defbd0abe1
P1 (must-fix bugs/gaps): 1. ASYM_RATIO_INDEX → LOSS_CAP_INDEX with explicit formula in §4.1 and §4.5 (was double source-of-truth — formula in §4.1, ISV slot orphaned) 2. Replay buffer schema change (per-bar aux_conf) added to §8 implementation footprint (~50 LoC additional, was invisible in original spec) 3. hold_baseline_buffer size specified = LOOKAHEAD_HORIZON_MAX = 30 bars (§4.2) 4. "4-tier gate" typo → "5-tier gate" in §7 P2 (clarifications): 5. alpha_ema centering documented as load-bearing for cold-start learning (§4.1) — not just for Q-target stability. EV is slightly negative at WR=46% with uninformed SP19 label; advantage-style centering rescues cold-start. 6. Q-scale asymmetry between Hold (uncentered) and trade (alpha_ema centered) documented as INTENTIONAL design choice (§4.2) — produces marginal Q(trade) > Q(Hold) preference that counteracts the Q(Hold) attractor. Behavioral test sp20_pure_noise validates the no-signal Hold default still works. P3 (tightening): 7. Behavioral test thresholds tightened (§4.6): - sp20_pure_trend: WR > 70% → > 90%, PF > 2.0 → > 3.0 - sp20_pure_noise: Hold% > 80% → > 95%, trades < 50 → < 20
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.