Stock Market Trends & Volatility Analysis Project Update

Posted on 2025-01-29

Analyzing Stock Market Trends with Python & Yahoo Finance API 📊

Project Overview

In this project, I’ve been working on analyzing stock market trends and volatility using Python and real-time stock data from the Yahoo Finance API. The goal is to track key financial metrics, including daily returns, moving averages, Bollinger Bands, and volatility to gain insights into the stock market's behavior.

What I’ve Accomplished So Far

✅ Data Collection & Preprocessing

  • Successfully pulled historical stock price data for Tesla (TSLA), Apple (AAPL), and Bitcoin (BTC) using yfinance.
  • Cleaned and structured the data for time-series analysis.
  • Handled missing values and ensured the dataset was optimized for analysis.

✅ Feature Engineering & Trend Analysis

  • Computed daily returns to measure how much each stock's price changed.
  • Implemented Simple Moving Averages (SMA) and Exponential Moving Averages (EMA) to smooth out price trends.
  • Applied Bollinger Bands to identify periods of high and low volatility.
  • Visualized comparisons between stocks and the S&P 500 to observe broader market trends.

✅ Data Visualization & Insights

  • Created time-series plots for Open, High, Low, Close (OHLC) prices.
  • Used Seaborn & Matplotlib to generate insightful visualizations.
  • Compared volatility patterns between Tesla, Apple, and Bitcoin.

Next Steps 🚀

  • 🔹 Implement technical indicators like Relative Strength Index (RSI) and MACD.
  • 🔹 Automate daily stock data fetching for real-time trend monitoring.
  • 🔹 Build an interactive dashboard to visualize key insights dynamically.
  • 🔹 Experiment with machine learning models for stock price prediction.

This project is an exciting dive into financial data analysis, blending data science and finance to uncover stock market patterns. Stay tuned for further updates as I refine the analysis and integrate AI-driven predictions! 🚀📈

Categories: Python, Finance