eeeb4e0a90
Complete temporal causal bottleneck implementation across all GPU paths: Experience collector (data collection): - Loads bn_tanh_concat_kernel from utility cubin - Bottleneck GEMM + tanh + concat runs per timestep before Q-forward - Same 2D compression as training → consistent Q-values for action selection - Buffers: exp_bn_hidden [N, bn_dim], exp_bn_concat [N, concat_dim] Backward pass (gradient kernels): - bn_tanh_backward_kernel: d_bn = d_concat * (1 - tanh^2) Reads saved tanh values from forward, applies derivative - bn_bias_grad_kernel: db_bn = sum(d_bn, dim=0) via atomicAdd - dW_bn via cuBLAS launch_dw_only: d_bn^T @ states[:, :market_dim] - All gradients accumulate into grad_buf at tensors 20-21 The 2D bottleneck is now end-to-end: Forward: states → W_bn GEMM → tanh → concat → h_s1 → ... → Q-values Backward: d_logits → ... → d_h_s1 → d_concat → d_bn (tanh') → dW_bn, db_bn Experience: states → bottleneck → Q-forward → action selection → env step Set bottleneck_dim=2 to enable. Default: 0 (disabled, backward compatible). Co-Authored-By: Claude Opus 4.6 (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.