
Training with the gate did NOT meaningfully improve absolute Sharpe.
The eval-best threshold remains 0.20-0.25 in BOTH runs (not 0.39).
The Q-network's primary contribution is the binary trade/don't-trade
decision; the action-choice (Buy direction + placement) is largely
determined by alpha sign — linear Q can't time entry better than the
threshold filter does on its own.
Honest analysis: the gap to Phase 1d.4 baseline (+4.4 at cost=0,
-4.0 at half-tick) is NOT architectural but ECONOMIC:
Env spread: bid/ask synthesized at ±0.125-tick around mid
→ round-trip spread cost = 0.25 per trade
At τ=0.20 with 168 trades/ep: 168 × 0.25 = 42 in spread costs
Mean reward = -5 → alpha extracts ~37 of value
All eaten by spread
Phase 1d.4 baseline likely trades much less (~20-50 trades/ep at best
operating point — pure threshold-only policy, no RL). Our policy
trades 3-8× more because the DQN's action choices add fine-grained
trade attempts beyond the threshold filter's wait/trade gate.
The control loop architecture (Phase E.1 + E.2 + E.3 gate consumption)
is VALIDATED — gate produces monotone Sharpe lift, +1.045σ rvr held,
trade-rate-self-correction converges cleanly. But beating Phase 1d.4's
absolute Sharpe requires:
1. MLP for the Q-network (more representation capacity for
entry-timing decisions within the alpha confidence band)
2. OR action-space constraints (collapse the 9-action space — drop
fine-grained L1/L2 placement, keep just {Wait, BuyMarket,
SellMarket, FlatMarket})
3. OR better fill economics (real LOB instead of fixed ±0.125-tick
synthesis)
These are Milestone E.3 follow-up work (Tasks 24-28 sweeps + future
architectural changes). The composition backtest validated what it
was designed to: the cost-edge frontier of the linear Q + Phase 1d.3
alpha + controller setup, and surfaced the next architectural
question (representation capacity vs action-space size vs fill
realism).
Branch: sp20-aux-h-fixed, pushed.
|
||
|
jgrusewski
|
a36ad53a57 |
feat(alpha): wire slot 543 consumption — 2D threshold × cost sweep
Phase E.3 Task 23 follow-up. Adds the confidence-threshold gate that
consumes the controller's ISV[543] output. Both binaries:
fn epsilon_greedy_gated(q, alpha_confidence, threshold, eps, rng) -> u8 {
if alpha_confidence < threshold { return 0; /* Wait */ }
epsilon_greedy(q, eps, rng)
}
State[1] is the env's alpha_confidence = |sigmoid(alpha_logit) - 0.5|
which is in [0, 0.5]; threshold is also clamped [0, 0.5], so direct
comparison is valid.
alpha_dqn_h600_smoke (closed-loop with controller):
Adds current_threshold: f32 cache, initialised to 0.0 (no gate),
refreshed via stream.clone_dtoh(&isv_dev) after each per-episode
controller invocation. Action selector reads current_threshold for
the NEXT episode's step decisions.
alpha_compose_backtest (2D sweep):
Adds --threshold-grid CLI flag (default [0.0, 0.05, 0.10, 0.15, 0.20,
0.25] — Phase 1d.4 pattern). Eval loop becomes 2D (threshold × cost).
Per-bin includes avg_n_trades for trade-rate visibility. End-of-run
prints BEST per-cost = max Sharpe_ann across τ.
Results (1000 train ep, 300 eval ep × 5 τ × 5 costs):
cost τ=0.00 best τ Sharpe lift trades/ep saved
------- ---------- --------- ----------- ---------------
0.0000 -41.78 -15.72 (τ=0.20) +26.1 477 → 168 (-65%)
0.0625 -71.46 -21.30 (τ=0.25) +50.2 476 → 138 (-71%)
0.1250 -86.78 -29.17 (τ=0.20) +57.6 482 → 167 (-65%)
0.2500 -108.57 -42.12 (τ=0.25) +66.5 480 → 132 (-73%)
0.5000 -146.76 -54.86 (τ=0.25) +91.9 478 → 136 (-72%)
Win rate at cost=0: 7.7% (no gate) → 20.3% (τ=0.20).
The gate architecture is VALIDATED: monotone improvement in win rate +
Sharpe + trade-rate reduction across all costs. The control loop
(controller → slot 543 → policy gate → observed rate feedback) is
sound. But the policy is STILL negative-Sharpe at every cost.
Phase 1d.4 baseline at half-tick: -4.0 (ours: -29.17). 25-pt gap.
Root cause of the remaining gap: the Q-network was TRAINED without
gate awareness. It learned Q-values for the over-trading regime. The
eval-only gate filters those decisions but can't fix miscalibrated
Q-values. Phase 1d.4 baseline beats us because its policy
(always-market-when-confident) is INHERENTLY gated by design — no
mismatched Q-values to fix.
Next iteration to close the 25-pt gap: train WITH gate on, so the
Q-network learns weights for the gated policy class. This means:
either (a) controller runs during training (smoke pattern) and the
threshold develops endogenously, or (b) fixed --train-threshold CLI
during training. Either way, the Q-network sees Wait-at-low-confidence
during the learning phase and adapts.
Files touched:
crates/ml/examples/alpha_dqn_h600_smoke.rs (gate + threshold cache)
crates/ml/examples/alpha_compose_backtest.rs (gate + 2D sweep)
config/ml/alpha_compose_backtest.json (2D verdict)
|
||
|
jgrusewski
|
2af8e02fd8 |
feat(alpha): Phase E.3 composition backtest — reveals slot 543 needs consumption
Phase E.3 Task 23. Trains the Phase E execution-policy DQN on the first
80% of fxcache snapshots, then evaluates the frozen policy (ε=0) on the
held-out 20% across a transaction-cost sweep. Compares absolute Sharpe
vs the Phase 1d.4 always-market-when-confident baseline.
Pipeline pieces:
- Shared loaders extracted into crates/ml/src/env/loaders.rs (used by
both alpha_dqn_h600_smoke and alpha_compose_backtest)
- alpha_compose_backtest.rs: train DQN on first n_train bars, then
frozen-eval n_eval episodes per cost level
- cost grid: [0.0, 0.0625, 0.125, 0.25, 0.5] (price units per
contract round-turn)
- Annualised Sharpe via per-episode Sharpe × sqrt(episodes/year)
where episodes/year ≈ 252 · 6.5h · 3600s / (horizon · 12s)
Run (horizon=600, 1000 train ep, 500 eval ep/cost, 1.5M snapshots):
cost n_ep mean_R std_R Sharpe/ep Sharpe_ann win_rate
0.0000 500 -11.09 8.03 -1.380 -39.50 0.090
0.0625 500 -20.68 9.88 -2.093 -59.89 0.012
0.1250 500 -29.38 9.49 -3.095 -88.58 0.000
0.2500 500 -48.23 11.90 -4.052 -115.98 0.000
0.5000 500 -84.59 17.43 -4.854 -138.92 0.000
Phase 1d.4 baseline for comparison: +4.4 ann. at cost=0, -4.0 at half-tick.
The Phase E policy LOSES MONEY across the whole cost grid — even at
frictionless cost=0. This is not a contradiction with the H=600 PASS
verdict (rvr=+1.04σ): the smoke's rvr is RELATIVE TO RANDOM, while
backtest Sharpe is ABSOLUTE. "Better than random by 1 std" is still
losing if random loses big.
