fa1f299147
Bug audit finding #2 (post train-d2b2s diagnostic — same Pattern 1 class as SP14 B.11 commit200f05fceand the 4-producer batch in5608b866b). SP5 Pearl 2 budget producer and SP7 loss-balance controller currently launch from process_epoch_boundary (fires once per epoch), but the loss-balance budget output (ISV[BUDGET_CQL_BASE..] / ISV[BUDGET_C51_BASE..]) is consumed EVERY training step via apply_c51_budget_scale (fused_training.rs:1941) and the dispatch kernel that resolves the cached value into lb_budget_effective_buf (fused_training.rs:3631). The dispatch kernel is per-step, but the underlying flatness signal ISV[FLATNESS_BASE..] is per-epoch. SP7's controller therefore reads (steps_per_epoch − 1)-step-stale flatness — the same failure mode that broke SP14's ALPHA_GRAD_SMOOTHED. The entire loss-balance budget system has been operating on stale-flatness state for the duration of training. Migration (atomic, preserves Pearl 2 → SP7 dependency): - Pearl 2 budget launch moved to submit_aux_ops (just-after the producer-cadence batch's MoE chain, just-before the IQL gather block). - SP7 loss-balance controller follows immediately (reads Pearl 2 output via ISV). - Same captured-into-aux_child graph-replay semantics as SP14 B.11. - training_loop.rs lines 4312, 4336 deleted; replaced with redirect comment. Per feedback_no_partial_refactor: this is the 7th cadence-fix in this branch since v8ztm. Other Pattern 1 candidates (SP5 Pearl 1 atom, Pearl 3 σ — Pearl 2 inputs, AND SP8 Fix 36 launch_max_budget_compute — SP7 controller input) deferred per scope-tightening rule. They sit one-epoch-stale at fold start; Pearl A bootstrap + the controller's internal cold-start branch keep behavior functional. Tracked in the audit doc top-of-file entry; independent migrations, land separately. Verification: cargo check -p ml --lib clean (dev profile); sp14_oracle_tests 7/7 pass on RTX 3050 Ti. 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.