bee81b4967
Critical fixes after honest review:
1. reward-audit measurement: split into 3 classes (additive / transform
/ sample-selector) — one-size-fits-all metric couldn't work for
PopArt normalization or CF flip mirroring
2. H1 detection: replace undefined "Sharpe/state" with concrete
forced-exploration protocol (epsilon=1.0 for 1 epoch at mid-train,
sample states × magnitudes, compare Sharpe-per-trade)
3. H4 scope: clarify this is the per-action-branch gradient (direction
vs magnitude head), NOT the per-loss-component (IQN/CQL/C51/Ens)
budget. Fix sketch updated to target the correct knob.
4. Validation gate: criterion B (multi-fold Sharpe + WinRate) is now
MANDATORY; criterion A gates are soft. Prevents "partial success"
hiding a policy-quality failure.
5. Trade-count gate: per-fold ≥100 on ≥5/6, not an average — 5 lean
folds pooled with 1 fat one should not pass.
Minor:
- Budget trimmed to 3.5–4 hrs L40S (was 6–10)
- L40S pool made explicit as target
- Phase 1 branches now pushed as wip/* for crash safety
- Hyperopt-params mismatch handling documented
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.