1e8188c947
First commit of the horizon-rebase refactor (full plan at docs/superpowers/plans/2026-05-22-horizon-rebase-n3.md). Motivated by empirical evidence from this session's investigations: - C1: ES MBP-10 input ACF dies by L=300; K=1000/6000 are below noise floor - D3: binarized labels ρ(30,6000)=0.06; K=6000 carries no signal - Heads_w1 mask experiment: h6000 AUC collapsed 0.73→0.54, confirming long-horizon prediction needs cross-channel integration that 5-horizon bucket structure couldn't provide New horizon set [10, 100, 1000] matches C1's empirical autocorrelation elbows (~50ms, ~3.5s, ~45s wall-clock at 74µs median Δt). Scope of this commit: - crates/ml-alpha/src/heads.rs: N_HORIZONS 5→3, HORIZONS=[10,100,1000] - crates/ml-alpha/src/cfc/bucket_routing.rs: MAX_BUCKET_DIM 28→96, BUCKET_DIM_K=[43,43,42], BUCKET_CHANNEL_OFFSET=[0,43,86,128] - crates/ml-alpha/src/data/loader.rs: 5 hardcoded [T;5] types migrated - crates/ml-backtesting/src/sim/mod.rs: broadcast_alpha signature, local N_HORIZONS re-exports ml_alpha::heads::N_HORIZONS - crates/ml-backtesting/src/harness.rs: local N_HORIZONS re-export, per-horizon log labels, MultiHorizonLoaderConfig.horizons Tests + examples + CUDA kernels still need migration (subsequent commits per plan tasks 2-8). 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.