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K=3 sparse cross-entropy reduce over the trade-outcome softmax tile produced by `aux_trade_outcome_forward` (Phase A3). Mirrors the K=2 sibling `aux_next_bar_loss_reduce` structurally: single-block shmem-tree reduce, two parallel partial strips (loss_numer + valid_count) reduced lockstep, fmaxf(p_tgt, 1e-30) numerical floor, fmaxf(valid, 1.0) all- skip-batch guard, valid_count_out[1] save-for-backward. Kept as SEPARATE kernel from the K=2 sibling: - Diagnostic isolation (distinct HEALTH_DIAG slot, distinct cubin in profiles for clean per-loss-source attribution) - Sparse-label semantic clarity (~95-99% mask=-1 vs ~50-100% valid for the K=2 next-bar head) - Future per-class weighting headroom (Profit/Stop/Timeout 3:1-10:1 imbalance will likely need class-weighted CE — surgical mod here without touching the K=2 head's contract) Phase A4 (this commit) is dead code — no Rust launcher yet. Phase A5 lands backward; Phase B wires the full forward→loss→backward chain. Discipline: feedback_no_atomicadd (single-block tree-reduce), feedback_ cpu_is_read_only (pure GPU), pearl_first_observation_bootstrap (sentinel 0 valid_count produces zero gradients gracefully on cold start). Audit: docs/dqn-wire-up-audit.md Phase A4 section. Cubin: aux_trade_outcome_loss_reduce_kernel.cubin (9.9 KB) compiles clean. 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.