Part 1. Python and finance. Why Python for finance -- Python infrastructure -- Part 2. Mastering the basics. Data types and structures -- Numerical computing with NumPy -- Data analysis with pandas -- Object-oriented programming -- Part 3. Financial data science. Data visualization -- Financial time series -- Input/output operations -- Performance Python -- Mathematical tools -- Stochastics -- Statistics -- Part 4. Algorithmic trading. The FXCM trading platform -- Trading strategies -- Automated trading -- Part 5. Derivatives analytics. Valuation framework -- Simulation of financial models -- Derivatives valuation -- Portfolio valuation -- Market-based valuation.
Summary:
Python has become the programming language of choice for data-driven and AI-first finance. Some of the largest investment banks and hedge funds now use Python and its ecosystem for building core trading and risk management systems. In the second edition of this guide, Yves Hilpisch shows developers and quantitative analysts how to use Python packages and tools for financial data science, algorithmic trading, and computational finance.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.