ce019c72d2
Per spec §6.6 / Q6 train/dev/test split (defaults Q1-Q7 train, Q8 dev, Q9 sealed final test): - DQNHyperparameters: holdout_quarters + dev_quarters (default 1+1) - crates/ml/examples/train_baseline_rl.rs: --holdout-quarters / --dev-quarters CLI flags forwarded to hyperparams (this is the actual training binary; bin/fxt/src/commands/train.rs is a gRPC client and services/ml_training_service/src/main.rs accepts training params via proto not CLI — see audit doc note). - DQNTrainer::train_walk_forward slices training_data BEFORE fold generation; folds run on Q1..Q(9 - holdout - dev) only. - DQNTrainer struct: dev_features/dev_targets/holdout_features/ holdout_targets fields stash trailing slices for end-of-training dev eval and the Phase 4.3 separate eval-only workflow. - debug_assert sealed-slice guard catches future refactors that re-introduce holdout into the training path. Per established Phase 1 precedent (1.1-1.5: kernel/state lands first, consumer wiring deferred to follow-up commit per feedback_no_partial_refactor): CLI plumbing + slicing + dev/holdout storage land in this commit. The post-final-fold dev evaluation call (consumer of dev_features) is deferred to a follow-up commit and will mirror Task 1.7's evaluate_dqn_graphed integration pattern. Phase 4.3 argo-eval-final.sh is the sole legitimate consumer of holdout_features (separate eval-only workflow that does NOT call train_walk_forward). cargo check -p ml --features cuda --example train_baseline_rl: clean cargo check -p fxt: clean (no fxt changes needed; gRPC client only) 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.