📊 Crypto Market Analytics Dashboard

An interactive Streamlit-based dashboard for analyzing cryptocurrency market trends, portfolio performance, risk metrics, and asset correlations using daily price data of the Top 100 cryptocurrencies.

This project is built as a personal portfolio project, focusing on data analysis, financial insights, and clean dashboard design.

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🚀 Overview

The Crypto Market Analytics Dashboard allows users to:

• Analyze overall cryptocurrency market trends
• Explore individual crypto assets
• Simulate and evaluate custom portfolios
• Assess risk using volatility and drawdowns
• Study correlations for diversification insights

The dashboard emphasizes clarity, modularity, and analytical value, avoiding unnecessary complexity or overengineering.

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📊 Dataset

• Source: Kaggle
• Dataset: Top 100 Cryptocurrencies – Daily Price Data (2025)
• Records: ~36,500+
• Frequency: Daily

The dataset includes price data, market capitalization, trading volume, daily returns, cumulative returns, moving averages, and volatility measures.

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🧩 Dashboard Pages

Market Overview

Provides a high-level view of the cryptocurrency market, including market capitalization trends, trading volume analysis, and top cryptocurrencies by market cap.

Crypto Explorer

Enables detailed exploration of individual cryptocurrencies, including price trends, moving averages, volatility behavior, and interactive asset comparison.

Portfolio Simulator

Allows users to construct custom crypto portfolios using selected assets and allocation weights, and visualize portfolio performance over time.

Risk Analysis

Focuses on risk assessment through volatility analysis, cumulative returns, and drawdown evaluation.

Correlation & Diversification

Displays correlation heatmaps of crypto returns to help identify relationships between assets and assess diversification potential.

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🛠️ Tech Stack

• Python
• Streamlit
• Pandas
• NumPy
• Plotly

▶️ How to Run the Project

Install dependencies:

pip install -r requirements.txt

Running the Dashboard

Install dependencies:

pip install -r requirements.txt

Run the Dashboard:

streamlit run app.py

app.py serves as the main entry point.

All dashboard pages are automatically loaded from the pages/ directory by Streamlit.

Design Philosophy

The dashboard follows a modular, page-based architecture, prioritizing readability, interpretability, and meaningful insights over visual clutter.
Logic is implemented directly within each page to maintain transparency and simplicity.

Notes & Limitations

• This is a personal portfolio project, not a production-grade trading or investment tool
• All analytics are intended for educational and exploratory purposes only
• The dashboard does not use real-time data or provide trading functionality

Potential Enhancements

• Advanced performance metrics (Sharpe Ratio, Sortino Ratio)
• Portfolio optimization techniques
• Clustering-based diversification analysis
• Exportable analytical reports

Acknowledgements

• Dataset sourced from Kaggle
• Built as part of a hands-on learning journey in data analytics and dashboard development