E-Books →Python for Algorithmic Trading Cookbook Recipes for designing, building

Key FeaturesFollow practical Python recipes to acquire, visualize, and store market data for market researchDesign, backtest, and evaluate the performance of trading strategies using professional techniquesDeploy trading strategies built in Python to a live trading environment with API connectivityPurchase of the print or Kindle book includes a free PDF eBookBook Description
Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.
Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You'll optimize strategy parameters with walk-forward optimization using vectorbt and construct a production-ready backtest using Zipline Reloaded. Implementing all that you've learned, you'll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.
By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.
What you will learnAcquire and process freely available market data with the OpenBB PlatformBuild a research environment and populate it with financial market dataUse machine learning to identify alpha factors and engineer them into signalsUse VectorBT to find strategy parameters using walk-forward optimizationBuild production-ready backtests with Zipline Reloaded and evaluate factor performanceSet up the code framework to connect and send an order to Interactive BrokersWho this book is for
Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be.
Table of ContentsAcquire Free Financial Market Data with Cutting-edge Python LibrariesAnalyze and Transform Financial Market Data with pandasVisualize Financial Market Data with MatDescriptionlib, Seaborn, and Descriptionly DashStore Financial Market Data on Your ComputerBuild Alpha Factors for Stock PortfoliosVector-Based Backtesting with VectorBTEvent-Based Backtesting Factor Portfolios with Zipline ReloadedEvaluate Factor Risk and Performance with Alphalens ReloadedAssess Backtest Risk and Performance Metrics with PyfolioSet Up the Interactive Brokers Python APIManage Orders, Positions, and Portfolios with the IB API(N.B. Please use the Read Sample option to see further chapters)