The diagnostic that the E.2 controller already surfaced:
ISV[543] STACKER_THRESHOLD saturated at upper clamp (0.5) — policy
trades 85% of the time vs the 8% target. Over-trading pays spread on
every bar regardless of alpha confidence. Even with perfect alpha
(Phase 1d.3 AUC=0.673), trading 85% × spread cost > alpha edge.
The Phase 1d.4 baseline beats us at cost=0 because it WAITS unless
|stacker_logit| > threshold — the threshold gate filters bars with
weak alpha signal. The Phase E controller PRODUCES slot 543 but the
DQN's action selection doesn't CONSUME it.
This is exactly what the E.3 backtest is FOR: revealing that the
Phase E.1/E.2 producer-side architecture without consumer-side gating
is incomplete. The composition backtest validates the architecture's
weak link.
NEXT (E.3 task 24-28 or a side fix): wire slot 543 consumption into
the action selection. At each step:
if |ISV[543] − 0.5| > |stacker_logit − 0.5|:
action = Wait // confidence below threshold, sit out
else:
action = argmax(Q)
Or equivalently: action = if confidence_high(alpha_logit, ISV[543])
{ argmax(Q) over Buy/Sell actions } else { Wait }.
Once slot 543 is consumed, re-run alpha_compose_backtest and expect
Sharpe to move toward / past the Phase 1d.4 baseline.
Loader refactor: extracted load_fill_model_from_json, load_alpha_cache,
load_snapshots_from_fxcache from alpha_dqn_h600_smoke.rs into
crates/ml/src/env/loaders.rs. The smoke now calls the shared module
via ml::env::loaders::*. ~150 lines of duplicated code removed.
Build + run verified: smoke still builds clean. Backtest runs in ~30s
(train 8s + eval 20s + setup).
Branch: sp20-aux-h-fixed, pushed.
|
||
|
jgrusewski
|
91383507fc |
feat(alpha): wire stacker-threshold controller into smoke rollout-end
Phase E.2 Task 17. Loads stacker_threshold_controller.cubin at smoke
startup, initialises ISV[544] (TRADE_RATE_TARGET) to 0.08 (CLI flag
--trade-rate-target, never reset), allocates a 3-float Wiener state
buffer for slot 545's Pearl A+D state.
Invokes the controller at every episode end with:
rollout_trade_count = count of non-Wait actions in the episode
rollout_total_decisions = actions_host.len() (= ep_len)
rollout_realized_sharpe = ep_terminal_R / RANDOM_BASELINE_STD
(per-rollout analog of the rvr metric;
lets the Kelly-atten controller respond
to in-policy performance vs the baseline
noise floor)
CLI args added:
--trade-rate-target default 0.08 (8% per-step trade rate target)
--k-threshold default 0.01
--k-atten default 0.005
--target-sharpe default 0.5
--wiener-alpha-floor default 0.4
--ctl-alpha-meta default 0.1
Periodic log line extended:
ep ... | KC q/H/rvr/ΔQ ... | CTL thresh=... obs=... atten=...
Final JSON adds:
final_stacker_threshold
final_trade_rate_observed_ema
final_stacker_kelly_attenuation
trade_rate_target
Smoke run (H=600, 1000 episodes) verifies the controller is alive:
ISV[543] STACKER_THRESHOLD: 0.000 → 0.5000 (saturated at ceiling)
ISV[545] TRADE_RATE_OBSERVED_EMA: 0.000 → 0.712
ISV[546] STACKER_KELLY_ATTENUATION:0.000 → 0.100 (hit floor)
Verdict: PASS — rvr=+1.043σ (unchanged from Task 12b PASS, expected
since smoke doesn't yet CONSUME slots 543/546).
Tuning notes (calibration for production, not bugs):
• Threshold saturating at 0.5 → policy trades ~85% (target 8%, off by
10×). Either re-calibrate target_trade_rate from realistic backtest
behaviour, or raise the clamp ceiling. Current ε-greedy with low
threshold-consumption gate produces high trade rate.
• Kelly atten hit floor (0.1) because rollout_sharpe (~-0.004) is far
below target_sharpe=0.5. The target needs to match the rollout
metric's scale, OR the metric should be time-normalised. The
current ep_terminal_R / baseline_std proxy is meaningful but its
scale doesn't match a typical annualised Sharpe target.
These tuning items don't gate Milestone E.2 — the producer-side
controller is correctly driving the ISV slots; *consuming* those slots
(threshold gate on alpha signal, Kelly-cap multiplier) is Phase E.3
work (alpha + execution composition).
Phase E.2 Tasks 16 + 17 close-out: kernel + launcher + GPU smoke test
+ wired into smoke binary + initialisation + verified end-to-end. Tasks
19-22 (NoisyNet) are gated on Task 12 FAIL, which we passed — skipped.
Task 18 (alpha-trust ablation, ~9-18 hours compute) deferred to a
dedicated session if needed.
|
||
|
jgrusewski
|
5c0bcb1fdb |
fix(alpha): MBP-10 parser full-levels copy + fit_poisson L2 regularization
Two carried-over limitations from Phase E.0 / E.1 fixed and verified.
1. MBP-10 parser bug fix (`parse_mbp10_streaming` + `parse_mbp10_file`)
The DBN crate's `Mbp10Msg` carries the FULL post-update top-10 book
in `levels: [BidAskPair; 10]` per message — not just the single
update event's price/size. Previously the parser only called
`update_level(0, ...)` with the update event's fields, leaving
`current_snapshot.levels[1..10]` at default-empty. Downstream:
- OFI calculator reading L2-L5 got zeros → produced wrong OFI
features (the canonical Phase 1c/1d 81-dim feature stack has
multi-level OFI as features 0..5; with the bug these were
constant zero).
- microprice (`snapshot.levels[1]`) got zeros.
- FillModel L2/L3 fit observations got zeros, so L2/L3
coefficients were undefined (we worked around by replicating
L1 with attenuated intercept).
Fix: after `update_level(0, ...)`, copy fields from
`mbp10.levels[lvl]` into `current_snapshot.levels[lvl]` for `lvl
in 1..max_lvl`. Field-by-field copy preserves the existing scale
convention (raw 1e9 fixed-point i64). Applied to both streaming
and async file-parse code paths.
Comment "For simplicity, store all updates in level 0 / A full
implementation would maintain proper level ordering" removed.
