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Lewinson E. Python for Finance Cookbook.pdf

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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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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For some time, Mr. Durov and a few dozen staffers had no fixed headquarters, but rather traveled the world, setting up shop in one city after another, he told the Journal in 2016. The company now has its operational base in Dubai, though it says it doesn’t keep servers there.Mr. Durov maintains a yearslong friendship from his VK days with actor and tech investor Jared Leto, with whom he shares an ascetic lifestyle that eschews meat and alcohol.

Importantly, that investor viewpoint is not new. It cycles in when conditions are right (and vice versa). It also brings the ineffective warnings of an overpriced market with it.Looking toward a good 2022 stock market, there is no apparent reason to expect these issues to change.

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