jgrusewski b508c38529 plan(crt-a): remove fallbacks — greenfields, resolve properly
Per user direction: no fallbacks, greenfields, kernel updates acceptable.

Changes:
  1. Task A0.5 ADDED — pre-planned task that fires if A0 investigation
     finds forward_only is stateless K-window (Case 2) or has periodic
     reset (Case 3). Refactors PerceptionTrainer to maintain persistent
     SSM state and expose forward_step(snapshot, b) that advances by 1
     event. Bit-identical to forward_only of equivalent window per a
     new golden test in incremental_forward.rs.

     This is NOT a fallback — it's a pre-planned task whose execution
     is determined by actual investigation outcome. Case 1 → A0.5 is
     a no-op. Case 2 or 3 → A0.5 fires in full. Either way the plan
     proceeds without user-approval pause.

  2. Task A2 reframed — Wiener-α conviction-EMA is a "load-bearing
     component of continuous control," not a "minimal Phase B subset."
     Without smoothing, event-rate trading is structurally incoherent.

  3. Task A2 test fallback REMOVED — instead of "if helpers don't
     exist, omit the unit test," the plan now says "add the helpers
     properly to the public API." Step 1 audits the LobSimCuda public
     API and adds missing accessors. Greenfields — accessors stay on
     the API permanently if testing needs them.

  4. Task A5 (conditional hyperactivity mitigation) DELETED. A2 is
     designed correctly the first time with Wiener-α floor at 0.4. If
     A4 cluster smoke shows hyperactivity, that's an A2 bug to fix
     properly, not a tuning knob to nudge.

  5. Scope contract updated — Phase A vs Phase B split is by code-path
     responsibility (continuous control infrastructure vs multi-horizon
     signal-driven policy), NOT by "shipping less to be safe."

  6. Notes for the Implementer — explicit "No fallbacks" section
     replaces the implicit-fallback language. STOP-and-notify-user for
     Case 2 deleted (A0.5 handles it inline).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-20 17:37:24 +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%
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