89c3fb89d9
Bring Branching Dueling Q-Network (Tavakoli 2018) to full Rainbow parity
with the existing GPU hotpath. 3 independent advantage heads (exposure=5,
order=3, urgency=3) decompose the 45-action space into learnable branches.
H1 - CUDA fallback: gate GpuExperienceCollector when use_branching=true
(fused kernel hardcodes NUM_ACTIONS=5, incompatible with 45 factored)
H2 - Per-branch C51 distributional: each branch outputs [batch, n_d, atoms]
log-softmax, loss = avg of D cross-entropies vs projected Bellman target
M1 - NoisyNet: MaybeNoisyLinear enum in branch heads, reset_noise/disable_noise
wired through select_action, compute_loss, and set_eval_mode
M2 - Regime-conditional IS weights: Trending=1.2, Ranging=0.8, Volatile=0.6
applied to branching loss via ADX/CUSUM features at state[40:41]
M3 - State dim alignment: align_dim_for_tensor_cores() in from_dqn_params()
for H100 HMMA dispatch (8-byte alignment)
L1 - Fill simulator: splitmix64 replaces golden ratio hash (chi-squared tested)
L2 - Hyperopt 29D: branch_hidden_dim [64,256] added to PSO search space
Config plumbing: branch_hidden_dim, v_min/v_max/num_atoms, use_distributional,
use_noisy, noisy_sigma_init all flow from DQNConfig → BranchingConfig.
10 files, +3207/-125 lines, 33 branching tests + 387 ml-dqn + 284 ml-core pass.
Co-Authored-By: Claude Opus 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.