ad5b29e652
Per docs/superpowers/specs/2026-05-31-c51-atom-span-math-validation.md, the binding constraint on atom_max during training is the dynamic Bellman bound `atom_max ≥ WIN + γ × atom_max`, not the overstated fixed-point `WIN/(1-γ)`. The math validation against local b=128 smoke confirmed atom_max can be 4.5× the fixed-point bound yet train cleanly (qpa=+0.969 at step 999). This commit adds derived diag fields under `risk_stack.atom_calibration` to expose the bound directly: - win_bound, atom_max, gamma (inputs) - dynamic_bound = WIN + γ × atom_max (the binding constraint) - atom_max_headroom (=atom_max - dynamic_bound; >0 = self-consistent) - popart_sigma, v_target_max_3sigma (statistical V_target estimate) - atom_max_over_3sigma (resolution waste ratio) These let future cluster runs measure CURRENT design's over-sizing empirically (smoke step 999 showed atom_max ~5× larger than 3σ of V_target requires — wasteful but safe). Future iterations can use this data to safely tighten atom_max anchor toward V_target_max without speculating about which design works. Pure additive diag — no kernel changes, no behavior change. Pulls values from existing ISV slots (RL_REWARD_CLAMP_WIN_INDEX, RL_C51_V_MAX_INDEX, RL_GAMMA_INDEX, RL_POPART_SIGMA_INDEX). Validates the math from 2026-05-31-c51-atom-span-math-validation.md empirically in every cluster run going forward.
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.