b5bed9f805
Local trace at base_lambda=0.1 showed the linear ratio
{1, 0.3, 0.1, 0.03, 0.005} = HORIZONS[0]/HORIZONS[h] gave h6000 a
200x stronger target-undershoot signal than h30. The controller
slammed h6000 with smoothness gradient (peak λ[h6000]≈60) while h30
saw nearly none. h6000 val_auc collapsed to 0.514 (near-random)
while h100/h300 improved.
Replace with sqrt(HORIZONS[0]/HORIZONS[h]) = {1, 0.548, 0.316, 0.173,
0.0707}. Caps the differential at ~14x. Verified locally at base=0.1:
- h6000 val_auc recovered to 0.599 (vs 0.514 collapse, vs 0.621 no-smooth)
- jitter ratio h6000/h30 = 0.51 (meaningful differentiation, was 0.84 unforced)
- λ[h6000] equilibrium = 0.7-3 (was 4-60 with linear ratio at same base)
The design target (h6000 jitter = 7% of h30) is less aggressive than
linear (was 0.5%) but produces a tractable training equilibrium that
preserves h6000 predictive capacity. Both 500-step local AND the full
40k-step L40S retrain will tell us where it lands at scale.
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.