https://github.com/jass024/bitcoin-historical-analysis

Performed comprehensive time-series analysis on 14+ years of Bitcoin historical data using Python (Pandas, NumPy, Plotly). Calculated key technical indicators (SMA, EMA, RSI, volatility) to identify market trends, potential anomalies, and areas of regulatory concern. Visualized results using interactive Plotly charts.
https://github.com/jass024/bitcoin-historical-analysis

numpy pandas python-3

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Performed comprehensive time-series analysis on 14+ years of Bitcoin historical data using Python (Pandas, NumPy, Plotly). Calculated key technical indicators (SMA, EMA, RSI, volatility) to identify market trends, potential anomalies, and areas of regulatory concern. Visualized results using interactive Plotly charts.

• Host: GitHub
• URL: https://github.com/jass024/bitcoin-historical-analysis
• Owner: jass024
• License: gpl-3.0
• Created: 2024-06-29T17:45:18.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-13T17:16:36.000Z (4 months ago)
• Last Synced: 2024-10-01T19:29:26.493Z (about 2 months ago)
• Topics: numpy, pandas, python-3
• Language: Python
• Homepage:
• Size: 8.51 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Bitcoin-Historical-Analysis

Performed comprehensive time-series analysis on 14+ years of Bitcoin historical data using Python (Pandas, NumPy, Plotly). Calculated key technical indicators (SMA, EMA, RSI, volatility) to identify market trends, potential anomalies, and areas of regulatory concern. Visualized results using interactive Plotly charts.

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This project provides a comprehensive analysis of Bitcoin's historical price, volume, and volatility data from 2010 to 2024. It leverages advanced data analysis techniques in Python, creates interactive visualizations, and investigates potential anomalies relevant to regulatory compliance. The project is designed to provide insights into Bitcoin's market dynamics and support data-driven decision-making for investors and regulators.

## Project Goals

*   Uncover long-term price trends, volatility patterns, and key market events that influenced Bitcoin's price.

*   Identify potential anomalies or patterns indicative of market manipulation or regulatory violations.

*   Assess the risk profile of Bitcoin investments based on historical volatility and price trends.

*   Provide an educational resource for understanding Bitcoin's market behavior and associated risks.

## Key Features

*   **Data Analysis:**

    *   Calculated daily returns, volatility, and various moving averages (SMA, EMA) to understand price trends.

    *   Analyzed volume patterns to gain insights into market activity.

    *   Computed the Relative Strength Index (RSI) to identify overbought/oversold conditions.

*   **Interactive Visualizations:**

    *   Created dynamic Plotly charts to visualize price trends, volatility, daily returns, RSI, moving averages, and total volume by month.

*   **Anomaly Detection (Optional):**

    *   Explore statistical methods and machine learning algorithms to identify unusual price movements or trading patterns. (Implementation in progress)

## Tech Stack

*   **Language:** Python

*   **Libraries:** pandas, NumPy, Plotly

*   **Data:** Historical Bitcoin data (CSV format)

## Repository Structure

*   `data/`: Contains the raw and processed Bitcoin data.

*   `src/`: Contains the Python scripts for data analysis and visualization.

*   `visualizations/`: Contains the generated Plotly charts (HTML files).

*   `README.md`: This file, providing project details.

## How to Run

1.  Clone this repository: `git clone https://github.com/jaswinder123/bitcoin-historical-analysis.git`

2.  Install required libraries: `pip install pandas numpy plotly`

3.  Run the analysis script: `python src/bitcoin_analysis.py`

4.  Open the generated HTML files in the `visualizations/` folder to view the charts.

## Future Enhancements

*   Integrate external data sources (e.g., news sentiment, social media data).

*   Develop more sophisticated anomaly detection models.

*   Implement time series forecasting to predict future price movements.

*   Explore potential correlations with macroeconomic indicators.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contact

Jaswinder Singh - [LinkedIn Profile](https://www.linkedin.com/in/jaswindersingh024/) - [Email Address]([email protected])