Investment Portfolio Dashboard

A Streamlit-based dashboard that provides a holistic view of investments. I created this because I have to check my different investment accounts separately, on my phone, but I increasingly want to load ONE app where I view everything together and I can understand and track how my investments are doing. I have integrated with Charles Schwab and Interactive Brokers, but you can use this framework to add more portfolios and it should work all the same - you just need to authenticate with the relevant brokerage and make sure their APIs support what you need.

Features

• Portfolio Overview: View total portfolio value and performance across all accounts
• Multi-Broker Support: Integrate with both Charles Schwab and Interactive Brokers
• Flexible Views: Filter by broker or account type
• Asset Allocation: Visualize portfolio allocation by security
• Performance Metrics: Track unrealized P/L, returns, and more

Screenshots

[Coming soon]

Requirements

• Python 3.8+
• Streamlit
• ngrok (for OAuth authentication)
• API credentials for Charles Schwab and/or Interactive Brokers

Installation

1. Clone the repository:

   git clone https://github.com/o-dia/streamlit-investment-dashboard.git
   cd streamlit-investment-dashboard
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Create a .env file with your credentials:

   SCHWAB_CLIENT_ID=your_client_id_here
   SCHWAB_CLIENT_SECRET=your_client_secret_here
   SCHWAB_REDIRECT_URI=your_ngrok_url_here/callback
   
   # For Interactive Brokers (optional)
   IB_CLIENT_ID=your_ib_client_id
   IB_GATEWAY_PORT=4001
   IB_HOST=127.0.0.1
   

Usage

1. Start ngrok to create a tunnel for OAuth:

   ngrok http 8501
   

2. Update your .env file with the ngrok URL

3. Run the app:

   streamlit run app.py
   

4. Open your browser to http://localhost:8501

5. Navigate to the Authentication tab to connect your brokerage accounts

Development Status

This project is currently in development. Authentication with Charles Schwab API is pending resolution.

Technologies Used

• Python
• Streamlit
• Plotly
• Pandas
• OAuth for API authentication