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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Chinaâs stock markets are some of the largest in the world, with total market capitalization reaching RMB 79 trillion (US$12.2 trillion) in 2020. Chinaâs stock markets are seen as a crucial tool for driving economic growth, in particular for financing the countryâs rapidly growing high-tech sectors.Although traditionally closed off to overseas investors, Chinaâs financial markets have gradually been loosening restrictions over the past couple of decades. At the same time, reforms have sought to make it easier for Chinese companies to list on onshore stock exchanges, and new programs have been launched in attempts to lure some of Chinaâs most coveted overseas-listed companies back to the country.
The SSE was the first modern stock exchange to open in China, with trading commencing in 1990. It has now grown to become the largest stock exchange in Asia and the third-largest in the world by market capitalization, which stood at RMB 50.6 trillion (US$7.8 trillion) as of September 2021. Stocks (both A-shares and B-shares), bonds, funds, and derivatives are traded on the exchange. The SEE has two trading boards, the Main Board and the Science and Technology Innovation Board, the latter more commonly known as the STAR Market. The Main Board mainly hosts large, well-established Chinese companies and lists both A-shares and B-shares.
