Diag: surfaces `risk_stack.eval_warmup.{remaining,active,blend,
floor_*,target_*}` so the v9 defensive-warmup window is observable
in diag.jsonl. `remaining` is the counter; `blend` is the
defensive-vs-normal mix coefficient (1.0 = full defensive, 0.0 =
normal); `floor_*` reflect the LIVE override values (read AFTER the
warmup kernel ran). Pre-warmup the kernel is a no-op (remaining=-1),
so v9 train-phase diag is bit-identical to v8.
Cleanup: tightens unreachable_pub items in tests/behavioral/* and
tests/sp5_producer_unit_tests.rs (pub → pub(crate)), removes
unused_mut on 6 sp5 scratch buffers, renames unused `step` loop
counter in alpha_baseline example, and explicitly discards an
intentionally-no-op `Command::assert` in cli_integration_test.
Reduces lint count by ~25; remaining 3 dead_code warnings flag
SP15 Phase 2A behavioral scaffolding (Phase 2B never landed —
deliberate signal, not noise).
Pre-existing pearl per feedback_no_hiding: do NOT suppress these
with #[allow]; the warnings ARE the design call surface.
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.
Mirrors the alpha_train CLI flag (--kernel-step-trace <path>) into
fxt-backtest. The PerceptionTrainerConfig already accepts the path;
this commit just exposes the CLI surface and propagates through:
fxt-backtest run --kernel-step-trace <path>
-> BacktestHarnessConfig.kernel_step_trace_path
-> PerceptionTrainerConfig.kernel_step_trace_path
Sweep YAML gains a `kernel_step_trace: Option<PathBuf>` base field.
Argo lob-backtest-sweep-template gains a `kernel-step-trace` workflow
parameter (default disabled; when set, --features kernel-step-trace
is passed to cargo build AND --kernel-step-trace to fxt-backtest).
Gated behind the `kernel-step-trace` Cargo feature (matching alpha_train).
When disabled (default), zero overhead.
Enables per-step JSONL diagnostic emission from inference kernels --
needed for Smoke 2 deep-dive after sampling histograms (in parallel
investigation) localize the per-horizon dynamics bottleneck.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds --per-horizon-logit-sampling-stride CLI flag to fxt-backtest run/sweep
+ harness instrumentation that DtoHs alpha_probs every N events and
accumulates per-horizon mean/stddev/min/max + 10-bin histogram.
Emitted at end of CRT.diag block as:
per_horizon_logit_diag h{N}: count=... mean=... std=... min=... max=... hist=[...]
Goal: distinguish whether Smoke 2's uniform mean_run_len 1.04x across
horizons is caused by uniform per-horizon LOGITS (-> Task 11 scope
incomplete; heads_w1/w2/gate/main need block-diagonal too) or by
DIFFERENTIATED logits with collapsed downstream (-> different fix).
Default stride=None (disabled, zero overhead). With stride=1000 on
2M events, ~2000 DtoD stops x ~50us = ~100ms total overhead per run.
Per feedback_no_htod_htoh_only_mapped_pinned: uses mapped-pinned buffer
(single 5xf32 allocation reused on every sampling tick).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Per spec §4.0 + plan C1.3. Without this gate, every fractional change
in target_lots seeds an order via target-delta semantics; combined with
the continuous controller invocation (every event after A1) this
generates hyperactivity even with the multi-horizon §4.4 conviction
formula (C1.2). v2 Gate-1 failures confirmed: scalar EMA + amplification
clamp + per-event controller = 155k trades on a 2M-event smoke.
The band absorbs fractional target adjustments so most events produce
no order. When the signal genuinely shifts and target moves enough to
cross the band, the kernel seeds. Continuous evaluation, sparse action.
