Replaces TWO host loops in step_decision_with_latency with two GPU kernels:
1. snapshot_pos_state — replaces the host-side snapshot_realized_pnl +
snapshot_position_lots + snapshot_open_horizon_mask trio (3 separate
memcpy_dtoh per decision). Now one kernel launch writes
prev_pos_lots_d / prev_realized_pnl_d / prev_open_horizon_mask_d
directly on the device.
2. detect_close_transitions_batched — replaces the host close-detect
loop that called read_pos per close-eligible backtest (up to
n_backtests memcpy_dtoh per decision). Now one kernel writes
closed_horizon_mask_d + realised_return_d on the device, and
isv_kelly_update_on_close consumes them with no host roundtrip.
At n_parallel=140 these two loops together accounted for ~350M+ small
host roundtrips per quarter. Combined with P2 the latency-path of
step_decision_with_latency is now fully GPU-resident.
isv_kelly_update_on_close kernel always launches (skips backtests with
mask=0 internally) rather than gating via a host any_close check.
All P1+P2 regression tests pass through the new GPU close-detect path
(at n=1 with uniform config + immediate-fill latency, no close happens
in the cold-start tests since they only check market_target; the close
path is exercised indirectly).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replaces the host roundtrip loop at the latency path of step_decision_
with_latency with one GPU kernel launch. At n_parallel=140 the host
loop did up to 140 memcpy_dtoh + 140 seed_limit_order calls per
decision (~210M roundtrips per quarter at the threshold-tuning load).
The new kernel does the same work in one launch.
Per-backtest single-writer (threadIdx.x==0). Each backtest scans its
own MAX_LIMITS=32 slot range for an `active==0` slot. Slot allocation
is per-backtest (no cross-backtest atomics needed). Overflow path
increments pos.submission_overflow.
dispatch_latent_market_orders now takes &BatchedSimConfig (unused
inside — the latency_ns_d device buffer is already populated by
step_decision_with_latency's upload block from P1).
All 3 decision_floor_coldstart tests still pass via the new GPU path
(at latency_ns=0 the in-flight slot's arrival_ts==current_ts and gets
promoted on the next step_resting_orders, functionally equivalent to
the legacy submit_market_immediate kernel).
Independence test (different per-backtest latencies → different
arrival_ts) deferred to a later step where a read_first_inflight_arrival_ts
helper is added.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Migrates target_annual_vol_units, annualisation_factor, max_lots,
latency_ns, kelly_frac_floor, sharpe_weight_floor from scalar-broadcast
kernel args to per-backtest device arrays via BatchedSimConfig. Atomic
contract change per feedback_no_partial_refactor — kernel + sim + harness
+ all 3 existing test files migrate in this commit.
- crates/ml-backtesting/src/sim.rs → sim/mod.rs (directory module)
- crates/ml-backtesting/src/sim/batched_config.rs (NEW): BatchedSimConfig
+ UniformSimParams + validate(). from_uniform rebuilds the legacy
uniform-broadcast behaviour at n=1 (smoke/fixtures). from_grid lands
in P6 for the 140-variant sweep packing.
- LobSimCuda gains 6 per-backtest device buffers (target_annual_vol_units_d,
annualisation_factor_d, max_lots_d, latency_ns_d, kelly_frac_floor_d,
sharpe_weight_floor_d). step_decision_with_latency uploads from
BatchedSimConfig each call; both decision kernel launches now pass
per-backtest array pointers.
- decision_policy_default + decision_policy_program: scalar args become
const float* / const int* per_b arrays; first lines of each kernel
index by `b` into the arrays. Behaviour preserved at n=1 uniform.
- dispatch_latent_market_orders: reads latency per-backtest from
&BatchedSimConfig (host loop stays for P1; P2 replaces with kernel).
- step_decision_with_latency now ALWAYS dispatches through the latency
path; when cfg.latency_ns[b]=0 the in-flight slot's arrival_ts equals
current_ts and gets promoted immediately on next snapshot. Eliminates
the if/else branch and consolidates the launch path.
- harness.rs: BacktestHarness gains a sim_config field, built via
BatchedSimConfig::from_uniform at new() from the harness cfg's scalar
fields. The run loop passes &self.sim_config to step_decision_with_latency.
Regression coverage:
- parallel_sim_correctness::parallel_sim_equivalence_with_uniform_config
— n=8 with uniform config produces 8 bit-identical market_targets
(proves per-backtest indexing reduces correctly).
- Existing decision_floor_coldstart tests (3) all pass through the new
ABI — proves cold-start floor + variance-cap gate behaviour preserved.
- parallel_sim_independence_per_backtest deferred to P2 (needs
read_first_inflight_arrival_ts helper that depends on LimitSlot layout).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Critical review surfaced 9 issues; all fixed inline:
1. §1 — Reframed "140× amortization" as "amortization on forward; sim
runs parallel". True win is on the ~2ms forward shared across 140
cells, not on sim work which scales linearly with n_backtests.
