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ISV bus tail-append: - [96] H_S2_RMS_EMA_INDEX — per-batch RMS(save_h_s2) EMA (alpha=0.05) - [97] ISV_LAYOUT_FINGERPRINT_LO_INDEX (shifted from 94) - [98] ISV_LAYOUT_FINGERPRINT_HI_INDEX (shifted from 95) - ISV_TOTAL_DIM 96 -> 99 - New LAYOUT_FINGERPRINT_CURRENT = 0x3e21acecd922e540 (was 0xcf3a24b0a1f70057) Producer kernel `h_s2_rms_ema_kernel.cu`: - Single-block reduction (256 threads, shmem-tree, no atomicAdd) of the online trunk's `save_h_s2 [B, SH2]` post-GRN activation - RMS = sqrt(sum_sq / (B*SH2)); EMA into ISV[96] with alpha=0.05 - Launched once per training step alongside reward_component_ema and the other Plan 3/4 ISV producers in training_loop.rs Constructor cold-start writes ISV[96]=1.0 (neutral RMS). StateResetRegistry: H_S2_RMS_EMA registered as FoldReset -> 1.0. `launch_h_s2_rms_ema` and the kernel both treat the unconditional constructor allocation as an invariant — debug_assert! on the host side, no defensive (N>0) ternary in the kernel — same proper-resolution pattern as the 2c.3c.4 followup. Producer-only commit. 2c.3c.6 wires the consumer in mag_concat_qdir's adaptive-scale path so the magnitude-branch decoder sees a scale- invariant residual stack regardless of GRN drift across training. Smoke deferred — producer-only with zero consumers, runtime behaviour structurally unchanged from 2c.3c.4 (which validated the GRN backward chain end-to-end with all 3 fold checkpoints). cargo check + cargo build (new h_s2_rms_ema_kernel.cubin compiles clean under nvcc sm_80) is the standing validation; meaningful multi_fold_convergence re-run lands with 2c.3c.6 when the consumer wires up. 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.