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This is not an investment lecture. It covers data collection, analysis, and visualization through securities data. It covers various data formats and teaches various text preprocessing techniques. It covers visualization techniques for time series data and methods for expressing scale. It covers several techniques for interpreting stock price data. This lecture is a course that teaches you how to gain insights through data analysis. The content learned through stock price data is organized so that you can apply it to collecting, analyzing, and visualizing data such as demand, inventory, sales, and traffic volume, where time series is used.

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Finding Signals and Noise with Python Stock Data Collection and Analysis

입문

초급

중급이상

파이썬

Python

플로틀리

Plotly

판다스

Pandas

넘파이

Numpy

씨본

Seaborn

(1) Finding signal and noise in securities data

(2.1) Collection of all KRX items

(2.2) Analysis of all KRX stocks

(3.1) Collecting daily prices with FinanceDataReader

(3.2) Comparison of returns on various items

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

(3.4) Visualizing daily prices using plotly

(4.1) Collection of all ETF items

(4.2) Preprocessing of collected data for all ETF items

(4.3) Analysis of all ETF items

(5.1) Industry/Theme Collection

(5.2) Industry/Theme Preprocessing

(5.3) Industry theme analysis

(6.1) Technical Analysis and Auxiliary Indicators

(6.2) Volatility indicator - Bollinger Bands

(6.3) Momentum indicator - RSI

(6.4) Trend Indicator - MACD

(6.5) Improving pandas and plotly productivity with cufflinks

Congratulations on making it this far!