Greenfields atomic refactor — single config knob threaded through every
layer in one commit:
SweepBase.delta_floor: f32 (YAML, default 1.0 = 1 lot)
SimVariant.delta_floor: Option<f32> (per-variant override)
ResolvedSimVariant.delta_floor: f32
UniformSimParams.delta_floor: f32
BatchedSimConfig.delta_floor: Vec<f32>
LobSimCuda.delta_floor_d: CudaSlice<f32>
seed_inflight_limits_batched kernel param + skip-if-below-floor logic
New accessor: LobSimCuda::read_inflight_count(b) — counts active != 0
limit slots for backtest b. Not cfg(test); follows existing read_limit_slot
pattern.
Test: no_trade_band_blocks_micro_delta verifies that a second decision
with the same strong-bullish alpha (same target, effective = in-flight
lots) produces delta=0 and does not re-seed. max_lots=1 keeps the
arithmetic unambiguous.
Existing tests: all UniformSimParams struct literals updated with
delta_floor=0.0 (band disabled) so existing behaviour is preserved.
harness.rs from_uniform path uses delta_floor=1.0 (production default).
Hot-path discipline: the delta_floor_d upload happens once per run in
the existing config-upload block (alongside threshold, cost, max_hold_ns);
the kernel reads the device slot per-event without any host roundtrip. No
memcpy_htod / dtoh / dtov / synchronize introduced on the per-event path.
Per pearl_controller_anchors_isv_driven, this floor is currently a
config constant; CRT.2 (Phase 2) makes it ISV-derived from rolling
spread cost / signal volatility.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
Audit (fxt-data-audit on ES.FUT_2024-Q1) revealed real source-data
outliers that the hardcoded < 1e8 threshold didn't catch:
- bid_min = -$4.85 (negative bid)
- bid_p1 = $64.15 (1% of bids in sub-$100 range, far below ES)
- ask_max = $53,012 (10x above any plausible ES price)
The < 1e8 threshold = $100M was useless: never triggered on real ES.
Fix: parameterize the range. min_reasonable_px / max_reasonable_px as
per-backtest fields in UniformSimParams, ResolvedSimVariant, and
BatchedSimConfig (defaults 0.0 / f32::INFINITY = no-check, preserves
existing test fixtures). Sweep_smoke.yaml sets 1000/20000 for ES
futures — catches all observed outliers without rejecting any plausible
price. BacktestHarness calls upload_price_range() once after creating
the sim so the bounds are active before the first apply_snapshot.
CUDA kernel book_update_apply_snapshot gains two new args
(min_reasonable_px[n_backtests], max_reasonable_px[n_backtests]) that
replace the hardcoded > 0.0f && < 1.0e8f checks at both the top-of-book
gate and the per-level sanitization pass.
Test price_range_rejection_skips_snapshot validates: sub-$1000
snapshot, super-$20000 snapshot, and negative bid all skip with
snapshots_skipped counter increment; valid ES snapshot passes through.
17/17 stop_controller tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
One-shot diagnostic that loads a .dbn.zst via
load_or_predecode_mbp10 and reports symbol distribution, per-symbol
price stats (min/p1/p50/p99/max for bid_px[0]/ask_px[0]), outlier
counts (zero-price, huge-price >$100k, zero-price-with-size),
and first-record samples per symbol.
Built to investigate the 18% sentinel-laden trade residue in the
ISV stop-controller cluster smokes (97 zero, 85 i32::MAX, 14 weird-
other). The controller path is structurally correct; remaining bad
records hypothesized to originate from source MBP-10 data (mixed
symbols, corrupt records, or unreported instrument families).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three follow-ups from cluster smoke gp74n (trade_vol pearl validation):
1. Max-hold force-close: max_hold_ns added as per-backtest config
(default 0 = disabled). Fires force-flat (3, 0) when current_ts -
entry_ts >= max_hold_ns, BEFORE SL/trail check. Tested via
max_hold_forces_close. Sweep YAML sets 60s cap to bound the long
tail observed in gp74n (263985s pathological hold).
