6e0cb3d62c
Task 3 refactored experience_state_gather (the WRITER) to use assemble_state() with canonical layout (OFI at [42..62)). But env_step and ofi_embed_build_input (the READERS) still hardcoded OFI at [66..84) — the OLD pre-refactor layout. This meant 7 locations were reading MTF/portfolio features as if they were OFI: 1. Line 1745: dense micro-reward ofi_cur = state+66 → actually MTF[4] 2. Line 1778: book_aggression = state[82] → actually plan_isv region 3. Line 1983: ps[30..37] OFI delta storage for NEXT bar — storing MTF data 4. Lines 5833/5836/5838/5840: ofi_embed_build_input — feeds 18→10 MLP into Mamba2 temporal SSM and attention. Entire temporal pipeline was training on MTF features dressed as OFI. Symptoms explained: - WinRate=20.9% on validation (anti-correlated): dense micro-reward computes quality=sign_pos × garbage_MTF_deltas, systematically rewarding wrong direction - mean_reward=+0.004 but Sharpe_raw=-0.0004: shaped reward exploits garbage signal, real portfolio loses money - grad_norm=23560 at epoch 2: gradients chasing noise - Q-value explosion to ±10 in one epoch: learning contradictions Fix: replaced all hardcoded 66/74/82/83 with SL_OFI_START from state_layout.cuh. Both reader kernels now use the same canonical layout as assemble_state(). Verified locally: smoke test passes, OFI_DIAG shows correct non-zero values (raw_mean=-0.36, delta_mean=-0.21, log_dur=-0.23). 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.