jgrusewski bbd52c3aa7 feat(sp22-vnext): FoldReset registry entries for B5b/C collector buffers
Two collector-side device buffers used by the K=3 trade-outcome head
were missing FoldReset registry coverage:

  - prev_aux_outcome_probs [alloc_episodes × 3]
    TRUE stale-read risk. Producer writes end-of-step; consumer
    (experience_state_gather) reads start-of-next-step into
    state[121..124). Without FoldReset the new fold's step-0 state
    gather would inject the previous fold's last-step softmax probs
    into the first batch's state slots.

  - exp_aux_to_input_buf [alloc_episodes × 262]
    Cleanliness-only. Concat kernel overwrites all 262 columns every
    step before the K=3 forward reads them, so no steady-state stale-
    read risk. Registered for parity with the rest of the K=3
    pipeline + to satisfy feedback_registry_entries_need_dispatch_
    arms (the pin test asserts every registry entry has a matching
    dispatch arm in reset_named_state).

Both fields promoted to pub(crate) on GpuExperienceCollector so
reset_named_state can reach them. Matching dispatch arms added with
the standard memset_zeros pattern (is_win_per_env / hold_baseline_
buffer style).

Tests: All 10 state_reset_registry tests pass, including the critical
every_fold_and_soft_reset_entry_has_dispatch_arm pin test that walks
the dispatch body and validates parity with registry entries. Full
lib suite 1015/1 (the failing test is the pre-existing
test_dqn_checkpoint_round_trip NoisyLinear flake — pred1/pred2 sign
mismatch surfacing ~30-50% of full-suite runs, documented in
project_sp22_h6_vnext_resume memory as unrelated to this work).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-14 13:12:34 +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
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Rust 88.2%
Cuda 7.7%
Python 1.3%
Shell 1.1%
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