a5f23b28f0
Third root cause of the apparent magnitude collapse in smoke-test data:
HEALTH_DIAG reads monitor.action_counts to compute dist_q/h/f and
ent_mag/ent_dir, but the block that populated action_counts from the
GPU summary ran AFTER HEALTH_DIAG. Every epoch saw all-zero counts so
dist_q=dist_h=dist_f=ent_mag=ent_dir=0 in every log line, and the
last_magnitude_dist cache the smoke test reads was always zeros.
Moved the GPU-summary download + monitor population block up above the
HEALTH_DIAG preparation block. Signal is now real per-epoch:
dist_q=0.60 dist_h=0.15 dist_f=0.25 (was 0/0/0)
ent_mag=0.83 ent_dir=0.89 (was 0/0)
magnitude_distribution smoke test now PASSES on local RTX 3050 Ti at
20 epochs. Final: Quarter=0.598 Half=0.154 Full=0.249 — all above 5%
smoke threshold.
Combined with 2fb30f098 (9→12 bin cascade + magnitude-preference
denominator restricted to tradable directions), this resolves the
Track-1 "magnitude collapse" pathology. The collapse was never a
policy-learning failure — it was a monitoring visibility + ordering
bug that made every reading look like mag=0 100%.
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.