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THE CORE QUESTION

+----------------------------------------------------------------------+
|                                                                      |
|   How do major technology stocks compare on trend, return, risk,     |
|            correlation, and simple strategy behaviour?               |
|                                                                      |
+----------------------------------------------------------------------+
  

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Executive Summary

This project transforms five years of historical stock price data into a polished Streamlit analytics dashboard focused on one central question:

How do major technology stocks compare on trend, return, risk, correlation, and simple strategy behaviour?

The app combines exploratory analysis, return diagnostics, volatility measurement, correlation mapping, moving-average signal exploration, and lightweight backtesting into a portfolio-ready analytics experience built around Apple, Amazon, Google, and Microsoft.

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Featured Preview

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Table of Contents

#	Section
01	Project Intention & Goals
02	Dataset at a Glance
03	Analysis Modules
04	Key Findings
05	Technical Pipeline
06	Technology Stack
07	Repository Structure
08	Run Locally
09	Dashboard Preview
10	About the Author
11	Suggested Resources

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Project Intention & Goals

This project was built to go beyond static price charts by treating historical stock data as a decision-support asset for comparative financial analysis.

What This Project Sets Out To Do

• Compare four major technology stocks through a unified analytical framework
• Measure return, volatility, and risk-adjusted performance in one consistent view
• Reveal how strongly the stocks move together through price and return correlation
• Explore how moving averages change trend interpretation
• Visualise crossover signals in a dashboard-friendly way
• Compare a simple strategy against buy-and-hold for analytical context
• Present the work as a polished Streamlit application, not just a notebook

Business Questions Addressed

• Which stock delivered the strongest total return over the common analysis period?
• Which stock produced the best Sharpe Ratio?
• Which stock carried the highest day-to-day volatility?
• Which stocks move most similarly in return terms?
• How useful are moving-average crossover signals as a basic timing framework?
• Does a simple moving-average rule outperform passive buy-and-hold in this universe?

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Dataset at a Glance

Metric	Value
Stocks Covered	4
Companies	Apple, Amazon, Google, Microsoft
Raw Dataset Range	Feb 08, 2013 to Feb 07, 2018
Common Comparison Window	Mar 2014 to Feb 2018
Total Rows	4,752
Data Frequency	Daily
Strongest Return	Amazon (+318.6%)
Best Sharpe Ratio	Amazon (1.40)
Highest Volatility	Amazon (1.844)
Strongest Return Correlation Pair	Amazon / Google

Data Sources Used

File	Purpose
individual_stocks_5yr/AAPL_data.csv	Apple historical daily price data
individual_stocks_5yr/AMZN_data.csv	Amazon historical daily price data
individual_stocks_5yr/GOOG_data.csv	Google historical daily price data
individual_stocks_5yr/MSFT_data.csv	Microsoft historical daily price data
Stock_price_analysis.ipynb	Notebook-based exploratory and analytical workflow
app.py + pages_content/	Streamlit dashboard interface and page routing

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Analysis Modules

The dashboard is organised as a compact analytical journey rather than a loose set of charts.

Core Coverage

• Data Layer: loading, standardization, feature engineering, and KPI helpers
• Dataset Overview: structural inspection, coverage, schema, and quality context
• Trend Analysis: price history comparison with selectable moving averages
• Return Analysis: daily returns, distribution diagnostics, and cumulative performance
• Risk & Volatility: volatility, rolling risk, and Sharpe Ratio comparison
• Correlation Analysis: price and return heatmaps with pairwise relationship insight
• Signal Explorer: moving-average crossover detection and signal event review
• Strategy & Backtesting: simple MA-rule comparison versus buy-and-hold
• Key Insights: final interpretation, leadership, laggards, and realistic framing

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Key Findings

Here is what the analysis says when the four-stock universe is compared on a consistent basis.

• Amazon led on absolute return at roughly +318.6%, making it the strongest performer in the group.
• Amazon also led on risk-adjusted return, posting the best annualised Sharpe Ratio at about 1.40.
• Higher reward came with higher risk, as Amazon also showed the highest volatility in the dataset.
• Google was the relative laggard in total-return terms within this four-stock universe.
• Amazon and Google formed the strongest return-correlation pair, indicating the closest day-to-day movement pattern.
• Technical signals add structure, not certainty. Moving averages help interpretation, but they are not treated as predictive truth.

