c0d3d7b2ff
After a first dispatch attempt of monolithic Task 2 (GRN ADOPT) was correctly refused with a thorough scope assessment, this revision records the decomposition and the structural facts that drove it: 1. layout_fingerprint_seed() only fingerprints ISV slots, not param-tensor layout. The "fingerprint will auto-update" assumption in the original Task 2 spec was wrong for param-tensor reshuffles. 2. compute_param_sizes has 86 tensors (docstring saying 42 is stale). Inserting GRN's 9 sub-tensors shifts 82 downstream tensors and 98 padded_byte_offset call sites. 3. At least 12 kernels consume h_s2 expecting ReLU non-negativity. GRN's LayerNorm output is zero-mean (signed). Per-consumer verification needed before swap. 4. crates/ml-supervised::tft::gated_residual is incompatible as a port: different formula (sigmoid gate, not GLU split) and incompatible tensor abstraction. New CUDA kernels from scratch. Decomposition: - Task 2a (research, no code): audit h_s2 consumers for ReLU vs LayerNorm semantics. Output: per-consumer table. - Task 2b (small, checkpoint break): extend fingerprint seed to include param-tensor names + sizes. Pearl-aligned: complete fingerprint coverage rather than partial. - Task 2c (large, checkpoint break): GRN kernels + 98 call-site migration + h_s2 consumer shims (per 2a). Blocked on 2a+2b. Recommended order updated to thread 2a → 2b → 2c. Tasks 1, 6, 3 remain independent of 2c and can land in parallel where useful. Pearl rules section added documenting the safety constraints applied throughout the plan. No code changes. Plan-doc only.
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.