72684ed3ef
reset_session_state was resetting four EMAs that calibrate signal scale rather than predict behavior: - RL_POS_SCALED_REWARD_MAX_EMA_INDEX (clamp scale anchor) - RL_NEG_SCALED_REWARD_MAX_EMA_INDEX (clamp scale anchor) - RL_REWARD_CLAMP_CLIP_RATE_EMA_INDEX (clamp feedback) - RL_POPART_MAX_ABS_REWARD_EMA_INDEX (F4 envelope) At the train→eval fold boundary this disabled the reward clamp (cap = pos_max_ema × ratio = 0) and let eval's first ~10 trade outliers blow up the popart envelope σ 1500× (57 → 4471) over ~14 steps. PPO surrogate (A_unnorm = σ × A_norm) ran with catastrophically mis-scaled gradients for the next ~1000 eval steps, accounting for the bulk of the -$185M eval loss at alpha-rl-6kghr fold 1. Refines pearl_adaptive_carryover_discipline: RESET PREDICTIVE EMAs (Kelly, dd, recency) but PRESERVE NORMALIZATION EMAs. Spec: docs/superpowers/specs/2026-05-31-eval-boundary-normalization-preservation-design.md Pearl: pearl_popart_reset_at_eval_boundary_shocks_normalization.md 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.