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Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environment Key Features • Follow practical Python recipes to acquire, visualize, and store market data for market research • Design, backtest, and evaluate the performance of trading strategies using professional techniques • Deploy trading strategies built in Python to a live trading environment with API connectivity Book 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. Who is this book 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. What you will learn • Acquire and process freely available market data with the OpenBB Platform • Build a research environment and populate it with financial market data • Use machine learning to identify alpha factors and engineer them into signals • Use VectorBT to find strategy parameters using walk-forward optimization • Build production-ready backtests with Zipline Reloaded and evaluate factor performance • Set up the code framework to connect and send an order to Interactive Brokers Cover Title Page Copyright and Credits Contributors Table of Contents Preface Acquire Free Financial Market Data with Cutting-Edge Python Libraries Technical requirements Diving into continuous futures data with Nasdaq Data Link Getting ready... How to do it... How it works... There’s more... See also Exploring S&P 500 ratios data with Nasdaq Data Link How to do it... How it works... There’s more... See also Working with stock market data with the OpenBB Platform Getting ready... How to do it... How it works... There’s more... See also Fetching historic futures data with the OpenBB Platform Getting ready... How to do it... There’s more... See also Navigating options market data with the OpenBB Platform Getting ready... How to do it... How it works... There’s more... See also Harnessing factor data using pandas_datareader Getting ready... How to do it... How it works... There’s more... See also Analyze and Transform Financial Market Data with pandas Diving into pandas index types How to do it... How it works... There’s more... See also Building pandas Series and DataFrames Getting ready How to do it... How it works... There’s more... See also Manipulating and transforming DataFrames Getting ready... How to do it... How it works... There’s more... See also Examining and selecting data from DataFrames How to do it... How it works... There’s more... See also Calculating asset returns using pandas How to do it... How it works... There’s more... See also Measuring the volatility of a return series How to do it... How it works... There’s more... See also Generating a cumulative return series Getting ready... How to do it... How it works... See also Resampling data for different time frames How to do it... How it works... There’s more... See also Addressing missing data issues Getting ready... How to do it... How it works... There’s more... See also Applying custom functions to analyze time series data Getting ready... How to do it... How it works... There’s more... See also Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash Quickly visualizing data using pandas How to do it... How it works... There’s more... See also Animating the evolution of the yield curve with Matplotlib How to do it... How it works... There’s more... See also Plotting options implied volatility surfaces with Matplotlib Getting ready... How to do it... How it works... There’s more... See also Visualizing statistical relationships with Seaborn How to do it... How it works... There’s more... See also Creating an interactive PCA analytics dashboard with Plotly Dash Getting ready... How to do it... How it works... There’s more... See also Store Financial Market Data on Your Computer Storing data on disk in CSV format How to do it... How it works... There’s more... See also... Storing data on disk with SQLite Getting ready... How to do it... How it works... There’s more... See also... Storing data in a PostgreSQL database server Getting ready... How to do it... How it works... There’s more... See also... Storing data in ultra-fast HDF5 format Getting ready... How to do it... How it works... There’s more... See also... Build Alpha Factors for Stock Portfolios Identifying latent return drivers using principal component analysis Getting ready How to do it... How it works... There’s more... See also Finding and hedging portfolio beta using linear regression Getting ready How to do it... How it works... There’s more... See also Analyzing portfolio sensitivities to the Fama-French factors Getting ready How to do it... How it works... There’s more... See also Assessing market inefficiency based on volatility How to do it... How it works... There’s more... See also Preparing a factor ranking model using Zipline Pipelines Getting ready How to do it... How it works... There’s more... See also Vector-Based Backtesting with VectorBT Building technical strategies with VectorBT Getting ready How to do it... How it works... There’s more... See also Conducting walk-forward optimization with VectorBT Getting ready How to do it... How it works... There’s more... See also Optimizing the SuperTrend strategy with VectorBT Pro Getting ready How to do it... How it works... There’s more... See also Event-Based Backtesting Factor Portfolios with Zipline Reloaded Technical Requirements For Windows, Unix/Linux, and Mac Intel users For Mac M1/M2 users Backtesting a momentum factor strategy with Zipline Reloaded Getting ready How to do it... How it works... There’s more... See also Exploring a mean reversion strategy with Zipline Reloaded Getting ready How to do it... How it works... There’s more... See also Evaluate Factor Risk and Performance with Alphalens Reloaded Preparing backtest results Getting ready... How to do it... How it works... There’s more... See also Evaluating the information coefficient Getting ready... How to do it... How it works... There’s more... See also Examining factor return performance How to do it... How it works... There’s more... See also Evaluating factor turnover How to do it... How it works... There’s more... See also Assess Backtest Risk and Performance Metrics with Pyfolio Preparing Zipline backtest results for Pyfolio Reloaded Getting ready... How to do it... How it works... There’s more... See also Generating strategy performance and return analytics Getting ready... How to do it... How it works... There’s more... See also Building a drawdown and rolling risk analysis Getting ready... How to do it... How it works... There’s more... See also Analyzing strategy holdings, leverage, exposure, and sector allocations Getting ready... How to do it... How it works... There’s more... See also Breaking Down Strategy Performance to Trade Level Getting ready... How to do it... How it works... There’s more... See also Set Up the Interactive Brokers Python API Building an algorithmic trading app Getting ready... How to do it... How it works... There’s more... See also Creating a Contract object with the IB API Getting ready... How to do it... How it works... There’s more... See also Creating an Order object with the IB API Getting ready... How to do it... How it works... There’s more... See also Fetching historical market data Getting ready... How to do it... How it works... There’s more... See also Getting a market data snapshot Getting ready... How to do it... How it works... There’s more... See also Streaming live market data Getting ready... How to do it... How it works... There’s more... See also Storing live tick data in a local SQL database Getting ready... How to do it... How it works... There’s more... See also Manage Orders, Positions, and Portfolios with the IB API Executing orders with the IB API Getting ready How to do it... How it works... There’s more... See also Managing orders once they’re placed Getting ready How to do it... How it works... There’s more... See also Getting details about your portfolio Getting ready How to do it... How it works... There’s more... See also Inspecting positions and position details Getting ready How to do it... How it works... There’s more... See also Computing portfolio profit and loss Getting ready How to do it... How it works... There’s more... See also Deploy Strategies to a Live Environment Calculating real-time key performance and risk indicators Getting ready How to do it... How it works... There’s more... See also Sending orders based on portfolio targets Getting ready How to do it... How it works... There’s more... See also Deploying a monthly factor portfolio strategy Getting ready How to do it... How it works... There’s more... See also Deploying an options combo strategy Getting ready How to do it... How it works... There’s more... See also Deploying an intraday multi-asset mean reversion strategy Getting ready How to do it... How it works... There’s more... See also Advanced Recipes for Market Data and Strategy Management Streaming real-time options data with ThetaData Getting ready How to do it... How it works... There’s more... See also Using the ArcticDB DataFrame database for tick storage Getting ready How to do it... How it works... There’s more... See also Triggering real-time risk limit alerts Getting ready How to do it... How it works... There’s more... See also Storing trade execution details in a SQL database Getting ready How to do it... How it works... There’s more... See also Index Other Books You May Enjoy Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environmentKey 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 DescriptionDiscover 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 forPython 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.