de922c6a4a
Component 5 / Kernel 3 of the SP20 fused-producer chain. Single-block BLOCK=256 kernel reads `aux_logits [B, 3]` (the SP14-C aux head's 3-class direction logits) and emits `[aux_conf_p50, aux_conf_std]` into a `MappedF32Buffer<2>`, where the per-row signal is `aux_conf[i] = max_c softmax(logits[i, *])[c] - 1/3`. p50 uses the inlined `sp4_histogram_p99` pattern (per-warp tile binning + cumulative-from-bottom, no atomicAdd per `feedback_no_atomicadd`); std uses two block tree-reductions sharing one shmem tile sequentially. One fused kernel streams `aux_logits` once for both stats per `pearl_fused_per_group_statistics_oracle`. Phase 1.4 wires the production launch site atomically with the rest of the SP20 reward chain per `feedback_no_partial_refactor`. This commit lands kernel + Rust launcher + GPU oracle tests + build entry + audit-doc entry together so the kernel is independently verifiable on RTX 3050 Ti (sm_86) and L40S (sm_89) before the EMA + controller producers (Phase 1.2 + 1.3) reference its outputs. Tests verify: - uniform logits → aux_conf = 0 → [p50, std] = [0, 0] - varied confidence (logit ramp 0 → 3) → matches CPU oracle - heterogeneous half-hot half-uniform → matches CPU oracle - empty batch → degenerate-guard writes [0, 0] All 4 GPU oracle tests + 4 launcher unit tests pass on RTX 3050 Ti. Test data uses per-row variance to avoid the `pearl_sp4_histogram_warp_tile_undercount` lockstep-uniform trap (concentrated values within one bin_width race the per-warp non-atomic increments). 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.