Detect Significant Price Drops in Stocks Using Python

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AI Summary

Video Summary: Python Program for Detecting Price Drops

• Introduction
  • Video about creating a Python program to detect significant asset price drops.
  • Reminder to subscribe, like, and hit the notification bell.
  • Support available on Patreon (patreon.com/computerscience).
  • Disclaimer: Material is educational, not professional investment advice.
• Getting Started
  • Using Google’s Colab website for Python programming.
  • Steps to create a new notebook and start coding.
• Importing Libraries
  • pandas as pd
  • datetime from datetime module
• Creating Functions
  • get_date_diff: Calculates days between two dates.
  • detect_significant_price_drops: Detects significant stock price drops based on a threshold.
• Getting Stock Data
  • Reading stock data from a CSV file (stock_prices.csv).
  • Data represents Amazon stock prices.
• Detecting Price Drops
  • Using the detect_significant_price_drops function to find days with price drops greater than a set threshold.
• Calculating Days Between Drops
  • Creating a list to store the number of days between each significant price drop.
  • Looping through the price drops to calculate the differences.
• Analyzing Price Drops
  • Calculating the average number of days between significant price drops.
  • Calculating the mean of the significant price drops to find the average drop percentage.
• Summary Print Statements
  • Printing the average percentage drop and the average number of days between drops.
  • Printing how often the threshold was met within the dataset.
  • Printing the biggest drop percentage.
• Conclusion
  • Thanks to viewers and Patreon supporters.
  • Encouragement to support the channel on Patreon.
  • Final remarks and anticipation for the next video.

Additional Notes:

• The video provides a step-by-step guide to programming in Python using Google Colab.
• The program is designed to help users understand stock price movements and is not intended for actual trading.
• The tutorial covers importing necessary libraries, creating functions for analysis, and working with stock data.
• The presenter emphasizes the educational nature of the content and provides a link to Patreon for those who wish to support the channel.