2. fit_poisson L2 regularization
New `fit_poisson_l2(features, observed, max_iters, lr, l2_lambda)`
API (the old `fit_poisson` delegates with l2_lambda=0). L2 penalty
applies to slope coefficients β[1..5] but NOT to intercept β[0]
(penalizing the intercept biases toward p≈0.5 for all-zero-feature
samples, breaking the recovery test). Per-iteration update:
β[0] -= lr · grad[0] / n (intercept)
β[k] -= lr · (grad[k] / n + λ · β[k]) (slope, k ∈ 1..5)
Canonical motivation: on real 5.2M-trade ES.FUT data the
unregularized fitter converged to β_spread ≈ -40 (Task 5c commit
|
||
|
jgrusewski
|
cd5aa3402b |
feat(alpha): wire Phase 1d.3 stacker into smoke — H=600 VERDICT PASS
Phase E.1 Task 12b complete. The H=600 DQN smoke now consumes real
alpha_logit from the Phase 1d.3 stacker (Mamba2 + 7-input MLP stacker
trained for AUC=0.673 on test), and PASSES all four kill criteria:
Q_SPREAD_EMA = 10.92 ≥ 0.05 PASS
ACTION_ENTROPY_EMA = 1.97 ≥ 1.099 PASS
RETURN_VS_RANDOM_EMA = +1.043 ≥ 0.0 PASS ← jumped +3.62σ
EARLY_Q_MOVEMENT_EMA = 0.099 ≥ 0.01 PASS
Overall: PASS (H=6000 scale-up VIABLE)
Before/after comparison (same env, same DQN, only alpha_logit changed):
alpha_logit=0 alpha_logit=Phase1d.3
rollout_R_mean (final) -18,272 -18
RETURN_VS_RANDOM_EMA -2.58σ +1.04σ
Overall verdict FAIL PASS
The 1000× reduction in episode loss + the +3.62σ rvr swing definitively
proves the "first-best-action lock-in" hypothesis from the previous FAIL
analysis was a SYMPTOM, not the cause. The cause was alpha_logit=0
placeholder starving the policy of directional signal. With real Phase
1d.3 alpha, the linear Q-network learns to use it cleanly — no
NoisyNet, no MLP, no architectural change needed.
Integration pieces in this commit:
1. Cargo workspace registration: ml-alpha added as a workspace dep,
ml's manifest now depends on ml-alpha for FxCacheReader access.
(ml-alpha already depends only on ml-core, so no circular risk.)
2. alpha_dqn_h600_smoke.rs: two new CLI args
--fxcache-path <PATH> load snapshots from precomputed fxcache
(mid from raw_close, bid/ask synthesized
at fixed half-tick, 81-dim features extracted
for spread_bps / l1_imbalance / ofi / mid_drift)
--alpha-cache <PATH> load Phase 1d.3 stacker logit cache produced
by `alpha_train_stacker --alpha-cache-out`.
Each cache entry aligns to the corresponding
fxcache bar, populates SnapshotRow.alpha_logit
(and derives alpha_confidence = |sigmoid(z)-0.5|).
3. Snapshot source selection: in main(), --fxcache-path takes priority
when both paths are set; --alpha-cache requires --fxcache-path
(alignment guarantee). Original --mbp10-dir path unchanged for
non-cached runs.
4. Two new helper fns: load_alpha_cache (binary [u32 n] + [f32; n]
reader), load_snapshots_from_fxcache (FxCacheReader → Vec<SnapshotRow>
with synthesized bid/ask and alpha_logit/alpha_confidence from cache).
alpha_logits_cache.bin (7.6 MB, 1.97M f32 entries) is .gitignore'd —
regenerable from `cargo run -p ml-alpha --release --example
alpha_train_stacker -- --fxcache-path <FXC> --alpha-cache-out
config/ml/alpha_logits_cache.bin` (~2 min on RTX 3050 Ti).
Reproduction of this PASS verdict:
cargo run -p ml --release --example alpha_dqn_h600_smoke -- \
--fxcache-path /home/jgrusewski/Work/foxhunt/test_data/feature-cache/9297....fxcache \
--alpha-cache config/ml/alpha_logits_cache.bin \
--horizon 600 --n-episodes 1000
Total run time ~10s after fxcache load. Verdict + per-checkpoint KC
trajectory in config/ml/alpha_dqn_h600_smoke.json.
NEXT: Task 13 — scale to H=6000 (the production horizon). Per the plan,
PASS at H=600 unlocks H=6000.
|
||
|
jgrusewski
|
8958637c77 |
feat(alpha): stabilize alpha_dqn_h600_smoke — reward norm + target net + grad clip
Three stabilizers applied to the H=600 DQN smoke after initial run showed
unstable training (early_mvmt=2268× at lr=1e-6, NaN at lr=1e-4):
1. Reward normalization (--reward-scale, default 1000)
Rewards divided by scale BEFORE the Munchausen target. TD error
drops from ~1000 (raw reward magnitude at H=600) into O(1) target /
gradient / weight-update scale. Action selection + rollout-R
reporting use ORIGINAL rewards (so rvr math stays correct against
the Task 7c baseline).
2. Target network (--target-update-every, default 10 episodes)
Separate w_target_dev / b_target_dev buffers. Q_next(s') forward
uses target weights; SGD updates online only. Hard-update copies
online → target every K episodes. Breaks the V_soft(s') chase-its-
own-tail divergence of online-only Munchausen.
3. Gradient clipping (--grad-clip, default 1.0)
New `alpha_clip_inplace_kernel` in alpha_linear_q.cu (element-wise
clamp). Applied to dW and db after grad, before SGD. Safety net.
Diagnostic fix: weight_norm was direction-insensitive — orthogonal
rotations don't change ||W||_F, so early_mvmt read ≈0 even when training.
Switched to weight_distance_from_init = ||W_now − W_init||_F +
||b_now − b_init||_F (captures rotation). q_early = q_init + distance
so kernel's |q_early − q_init| / |q_init| ratio = distance / ||W_init||_F.
With lr bumped back up to 1e-4 (default for the stabilized config),
verified at horizon=100, n_episodes=200:
Q_SPREAD_EMA = 23.64 (≥ 0.05) PASS
ACTION_ENTROPY_EMA = 1.86 (≥ 1.0986) PASS
RETURN_VS_RANDOM_EMA = +0.586 (≥ 0.0) PASS
EARLY_Q_MOVEMENT_EMA = 0.0212 (≥ 0.01) PASS
Overall: PASS (H=6000 scale-up VIABLE)
early_mvmt grew monotonically (0.005 → 0.021) across the 200-episode
run — direction-sensitive diagnostic confirms genuine policy learning.
Audit doc docs/isv-slots.md updated per Invariant 7.
Next: H=600 / 1000-episode run on full data; if PASS holds, Task 13
(H=6000 scale-up) unlocks.
|
||
|
jgrusewski
|
fa30c2dd66 |
feat(alpha): alpha_dqn_h600_smoke — runnable Task 12 DQN smoke
Phase E.1 Task 12. Linear Q-network (W [9×10] + b [9], no hidden layer)
trained with ε-greedy + Munchausen target on the Phase E ExecutionEnv.
End-to-end runnable: load env, train, periodically launch
alpha_kill_criteria + apply_pearls_ad chain at episode boundaries, emit
PASS/FAIL verdict against the 4 kill criteria thresholds.
Pipeline per training step (all on GPU):
1. forward Q_current on s_batch via alpha_linear_q_forward
2. forward Q_next on s'_batch via alpha_linear_q_forward
3. alpha_munchausen_target → targets[batch]
4. alpha_linear_q_grad → dW, db (sparse over taken actions)
5. alpha_linear_q_sgd_step on W and b (separate launches)
6. every K episodes: kill_criteria + apply_pearls_ad chain → ISV[539..542]
Pipeline visibility bumps so examples can reach launchers:
- cuda_pipeline::alpha_kernels module → pub
- All launch_alpha_* fns → pub
- launch_apply_pearls → pub
- ALPHA_LINEAR_Q_CUBIN → pub
These are appropriate pub exports (Phase E.1 public API surface).
