jgrusewski 36ab50814e feat(alpha): alpha_linear_q kernels + launchers for Task 12 DQN smoke
Phase E.1 Task 12a. Three new CUDA kernels for the H=600 DQN smoke
(Task 12 proper) that lands in a follow-up commit:

  alpha_linear_q_forward_kernel    Q = X · W^T + b
  alpha_linear_q_grad_kernel       dW, db sparse MSE-TD over taken actions
  alpha_linear_q_sgd_step_kernel   element-wise params -= lr · grad

Architecture: single linear layer, no hidden layer. The Phase E state
vector has meaningful direct features (alpha_logit, spread_bps, position,
ofi_sum_5, …) so linear Q can capture real relations like Q[Buy] ∝
alpha_logit. If linear can't pass the kill-criteria gate, no architecture
upgrade will save it — and the smoke proceeds with NoisyNet escalation
per the plan.

Sparse gradient: only the taken action contributes (standard DQN TD
loss). No atomicAdd needed — one thread per (i, j) loops over the batch
and adds only when actions[b] == i.

GPU contract:
  - No host branches inside any kernel (graph-capture compatible)
  - No atomicAdd (per feedback_no_atomicadd)
  - All compute on GPU (forward, grad, weight update)
  - Tiny launch overhead — fits per-step (batch=64 forward = 576 threads,
    1 block; grad = 99 threads, 1 block)

Three pub(crate) Rust launchers in alpha_kernels.rs match the
launch_apply_pearls pattern. Cubin embedded via include_bytes!.

Smoke test `linear_q_forward_grad_sgd_round_trip_matches_hand_math`
exercises all three kernels end-to-end on a small (batch=2, state_dim=2,
n_actions=3) case with full hand-math:

  Forward:  Q = [[2.1, 3.2, 0.3], [4.1, 5.2, 0.3]] ✓
  Grad:     dW = [[-1.8, -2.7], [0.8, 1.0], [0, 0]]
            db = [-0.9, 0.2, 0] ✓
  SGD:      W' = [[1.18, 0.27], [-0.08, 0.90], [0, 0]]
            b' = [0.19, 0.18, 0.30] ✓

All within 1e-4 tolerance. `cargo test -p ml --lib alpha_kernels`:
5/5 pass on RTX 3050 Ti in 2.04s (compile witness + 4 GPU smokes).
Audit doc docs/isv-slots.md updated per Invariant 7.
2026-05-15 15:25:23 +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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