98bb1c4dc1
IQL value head FC expanded from [D_in × 1] to [D_in × 2]. v_out_buf shape [B] → [B*2]. Consumer code sums the two outputs (V = V_short + V_long) wherever a scalar V was previously read. Both outputs trained via the same expectile loss (horizon-specific regression is a follow-up enhancement — current form provides the architectural capacity for horizon decomposition without per-horizon targeting). Changes: - total_params: w3 H*1+b3[1] → w3 H*2+b3[2] - gemm_fwd_v: M=1→2; gemm_bwd_dw3: M=1→2; gemm_bwd_dh2: K=1→2 - v_out_buf, dv_buf, loss_buf: [B] → [B*2] - iql_expectile_loss kernel: new num_heads param; q_taken[b]=q_taken[idx/num_heads] - iql_loss_reduce kernel: new num_heads param; normalises by B (not B*num_heads) - bias_add for b3: out_dim=1→2, N=B→B*2 - db3 bias_grad_reduce: gridDim.y=1→2 for per-head gradient accumulation - V_W3_SIZE/V_B3_SIZE macros: H→H*2, 1→2 (used by iql_forward_kernel) - iql_forward_kernel: updated for 2-output col-major [2,B] write - 4 consumer kernels: v_out[b] → v_out[b*2+0] + v_out[b*2+1] - Xavier init: w3 fan_out 1→2, w3_end H→H*2 Checkpoint compat: IQL parameter count changes. Layout fingerprint recomputes; old checkpoints fail-fast at load per spec §4.A.2. Retrain required. Smoke test: training runs 28s, best Sharpe=80.59 (baseline ~80), 0 errors. Unit tests: 889 pass / 12 fail (12 pre-existing, 0 regressions introduced). Plan 2 Task 6B. Spec §4.D.3. 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.