9c26e78cdc
32 tasks across 5 milestones (E.0 foundation → E.4 shadow-mode), with locked design decisions from three rounds of focused research memos: - Q1 (fill sim): medium-tier Poisson regression from 5.2M trade tape - Q2 (reward): terminal-only, n-step credit (consumes ISV slot 517) - Q3 (alpha trust): implicit calibrated trust via state features - Q4 (state window): current snapshot + 2 short-horizon scalars - Q5 (sizing): hybrid decoupled fractional Kelly × Phase E attenuation Trainer choice: DQN primary (Rainbow + Munchausen target), PPO control on H=600 truncated only if kill criteria fire. Exploration: ε-greedy with kill-criteria gate at end of week 2; NoisyNet escalation (4-6 days due to dead scaffolding in our codebase) if criteria fail; RND beyond that. ISV consumption: 5 existing slots (n_step=517, γ=43-46, ε=41, Kelly=280, reward_caps=452-453); new block 539..550 reserved for Phase E (10 in active use, 2 spare). One new controller (stacker-threshold engagement-rate-self-correction at slot 543). Hardcoded by design: Kelly contract cap (Category-1 safety), kill-criteria thresholds (circuit breakers). All other knobs are ISV-driven per pearl_controller_anchors_isv_driven. Decisive gates at week 1 (H=600 kill criteria), week 4 (composition backtest Sharpe at half-tick > 0), and week 5 (shadow-vs-backtest PnL within 30%). 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.