857722e774
Per-step launches (in graph capture order): 1. Forward (existing) 2. Action select (existing) → q_dir_logits available 3. launch_sp14_q_disagreement_update → ISV[383, 384, 389] 4. launch_sp14_alpha_grad_compute → ISV[385..395] (consumes q_disagreement) 5. Backward (existing) — wire-col scale at B.10 reads ISV[393] Per-epoch launch (end of epoch): 6. launch_sp14_gradient_hack_detect → circuit breaker α_short=0.3, α_long=0.05, α_var=0.05 per spec; warmup_gate derived from steps_in_fold / WARMUP_STEPS_FALLBACK. Var_aux producer gap closed (option C from B.4): alpha_grad_compute_kernel now also writes ISV[VAR_AUX_INDEX=388] via Welford EMA against (aux_dir_acc_short - aux_dir_acc_long). Adaptive k_aux is now functional (was degenerate at K_BASE_AUX=20.0 constant pre-B.11). Closes the "adaptive_k_aux currently degenerate" concern flagged in B.4 commit. After this commit, the EGF pearl is FULLY ACTIVE end-to-end: - Forward: aux signal feeds direction Q-head input (B.8/B.9) - Backward: wire-col gradient gated by α_grad_smoothed (B.10) - Producers: α_grad computed every step from real driver signals (B.11) - Pre-B.11 force-closed gate (sentinel 0.0) → post-B.11 responsive gate Build clean: 18 warnings pre-existing baseline, 0 new. Tests: 4/4 P0b aux_w tests pass (no regression). 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.