jgrusewski e27fc90b9b arch(crt-1): open_trade_state 24→64 byte expansion — atomic refactor (C1.1)
Per spec §7 (v3 architecture reset, promoted from v2 Phase B into CRT.1).
Single-cache-line layout. Every reader and writer of open_trade_state
migrates in this commit per feedback_no_partial_refactor.

New 64-byte layout (existing 24-byte fields preserved at original offsets):
  Offset  Size  Field
    0     8     entry_ts_ns                              (u64)
    8     4     entry_px_x100                            (i32)
   12     4     entry_size                               (i32)
   16     4     realized_at_open                         (f32)
   20     4     conviction_at_entry                      (f32)  ← NEW
   24     4     conviction_ema                           (f32)  ← NEW
   28    16     conviction_per_horizon_ema [4 × f32]    ← NEW
   44     4     pnl_adjusted_conviction_ema              (f32)  ← NEW
   48     4     peak_unrealized_pnl                      (f32)  ← NEW
   52     4     degradation_consecutive_events           (u32)  ← NEW
   56     4     disagreement_consecutive_events          (u32)  ← NEW
   60     1     horizon_idx_dominant_at_entry            (u8)   ← NEW
   61     3     pad

The 24-byte prefix is unchanged so downstream readers (stop_check_isv in
decision_policy.cu, max_hold event-rate check in resting_orders.cu) need
only update the stride constant. The new fields at offsets 20..63 are
populated by subsequent CRT.1 tasks (C1.2 multi-horizon conviction, C1.4
composite exit signal).

Open branch in pnl_track.cu zeros the new fields implicitly because
alloc_zeros on the device slot zero-initialises everything; subsequent
writes only touch the 0-23 byte range (existing fields). Close branch
reset-loop iterates OPEN_TRADE_STATE_BYTES which now zeros all 64.

Files changed:
  crates/ml-backtesting/src/lob/mod.rs  — pub const OPEN_TRADE_STATE_BYTES: usize = 64
  crates/ml-backtesting/cuda/pnl_track.cu  — #define OPEN_TRADE_STATE_BYTES 64
  crates/ml-backtesting/cuda/resting_orders.cu — comment + stride literal 24→64
  crates/ml-backtesting/tests/stop_controller.rs — open_trade_state_64_byte_layout test

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-20 20:25:05 +02:00

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.

Description
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