55d049ecf4
apply_reward_scale.cu's post-scale [-3,+1] clamp was masking ~99.99% of realized tail magnitudes from the agent's reward signal: local b=16 smoke showed train pnl_cum_usd −$0.63 (clamp-truncated) vs realized_pnl_cum_usd −$8,574.63 (raw raw_rewards sum), a 13,600× compression. Per van Hasselt 2016, popart standardization + F4 envelope are designed to handle tail magnitudes; the clamp fights them. Changes: - RL_REWARD_CLAMP_ENABLED_INDEX (slot 724): default 0 (disabled). When 0 the kernel skips the asymmetric clamp; scaled rewards pass through to rewards[b] unchanged. Legacy behavior restored by setting to 1. - DiagInputs.realized_pnl_cum_usd: parallel counter computed from raw_rewards (pre-scale, pre-clamp shaped pnl). Compare against trading.pnl_cum_usd to surface clamp-truncation gaps. - Trainer accumulates realized_pnl_cum_usd in both train + eval loops per closed-trade done-step (same pattern as pnl_cum_usd). - EXPECTED_LEAVES 651 → 652 for the new diag leaf. Validation: - 200+100 b=16 fold-1 smoke clean; train leaves = eval leaves = 652; realized_pnl_cum_usd diverges from pnl_cum_usd as expected when clamp disabled (the reward-hacking gap is now observable). - compute-sanitizer pending (cluster). B-8 (popart σ_welford disaggregation) and B-9 (C51 Bellman-target saturation observability) specs at docs/superpowers/specs/2026-06-01-* build on this slot allocation (725, 726-729 next). 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.