jgrusewski e1aef53373 docs(sp20): plan accuracy errata for Phase 2 (Tasks 2.0/2.1/2.2/2.3/2.4)
Append a "Plan Accuracy Errata" section to the SP19+20 plan
documenting the 6 deviations from the original Phase 2 plan that
emerged during implementation. Each entry captures the gap, the
decision made, and the rationale, so future implementers see what
was actually built vs what was specified.

Gaps documented:

  1. Task 2.0 (label-at-open infrastructure) was NEW — split out from
     Task 2.2 to land buffer + alloc + reset + write site atomically
     before Task 2.2's consumer ships. Sign convention mapping detail
     captured (kernel emits {0, 1, -1}, SP20 spec uses {-1, 0, +1}).

  2. Per-env alpha plumbing was not in Task 2.2 plan scope — built in
     Task 2.2 (NOT deferred to Phase 4) to preserve the
     `feedback_no_partial_refactor` contract atomicity. Touches 5
     files in one commit.

  3. Per-bar SP18 D-leg sites — KEEP for Phase 2 per `feedback_no_stubs`.
     Plan's wording "DELETE [these helpers]" was overbroad; only the
     trade-close-site call is deleted. The phantom
     `compute_sp12_reward_with_cost` doesn't exist as a function (the
     SP12 v3 reward is the inlined block).

  4. `sp20_compute_event_reward` placement — new dedicated
     `sp20_reward.cuh` header (NOT inside `experience_kernels.cu`,
     NOT inside `trade_physics.cuh`). Mirrors the
     compute_asymmetric_capped_pnl / compute_min_hold_penalty
     header-only pattern; needed for GPU oracle test wrapper to share
     the function bit-for-bit per `feedback_no_cpu_test_fallbacks`.

  5. Task 2.3 was subsumed by Task 2.2 — the existing Path C chain
     consumes the new `alpha` field automatically once `alpha_per_env`
     is wired; no separate `sp20_emas_compute` producer call needed.

  6. `min_hold_*` kernel-arg trio cleanup deferred to Task 2.4 — the 3
     kernel args were deleted in Task 2.2 (per `feedback_no_hiding`)
     but the upstream producer chain (`min_hold_temperature_update_kernel`,
     ISV[460], `read_min_hold_temperature_from_isv`, `config.min_hold_*`)
     deferred to Task 2.4 because it touches SP14 ISV slot registry +
     StateResetRegistry + ISV layout fingerprint bump.

Implementation-level details remain in `docs/dqn-wire-up-audit.md`
Task 2.0 / 2.1 / 2.2 entries; this errata is the plan-level
"what was actually built vs what was specified".

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-10 00:30:33 +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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