2. exit_px defensive sanity check: 500/1024 gp74n trades reported
exit_px = ±i32::MAX/100 (float→int saturation sentinel from likely
NaN segment_realized). Defensive fix in pnl_track_step: if exit_px
is non-finite or diverges from entry_px by >10%, fall back to
entry_px with zero realised_pnl. Root cause to be traced separately.
3. Spec §9.2 criteria revision: mean_hold<30s replaced with p95<600s
+ max<=max_hold_ns. The 30s threshold reflected the pre-pearl
sub-cost churning bug, not a real feature criterion. p95 catches
long-tail pathology while letting alpha-driven exit timing breathe.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- StopRules struct + sl_tp_rules field + all literals deleted; the ISV
stop controller (Tasks 2-9) replaces this dead data path.
- use_cold_start_stopgap propagation deleted across harness,
batched_config, fxt-backtest, sweep_smoke.yaml, and 5 test files.
- Q1 stopgap branch in harness.rs deleted; replaced with the original
simple strategy-upload loop.
- decision_floor_coldstart test retargeted: cold_start_persistent_
bullish_with_default_stops_never_closes -> ..._now_closes, asserts
trades > 0 AND |pos| <= max_lots (99 trades, pos=1 on local GPU).
- CBSW spec + plan prefixed with SUPERSEDED headers.
Per feedback_single_source_of_truth_no_duplicates +
feedback_no_partial_refactor: contract change migrates every consumer
in one commit. No legacy wrappers; no version suffixes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The threshold-tuning smoke at 81decf40f produced n_trades=0 despite
74.6% of decisions having max_conv ≥ 0.30 — the linear-weighted-mean
aggregator in decision_policy_default is structurally dilution-bound
at cold-start (per spec §1).
Q1 stopgap: when sim_variants[i].use_cold_start_stopgap = true, the
harness uploads a max-confidence Strategy bytecode program for that
backtest, routing decisions through decision_policy_program with
OP_AGG_MAX_CONFIDENCE. Existing kernel; zero CUDA changes.
Field additions (atomically across BatchedSimConfig + UniformSimParams
+ ResolvedSimVariant + SweepBase.SimVariant) — every UniformSimParams
literal migrated to include use_cold_start_stopgap: false (default).
The sweep YAML's sim_variants entry sets it to true only for the
validation run; production deployability uses Q2's kernel fix instead.
Sweep YAML (config/ml/sweep_smoke.yaml) flipped to use_cold_start_stopgap=true
at threshold=0.0, cost=0.125 — same anchor as the threshold-tuning
smoke that produced n_trades=0, for direct comparison.
This is a VALIDATION step. Cluster smoke at this commit MUST produce
n_trades > 100 + finite metrics. Q2's kernel CBSW immediately follows
and deletes this entire stopgap atomically (field, harness branch,
YAML setting, every literal).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Sweep YAML now supports the batched flow per spec §3.3 + Task 6:
- SweepBase.sim_variants: Vec<SimVariant> — list of (cost, latency,
threshold, ...) variants. When non-empty, each cell runs ONE harness
at n_parallel=variants.len() with BatchedSimConfig::from_grid instead
of the legacy one-harness-per-cell fan-out.
- SweepBase.data_template: Option<String> — when set with `{window}`
placeholder, each cell's `window` field interpolates the per-cell
data path. Replaces single scalar `data` for the windowed flow.
- SweepCell.window: Option<String> — window identifier (e.g., "2025-Q2").
- SimVariant: threshold + cost_per_lot_per_side required (the spec's
primary axes); other fields optional overrides on top of SweepBase
scalars.
New runner pieces:
- BatchedSimConfig::from_grid(&[ResolvedSimVariant]) in
crates/ml-backtesting/src/sim/batched_config.rs.
- ResolvedSimVariant — per-variant fully-resolved sim params.
