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✨ Portfolio 360 - Economic, Financial and Invesment Insights Platform

🌟 Overview

Portfolio 360 is a powerful platform for economic and financial analysis, offering tools to analyze historical stock data, evaluate economic factors, and build optimized portfolios. The platform leverages cutting-edge data engineering, predictive analytics, and interactive dashboards to provide investors with actionable insights.

Built on a Medallion Architecture (Bronze-Silver-Gold), InsightInvest ensures scalability, data accuracy, and seamless cloud migration—future-proofed for Microsoft Fabric.

🎯 Purpose

Empowering investors through data-driven decision-making:

  • Identify high-potential investments using market and economic trends.
  • Optimize portfolios by aligning with modern investment strategies.
  • Track performance dynamically with actionable and visually engaging insights.

🚀 Objectives

  1. Data Engineering:
    • Build scalable data pipelines to collect, clean, and process stock/economic data.
    • Ensure cloud compatibility for effortless migration to Microsoft Fabric.
  2. Data Science:
    • Implement advanced models for stock analysis and portfolio optimization.
    • Evaluate macroeconomic factors to pinpoint high-potential industries.
  3. Data Analysis:
    • Create interactive dashboards with Power BI for performance tracking.
    • Deliver insights through visual storytelling and user-friendly analytics.

🛠️ Architecture

📂 Local Setup (Development Phase)

  • Bronze Layer:
    Raw data storage in MinIO (JSON, CSV formats).
  • Silver Layer:
    Cleaned, structured data in Delta Lake (Parquet, Delta formats).
  • Gold Layer:
    Analytics-ready data stored in PostgreSQL for reporting.

☁️ Cloud Setup (Future Migration)

  • Bronze Layer:
    Raw data stored in OneLake (Microsoft Fabric).
  • Silver Layer:
    Structured data processed using Delta Lake on Fabric.
  • Gold Layer:
    Analytics-ready tables in Fabric Lakehouse Tables or SQL Tables.

🧰 Tools and Technologies

Data Engineering

Empowering robust data pipelines and storage:

  • 🔗 MinIO: Object storage for raw and cleaned datasets.
  • ⚡ Delta Lake: ACID-compliant scalable data lake for optimized processing.
  • 🚂 Apache Spark: Quick and scalable batch data processing.
  • 📅 Airflow: Workflow orchestration for seamless automation.
  • 📦 Microsoft Fabric: Centralized storage, ETL, and reporting (future-proof).

Data Science

Deriving profound insights from predictive modeling:

  • 🧪 Python: Libraries like scikit-learn, TensorFlow, statsmodels.
  • 📊 Machine Learning: Forecasting and portfolio optimization models.
  • 📈 Time Series: ARIMA, Prophet, and LSTM for trend predictions.

Data Analysis

Turning insights into intuitive, interactive visuals:

  • 📊 Power BI: Dynamic dashboards for portfolio tracking and analysis.
  • 🛢️ PostgreSQL: Back-end repository for analytics-ready data.
  • 🎨 Visualization: Tools like matplotlib, seaborn, and Plotly.

Key Features

Data Engineering

  • Scalable ETL Pipelines:

    • Ingest stock prices, economic indicators, and industry data from public APIs.
    • Clean, deduplicate, and transform data using Apache Spark.
    • Automate workflows with Airflow for reliable processing.

  • Data Storage:

    • Use MinIO for local raw and cleaned data storage.
    • Store structured analytics-ready data in PostgreSQL.
    • Maintain compatibility with Microsoft Fabric for seamless migration.

Data Science

  • Stock and Industry Selection:

    • Analyze historical stock data to identify high-performing stocks.
    • Evaluate macroeconomic trends (e.g., GDP, inflation) and industry correlations.

  • Portfolio Optimization:

    • Build efficient portfolios using Mean-Variance Optimization and Modern Portfolio Theory.
    • Apply predictive models to forecast GDP growth and asset returns.

  • Time Series Forecasting:

    • Use ARIMA and LSTMs for trend prediction.
    • Incorporate external factors like news sentiment for dynamic decision-making.

Data Analysis

  • Portfolio Tracking:

    • Develop Power BI dashboards to compare Portfolio A/B with indices.
    • Track KPIs such as total value, volatility, and Sharpe Ratio.

  • Performance Analysis:

    • Analyze cumulative returns and excess returns over time.
    • Visualize industry correlations and sector allocations.

  • Actionable Insights:

    • Highlight top-performing stocks and industries.
    • Identify diversification opportunities through correlation heatmaps.

Database Schema (Gold Layer)

Tables

  1. Portfolio Holdings:
    • Track stock positions in each portfolio.
  2. Portfolio Performance:
    • Daily performance metrics (e.g., return, volatility, benchmark comparison).
  3. Indices:
    • Historical index data for benchmarks (e.g., S&P 500).
  4. Stocks:
    • Metadata about stocks (e.g., ticker, company, sector).
  5. Portfolios:
    • Portfolio metadata and strategies.

Power BI Dashboard Structure

  1. Portfolio Overview:
    • Compare Portfolio A vs. Portfolio B: Total value, returns, and sector allocation.
  2. Performance Over Time:
    • Cumulative returns for portfolios and benchmarks (e.g., S&P 500).
  3. Risk Analysis:
    • Correlation heatmap for stocks and industries.
    • Volatility trends for each portfolio.
  4. Holdings Analysis:
    • Pie charts for sector allocation.
    • Bar charts for top-performing stocks.

Next Steps

Phase 1: Local Development

  1. Set up the Bronze-Silver-Gold pipeline with MinIO, Delta Lake, and PostgreSQL.
  2. Implement ETL workflows using Apache Spark and Airflow.
  3. Develop Power BI dashboards for portfolio tracking.

Phase 2: Migration to Microsoft Fabric

  1. Migrate data storage to OneLake and Delta Lake on Fabric.
  2. Replace Airflow with Fabric Pipelines for orchestration.
  3. Optimize Power BI dashboards to query Fabric-native SQL tables.

Conclusion

InsightInvest bridges data engineering, data science, and data analysis to provide comprehensive insights into stock and industry performance. With its scalable architecture and modular design, it supports seamless migration to Microsoft Fabric, ensuring future readiness for enterprise-scale data workflows.