f7a91d906d
SoftReset registry category (isv_grad_balance_targets, isv_grad_scale_limit) implemented via bootstrap-write + existing kernel's EMA. CPU writes bootstrap values (1.0 for targets, 2.0 for limit) at fold boundary; grad_balance_isv_update kernel's adaptive-rate EMA (alpha in [0.01, 0.30]) converges from bootstrap toward observed values over subsequent epochs (implicit decay_bars via EMA time constant). Option A chosen (minimal): the existing grad_balance_isv_update kernel already uses adaptive-rate EMA — not a hard-write — so bootstrap-at-fold-boundary is sufficient. The EMA time constant ~1/alpha provides the decay, satisfying the decay_bars=500 intent without a new ISV slot (Option B rejected as over- engineered: new ISV slot + kernel change for no material behavioral improvement). Changes: - training_loop.rs::reset_named_state: add dispatch arms for both SoftReset entries, writing bootstrap constants (CPU-born input, not adaptive output; complies with spec §4.C.6 GPU-drives-CPU-reads) - trainer/mod.rs: fold-boundary reset loop now calls both fold_reset_entries() and soft_reset_entries(); stale "handled separately (Plan 2)" comment removed - smoke_tests/soft_reset.rs: 4 CPU-only tests verify registry classification, entry count, mutual exclusivity from fold_reset, and bootstrap constant invariants - docs/dqn-wire-up-audit.md: D.5 SoftReset dispatch row added No CPU-side adaptive computation. No new ISV slot. No behavioral change to training beyond writing known-good bootstrap values at fold boundaries. Plan 2 Task 4. Spec §4.D.5. 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.