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Interactive Backtest Dashboard

Streamlit-powered interactive visualization dashboard for backtest results.

Features

Performance Metrics

  • Total Return: Overall P&L percentage
  • Sharpe Ratio: Risk-adjusted returns
  • Maximum Drawdown: Worst peak-to-trough decline
  • Win Rate: Percentage of profitable trades
  • Volatility: Annualized return volatility

Visualizations

  1. Equity Curve & Drawdown

    • Interactive time-series plot
    • Shows portfolio value over time
    • Drawdown visualization

  2. Execution Analytics

    • Slippage distribution (basis points)
    • Latency distribution (milliseconds)
    • P99 latency tracking

  3. Market Microstructure

    • Volume imbalance (bid/ask ratio)
    • Color-coded bar chart
    • Helps identify market conditions

  4. Trade Log

    • Recent trades table
    • Symbol, side, quantity, price
    • Slippage and latency per trade

Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Run Dashboard

# From project root
streamlit run src/dashboard/app.py

# Or with custom port
streamlit run src/dashboard/app.py --server.port 8502

3. Access Dashboard

Open browser to http://localhost:8501

Configuration

Sidebar Options

  • Show Trade Markers: Display trade execution points
  • Show Drawdown: Toggle drawdown subplot

Quick Stats

  • Real-time metrics in sidebar
  • Delta indicators for quick assessment

Data Sources

The dashboard can load data from:

  1. Sample Data (default): Synthetic data generator
  2. JSON Files: Load from src/dashboard/data/*.json
  3. Database: Connect to PostgreSQL/MongoDB
  4. Live Backtest: Real-time updates

Customization

Add New Visualizations

# In app.py
st.header("My Custom Chart")
fig = go.Figure()
fig.add_trace(go.Scatter(x=data['x'], y=data['y']))
st.plotly_chart(fig, use_container_width=True)

Change Color Scheme

# Modify color palette
COLOR_SCHEME = {
    'primary': '#2E86AB',
    'secondary': '#A23B72',
    'accent': '#F18F01',
    'success': '#06A77D'
}

Screenshots

(Run the dashboard to see live interactive charts)

Tech Stack

  • Streamlit: Web framework
  • Plotly: Interactive charts
  • Pandas: Data manipulation
  • NumPy: Numerical computations

For Recruiters

This dashboard demonstrates:

  • Full-stack capability: Backend (Python) + Frontend (Streamlit)
  • Data visualization: Complex financial metrics
  • UX design: Clean, professional interface
  • Production-ready: Deployable to cloud (Streamlit Cloud, AWS, GCP)

Deployment

Streamlit Cloud

# Free hosting
1. Push to GitHub
2. Connect to streamlit.io
3. Deploy from repo

Docker

FROM python:3.10
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["streamlit", "run", "src/dashboard/app.py"]

License

Same as parent project.