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Python for Algorithmic Trading Cookbook - Second Edition

Name: Python for Algorithmic Trading Cookbook - Second Edition
Author: Jason Strimpel
ISBN: 9781806662036

by Jason Strimpel

July 2026

Intermediate to advanced

536 pages

10h 39m

English

Packt Publishing

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Preface
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Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries
Technical requirementsWorking with stock market data on the OpenBB platformComparison of fundamental dataBuilding stock screenersFetching historical futures data with the OpenBB platformNavigating options market data with the OpenBB platformOption GreeksHarnessing factor data using pandas_datareader
Chapter 2: Analyze and Transform Financial Market Data with pandas
Diving into pandas index typesDatetimeIndexPeriodIndexMultiIndexBuilding pandas Series and DataFramesBuilding a MultiIndex DataFrame from scratchReindexing an existing DataFrame with a MultiIndex objectManipulating and transforming DataFramesCreating new columns using aggregates, Booleans, and stringsConcatenating two DataFrames togetherPivoting a DataFrame such as ExcelGrouping data on a key or index and applying an aggregateJoining options data together to create straddle pricesGrouping by multiple columnsApplying different methods to different columnsApplying custom functionsGrouping and transforming dataExamining and selecting data from DataFramesSelection by label using locSelection by position using ilocSelection by Boolean indexingPartial string indexingUsing at for fast access to a scalarUsing nsmallest and nlargestUsing the query method to query DataFramesCalculating asset returns using pandasMeasuring the volatility of a return seriesGenerating a cumulative return seriesThe cumulative sum of simple returnsThe cumulative sum of compound returnsResampling data for different time framesAddressing missing data issuesApplying custom functions to analyze time series data
Chapter 3: Accelerate Financial Market Data Analysis with Polars and DuckDB
Technical requirementsConverting CSV files to parquet for efficient storageUsing asynchronous python to speed up processingManipulating financial market data with polarsIntegrating polars with DuckDB for analysisBuilding a high-performance data lake over parquet files with DuckLake
Chapter 4: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash
Quickly visualizing data using pandasAnimating the evolution of the yield curve with matplotlibPlotting options implied volatility surfaces with matplotlibVisualizing statistical relationships with SeabornCreating an interactive PCA analytics dashboard with Plotly Dash
Chapter 5: Build a Quantamental Research Database with Hedge Fund Tools
Technical requirementsCreating bias-free historical datasets with versioned data and time travelCompute rolling metrics on millions of rows of historic price dataBuilding a Warren Buffett value stock screener with historical fundamental dataBuilding an options data analytics query tool with millions of rows of options data
Chapter 6: Conduct Market Research with Advanced AI and Agentic Workflows
Building your own AI equity research analyst to analyze financial statementsWriting Python code to analyze stock price performance automatically with AIUsing AI agentic workflows to automate comparative analysisAutomating equity research reports with AIConverting trading strategy research papers into code with LlamaIndex
Chapter 7: Build Alpha Factors for Stock Portfolios
Identifying latent return drivers using principal component analysisFinding and hedging portfolio beta using linear regressionAnalyzing portfolio sensitivities to the Fama-French factorsAssessing market inefficiency based on volatilityPreparing a factor ranking model using zipline pipelineFor windows, Unix/Linux, and mac intel usersFor apple silicon users
Chapter 8: Vector-Based Backtesting with VectorBT
Building technical strategies with VectorBTConducting walk-forward optimization with vectorbtOptimizing the SuperTrend strategy with VectorBT ProFor Windows, Unix/Linux, and Mac Intel usersFor Apple Silicon users
Chapter 9: 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 ReloadedExploring a mean reversion strategy with Zipline Reloaded

Chapter 10: Evaluate Factor Risk and Performance with Alphalens Reloaded
Preparing backtest resultsEvaluating the information coefficientExamining factor returns performanceEvaluating factor turnover
Chapter 11: Assess Backtest Risk and Performance Metrics with Pyfolio
Preparing Zipline backtest results for Pyfolio ReloadedGenerating strategy performance and return analyticsBuilding a drawdown and rolling risk analysisAnalyzing strategy holdings, leverage, exposure, and sector allocationsBreaking down strategy performance to trade level
Chapter 12: Set Up the Interactive Brokers Python API
Building an algorithmic trading appRequest-callback patternInheritance and overridingOur trading app classCreating a Contract object with the IB APICreating an Order object with the IB APIFetching historical market dataRequesting historic dataReceiving historic dataGetting a market data snapshotStreaming live market dataRequesting streaming dataReceiving streaming dataStoring live tick data in a local SQL database
Chapter 13: Manage Orders, Positions, and Portfolios with the IB API
Executing orders with the IB APIGetting details about your portfolioInspecting positions and position detailsComputing portfolio profit and loss
Chapter 14: Deploy Strategies to a Live Environment
Calculating real-time key performance and risk indicatorsCumulative returnsMax drawdownVolatilityOmega ratioSharpe ratioConditional value at riskSending orders based on portfolio targetsOrdering for a fixed amount of money with order_valueOrdering to adjust a position to the target number of shares with order_target_quantityOrdering to a given percent of current portfolio value with order_percentOrdering to adjust a position to a target value with order_target_valueOrdering to adjust a position to a target value with order_target_valueOrdering to adjust a position to a target percent of the current portfolio value with order_target_percentDeploying a monthly factor portfolio strategyDeploying an options combo strategyDeploying an intraday multi-asset mean-reversion strategy
Chapter 15: Advanced Recipes for GPU-Accelerated Trading Research
Technical requirementsAccelerating large-scale financial data wrangling with cuDFSpeeding up factor model training with cuMLBuilding GPU-accelerated asset correlation networks with cuGraphRunning GPU-accelerated portfolio optimization with cuOpt
Chapter 16: Unlock Your Exclusive Benefits
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Index

Content preview from Python for Algorithmic Trading Cookbook - Second Edition

Index

equity research reports, automating with206 – 212

AI agentic workflows

using, to automate comparative analysis196 – 199, 202 – 206

AI equity research analyst

building, to analyze financial statements186 – 190

AQR Capital Management25

Alphalens Reloaded307

backtest results, preparing308 – 313

factor returns performance, examining320 – 326

factor turnover, evaluating327 – 330

factor's alpha, calculating326, 327

get_clean_factor_and_forward_returns function313

Information Coefficient (IC), evaluating314 – 318

installing308

Anthropic Claude

documentation link191

Applied Quantitative Research (AQR)327

ArcticDB157

query builder functionality178

time travel and versioning feature158

Augmented Dickey-Fuller (ADF) test294

Average True Range ...

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Python for Algorithmic Trading Cookbook - Second Edition

by Jason Strimpel

Index

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,
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