CryptoQuantEngine

High-Frequency Level 2 Trading Backtesting Engine

CryptoQuantEngine is a high-performance C++ framework for backtesting high-frequency trading (HFT) strategies on Level 2 crypto market data.

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Features

• Full Depth-of-Book Simulation
  Accurately reconstructs and simulates the entire order book from Level 2 market data, supporting market-by-price feeds.

• Realistic Latency Modeling
  Simulates order entry, order response, and market data feed latencies for robust evaluation of latency-sensitive strategies.

• Queue Position Tracking
  Estimates queue position for passive orders, enabling realistic fill probability and execution modeling.

• Flexible Order Matching Engine
  Provides configurable and extensible order matching logic for both market-making and liquidity-taking strategies.

• Modular Strategy Framework
  Easily implement custom strategies by inheriting from the abstract strategy class and accessing market/order management APIs.

• Comprehensive Performance Analytics
  Reports key metrics such as Sharpe ratio, Sortino ratio, max drawdown, and detailed trading statistics after each backtest run.

• Automated Visualization
  Generates CSV outputs and Python-based plots for equity curves and position tracking.

• Robust Unit Testing
  Includes a suite of unit tests to ensure reliability and correctness of core components.

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Quick Start

Prerequisites

• C++20-compatible compiler
• CMake version 3.12 or later
• Python 3.7+ (for plotting)
• Python packages: matplotlib, pandas

Build Instructions

Clone the repository

git clone https://github.com/caped-doshi/cryptoquantlab.git

Create and enter build directory

mkdir build && cd build

Configure the project with CMake

cmake -DCMAKE_BUILD_TYPE=Release ..

Compile the engine

cmake --build .

Run unit tests

ctest --output-on-failure

Run the backtest engine

./backtest

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This will:

• Load configuration files
• Run the backtest engine for the specified number of iterations
• Print final equity, performance metrics, and trading statistics
• Generate plots of equity and position (requires Python)

See Usage Guide for more details.

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Example Strategy: Grid Trading

A sample grid trading strategy is included.
See Examples for implementation, configuration, and usage.

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Extending the Framework

To implement your own strategy:

• Inherit from the abstract Strategy class
• Implement initialize() and on_elapse()
• Use the BacktestEngine for market data and order management

See Strategies Guide for instructions.

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Performance Metrics

After each run, the engine reports:

• Sharpe Ratio — risk-adjusted return
• Sortino Ratio — downside risk-adjusted return
• Max Drawdown — largest peak-to-trough loss
• Trading statistics — number of trades, volume, value

Plots are generated as CSV and visualized using Python.

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License

MIT License. See LICENSE for details.

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Further Reading

• Configuration Guide
• Usage Guide
• Strategies Guide
• Examples
• API Reference