cc55c8a25c
Plan C Phase 2 T8. The plan-prescribed pre-T2 snapshot approach was impossible (T2 had already landed); replacement strategy (ii) from the dispatch brief — behavioral parity vs an analytical Boltzmann reference computed in Rust — is used. Setup forces direction = Long (d=2) deterministically via peaked C51 logits (Long peaked at v=+0.8 atom; other directions at v=-0.5), so the kernel's Hold/Flat → mag_idx=0 short-circuit doesn't mask the magnitude branch's Boltzmann sampling. q_values are crafted with each branch (mag/ord/urg) peaked at a single bin with magnitude 1.0: Mag Q = [0.0, 0.0, 1.0] peak at Full (mag=2) Order Q = [1.0, 0.0, 0.0] peak at Market (ord=0) Urgency Q = [0.0, 1.0, 0.0] peak at urg=1 With q_range=1.0 in all three branches, tau collapses to 1.0 and the analytical Boltzmann probabilities are: P(best) = 1/(1 + 2/e) ≈ 0.5767 P(other) = 1/e/(1 + 2/e) ≈ 0.2117 Tolerance: at batch=8192 the 1-σ Bernoulli noise is ~0.0055 for p≈0.58; ±5% absolute tolerance covers ~9σ. Algorithmic divergence (e.g. an inadvertent strict-argmax substitution) would shift P(best) to 1.0 — trivially detected by the ±5% tolerance. Assertions: - dir_idx == Long for every sample (eval argmax E[Q] over peaked C51) - mag/ord/urg histograms each within ±5% of the Boltzmann reference 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.