Getting Started

Welcome to the getting started guide for dxlib! This guide will walk you through the initial steps to install, set up, and start using dxlib in your Python projects.

What is dxlib for?

dxlib is a powerful Python library designed specifically for quantitative finance, offering a comprehensive suite of tools for financial modeling, analysis, and algorithmic trading. Whether you’re a quantitative analyst, algorithmic trader, or financial researcher, dxlib provides the building blocks needed to develop sophisticated trading strategies, optimize portfolios, and analyze financial data with ease.

  • Core Components: dxlib’s core includes essential components such as history objects for data storage and retrieval, strategy executors for executing trading strategies, inventory management for tracking positions, and security models for managing financial instruments.

  • Indicators: Leveraging a wide range of technical indicators, time series analysis tools, and statistical functions, dxlib empowers users to perform in-depth market analysis, generate trading signals, and gain insights into market trends and patterns.

  • Portfolio Management: dxlib simplifies portfolio management by offering tools for creating and managing portfolios, loading and processing data, and applying portfolio transformations to optimize performance and manage risk.

  • Financial Structures: From options contracts and option models to fixed income rates and financial terms, dxlib provides a flexible framework for modeling a variety of financial instruments and structures.

  • Trading Tools: With dxlib’s trading tools, users can manage orders, generate trading signals based on predefined strategies, record transactions, and streamline the execution of trading algorithms.

  • Interface Integration: dxlib seamlessly integrates with external data sources such as YFinance, Alpaca Markets, and IBKR, allowing users to access real-time market data and historical information. Additionally, internal interfaces facilitate communication between different components within the library, enabling efficient data flow and system integration.

Who should use dxlib?

  • Backtesting Strategies: Use dxlib to backtest trading strategies against historical data, evaluate performance metrics, and refine strategies for live trading.

  • Algorithmic Trading: Develop custom trading algorithms using technical indicators, market signals, and risk management tools provided by dxlib to automate trading decisions and execute trades.

  • Portfolio Optimization: Utilize dxlib’s portfolio management capabilities to construct diversified portfolios, rebalance asset allocations, and optimize portfolio performance based on risk-return objectives.

  • Real-Time Analysis: Leverage dxlib for real-time data analysis, decision-making, and trade execution in fast-paced financial markets, gaining a competitive edge in algorithmic trading.

  • Financial Research: Conduct research and development of new financial instruments, derivative models, and trading strategies using dxlib’s flexible framework and extensive toolset.