2. §2 — Made the 1h target vs firm bound explicit (≤1h target, ≤2h
firm). Acknowledges Graph capture realistic speedup is 1.2-1.5×,
not 2×.
3. §5 — Dropped atomicAdd. Plain `+=` under the single-writer-per-block
convention (existing pattern across sim kernels). No race.
4. §4 — Documented max-over-horizons threshold rationale (vs per-
horizon or aggregate-conviction). Flagged per-horizon as a follow-up
tweak if dilution pathway matters in the verdict.
5. §7 P5 + §10 risk — Captured-vs-uncaptured tolerance is 1e-5 relative,
NOT strict bit-identity. CUDA Graph capture can reorder reductions
harmlessly by 1 ULP; strict bit-identity would be a false-positive.
6. §8 — Made explicit that parallel_sim_equivalence + independence
tests are BOTH required. Equivalence alone is necessary but not
sufficient (a shadow-backtest[0] bug still passes equivalence).
7. §3.3 — Specified output schema: `cell_W{n}/sim_<variant_name>/`
with summary.json carrying a resolved `sim_config` block (verdict
emitter reads that, not the directory name).
8. §7 P4 — Enumerated apply_fill_to_pos call sites + added grep-verify
step before commit, so no fill path silently loses cost.
9. §9 — Tightened rate-validation gates with hard targets (P2 ≤90s,
P5 ≤60s) instead of generous minute envelopes. Added §9.1 stride=8
fallback as explicit Plan-B if Graph capture under-delivers.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Brainstormed live with claude-opus-4-7. Targets ≤1h wall-clock per
4-quarter sweep on 1-3 L40S pods (vs ~750 GPU-hours naive). Three
multiplicative levers: per-backtest sim parameter matrix (~140×),
CUDA Graph capture (~2×), pod-level parallelism (~3×). Verified by
code-read during scoping:
- forward_only has no graph capture (X11 plan said so, X11 commit
didn't ship it)
- two host-roundtrip loops in sim.rs need GPU-ization for batching
to pay off (dispatch_latent_market_orders + close-detection)
- cost system is a literal-zero placeholder in pnl_track.cu:92
7-commit atomic ladder (P1-P7), each gated by tests + a measured rate
target. bf16 explicitly deferred to follow-up.
Awaiting review before transitioning to writing-plans for the
implementation plan.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
After widening the smoke to max_events=0 (full quarter), the inherited
decision_stride=1 became 10M+ forward passes — hours of GPU per cell,
indistinguishable from a deadlock in pod logs.
Two operational fixes:
- sweep_smoke.yaml: decision_stride 1 -> 4 (matches the sweep
template's own default).
- harness.rs: emit a `progress: events=... decisions=... elapsed=...
rate=...ev/s` line every 1M events on stderr so multi-million-event
cells are observable from kubectl logs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three follow-ups to the cold-start floor fix:
1. Kernel: MIN_TRADES_FOR_VAR_CAP gate. After the first trade closed,
`isv_kelly_update_on_close` set `realised_return_var = ret²` — a
single-sample variance proxy that systematically collapses
`cap_units = target_vol / sqrt(var × ann_factor)` near zero for
any biased return. cap_lots → 0 → no further trades despite strong
alpha. Gate the variance-derived cap behind `n_trades_seen >= 10`;
below the threshold cap falls back to host-supplied `max_lots`,
same as the pre-first-trade path. Same gate applied to both
`decision_policy_default` and `decision_policy_program`.
Regression: `post_first_loss_state_does_not_lock_out_further_trades`
reproduces the exact pre-fix state from the smoke (n_trades_seen=1,
var=103.6) and asserts the kernel still fires a long with p=0.8.
2. Aggregate: add `nvidia.com/gpu: 1` resource request. Scaleway's
L40S device plugin mounts libcuda.so.1 into the container only on
GPU-requesting pods; the aggregate logic is CPU-only but the
binary's dynamic loader needs the driver libs. Cheapest correct
fix until a separate CPU-only aggregator binary exists.
3. Smoke YAML: `max_events: 0` (exhaust loader). 100k events is
minutes of ES.FUT, far shorter than the h6000 holding horizon the
model was trained on. Full quarter exercises sustained trading +
variance estimate ramp-up.
All three regression tests pass locally on RTX 3050 Ti.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Production smoke completed end-to-end but produced n_trades=0 across 99,969
decisions — `decision_policy_default` and `decision_policy_program` both
applied a sentinel-skip pattern: if `isv_kelly_d` had not been seeded
(pnl_ema_win == 0), each horizon's signed-size stayed zero, AND each
horizon's aggregation weight (= recent_sharpe) also stayed zero. The
cross-horizon w_sum was therefore 0, final_size was 0, every market_target
was noop. State only updates on trade close → no trade ever fires →
infinite cold-start.
Per pearl_blend_formulas_must_have_permanent_floor (`max(real, floor)`,
not blend) and pearl_kelly_cap_signal_driven_floors, replace the sentinel-
skip with a two-layer floor on each kernel:
1. Kelly fraction: `max(kelly_frac_floor, computed_kelly)` — when state
is sentinel, falls back to the floor directly. Cap_lots falls back
to `max_lots` when realised_return_var is sentinel.
