jgrusewski 3e5e3ff206 feat(dqn-v2): D.6 plan_isv[6] = remaining_fraction
Adds 7th plan_isv dimension: PLAN_ISV_REMAINING_FRACTION = max(0, min(1,
(plan_target_bars - hold_time) / plan_target_bars)) when plan active, else
0. Exposes temporal pressure to the policy.

State vector grows 104 → 112 (105 + 7 padding for 8-alignment).
SL_PORTFOLIO_PLAN_DIM 6 → 7. SL_PADDING_DIM 0 → 7.

Both training (experience_env_step) and val (backtest_plan_state_isv) write
the new slot identically — preserves train/val state-distribution parity.

All hardcoded stride-6 references in backtest_plan_kernel.cu replaced with
SL_PORTFOLIO_PLAN_DIM. plan_isv_buf allocation updated to n_windows * 7.
Stale offset comments ([86..92)) corrected to [98..105) across all files.

No behavioural change to existing dimensions. New signal is additive.

Plan 2 Task 6A. Spec §4.D.6.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-24 20:36:46 +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
No description provided
Readme 849 MiB
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Cuda 7.7%
Python 1.3%
Shell 1.1%
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