87b8303950
Per project_ml_alpha_starting_capital greenfield posture: there is no V1 to differentiate from (the V1 trunk forward was dead code, no V1 checkpoint files exist in the wild). The 'v2' prefix on every identifier was historical baggage from the migration period. Renames: - CheckpointV2 -> Checkpoint (also drops the version: u32 field — bincode either deserialises a current envelope or errors; no migration path needed) - CheckpointVersionProbe removed (was only for V1 rejection) - LAYER_NORM_CUBIN_V2 / VARIABLE_SELECTION_CUBIN_V2 / ATTENTION_POOL_CUBIN_V2 -> LAYER_NORM_CUBIN / VARIABLE_SELECTION_CUBIN / ATTENTION_POOL_CUBIN - _ln_module_v2 / _vsn_module_v2 / _attn_module_v2 -> drop _v2 suffix - smoke_load_v2_checkpoint test -> smoke_load_checkpoint - config/ml/sweep_v2_*.yaml -> config/ml/sweep_*.yaml - migration-era 'V2 weight skeleton' / 'V2 fields' / etc. comments cleaned to remove the v2 prefix Pre-existing 'v2' references in ml-backtesting CUDA files (decision_policy.cu, pnl_track.cu) are NOT touched — those refer to future planned 'v2' refinements (Portfolio mode, multi-fill averaging) from the C1-C19 commits and reflect aspirational features unrelated to this session's trunk-grows work. Verification: ml-alpha + ml-backtesting + fxt-backtest all build clean. perception_forward_golden bit-exact (max_diff = 0.000000).
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.