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Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python
Transform financial market data into algorithmic trading strategies and deploy them into a live trading environment with recipes leveraging modern Python libraries like pandas, Polars, and DuckDB
Key FeaturesBacktest Python trading strategies with VectorBT and Zipline Reloaded using walk-forward analysisMeasure risk, performance, and alpha quality with Alphalens Reloaded and PyFolioAutomate strategy execution with the Interactive Brokers API for live tradingBook DescriptionGet 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.
What you will learnAcquire equities, futures, and options data using OpenBB and FMPProcess and analyze time series data efficiently with pandas and PolarsStore and query massive datasets with ArcticDB, DuckDB, and ParquetVisualize trading data using Matplotlib, Seaborn, and Plotly DashEngineer alpha factors using PCA, regression, and Fama-French modelsBacktest strategies with VectorBT and Zipline Reloaded frameworksEvaluate performance and risk using Alphalens Reloaded and PyFolioDeploy and automate live trades using the Interactive Brokers APIWho this book is forThis book is for traders, investors, and Python enthusiasts who need practical code to acquire, analyze, and automate algorithmic trading strategies using modern, high-performance Python tools. Readers should have some exposure to investing or trading, a basic familiarity with Python syntax, and a basic knowledge of libraries such as Pandas and NumPy. This book is ideal for discretionary traders who want to adopt a systematic approach and apply professional techniques, such as factor modeling, backtesting, and execution automation, to trading workflows using Python.
Table of ContentsAcquire Free Financial Market Data with Cutting-Edge Python LibrariesAnalyze and Transform Financial Market Data with pandasAccelerate Financial Market Data Analysis with Polars and DuckDBVisualize Financial Market Data with Matplotlib, Seaborn, and Plotly DashBuild a Quantamental Research Database with Hedge Fund ToolsConduct Market Research with Advanced AI and Agentic WorkflowsBuild Alpha Factors for Stock PortfoliosVector-Based Backtesting with VectorBTEvent-Based Backtesting Factor Portfolios with Zipline ReloadedEvaluate Factor Risk and Performance with Alphalens ReloadedAssess Backtest Risk and Performance Metrics with Pyfolio(N.B. Please use the Read Sample option to see further chapters)
Key FeaturesBacktest Python trading strategies with VectorBT and Zipline Reloaded using walk-forward analysisMeasure risk, performance, and alpha quality with Alphalens Reloaded and PyFolioAutomate strategy execution with the Interactive Brokers API for live tradingBook DescriptionGet 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.
What you will learnAcquire equities, futures, and options data using OpenBB and FMPProcess and analyze time series data efficiently with pandas and PolarsStore and query massive datasets with ArcticDB, DuckDB, and ParquetVisualize trading data using Matplotlib, Seaborn, and Plotly DashEngineer alpha factors using PCA, regression, and Fama-French modelsBacktest strategies with VectorBT and Zipline Reloaded frameworksEvaluate performance and risk using Alphalens Reloaded and PyFolioDeploy and automate live trades using the Interactive Brokers APIWho this book is forThis book is for traders, investors, and Python enthusiasts who need practical code to acquire, analyze, and automate algorithmic trading strategies using modern, high-performance Python tools. Readers should have some exposure to investing or trading, a basic familiarity with Python syntax, and a basic knowledge of libraries such as Pandas and NumPy. This book is ideal for discretionary traders who want to adopt a systematic approach and apply professional techniques, such as factor modeling, backtesting, and execution automation, to trading workflows using Python.
Table of ContentsAcquire Free Financial Market Data with Cutting-Edge Python LibrariesAnalyze and Transform Financial Market Data with pandasAccelerate Financial Market Data Analysis with Polars and DuckDBVisualize Financial Market Data with Matplotlib, Seaborn, and Plotly DashBuild a Quantamental Research Database with Hedge Fund ToolsConduct Market Research with Advanced AI and Agentic WorkflowsBuild Alpha Factors for Stock PortfoliosVector-Based Backtesting with VectorBTEvent-Based Backtesting Factor Portfolios with Zipline ReloadedEvaluate Factor Risk and Performance with Alphalens ReloadedAssess Backtest Risk and Performance Metrics with Pyfolio(N.B. Please use the Read Sample option to see further chapters)
- GenresProgrammingFinance
536 pages, Paperback
Published July 10, 2026
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Displaying 1 - 1 of 1 review
July 24, 2026
It is a cookbook of 68 recipes that take you the whole way from sourcing market data to placing live orders through a broker. It builds up from acquiring and accelerating data (OpenBB, pandas, Polars, DuckDB), through a research layer (an ArcticDB research database, AI and agentic workflows, alpha factors), into backtesting and evaluation (VectorBT, Zipline Reloaded, Alphalens, Pyfolio), then live execution on the Interactive Brokers API, and finally GPU acceleration with NVIDIA RAPIDS. A few things I liked:
• It reaches live execution instead of stopping at the backtest: a modular Interactive Brokers app plus three deployable strategies (a monthly factor portfolio, an options combo, and an intraday mean-reversion strategy).
• Rigorous validation runs through it: versioned data with ArcticDB time travel to keep lookahead bias out, and walk-forward optimization that explains why you cannot shuffle folds on time series.
• Professional tooling brought to non-professionals: OpenBB, ArcticDB, the "Reloaded" backtesting ecosystem, and RAPIDS on the GPU.
• An AI chapter with a retrieval-augmented equity analyst on LangChain/Chroma/Claude, LlamaIndex agents that write and run their own Python, and a recipe that turns a strategy paper into runnable code.
• It reaches live execution instead of stopping at the backtest: a modular Interactive Brokers app plus three deployable strategies (a monthly factor portfolio, an options combo, and an intraday mean-reversion strategy).
• Rigorous validation runs through it: versioned data with ArcticDB time travel to keep lookahead bias out, and walk-forward optimization that explains why you cannot shuffle folds on time series.
• Professional tooling brought to non-professionals: OpenBB, ArcticDB, the "Reloaded" backtesting ecosystem, and RAPIDS on the GPU.
• An AI chapter with a retrieval-augmented equity analyst on LangChain/Chroma/Claude, LlamaIndex agents that write and run their own Python, and a recipe that turns a strategy paper into runnable code.
Displaying 1 - 1 of 1 review