0c99e08002
Smoke smoke-test-dr2bn (commit19b008e1c) failed all 7 SP1 pass criteria. F1 first-fire signature at step 240: flagged=[6=grad_buf, 12=save_h_s2, 26=iqn_trunk_m, 32=bn_d_concat_buf] — IDENTICAL to pre-fix smoke-xvzgk but at step 240 vs 890. The surgical fix at19b008e1cmade F1 NaN EARLIER, not prevented it. Diagnosis: the ε floor `(1e6 * isv).max(1e3)` clips legitimate F1 startup gradients to ±1e3 when fold-boundary ISV reset puts ISV[96] or ISV[21] near 0. Clipped gradients destabilize Adam EMAs → cuBLAS GEMM accumulator overflow → slot 26 + 32 NaN. F0 regression unchanged across Phase C (35.24 vs pre-fix 34.55). Confirms the F0 regression is a Phase B instrumentation side effect, not Phase C. Separate investigation thread within SP1. Next iteration: Task 6 fix-up #2 changes ε floor to `(1e6 * isv.max(1.0))` guaranteeing max_abs ≥ 1e6 regardless of ISV state. Removes cold-start clamp pathology while preserving F0 paper-review intent.
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.