jgrusewski f50a974fb6 docs(sp22): H1 falsified + H6 aux→policy state bridge design
H1 result (commit 9adbca826, smoke train-5zmkr, reverted at e8814079d):
  aux_dir_acc HD[2] = 78% with H=200 — aux head LEARNS direction at
  longer horizon. But policy WR stayed pinned at 50.1-50.2% — the
  learned aux signal does NOT propagate to policy (per
  pearl_separate_aux_trunk_when_shared_starves: aux on separate
  trunk with stop-grad to policy).

H6 design (synthesized from all v5-v11 + H1 evidence):

  Wire the aux head's directional probability into the policy STATE
  as an input feature.
    - Policy gradient flows THROUGH the feature (uses it)
    - Stop-grad blocks gradient BACK (aux trunk unaffected, pearl
      preserved)
    - Uses existing padding slot [121..128) in STATE_DIM=128 (no
      layout growth)

  Why this is the structural fix:
    - Aux PROVED directional signal is in the features (78% at H=200)
    - Policy PROVED it can't extract direction (WR=50% across all
      v5-v11 conditions)
    - Bridge connects the two without violating trunk separation
    - Information-theoretic: gives policy a feature it provably
      can't compute itself

  Test outcome interpretation:
    WR > 50.5%  → Mechanism 1 was binding (trunk separation gap)
    WR pinned   → Mechanism 2 (reward density) or Mechanism 3 (V/A
                  unidentifiability) dominates → H3 or V/A fix next

Files changed:
  - docs/plans/2026-05-12-sp22-wr-plateau-investigation.md:
      H6 added as new primary hypothesis after H1; experiment order
      revised
  - docs/dqn-wire-up-audit.md: H6 design entry with three-mechanism
      synthesis

Implementation scope (separate commit):
  1. State layout: claim slot in padding [121..128) for aux_dir_prob
  2. Aux trunk export: pull "up" probability per bar from aux forward
  3. Experience collection: write aux_dir_prob into per-bar state
  4. Stop-grad verification: confirm policy gradient blocked
  5. Trade-open persistence: latch aux_dir_prob for trade duration

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-12 20:58:13 +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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