54aa69c108
Documents the design for fixing the single-SM bottleneck in five backward/K-loop kernels: cfc_step (fwd+bwd), GRN bwd, VSN bwd, and attention_pool bwd. All currently use grid=(1,1,1) with an internal n_batch loop — on L40S (142 SMs) with B=32 this puts <1% of the GPU to work in the K-loop critical path. Architecture: block-per-batch (grid=(B,1,1)) for the kernel body, plus per-batch grad scratch buffers reduced via a single parameterised reduce_axis0 kernel (block tree-reduce, no atomicAdd per feedback_no_atomicadd.md). Same pattern as the existing LayerNorm bwd reducer — CUDA-Graph-safe, debuggable, and consistent with foxhunt's no-cooperative-groups discipline. Target: ≥3× epoch wall speedup (stretch 8-15×). Makes 3-fold CV tractable (10.5h → 2-3h) and unblocks decision-stride / state-dim sweeps that compound the gain. Acceptance gates: (a) all 8 perception_overfit smokes still converge, (b) new B=1 bit-equivalence test asserts the refactored batched bwd kernel at B=1 matches the existing single-sample helper byte-for-byte, (c) cluster A/B vs t6z89 baseline shows AUC trajectory within ±0.005 and epoch wall ≥3× faster. Atomic refactor per kernel — one commit per kernel covering the kernel rewrite, scratch buffer alloc, reducer launch wiring, and smoke. No "_legacy" parallel kernels per feedback_no_legacy_aliases.md + feedback_no_partial_refactor.md. Open implementation-plan decisions: exact memset_zeros ordering inside the captured graph, batch-vs-per-tensor reducer launches, optimal block_dim for reduce_axis0 itself. Co-Authored-By: Claude Opus 4.7 <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.