Real-Time Bitcoin Price Monitoring with Streamlit

Table of Contents

• Overview
• Features
• Repository Structure
• Prerequisites
• Installation
  • 1. Clone the Repository
  • 2. Set Up a Virtual Environment (Recommended)
  • 3. Install Python Dependencies
• Running the App Locally
• Docker Deployment
  • Quickstart with Scripts
  • Manual Docker Commands
• Configuration
• Jupyter Notebooks
• Contributing

Overview

This project demonstrates how to transform a pure-Python data-science workflow into a polished, interactive web dashboard using Streamlit—without writing any HTML, CSS, or JavaScript. It ingests live and historical cryptocurrency data from the CoinGecko REST API, enriches it with technical-analysis metrics, detects anomalies, and provides probabilistic forecasts via Prophet. You can run it locally, in a Docker container, or explore the analysis step-by-step in the provided Jupyter notebooks.

Author: Manan J. Ambaliya UID: 121118776 Email: manan001@umd.edu

Features

• Live Price Monitoring: Fetch real-time cryptocurrency prices (e.g., BTC, ETH, ADA).
• Historical Data Analysis: Retrieve and visualize up to 365 days of price history.
• Technical Indicators: Compute Moving Averages, RSI, MACD, Bollinger Bands, and more via ta-lib wrappers.
• Anomaly Detection: Highlight outliers in daily returns using Z-score methods.
• Forecasting: Generate probabilistic price projections with Facebook Prophet.
• Portfolio Tracking: (Optional) Maintain coin holdings across sessions and compute current valuations.
• Dockerized Deployment: One-click container build and run via helper scripts.
• Modular Codebase: Shared utility layer in Streamlit_utils.py for clean, reusable functions.

Repository Structure

├── Dockerfile                     # Container specification
├── docker_build.sh                # Build Docker image
├── docker_run.sh                  # Launch Docker container
├── docker_bash.sh                 # Open a shell inside the container
├── docker_clean.sh                # Remove containers and images
├── requirements.txt               # Python dependencies
├── Streamlit_utils.py             # API wrapper & utility functions
├── Streamlit.example.py           # Production-ready Streamlit app entry point
├── Streamlit.example.ipynb        # Notebook version of the Streamlit pipeline
├── Streamlit.example.md           # Documentation for the example app
├── Streamlit.API.ipynb            # Notebook demonstrating the raw API wrapper
├── Streamlit.API.md               # Documentation for the API notebook
└── README.md                      # (This file)

Prerequisites

• Python: Version 3.10 or higher
• pip: Package installer for Python
• Git: To clone the repository
• Docker (optional): For containerized deployment

Installation

1. Clone the Repository

git clone https://github.com/<your-username>/<your-repo>.git
cd <your-repo>

2. Set Up a Virtual Environment (Recommended)

python3 -m venv .venv
source .venv/bin/activate   # macOS/Linux
.\.venv\Scripts\activate  # Windows

3. Install Python Dependencies

pip install --upgrade pip
pip install -r requirements.txt

Running the App Locally

streamlit run Streamlit.example.py --server.port=8501

Then open your browser at http://localhost:8501.

Docker Deployment

This project provides helper scripts to simplify Docker workflows:

Script	Purpose
docker_build.sh	Build the Docker image
docker_run.sh	Launch a container and expose port 8501
docker_bash.sh	Open an interactive shell inside the container
docker_clean.sh	Stop and remove containers/images
docker_dev.sh	All in one, First Clear the previous image then Build the image then launch the container

Quickstart with Scripts

# Make all scripts executable
chmod +x docker_*.sh

# Build the image
./docker_build.sh

# Run the container
./docker_run.sh

# (Optional) Get a shell inside the container
./docker_bash.sh

# (Optional) Clean up containers and images
./docker_clean.sh

# (Optional) Clean up, Build, and Run
./docker_dev.sh

Manual Docker Commands

If you prefer manual steps:

# Build the image
docker build -t streamlit-bitcoin-tracker .

# Run the container
docker run -d -p 8501:8501 --name streamlit-bitcoin-tracker streamlit-bitcoin-tracker

# (Optional) Access a container shell
docker exec -it streamlit-bitcoin-tracker /bin/bash

# (Optional) Stop and remove container & image
docker stop streamlit-bitcoin-tracker
docker rm streamlit-bitcoin-tracker
docker rmi streamlit-bitcoin-tracker

Configuration

• Sidebar Controls in Streamlit.example.py allow you to select:
  • Cryptocurrency symbol (e.g., BTC, ETH, ADA)
  • Date range (7–365 days)
  • Moving average window
  • Anomaly detection threshold
  • Forecast horizon
• To support additional coins, edit the CRYPTO_LIST constant in Streamlit_utils.py or directly in Streamlit.example.py.

Jupyter Notebooks

• Streamlit.example.ipynb: Mirrors the production pipeline step-by-step with inline narrative, tables, and plots—ideal for teaching or exploration.
• Streamlit.API.ipynb: Demonstrates usage of the raw CoinGecko API via Streamlit_utils.py for custom analytics tasks.
• Documentation for each notebook is available in Streamlit.example.md and Streamlit.API.md, respectively.

Contributing

Contributions, issues, and feature requests are welcome! Please:

1. Fork the repository
2. Create a new branch (git checkout -b feature-name)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to the branch (git push origin feature-name)
5. Open a Pull Request

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Last updated: May 12, 2025