47a605e4c5
Replaces CheckpointV1 with CheckpointV2 — covers the full v2 inference graph: VSN, Mamba2 stacks 1+2 (in/a/b/c/out weights + biases), LN_a/LN_b, attention-pool, CfC (now v2-shaped via cfc_n_in=HIDDEN_DIM), and the full GRN heads (10 tensors: w1/b1, w2/b2, w_gate/b_gate, w_main/b_main, w_skip/b_skip). save_checkpoint reads every trunk weight tensor via memcpy_dtoh and packs into the CheckpointV2 bincode envelope. load_checkpoint peeks the version first (CheckpointVersionProbe), rejects non-2 versions, deserialises into CheckpointV2, validates n_in / n_hid / cfc_n_in / mamba2_state_dim match the supplied cfg, and uploads each weight tensor with size-checked memcpy_htod. V1 envelopes hard-rejected — alpha_train never produced V1 files, so no migration. The old V1-shaped roundtrip test is removed; new V2 round-trip test will land alongside the alpha_train wiring (X14). Verification: - perception_forward_golden: PASS (max_diff = 0.000000) - ml-alpha lib tests: 34 pass - fxt-backtest binary builds clean Per spec §1.2 (X12).
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.