d2bfeaa983
Adds a tiny alpine pod (check-cache) that runs first on the platform
pool (no autoscaler delay, ~3 sec end-to-end) and probes the
training-data PVC for /data/bin/$SHA/alpha_train. Outputs:
- sha: short SHA used for binary cache keying
- cache: "hit" or "miss"
ensure-binary now has `when: cache == miss` — when the binary is
already cached for the current SHA, the entire ~4.8GB ci-builder
image pull + sccache compile cycle is skipped. Re-runs on the same
SHA now go straight from submission to training in ~30 seconds
instead of ~3 minutes.
train depends on check-cache + ensure-binary; sources the SHA from
check-cache's output (works whether ensure-binary ran or was
skipped — Argo treats `when:` skip as a satisfied dependency).
Submission script (scripts/argo-alpha-perception.sh) now pre-resolves
commit-sha=HEAD to an actual git SHA via `git rev-parse origin/<branch>`
before submission. This lets the alpine check-cache pod work without
installing git in the container.
Also removed the now-stale `ci-training-h100x2|ci-training-h100-sxm`
case branch from the SM-arch detection — those pools no longer exist
post-pool-cleanup commit a252119fd.
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.