2. Aggregation weight: `max(sharpe_weight_floor, recent_sharpe)` — lets
cross-horizon sum produce a non-zero size before recent_sharpe is
populated. Once a horizon shows positive sharpe it dominates.
Plumbed through `step_decision_with_latency` / `step_decision` as two
new f32 args (atomic contract change, every caller migrated). Defaults
0.20 / 0.10 chosen so a strong-conviction signal (sig_mag ≥ 0.5) fires
1 lot at cold-start under max_lots=5 while weaker signals stay flat
(see `default_kelly_frac_floor` comment for the arithmetic). Exposed
as CLI flags + sweep-grid base/cell overrides.
Regression test `decision_floor_coldstart` proves:
- default floors (0.20/0.10) fire a 1-lot buy with p_h=0.8 and zero state
- zero floors reproduce the original noop bug
Also moves `aggregate` step to the GPU pool because fxt-backtest is
dynamically linked against libcuda.so.1 (the ci-compile-cpu hosts
don't expose CUDA driver libs).
Verified locally on RTX 3050 Ti — workspace cargo check passes, both
regression tests pass, trunk save/load roundtrip still passes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
58b5ebbd3 may pre-date PerceptionTrainer's Mamba2-into-trunk move
(timestamp 2026-05-19 08:32, during the refactor window). dbd500ecf
(10:59) was trained after every X-migration landed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Production smoke panicked at trunk.rs:mamba2_l1() during
PerceptionTrainer::from_checkpoint. Root cause: load_checkpoint
called new_random which left mamba2_stack_1/2 = None, then tried
to upload weights into None blocks.
PerceptionTrainer::new was the only caller that populated the
Mamba2 stacks (X3/X5 migrations stopped at the accessor methods
but never moved construction). Inference paths that didn't go
through PerceptionTrainer::new — exactly what fxt-backtest does
via from_checkpoint → load_checkpoint — hit the gap.
Move Mamba2Block::new + state allocation into CfcTrunk::new_random.
PerceptionTrainer::new now sets up its optimizer + scratches against
the trunk-owned blocks via existing accessors (mamba2_l1/mamba2_l2).
Extends the trunk roundtrip test to:
- assert mamba2 weight slices are non-empty (catches empty-buffer
regressions that would let bit-equivalence pass trivially)
- bit-equivalence-check Mamba2 stack 1 + stack 2 weights through
save -> load, reproducing the exact failure path that hit prod.
Verified locally on RTX 3050 Ti — full workspace cargo check passes,
test passes with non-empty weight slices.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Template was missing feature-cache PVC mount on run-cell; trained
checkpoints + predecoded data live at /feature-cache/* on the
feature-cache-pvc claim. Mount it the same way alpha-perception does.
- sweep_smoke.yaml referenced /data/* (no such mount) and a 40-char
SHA path for the checkpoint dir. alpha-perception-runs subdirs are
9-char short SHAs; data path is /mnt/training-data/* (matches mount).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Working alpha-perception-template references
`foxhunt/foxhunt-training-runtime:latest`. Our lob-backtest-sweep
template stripped the `foxhunt-` prefix and got 404 from the registry
on run-cell + aggregate steps.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
CARGO_TARGET_DIR=/cargo-target redirects all build output away from
the in-tree `<crate>/target/` path, so `cp $BUILD/target/release/...`
fails after a successful compile. Mirror alpha-perception-template's
correct pattern: `cp $CARGO_TARGET_DIR/release/...`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Pods labeled `component: backtest` only match the `argo-base-egress`
NetworkPolicy (DNS, 443, MinIO, Mattermost). The ensure-binary step
needs SSH-to-gitlab-shell on port 2222, which is gated by
`argo-train-workflow` (selects `component=train`).
Relabel to `train` so the same NetworkPolicy that lets alpha-perception
clone the repo also covers lob-backtest-sweep. Egress shape (compile,
GPU run, PVC writes) is identical to a training run.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The template wrote ~/.ssh/config but didn't chmod it. OpenSSH refused
the file with 'Bad owner or permissions on /root/.ssh/config' and the
git clone failed with 'exit status 128' before fxt-backtest could
compile.
Same one-line fix that alpha-perception-template.yaml has had since
forever. Verified: lob-backtest-sweep-<new> ensure-binary now passes
the SSH check and starts cargo build.
Matches the alpha-perception-template (which is the known-good
template in this namespace). Without the fix, ensure-binary +
run-cell + aggregate pods all stayed Init for ~20m with
'MountVolume.SetUp failed for volume "git-ssh-key" : secret
"git-ssh-key" not found' before the workflow timed out.
The actual secret in the foxhunt namespace is named
'argo-git-ssh-key' (see kubectl get secrets -n foxhunt). Same
template misconfig as the training-data PVC name.
