f7697851f7
Plan 4 Task 2c.3a. Build-clean prep commit. Lays down the GRN param-tensor layout (86 -> 95 tensors) and migrates all 93 padded_byte_offset call sites + ancillary index references in lockstep. Does NOT swap the trunk forward — that's Task 2c.3b. Explicit panics at every trunk-forward and trunk-backward caller ensure any accidental runtime execution fails loudly rather than producing silent garbage. Param-tensor reshuffle: - Delete: W_S1, b_S1, W_S2, b_S2 (old trunk's 4 Linear tensors) - Insert: 7 h_s1 GRN tensors (W_a, b_a, W_b, b_b, W_residual, gamma, beta) - Insert: 6 h_s2 GRN tensors (no W_residual — SH1 == SH2 makes residual identity) - All tensor indices >= 4 in OLD layout shift +9 in NEW layout - NUM_WEIGHT_TENSORS 86->95; FIRST_ISV_TENSOR 68->77 layout_fingerprint_seed() updated. New LAYOUT_FINGERPRINT_CURRENT: 0xcf3a24b0a1f70057 (was 0xa504d3c2f275b8af). Xavier init paths added for the 13 new tensors: - W matrices (Linear_a, Linear_b, Linear_residual): Xavier - LayerNorm gamma_h_s1, gamma_h_s2: 1.0 - LayerNorm beta_h_s1, beta_h_s2: 0.0 - All biases (b_a_h_s1, b_b_h_s1, b_a_h_s2, b_b_h_s2): 0.0 Encoder gates: BatchedForward::encoder_forward_only, BatchedForward::forward_target_raw, BatchedForward::forward_online_f32, BatchedBackward::backward_full, GpuDqnTrainer::apply_iqn_trunk_gradient, GpuDqnTrainer::apply_ensemble_diversity_backward all panic at function entry. Original bodies preserved (with #[allow(unreachable_code, unused_variables)]) for Task 2c.3b/c to swap GRN forward + backward in place. Spectral-norm descriptor (13 matrices): slots [0]/[1] re-mapped to GRN's w_a_h_s1/w_a_h_s2 — shapes match legacy W_s1/W_s2 exactly, so the existing spectral-norm constraint transfers cleanly to the GRN's first Linear. Linear_b / Linear_residual not yet covered (Task 2c.3c). Smoke intentionally NOT run. Build-clean is the validation. Task 2c.3b will swap the encoder forward (gpu_grn::GrnBlock::forward); Task 2c.3c will swap the trunk backward (gpu_grn::GrnBlock::backward) and run smoke. Validation: - cargo check --workspace: 11 baseline warnings (unchanged) - cargo build -p ml --lib: all 58 cubins compile clean - 93 padded_byte_offset call sites all migrated; spectral_norm descriptor + branch_w_base + decoder_forward_only + value_head indices all updated in lockstep per feedback_no_partial_refactor.md 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.