78b781a523
Two architectural fixes addressing the "never close" overfit attractor that caused walk-forward fold 0 to converge to dones=0 by step 2000. 1. Asymmetric clip on unrealized_R in trade_context (value leak fix): trade_context[1] = fminf(0.0f, unrealized_R) The model previously saw unrealized_R as a state feature. Q learned "high unrealized = high V(state)" → Q(close) < V(hold) → never close. Now Q only sees the LOSS side: losing positions visible (cut losses), winning positions invisible (close decision driven by general policy, not state-conditioned on profit). Realized PnL on close still teaches "take profits" via Bellman backup. 2. Enable max_hold_ns = 60s (environment constraint): LobSimCuda::max_hold_ns_d was alloc_zeros (disabled). The lobsim has session-gap force-close (>1 hour gaps) but no per-trade timeout, letting the agent hold positions indefinitely within sessions. Now alpha_rl_train sets max_hold to 60s via new upload_max_hold_ns method backed by fill_u64.cu kernel (device-side, complies with feedback_no_htod_htoh_only_mapped_pinned). These are environment/architectural fixes, NOT reward shaping hacks. Together they address why the model COULD learn "never close" and ensure dones signal density for Q-learning. 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.