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21 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
jgrusewski
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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. |
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jgrusewski
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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.
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jgrusewski
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783297e002 | feat(loader): instrument_id filter + outdated test fix + sp18 fingerprint | ||
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jgrusewski
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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> |
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jgrusewski
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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> |
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jgrusewski
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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>
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jgrusewski
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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>
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jgrusewski
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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>
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jgrusewski
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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>
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jgrusewski
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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>
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jgrusewski
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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> |
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jgrusewski
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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> |
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jgrusewski
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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> |
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jgrusewski
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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>
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jgrusewski
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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)
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jgrusewski
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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.
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jgrusewski
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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.
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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
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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.
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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.
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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.
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