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Python for Algorithmic Trading Cookbook

Python for Algorithmic Trading Cookbook

By : Jason Strimpel
4.3 (20)
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Python for Algorithmic Trading Cookbook

Python for Algorithmic Trading Cookbook

4.3 (20)
By: Jason Strimpel
Buy this Book

Overview of this book

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.
Table of Contents (16 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the example code files
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Conventions used
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Get in touch
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Making the Most Out of This Book – Get to Know Your Free Benefits
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1
Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries
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Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries
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Technical requirements
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Working with stock market data with the OpenBB Platform
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Fetching historic futures data with the OpenBB Platform
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Navigating options market data with the OpenBB Platform
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Harnessing factor data using pandas_datareader
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Chapter 2: Analyze and Transform Financial Market Data with pandas
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Chapter 2: Analyze and Transform Financial Market Data with pandas
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Diving into pandas index types
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Building pandas Series and DataFrames
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Manipulating and transforming DataFrames
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Examining and selecting data from DataFrames
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Calculating asset returns using pandas
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Measuring the volatility of a return series
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Generating a cumulative return series
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Resampling data for different time frames
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Addressing missing data issues
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Applying custom functions to analyze time series data
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Chapter 3: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash
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Chapter 3: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash
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Quickly visualizing data using pandas
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Animating the evolution of the yield curve with Matplotlib
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Plotting options implied volatility surfaces with Matplotlib
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Visualizing statistical relationships with Seaborn
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Creating an interactive PCA analytics dashboard with Plotly Dash
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Chapter 4: Store Financial Market Data on Your Computer
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Chapter 4: Store Financial Market Data on Your Computer
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Storing data on disk in CSV format
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Storing data on disk with SQLite
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Storing data in a PostgreSQL database server
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Storing data in ultra-fast HDF5 format
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Chapter 5: Build Alpha Factors for Stock Portfolios
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Chapter 5: Build Alpha Factors for Stock Portfolios
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Identifying latent return drivers using principal component analysis
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Finding and hedging portfolio beta using linear regression
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Analyzing portfolio sensitivities to the Fama-French factors
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Assessing market inefficiency based on volatility
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Preparing a factor ranking model using Zipline Pipelines
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Chapter 6: Vector-Based Backtesting with VectorBT
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Chapter 6: Vector-Based Backtesting with VectorBT
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Building technical strategies with VectorBT
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Conducting walk-forward optimization with VectorBT
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Optimizing the SuperTrend strategy with VectorBT Pro
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Chapter 7: Event-Based Backtesting Factor Portfolios with Zipline Reloaded
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Chapter 7: Event-Based Backtesting Factor Portfolios with Zipline Reloaded
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Technical Requirements
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Backtesting a momentum factor strategy with Zipline Reloaded
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Exploring a mean reversion strategy with Zipline Reloaded
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Chapter 8: Evaluate Factor Risk and Performance with Alphalens Reloaded
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Chapter 8: Evaluate Factor Risk and Performance with Alphalens Reloaded
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Preparing backtest results
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Evaluating the information coefficient
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Examining factor return performance
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Evaluating factor turnover
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Chapter 9: Assess Backtest Risk and Performance Metrics with Pyfolio
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Chapter 9: Assess Backtest Risk and Performance Metrics with Pyfolio
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Preparing Zipline backtest results for Pyfolio Reloaded
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Generating strategy performance and return analytics
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Building a drawdown and rolling risk analysis
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Analyzing strategy holdings, leverage, exposure, and sector allocations
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Breaking Down Strategy Performance to Trade Level
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Chapter 10: Set Up the Interactive Brokers Python API
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Chapter 10: Set Up the Interactive Brokers Python API
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Building an algorithmic trading app
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Creating a Contract object with the IB API
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Creating an Order object with the IB API
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Fetching historical market data
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Getting a market data snapshot
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Streaming live market data
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Storing live tick data in a local SQL database
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Chapter 11: Manage Orders, Positions, and Portfolios with the IB API
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Chapter 11: Manage Orders, Positions, and Portfolios with the IB API
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Executing orders with the IB API
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Managing orders once they’re placed
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Getting details about your portfolio
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Inspecting positions and position details
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Computing portfolio profit and loss
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Chapter 12: Deploy Strategies to a Live Environment
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Chapter 12: Deploy Strategies to a Live Environment
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Calculating real-time key performance and risk indicators
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Sending orders based on portfolio targets
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Deploying a monthly factor portfolio strategy
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Deploying an options combo strategy
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Deploying an intraday multi-asset mean reversion strategy
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Chapter 13: Advanced Recipes for Market Data and Strategy Management
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Chapter 13: Advanced Recipes for Market Data and Strategy Management
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Streaming real-time options data with ThetaData
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Using the ArcticDB DataFrame database for tick storage
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Triggering real-time risk limit alerts
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Storing trade execution details in a SQL database
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Index
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Index
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Why subscribe?
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