21b6058e07
Folds two new producer signals into SP4 scope, eliminating the earlier "carved out" exception: WEIGHT DECAY (per param-group, 7 new ISV slots): λ = |w·g| / max(||w||², ε) Derived from equilibrium analysis of d/dt(||w||²) = 2(w·g) - 2λ||w||². The equilibrium gradient-projection-onto-weight-direction divided by weight norm. Same theoretical-derivation category as Adam β values. EMA half-life α=0.005 (~140 steps). Bootstrap 1.0. L1 LAMBDA (trunk only, 1 new ISV slot — NOVEL PEARL): λ = (mean(|g|) / mean(|w|)) × D where D = (log K - H_observed) / log K is gradient-direction entropy deficit and H_observed = -Σ p[i]·log p[i], p[i] = ||g[:,i]|| / Σ ||g[:,j]|| L1 regularization-strength derives from gradient-direction entropy deficit across input features. When gradient is uniform across features (D≈0): network hasn't differentiated, λ=0 (no pruning). When gradient concentrates on few features (D≈1): network has identified what matters, λ ramps up to prune the rest. Self-curriculum — L1 strength tracks the emergence of feature differentiation. This extends pearl_adaptive_moe_lambda (regularization strength = EMA- tracked deficit of regularized quantity) to feature-redundancy domain. Pearl-name candidate (post-validation): pearl_signal_driven_regularisation_strength. Bootstrap λ=0 means cold-start = no L1 pruning; ramp-up only after gradient differentiates. Worst-case behavior is "L1 disabled" — graceful. Total ISV slot count: 28 → 36. Total producer kernels: 28 → 36. Effort estimate: 3000-4000 → 3500-4500 LOC, 1-1.5 → 1.5-2 weeks. Acknowledged limitations updated: removed item #8 (carve-out) since no carve-outs remain. Added items for L1 pearl novelty (untested) and weight decay equilibrium-formula non-stationarity. Both have graceful worst-case behavior and explicit validation criteria (#10 and #11) to detect anomalies. SP4 now closes 100% of the magnitude/regularization surface — no hardcoded scalars remain in the entire chain. AdamW config fields for weight_decay and l1_lambda removed from HyperParams to prevent accidental hardcoding regression. 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.