3f8e1fb553
Re-derive 3-element fixtures for [10, 100, 1000] preserving geometric
decay invariant. Rename excess_at_h6000_lifts_lambda_proportionally to
excess_at_h1000_lifts_lambda_proportionally.
Critical correction during execution: the kernel uses SQRT-anchored
TARGET_K_RATIO (since commit b5bed9f80 "sqrt K-ratio") not linear ratio.
The lifted-fixture computation mirrors the kernel's sqrt constant
(TARGET_K_RATIO_H2 = sqrt(10/1000) ≈ 0.3162) so the test fires the
lambda = 10 × base invariant under the actual kernel math.
Side-discovery (flagged for Task 5 scope expansion):
- cuda/smoothness_lambda_controller.cu:30 still has SLC_N_HORIZONS = 5
- TARGET_K_RATIO at lines 45-51 uses old-horizon formula {30/30, 30/100,
30/300, 30/1000, 30/6000}. With N_HORIZONS=3 the kernel reads only
slots [0..3] = {1.0, 0.5477, 0.3162} — those correspond to old
30/30, 30/100, 30/300 ratios. h1000's smoothness target is currently
anchored to OLD h300 ratio (functional bug requiring kernel update).
cargo test --test smoothness_lambda_controller_invariants: 4 passed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.