dab5990287
Brainstorm session 2026-04-29 produced this spec scoping Sub-project 1 of a three-sub-project decomposition addressing the F1 ep2 NaN explosion that persists across 9+ defensive layers in Plan C Phase 2 (commits e445d07a..e9096c7be). Decomposition: - SP1 (this spec): F1 NaN root-cause; γ audit + β always-landing instrumentation + surgical fix + multi-fold validation - SP2 (future): numerical stability framework — codify guards - SP3 (future): Q-learning structural stability — target-Q clip, pessimistic ensemble, atom-range governance Operating principles: - No deferrals (anomalies fixed within SP1, not punted) - Combined fixes — rich commits (per feedback_no_partial_refactor) - Always-landing diagnostic instrumentation (24-slot nan_flags_buf expands to 48; permanent regression sentinel) - F0 Best Sharpe ≥ 55 preserved (no regression on the working path) Pass criterion: all 3 folds train 5 epochs, F0+F1+F2 Sharpe ≥ 0, zero NaN-CLAMPED-TO-ZERO log lines, all 48 flag slots remain zero. 5 design sections approved iteratively: Architecture, Components, Data Flow, Error Handling, Testing. Next: writing-plans produces implementation plan.
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.