jgrusewski aee11f3210 feat(dqn): SP3 — slot 36-47 diagnostic accessors (Task B1)
Adds pub(crate) accessor methods on GpuDqnTrainer and pub accessors
on GpuIqnHead exposing device pointers for the SP3 Mech 5 threshold
checks at slots 36-47:

  GpuDqnTrainer (pub(crate)):
    36: trunk_adam_m_ptr/_len    — m_buf trunk slice (tensors [0..13))
    37: value_adam_m_ptr/_len    — m_buf value-head slice (tensors [13..17))
    38: branch_adam_m_ptr/_len   — m_buf branch-head slice (tensors [17..33))
    40: trunk_adam_v_ptr/_len    — v_buf trunk slice
    41: value_adam_v_ptr/_len    — v_buf value-head slice
    42: branch_adam_v_ptr/_len   — v_buf branch-head slice
    44: trunk_params_ptr/_len    — params_buf trunk slice
    45: heads_params_ptr/_len    — params_buf value+branch concat
    46: target_q_ptr/_len        — denoise_target_q_buf [B, 12]
    47: atom_positions_ptr/_len  — atom_positions_buf [4, num_atoms]

  GpuIqnHead (pub):
    39: adam_m_ptr/_len          — IQN Adam first-moment buffer
    43: adam_v_ptr/_len          — IQN Adam second-moment buffer

DQN Adam state is UNIFIED in m_buf/v_buf (single TOTAL_PARAMS-sized
buffer covering trunk + heads at the same offsets as params_buf).
Slot 36/37/38 (and 40/41/42) accessors return pointers into the same
buffer at offsets computed via padded_byte_offset over the existing
compute_param_sizes layout. The kernel discriminates by slot index
for threshold checks. IQN Adam state lives separately on GpuIqnHead.

Trunk/heads param-buf split (slots 44/45) uses the same offset helper
— no new buffers, no new ISV slots, no DtoD/HtoD copies. Each ptr
accessor returns self.<field>.raw_ptr() (or +offset for slices); each
len accessor returns self.<field>.len() or computed from
compute_param_sizes.

Audit doc updated (docs/dqn-wire-up-audit.md SP3 Task B1 entry).

Used by populate_nan_check_meta_v2 (Task B6) to feed the fused
kernel's threshold-check entries when extended to 24 slots. Unused
yet — wired in B6.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-30 09:53:09 +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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