093feac3da
Three coordinated changes to remove anti-aligned components from the reward pipeline. Predicted Pearson trajectory based on Phase 3A audit was wrong (PopArt + hindsight only delivered +0.04 not +0.4), but the deep Phase 3B-Z audit revealed the "low Pearson" was largely a measurement artifact: raw_reward tracks ALL PnL events (including fees + partial closes via pos.realized_pnl_delta) while diag's realized_pnl_usd_cum tracks only full closures. The actual training reward IS aligned with total realized PnL evolution. ## Changes * Slot 753 RL_SURFER_SCAFFOLD_WEIGHT_INDEX bootstrap 1.0 → 0.0 (Phase 5 hold-bonus/entry-cost/short-hold-penalty/long-ride-bonus shaping disabled — was the dominant anti-aligned contaminant per Phase 3A audit and cluster 2A-E evidence of +$201M shaped vs -$330M actual). * New ISV slot 793 RL_POPART_NORMALIZE_ENABLED_INDEX bootstrap 0.0 (PopArt batch normalization disabled — was found to sign-flip rewards under adverse batch composition). * Hindsight injection (rl_hindsight_inject.cu wants_inject) extended with scaffold_w > 0.5 condition (when slot 753 = 0, hindsight off). Semantically pairs the two training scaffolds. ## Verification * 13/13 multi_head_policy_invariants PASS (no regression). * FOXHUNT_USE_MULTI_HEAD_POLICY=0/1 ./scripts/determinism-check.sh --quick: both exit 0. * Local single-seed mid-smoke (b=128, seed 42): - eval pnl: -$4.6M (vs Phase 0 baseline -$4.46M, within noise) - 14,691 trades training / 3,268 eval (healthy, not paralyzed) - action_entropy 1.82 at step 1999 (below uniform 2.40, learning) - 0 NaN, exit 0 ## Pearson interpretation (Phase 3B-Z audit) The Pearson 0.38 between raw_reward and pnl_step_close_usd_delta is a structural mismatch between two valid PnL views (all events vs close events only), NOT an anti-alignment. raw_reward at slot 753 = 0 is the delta of pos.realized_pnl which already aggregates the events we care about for total-USD optimization. Cluster verification (Phase 3C, next) will reveal whether the removal of Phase 5 + popart + hindsight produces a base policy that can learn at scale. The 2A-E cluster catastrophe (-$330M) was driven by Phase 5 shaping that's now disabled. Plan: docs/superpowers/plans/2026-06-03-reward-alignment-gae- combined.md Linked pearls: * pearl_reward_signal_anti_aligned_with_pnl * pearl_phase5_term_4_is_almost_potential * pearl_popart_blind_to_session_signals * pearl_pure_pnl_mode_starves_b16_controllers Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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.