e4c3cc60d2
Replaces the flawed Phase F + G arc preserved on branch `ml-alpha-phase-f-g-flawed` (commits 99a125cdb..b3808a5ac). The prior attempt shipped a trainer with multiple production-blocking defects: 1. ISV[400..406] uninitialised → kernels read γ=0, ε=0, entropy=0 2. rl_reward_scale_controller drifted to 1e3 on no-trade steps 3. 6 controllers exist as .cu but never launched 4. Target net never soft-updated (τ has no consumer) 5. step_with_lobsim violated feedback_cpu_is_read_only with host Thompson sampling + EMA tracking + advantage/return loops 6. "toy" framing leaked into production (alpha_rl_train.rs shipped with next_snapshots=snapshots — the F.4 next-state code path was a no-op until that one issue got caught mid-review) 7. No NaN abort in production CLI The convergence-gate fixtures (dqn_toy/ppo_toy → renamed dqn_reward_signal/ppo_reward_signal on the flawed branch) hid every defect because MockLobEnv is state-invariant with horizon=1. The rebuild is GPU-pure: kernel-driven action sampling, kernel-driven EMA tracking, kernel-driven advantage/return, ISV bootstrap at trainer construction, all 7 controllers wired with device-resident EMA inputs, target-net soft update consumer, NaN abort, no LobEnv trait (drives LobSimCuda via the existing decision-policy kernel pattern). Sequenced as R1..R9 with falsifiability gates G1..G7 that exercise the specific failure modes the convergence-gate fixtures couldn't catch. Calendar ~8.5 dev days. Also adds memory pearl feedback_extending_existing_code_audits_for_existing_violations capturing the lesson: extending pre-existing CPU/orphan-controller violations is how this happened. 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.