4f71ab32ae
Phase E.0 Task 7c. Ran phase_e_random_baseline against the fitted L1 FillModel on 500K MBP-10 snapshots from ES.FUT 2024-Q1. Completed in ~2 minutes (snapshot load dominated; episode loop ~150ms total). Results: mean reward = -5185.13 std reward = 4952.85 p05 = -13972.31 p25 = -7251.85 p50 (median) = -2804.56 p75 = -1787.90 p95 = -954.75 (best 5% of random episodes still lose) kill threshold = +4720.57 (= mean + 2σ; E.1 DQN must exceed) avg fills/ep = 139.22 (~1 fill every 4.3 steps) These numbers feed ISV slots: 547 (RANDOM_BASELINE_MEAN_INDEX) = -5185.13 548 (RANDOM_BASELINE_STD_INDEX) = 4952.85 Interpretation: the broken fitter (β_spread = -40 → near-zero limit fill probability at typical spreads) causes the random policy to over-rely on market orders, paying full spread + fee on every flip. With 139 fills per episode this compounds into the strongly-negative baseline. The baseline is *still meaningful* — the DQN will face the same env and the same fill model, so a DQN that beats this learns something real. Open follow-up for Phase E.1: regularise fit_poisson (add L2 penalty on β to prevent runaway β_spread on wide-spread tail samples), then re-run both Task 5 and Task 7. Until then, the current baseline is the operational reference point.
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.