731cae4c80
The pre-fix clamp at gpu_dqn_trainer.rs:14722 was [0.05, 0.3] — the SP11-era cap. P0b's controller computes aux_w in [0.15, 1.5] (base 0.5 × [0.3, 3.0]) but the setter silently chopped everything above 0.3, masking the deficit-amplification term entirely. Smoke A trace confirmed: Fold 0/1: raw aux_w = 0.66-0.80 → clamped to 0.30 (deficit invisible) Fold 2: raw aux_w = 0.164 (stagnation; below clamp) → 45% deficit Post-fix: deficit-amp term `(1 + 5 × deficit)` actually expresses through to the trainer. Fold 2 stagnation will get the designed floor 0.15 instead of being capped at 0.3 — but the upper range 1.5 also opens, so deficit-amp can pull aux_w up when accuracy is below target. Constants imported from sp13_isv_slots.rs (AUX_W_BASE=0.5, AUX_W_HARD_FLOOR_RATIO=0.3, AUX_W_HARD_CEIL_RATIO=3.0). No new slots; existing constants exposed as the clamp bounds. Test: set_aux_weight_clamp_range verifies constants resolve correctly. A.1 (C51 atom-probability floor) deferred — Phase 0 verification found the spec's stated `−log(p)/p · ∂p/∂z` divide does NOT exist in c51_grad_kernel.cu at HEAD 037c24116; actual kernel uses the numerically-stable `expf(lp) - proj`. The 1109 GRAD_CLIP_OUTLIER events in Smoke A are real but their mechanism is different. Will be re-spec'd as a separate task post-SP14. 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.