python algorithmic trading cookbook von jason strimpel (20 Ergebnisse)

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

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Paperback. Zustand: 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. Zustand: 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.

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Zustand: As New. Unread book in perfect condition.

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paperback. Zustand: Sehr gut. 412 Seiten; 9781835084700.2 Gewicht in Gramm: 1.

Paperback. Zustand: new. Paperback. Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environmentGet With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader FreeKey FeaturesFollow practical Python recipes to acquire, visualize, and store market data for market researchDesign, backtest, and evaluate the performance of trading strategies using professional techniquesDeploy trading strategies built in Python to a live trading environment with API connectivityBook DescriptionDiscover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. Youll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that youve learned, youll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.What you will learnAcquire and process freely available market data with the OpenBB PlatformBuild a research environment and populate it with financial market dataUse machine learning to identify alpha factors and engineer them into signalsUse VectorBT to find strategy parameters using walk-forward optimizationBuild production-ready backtests with Zipline Reloaded and evaluate factor performanceSet up the code framework to connect and send an order to Interactive BrokersWho this book is forPython for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be. Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Zustand: 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. Zustand: 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. Zustand: new. Paperback. Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environmentGet With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader FreeKey FeaturesFollow practical Python recipes to acquire, visualize, and store market data for market researchDesign, backtest, and evaluate the performance of trading strategies using professional techniquesDeploy trading strategies built in Python to a live trading environment with API connectivityBook DescriptionDiscover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. Youll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that youve learned, youll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.What you will learnAcquire and process freely available market data with the OpenBB PlatformBuild a research environment and populate it with financial market dataUse machine learning to identify alpha factors and engineer them into signalsUse VectorBT to find strategy parameters using walk-forward optimizationBuild production-ready backtests with Zipline Reloaded and evaluate factor performanceSet up the code framework to connect and send an order to Interactive BrokersWho this book is forPython for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be. 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 / softback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.

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Paperback. Zustand: new. Paperback. Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environmentGet With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader FreeKey FeaturesFollow practical Python recipes to acquire, visualize, and store market data for market researchDesign, backtest, and evaluate the performance of trading strategies using professional techniquesDeploy trading strategies built in Python to a live trading environment with API connectivityBook DescriptionDiscover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. Youll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that youve learned, youll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.What you will learnAcquire and process freely available market data with the OpenBB PlatformBuild a research environment and populate it with financial market dataUse machine learning to identify alpha factors and engineer them into signalsUse VectorBT to find strategy parameters using walk-forward optimizationBuild production-ready backtests with Zipline Reloaded and evaluate factor performanceSet up the code framework to connect and send an order to Interactive BrokersWho this book is forPython for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be. 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.

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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environmentKey Features: Follow practical Python recipes to acquire, visualize, and store market data for market research Design, backtest, and evaluate the performance of trading strategies using professional techniques Deploy trading strategies built in Python to a live trading environment with API connectivity Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You'll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you've learned, you'll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.What You Will Learn: Acquire and process freely available market data with the OpenBB Platform Build a research environment and populate it with financial market data Use machine learning to identify alpha factors and engineer them into signals Use VectorBT to find strategy parameters using walk-forward optimization Build production-ready backtests with Zipline Reloaded and evaluate factor performance Set up the code framework to connect and send an order to Interactive BrokersWho this book is for:Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be.Table of Contents Acquire Free Financial Market Data with Cutting-edge Python Libraries Analyze and Transform Financial Market Data with pandas Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash Store Financial Market Data on Your Computer Build Alpha Factors for Stock Portfolios Vector-Based Backtesting with VectorBT Event-Based Backtesting Factor Portfolios with Zipline Reloaded Evaluate Factor Risk and Performance with Alphalens Reloaded Assess Backtest Risk and Performance Metrics with Pyfolio Set Up the Interactive Brokers Python API Manage Orders, Positions, and Portfolios with the IB API Deploy Strategies to a Live Environment Advanced Recipes for Market Data and Strategy Management.

Taschenbuch. Zustand: Neu. Python for Algorithmic Trading Cookbook | Recipes for designing, building, and deploying algorithmic trading strategies with Python | Jason Strimpel | Taschenbuch | Englisch | 2024 | Packt Publishing | EAN 9781835084700 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.