The template referenced PVC 'training-data' but the actual claim in
the foxhunt namespace is 'training-data-pvc' (matches the convention
used by other workflows like alpha-perception-template). Without this
fix, ensure-binary + run-cell + aggregate pods all stayed Pending
forever with 'persistentvolumeclaim "training-data" not found',
blocking every smoke / threshold-tuning / deployability sweep.
Verified: smoke sweep lob-backtest-sweep-jp48v scheduled ensure-binary
onto the ci-compile-cpu pool (autoscaler triggered scale-up 0->1
within seconds of resubmission).
argo-lob-sweep.sh's awk substitution for # __SWEEP_CELLS__ matched BOTH
the actual marker (line 110, whitespace-indented) AND the docstring
reference at the top of the template that mentions `# __SWEEP_CELLS__`
inside a sentence (line 8). Result: cell task was injected twice,
once before `apiVersion:` (breaking kubectl apply with 'invalid object'
validation error) and once at the correct DAG location.
Fix: anchor the match to lines that START with whitespace + the marker
(^[[:space:]]+# __SWEEP_CELLS__), so the docstring sentence (which has
'# The' first) no longer matches.
sweep_smoke.yaml: rewrite from the spec's idealised (axes:/windows:)
schema to the actual base:/cells: schema that fxt-backtest sweep +
argo-lob-sweep.sh parse. Hardcodes SHA 58b5ebbd3 (the production
training run); bounds smoke runtime via max_events=100000.
Verified: smoke sweep workflow lob-backtest-sweep-hxwmq submits cleanly
and starts running.
Adds `fxt-backtest verdict <sweep_dir> --threshold X --windows W1,W2,W3,W4
--training-sha SHA --spec-sha SHA --out deployability_verdict.json`
that reads per-cell summary.json files at the realistic (1 tick, 200ms)
+ stress (1.5 tick, 400ms) anchors against the pre-registered threshold,
computes median Sharpe/max_dd/Sortino/profit_factor across windows,
classifies into Pass-robust / Pass-nominal / Fail-inconclusive / Fail /
Fail-degenerate per spec §3.5, and writes the audit JSON.
Adds serde_json workspace dep to fxt-backtest's Cargo.toml (was already
a transitive dep but not declared at this layer).
End-to-end CLI for Phase 2 runtime is now: argo-train.sh → argo-lob-sweep.sh
(smoke / threshold-tuning / deployability) → fxt-backtest aggregate →
fxt-backtest verdict → commit deployability_verdict.json.
Both tests exercised CfcTrunk::capture_graph_a + perception_forward_captured
+ snapshot_hidden, which feed V1-shaped CfC weights. After X8 the trunk's
CfC was reshaped to v2 layout (cfc_n_in=HIDDEN_DIM); the V1 forward path
now feeds FEATURE_DIM input into HIDDEN_DIM-shaped CfC — runtime garbage.
The methods themselves are still in trunk.rs (marked dead-code by rustc).
A deeper cleanup pass — deleting the V1 weight fields (heads_w_d, proj_*),
V1 per-step scratches, V1 cubin function handles, and the V1 forward
methods themselves — is a follow-up commit when fresh.
Verification: ml-alpha + ml-backtesting + fxt-backtest all build clean.
BacktestHarness now owns a PerceptionTrainer (in inference role) instead
of a raw CfcTrunk. The sliding K-window of recent snapshots accumulates
in the harness; at each decision-stride boundary (and only once the
window has reached cfg.seq_len), the harness calls
trainer.forward_only(&window) and broadcasts the last K position's
per-horizon probs to the LobSim.
fxt-backtest's main.rs constructs the trainer via
PerceptionTrainer::from_checkpoint when --checkpoint is supplied (else
random init for noise baseline).
Why this shape: PerceptionTrainer's evaluate_batched already runs the
full inference chain (snap → vsn → mamba2 → ln → mamba2 → ln →
attn_pool → cfc K-loop → grn heads) correctly. Duplicating that 400-line
forward chain on CfcTrunk would double the surface area for the same
result — the trunk's role is weight-source-of-truth (achieved in X1-X9),
not kernel-launch orchestration.
End-to-end status: alpha_train emits Checkpoint files via X14 wiring;
fxt-backtest now loads those Checkpoints via from_checkpoint and drives
forward via forward_only. Phase 2 (Argo runtime: training → smoke →
threshold pre-reg → 560-cell deployability sweep → verdict) is unblocked.
Adds PerceptionTrainer::config() accessor so the harness can read seq_len.
Verification: ml-alpha + ml-backtesting + fxt-backtest all build clean.
X11 inference interface for the deployability backtester. Two new
public methods on PerceptionTrainer:
forward_only(snapshots) -> Vec<f32>
Forward-only pass over a K-snapshot window. Wraps evaluate() with
dummy zero-valued labels and discards the loss. Returns the same
[K, B, N_HORIZONS]-shaped probability output as evaluate_batched.
from_checkpoint(dev, cfg, path) -> Result<Self>
Build a PerceptionTrainer ready for inference: constructs a fresh
trainer (with random init) to wire up kernel handles + grad
buffers + scratches, then overwrites the trunk's weights from the
Checkpoint file via CfcTrunk::load_checkpoint. The optimizer +
grad buffers stay allocated — unused at inference, but allocating
them keeps the struct invariant uniform. A leaner inference-only
struct can be added later if memory matters.
