3f089210f3
cnjfl wall-time analysis: 80% of training time was disk IO. The MultiHorizonLoader was constructed fresh per epoch in the CLI loop, forcing 9 file deserializations from bincode (~50s/file × 9 = 7-8 min) per epoch. For a 6-epoch run that was ~45 min of pure IO out of ~50 min total. Now loaders preload ALL files into RAM at construction (one-time ~7 min startup) and expose a `reset(seed: u64)` method that re-seeds anchor sampling per epoch — no disk IO between epochs. Memory cost: ~13-15 GB for the 9-quarter ES.FUT dataset (45M snapshots × ~280 bytes). Well under the training pod's 64 GB limit; current 16 GB request remains sufficient since the resident set fits. Expected wall-time impact at K=96, B=8, 16K seqs: 6 epochs: ~50 min → ~13 min (~3.7×) 15 epochs: ~120 min → ~23 min (~5×) The internal LoadedFile-cache-with-cycling logic is gone — the loader now holds Vec<LoadedFile> with all files resident. Per-call `next_sequence` picks a uniformly random file + uniformly random anchor inside it (instead of cycling files with a per-file budget). Distribution is equivalent: each file contributes ~n_max_sequences / n_files samples per epoch in expectation. 77 ml-alpha tests pass. Co-Authored-By: Claude Opus 4.7 <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.