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Technical Pipeline

Raw CSV Files
      |
      v
Data Loading and Validation
      |
      v
Feature Engineering
(daily returns, moving averages)
      |
      +----------------------+
      |                      |
      v                      v
Comparative Metrics     Signal Generation
(return, risk, corr)    (MA crossover logic)
      |                      |
      +----------+-----------+
                 |
                 v
Interactive Visualisation
(Plotly + Streamlit UI)
                 |
                 v
Portfolio-Style Dashboard
stock-price-analytics.streamlit.app
  

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Technology Stack

Layer	Tools
Data Wrangling	pandas, numpy
Visualization	plotly
Dashboard	streamlit
Project Utilities	pathlib
Notebook Analysis	jupyter

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Repository Structure

stock-price-analysis-time-series-python/
|
|-- app.py
|-- README.md
|-- requirements.txt
|-- Stock_price_analysis.ipynb
|
|-- assets/
|   |-- profile.jpg
|   |-- stock_price_trend_analysis.png
|   |-- daily_return_analysis.png
|   |-- volatility_comparison.png
|   |-- risk_vs_return.png
|   |-- corr_closing_price.png
|   |-- corr_daily_return.png
|   |-- moving_average_analysis.png
|   |-- golden_cross_and_death_cross_analysis.png
|   `-- app_sell_buy.png
|
|-- individual_stocks_5yr/
|   |-- AAPL_data.csv
|   |-- AMZN_data.csv
|   |-- GOOG_data.csv
|   `-- MSFT_data.csv
|
|-- pages_content/
|   |-- about.py
|   |-- correlation.py
|   |-- dataset.py
|   |-- home.py
|   |-- insights.py
|   |-- returns.py
|   |-- risk.py
|   |-- signals.py
|   |-- strategy.py
|   `-- trend.py
|
`-- utils/
    |-- charts.py
    |-- data.py
    |-- sidebar.py
    `-- styles.py

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Run Locally

Step 1 - Clone the Repository

git clone https://github.com/doyancha/stock-price-analysis-time-series-python.git
cd stock-price-analysis-time-series-python

Step 2 - Install Dependencies

pip install -r requirements.txt

Step 3 - Launch the Dashboard

streamlit run app.py

The app will open locally at http://localhost:8501.

Requirements

streamlit
pandas
numpy
plotly

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Dashboard Preview

The repository already includes exported visuals in the assets folder, which makes it possible to preview the analytical scope even before launching the app.

Page	Description
Home	Executive overview with KPIs and market-wide summary visuals
Dataset Overview	Structural inspection of rows, dates, coverage, and preprocessing
Trend Analysis	Price history and moving-average exploration
Return Analysis	Daily return behavior, distribution, and cumulative performance
Risk & Volatility	Volatility, Sharpe Ratio, and risk-return positioning
Correlation Analysis	Closing-price and return-level correlation mapping
Signal Explorer	Moving-average crossover events and stock-level signal tracking
Strategy & Backtesting	Simple MA strategy comparison versus buy-and-hold
Key Insights	Consolidated takeaways for interpretation and storytelling
About Project	Author profile, project context, and technology stack

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About the Author

+---------------------------------------------------------+
|                                                         |
|               MIR SHAHADUT HOSSAIN                      |
|          Data Analyst | Streamlit Developer             |
|                                                         |
|    Turning historical data into structured insight.     |
|                                                         |
+---------------------------------------------------------+
  

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Suggested Resources

Documentation and Libraries Used

Resource	Link
Streamlit Docs	docs.streamlit.io
Pandas Documentation	pandas.pydata.org/docs
NumPy Documentation	numpy.org/doc
Plotly Python Docs	plotly.com/python
Python pathlib Docs	docs.python.org/pathlib

Dataset and Domain References

Resource	Link
Kaggle Stock Price Dataset Concepts	kaggle.com
Investopedia Sharpe Ratio Guide	investopedia.com/sharpe-ratio
Investopedia Correlation Guide	investopedia.com/correlation
Streamlit Community Cloud	docs.streamlit.io/streamlit-community-cloud

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 Built by Mir Shahadut Hossain | 2025-2026
 Data Analyst | Python Developer | Streamlit Builder
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