book

Python for Algorithmic Trading Cookbook

Name: Python for Algorithmic Trading Cookbook
Author: Jason Strimpel
ISBN: 9781835084700

by Jason Strimpel

August 2024

Intermediate to advanced

406 pages

9h 25m

English

Packt Publishing

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Python for Algorithmic Trading Cookbook
Contributors
About the author
About the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsMaking the Most Out of This Book – Get to Know Your Free BenefitsStep 1Step 2Step 3
Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries
Technical requirementsWorking with stock market data with the OpenBB PlatformGetting ready…How to do it…How it works…There’s more…See alsoFetching historic futures data with the OpenBB PlatformGetting ready…How to do it…There’s more…See alsoNavigating options market data with the OpenBB PlatformGetting ready…How to do it…How it works…There’s more…See alsoHarnessing factor data using pandas_datareaderGetting ready…How to do it…How it works…There’s more…See also
Chapter 2: Analyze and Transform Financial Market Data with pandas
Diving into pandas index typesHow to do it…How it works…There’s more…See alsoBuilding pandas Series and DataFramesGetting readyHow to do it…How it works…There’s more…See alsoManipulating and transforming DataFramesGetting ready…How to do it…How it works…There’s more…See alsoExamining and selecting data from DataFramesHow to do it…How it works…There’s more…See alsoCalculating asset returns using pandasHow to do it…How it works…There’s more…See alsoMeasuring the volatility of a return seriesHow to do it…How it works…There’s more…See alsoGenerating a cumulative return seriesGetting ready…How to do it…How it works…See alsoResampling data for different time framesHow to do it…How it works…There’s more…See alsoAddressing missing data issuesGetting ready…How to do it…How it works…There’s more…See alsoApplying custom functions to analyze time series dataGetting ready…How to do it…How it works…There’s more…See also
Chapter 3: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash
Quickly visualizing data using pandasHow to do it…How it works…There’s more…See alsoAnimating the evolution of the yield curve with MatplotlibHow to do it…How it works…There’s more…See alsoPlotting options implied volatility surfaces with MatplotlibGetting ready…How to do it…How it works…There’s more…See alsoVisualizing statistical relationships with SeabornHow to do it…How it works…There’s more…See alsoCreating an interactive PCA analytics dashboard with Plotly DashGetting ready…How to do it…How it works…There’s more…See also
Chapter 4: Store Financial Market Data on Your Computer
Storing data on disk in CSV formatHow to do it…How it works…There’s more…See also…Storing data on disk with SQLiteGetting ready…How to do it…How it works…There’s more…See also…Storing data in a PostgreSQL database serverGetting ready…How to do it…How it works…There’s more…See also…Storing data in ultra-fast HDF5 formatGetting ready…How to do it…How it works…There’s more…See also…
Chapter 5: Build Alpha Factors for Stock Portfolios
Identifying latent return drivers using principal component analysisGetting readyHow to do it…How it works…There’s more…See alsoFinding and hedging portfolio beta using linear regressionGetting readyHow to do it…How it works…There’s more…See alsoAnalyzing portfolio sensitivities to the Fama-French factorsGetting readyHow to do it…How it works…There’s more…See alsoAssessing market inefficiency based on volatilityHow to do it…How it works…There’s more…See alsoPreparing a factor ranking model using Zipline PipelinesGetting readyHow to do it…How it works…There’s more…See also
Chapter 6: Vector-Based Backtesting with VectorBT
Building technical strategies with VectorBTGetting readyHow to do it…How it works…There’s more…See alsoConducting walk-forward optimization with VectorBTGetting readyHow to do it…How it works…There’s more…See alsoOptimizing the SuperTrend strategy with VectorBT ProGetting readyHow to do it…How it works…There’s more…See also

