ISBN: 9781806662036 - python for algorithmic trading cookbook: recipes for designing, building, and deploying algorithmic trading strategies with python (13 risultati)

Paperback or Softback. Condizione: New. Python for Algorithmic Trading Cookbook - Second Edition: Recipes for designing, building, and deploying algorithmic trading strategies with Python. Book.

Condizione: New.

Paperback. Condizione: New. Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies. By following step-by-step instructions, you'll be proficient in trading concepts and have hands-on experience in a live trading environment.

Paperback. Condizione: New. Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies. By following step-by-step instructions, you'll be proficient in trading concepts and have hands-on experience in a live trading environment.

Paperback. Condizione: New. Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies. By following step-by-step instructions, you'll be proficient in trading concepts and have hands-on experience in a live trading environment.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condizione: Neu. Neuware - Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies.

Paperback. Condizione: New. Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies. By following step-by-step instructions, you'll be proficient in trading concepts and have hands-on experience in a live trading environment.

Taschenbuch. Condizione: Neu. Python for Algorithmic Trading Cookbook - Second Edition | Recipes for designing, building, and deploying algorithmic trading strategies with Python | Jason Strimpel | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781806662036 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.…

Paperback. Condizione: new. Paperback. 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 DuckDBKey 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.Youll source equities, options, and futures data with OpenBB and FMP, then accelerate Python for data analysis workflows with Pandas, Polars, Parquet, DuckDB, and ArcticDB. Youll 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. Youll 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, youll connect to the Interactive Brokers API to stream ticks, manage orders, retrieve portfolio state, and monitor live trading workflows. By the end, youll 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. Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. 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 DuckDBKey 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.Youll source equities, options, and futures data with OpenBB and FMP, then accelerate Python for data analysis workflows with Pandas, Polars, Parquet, DuckDB, and ArcticDB. Youll 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. Youll 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, youll connect to the Interactive Brokers API to stream ticks, manage orders, retrieve portfolio state, and monitor live trading workflows. By the end, youll 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. Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. 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 DuckDBKey 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.Youll source equities, options, and futures data with OpenBB and FMP, then accelerate Python for data analysis workflows with Pandas, Polars, Parquet, DuckDB, and ArcticDB. Youll 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. Youll 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, youll connect to the Interactive Brokers API to stream ticks, manage orders, retrieve portfolio state, and monitor live trading workflows. By the end, youll 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. Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: Brand New. 536 pages. 7.50x1.21x9.25 inches. In Stock. This item is printed on demand.