d49fbbe4e2
User correction: curiosity is the *fix* for the ep1-peak overfitting pathology, not a hazard to defend against. Reframed §7 from "Risks" to "Design notes" — curiosity bound is a signal-relative scale, not a defensive cap. Fix the contradiction this exposed in the formula: previous `curiosity_pressure = stagnant_or_worse * curiosity_bound` went to zero when improving, which would cancel the always-on exploration the §7 narrative now relies on. Replace with permanent-floor pattern per pearl_blend_formulas_must_have_permanent_floor: curiosity_floor = 0.2 * curiosity_bound (CURIOSITY_PERMANENT_FRACTION) curiosity_dynamic = stagnant_or_worse * curiosity_bound curiosity_pressure = max(curiosity_dynamic, curiosity_floor) Now curiosity is always ≥ 20% of bound (anti-overfitting baseline) and rises toward the bound when stagnant (stagnation breaker). Updated unit-test guidance to assert pressure > 0 even at z=+10. CURIOSITY_PERMANENT_FRACTION=0.2 added to Invariant-1 fraction list. Saboteur-rising-with-improvement reframed as adversarial-load feature rather than over-stress risk.
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.