nextzang6546

投資に関する講義ではありません。証券データを使用して、データの収集、分析、および視覚化について説明します。さまざまなデータ形式を取り扱い、さまざまなテキスト前処理手法を習得します。時系列データの視覚化手法とスケール表現方法、株価データの解釈方法について説明します。この講義は、データ分析を通じてインサイトを得る方法を学ぶ講義です。株価データから学んだ内容を、時系列が使用される需要量、在庫量、販売量、トラフィック量などのデータの収集、分析、視覚化に活用できるように構成されています。

todaycode

Python を使用して証券データを集め、分析してシグナルとノイズを見つける

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.

입문

초급

중급이상

파이썬

Python

플로틀리

Plotly

판다스

Pandas

넘파이

Numpy

씨본

Seaborn

Finding Signals and Noise with Python Stock Data Collection and Analysis

(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!