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  • Book Overview & Buying Python for Algorithmic Trading Cookbook
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Python for Algorithmic Trading Cookbook

Python for Algorithmic Trading Cookbook - Second Edition

By : Jason Strimpel
Buy this Book
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Python for Algorithmic Trading Cookbook

Python for Algorithmic Trading Cookbook

By: Jason Strimpel
Buy this Book

Overview of this book

Get practical Python code for algorithmic trading from Jason Strimpel, founder of PyQuant News and a veteran of global trading, risk management, and machine learning. This hands-on guide shows you how to turn market data into tested, automated trading strategies using modern Python tools. You’ll source equities, options, and futures data with OpenBB and FMP, then accelerate Python for data analysis workflows with Pandas, Polars, Parquet, DuckDB, and ArcticDB. You’ll visualize market data with Matplotlib, Seaborn, and Plotly Dash before moving into alpha research and quantitative trading techniques. Detailed recipes help you engineer alpha factors with PCA, regression, Fama-French models, SciPy, and statsmodels. You’ll design and evaluate quantitative trading strategies using VectorBT, Zipline Reloaded, Alphalens Reloaded, and PyFolio, including walk-forward analysis and risk-aware performance review. For execution, you’ll connect to the Interactive Brokers API to stream ticks, manage orders, retrieve portfolio state, and monitor live trading workflows. By the end, you’ll have reusable Python templates for researching, backtesting, evaluating, and operating algorithmic trading strategies.
Table of Contents (19 chapters)
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Preface
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Preface
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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 on the OpenBB platform
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Fetching historical 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: Accelerate Financial Market Data Analysis with Polars and DuckDB
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Chapter 3: Accelerate Financial Market Data Analysis with Polars and DuckDB
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Technical requirements
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Converting CSV files to parquet for efficient storage
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Using asynchronous python to speed up processing
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Manipulating financial market data with polars
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Integrating polars with DuckDB for analysis
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Building a high-performance data lake over parquet files with DuckLake
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Chapter 4: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash
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Chapter 4: 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 5: Build a Quantamental Research Database with Hedge Fund Tools
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Chapter 5: Build a Quantamental Research Database with Hedge Fund Tools
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Technical requirements
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Creating bias-free historical datasets with versioned data and time travel
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Compute rolling metrics on millions of rows of historic price data
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Building a Warren Buffett value stock screener with historical fundamental data
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Building an options data analytics query tool with millions of rows of options data
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Chapter 6: Conduct Market Research with Advanced AI and Agentic Workflows
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Chapter 6: Conduct Market Research with Advanced AI and Agentic Workflows
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Building your own AI equity research analyst to analyze financial statements
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Writing Python code to analyze stock price performance automatically with AI
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Using AI agentic workflows to automate comparative analysis
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Automating equity research reports with AI
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Converting trading strategy research papers into code with LlamaIndex
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Chapter 7: Build Alpha Factors for Stock Portfolios
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Chapter 7: 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 pipeline
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Chapter 8: Vector-Based Backtesting with VectorBT
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Chapter 8: 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 9: Event-Based Backtesting Factor Portfolios with Zipline Reloaded
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Chapter 9: 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 10: Evaluate Factor Risk and Performance with Alphalens Reloaded
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Chapter 10: 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 returns performance
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Evaluating factor turnover
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Chapter 11: Assess Backtest Risk and Performance Metrics with Pyfolio
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Chapter 11: 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 12: Set Up the Interactive Brokers Python API
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Chapter 12: 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 13: Manage Orders, Positions, and Portfolios with the IB API
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Chapter 13: Manage Orders, Positions, and Portfolios with the IB API
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Executing orders with the IB API
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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 14: Deploy Strategies to a Live Environment
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Chapter 14: 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 15: Advanced Recipes for GPU-Accelerated Trading Research
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Chapter 15: Advanced Recipes for GPU-Accelerated Trading Research
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Technical requirements
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Accelerating large-scale financial data wrangling with cuDF
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Speeding up factor model training with cuML
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Building GPU-accelerated asset correlation networks with cuGraph
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Running GPU-accelerated portfolio optimization with cuOpt
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Chapter 16: Unlock Your Exclusive Benefits
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Chapter 16: Unlock Your Exclusive Benefits
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Unlock this Book's Free Benefits in 3 Easy Steps
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Other Books You May Enjoy
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Other Books You May Enjoy
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Index
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Index

Manipulating and transforming DataFrames

Before moving on to more advanced recipes, it's important to understand the fundamentals of working with data. DataFrames are the most common pandas data structures you'll work with. Despite the existence of hundreds of methods for DataFrame manipulation, only a subset of these are regularly used.

In this recipe, we will show you how to manipulate a DataFrame using the following common methods:

  • Creating new columns using aggregates, Booleans, and strings
  • Concatenating two DataFrames together
  • Pivoting a DataFrame such as Excel
  • Grouping data on a key or index and applying an aggregate
  • Joining options data together to create straddle prices

Getting ready

We'll start by importing the necessary libraries and downloading market price data:

  1. Start by importing NumPy, pandas, and the OpenBB Platform: 
    import numpy as np
import pandas as pd
from openbb import obb
obb.user.preferences.output_type = "dataframe"
  2. Load some stock price...
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Python for Algorithmic Trading Cookbook
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