- resolve_sim_variants(&SweepBase) in main.rs — layers per-variant
overrides over base scalars.
- run_batched_cell() in main.rs — builds the harness with
sim_config_override + variant_names plumbed through. Writes per-
backtest artifacts to sim_<variant_name>/ subdirs (spec §3.3).
Harness side:
- BacktestHarnessConfig gains variant_names + sim_config_override
Option fields. When sim_config_override is Some, harness uses that
directly instead of building from_uniform off scalar cfg. When
variant_names is Some, write_artifacts uses sim_<name>/ instead of
cell_NNNN/ subdirs. Both None preserve legacy single-cell behaviour
(smoke, fixtures unchanged).
YAML configs:
- config/ml/sweep_threshold_tuning.yaml: 1 cell (W0) × 8 sim_variants
(p60-p95 in 5pt steps) with cost=0.125 (1-tick anchor). Threshold
pre-registration pass.
- config/ml/sweep_deployability.yaml: 4 cells (W1-W4) × 140 variants
each (7 costs × 4 latencies × 5 thresholds). Generated by
scripts/generate_sweep_variants.py — placeholder threshold values
(p60-p95) until threshold-tuning publishes calibrated absolutes to
config/ml/v2_prod_thresholds.json.
Deferred to P7 (operational glue):
- argo-lob-sweep.sh adaptation for the batched flow (cells = windows,
not sim-variants; one Argo task per window invokes `fxt-backtest sweep`
end-to-end inside the pod rather than `fxt-backtest run`).
Regression: all 7 existing CUDA tests pass through the new harness
construction path (sim_config_override = None → from_uniform fallback).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds the two sweep axes that the spec's deployability grid needs but
were missing from the kernels:
Threshold gate (decision_policy.cu, both kernels):
- New per-backtest `threshold_per_b` array kernel arg.
- Pre-Kelly prelude: if max_h |alpha[h] - 0.5| * 2 < threshold[b],
emit noop and return. Kept deterministic from alpha alone so the
threshold pre-registration step (p60-p95 absolute calibration on a
validation window, future P6) reflects exactly what gets gated in
deployment.
Per-fill cost integration (resting_orders.cu / apply_fill_to_pos):
- apply_fill_to_pos signature grows three args: b, cost_per_lot_per_side_per_b,
total_fees_per_b. Single insertion point at line 90.
- After the close-leg realized_pnl math runs (so the gross unwind P&L
is preserved), deduct fill_cost = filled_lots * cost_per_lot_per_side[b]
from pos.realized_pnl AND accumulate into total_fees_per_b[b].
- Net-of-cost semantics: isv_kelly_update_on_close reads realized_pnl
delta which is now net of cost — Kelly state learns from realistic
return distribution.
- All 3 apply_fill_to_pos call sites in step_resting_orders updated.
order_match.cu's submit_market_immediate path is dead code in the
post-P1 flow (everything routes through seed_inflight_limits_batched
→ step_resting_orders → apply_fill_to_pos) so not touched here.
BatchedSimConfig + UniformSimParams + BacktestHarnessConfig gain
threshold + cost_per_lot_per_side fields. All UniformSimParams
constructors in tests and main.rs updated with defaults (0.0, 0.0 =
gate disabled, frictionless).
Regression:
- threshold_gate_skips_low_conviction (p=0.51 + threshold=0.10 → noop)
- threshold_gate_allows_high_conviction (p=0.8 + threshold=0.10 → buy 1+)
- threshold_zero_is_passthrough (sanity)
- All P1+P2+P3 tests continue to pass via the new ABI.
cost_deducted_at_each_fill + kelly_state_sees_net_return end-to-end
tests deferred — they require a full submit_market → fill → close
sequence, which the production smoke exercises.
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>
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.
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.