Chapter 7: Event-Based Backtesting Factor Portfolios with Zipline Reloaded
Technical RequirementsFor Windows, Unix/Linux, and Mac Intel usersFor Mac M1/M2 usersBacktesting a momentum factor strategy with Zipline ReloadedGetting readyHow to do it…How it works…There’s more…See alsoExploring a mean reversion strategy with Zipline ReloadedGetting readyHow to do it…How it works…There’s more…See also
Chapter 8: Evaluate Factor Risk and Performance with Alphalens Reloaded
Preparing backtest resultsGetting ready…How to do it…How it works…There’s more…See alsoEvaluating the information coefficientGetting ready…How to do it…How it works…There’s more…See alsoExamining factor return performanceHow to do it…How it works…There’s more…See alsoEvaluating factor turnoverHow to do it…How it works…There’s more…See also
Chapter 9: Assess Backtest Risk and Performance Metrics with Pyfolio
Preparing Zipline backtest results for Pyfolio ReloadedGetting ready…How to do it…How it works…There’s more…See alsoGenerating strategy performance and return analyticsGetting ready…How to do it…How it works…There’s more…See alsoBuilding a drawdown and rolling risk analysisGetting ready…How to do it…How it works…There’s more…See alsoAnalyzing strategy holdings, leverage, exposure, and sector allocationsGetting ready…How to do it…How it works…There’s more…See alsoBreaking Down Strategy Performance to Trade LevelGetting ready…How to do it…How it works…There’s more…See also
Chapter 10: Set Up the Interactive Brokers Python API
Building an algorithmic trading appGetting ready…How to do it…How it works…There’s more…See alsoCreating a Contract object with the IB APIGetting ready…How to do it…How it works…There’s more…See alsoCreating an Order object with the IB APIGetting ready…How to do it…How it works…There’s more…See alsoFetching historical market dataGetting ready…How to do it…How it works…There’s more…See alsoGetting a market data snapshotGetting ready…How to do it…How it works…There’s more…See alsoStreaming live market dataGetting ready…How to do it…How it works…There’s more…See alsoStoring live tick data in a local SQL databaseGetting ready…How to do it…How it works…There’s more…See also
Chapter 11: Manage Orders, Positions, and Portfolios with the IB API
Executing orders with the IB APIGetting readyHow to do it…How it works…There’s more…See alsoManaging orders once they’re placedGetting readyHow to do it…How it works…There’s more…See alsoGetting details about your portfolioGetting readyHow to do it…How it works…There’s more…See alsoInspecting positions and position detailsGetting readyHow to do it…How it works…There’s more…See alsoComputing portfolio profit and lossGetting readyHow to do it…How it works…There’s more…See also
Chapter 12: Deploy Strategies to a Live Environment
Calculating real-time key performance and risk indicatorsGetting readyHow to do it…How it works…There’s more…See alsoSending orders based on portfolio targetsGetting readyHow to do it…How it works…There’s more…See alsoDeploying a monthly factor portfolio strategyGetting readyHow to do it…How it works…There’s more…See alsoDeploying an options combo strategyGetting readyHow to do it…How it works…There’s more…See alsoDeploying an intraday multi-asset mean reversion strategyGetting readyHow to do it…How it works…There’s more…See also
Chapter 13: Advanced Recipes for Market Data and Strategy Management
Streaming real-time options data with ThetaDataGetting readyHow to do it…How it works…There’s more…See alsoUsing the ArcticDB DataFrame database for tick storageGetting readyHow to do it…How it works…There’s more…See alsoTriggering real-time risk limit alertsGetting readyHow to do it…How it works…There’s more…See alsoStoring trade execution details in a SQL databaseGetting readyHow to do it…How it works…There’s more…See also
Index
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Content preview from Python for Algorithmic Trading Cookbook

1 Acquire Free Financial Market Data with Cutting-Edge Python Libraries

A May 2017 Economist cover declared data to be the world’s most valuable resource. It’s none truer than in algorithmic trading. As algorithmic traders, it’s our job to acquire and make sense of billions of rows of market data for use in trading algorithms. In this context, it’s crucial to gather high-quality, reliable data that can adequately support trading algorithms and market research. Luckily for us, it’s possible to acquire high-quality data for free (or nearly free).

This chapter offers recipes for a series of different Python libraries—including the cutting-edge OpenBB Platform—to acquire free financial market data using Python. One of the primary challenges most ...

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Python for Algorithmic Trading Cookbook

by Jason Strimpel

1

Acquire Free Financial Market Data with Cutting-Edge Python Libraries

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.