ETF-Analyzer

Financial Database and Web Application using SQL, Python, and Voilà library to analyze the performance of a hypothetical ETF.

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Technologies

This project leverages python 3.7 with the following:

• JupyterLab - JupyterLab is a web-based user interface designed for data analysis.

• pandas - Flexible and powerful data analysis / manipulation library for Python.

• numpy - The fundamental package for scientific computing with Python.

• sqlalchemy - Object Relational Mapper that gives the full power and flexibility of SQL.

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Installation Guide

Before running the application first install the following dependencies.

  pip install jupyterlab
  pip install pandas
  pip install numpy
  pip install sqlalchemy

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Examples

Writing a SQL SELECT statement that reads all the PYPL data from the database. Reads the PYPL data from the database into a Pandas DataFrame.

# Write a SQL query to SELECT all of the data from the PYPL table
query =  """
SELECT * 
FROM PYPL
"""

# Use the query to read the PYPL data into a Pandas DataFrame
pypl_dataframe = pd.read_sql_table('PYPL', con=engine)

Using hvPlot to create an interactive visualization for the PYPL daily returns.

# Create an interactive visualization with hvplot to plot the daily returns for PYPL.
pypl_dataframe[['time', 'daily_returns']].hvplot(
    x='time', y='daily_returns', xlabel='Date', ylabel='Daily Returns',
    title="PayPal Daily Returns (2016-12-16 to 2020-12-04)")

Calculating annualized returns for the portfolio and displaying result.

# Create a DataFrame that displays the mean value of the “daily_returns” columns for all four assets.
etf_portfolio_returns = etf_portfolio['daily_returns'].mean(axis=1)

# Use the average daily returns provided by the etf_portfolio_returns DataFrame 
# to calculate the annualized return for the portfolio. 
annualized_etf_portfolio_returns = etf_portfolio_returns.mean() * 252

# Display the annualized return value of the ETF portfolio.
print(f"The Annualized Return of the Portfolio is {annualized_etf_portfolio_returns*100:.2f}%")

Using hvPlot to create an interactive visualization for the cumulative return values of the ETF portfolio.

# Using hvplot, create an interactive line plot that visualizes the ETF portfolios cumulative return values.
etf_cumulative_returns = pd.DataFrame(etf_cumulative_returns)
etf_cumulative_returns.columns = ['cumulative_returns']
etf_cumulative_returns = etf_cumulative_returns.join(pypl_dataframe['time'])

#etf_cumulative_returns

etf_cumulative_returns.hvplot(
    x='time', y='cumulative_returns', xlabel='Date', ylabel='Cumulative Returns',
    title="ETF Cumulative Returns (2016-12-16 to 2020-12-04)")

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Usage

To use the etf_analyzer application simply clone the repository and run the etf_analyzer.ipynb with:

python etf_analyzer.ipynb

Below you will find screenshot demonstrations.

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Contributors

Brought to you by Robert Giannini. LinkedIn: https://www.linkedin.com/in/robertgianninijr/

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License

MIT