Initial micro-smoke (horizon=100, n_episodes=50, lr=1e-6):
Q_SPREAD_EMA = 3.12 (≥ 0.05) PASS
ACTION_ENTROPY_EMA = 2.12 (≥ 1.0986) PASS
RETURN_VS_RANDOM_EMA = +1.03 (≥ 0.0) PASS
EARLY_Q_MOVEMENT_EMA = 2268 (≥ 0.01) PASS [unphysical scale]
Overall: PASS (uncalibrated)
Known stability issues — flagged in the binary's CLI docstring:
- lr=1e-4 diverges to NaN (Q grows, Munchausen target explodes)
- lr=1e-6 stays finite but Q grows 2000× over 50 episodes
- Follow-ups: gradient clipping, target network, reward normalisation
Bug fixed during development: `stream.memcpy_htod(&host, &mut buf.clone())`
was uploading to a TEMPORARY clone (dropped immediately) — `kc_scalar_dev`
and `kc_action_counts_dev` never got their host data → entropy=0, early_mvmt=0,
rvr stuck at the alloc-zeros default. Fixed by removing `.clone()` and using
direct `&mut` refs.
Reads:
config/ml/alpha_fill_coeffs.json (Task 5c)
ISV slots 547/548 (Task 7c baseline)
Writes:
config/ml/alpha_dqn_h600_smoke.json (verdict + per-checkpoint KC trajectory)
Reproduction:
cargo run -p ml --release --example alpha_dqn_h600_smoke -- \
--mbp10-dir /home/jgrusewski/Work/foxhunt/test_data/futures-baseline-mbp10/ES.FUT \
--horizon 600 --n-episodes 1000
Audit doc docs/isv-slots.md updated per Invariant 7.
|
||
|
jgrusewski
|
91d1a52b9c |
refactor(alpha): rename phase_e_* → alpha_* — system-scoped naming
The kill-criteria producer, Munchausen target kernel, Rust launchers,
fit/baseline binaries, and their output JSON artifacts are *durable
infrastructure* of the alpha trading system (live across Phase E/F/G/...),
not milestone-scoped to Phase E specifically. Aligns with the earlier
`phase_e_isv_slots.rs` → `alpha_isv_slots.rs` rename rationale.
What was renamed:
Code files:
crates/ml/src/cuda_pipeline/phase_e_kill_criteria.cu → alpha_kill_criteria.cu
crates/ml/src/cuda_pipeline/phase_e_munchausen_target.cu → alpha_munchausen_target.cu
crates/ml/src/cuda_pipeline/phase_e_kernels.rs → alpha_kernels.rs
crates/ml/examples/phase_e_fit_fill_model.rs → alpha_fit_fill_model.rs
crates/ml/examples/phase_e_random_baseline.rs → alpha_random_baseline.rs
Artifacts:
config/ml/phase_e_fill_coeffs.json → alpha_fill_coeffs.json
config/ml/phase_e_random_baseline.json → alpha_random_baseline.json
Kernel function names:
phase_e_kill_criteria_compute_kernel → alpha_kill_criteria_compute_kernel
phase_e_munchausen_target_kernel → alpha_munchausen_target_kernel
Rust launcher names:
launch_phase_e_kill_criteria → launch_alpha_kill_criteria
launch_phase_e_munchausen_target → launch_alpha_munchausen_target
Static cubin names:
PHASE_E_MUNCHAUSEN_TARGET_CUBIN → ALPHA_MUNCHAUSEN_TARGET_CUBIN
Historical milestone tags in doc-comments ("Phase E.1 Task N (2026-05-15)")
are RETAINED — they record WHEN the work landed and what plan it
implemented, which doesn't change with the system-scoped rename.
Plus: ADDS the alpha_munchausen_target GPU smoke test in alpha_kernels.rs.
End-to-end validates the launcher + kernel against hand-computed expected
values: batch=2 with one terminal sample; expected targets [29.8, 1.1];
got match within 0.05 tolerance on RTX 3050 Ti. PROVES the Task 9/10
kernels actually run on GPU.
All affected references updated in:
- build.rs (kernel compile list)
- mod.rs (module registration)
- state_reset_registry.rs (4 RegistryEntry descriptions for slots 539-542)
- alpha_isv_slots.rs (slot table comment)
- docs/isv-slots.md (audit-doc cross-references)
Verified:
cargo test -p ml --lib alpha_kernels: 2/2 pass (including GPU smoke)
cargo test -p ml --lib state_reset_registry: 10/10 pass
cargo build -p ml --release --example alpha_fit_fill_model --example alpha_random_baseline: clean
|
||
|
jgrusewski
|
9cfc6d8502 |
feat(alpha): phase_e_random_baseline example + reset_at extension
Phase E.0 Task 7b. Random-uniform policy reward baseline binary, plus a
small `ExecutionEnv::reset_at(seed, start_cursor)` extension so episodes
can sample random starting points across a long snapshot replay.
The binary loads MBP-10 snapshots, constructs SnapshotRow values (with
L2/L3 synthesized at ±0.25-tick offsets per the L1-only parser
limitation), loads the fitted FillModel from JSON, then runs N random
episodes from random start cursors. Reports mean / std / quintile
percentiles + kill threshold (mean + 2σ) for E.1 to exceed.
Smoke run (500 episodes, horizon 600, 100K snapshots):
mean = -5600 (dominated by terminal force-close variance + market-order
over-reliance because fit converged to β_spread = -40
→ limit fill probability ~0 at typical spreads)
std = 5383
p95 = -895
kill threshold (mean + 2σ) = +5167
The deeply negative baseline is correct *for this env* even though it
doesn't reflect realistic random-policy P&L. The DQN will face the same
env (same fill model, same cost structure), so the comparison stays
fair. Fitter regularisation (to prevent β_spread runaway) is a Phase E.1
follow-up.
Run:
cargo run -p ml --release --example phase_e_random_baseline -- \
--mbp10-dir /home/jgrusewski/Work/foxhunt/test_data/futures-baseline-mbp10/ES.FUT \
--fill-coeffs config/ml/phase_e_fill_coeffs.json \
--horizon 600 \
--n-episodes 10000 \
--out-path config/ml/phase_e_random_baseline.json
env.reset_at also called by reset() (1-line refactor); no behavior change.
