a3bb040bc2
Algorithmic property test (CPU). Confirms Thompson exploration discovers KNOWN +0.005 edge in 100 iterations on a 1-state bandit, while argmax-only training never updates Q[Long]. Setup revised from plan A draft (option 3 — production-realistic): p_long initial = [0.10, 0.20, 0.40, 0.20, 0.10] (uniform, E=0, has σ) p_flat initial = [0, 0, 1, 0, 0] (δ(v=0), deterministic) Argmax with strict-> ties at E=0 → always picks Flat → never explores Long → Q[Long] stays at 0, never discovers edge. Thompson samples Long > 0 with P≈0.30 → ~30 effective updates → mean drifts toward +0.005, crosses Q[Flat]=0 within budget. Plan's prior draft (initial p_long with mean=-0.015 + p_flat=δ(0)) was calibration-bound: Thompson drift was directionally correct (-0.015 → -0.005) but didn't cross zero in 100 iters. Revised setup eliminates the artificial initial bias and matches production reality more closely (Flat = δ(0) by construction; Long starts spread from random init, then accumulates true edge). Stop condition: if Thompson e_long ≤ e_flat with this setup, the hypothesis is genuinely wrong and reward shaping must change before proceeding to Phase 2. Observed (local RTX 3050 Ti, ~0.00s test wall, ~1.57s 5-test suite): argmax : e_long=0.000000, e_flat=0.000000 (asserts e_long ≤ 0.001 OK) thompson: e_long=0.003550, e_flat=0.000000 (asserts e_long > e_flat OK) All 5 Phase 0 tests pass: 0.A bias-reproduces, 0.B inverse-CDF, 0.C IQN symmetry, 0.D Thompson-reverses, 0.E synthetic-edge.
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.