737c2c1305
Scaleway BSSD PVCs (cargo-target-*, sccache-*, feature-cache-pvc,
bin-cache, training-data-pvc, test-data-pvc, platform service PVCs) are
single-zone-locked at create time. The cluster's node pools in main.tf
set `region` but no `zone`, so each autoscale event picks a zone
arbitrarily. When the autoscaler spins up a node in a zone that doesn't
match an existing PVC, scheduling fails with
"1 node(s) didn't match PersistentVolume's node affinity"
until the autoscaler eventually retries in the right zone. We hit this
on the HM pool creation (fixed manually with zone=fr-par-2) and again
on this commit's ensure-binary autoscale.
Track 1 (this commit): add `topology.kubernetes.io/zone: fr-par-2` to
every k8s.scaleway.com/pool-name nodeSelector entry across the 11 argo
workflow templates (34 entries total). The Kubernetes scheduler AND
cluster-autoscaler both honor topology keys when deciding placement /
provisioning — so future autoscale events will only spin up fr-par-2
nodes, and PVC binding is guaranteed.
Track 2 (future, structural): add `zone = "${var.region}-2"` to each
scaleway_k8s_pool in infra/modules/kapsule/main.tf so the pools never
provision in any other zone. Requires terraform-state cleanup (the
GitLab http backend currently has no states; the HM pool was created
out-of-band) and drain/recreate of any existing mixed-zone nodes —
deferred.
Verification: pool=N / paired=N coverage report shows 1:1 pool-name
to topology.kubernetes.io/zone entries in every file. kubectl apply -f
of all 11 templates returned "configured" for each.
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.