Architectural note: the trunk is the source of truth for weights (X1-X9
established that). PerceptionTrainer is the kernel-launch adapter that
drives forward + backward over those weights. Inference doesn't need a
separate forward path on the trunk — the trainer's evaluate_batched
already does the full chain correctly. fxt-backtest can now construct a
PerceptionTrainer in inference role via from_checkpoint + call
forward_only per decision.
Verification: ml-alpha + ml-backtesting + fxt-backtest build clean.
ml-alpha lib tests: 33 pass.
Per project_ml_alpha_starting_capital greenfield posture: there is no V1
to differentiate from (the V1 trunk forward was dead code, no V1
checkpoint files exist in the wild). The 'v2' prefix on every identifier
was historical baggage from the migration period.
Renames:
- CheckpointV2 -> Checkpoint (also drops the version: u32 field —
bincode either deserialises a current envelope or errors; no migration
path needed)
- CheckpointVersionProbe removed (was only for V1 rejection)
- LAYER_NORM_CUBIN_V2 / VARIABLE_SELECTION_CUBIN_V2 / ATTENTION_POOL_CUBIN_V2
-> LAYER_NORM_CUBIN / VARIABLE_SELECTION_CUBIN / ATTENTION_POOL_CUBIN
- _ln_module_v2 / _vsn_module_v2 / _attn_module_v2 -> drop _v2 suffix
- smoke_load_v2_checkpoint test -> smoke_load_checkpoint
- config/ml/sweep_v2_*.yaml -> config/ml/sweep_*.yaml
- migration-era 'V2 weight skeleton' / 'V2 fields' / etc. comments
cleaned to remove the v2 prefix
Pre-existing 'v2' references in ml-backtesting CUDA files
(decision_policy.cu, pnl_track.cu) are NOT touched — those refer to
future planned 'v2' refinements (Portfolio mode, multi-fill averaging)
from the C1-C19 commits and reflect aspirational features unrelated to
this session's trunk-grows work.
Verification: ml-alpha + ml-backtesting + fxt-backtest all build clean.
perception_forward_golden bit-exact (max_diff = 0.000000).
Adds CfcConfig.n_batch (default 1) and CfcConfig.seq_len (default 32).
PerceptionTrainer's trunk construction passes its training-time
n_batch/seq_len. Default values support backtester inference (B=1, K=32).
X11 foundation: subsequent commits add intermediate buffer fields +
forward_v2 method on CfcTrunk sized from these config fields. With X11
complete, fxt-backtest can drive the v2 forward through the trunk
without instantiating a full PerceptionTrainer.
Verification: ml-alpha + ml-backtesting + fxt-backtest all build clean.
Per spec §1.1 (X11 foundation).
Adds #[ignore]d integration test that loads a CheckpointV2 file produced
by alpha_train and verifies CfcTrunk::load_checkpoint accepts it.
Activated via FOXHUNT_SMOKE_CKPT env var on a CUDA-capable host.
Also adds a compile-time witness test confirming Summary.max_drawdown_pct
field exists post-X16 (required by emit_deployability_verdict).
Note: full BacktestHarness end-to-end smoke (running one cell against
fixture MBP-10) requires the v2 forward path to live on the trunk
(X11 — deferred). This commit verifies the producer/consumer wire-up.
Per spec §3.4, §4.1 (X18).
X16: Adds Summary.max_drawdown_pct field as |max_drawdown_usd| /
STARTING_CAPITAL_USD (pinned at $35k per project_ml_alpha_starting_capital
memory — realistic ES single-contract small-account anchor). Used by
the verdict emitter as the capital-deployability hard gate (median
across windows < 20%).
X17: Adds VerdictTier enum (Pass-robust / Pass-nominal / Fail-inconclusive
/ Fail / Fail-degenerate), AnchorSpec / AnchorReport / DeployabilityVerdict
types, classify_verdict (tiered logic per spec §3.5), and
emit_deployability_verdict that reads per-cell summary.json files at
both realistic (1.0 tick, 200 ms) and stress (1.5 tick, 400 ms) anchors,
computes median Sharpe / Sortino / max_dd_pct / profit_factor across
walk-forward windows, and applies the gates.
Per spec §3.2 (X16), §3.5 (X17).
X13: Adds PerceptionTrainer::save_checkpoint as a thin delegate to
self.trunk.save_checkpoint. Inference-only serialization — grads + AdamW
state aren't included.
X14: Inside the existing auc_h6000_improved block in alpha_train.rs,
calls trainer.save_checkpoint(out_dir / 'trunk_best_h6000.bin') so the
trained trunk lands alongside alpha_train_summary.json. Extends
AlphaTrainSummary with best_h6000_ckpt_path (Option<String>) so
downstream tooling (fxt-backtest --checkpoint) can locate the file
without re-deriving the path.
