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This is not an investment lecture. It covers data collection, analysis, and visualization using stock market data. You will work with various data formats and learn various text preprocessing techniques. It covers visualization techniques for time-series data, methods for representing scales, and several techniques for interpreting stock price data. This course is about learning how to gain insights through data analysis. It is designed so that the concepts learned through stock price data can be applied to collecting, analyzing, and visualizing other time-series data such as demand, inventory, sales, and traffic volume.

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Finding Signals and Noise through Python Stock Data Collection and Analysis

입문

초급

중급이상

파이썬

Python

플로틀리

Plotly

판다스

Pandas

넘파이

Numpy

씨본

Seaborn

(1) Finding Signals and Noise with Securities Data

(2.1) Collect all KRX stocks

(2.2) Analysis of All KRX Listed Stocks

(3.1) Collecting daily quotes with FinanceDataReader

(3.2) Comparing Returns of Multiple Stocks

(3.3) Web Crawling? Web Scraping! Collecting Stock Prices Directly with a Few Lines of Code

(3.4) Visualization of daily prices using plotly

(4.1) Collecting all ETF items

(4.2) Data Preprocessing for All ETF Items Collected

(4.3) Analysis of All ETF Items

(5.1) Sector/Theme Collection

(5.2) Industry/Theme Preprocessing

(5.3) Industry Theme Analysis

(6.1) Technical Analysis and Auxiliary Indicators

(6.2) Volatility Indicators - Bollinger Bands

(6.3) Momentum Indicator - RSI

(6.4) Trend Indicators - MACD

(6.5) Increasing productivity with pandas and plotly through cufflinks

Congratulations on making it this far!