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Comprehensive Python Finance: Stock Analysis & Visualization

Master Python for Stock Analytics: From Data Download to Custom Analysis Tools

5.0 (34)
293 learners

What you'll learn

Analyze and filter stock data using Python and Pandas.
Download and manage historical stock data in CSV format.
Visualize stock performance with plots and correlation matrices.
Create and install a custom stock analysis library with Python.

This course includes

  • 1.5 hours of video
  • Certificate of completion
  • Access on mobile and TV

Course content

1 modules • 14 lessons • 1.5 hours of video

Comprehensive Python Finance: Stock Analysis & Visualization
14 lessons • 1.5 hours
  • **Python Stock Tickers: Download ALL Exchange Data (API to Pandas DataFrame) Part 1** 🐍05:34
  • Python Data Curation: Filter Stock Tickers by S&P 500 & Sector with Pandas | Part 2 📊06:52
  • Python Download Historical Stock Data & Save to CSV (eod Library & Pandas) | Part 3 💾11:24
  • Python Aggregate Stock Data: Combine Multiple Price CSV Files into One Pandas DataFrame | Part 4 🛠️06:43
  • Python Financial Analysis: Returns, Correlation Matrix, & Performance Plots | Part 5 📈07:20
  • Python: Calculate Stock Returns & Save to Multi-Sheet Excel File (Pandas) | Part 6 📊05:25
  • Python Matplotlib: Plot Performance Grid of Multiple Stocks with Dynamic Subplots | Part 7 🖼️04:34
  • Python Financial Events: Find Stocks Announcing Earnings or Dividends (EOD API) | Part 8 📅05:36
  • Python Stock Screener: Calculate Close to 52-Week High Ratio (EOD API & Pandas) | Part 9 🔎07:36
  • Python OOP for Finance: Building a Stock Analysis Class to Manage Individual Securities | Part 10 🛠️07:03
  • Python Class: Calculate Log Returns, Rolling Volatility, & Plot Distribution | Part 11 📊05:06
  • Python Matplotlib: Visualize Returns, Volatility Scatter, & Relative Performance | Part 12 📈05:05
  • Python Pandas: Filter Time Series Data by Option Expiration & Low Volatility Duration | Part 13 📅06:01
  • Python Package Guide: Create, Publish & Install a Custom Stock Analysis Library with Pip | Part 14 📦10:57

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