Use powerful Python libraries such as pandas, NumPy, and SciPy
In this book, youâll cover different ways of downloading financial data and preparing it for modeling. Youâll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, and RSI, and backtest automatic trading strategies. Next, youâll 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. Youâll 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, youâll work through an entire data science project in the finance domain. Youâll also learn how to solve credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models.
Use powerful Python libraries such as pandas, NumPy, and SciPy
In this book, youâll cover different ways of downloading financial data and preparing it for modeling. Youâll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, and RSI, and backtest automatic trading strategies. Next, youâll 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. Youâll 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, youâll work through an entire data science project in the finance domain. Youâll also learn how to solve credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models.
BY Python đ Work With Data
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That growth environment will include rising inflation and interest rates. Those upward shifts naturally accompany healthy growth periods as the demand for resources, products and services rise. Importantly, the Federal Reserve has laid out the rationale for not interfering with that natural growth transition.It's not exactly a fad, but there is a widespread willingness to pay up for a growth story. Classic fundamental analysis takes a back seat. Even negative earnings are ignored. In fact, positive earnings seem to be a limiting measure, producing the question, "Is that all you've got?" The preference is a vision of untold riches when the exciting story plays out as expected.
