d482efc416
v8 smoke (train-96wfk) confirmed win_conc=0.0000 across all 9 cycles
even when PF crossed 1.0. The Phase 8.2/8.4 threshold tweaks couldn't
fix it because the underlying formula `top_mean / all_mean` is
sign-unstable around `all_mean=0`. PER alpha boost path was dark for
any cold-start policy below PF=1 — exactly the regime where the
boost matters most.
Old: if all_mean ≤ (0.01 * pnl_std).max(0.0) { return 0.0; }
top_mean / all_mean
→ guard trips at PF≤1 → 0.0 (every v8 cycle)
New: ((top_mean - all_mean) / pnl_std).max(0.0)
→ z-score separation: how many pnl_stds above the overall mean
does the top decile sit? By construction top_mean ≥ all_mean,
so separation is non-negative even when all_mean is negative.
→ bounded [0, ∞), scale-invariant, profitability-agnostic.
Expected v9 magnitudes (v8 cycle-1-like inputs):
top_mean ≈ 5e-6, all_mean ≈ 1e-7, pnl_std ≈ 1.08e-5
separation = (5e-6 - 1e-7) / 1.08e-5 ≈ 0.45
Healthy PF>1 cycles likely 0.2..1.5 range.
Pearls honoured:
- pearl_controller_anchors_isv_driven: pnl_std is the signal-driven
scale anchor (replaces hardcoded all_mean denominator)
- feedback_isv_for_adaptive_bounds: z-score formulation removes the
hardcoded multiplier dependency that motivated 8.2 and 8.4's
threshold adjustments
- feedback_no_quickfixes: structural reformulation, not a threshold
tweak (8.2 and 8.4 already showed threshold tweaks couldn't fix
the sign-instability)
Verification:
cargo check -p ml --features cuda # clean
Co-Authored-By: Claude Opus 4.7 (1M context) <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.