5ee795f14f
First of 3 Layer D producer kernels. Replaces host-side trade-PnL
aggregation loop in training_loop.rs with a GPU kernel chained through
apply_pearls_ad_kernel for smoothing. Per feedback_no_cpu_compute_strict.
Additive only — no consumer wiring, no behavior change. The kernel +
launcher are loaded into GpuDqnTrainer and the slots are reserved on the
ISV bus, but the host-side aggregation continues to run unchanged. D4
(atomic Layer D commit) wires this and the other two D2/D3 kernels to
call sites in the same atomic refactor per feedback_no_partial_refactor.
What this lands:
- pnl_aggregation_kernel.cu (single-block tree-reduce, no atomicAdd)
- Rust launcher launch_sp5_pnl_aggregation()
- 4 new ISV slots (PNL_TOTAL_INDEX..PNL_MAX_DD_INDEX, slots 286..290)
- ISV_TOTAL_DIM 286 → 290; SP5_PRODUCER_COUNT semantics formalised as
wiener-buffer linear-span (110 → 116; the unique-slot count diverged
from the linear span at D1 — pre-D1 they coincided by accident at the
Pearl 6 carve-out's introduction)
- LAYOUT_FINGERPRINT_FRAGMENT extended with PNL_* entries
- StateResetRegistry sp5_pnl_aggregation entry + dispatch arm
(sentinel 0.0; fold-reset; PnL is NOT cross-fold persistent unlike
Pearl 6 Kelly stats, so it takes the standard sentinel-bootstrap
path per pearl_first_observation_bootstrap)
- build.rs cubin registration
- GPU-gated unit test pnl_aggregation_kernel_correctness with
analytical ground truth (no CPU reference per
feedback_no_cpu_test_fallbacks)
- Audit doc append documenting D1 + the SP5_PRODUCER_COUNT semantic
clarification
Tests: cargo check + cargo build --release --features cuda clean;
ISV slot + state_reset_registry unit tests 6/6 pass (incl new
pnl_aggregation_slots_contiguous_and_above_kelly_block); GPU
correctness test fires on next L40S smoke alongside existing 17 SP5
producer unit tests.
Refs: SP5 plan §D Task D1, validated SP5 Layer A/B at 5845e4403.
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.