f86353840e
`apply_iqn_trunk_gradient` and the parallel VSN-range SAXPY both scaled their contribution by `iqn_lambda × iqn_readiness × iqn_budget`. The readiness scalar initialises to 0.0 and only ramps up when `iqn_loss_ema` drops below `iqn_loss_initial` — but that improvement requires the trunk to learn IQN's gradient, which the readiness gate just blocked. Bootstrap deadlock: trunk_iqn=0.0000 across every observed L40S epoch, downstream strangling direction-Q discrimination → eval strict-argmax glues to one direction → 22-34 trades per 858k-bar window vs healthy 1257-trade burst at the one epoch where the gate momentarily lifted. iqn_budget already throttles the IQN contribution via the per-component budget controller (60% IQN, ISV-driven), so readiness was an additive band-aid that became load-bearing. New scale: `iqn_lambda × iqn_budget`. The `iqn_readiness` field stays on `self` because the C51 loss kernel launch site reuses `iqn_readiness_dev_ptr` as a CVaR-alpha pointer (gpu_dqn_trainer.rs:~16227) — that semantic overload is broken in a different way (CVaR α=0 is degenerate) and is tracked for follow-up. Verified on cluster trace `train-multi-seed-vg2r9` (epochs 0–13): trunk_iqn=0.0000 every epoch, q_gap_comp=0.00 every epoch, val trade_count locked at 22–34 except epoch 2 (1257 trades) where the gate accidentally cleared. Co-Authored-By: Claude Opus 4.7 (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.