47c8b783c4
Companion to Plan 5 Task 1A (multi-seed Argo DAG). Adds the post-run aggregation pipeline: - scripts/gather-multi-seed-metrics.sh: thin wrapper that pulls Argo logs for every workflow tagged foxhunt-tag=<tag>, concatenates them into /tmp/all-logs-<tag>.txt, then dispatches to the Python aggregator. - scripts/aggregate-multi-seed-metrics.py: stdlib-only HEALTH_DIAG parser. Recognises both bare and JSON-envelope-wrapped HEALTH_DIAG[<epoch>] lines, parses every <block>[<key=val> ...] segment, and emits per- (epoch, metric_name) mean / std / median / min / max across streams (where each stream is one (seed, fold) training run, attributed by pod-name prefix when present, else by epoch-rewind detection — naive epoch=0 trigger over-segments the multi-line per-epoch HEALTH_DIAG output, fixed by requiring epoch < last_epoch to start a new stream). Output JSON schema matches the plan example (top-level: tag, multi_seed, folds, warmup_end_epoch, streams_seen, aggregates; per-entry: epoch, mean, std, median, min, max, n_samples). - scripts/aggregate-norm-stats.py: stdlib-only merger for norm_stats_foldN_seedM.json files. Per-fold output collapses N seeds into mean/median/std/std_dispersion arrays consumed by the `evaluate` step's inference normaliser. - scripts/requirements.txt: declares numpy/scipy/matplotlib for downstream Plan 5 T4-T5 tier-validation/plotting scripts. The aggregators themselves are stdlib-only (statistics module). Smoke-validated on /tmp/p4t6-cleanroom-smoke.log: detects 3 streams (matching the 3 fold runs in that log), 90 unique metrics, n_samples=3 per (epoch, metric), schema matches plan. 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.