ab9c7e43a8
The liquid_tau_rk4_step kernel modelled f(x) = (1/tau)*(1-x), which has fixed point x=1.0 for any tau. liquid_mod_buf was initialised to [1.0;4] and the dynamics could never move it away from that fixed point since every kernel call reduces to f(1.0)=0 (no perturbation path exists). The velocity_mod multiplier in c51_grad_kernel was therefore always 1.0 — a mathematical identity with zero measurable effect on spread_scale. Additionally, no ISV slot existed for liquid_mod (violates §4.C.6 GPU-drives spec), and the associated LiquidTrainableAdapter supervised path was never wired into the DQN backward pass. Decision C (delete): remove liquid_tau_rk4_step kernel (experience_kernels.cu), liquid_mod param + velocity_mod line (c51_grad_kernel.cu), liquid_mod_buf allocation, liquid_tau_rk4_kernel field, update_liquid_tau() method and its call site in update_eval_v_range() (gpu_dqn_trainer.rs). per_branch_q_gap_ema_buf is retained — it is used independently for trajectory backtracking state snapshot/restore. Supervised LiquidTrainableAdapter unchanged. cargo check -p ml: 8 warnings (baseline), 0 errors. Audit docs updated: dqn-wire-up-audit.md + ml-supervised-to-dqn-concept-audit.md. 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.