jgrusewski 494125c104 spec(dqn): distributional RL aggregation — UCB on C51+IQN σ for action selection
Designs the structural fix for the C51 expected-Q Hold/Flat bias exposed
by the Kelly val-Flat-collapse fix. The bias is a manifestation of a
deeper project-wide pearl:

  "Distributional RL aggregation discards uncertainty;
   action selection must restore it."

Any value head representing Q as a distribution (atoms, quantiles,
ensembles) MUST expose both E[Q] and σ(Q) to action selection. Boltzmann
on E[Q] alone produces structural bias toward low-variance actions
regardless of expected payoff — the C51+IQN Flat-attractor is one
instance of this lost-information pattern.

Fix: extract σ(Q) from BOTH C51 atoms (closed form) and IQN quantiles
(IQR/1.349), blend by loss-time weight, feed Q_eff = E[Q] + κ·σ
(κ=1.0 structural identity) to direction-branch Boltzmann ONLY.

4-phase implementation:
  Phase 0 — TDD hypothesis verification (Rust mirror functions + 5 unit
            tests including GPU integration on real checkpoint)
  Phase 1 — Audit existing reward levers (B.2, CF, PopArt, Q-target)
            via 6 unit tests; fix any bugs found
  Phase 2 — UCB integration: new compute_q_with_uncertainty kernel,
            modified action_select, Rust orchestration, project-wide
            aggregation contract in dqn-wire-up-audit.md
  Phase 3 — Verification per Plan 5 Task 5 multi-seed × multi-fold

5-layer verification gate; 8 risks with mitigations; 4 stop conditions
that halt execution and force redesign.

Existing band-aid fixes (Kelly cap, eps-floor, tau-floor) stay — they
address symptoms at different layers. UCB adds the missing aggregation
step that was the common root across all the symptoms.

Direction-branch only — magnitude/order/urgency don't have the
Flat-attractor (atom-mass collapse asymmetry).

5-7 days active work across 4 sub-plans; each gets its own
writing-plans cycle.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-26 23:24:25 +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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