> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/dhir1007/nanoARB/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Learn about NanoARB, a production-grade high-frequency trading engine for CME futures markets

## What is NanoARB?

NanoARB is a nanosecond-level high-frequency trading engine designed for CME futures markets (ES, NQ). It combines cutting-edge machine learning models with ultra-low-latency Rust infrastructure to achieve institutional-grade performance for statistical arbitrage and market-making strategies.

Built entirely in Rust with zero Python at runtime, NanoARB delivers sub-microsecond tick-to-trade latency while maintaining production-grade reliability and realistic backtesting capabilities.

## Key features

<CardGroup cols={2}>
  <Card title="Sub-microsecond inference" icon="bolt">
    \< 800ns end-to-end tick-to-trade latency with median total latency of 780ns
  </Card>

  <Card title="Production Rust codebase" icon="rust">
    Zero Python at runtime, built for maximum performance and reliability
  </Card>

  <Card title="State-of-the-art ML" icon="brain">
    Mamba State Space Models for 10-50x faster inference than Transformers
  </Card>

  <Card title="Realistic backtesting" icon="chart-line">
    Latency simulation, queue position modeling, and adverse selection
  </Card>
</CardGroup>

### Performance benchmarks

Measured on AMD EPYC 7763 (AWS c6a.8xlarge):

| Operation               | Median    | P95       | P99       |
| ----------------------- | --------- | --------- | --------- |
| LOB Update              | 45ns      | 62ns      | 78ns      |
| Feature Extraction      | 120ns     | 145ns     | 168ns     |
| Model Inference         | 580ns     | 720ns     | 890ns     |
| **Total Tick-to-Trade** | **780ns** | **950ns** | **1.2μs** |

### Trading capabilities

**Data Pipeline**

* CME MDP 3.0 binary protocol parser with SBE encoding
* Zero-copy message parsing using `nom`
* Synthetic data generator for development and testing
* Historical replay support

**Order Book Engine**

* 20-level price aggregation with O(log n) updates using `BTreeMap`
* Real-time feature extraction: Microprice, OFI, VPIN, Book Imbalance
* Tensor serialization for ML inference

**ML Models**

* **Mamba-LOB**: State Space Model for sequence modeling (\~500K parameters, \<800ns inference)
* **Decision Transformer**: Offline RL for market-making
* **IQL**: Implicit Q-Learning with expectile regression
* ONNX export for Rust inference via `ort`

**Backtesting**

* Event-driven architecture with configurable latency models
* Realistic fill simulation with queue position tracking
* Walk-forward and purged cross-validation

## Architecture overview

NanoARB is built as a modular Rust workspace with the following crates:

| Crate           | Description                                   |
| --------------- | --------------------------------------------- |
| `nano-core`     | Core types, traits, error handling            |
| `nano-feed`     | CME MDP 3.0 parser, synthetic data generator  |
| `nano-lob`      | Order book reconstruction, feature extraction |
| `nano-model`    | ONNX inference, signal generation             |
| `nano-backtest` | Event-driven backtesting engine               |
| `nano-strategy` | Trading strategies, RL environment            |
| `nano-gateway`  | Entry point, metrics, configuration           |

```
┌─────────────────────────────────────────────────────────────────────┐
│                         NanoARB Architecture                        │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐           │
│  │  CME MDP 3.0 │───▶│  nano-feed   │───▶│   nano-lob   │           │
│  │  Market Data │    │   Parser     │    │  Order Book  │           │
│  └──────────────┘    └──────────────┘    └──────┬───────┘           │
│                                                  │                  │
│                                                  ▼                  │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐           │
│  │  nano-model  │◀───│   Features   │◀───│   Snapshot   │           │
│  │ ONNX Infer.  │    │  Extraction  │    │ Ring Buffer  │           │
│  └──────┬───────┘    └──────────────┘    └──────────────┘           │
│         │                                                           │
│         ▼                                                           │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐           │
│  │nano-strategy │───▶│nano-backtest │───▶│ nano-gateway │           │
│  │  MM / Signal │    │   Engine     │    │   Metrics    │           │
│  └──────────────┘    └──────────────┘    └──────────────┘           │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘
```

## Who should use NanoARB?

NanoARB is designed for:

* **Quantitative researchers** developing high-frequency trading strategies
* **Algorithmic traders** who need realistic backtesting with sub-microsecond precision
* **HFT developers** building low-latency trading systems in Rust
* **Academic researchers** studying market microstructure and ML for trading
* **Trading firms** evaluating market-making and statistical arbitrage strategies

<Warning>
  **Educational and research purposes only**

  This software is for educational and research purposes only. It is not financial advice. Trading futures involves substantial risk of loss. Past performance does not indicate future results.
</Warning>

## Next steps

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Get NanoARB up and running in minutes
  </Card>

  <Card title="Installation" icon="download" href="/installation">
    Detailed installation instructions and requirements
  </Card>
</CardGroup>