|
||
|
jgrusewski
|
12151ccf6a |
feat(alpha): fitted FillModel coefficients from 500K ES.FUT snapshots
Phase E.0 Task 5c. Ran phase_e_fit_fill_model on the ES.FUT 2024-Q1 MBP-10 + trade tape (5.2M trades, 3.9M MBP-10 events, 500K snapshots accumulated at snapshot_interval=50 over a ~24-minute window). Total runtime ~80s. Empirical fill rates within 60s window: - bid_l1: 4.97% (matches L1 maker-side activity in trending market) - ask_l1: 71.34% (high — most 60s windows see an aggressive buy) Fitted L1 cloglog coefficients (all 5 features): BID L1: β_0=-0.213 β_spread=-2.064 β_imbal=-0.099 β_ofi=-0.006 β_logτ=-0.286 ASK L1: β_0=+0.016 β_spread=-40.336 β_imbal=+0.041 β_ofi=+0.652 β_logτ=-0.055 Sanity (sign checks all pass): - β_spread < 0 both sides (wider spread → fewer fills) ✓ - bid β_imbal < 0 (more bid stack → harder to get hit by sell) ✓ - ask β_ofi > 0 (buying pressure correlates with ask fills) ✓ - β_logτ < 0 both sides (quieter markets → slower execution) ✓ L1-only limitation: as documented in the binary header, the parser only populates levels[0]; L2/L3 in the JSON are L1 with β_0 -= ln(L+1) attenuation. Default --out-path bumped to config/ml/phase_e_fill_coeffs.json so future re-runs land in the same committed location. |
||
|
jgrusewski
|
3bdf74018d |
feat(alpha): phase_e_fit_fill_model example — cloglog FillModel calibration
Phase E.0 Task 5b. Calibrates FillModel coefficients from historical MBP-10
+ trade tape. Streams snapshots concurrently with time-sorted trades; per
snapshot, determines binary fill outcome ("would a posted L1 limit have
been hit within next --window-seconds?"), accumulates (FillFeatures, y),
calls fit_poisson (cloglog Bernoulli, see
|
||
|
jgrusewski
|
db874b1841 |
feat(foxhuntq): Phase 1c snapshot-resolution alpha + leakage fix + variable-dim fxcache
Three things landing atomically because they're load-bearing for each other: 1. **Trend-scanning leakage fix** — trend_scanning.rs was emitting OLS slope+t-stat over a *forward* window [t, t+L]. With the Phase 1a label = sign(price[t+60] − price[t]), the forward feature window overlaps the label window, contaminating it. Purged walk-forward only sterilizes forward-looking *labels* that cross the train/val split, not forward-looking *features* that peek inside the same horizon the label measures. The leak inflated MLP accuracy from 0.49 (legacy 74-dim baseline) to 0.75 — vanished to 0.50 after switching to a trailing window. Bounded the perfect-fit t-stat sentinel from ±1e6 → ±20 (p<1e-30 is already meaningless); eliminated the 16k corruption-cap drops. 2. **Variable-dim alpha column** — fxcache schema now carries the alpha-feature width via metadata (`alpha_feature_dim`), not a compile-time constant. Same on-disk format hosts the 134-dim bar-level stack OR the 81-dim snapshot stack. Reader + auto-detect honor the metadata-declared dim; downstream MLP auto-sizes `in_dim`. Single schema, no forks. 3. **Snapshot pipeline (Phase 1c falsification)** — `snapshot_pipeline.rs`: 81-dim per-MBP10-snapshot extractor reusing 10 snapshot-native alpha blocks + 6 new snapshot-specific features (time-since-trade, time-since-snap, event-rate, spread-bps, L1-imbalance, microprice-mid drift). `precompute_features` gets `--row-unit snapshot` flag; emits one fxcache row per LOB update (1.97M rows from MBP-10 data vs 206K for bar mode). **Smoke verdict on real data** (ES.FUT, 1.97M snapshots, 384K val): - Bar-level honest alpha: accuracy=0.5005, AUC=0.5043 (no signal) - **Snapshot-level alpha**: accuracy=0.5241, AUC=0.6849 (real signal, 384K val) - GBM corroboration: accuracy=0.5401 (non-linear partitioning sees more) - Horizon decay: alpha peaks at K=20-50 snapshots (~5-25ms), gone by K=500 - Regime-conditional: spread-Q4 quintile hits 0.752 accuracy on 76k samples Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|
jgrusewski
|
5694eb4df2 |
fix(sp21): T2.2 Phase 8.5 — wire factored-action branch sizes into closure-based eval (atomic)
v7 smoke (train-fv4s8, commit
|
||
|
jgrusewski
|
23b89a90e9 |
fix(sp21): T2.2 Phase 8.3+9 — eval pipeline GPU-only, hard-fail, delete CPU path (atomic)
Combined Phase 8.3 (visibility + hard-fail) and Phase 9 (CPU path
removal) per pearl_no_deferrals_for_complementary_fixes. Surfaced by
v6 smoke (train-x4m96) where:
[DQN GPU] Fold 0 GPU eval failed: GpuBacktestEvaluator::evaluate failed
for fold 0. Falling back to CPU path.
[DQN] Fold 0 evaluation failed: Failed to create DQN state tensor for
bar 0: Dimension mismatch: expected 54, got 45
Both fold 0 and fold 1 hit this; workflow exited 0, masking eval
failure for every smoke run since STATE_DIM grew beyond legacy 54.
Root cause (silent GPU failure):
evaluate_dqn_fold_gpu calls evaluator.evaluate(closure, portfolio_dim: 3)
but GpuBacktestEvaluator initialises portfolio_dim = PORTFOLIO_BASE_DIM (8)
+ MTF_DIM (16) = 24 per canonical state layout. gather_states asserts
match → returns MLError::ConfigError. Caller wraps with .with_context()
+ warn!("... {}", e) — `{}` strips anyhow chain, hiding root cause.
The CPU fallback runs with a separate stale 45-dim state builder
(42 from extract_ml_features + 3 portfolio) → fails at GpuTensor::
from_host shape validation against the model's 54-feature default.
Fixes (all atomic):
1. portfolio_dim: 3 → 24 at all 4 call sites (DQN x2, PPO, supervised).
The GPU evaluator's gather_kernel handles full 128-dim state
assembly (Market 42 + OFI 32 + TLOB 16 + MTF 16 + Portfolio 8 +
PlanISV 7 + Padding 7); caller just declares correct portfolio_dim.
2. Surface anyhow chain: {} → {:#} in error messages.
3. Hard-fail on GPU eval failure: anyhow::bail! (no CPU fallback).
Per user directive: "hard fail on gpu panic, cpu path strictly
forbidden should be removed entirely!"
4. DELETE CPU DQN eval path entirely:
- fn evaluate_dqn_fold
- fn build_chunk_states (stale 45-dim state builder)
- fn simulate_chunk_trades
- fn compute_metrics + struct ComputedMetrics
- struct PortfolioState
5. DELETE coupled surrogate-noise machinery:
- struct SurrogateSampler + impl
- fn load_surrogate_marginals
- fn compute_pooled_sharpe
- Surrogate init blocks in main
- ACTION_MARGINALS / POOLED_SHARPE emission blocks
6. DELETE coupled CLI flags:
- --gpu-eval / --no-gpu-eval (GPU mandatory)
- --surrogate-mode, --surrogate-seed, --surrogate-marginals
- --emit-action-marginals, --emit-pooled-sharpe
7. Collapse `if args.gpu_eval { ... }` blocks to direct calls; cleaner
control flow, no gpu_handled tracking.
Pearls honoured:
- feedback_no_cpu_test_fallbacks: GPU oracle only
- feedback_no_partial_refactor: stale CPU layout from pre-STATE_DIM=128 era
- feedback_no_hiding: error chain now visible via {:#}
- feedback_no_legacy_aliases: no deprecated --no-gpu-eval wrapper
- pearl_no_deferrals_for_complementary_fixes: 8.3+9 combined
Files changed:
- crates/ml/examples/evaluate_baseline.rs:
−892 net lines (1066 del, 174 ins; 2817 → 1925)
- docs/dqn-wire-up-audit.md: 2026-05-12 audit entry
Verification:
- cargo check -p ml --example evaluate_baseline --features cuda # clean
- cargo check --workspace --features cuda # clean
- cargo test -p ml --lib --features cuda financials # 7/7
Note: OFI/TLOB/MTF feature-set fidelity is a separate concern. The GPU
gather_kernel handles state assembly; caller currently provides zeroed
OFI (LobBar.ofi = 0.0) and no MTF data. Eval will run, but on degraded
features. Faithful feature wiring deferred to a later Phase once
eval-runs-at-all is validated by v7 smoke.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
|
||
|
jgrusewski
|
62b5a50e8b |
fix(eval): shape-mismatch on checkpoint load — read arch from safetensors metadata (atomic)
Smoke v1 (train-grfcw) evaluate phase failed with "Failed to load DQN
checkpoint" for fold 0 and fold 1. MinIO log inspection confirmed
checkpoints WERE saved (1431144 bytes each) — the failure was
eval-side shape mismatch.
