2d226e6e76
Per spec §8.2 (3.1) post-amendment-2 fix: ALPHA_SPLIT slot initialized
DIRECTLY to 0.5 in trainer constructor. Formula α = grad_norm_q /
(grad_norm_q + grad_norm_d + ε) takes over only after BOTH grad-norm
EMAs accumulate ≥ N_WARM=100 non-zero observations.
Two kernels in r_quality_discipline_split_kernel.cu (single cubin per
established 1:1-source-to-cubin pattern with multiple kernels):
- r_quality_discipline_split_kernel: per-step composition + warm count
- alpha_split_producer_kernel: per-step ALPHA_SPLIT update from grad ratio
(gated on warm count to prevent premature formula activation)
3 ISV slots (417 ALPHA_SPLIT, 418 GRAD_NORM_QUALITY, 419 GRAD_NORM_DISCIPLINE)
+ sp15_alpha_warm_count [1] mapped-pinned scratch buffer on the trainer
struct. 4 fold-reset registry entries + dispatch arms (one for the
non-ISV warm-count buffer mirrors the sp11_novelty_hash host_slice_mut
pattern).
Per established Phase precedent: kernels + launchers land first; consumer
migration (per-step launches in training_loop.rs reward composition site)
deferred to a follow-up commit per feedback_no_partial_refactor.
Anchor tests: 2.4 cost_sensitivity + 2.6 regime_silences (Phase 2B
contracts) — green via Phase 3.4 regret + 3.2 cost; this commit lands
the split structure they depend on.
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.