89fadec24a
GpuIqnHead gains 12 new CudaSlice<f32> buffers (online_taus_branch[4], target_taus_branch[4], cos_features_branch[4]) allocated at construction time via alloc_f32. Each slab is [B,N] for taus and [D,N] for cos_features — same sizes as the existing main buffers. refresh_taus_for_branch(branch_idx, tau5): uploads one branch's 5-quantile τ schedule from ISV[IQN_TAU_BASE + b*5 .. +5] to per-branch slabs with cold-start floor (FIXED_TAUS[q] when ISV slot is zero). No cross-branch averaging. activate_branch_taus(b) / deactivate_branch_taus(b): symmetric mem::swap helpers install/restore one branch's slab into self.online_taus/target_taus/cos_features for a per-branch IQN forward pass. activate→deactivate(b) is its own inverse. fused_training.rs: - Tau refresh block calls refresh_taus_for_branch(b, tau5) for all 4 branches, then refresh_taus_from_isv for the averaged main buffer (CVaR backward compat). - grad_decomp_snapshot_iqn() moved BEFORE the parallel/sequential fork so the snapshot is taken before any of the 4 per-branch apply_iqn_trunk_gradient calls. - Parallel path: 4 sequential IQN passes on iqn_stream; after each pass, event sync to main stream, apply_iqn_trunk_gradient(iqn_budget/4), re-fork so next pass starts after main has consumed d_h_s2_buf. iqn_done_event recorded after all 4 passes. - Sequential path: 4 sequential IQN passes on main stream; apply_iqn_trunk_gradient (iqn_budget/4) inline after each pass while d_h_s2_buf holds that branch's result. - Post-join: single apply_iqn_trunk_gradient removed (now inline); target_ema_update and PER loss cast remain. ÷4 normalization: both apply_iqn_trunk_gradient call sites use iqn_budget_per_branch = iqn_budget / 4.0_f32 so 4 × (budget/4) = budget total — matching SP5 Layer B gradient magnitude contract exactly. docs/isv-slots.md: add SP6 Pearl 5 consumer wiring section under the SP5 table. Co-Authored-By: Claude Sonnet 4.6 <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.