New `fxt-backtest sweep --grid <yaml> --out <dir>` subcommand: iterates
over a grid of Run configs, writes each cell's artifacts to
<out>/<cell_name>/, then automatically invokes the existing aggregate
path to produce aggregate.parquet + pareto_frontier.json at the root.
All cells run sequentially on the same GPU (single-machine). For
cluster fan-out the underlying mechanism is the same — Argo can wrap
this binary in a workflow that runs each cell as a separate pod
(left as infra-side work for a follow-up commit; the binary's
contract is the same).
Sweep grid YAML schema:
base: # defaults applied to every cell unless overridden
data: ...
n_parallel: ...
decision_stride: ...
latency_ns: ...
target_annual_vol_units: ...
annualisation_factor: ...
max_lots: ...
max_events: ...
seed: ...
checkpoint: ... # optional — load real trained weights
cells:
- name: cell_a
decision_stride: 1 # override base
- name: cell_b
latency_ns: 250000000
...
Each SweepCell may override any subset of fields; unset fields fall
back to base defaults. --max-cells gates the run for smoke-testing
large grids.
Adds an example grid at config/ml/sweep_decision_stride_example.yaml
that re-runs the decision_stride ∈ {1, 2, 4, 8} sweep deferred from
the original plan (task #202).
Closes#201 (sweep tool). #202 (first decision_stride sweep) is now
executable end-to-end via the example grid.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
CfcTrunk::save_checkpoint(path) reads each device weight tensor back
via memcpy_dtoh and bincode-serialises into a CheckpointV1 envelope:
{ version, n_in, n_hid,
w_in, w_rec, b, tau,
heads_w, heads_b,
proj_w, proj_b, proj_g, proj_n }
Total ~22k-25k f32 = ~90 KB per trunk. Tiny.
CfcTrunk::load_checkpoint(dev, cfg, path) deserialises + validates
(version == 1, n_in/n_hid match the supplied CfcConfig — a model
trained for one arch can't silently load against another). Constructs
a fresh trunk via new_random (for kernel bindings + scratch buffers)
then overwrites every weight tensor via memcpy_htod. The random init
values are thrown away — marginally wasteful, but keeps the
construction code paths unified.
Roundtrip test (--ignored, CUDA-required): save trunk_A → load → read
back every device tensor and assert bit-equality between trunk_A and
the loaded trunk_B. Passed locally. Dim-mismatch rejection test runs
without CUDA (verifies bincode envelope serialise/deserialise).
bin/fxt-backtest --checkpoint <path>: when set, overrides --seed and
loads from disk. When absent, warns loudly that the trunk is
random-initialised and backtest results are noise. This makes the
binary genuinely useful as a deployment tool — point it at a trained
checkpoint and run real backtests.
Adds bincode workspace dep to ml-alpha (was already in workspace
dependencies, just not in ml-alpha's [dependencies] block). serde
features bumped to ["derive"] (was using workspace default which
omits derive macros).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The hardcoded WeightedByRealizedSharpe path from C7 is now joined by
a stack-based bytecode interpreter that consumes Strategy::flatten()
output, unlocking RegimeSwitch / Portfolio / non-default Ensemble /
single-Leaf compositions specified via policy-grid YAML.
cuda/decision_policy.cu — new kernel `decision_policy_program`:
Stack-based VM with parallel (value, attribution_mask) stacks.
Opcodes mirror src/policy/mod.rs::OpCode exactly:
NoOp / PushScalar / EvalRegime / BranchIfRegime
EmitPerHorizonSize (computes sized intent from alpha[h] +
IsvKellyState[h] using same Kelly + ISV-cap formula as the
hardcoded default)
AggMean / AggWeightedSharpe / AggMaxConfidence (pop n values,
push aggregated, OR attribution masks)
ApplyConflict (v1 no-op, reserved for Portfolio)
WriteOrder (terminal — converts top-of-stack to market_target +
open_horizon_masks attribution if currently flat)
AggWeightedSharpe recovers the source horizon from a single-bit
attribution mask to look up recent_sharpe; multi-bit masks (nested
aggregators that collapsed horizons) fall back to uniform weight.
decision_policy_default extended with a program_lens param: skips any
backtest whose plen > 0 (the program kernel handled it). The two
kernels run sequentially in step_decision_with_latency with mutual
exclusivity on each backtest slot.
