e412f12e33
The C2 fix from the previous session was too aggressive — it eliminated ALL exploration mechanisms simultaneously: 1. epsilon forced to 0.0 when noisy nets active (select_action) 2. count bonus removed from Q-value computation (metrics only) 3. noisy_epsilon_floor config field declared but never read This left noisy nets as the sole exploration mechanism, which produces perturbations too small to overcome Q-value gaps (A4=0.12 vs others≈0.02). Result: 20/20 hyperopt trials hit fallback objective with 1/5 diversity. Fixes: - select_action: use noisy_epsilon_floor (not 0.0) as effective_epsilon when noisy nets active — guarantees minimum random action rate - select_action: re-enable UCB count bonus on Q-values before argmax (both IQN and standard paths) for directed exploration - select_action_with_confidence: same fixes for consistency - trainer: set epsilon to noisy_epsilon_floor (not 0.0) at init - hyperopt: widen noisy_epsilon_floor range from [0.0, 0.05] to [0.03, 0.15] with default 0.05 Exploration now has two complementary mechanisms: - noisy_epsilon_floor: random actions feed diverse replay buffer - count bonus: UCB term biases greedy selection toward under-visited actions - noisy nets: weight perturbation adds stochasticity to Q-values select_action_inference (production) is unchanged — pure exploitation. 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.