Analyzing Stock Prices

  • Guided Project
  • 0.2 hours
  • Intermediate
  • Python

Practice analyzing stock price data using Python dictionaries and data structures to uncover market trends and insights.

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Overview

In this project, you'll assume the role of a financial analyst to explore historical stock price data from the NASDAQ stock exchange. Using your Python skills, you'll read in CSV data, store it in appropriate data structures like dictionaries, and analyze it to uncover insights about the stock market. You'll compute metrics like the average closing price for each stock, determine the most traded stocks per day, search for days with unusually high trading volume, and identify the most profitable stocks over the time period. This project allows you to apply fundamental Python concepts to a real-world financial dataset, building your data analysis skills. **Objective:** Analyze historical NASDAQ stock price data using Python to identify trends, uncover market insights, and find the most profitable stocks.

What You'll Learn

  • ✓ Compute aggregates using hash tables
  • ✓ Implement search algorithms

Before You Start

  • ✓ Implementing hash tables and linked lists in Python
  • ✓ Employing linked nodes to create data structures
  • ✓ Defining LIFO and FIFO data structures
  • ✓ Developing binary search and linear search algorithms

Project Steps

7 steps

  1. 1 Stock Price Data
  2. 2 Minimum and Maximum Average Closing Prices
  3. 3 Grouping Trades per Day
  4. 4 Finding the Most Traded Stock Each Day
  5. 5 Searching for High Volume Days
  6. 6 Finding Profitable Stocks
  7. 7 Next Steps
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