Python for Finance Cookbook Over 50 recipes for applying modern Python

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Python for Finance Cookbook Over 50 recipes for applying modern Python

160,000₫

In this book, you'll cover different ways of downloading financial data and preparing it for modeling.

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Mã sản phẩm: STA602
Danh mục: Sách IT, Sách chuyên ngành
Tình trạng: Còn hàng

Mô tả

Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to quantitative finance to analyze data ( sách gia công, bìa mềm )

Python for Finance Cookbook Over 50 recipes for applying modern Python

Thể loại:Computers - Databases

Năm:2020

Ngôn ngữ:English

Trang:410 / 426

Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas

Key Features

• Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data

• Explore unique recipes for financial data analysis and processing with Python

• Estimate popular financial models such as CAPM and GARCH using a problem-solution approach

Book Description

Python is one of the most popular languages used with a huge set of libraries in the financial industry.

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. You'll then be able to tune the hyperparameters of models and handle class imbalance. Finally, you'll focus on solving problems in finance with deep learning using PyTorch.

By the end of this book, you'll have learned how to effectively analyze financial time series using a recipe-based approach.