# NanoARB

## Docs

- [BacktestConfig](https://mintlify.wiki/dhir1007/nanoARB/api/backtest/config.md): Configuration for backtest engine including latency, fees, risk, and execution parameters
- [BacktestEngine](https://mintlify.wiki/dhir1007/nanoARB/api/backtest/engine.md): Core backtest engine for simulating trading strategies with realistic market conditions
- [Performance Metrics](https://mintlify.wiki/dhir1007/nanoARB/api/backtest/metrics.md): Performance metrics and statistics for backtest analysis
- [LatencyConfig](https://mintlify.wiki/dhir1007/nanoARB/api/config/latency.md): Latency simulation configuration for realistic backtesting
- [RiskConfig](https://mintlify.wiki/dhir1007/nanoARB/api/config/risk.md): Risk management configuration and limits
- [TradingConfig](https://mintlify.wiki/dhir1007/nanoARB/api/config/trading.md): Trading configuration for live and paper trading
- [Error Handling](https://mintlify.wiki/dhir1007/nanoARB/api/core/errors.md): Error types and result handling patterns
- [Core Traits](https://mintlify.wiki/dhir1007/nanoARB/api/core/traits.md): Interfaces for implementing trading components
- [Core Types](https://mintlify.wiki/dhir1007/nanoARB/api/core/types.md): Fundamental data types for high-frequency trading operations
- [LOB Feature Extraction](https://mintlify.wiki/dhir1007/nanoARB/api/lob/features.md): Extract machine learning features from limit order book snapshots
- [OrderBook](https://mintlify.wiki/dhir1007/nanoARB/api/lob/orderbook.md): High-performance limit order book implementation with 20 price levels
- [BaseStrategy](https://mintlify.wiki/dhir1007/nanoARB/api/strategy/base.md): Core strategy implementation with position tracking and P&L calculation
- [MarketMakerStrategy](https://mintlify.wiki/dhir1007/nanoARB/api/strategy/market-maker.md): Automated market-making with inventory management and quote generation
- [Signal Strategy](https://mintlify.wiki/dhir1007/nanoARB/api/strategy/signals.md): Signal-based trading with confidence thresholds and position sizing
- [Crate Structure](https://mintlify.wiki/dhir1007/nanoARB/architecture/crates.md): Detailed documentation of NanoARB's 7 core crates and their responsibilities
- [Data Flow](https://mintlify.wiki/dhir1007/nanoARB/architecture/data-flow.md): Detailed walkthrough of how market data flows through NanoARB from CME packet to order execution
- [Architecture Overview](https://mintlify.wiki/dhir1007/nanoARB/architecture/overview.md): High-level architecture of the NanoARB high-frequency trading framework
- [Backtest Configuration](https://mintlify.wiki/dhir1007/nanoARB/backtesting/configuration.md): Configure backtest parameters, latency models, fees, risk limits, and execution simulation
- [Performance Metrics](https://mintlify.wiki/dhir1007/nanoARB/backtesting/performance-metrics.md): Understand backtest metrics including Sharpe ratio, drawdown, win rate, and profit factor
- [Running Backtests](https://mintlify.wiki/dhir1007/nanoARB/backtesting/running-backtests.md): Execute backtests via Rust API, load market data, and interpret results
- [Event-Driven Architecture](https://mintlify.wiki/dhir1007/nanoARB/concepts/event-driven.md): Event scheduling, processing, and timing in NanoARB
- [Order Book Implementation](https://mintlify.wiki/dhir1007/nanoARB/concepts/order-books.md): High-performance limit order book with 20 price levels and O(log n) updates
- [Price & Quantity Types](https://mintlify.wiki/dhir1007/nanoARB/concepts/price-types.md): Fixed-point arithmetic for deterministic HFT calculations
- [Trading Strategies](https://mintlify.wiki/dhir1007/nanoARB/concepts/strategies.md): Strategy trait and implementations for algorithmic trading
- [Feature Extraction](https://mintlify.wiki/dhir1007/nanoARB/data/feature-extraction.md): LOB feature engineering for machine learning models
- [Market Data Feeds](https://mintlify.wiki/dhir1007/nanoARB/data/market-data-feeds.md): CME MDP 3.0 protocol implementation and synthetic data generation
- [ML Integration](https://mintlify.wiki/dhir1007/nanoARB/data/ml-integration.md): Machine learning pipeline with Mamba-LOB architecture and ONNX inference
- [Docker Deployment](https://mintlify.wiki/dhir1007/nanoARB/deployment/docker.md): Deploy NanoARB using Docker containers for isolated and reproducible environments
- [Monitoring & Metrics](https://mintlify.wiki/dhir1007/nanoARB/deployment/monitoring.md): Monitor NanoARB performance with Prometheus and Grafana for real-time insights
- [Production Deployment](https://mintlify.wiki/dhir1007/nanoARB/deployment/production.md): Best practices for deploying NanoARB in production high-frequency trading environments
- [Installation](https://mintlify.wiki/dhir1007/nanoARB/installation.md): Complete installation guide for NanoARB with system requirements and troubleshooting
- [Introduction](https://mintlify.wiki/dhir1007/nanoARB/introduction.md): Learn about NanoARB, a production-grade high-frequency trading engine for CME futures markets
- [Quickstart](https://mintlify.wiki/dhir1007/nanoARB/quickstart.md): Get NanoARB up and running in minutes with this step-by-step guide
- [Market Making](https://mintlify.wiki/dhir1007/nanoARB/strategies/market-making.md): Build market-making strategies with inventory management and quote optimization
- [Reinforcement Learning Strategies](https://mintlify.wiki/dhir1007/nanoARB/strategies/rl-strategies.md): Train and deploy RL agents for market making with IQL and Decision Transformer
- [Signal Generation](https://mintlify.wiki/dhir1007/nanoARB/strategies/signal-generation.md): Build signal-based trading strategies with ML models and feature engineering
- [Strategy Trait](https://mintlify.wiki/dhir1007/nanoARB/strategies/strategy-trait.md): Core Strategy trait and lifecycle management for building trading strategies