878cc9ba72
The F-3c commit (cdd3dc6ed) pushed `aux_trade_outcome_ce_ema` into
the regression-detection metrics vec but never wired a `tracing::
info!` console emit. Smoke `train-q5k5k` ran clean past the
rollout + post-rollout phases — but `argo logs train-q5k5k | grep
aux_trade_outcome` returned zero hits, so the operator had no way
to read the K=3 head's CE EMA trajectory.
Fix: extend the existing K=2 aux HEALTH_DIAG print
HEALTH_DIAG[N]: aux [next_bar_mse=… regime_ce=… w=…]
to include `trade_outcome_ce=…`:
HEALTH_DIAG[N]: aux [next_bar_mse=… regime_ce=…
trade_outcome_ce=… w=…]
Reads ISV[538] via the same `trainer.read_isv_signal_at(...)`
accessor the regression-detection vec uses — single source of
truth, no duplicated mirroring.
Expected operator-visible trajectory across smoke epochs:
Epoch 0 (cold-start, Pearl A sentinel): 0.000
Epoch 1+ (first non-zero bootstrap): ~1.099 (= ln(3))
Healthy learning: 0.5 - 0.7
Pinned at ln(3) for many epochs: head can't learn
cargo check -p ml clean. Audit doc updated.
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.