8f218cab24
Two independent bugs surfaced by Class C (frame-shift) + Class A (hardcoded bounds) audits, both implicated in the months-long WR-stuck-at-46-48% plateau across 11 superprojects. ## Bug 1: Replay buffer Sutton's deadly triad experience_kernels.cu:2275 was writing the original INTENT action to out_actions, but the reward in the replay buffer was computed from the REALIZED position (post-enforcement: Kelly cap, capital floor, trail-stop, broker cap can clamp Long→Flat). Replay buffer stored (s, intent, r_realized, s'). Q(s, Long) was therefore trained against r(s, Flat) whenever env clamped the intent. This explains the train_active_frac=0.40 vs val_active_frac=0.05 gap: train measures intent (40% Long/Short), eval measures realized (5% Long/Short). The 8× gap is env physics draining intent. Fix: after unified_env_step_core resolves actual_dir_core/actual_mag_core, overwrite out_actions[out_off] with the realized action (same encoding as backtest_env_kernel.cu:323-330, which has been doing it correctly all along). Order/urgency preserved from intent. ## Bug 2: Kelly cap update kernel ignored existing ISV warmup floor kelly_cap_update_kernel.cu:53 hardcoded the kelly_f floor at 0.0f. Cold path (per-epoch boundary). Per project_magnitude_eval_collapse_kelly_capped, this collapses kelly_cap to 0 → max position pinned to Quarter for cold start. The val-mag pathology. The warmup floor producer (ISV[KELLY_WARMUP_FLOOR_INDEX=330], SP9 Fix 37) was already populated and consumed by the per-step path at trade_physics.cuh:377-384, but this cold-path kernel never read it. Partial wiring. Fix: replace fmaxf(kelly_f, 0.0f) with fmaxf(kelly_f, isv[330]). One-line change. ## Predicted effect - train_active_frac and val_active_frac should converge (Bug 1 inflated train by counting overridden intents) - Magnitude distribution should escape Quarter-only (Bug 2 was pinning it) - WR ceiling at 46-48% may finally move (Bug 1 broke Bellman consistency; Bug 2 prevented edge realization) Falsification: 5-epoch L40S smoke. If unmoved by ep5, the plateau is deeper still (Class A P0-A REWARD_POS_CAP/NEG_CAP next). 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.