Root cause:
- Training uses STATE_DIM=128 (ml_core::state_layout), num_actions=108
(factored b0*b1*b2*b3=4*3*3*3), num_order_types=3,
num_urgency_levels=3.
- evaluate_baseline CLI defaults: --feature-dim=54, --num-actions=5
(legacy from pre-branching DQN era).
- Loading 128-state-dim 108-action checkpoint into 54-feature 5-action
net → tensor shape mismatch → `load_from_safetensors` returned
parse error → `with_context(...)` wrapped it as the generic "Failed
to load DQN checkpoint" message, hiding the actual shape error.
- Both GPU and CPU eval paths hit the same root cause.
Fix:
Both eval paths now call `DQNConfig::from_safetensors_file(&ckpt_path)`
to read architecture-critical fields from the checkpoint's embedded
metadata (state_dim, num_actions, hidden_dims, num_order_types,
num_urgency_levels, dueling_hidden_dim, num_atoms, gamma). Eval-time
fields (LR, epsilon, buffer caps) overridden; hyperopt-derived gamma/
v_min/v_max applied if present in hyperopt config.
Older checkpoints without embedded metadata fall back to CLI-args-built
config + warn! log. All production SP21+ checkpoints embed metadata
via the existing DQNConfig::checkpoint_metadata path.
Files changed:
- crates/ml/examples/evaluate_baseline.rs: shape-aware config for both
dqn_eval_gpu_path (line ~1238) and dqn_eval_cpu_path (line ~1029)
- docs/dqn-wire-up-audit.md: 2026-05-11 audit entry
Verification:
- cargo check -p ml --examples --features cuda: 0 errors
- cargo test -p ml --lib financials: 7/7 (unchanged)
- cargo test -p ml --lib sp21_isv_slots: 4/4 (unchanged)
Behavioral gate: smoke v3 (train-psf86, in-flight on
|
||
|
jgrusewski
|
032026dc07 |
feat(sp20): parallelise per-bar OFI extraction via time-bucket sharding
Replaces the sequential per-bar OFI loop in
`crates/ml/examples/precompute_features.rs:578-734` with a call into a
new `ml-features` library function that shards bars across CPU cores
with warmup overlap. On `ci-compile-cpu` (28 cores), large fxcache
runs (~50-200k bars) get a meaningful speedup; small datasets (<10k
bars) may run slightly slower due to amortisation cost — see test
output below.
Strategy: time-bucket sharding with warmup overlap
- Partition core bars [0, n) into K contiguous shards (K = rayon
current threads, or `OFI_SHARD_COUNT` env override).
- For each shard [start_k, end_k):
1. Construct a fresh `OFICalculator`.
2. Replay the `WARMUP_BARS = 5000` preceding bars (or fewer if
start_k < 5000) by feeding trades + calling
`calculate(last_snap)` and DISCARDING outputs. This populates
VPIN (50 buckets × 50k contracts), Kyle (100 trades),
trade-imbalance (100 trades), OFI-stats (300 snapshots), and
prev_snapshot to bit-identical sequential-walk state.
3. Process core bars and emit ofi_per_bar rows.
- Concatenate shard outputs in order, then run post-loop passes
(lag-1 deltas, log_bar_duration) over the full Vec — both are pure
functions of the concatenated output / bar timestamps and have no
shard interaction.
Bit-equivalence guarantee
=========================
Each shard's warmup phase replays the same prefix of trades+snapshots
into a freshly initialised local `OFICalculator`. Once the warmup has
covered enough bars to fully populate every rolling window AND any
post-warmup-window state has been carried forward, the shard's
calculator state at the warmup→core boundary is bit-identical to the
sequential walk. `MicrostructureState` is constructed fresh per-bar
(no cross-bar state) so its semantics are unchanged.
Verified by `crates/ml-features/tests/ofi_parallel_bit_equiv_test.rs`:
| test | result | max abs diff |
|-------------------------------------------------|--------|--------------|
| parallel_matches_sequential_within_1e9_k4 | pass | ≤ 1e-9 |
| parallel_matches_sequential_within_1e9_k8 | pass | ≤ 1e-9 |
| parallel_k1_is_sequential | pass | exactly 0 |
| parallel_handles_no_trades | pass | ≤ 1e-9 |
| parallel_speedup_smoke (ignored, bench-shaped) | n/a | n/a |
Speedup smoke (release, K=8, synthetic data):
n=8000 bars : seq=16.36ms par=18.98ms speedup=0.86x
n=30000 bars: seq=50.89ms par=29.56ms speedup=1.72x
n=80000 bars: seq=142.12ms par=55.13ms speedup=2.58x
Speedup grows with N because the 5000-bar warmup overhead
amortises. At production scale (50-200k bars × ~3 snap/bar × ~5
trades/bar) real workloads will see closer to K-1.x speedup. Small
datasets (<10k) regress slightly — by design; warmup must be ≥
rolling-window depth for bit-equivalence and we will not relax that
for marginal wall-clock gains on tiny inputs.
Diff
====
- `crates/ml-features/src/ofi_calculator.rs` (+356 LOC):
`compute_ofi_per_bar_sequential` (reference impl),
`compute_ofi_per_bar_parallel` (rayon par_iter over shard ranges),
`process_one_bar` (shared per-bar body — single source of truth
for the loop semantics, no duplication between paths),
`apply_post_loop_passes`, `PerBarOfiInputs` struct,
`PER_BAR_OFI_DIM=32` const, `DEFAULT_OFI_PARALLEL_WARMUP_BARS=5000`
const.
- `crates/ml-features/src/lib.rs` (+4 LOC): re-export new public API.
- `crates/ml/examples/precompute_features.rs` (-132 +35 LOC): replace
the inline 132-line OFI loop with a call to
`compute_ofi_per_bar_parallel`. Reads `OFI_SHARD_COUNT` env or
falls back to `rayon::current_num_threads()`.
- `crates/ml-features/tests/ofi_parallel_bit_equiv_test.rs` (+~280 LOC,
new): synthetic-data bit-equivalence tests at K=4, K=8, K=1, and
no-trades.
Memory budget per shard: one cloned `OFICalculator` (≤10 MB carrying
the rolling-window state). K=8 ≈ 80 MB extra. Manageable on the 28-core
CPU node.
No `mbp10_to_imbalance_bars` / `filter_front_month_mbp10` changes
(out of scope; those were just fixed and a separate smoke is running).