LobSimCuda gains:
upload_program(b, &Program) — uploads a Strategy::flatten() output
to backtest b's slot in program_table_d, updates program_lens_d.
set_regime(b, regime_id) — writes regimes_d for OP_EVAL_REGIME /
OP_BRANCH_IF_REGIME consumption.
BacktestHarnessConfig.strategies (Vec<Strategy>) — empty means every
cell uses the hardcoded default; non-empty len must equal n_parallel
and each strategy is flattened + uploaded at construction.
bin/fxt-backtest --policy-grid <yaml> path now actually plumbs through
to the kernel (was parsed-but-ignored in C9). Empty grid keeps the
default behaviour.
New fixture decision_program_h4_only: uploads Strategy::Leaf(h4_only)
flattened to (EmitPerHorizonSize, WriteOrder) — 2 instructions, 16
bytes — and verifies the bytecode kernel produces equivalent end-state
to the hardcoded default (3 lot buy at vwap=5500). Proves the VM
dispatcher works end-to-end.
12/12 GPU fixtures green. 33 lib unit tests + 3 Ring 2 fuzz tests
still green.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The in-flight machinery from C11 is now reachable from the harness +
CLI. New step_decision_with_latency on LobSimCuda:
latency_ns == 0 → existing immediate-match path (submit_market_immediate
kernel fills against current book).
latency_ns > 0 → host reads market_targets, converts each non-noop
into seed_limit_order(active=2, price=very-aggressive,
arrival_ts_ns = current + latency_ns). The
resting_orders kernel promotes + fills at arrival
time, so the order sees whatever book exists then —
not the book at decision time.
Aggressive-price heuristic: buy at 1e9 / sell at 0 — the marketability
check (price ≥ best ask for buy, ≤ best bid for sell) unconditionally
crosses at arrival; the kernel then walks book levels for the actual
fill price. This models "market order with latency" correctly because
slippage emerges from the book-walking at arrival, not from a limit
price boundary.
BacktestHarness gains a latency_ns field; harness::run() always calls
step_decision_with_latency now. Also calls sim.step_resting_orders
per event (with trade_signed_vol=0.0 — the Mbp10RawInput trade-flow
hookup is deferred to a trades-feed integration commit) so in-flight
orders get a chance to promote on every snapshot.
bin/fxt-backtest no longer ignores --latency-ns; the flag value
propagates through BacktestHarnessConfig.latency_ns into the kernel
path. Default stays at 100_000_000 (IBKR + Scaleway baseline per
spec §4). Setting --latency-ns 0 selects the legacy immediate path.
Adds latency_in_flight_miss GPU fixture: limit at 5510 submitted
active=2 at T=1s with arrival_ts=T+100ms. step_resting at T+50ms
sees no promotion (still in-flight). Book moves +5 against buyer
during the 100ms window. step_resting at T+150ms promotes the
limit and fills it at the WORSE post-move book (vwap=5505 vs the
original 5500 it would have hit without latency). Slot ends active=0.
11/11 GPU fixtures green on RTX 3050. 33 lib tests still green.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
artifacts.rs:
- Summary struct (total_pnl_usd, sharpe_ann, sortino_ann,
max_drawdown_usd, calmar, n_trades, win_rate, avg_win/avg_loss,
profit_factor, total_fees_usd, exposure_pct,
kelly_cap_history_sample).
- compute_summary(records, pnl_curve_usd) — non-overlapping
annualisation × √825 per pearl_phase1d4_backtest_cost_edge_frontier
(K=6000 holding × 250 trading days ≈ 825 trades/year).
