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Verlag: Packt Publishing 1/31/2020, 2020
ISBN 10: 1789618517 ISBN 13: 9781789618518
Sprache: Englisch
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In den WarenkorbPaperback or Softback. Zustand: New. Python for Finance Cookbook 1.63. Book.
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In den WarenkorbZustand: New. 2020. Paperback. . . . . .
Verlag: Packt Publishing Limited, GB, 2020
ISBN 10: 1789618517 ISBN 13: 9781789618518
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandasKey FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook DescriptionPython is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries.In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks.By the end of this book, you'll have learned how to effectively analyze financial data using a recipe-based approach.What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is forThis book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
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In den WarenkorbZustand: New.
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EUR 51,94
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In den WarenkorbZustand: New. In.
Verlag: Packt Publishing 12/30/2022, 2022
ISBN 10: 1803243198 ISBN 13: 9781803243191
Sprache: Englisch
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In den WarenkorbPaperback or Softback. Zustand: New. Python for Finance Cookbook - Second Edition: Over 80 powerful recipes for effective financial data analysis 2.75. Book.
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In den WarenkorbZustand: New.
Verlag: Packt Publishing Limited, GB, 2020
ISBN 10: 1789618517 ISBN 13: 9781789618518
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandasKey FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook DescriptionPython is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries.In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks.By the end of this book, you'll have learned how to effectively analyze financial data using a recipe-based approach.What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is forThis book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
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In den Warenkorbpaperback. Zustand: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!