No audit-doc update required: changes are confined to ml-features +
ml/examples and do not touch crates/ml/src/(cuda_pipeline|trainers/dqn)/
which is the trigger scope for the Invariant-7 audit-doc check.
|
||
|
jgrusewski
|
abd7e533bc |
fix(architectural): volume_bar_size in cache key + OFI front-month filter
## Two architectural cleanups, both surfaced by the wgdc8 experiment ### Part 1: volume_bar_size in cache key Mirrors the imbalance_bar_threshold/ewma_alpha fix from `f7718b376`. The volume bar size constant (100 contracts/bar) was previously hardcoded and not in the fxcache key. Tuning it would have hit the same fossilization bug as imbalance_bar_threshold did pre-fix. Changes: - `Hyperparams.volume_bar_size: u64` field added (default 100, matches `DEFAULT_VOLUME_BAR_SIZE` for backwards compat). - TrainingProfile loader reads `volume_bar_size` TOML key. - `calculate_dbn_cache_key_full` signature 7 → 8 args. Hashed via `to_le_bytes()`. Test `test_cache_key_includes_volume_bar_size` added; passes alongside the 5 existing tests. - 4 callers updated atomically (per `feedback_no_partial_refactor`): `discover_and_load`, `data_loading.rs:146`, `train_baseline_rl.rs:599`, `precompute_features.rs:259,720`. - `data_loading.rs:279` now passes `self.hyperparams.volume_bar_size` to `build_volume_bars` instead of the hardcoded `DEFAULT_VOLUME_BAR_SIZE`. - New `--volume-bar-size` CLI arg on both binaries (default 100). - New Argo workflow params `volume-bar-size` (default "100") and `data-source` (default "mbp10") on both `train-template.yaml` and `train-multi-seed-template.yaml`. Threaded into precompute + trainer invocations. - `scripts/argo-train.sh` exposes `--volume-bar-size <n>` and `--data-source <s>` for ad-hoc overrides. ### Part 2: OFI front-month filter (latent bug fix) `crates/ml/examples/precompute_features.rs:539-557` (the OFI/VPIN/Kyle's Lambda computation branch when MBP-10 + trades data is available) was loading trades unfiltered for per-bar microstructure feature computation. The volume bar formation path filters front-month per-file (line 354), but the OFI path did not. Effect pre-fix: during contract rollover windows (e.g., ESZ24 → ESH25), OFI per-bar microstructure features included trades from BOTH contracts simultaneously, distorting VPIN, Kyle's Lambda, and trade imbalance signals. Severity in production: small (front-month dominates ES.FUT volume by 10-100×) but real and present in every prior MBP-10+trades production run. Fix: mirror the per-file `filter_front_month` call from the volume bar path. Volume bar formation and OFI computation now both see the same in-month trade tape. Added log line shows raw vs filtered count per file for transparency. ## Why bundled Both fixes touch trade-data plumbing in `precompute_features.rs` and the fxcache key contract. Per `feedback_no_partial_refactor`, related architectural cleanups land atomically. Both surfaced from the same wgdc8 audit; bundling avoids two cache-key-invalidating commits in sequence (each would force full fxcache regen). ## Compatibility - `volume_bar_size` defaults to 100 → existing wgdc7-equivalent runs reproduce, but with a *new* fxcache key (the f7718b376-era cache file is unreachable; harmless, can GC manually). - OFI fix is strictly more correct; no opt-out needed. Existing models trained on contaminated OFI features may show slight feature distribution drift on first cache regen — expected, not a regression. - `data_source = "ohlcv"` Argo param now possible; routes precompute through volume bar branch directly. wgdc8 experiment uses this to test bar resolution sensitivity at volume_bar_size=500 (5× DEFAULT). Tests: 6/6 feature_cache tests pass. Workspace + examples compile clean. Audit-doc: `docs/dqn-wire-up-audit.md` updated. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
||
|
jgrusewski
|
f7718b3761 |
fix(architectural): include bar formation params in fxcache key + actually USE imbalance bars
## The bug (audit 2026-05-09) `crates/ml/src/feature_cache.rs:calculate_dbn_cache_key_full` hashed only `(symbol, data_source, dbn_filenames+sizes)` — NOT `imbalance_bar_threshold` or `imbalance_bar_ewma_alpha`. Combined with `precompute_features.rs:346` unconditionally calling `build_volume_bars` regardless of `data_source`, this meant: 1. 14 audited production runs (Apr 11–May 8) all collided on the same fxcache key (`a3f933aa...` / `c07c960a...`) regardless of TOML `imbalance_bar_threshold` value 2. The imbalance-bar code path was reachable only via fxcache MISS, which never happens in production because `ensure-fxcache` always populates first 3. Every "tuning" of `imbalance_bar_threshold` across 16+ SP runs was a silent no-op — the system was actually running volume bars at DEFAULT_VOLUME_BAR_SIZE (100 contracts/bar) ## The fix (this commit) **Part A — cache key includes bar formation params:** - `calculate_dbn_cache_key_full` signature: 5 args → 7 args. Two new f64 params hashed via `to_le_bytes()`. - 4 callers updated atomically (per `feedback_no_partial_refactor`). - 2 new unit tests (`test_cache_key_includes_bar_threshold`, `test_cache_key_includes_bar_alpha`) pin the contract. **Part B — precompute_features actually USES data_source:** - New CLI args `--imbalance-bar-threshold` (default 0.5) and `--imbalance-bar-ewma-alpha` (default 0.1) on both train_baseline_rl and precompute_features. - `precompute_features.rs:346` now branches: when `data_source == "mbp10"` AND `mbp10_data_dir.is_some()`, calls `mbp10_to_imbalance_bars` instead of `build_volume_bars`. **Argo plumbing:** - `train-template.yaml` + `train-multi-seed-template.yaml`: new workflow parameters threaded into BOTH precompute and trainer invocations so both compute the same fxcache key. - `scripts/argo-train.sh`: new CLI flags for ad-hoc overrides. - ensure-fxcache regen path: removed `rm -f /feature-cache/*.fxcache` (with bar-params now in key, parallel experiments coexist). ## Effects going forward - Tuning `imbalance_bar_threshold` actually changes bar density - Configuring `data_source = "mbp10"` actually produces imbalance bars - Multiple parallel experiments at different thresholds coexist on PVC - `dqn-production.toml: imbalance_bar_threshold = 0.5` no longer ignored Default values match prior production behavior → existing wgdc7-equivalent runs reproduce, just with a *new* fxcache key (the old volume-bar cache file is still on disk but won't be hit; harmless, can GC manually). Audit-doc: `docs/dqn-wire-up-audit.md` updated with full context. Tests: 5/5 feature_cache tests pass, full workspace + examples compile clean. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
||
|
jgrusewski
|
d39005c6f4 |
feat(sp19 commit b): producer-side multi-horizon reward blend at fxcache write time
Path (B) Commit B — load-bearing semantic change for the SP19 multi-
horizon reward augmentation. At fxcache-write time, blend 1-bar / 5-bar
/ 30-bar log-returns into `tgt[1]` (`preproc_next`) using equal-thirds
weights with `1/sqrt(N)` vol-scale correction:
blended = (1/3) * r_1
+ (1/3) * r_5 / sqrt(5)
+ (1/3) * r_30 / sqrt(30)
The vol-scale correction applies INSIDE the blend (before weighting) so
each horizon's log-return is at unit-volatility-equivalent under
random-walk assumption — drops naturally into the existing `tgt[1]`
preprocessing pipeline (consumed in `experience_kernels.cu:1556` and
`cuda_pipeline/mod.rs:508`) without double-scaling.
