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Derived the projection-arithmetic transformation that collapses per-(b, a) atom-position shift into a SINGLE per-sample reward adjustment: effective_reward = reward + gamma_eff * Delta_target * (1-done) - Delta_online where Delta_online = W[a_d] * state_121[b] (taken-action shift on online support) Delta_target = W[a*] * next_state_121[b] (sampled-target shift on target dist) The Bellman projection (block_bellman_project_f body) needs NO arithmetic change — only the reward arg passed in. This dramatically reduces Step 7's kernel surgery surface area. For Step 8 backward, derived the gradient paths: dL/dDelta_online = (1/dz) * sum_n p_target_n * (log_p_online[upper_n] - log_p_online[lower_n]) dL/dDelta_target = -gamma*(1-done) * dL/dDelta_online dL/dW[a_d] += dL/dDelta_online * state_121 dL/dW[a*] += dL/dDelta_target * next_state_121 dL/dstate_121[b] += dL/dDelta_online * W[a_d] dL/dnext_state_121[b] += dL/dDelta_target * W[a*] Critical constraint documented: Steps 7+8+11 MUST land atomically. Splitting Step 7 forward from Step 8 backward creates a silent gradient path through the aux head (missing dDelta_online/dnetwork_weights). Splitting Step 8 from Step 11 Adam accumulates dW without updating W — no learning. Per feedback_no_partial_refactor.md. The math now makes Step 7+8 a transcription job rather than exploration. 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.