Stock Market Analysis

Overview

This project focuses on analyzing stock market trends using historical price data and various financial indicators. The goal is to identify patterns, predict stock movements, and provide insights into market behavior using data-driven approaches.

Dataset

The dataset consists of historical stock data, including:

• Date – Trading date
• Open Price – Stock price at market opening
• Close Price – Stock price at market closing
• High Price – Highest price during the trading session
• Low Price – Lowest price during the trading session
• Volume – Number of shares traded
• Technical Indicators – Moving Averages, RSI, MACD, etc.

Objectives

• Perform Exploratory Data Analysis (EDA) to uncover market trends.
• Apply time-series analysis to study stock price fluctuations.
• Implement predictive modeling to forecast future stock prices.

Methodology

1. Data Preprocessing

• Handling missing values and outliers.
• Feature engineering using technical indicators such as Moving Averages, RSI, and Bollinger Bands.
• Normalizing stock price data for improved model performance.

2. Exploratory Data Analysis (EDA)

• Visualizing stock price trends over time.
• Identifying volatility using standard deviation and Bollinger Bands.
• Analyzing trading volume patterns.

3. Stock Price Prediction Models

• ARIMA (AutoRegressive Integrated Moving Average) – Time-series forecasting.
• LSTM (Long Short-Term Memory Networks) – Deep learning-based stock price prediction.
• Random Forest Regressor – Machine learning model for non-linear stock trends.

4. Model Evaluation

• Comparison of actual vs. predicted stock prices.
• Analysis of trend accuracy using visualization techniques.

Outputs

1. Key Insights

• Stocks exhibited seasonal patterns, with price spikes around earnings reports and major news events.
• Technical indicators such as RSI and MACD provided strong signals for bullish and bearish trends.
• LSTM-based prediction captured short-term fluctuations effectively, while ARIMA worked well for long-term forecasting.

Technologies Used

• Python
• Pandas, NumPy, Matplotlib, Seaborn
• Scikit-learn, TensorFlow/Keras
• ARIMA, LSTM, Random Forest
• Jupyter Notebook

Conclusion

This project demonstrates how machine learning and deep learning models can be used for stock market analysis and prediction. While LSTMs performed well for forecasting, technical indicators and ARIMA models provided valuable insights for long-term investment decisions.

Future Enhancements

• Implement Reinforcement Learning for adaptive trading strategies.
• Develop a real-time stock price prediction dashboard.
• Integrate sentiment analysis using financial news and social media data.