e00736ce9b
Comprehensive design replacing every hardcoded magnitude multiplier in the SP3 mechanism stack (Mechs 1, 2, 5, 6, 9, 10) plus pre-SP3 mechs in the same magnitude-control surface, per feedback_isv_for_adaptive_bounds and feedback_adaptive_not_tuned. Core principle: the BOUND lives in an ISV slot, computed by a producer kernel as p99 EMA of observed signal magnitude. Consumer reads the slot and clamps directly — no multiplier between ISV read and clamp. Cold- start ε from theoretical-init bootstrap (Xavier, etc.) — same theoretical- constant category as Adam β values, not tuning knobs. Architecture: - 28 new ISV slots (7 base bounds × per-param-group split where appropriate) - Per-signal P² (Jain-Chlamtac) quantile producer kernels - Diagnostic = clamp engagement (sticky flag from producer's max-comparison) - Migration in 3 layers: additive infra → atomic consumer flip → smoke Out of scope: theoretical/structural constants (Adam β, Xavier formula, attention 1/√d, hidden_dim, num_atoms). EMA rates stay as documented statistical-design parameters (half-life ≈ observation time-window). Estimated: ~2-3000 LOC across 3 layers, 1-2 weeks, 1 L40S smoke. Awaiting user spec review before writing implementation plan. 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.