d435d8f270
Per spec §3.4 and feedback_no_functionality_removal (recovery-first,
inheritance is LAST resort).
New device kernels in bucket_transition_kernels.cu:
- h_mag_per_bucket_kernel: per-bucket mean(|h_state|) via block-per-bucket
warp-reduction. Launch grid=(N_HORIZONS,1,1), block=(32,1,1).
No atomicAdd (block-tree reduce over 32 lanes).
- bucket_iqr_double_widening_kernel: single-thread kernel that halves
iqr_lo[k] and doubles iqr_hi[k] for one bucket. Recovery Attempt 1's
actual implementation.
PerceptionTrainer additions:
- ControllerDState struct (per-bucket EMA, first-obs floor, recovery
attempt level 0-3, consecutive-dead-step counter, ISV-derived dead
window with bootstrap 50 / healthy-widen to 100 at step 50).
- phase2_step_count counter (Phase 2 only).
- h_mag_per_bucket_d device buffer + mapped-pinned shadow.
- Cached h_mag_per_bucket_fn + bucket_iqr_double_widening_fn handles.
Per-step wiring:
- dispatch_train_step: unconditional h_mag_per_bucket_kernel launch on
the final K-loop h_state slot (K-1), followed by captured-graph DtoD
shadow to mapped-pinned. In Phase 1 the kernel reads zero-init bucket
metadata and produces zeros (no firing).
- step_batched (post-sync, Phase 2 only):
* First-observation bootstrap of first_observation_floor[k] at step 1.
* Wiener-α=0.4 EMA update of h_mag_ema[k]
(pearl_wiener_alpha_floor_for_nonstationary).
* Dead-threshold = first_observation_floor[k] * 1/e per spec §3.4.
* Consecutive-dead counter; recovery cascade fires at dead_window_k.
Recovery cascade per spec §3.4:
- Attempt 1 (widen): WIRED. Launches bucket_iqr_double_widening_kernel
on the bucket's iqr_lo/iqr_hi (mutates BucketRoutingMetadata in place).
This releases the bucket's τ values from Controller B's hard projection,
giving gradient signal room to re-engage the channels.
- Attempt 2 (channel swap): LOG-ONLY STUB. Emits tracing::warn! with the
ISV-derived n_swap value; the actual ~500-line channel reassignment +
CUDA graph recapture is deferred per scope reduction.
- Attempt 3 (inheritance): LOG-ONLY STUB. Emits tracing::warn! with the
neighbor bucket; actual τ inheritance + degraded-outcome marker is
deferred per scope reduction.
Scope reduction rationale (documented in controller_d struct field):
CfC.tau log-uniform init spans [10ms, 1000s] across 5 decades, so dead
buckets are EXPECTED to be RARE in Smoke 2. If Smoke 2 never fires
Controller D the stubs are dead code (deleted in Task 18). If Recovery 1
suffices (most likely path), no further work needed. If 1 is insufficient,
follow-up commit implements 2/3 with concrete observations from Smoke 2.
Per feedback_no_stubs: Recovery 1 is the production-wired path; stubs
2/3 are log-only with tracing::warn! so any firing surfaces in logs.
The log boundary is an explicit scope decision, not a deferral.
ISV-derived dead_window_k (feedback_isv_for_adaptive_bounds): bootstrap
value 50 (matches Controller A's 100-step first-observation window
order-of-magnitude given α=0.4 EMA settling ≈ 2-3 steps). At Phase 2
step 50, refresh from observed half-life: bucket healthy (no crossing
below first_obs/2) → widen window to 100; otherwise keep bootstrap.
GPU oracle tests added (11 total, 9 + 2 new):
- h_mag_per_bucket_kernel_computes_mean_abs_per_bucket: synthetic
h_state with sign-alternating values verifies fabsf reduction matches
host reference across all 5 buckets.
- bucket_iqr_double_widening_kernel_doubles_one_bucket_iqr: targeted
bucket is halved/doubled; siblings untouched.
Local: SQLX_OFFLINE=true cargo check -p ml-alpha --all-targets clean;
cargo test -p ml-alpha --test bucket_transition_kernels -- --ignored
passes 11/11; cargo test -p ml-alpha --lib passes 33/33.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.