3ad5e011b9
Comprehensive review of Layer B (commit 99367b9c6) caught one critical
and two important findings. All three fix in one atomic commit.
Critical: IQL Adam groups 4+5 (IqlHigh, IqlLow) missed the Pearl 4
migration. gpu_iql_trainer.rs::train_value_step still read
self.config.beta1=0.9, self.config.beta2=0.999 at runtime. ISV[230]
(adam_beta1(4)) and ISV[231] (adam_beta1(5)) were populated by the
Layer A producer but unconsumed — partial refactor of the 8-group
Pearl 4 contract. Fix mirrors the Curiosity pattern: 3 new
iql_beta1/beta2/epsilon parameters threaded through the IQL Adam call,
2 ISV reads at each of the 2 train_value_step call sites in
fused_training.rs.
Important: consumer clamp envelopes were wider than the SP4 producer
range (0.5..0.9999 vs producer's 0.85..0.95 for β1, etc.). At
cold-start with ISV=0, this produced β1=0.5 (more aggressive momentum
than the SP4-anchored 0.85 floor). Tightened all Pearl 4 consumer
clamps to match the producer envelope: β1∈[0.85,0.95], β2∈[0.99,0.9995],
ε∈[1e-10,1e-6].
Important: gpu_curiosity_trainer.rs:66-68 left dead ADAM_BETA1/BETA2/
EPS constants after the Pearl 4 migration. Removed.
8/8 Pearl 4 groups now ISV-driven (DqnTrunk, Value, Branches, IQN,
IqlHigh, IqlLow, Attn, Curiosity). cargo check + cargo build + lib
tests all clean.
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.