e9b85df72b
At threshold=0.5 the sampler produced 209M bars from 209M trade ticks (1:1 ratio, essentially per-tick) on workflow f5wnd today. Feature extraction hit 54Gi/56Gi memory limit — near OOM. The default was wrong: with EWMA bypassed (per1aaf94306), threshold=0.5 contracts of imbalance is below the natural ES.FUT trade size, so every trade fires a bar. threshold=20 produces ~5-6M bars matching the volume-bar density baseline (5.74M bars at 100 contracts). Math: 209M / 5.74M = 36×, so threshold needs 0.5 × 36 ≈ 18 → round to 20. Three files updated atomically per feedback_no_partial_refactor: - config/training/dqn-production.toml: 2.5 → 20.0 (wgdc8 left it at 2.5) - infra/k8s/argo/train-template.yaml: workflow default 0.5 → 20.0 - infra/k8s/argo/train-multi-seed-template.yaml: workflow default 0.5 → 20.0 The 1:1 bar:tick observation alongside the price-continuity proof (22 jumps out of 209M = ~1e-7 — front-month filter works) confirms the front-month fix on this branch (6c1ab8850+3b5f17913) is correct. Threshold was the remaining mistuning. Cache-key implication: changing the threshold invalidates all existing fxcache files (per the cache-key architectural fix). Next dispatch on this branch will regenerate fxcache from scratch. 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.