Producer trim: valid range shrinks by `LOOKAHEAD_HORIZON_MAX = 30`
bars since `r_30` needs `close[i + WARMUP + 30]`. Walk-forward fold
construction is dataset-relative, so trimming at producer time shifts
every fold's tail by 30 bars — one-time cost per fxcache regen.
Both producer call sites (`precompute_features.rs` for the
feature-precompute binary, `data_loading.rs` for the DBN-fallback
in-process path) change atomically per `feedback_no_partial_refactor`.
Kernel consumers are unchanged — only `tgt[1]`'s value composition
differs from the consumer's perspective.
ISV slots [507..510) (allocated in Commit A) are NOT consumed at
producer time — `precompute_features` runs BEFORE training so the
ISV bus isn't initialised when the blend happens. Producer hardcodes
1/3 each via `SP19_HORIZON_BLEND_WEIGHTS`. Future Path (A) refactor
would bump TARGET_DIM to carry per-horizon log-returns and read
ISV at training-step time; for the empirical hypothesis test
("does multi-horizon blend lift WR?") equal-thirds is sufficient.
fxcache schema invalidation: `FXCACHE_VERSION` 8 → 9. Existing
`.fxcache` files (1-bar-only `preproc_next`) fail validation at load
and trigger Argo's ensure-fxcache regen step. First L40S run after
this commit takes ~10-15 min longer for the regen — one-time cost,
expected.
Touches:
- crates/ml/examples/precompute_features.rs: SP19_HORIZON_BLEND_WEIGHTS
constant + LOOKAHEAD_HORIZON_MAX trim + blend in tgt[] writer +
early-bail-out check on minimum dataset size.
- crates/ml/src/trainers/dqn/data_loading.rs: same blend +
trim in DBN-fallback path; `last sample targets itself` block
removed (the trimmed range guarantees feature-vector and target
lengths match without a sentinel last row).
- crates/ml/src/fxcache.rs: FXCACHE_VERSION 8 → 9 + v9 docstring entry.
- crates/ml/tests/multi_horizon_reward_blend_test.rs (NEW): CPU-only
oracle behavioural test — 4 cases including known returns, trim
contract, zero-close short-circuit, constant-price blend.
- docs/dqn-wire-up-audit.md: Concerns subsection appended to the
SP19 Commit B entry already drafted in Commit A (pre-existing
test_fxcache_empty + test_dqn_checkpoint_round_trip flakiness
documented for Invariant 7).
Verification:
SQLX_OFFLINE=true CUDA_COMPUTE_CAP=86 cargo check --workspace clean
cargo test -p ml --test multi_horizon_reward_blend_test 4/4 pass
cargo test -p ml --test fxcache_roundtrip_test test_fxcache_f32_roundtrip passes (TARGET_DIM unchanged)
cargo test -p ml --lib ... 13 baseline failures (pre-existing); zero new regressions
bash scripts/audit_sp18_consumers.sh --check exit 0
Pre-existing test_fxcache_empty failure: hand-crafts a 64-byte header
but the actual header is 72 bytes; reader bails early on
"failed to fill whole buffer" instead of "bar_count=0". Test setup
bug, NOT a regression. Pre-Commit B failure count: 13. Post-Commit B
failure count: 13 (the test_dqn_checkpoint_round_trip test is flaky
and toggles independently of this change — verified by stashing and
re-running 3× pre-Commit B).
Atomic-refactor invariant satisfied: Commit A (slot reservations) +
Commit B (producer-side blend) land on the same branch with no L40S
dispatch between. Per task instruction the branch stays unpushed
pending user review.
DBN-fallback path: applied identically in `data_loading.rs:497-528`.
Both producers use the same `SP19_HORIZON_BLEND_WEIGHTS` constant and
the same `LOOKAHEAD_HORIZON_MAX = 30` trim.
Vol-scale correction site: applied inside the blend (before weighting)
in BOTH producers. The existing `tgt[1]` preprocessing pipeline does
NOT double-scale — `experience_kernels.cu:1556` and
`cuda_pipeline/mod.rs:508` consume `tgt[1]` as a unit-scale log-return
exactly as before the blend was added; the blended value drops in
without further scaling.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
|
||
|
jgrusewski
|
ce841eb56e |
fix(audit): lookahead housekeeping — purge gap + per-fold NormStats
Closes recs 2 + 3 in lookahead-bias-audit-2026-04-28.md.
Fix 2A (rec 2): add walk_forward::PURGE_BARS = 5 constant and insert
val_start = train_end + PURGE_BARS in all 4 fold-construction sites:
generate_walk_forward_indices, _from_timestamps, _windows (date-based,
drops first PURGE_BARS bars of val), and gpu_walk_forward::generate_folds
(both stratified variants preserve the gap on boundary shift). 5-bar
width matches max per-bar feature lookback (autocorr lags 1/5/10);
compile-time per feedback_isv_for_adaptive_bounds since the
feature-pipeline lookback is itself compile-time.
Fix 2B (rec 3): per-fold NormStats fit from train-only data.
- walk_forward.rs: add from_features_slice + denormalize/denormalize_batch
helpers (inverse of normalize_batch for clamp-bounded round-trip).
- train_baseline_rl.rs fold loop: load fxcache sidecar norm_stats.json,
denormalise back to RAW, refit per-fold via from_features_slice,
renormalise full dataset, re-upload via init_from_fxcache, save
per-fold norm_stats_fold{N}.json. DBN-fallback path keeps legacy
behaviour (no sidecar to denormalise from) with a warn! log.
Ensemble trainer block is documented follow-up (separate code path).
Behavioral tests:
- test_norm_stats_per_fold_fit_train_only: synthetic train-mean=1.0 /
val-mean=9.0 dataset; from_features_slice recovers train-only mean to
ε=1e-5 while from_features returns global 5.0
- test_norm_stats_denormalize_roundtrip: non-clamped features round-trip
bit-identically
- test_walk_forward_purge_gap_indices_from_timestamps: every fold has
val_start - train_end == PURGE_BARS
- test_fold_generation_basic (gpu_walk_forward): updated to expect the
purge gap
Validation: cargo check --workspace clean (12.30s); 20/20 walk_forward
+ gpu_walk_forward tests pass; audit_sp18_consumers.sh --check exit 0.
Pre-existing test failures on local RTX 3050 Ti are unrelated SIGSEGVs
(VRAM exhaustion in chunked Thompson tests, pre-existing on baseline).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
|
||
|
jgrusewski
|
81a9319d84 |
fix(sp15-wave3b-followup): migrate evaluate_baseline.rs 3 GpuBacktestEvaluator::new sites — Wave 3b missed the example binary
Wave 3b (
|
||
|
jgrusewski
|
ce019c72d2 |
feat(sp15-p1.6): --holdout-quarters + --dev-quarters CLI flags + sealed Q1-Q7/Q8/Q9 split
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> |
||
|
jgrusewski
|
5845e44031 |
fix(data): DBN spread-instrument filter — root cause of Bug 2 contamination
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>
|
||
|
jgrusewski
|
8434737a69 |
fix(data): structural defense at data-loading boundary — 5 layers
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> |
||
|
jgrusewski
|
5a5dd0fed1 |
fix(fxcache): target column [0:1] log-return-normalized, not raw price
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> |