Author: Bala Swapnika Gopi Date: May 17, 2025
Course: DATA605 — Spring 2025

This project builds an automated pipeline to:

1. Fetch real-time Bitcoin price data using CoinGecko API
2. Process data using Apache Ray for distributed computing
3. Perform time-series analysis with moving averages
4. Generate interactive visualizations

There are two primary entry points:

• bitcoin_API.ipynb — Interactive API exploration and documentation
• bitcoin_example.ipynb — End-to-end pipeline demonstration

For detailed API documentation, refer to bitcoin_API.md.

bitcoin_utils.py               # shared utilities for data processing and analysis
bitcoin_API.ipynb             # interactive API walkthrough
bitcoin_API.md               # API documentation
bitcoin_example.ipynb        # example pipeline implementation
bitcoin_example.md          # pipeline documentation
bitcoin_7d_plus_realtime.csv # historical Bitcoin price data

architecture.png            # system architecture diagram
bitcoin_flowchart.png      # process flow visualization
price_moving_avg_runtime.png # performance metrics visualization

Dockerfile            # container specification
docker_build.sh      # build container
docker_bash.sh       # start interactive shell
docker_jupyter.sh    # launch JupyterLab
docker_exec.sh       # execute commands
docker_push.sh       # push to registry
docker_clean.sh      # cleanup
docker_name.sh       # naming helper

requirements.txt      # Python dependencies
install_jupyter_extensions.sh  # Jupyter setup
install_project_packages.sh    # Project dependencies
bashrc               # Shell configuration

1. Clone the repo & navigate

   git clone https://github.com/[username]/tutorials.git
   cd tutorials/DATA605/Spring2025/projects/TutorTask93_Spring2025_Real-Time_Bitcoin_Data_Processing_with_Apache_Ray

2. Install Dependencies

   • Python 3.8+
   • Required packages:
     • ray==2.10.0
     • pandas
     • requests
     • plotly
     • yfinance
     • scikit-learn
     • statsmodels
     • jupyterlab
     • matplotlib
     • tensorflow

3. Docker Setup

   • Install Docker (Desktop/Engine)
   • Build the container:

     chmod +x docker_*.sh
     ./docker_build.sh

1. Build the image

   ./docker_build.sh (If you use ubuntu add "sudo" when you run docker commands, for eg: "sudo ./docker_build.sh")

2. Start JupyterLab

   ./docker_jupyter.sh

   • Access by copy pasting one of the links generated

3. Interactive Shell

   ./docker_bash.sh

Open bitcoin_API.ipynb in JupyterLab to:

• Learn about available API endpoints
• Test data fetching
• Explore real-time processing

Open bitcoin_example.ipynb to see:

• Complete data pipeline
• Analysis examples
• Visualization demos

1. Real-time Data Processing

   • Live Bitcoin price fetching
   • Distributed processing with Ray
   • Automated data collection

2. Analysis Capabilities

   • Moving averages calculation
   • Basic statistics
   • Time series analysis

3. Visualization

   • Interactive price charts
   • Moving average plots
   • Performance metrics

• Ray Documentation
• CoinGecko API Docs
• JupyterLab Documentation