55aeddaebd
L2 anchor regularization toward initialization (axis B). Anchors
horizon_tokens + Q + MoE experts toward their init values to prevent
the calibration drift observed in v1 (where val_loss climbed as α
opened past epoch 1 in 2 of 3 folds).
KERNEL (`anchor_l2_fwd_bwd`):
loss_out = λ · Σ_i (p[i] − p_init[i])²
grad_p[i] += 2λ · (p[i] − p_init[i])
- Warp-shuffle reduce; one block per parameter group; strided thread
loop over n. Cross-warp reduce uses one __syncthreads.
- Coalesced grad write via stride loop.
- λ passed as device-side [1]-buffer (host writes scalar before launch
— capture-safe).
CONTROLLER (`trainer::anchor_controller::AnchorController`):
- Signal-driven λ floor: λ_floor = ‖p_init‖ / (100 · √numel).
Cross-fold-persistent per pearl_kelly_cap_signal_driven_floors.
- Wiener-α smoother (α = diff_var / (diff_var + sample_var + ε))
on val_loss change; α floored at 0.4 per
pearl_wiener_alpha_floor_for_nonstationary.
- λ blends toward target = |ema_change|·scale with α; floored at
λ_floor per pearl_blend_formulas_must_have_permanent_floor.
- First-observation bootstrap (sentinel state replaced directly on
first step) per pearl_first_observation_bootstrap.
- 4/4 unit tests PASS: signal-floor init, bootstrap returns floor,
floor protection across 1000 steps, λ_max cap.
NUMGRAD VERIFICATION (RTX 3050 sm_86):
anchor_l2_numgrad PASSES with closed-form parity (machine precision)
and central-difference parity (4 random positions) within 5e-2 rel.
Co-Authored-By: Claude Opus 4.7 <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.