34586dad68
Rename binary alpha_compose_backtest → alpha_baseline and remove the boolean flags whose features are now mandatory: --c51 (always C51 distributional Q) --temporal (always Mamba2 temporal encoder) --isv-continual (controller always fires per eval episode) --regime-scale (vol-EMA regime defense always on) --pruned-actions (FALSIFIED 2026-05-15 per pearl_action_pruning_falsified) Every dependent code path was stripped, not just gated: - Linear-Q kernels (lq_fwd, lq_grad, munch_kernel) and their cubin loads are gone — C51 is the only Q-network. - Single-env push_kernel / h_store_kernel loads removed; the backtest has been batched-parallel-env since T14 and only the _batched variants are called here. (The smoke binary still uses single-env variants because one env per episode is its job.) - Dead transition buffers removed: states_dev, next_states_dev, actions_dev, rewards_dev, dones_dev, q_current_dev, q_next_dev, target_dev, single_state_dev, single_q_dev, probs_current_dev, probs_next_dev, m_dev, single_probs_dev, single-env state_pinned, action_pinned, window_tensor, h_enriched_buf_dev. - Dead constants and helpers: PRUNED_ACTIONS, N_WEIGHTS, N_BIASES, epsilon_greedy, epsilon_greedy_gated. End-to-end verification on the existing Q1 fxcache (rebuild was OOM locally; full multi-quarter validation is the next phase): cost=0.0000 best τ=0.250 Sharpe_ann=+36.83 win=0.984 trades/ep=83.3 cost=0.0625 best τ=0.250 Sharpe_ann=+38.53 win=0.996 trades/ep=83.2 cost=0.1250 best τ=0.250 Sharpe_ann=+38.37 win=0.994 trades/ep=83.3 cost=0.2500 best τ=0.250 Sharpe_ann=+34.24 win=0.990 trades/ep=85.6 cost=0.5000 best τ=0.250 Sharpe_ann=+31.83 win=0.946 trades/ep=84.8 Numbers track the prior T16-flag config (within stochastic noise), confirming the conditional-stripping was a pure simplification — no behavioral change, just a smaller, honester binary. Also updated: - scripts/alpha_pipeline.sh — A/B conditions collapse to fixed-cost vs cost-randomized training (the only opt-in left). - scripts/walk_forward_cv.sh — drop legacy flags, pass --window-k only. - crates/ml/src/env/loaders.rs — module doc-comment updated. 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.