hima-v / financial-markets-analysis

Financial Stock Market Analysis

This repository contains code and documentation for analyzing financial stock market data using various statistical and machine learning techniques. The analysis was performed on a dataset spanning five years (2016-2021) and covering 30 different stocks.

Dataset Description

The financial market dataset comprises a comprehensive collection of daily trading information for multiple stocks over a specified time period. Each row in the dataset represents a single trading day for a particular stock, with various attributes captured for analysis and modeling.

Key Attributes:

1. Date: The date of the trading session.
2. Symbol: The unique identifier for the stock.
3. Series: The category or type of security (e.g., equity).
4. Previous Close: The closing price of the stock from the previous trading day.
5. Open: The opening price of the stock at the beginning of the trading session.
6. High: The highest price observed during the trading session.
7. Low: The lowest price observed during the trading session.
8. Last: The last traded price of the stock during the trading session.
9. Close: The closing price of the stock at the end of the trading session.
10. VWAP (Volume Weighted Average Price): The average price of a stock weighted by trading volume over a specified time period.
11. Volume: The total number of shares traded during the trading session.
12. Turnover: The total value of all trades executed during the trading session.
13. Trades: The total number of trades executed during the trading session.
14. Deliverable Volume: The volume of shares that were delivered (settled) during the trading session.
15. % Deliverable: The percentage of deliverable volume relative to total volume traded during the session.

Experiments

1. Descriptive, Prescriptive, and Predictive Statistics: Analyzed the dataset to understand its characteristics and make predictions about future stock prices.
2. Linear Regression: Applied linear regression to model the relationship between independent variables and stock prices.
3. Logistic Regression: Used logistic regression to perform classification tasks on the stock market dataset.
4. Data Visualization: Created visualizations to explore patterns and trends in the stock market data.
5. Correlation Analysis: Calculated the correlation matrix and plotted correlation plots to understand relationships among different variables in the dataset.
6. Matrix Decomposition: Performed Singular Value Decomposition (SVD) or other matrix decomposition techniques to analyze the structure of the dataset.
7. Hypothesis Testing: Conducted hypothesis testing to make inferences about population parameters based on sample data.
8. ANOVA Testing: Applied analysis of variance (ANOVA) testing to compare means across multiple groups in the dataset.
9. Classification: Installed relevant packages for classification and evaluated the performance of classifiers using techniques like Particle Swarm Optimization for Support Vector Machine.
10. Optimization Algorithm Research: Prepared a research paper discussing the application of optimization algorithms to stock market analysis.

Files

• Financial_Stock_Market_Analysis.ipynb: Jupyter notebook containing code for all experiments.
• Dataset for the Stock Market
• research_paper.pdf: Research paper discussing optimization algorithms for stock market analysis.

Requirements

• Python 3.x
• Jupyter Notebook
• pandas
• scikit-learn
• matplotlib
• seaborn
• scipy

Usage

1. Clone the repository:

   git clone https://github.com/your-username/financial-stock-market-analysis.git
   

2. Navigate to the project directory

   cd financial-stock-market-analysis