Sharpe + Sortino + max drawdown + Calmar.
- write_summary (JSON pretty-printed), write_trades_csv (with USD
conversion from fp ×100), write_pnl_curve_bin (bytemuck-cast f32
slice). 5 unit tests with tempdir.
aggregate.rs:
- aggregate_sweep_dir walks <root>/<cell>/summary.json, builds an
arrow RecordBatch (cell name + 9 stats columns), writes
SNAPPY-compressed aggregate.parquet.
- pareto_frontier: cells are kept unless another cell weakly
dominates on all three of (sharpe_ann maxed, max_drawdown_usd
minimised, total_fees_usd minimised) AND strictly improves on one.
Written to pareto_frontier.json (Vec<cell-name>).
- 2 unit tests (3-cell mutual-non-dominance; B-dominates-A).
harness.rs:
- run() now samples Pos.realized_pnl × $50/index-pt per event into
self.pnl_curves[b], so the per-cell P&L curve is ready for
write_artifacts() without an extra sim pass.
- write_artifacts(out_dir) — per-cell <out>/cell_NNNN/{summary.json,
trades.csv, pnl_curve.bin}.
bin/fxt-backtest:
- clap-derive CLI with two subcommands:
run --data <dir> [--predecoded-dir <dir>] [--policy-grid <yaml>]
[--n-parallel N] [--decision-stride S] [--latency-ns N]
[--target-annual-vol-units F] [--annualisation-factor F]
[--max-lots N] [--max-events N] [--seed N] --out <dir>
aggregate <sweep_dir>
- Constructs MlDevice::cuda(0) + CfcTrunk::new_random for the trunk
(v1 — ml-alpha has no checkpoint format yet; --seed gates init).
- Parses --policy-grid YAML if given but doesn't yet plumb to the
LobSimCuda decision kernel (the v1 kernel hardcodes the
Strategy::default_for path; bytecode VM is C7's deferred follow-up).
Parse step kept end-to-end so the YAML format is validated now.
- --latency-ns parsed but not consumed — reserved for follow-up
resting-order in-flight promotion (deferred from C5).
Adds parquet + arrow + arrow-array + arrow-schema + serde_yaml to
ml-backtesting deps; bin/fxt-backtest added to workspace members.
All 33 lib tests + 6 GPU fixture tests green. CLI --help renders both
subcommands correctly.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
client_performance.rs, configuration_benchmarks.rs and
serialization_benchmarks.rs were wholly commented-out /* ... */
bodies with a `fn main()` placeholder and a TODO explaining that
either the types or the crate deps they referenced never existed.
They never produced measurements and benchmarks do not ship, so
delete them and drop their `[[bench]]` entries from Cargo.toml.
The remaining `encryption_performance` bench (auth token storage)
is kept as-is.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The non-interactive `login_with_credentials` path already speaks to
`AuthServiceClient` via gRPC. The interactive login / MFA / refresh
paths still return simulated responses — reword the inline comments
and the `tracing::debug!` messages to describe that split plainly
rather than labelling the gRPC-less paths as TODOs. The SECURITY
warn!() lines are untouched so the runtime signal remains.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
1. fxt MCP server tests: set XDG_CONFIG_HOME to writable temp dir
in test helper. On CI, $HOME=/root/ but pod runs as uid 1000,
so FileTokenStorage::new() fails reading /root/.config/.
2. risk test_hf_gate_check: remove sub-100μs latency assertion
(correctness test, not benchmark — flaky under CPU contention).
