82572ff3bd
Formal proof of why the current `atom_max = EWMA(WIN_bound)` design trains successfully despite empirical violations of the "structural minimum atom_max ≥ WIN/(1-γ)" claim made in 2026-05-30-c51-atom- resolution-design-alternatives.md. Key findings (proven by local smoke + cluster v8 trajectory): 1. The fixed-point bound `WIN/(1-γ)` is overstated as a structural constraint. Empirical: smoke step 999 atom_max = 4.5× the bound, system trains healthy (qpa=+0.969). 2. The actually-binding constraint is the dynamic bound: `atom_max ≥ max V_target_observed ≤ WIN + γ × V_max_observed` Self-consistent and satisfied with margin 23 at smoke step 999. 3. Both speculative alternatives (Fix F = mean_abs_pnl anchor, Fix F-v2 = V_target_observed anchor) have closed-form instability at γ(1+ε) ≥ 1 — for γ=0.99 only ε ≤ 0.01 stable, no resolution benefit over current design. 4. Current design is *conservative* (atom_max ~5× larger than V_target 3σ requires) but *correct* — slow EWMA hysteresis absorbs WIN transients and keeps Q-V Bellman self-consistent. Includes empirical trajectory data: - Local smoke (b=128, HEADd57bee054, 1000 steps): atom_max 1798→1468, qpa +0.65→+0.97, clip_rate 5-11%, dynamic bound satisfied with margin 23 at convergence. - Cluster v8 (b=1024, alpha-rl-vxbpq @6d4a962e5): popart_σ collapses 56→1.69 from step 100→16830, qpa stays >+0.93, pnl_cum $164M. Saves pearl_atom_span_dynamic_vs_fixed_point for future sessions. Diagnostic emit of V_target_max_observed proposed (§5) but deferred — current signals (WIN + γ × atom_max) already provide the bound indirectly via JSONL. Real-time V_target_max is a nice-to-have for future calibration work, not required to validate the current design.
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.