After this commit, every alpha-perception Argo workflow run produces
a CheckpointV2 file at every new-best-h6000 epoch, ready for backtest
consumption.
Verification:
- ml-alpha lib tests: 34 pass
- alpha_train example builds clean (release)
Per spec §1.1 (X13+X14).
Replaces CheckpointV1 with CheckpointV2 — covers the full v2 inference
graph: VSN, Mamba2 stacks 1+2 (in/a/b/c/out weights + biases), LN_a/LN_b,
attention-pool, CfC (now v2-shaped via cfc_n_in=HIDDEN_DIM), and the
full GRN heads (10 tensors: w1/b1, w2/b2, w_gate/b_gate, w_main/b_main,
w_skip/b_skip).
save_checkpoint reads every trunk weight tensor via memcpy_dtoh and
packs into the CheckpointV2 bincode envelope. load_checkpoint peeks
the version first (CheckpointVersionProbe), rejects non-2 versions,
deserialises into CheckpointV2, validates n_in / n_hid / cfc_n_in /
mamba2_state_dim match the supplied cfg, and uploads each weight
tensor with size-checked memcpy_htod.
V1 envelopes hard-rejected — alpha_train never produced V1 files, so
no migration. The old V1-shaped roundtrip test is removed; new V2
round-trip test will land alongside the alpha_train wiring (X14).
Verification:
- perception_forward_golden: PASS (max_diff = 0.000000)
- ml-alpha lib tests: 34 pass
- fxt-backtest binary builds clean
Per spec §1.2 (X12).
PerceptionTrainer's evaluate_batched + step_batched now read forward
kernel handles from self.trunk (snap_batched_fn, vsn_fwd_fn, ln_fwd_fn,
attn_fwd_fn, step_batched_fn, heads_grn_fwd_fn, transpose_3d_fn).
Trainer's duplicate cubin/function loading stays in place for now;
deferred dead-code cleanup.
Backward kernels (vsn_bwd_fn, ln_bwd_fn, etc.) stay on trainer — they
are training-only and don't belong on the inference trunk.
Verification:
- perception_forward_golden: PASS (max_diff = 0.000000)
Per spec §2.2 (X10b).
Adds the v2 forward kernel cubins (layer_norm, variable_selection,
attention_pool) and function handles (vsn_fwd_fn, ln_fwd_fn, attn_fwd_fn,
snap_batched_fn, step_batched_fn, heads_grn_fwd_fn, transpose_3d_fn)
onto CfcTrunk. The trunk now owns every kernel handle the v2 forward
chain needs.
PerceptionTrainer still loads its own copies of these cubins (duplicate
loading) and uses its own handles in evaluate_batched / step_batched —
the trainer-side consolidation lands in X10b. This commit is the
foundation: X11 will build capture_graph_a using these trunk-owned
handles, and X12 (CheckpointV2) doesn't depend on the consolidation.
Verification:
- perception_forward_golden: PASS (max_diff = 0.000000)
- ml-alpha lib tests: 34 pass
Per spec §2.2 (X10).
Adds the 4 missing GRN head fields (heads_w1, heads_b1, heads_w2, heads_b2)
that X1's skeleton under-modeled. Trunk now owns all 10 GRN head tensors:
input projection (HIDDEN→HEAD_MID), mid→mid layer, gate/main/skip outputs.
Trainer's 10 head fields removed. Trainer init still draws heads from
its ChaCha8Rng chain at the same call position, then memcpy_htod's them
into the trunk's now-allocated slots. Forward, backward, and AdamW
access sites redirect to self.trunk.heads_*. Gradient buffers + AdamW
state stay at trainer level.
Verification:
- perception_forward_golden: PASS (max_diff = 0.000000)
- ml-alpha lib tests: 34 pass
Per spec §2.2 (X9). After this commit, the trunk owns ALL v2 inference
weights — the source of truth for downstream checkpoint serialization.
Adds CfcConfig.cfc_n_in field (default HIDDEN_DIM) so the trunk's CfC
layer is sized for v2 usage (CfC input = LN_b output = HIDDEN_DIM) rather
than V1 usage (CfC input = snap features = FEATURE_DIM). The previous
CfcConfig.n_in field stays as "raw snap feature dim" for any V1 callers
still in the tree (fxt-backtest, trunk_forward.rs, graph_a_replay.rs);
their forward paths will be cleaned up in X10/X11 when the v2 forward
graph lives natively on the trunk.
Trainer's w_in_d / w_rec_d / b_d / tau_d fields are removed. Trainer
init still draws CfC weights from its ChaCha8Rng chain at the same
call position, then memcpy_htod's them into the trunk's now-v2-shaped
slots. Forward, backward, and AdamW access sites redirect to
self.trunk.{w_in,w_rec,b,tau}_d.
Verification:
- perception_forward_golden: PASS (max_diff = 0.000000)
- ml-alpha lib tests: 34 pass
Per spec §2.2 (X8).
