Python 🐍 Work With Data

Lewinson E. Python for Finance Cookbook.pdf

32.8 MB

Lewinson E. Python for Finance Cookbook.pdf

Use powerful Python libraries such as pandas, NumPy, and SciPy

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, and 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 Fama-French's 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 finance domain. You’ll also learn how to solve credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models.

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820 viewsJul 6, 2020 at 07:00

Lewinson E. Python for Finance Cookbook.pdf

Use powerful Python libraries such as pandas, NumPy, and SciPy

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, and 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 Fama-French's 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 finance domain. You’ll also learn how to solve credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models.

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