316db416bb
Per Class A audit: MIN_HOLD_TARGET=30.0f hardcoded was creating a
deterministic gradient pushing trades toward 30-bar holds regardless of
edge expiry. User's trading frequency is between HFT-MFT and varies by
regime; a 30-bar fixed target kills MFT-frequency alpha when the
optimal hold for current data is shorter (or longer).
The producer slot ISV[AVG_WIN_HOLD_TIME_BARS_INDEX=451] already exists
from SP14 Layer C Phase C.4b (commit 3b71d2183) — Pearl-A-bootstrapped
Welford EMA of observed winning trade hold times. Wiring fix only.
Cold-start fallback: when slot still at sentinel (no winning trades
observed yet), use MIN_HOLD_TARGET=30.0f as safety floor. Once a
winning trade closes and the EMA bootstraps, the adaptive value
takes over.
Validity window: isv_hold_target > 0.0f && < 240.0f; outside window
falls back to min_hold_target param (= MIN_HOLD_TARGET=30).
Added #define AVG_WIN_HOLD_TIME_BARS_INDEX 451 to state_layout.cuh
(C-side mirror of sp14_isv_slots.rs:97).
Per feedback_isv_for_adaptive_bounds: every adaptive bound in ISV.
Fixes the third Class A P0 hardcoded constant.
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.