LN_b (formerly ln_gain_d / ln_bias_d on trainer) now lives at
self.trunk.ln_b_gain_d / ln_b_bias_d. LN_b is the LayerNorm after
Mamba2 stack 2.
Verification: golden bit-exact; ml-alpha lib green.
Per spec §2.2 (X6).
Same pattern as X3: trainer constructs mamba2_l2 + Mamba2AdamW (against
&mamba2_l2), then moves the block into trunk.mamba2_stack_2. All
forward/backward/AdamW access sites redirect to self.trunk.mamba2_l2_mut().
Gradient buffers + AdamW state remain at trainer level.
Verification: golden bit-exact; ml-alpha lib green.
Per spec §2.2 (X5).
LN_a gain/bias tensors now live at self.trunk.ln_a_gain_d / ln_a_bias_d.
Trainer-side initialization values still drive the upload, preserving
PRNG-driven init values. Gradient buffers + AdamW state remain at
trainer level.
Verification: golden bit-exact (max_diff = 0.000000); ml-alpha lib green.
Per spec 2026-05-19-ml-alpha-v2-trunk-grows-and-deployability-design.md §2.2 (X4).
PerceptionTrainer no longer owns the Mamba2 stack-1 block; the trunk's
mamba2_stack_1 Option is filled in after Mamba2Block::new (which still
runs at the same point in PerceptionTrainer::new so the Mamba2 weight
init order is unchanged). Mamba2AdamW was already constructed against
&mamba2 before the move, so optimizer state is preserved.
CfcTrunk gains mamba2_l1_mut() / mamba2_l1() / mamba2_l2_mut() / mamba2_l2()
accessors that unwrap the Options. Forward, backward, and AdamW step
sites in evaluate_batched / step_batched / evaluate redirect through the
new accessors.
Gradient buffers (mamba2_grads_buffers) and AdamW state (mamba2_adamw)
remain at trainer level — training-only state stays with the trainer.
Verification:
- perception_forward_golden: PASS (max_diff = 0.000000)
- ml-alpha lib tests: 34 pass
Per spec 2026-05-19-ml-alpha-v2-trunk-grows-and-deployability-design.md §1.1, §2.2 (X3).
EOF
)
PerceptionTrainer gains a `trunk: CfcTrunk` field constructed at the
top of `new()` (after the determinism seed guard). VSN's
weight tensors (`vsn_w_d`, `vsn_b_d`) now live on `self.trunk`; the
trainer-owned copies are removed. Forward + backward + AdamW access
sites redirect to `self.trunk.vsn_w_d` / `self.trunk.vsn_b_d`.
PRNG-state preservation: the trainer's ChaCha8Rng chain still draws
VSN values at the same call position as before, then memcpy_htod's
them into the trunk's zero-initialised VSN slots from X1. This keeps
every downstream weight (attn_q, ...) bit-identical to the pre-X2
layout — perception_forward_golden continues to pass with
max_diff = 0.000000.
Gradient buffers (`grad_vsn_w_d`, `grad_vsn_b_d`) and AdamW state
(`opt_vsn_w`, `opt_vsn_b`) remain at trainer level — they're
training-only and shouldn't move to the inference trunk.
Verification:
- perception_forward_golden: PASS (max_diff = 0.000000)
- ml-alpha lib tests: 34 pass
- alpha_train example builds clean
Per spec 2026-05-19-ml-alpha-v2-trunk-grows-and-deployability-design.md
§1.1, §2.2 (X2).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds zero-initialised v2 weight tensors (VSN, LN_a/b, attn_q, GRN heads)
and Option<Mamba2Block> slots for stacks 1 and 2 to CfcTrunk. Extends
CfcConfig with mamba2_state_dim (default 16, matches
PerceptionTrainerConfig::default). Imports HEAD_MID_DIM for GRN head
sizing.
No forward path changes — fields allocated, not yet read. Existing
new_random init for V1 fields (CfC + simple heads + projection) is
preserved unchanged so the trunk's forward kernels still produce the
same output.
Skeleton for X2..X9 weight-group migrations.
Verification:
- ml-alpha lib tests: 34 pass
- perception_forward_golden bit-equivalence: PASS (max_diff = 0.000000)
Per spec 2026-05-19-ml-alpha-v2-trunk-grows-and-deployability-design.md
§1.1, §2.2 (X1).
ml_core::cuda_autograd::init::generate_uniform (backing xavier_uniform,
kaiming_uniform, bias_uniform, near_zero_xavier) defaulted to seeding
from SystemTime::now() + thread_id, producing non-reproducible weights
across processes. Mamba2 stacks initialise via OwnedGpuLinear::xavier,
which routes through this helper — so PerceptionTrainer.evaluate output
diverged 5-30% across fresh-process runs with identical cfg.seed.
Fix: thread-local seedable RNG override. New API:
let _g = ml_core::cuda_autograd::init::scoped_init_seed(seed);
// ... all xavier/kaiming/bias/near_zero calls draw from
// StdRng::seed_from_u64(seed) chain while _g is alive ...
