a252119fd4
Reasons: - h100-sxm: Scaleway account quota is 0/0 for the SXM instance type (cp_servers_type_H100_SXM_2_80G). The pool was perpetually trying to maintain size=1 with a creation_error node, which JAMMED the cluster autoscaler. That blocked L40S scale-up entirely during today's alpha-perception cluster run. - h100x2: more expensive than current workloads justify. Every training path (alpha perception, future PPO) fits on either the single-GPU H100 or L40S. Removed: - Two `scaleway_k8s_pool` resources from infra/modules/kapsule/main.tf - Six variables (enable + type + max_size for each pool) - Two outputs (pool_id for each) - Corresponding inputs in infra/live/production/kapsule/terragrunt.hcl The live cluster has the SXM pool stuck node manually deleted via `scw k8s node delete` (this commit-session); the pool resource itself will be destroyed on next `terragrunt apply`. Post-cleanup pool inventory: - platform (DEV1-L × 3) - ci-training-h100 (H100-1-80G, max 1) <- regular single-GPU - ci-training-l40s (L40S-1-48G, max 1) <- primary training target - ci-compile-cpu (POP2-HC, max 4) - ci-compile-cpu-hm (POP2-HM, max 1) 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.