Python algorithmic trading cookbook par jason strimpel (36 résultats)
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Paperback. Etat : 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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Paperback. Etat : 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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Paperback. Etat : 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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Paperback. Etat : 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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Paperback. Etat : 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.
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Paperback. Etat : 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.
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Paperback. Etat : 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.
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Paperback. Etat : 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.