// _g dropped here -> restores default time-based seeding
PerceptionTrainer::new now installs the guard before any Mamba2Block
construction, so the trainer is reproducible from cfg.seed end-to-end.
CfC/VSN/heads already used explicit ChaCha8Rng::seed_from_u64 — only
Mamba2 was affected.
Production behavior unchanged when no guard is set. ml-core: 306 tests
pass, ml-alpha: 34 lib tests pass.
Regression test: crates/ml-alpha/tests/perception_forward_golden.rs
captures bit-exact PerceptionTrainer.evaluate output (loss + 160 probs
on a deterministic seed=42 fixture) into a 644-byte golden file.
Three consecutive runs now produce max_abs_diff=0; pre-fix runs varied
by 0.1-0.3 absolute on individual probs.
.gitignore: added exception for crates/ml-alpha/tests/fixtures/*.bin
so deterministic test fixtures land in repo.
Per pearl_scoped_init_seed_for_reproducibility in project memory.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Supersedes the 2026-05-19 deployability spec (commit 07d5de504). The
prior spec assumed CfcTrunk::save_checkpoint was the producer-side
wiring point — discovered at execution time that alpha_train trains via
PerceptionTrainer (full v2: VSN + Mamba2 ×2 + LN ×2 + attn-pool + CfC +
heads), not the simpler CfcTrunk. The existing LOB backtester loads
CheckpointV1 envelopes that only know about CfC weights, so there is no
producer for a checkpoint containing the full v2 model.
New scope: one bigger spec covering refactor + deployability end-to-end.
Phase 1 (X0–X19, code commits): grow CfcTrunk to own the full v2
inference graph; restructure PerceptionTrainer to wrap a trunk + add
training-only state (grads, AdamW). Discipline: bit-equivalence golden
fixture (X0) gates every refactor commit (X1–X11). CheckpointV2
envelope (X12) replaces V1. Verdict emitter (X17) reuses the tiered
classification (Pass-robust / Pass-nominal / Fail-inconclusive / Fail /
Fail-degenerate) from the superseded spec.
Phase 2 (Argo runtime): production training → smoke gate → threshold
pre-registration → 560-cell deployability sweep → verdict + memory
update.
Hard gate before Phase 2: post-refactor fold-0 smoke must reproduce
recorded 3-fold A/B numbers (best_mean_auc 0.7529, best_h6000 0.7639,
both within ±0.010 absolute) from project_ml_alpha_v2_ab_verdict
memory. Prior spec marked SUPERSEDED in its header, kept in history as
audit trail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
User revisions to design spec from this session:
1. §2.1 — split anchor into realistic (200 ms, 1 tick) and stress (400 ms,
1.5 tick). Realistic remains the hard verdict gate; stress grades the Pass
into Pass-robust vs Pass-nominal.
2. §2.2 — expand metrics from Sharpe-only to four: annualized daily Sharpe
and max-drawdown are hard gates (median across windows > 1.0 and < 20%
respectively); Sortino and profit factor are diagnostics. Per-window
summary.json schema extended.
3. §2.6 — verdict emitter rewrites to two-anchor logic, tiered output:
Pass-robust / Pass-nominal / Fail-inconclusive / Fail / Fail-degenerate.
4. §3.3 — added max-dd computation and zero-trade-window failure modes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes the wiring gap between the existing real-LOB backtest system (C1–C19 on
this branch) and the v2 ml-alpha model. alpha_train.rs currently never calls
save_checkpoint, so the LOB harness/sweep/aggregate machinery has never been
pointed at a real trained model.
Design defines a single-pass falsifiable deployability gate: produce a
production checkpoint (cv-n-folds=1, cv-train-window=4 → train on 2024
quarters, val on 2025-Q1, hold out 2025-Q2..2026-Q1), pre-register one
threshold on the W0 val window, then evaluate median Sharpe across 4
held-out walk-forward quarters at the realistic Scaleway→IBKR anchor (200ms
RTT, 1-tick all-in cost). Pass iff median > 1.0; inconclusive in [0.8, 1.0]
counts as fail; smoke gate halts the full sweep on any wiring failure.
Approved through brainstorming. Awaiting user spec-review before plan
handoff.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The motivating "50% saturation hit-rate" observation came from gm67g
fold 0 (Option-2 config, commit 004b662c8 — itself a -0.003 mean_auc
regression vs ISV-σ at 410ab6b0e). Re-running the same diagnostic on
the actual production baseline (ISV-σ 3 folds: rxm5t/r57lx/x24d6,
logs retrieved from MinIO argo-logs bucket) on 215 horizon-epoch
observations:
saturated λ→AUC up: 8/13 = 61.5% median Δauc = +0.0025
non-saturated→AUC up: 96/202 = 47.5% median Δauc = -0.0012
difference: +14pp in favor of the controller working
The BCE-z-score controller is empirically correlated with the
optimization target. No evidence it's misaligned with AUC. Spec
premise falsified.
Spec retained in tree as historical record. The diagnostic
methodology itself (saturation→Δauc analysis on archived MinIO logs)
is the durable artifact and is documented in the supersede block.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
