jgrusewski e0d3abd9d2 plan(sp11): patch all 5 violations + 2 gaps from review
feedback_no_htod_htoh_only_mapped_pinned (tests not exempt):
  - All test fixtures converted from htod_copy/dtoh_sync_copy to
    MappedF32Buffer with host_slice / host_slice_mut access.
  - Novelty hash buffer + projection matrix changed from
    cudarc::CudaSlice<f32> + alloc_zeros to MappedF32Buffer.

feedback_no_cpu_forwards (CPU is read-only):
  - Projection matrix initialization changed from host-side StdRng +
    host_slice_mut writes to a one-shot GPU init kernel
    (novelty_simhash_proj_init_kernel) using Philox seeded from
    config.seed. No host RNG, no host writes.

feedback_no_cpu_compute_strict (saboteur multiplication):
  - B1 step 5 reverted from Rust-side `read_isv_slot * scale` to
    GPU-side: pass base scale + ISV pointer + slot index to the
    saboteur perturbation kernel; multiplication happens on-device.

feedback_trust_code_not_docs (grad-ratio terminology):
  - Spec §3.3.1 "per-component grad EMA" was wrong — SP4 grad-balancer
    is per-branch (4 slots), not per-reward-component. Renamed:
      reward_component_grad_ratio_compute_kernel
        → reward_component_mag_ratio_compute_kernel
      REWARD_COMPONENT_GRAD_RATIO_BASE
        → REWARD_COMPONENT_MAG_RATIO_BASE
    Source: existing REWARD_POPART_EMA_INDEX..+6 (per-component reward
    magnitude EMAs from SP4 reward_component_ema_kernel). Semantic
    equivalent for the controller's exploit/diversify blend.

feedback_no_stubs (dead parameter):
  - Removed `eps_div_idx_unused` from mag_ratio kernel signature.

Saboteur engagement: missing producer specified
  - Spec §3.3.1's two-reward-arrays formulation replaced with single
    `saboteur_delta_reward_buf` produced by the saboteur perturbation
    kernel itself (single reward computation, diff emitted as side
    output). Engagement kernel signature simplified to one input array.

PNL_REWARD_MAGNITUDE_EMA_INDEX (slot 359): producer wired
  - mag-ratio kernel mirrors `isv[REWARD_POPART_EMA_INDEX]` to
    scratch_out[6]; chained apply_pearls_ad targets slot 359.

Replay sample kernel location specified
  - graph_utility_kernels.cu:71 (gather_f32_scalar). New sibling kernel
    `gather_replay_reward_with_curiosity` defined; replaces the existing
    scalar gather (no legacy alias per feedback_no_legacy_aliases).
    novelty_simhash_lookup runs before, novelty_simhash_update after.

A2 controller test placeholders → full GPU oracle assertions
  - Three controller tests (z=0 midpoint, weight renorm, saboteur clamp)
    have full mapped-pinned fixtures with assertions on weight sum,
    individual values, post-clamp bounds.

Plan now passes:
  - feedback_no_htod_htoh_only_mapped_pinned (tests + production)
  - feedback_no_cpu_forwards (CPU never writes/computes for GPU)
  - feedback_no_cpu_compute_strict (all multiplications GPU-side)
  - feedback_no_atomicadd (race-tolerated non-atomic, safety documented)
  - feedback_no_partial_refactor (Layer B atomic; saboteur kernel sig
    change touches all callers in the same commit)
  - feedback_no_stubs (no dead parameters)
  - feedback_trust_code_not_docs (corrected spec terminology)
  - feedback_wire_everything_up (every new field has init + producer)
  - feedback_no_legacy_aliases (old gather_f32_scalar replaced, deleted)

1447 lines, +268 from previous version.
2026-05-04 00:40:36 +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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Readme 849 MiB
Languages
Rust 88.2%
Cuda 7.7%
Python 1.3%
Shell 1.1%
PLpgSQL 0.8%
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