71aade18cf
Component 5 / Kernel 1 of the SP20 design — central state-tracker
that updates 8 Wiener-α EMAs per training step:
- 4 ISV slots: ALPHA_EMA (511), WR_EMA (512),
HOLD_PCT_EMA (515), HOLD_REWARD_EMA (516)
- 4 internal: trade_duration_ema, aux_conf_p50_ema,
aux_conf_std_ema, aux_dir_acc_ema (private
scratch consumed by Phase 1.3 controllers)
Per-EMA i32 observation counters (mapped-pinned obs_count[8])
handle the pearl_first_observation_bootstrap sentinel transition
correctly even when 0.0 is a legitimate observation (e.g.,
first-loss WR=0). count==0 ⇒ replace, count>0 ⇒ Wiener-blend at
α = 0.4 (WIENER_ALPHA_FLOOR per
pearl_wiener_alpha_floor_for_nonstationary).
Phase 1.2 lands kernel + launcher + 5 tests (4 GPU oracle, 1
floor lock) + build entry + audit doc atomically per
feedback_no_partial_refactor. Production wire-up (Phase 1.4)
deferred — kernel is dead code until then.
Verified on RTX 3050 Ti (sm_86):
- 4 GPU oracle tests pass: first_observation_replaces_sentinel,
wiener_alpha_converges_to_long_run_mean,
hold_reward_ema_gated_on_hold_bars,
per_step_emas_fire_unconditionally
- 4 launcher unit tests pass (constants + struct sanity)
- 1 floor-lock test pass (WIENER_ALPHA_FLOOR == 0.4)
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.