f3a8a5ff62
User pearl (2026-04-24): the DQN already has every primitive TLOB needs (gpu_attention, batched_forward/backward, GpuLinear, cuBLASLt handles, cuda_autograd). Port TLOB's Q/K/V + attention as a composition of existing primitives. Random-init, trainable end-to-end from the DQN's reward signal. Uniform with Mamba2, IQL, atoms/γ/τ/ε — all of which already follow this pattern. Eliminates: - ONNX Runtime dependency (was already dead — stripped from ml-supervised) - Separate supervised pretraining pipeline - "Freeze vs fine-tune" false dichotomy - Pretrained-checkpoint-file-not-found failure mode Prerequisites for the cuBLAS-native design (all satisfied): - gpu_attention.rs exists - GpuLinear trainable layer exists - cuda_autograd over cuBLAS exists - MBP-10 data already in the DQN data pipeline What was "BLOCKED on prerequisites" in the Task 6C audit referred to the OLD pretrained-ONNX design. The cuBLAS-native design has all prerequisites satisfied — Task 6C can proceed under the new scope. Pearl captured in memory: pearl_tlob_no_pretraining.md. Generalises to any future attention/state-space/Neural ODE module: port to cuBLAS, random init, let the DQN teach it. 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.