The financial industry has adopted Python at a tremendousrate recently, with some of the largest investment banks and hedge funds usingit to build core trading and risk management systems. This hands-on guide helpsboth developers and quantitative analysts get started with Python, and guidesyou through the most important aspects of using Python for quantitativefinance.
Using practical examples through the book, author YvesHilpisch also shows you how to develop a full-fledged framework for Monte Carlosimulation-based derivatives and risk analytics, based on a large, realisticcase study. Much of the book uses interactive IPython Notebooks, with topicsthat include:
- Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
- Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
- Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies