Time_Series_Data_Analysis_on_Stocks

Description:

This repository is dedicated to performing exploratory time series data analysis on daily stock prices of key tech companies: Apple, Microsoft, Google, and Amazon, over a span of 5 years.

Update v2:

The project now includes features for fetching real-time stock data using Yahoo Finance API, storing it in MongoDB, and performing various statistical and financial analyses. Additionally, it now features an interactive web-based dashboard built with Dash that allows for dynamic visualization and deeper analysis of stock data.

🛠 Libraries Used

• Pandas - For data manipulation and analysis
• Numpy - Support for large, multi-dimensional arrays and matrices
• Seaborn & Matplotlib - For plotting graphs for data visualization
• MongoDB - Used for storing fetched stock data
• yfinance - Used to fetch live stock data
• Dash: Used for building web-based application dashboards.
• TA-Lib: For calculating technical indicators
• Plotly: For creating interactive plots.

🗂 Dataset

Explore the historical and the most recent stock data:

Historical Data: Navigate to the link to view the .csv files for each company https://github.com/ManikantaSanjay/Time_Series_Data_Analysis_on_Stocks/tree/main/individual_stocks_5yr 🔗

Real-time Data: Data is fetched daily using the yfinance library and stored in MongoDB

📊 Tasks Performed

Task 1 : Analysing the Closing Price of all the stocks

Task 2 : Analysing the Total Volume of stocks being traded each day

Task 3 : Analysing the Daily Price Change in stock.

Task 4 : Analysing the Monthly Mean of Close Column.

Task 5 : Analysing the Correlation between the Stock Prices of these Tech companies.

Task 6 : Analysing the Daily Return of Each Stock & Their Co-Relation

Task 7 : Value at Risk Analysis for Apple Stocks

Update v2:

Task 8 : Fetch and Update Stock Data Daily: Script to fetch daily stock data from Yahoo Finance and update the MongoDB database.

Task 9: Advanced Financial Calculations/Technical Indicators:

• Stochastic Oscillator and RSI (Relative Strength Index) calculations to measure stock momentum.
• Historical Volatility analysis of closing prices for each month.
• CAGR (Compound Annual Growth Rate) to measure the mean annual growth rate of investment.
• MACD (Moving Average Convergence Divergence) to reveal changes in the strength, direction, momentum, and duration of a trend in a stock's price.
• Advanced Candlestick pattern detection for strategic trading insights.
• Analysis of Money Flow Index (MFI) to identify overbought or oversold conditions.
• Detection of divergences between price movements and MFI, highlighting potential reversal points.

Task 10: Create dynamic dashboards using Plotly Dash to visualize each of the technical indicators

⚙ Setup and Installation

Ensure you have Python and MongoDB installed on your system. Install the necessary Python libraries using:

pip install pandas numpy seaborn matplotlib pymongo yfinance dash plotly talib
python dashboard.py

Feel free to fork this repository or contribute by providing suggestions to improve the analysis or adding new features to enhance the stock data exploration.

Add a star 🌟 to the repo if u like it. 😃 Thank You !!