5265a0186c
System-scoped naming for the alpha trading system's training binaries — same rationale as the earlier phase_e_* → alpha_* rename. These binaries produce / validate the durable alpha-system components (Mamba2 + stacker + calibration); they're tooling, not milestone artifacts. phase1a.rs → alpha_bar_baseline.rs phase1a_detailed.rs → alpha_bar_detailed.rs phase1d_calibrate.rs → alpha_calibrate.rs phase1d_mamba.rs → alpha_mamba_baseline.rs phase1d_long_horizon.rs → alpha_train_stacker.rs clap `name = "..."` strings updated to match new filenames; cross-refs in docstrings (alpha_calibrate.rs, gbm_baseline.rs) fixed. Plus: NEW `--alpha-cache-out <PATH>` flag on alpha_train_stacker.rs (Phase E.1 Task 12b). After the existing Mamba2 + stacker training completes, runs stacker inference on ALL bars (not just val/test) and dumps the resulting alpha_logits as a little-endian binary file: [u32 n_bars] [f32 logits[n_bars]] Bars `< seq_len − 1` are written as 0.0 (Pearl A sentinel — no history). Inference uses the same Block-S column normalisation (col_mean/col_std) computed during stacker training, applied to all bars consistently. This cache is consumed by alpha_dqn_h600_smoke.rs (next commit) which loads it and populates SnapshotRow.alpha_logit — replacing the current hardcoded 0.0 placeholder with the real Phase 1d.3 stacker output. Build verified: `cargo build -p ml-alpha --release --example alpha_train_stacker` completes clean in 40s.
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.