31c610b69a
Phase 1 — Training quality (14 structural bugs): - Reward: sqrt micro-reward scaling, remove sparse normalization, Kelly penalty, additive OFI, magnitude-scaled capital floor penalty - Gradient: CQL 25→10%, IQN 60→75%, MSE magnitude 4×, 3-phase counterfactual, tau_anneal 100K→500K, epsilon floor 2%, PopArt fold reset, dd_threshold 0.5% - Monitoring: per-branch Q-value instrumentation - Hold: min_hold_bars 5→1 Phase 2 — VarStore elimination: - Deleted copy_weights_from from all network types - Deleted to_varstore from DuelingQNetwork + DistributionalDuelingQNetwork - Deleted branching_to_varstore, 7 extract/sync VarStore functions - Deleted update_target_networks, legacy CPU training paths - Deleted iqn_target_network, target_network fields - Deleted gpu_kernel_parity_test.rs (705 lines) - Refactored GpuExperienceCollector to single flat buffer - Added weight_sets_from_branching() zero-copy replacement - EMA tau=1.0 init for target_params_buf KNOWN ISSUE: params_buf is zero-initialized but from_flat_buffer() creates weight set pointers into it BEFORE flatten_online_weights runs. The online forward reads zeros → NaN. Needs xavier_init_params_buf() at construction. 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.