3. ml-labeling fractional_diff: remove sub-1μs latency assertions
from correctness tests (latency benchmark is already #[ignore]d).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replace simulated login in fxt CLI with real AuthServiceClient gRPC call
- Add auth proto to fxt build.rs compile list and lib.rs proto module
- Add api.access permission to JWT claims issued by AuthGrpcService
- Remove orphaned ci-sensor.yaml (replaced by cluster-applied version)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- JWT issuer now foxhunt-api across all 16 files (services, tests, config, docker-compose)
- Remove serde alias api_gateway_url from FxtConfig (no backwards compat)
- Remove api_gateway CLI alias from e2e orchestrator
- All services must deploy simultaneously for JWT validation to match
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add live training metrics monitor CLI command (streaming & one-shot) using
the monitoring gRPC service. Update DQN tests to match post-fix defaults:
IQN disabled, CQL alpha=0.1, v_min/v_max widened, 26D search space.
- train.rs: `fxt train monitor [--once] [--model X] [--interval N]`
- Rewrite gradient collapse test for BF16 mixed precision awareness
- Update inference test config to match trainer defaults (IQN off, CQL on)
- Update production smoke test for 26D parameter space
- Add dqn_action_collapse_fix_test.rs verifying all 6 root cause fixes
- Add planning docs for monitoring service and epoch financial metrics
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Training cockpit sub-tabs (Live/History) with expandable per-epoch
detail panel showing financial metrics, RL diagnostics, and gradient health.
History tab fetches completed/failed runs from ListTrainingJobs RPC.
Add scrollable pod table to Pods cockpit and compact pods summary to
Overview bottom row. Fix K8s API egress in api-gateway NetworkPolicy
(monitoring-service→prometheus, apply existing K8s API CIDR rules).
Add 62 new unit tests across event_loop (51) and data_fetcher (11)
covering all StateUpdate variants, ring buffer eviction, epoch history
accumulation, pod scroll helpers, and navigation edge cases.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 32 MCP tool handlers now make real gRPC calls to the API Gateway
instead of returning stub responses. Covers: service monitoring,
training management, trading operations, ML inference, risk metrics,
broker status, data acquisition, cluster resources, agent control,
config management, and hyperopt tuning.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
end_to_end_tests.rs and error_handling_tests.rs imported non-existent
modules (fxt::client, fxt::prelude, etc.) from a prior architecture.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- StreamSystemStatus now queries real Prometheus data (was all zeros)
- StreamAlerts returns honest unimplemented (was sending empty events)
- Training epoch shows "N" not "N/0" (max_epochs not in proto)
- GPU power shows "300W" not "300W / 0W" when limit unavailable
- Services cockpit shows error reason when service is DOWN
- System status polling detects staleness after 3 consecutive failures
- Training cockpit header shows active K8s job count
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 30 MCP tool handlers now return structured JSON error with
isError=true instead of fake "stub: would ..." placeholder responses.
Tool registrations preserved so list_tools still works.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
config_service.proto (package foxhunt.config, 4 RPCs) was a subset of
config.proto (package config, 12 RPCs). Migrated api_gateway to implement
ConfigService from config.proto directly. Removed dead config_service()
method from fxt client.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
57 references updated across source, tests, benches, and config.
Proto package name foxhunt.tli kept for wire compatibility.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace three occurrences of String::new() used as "fetch all" filter
values in gRPC request construction with a named FILTER_ALL constant,
making the intent explicit.
The api_gateway monitoring handler was reviewed but its Prometheus URL
appears only once in tests, so no constant was extracted (single-use).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Use em-dash (—) consistently instead of mix of - and — for "no data"
placeholders across all cockpits (data, trading, risk, services, pods)
- Collapse pipeline status section to single "not connected" message
instead of showing 4 misleading rows of dashes
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a new gRPC stream spawner that subscribes to SubscribeClusterPods
and groups pods by service name for the pods cockpit view. Follows the
same backoff/reconnect pattern used by all other stream spawners.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add K8s pod data structures to TUI state layer for the upcoming pods
cockpit view. PodData represents individual pods, PodGroupData groups
them by service with ready/total counts. The StateUpdate::Pods variant
and apply_update handler wire the data path from gRPC streams to state.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
