📊 Macroeconomic Insights & Portfolio Response Dashboard

An interactive, research-driven dashboard that models the impact of macroeconomic indicators on financial asset classes using academic theory, data pipelines, and predictive analytics.

Deployed on Streamlit Cloud : https://macroeconomic-dashboard-by-triumph-kia-teh.streamlit.app/#%F0%9F%94%A2-key-performance-indicators-fred

---

👥 Contributors

Developed by:

• Triumph Kia Teh — Quantitative Modeling & Backend (CS + Econ + Math)

For collaboration or feedback, reach out via LinkedIn or GitHub.

---

📌 Project Objective

This dashboard simulates how asset classes (equities, bonds, FX, commodities) respond to changes in key macroeconomic indicators (e.g., GDP, inflation, ΔCLI) under various economic scenarios. It integrates:

• Academic research (e.g., Di Bonaventura & Morini, Long et al.)
• Real-time & historical economic data via public APIs
• Predictive models (ARIMA, linear regression)
• Visual tools (Tableau, Power BI, Streamlit)

---

🧠 Research Backbone

• Di Bonaventura & Morini (2024) – Macro-Financial Factors and Asset Classes
• Macrosynergy (2024) – Macroeconomic Trends and Financial Markets
• Long et al. (2022) – ΔCLI and Global Stock Returns

📄 See the Research Digest for full literature integration.

---

📅 Project Timeline

Executed in 6 structured phases over 12 weeks:

1. Project Setup, Research Review & Data Collection
2. Data Cleaning, Structuring & Literature Alignment
3. Dashboard Development & Scenario Modeling
4. Correlation Analysis & Research Validation
5. Deployment & UX Design
6. Predictive Analytics & Simulation Interface

---

📁 Repository Structure

macroeconomic-dashboard/
├── data/              # Raw and cleaned datasets
├── notebooks/         # EDA and modeling notebooks
├── dashboards/        # Streamlit, Tableau, and Power BI interfaces
├── scripts/           # ETL, modeling, visualization scripts
├── research/          # Research digest and academic references
├── deployment/        # Deployment configs and cron automation
├── requirements.txt   # Project dependencies
└── README.md

---

🚀 Features

• 📈 Macroeconomic indicator panels (CLI, inflation, interest rates)
• 🗺️ Scenario simulations (e.g., 2008, COVID-19, rate hikes)
• 📊 Cross-asset correlation heatmaps with time-lag analysis
• 🧠 Predictive models (ARIMA, ΔCLI-based regressions)
• 💻 Fully deployed dashboards (Streamlit, Tableau Public, Power BI Service)

---

📡 Data Sources

• FRED API
• OECD CLI
• Yahoo Finance
• World Bank Indicators

---

🧪 Installation & Usage

# 1. Clone the repository
git clone https://github.com/yourusername/macroeconomic-dashboard.git
cd macroeconomic-dashboard

# 2. Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # For Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the Streamlit app
cd dashboards/streamlit_app
streamlit run app.py

---

📸 Preview

Add screenshots or a GIF preview here once dashboard visuals are live.

---

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.