jgrusewski 0fe825a8c5 fix(rl): decouple C51 atom span from reward-clamp ceiling
Spec: docs/superpowers/specs/2026-05-30-c51-atom-span-decouple-from-clamp.md

Diagnosed in cluster v5 (alpha-rl-rjsjq SHA 7064c9269) at step 371:
c51_v_max ratcheted up 1 → 24 → 53 → 859 → 1938 while WIN clamp swung
4910 → 568 → 7540 → 44 (volatile from MARGIN saturation). The slow
EWMA tracking win_bound time-averaged the early spikes, locking
atom span at extreme-value magnitude. Δz reached ~184 per atom — Q
could no longer discriminate +$500 from +$5000 wins.

Root cause: atom-span target was anchored on `MARGIN × pos_max_ema`,
where pos_max is the EXTREME-VALUE statistic across b=1024 batch
elements. The clamp bound (rightly fat-tail) and the atom span (should
be typical-magnitude) were treating two distinct concerns as one.

Fix F: anchor atom span on `atom_margin × mean_abs_pnl_ema` — true
central-tendency signal — while leaving the reward clamp on
pos_max_ema. The clamp can still admit fat-tail wins ($42k → +245)
and V regression (scalar head) retains the magnitude for PPO advantage,
but Q's CATEGORICAL distributional support now sizes itself for the
trades where most learning happens.

New ISV slot 685 (RL_C51_ATOM_MARGIN_INDEX, bootstrap 10.0).
RL_SLOTS_END 685 → 686.

Validation (local b=128 1k smoke):
  metric          | pre-Fix-F      | with Fix F
  v_max final      | 1929           | 86.6        (22× tighter)
  Δz per atom      | 184            | 8.7         (resolution restored)
  qpa final        | +0.957         | +0.958      (Q-π alignment preserved)
  mean_active      | +$47k          | +$34k       (within smoke variance)

13/13 risk_stack_invariants + 20/20 trade_management_kernels +
integrated_trainer_smoke pass.

Tradeoff documented in spec: Q loses fat-tail categorical magnitude
(rewards beyond ±87 project to top/bottom atom), but V regression
preserves it. Acceptable for the lottery-ticket strategy where typical-
magnitude resolution matters more than fat-tail magnitude precision.
2026-05-30 23:52:43 +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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