61b2fa962b
Deep audit onb435d25befound a third post-B1b bug in same class as the slot 63 overload: experience_env_step writes cf_reward_weighted (= w_cf × cf_reward, post-controller-weight) to reward_components_ per_sample[+1] at line ~3696. SP4's reward_component_ema_kernel EMAs this into ISV slot 64 (REWARD_CF_EMA_INDEX) which the SP11 mag-ratio canary reads as ratio[1] = slot[64] / Σ. Self-reinforcing loop: high w_cf → high cf_reward_weighted → high slot 64 → high ratio[1] → controller raises w_cf → tighter loop Until mean=1 normalization saturates other components to floor. The other 5 component slots correctly write RAW pre-weight values: - rc[+0] = total_reward (intentional, PopArt input — post-composition) - rc[+2..+5] = r_trail / r_micro / r_opp_cost / r_bonus (all raw, pre-Σ) - Slot 360 (popart-component) fed by `popart_component_per_sample` which receives `r_popart` raw Only rc[+1] was wrong. Fixed: write raw cf_reward to rc[+1] so the canary tracks intrinsic cf magnitude. The replay-buffer cf-tuple reward (out_rewards[cf_off]) still uses cf_reward_weighted — that's correct, loss kernels train on controller-weighted signal. This was the third bug in a class — pre-SP11 invariants exposed by post-decomposition semantic. Trio: (1) slot 63 overload (5e16b67ca), (2) stale rc[] init + cf_flip ordering (b435d25be), (3) cf-component feedback loop (this commit). cargo check + build clean; 6/6 SP11 GPU tests + 14/14 contract tests still pass. After this fix-up, all known SP11 reward-system bugs identified by deep audit are resolved. L40S smoke validates empirical sharpe recovery.
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.