da1dd92bf8
Supersedes the 2026-05-19 deployability spec (commit 07d5de504). The
prior spec assumed CfcTrunk::save_checkpoint was the producer-side
wiring point — discovered at execution time that alpha_train trains via
PerceptionTrainer (full v2: VSN + Mamba2 ×2 + LN ×2 + attn-pool + CfC +
heads), not the simpler CfcTrunk. The existing LOB backtester loads
CheckpointV1 envelopes that only know about CfC weights, so there is no
producer for a checkpoint containing the full v2 model.
New scope: one bigger spec covering refactor + deployability end-to-end.
Phase 1 (X0–X19, code commits): grow CfcTrunk to own the full v2
inference graph; restructure PerceptionTrainer to wrap a trunk + add
training-only state (grads, AdamW). Discipline: bit-equivalence golden
fixture (X0) gates every refactor commit (X1–X11). CheckpointV2
envelope (X12) replaces V1. Verdict emitter (X17) reuses the tiered
classification (Pass-robust / Pass-nominal / Fail-inconclusive / Fail /
Fail-degenerate) from the superseded spec.
Phase 2 (Argo runtime): production training → smoke gate → threshold
pre-registration → 560-cell deployability sweep → verdict + memory
update.
Hard gate before Phase 2: post-refactor fold-0 smoke must reproduce
recorded 3-fold A/B numbers (best_mean_auc 0.7529, best_h6000 0.7639,
both within ±0.010 absolute) from project_ml_alpha_v2_ab_verdict
memory. Prior spec marked SUPERSEDED in its header, kept in history as
audit trail.
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.