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Disclaimer:
The files and tools available for download on this page are provided solely for educational and informational purposes. Alvestor does not provide investment, financial, or trading advice, nor is it legally qualified to do so. The tools offered, namely their use of mathematical and financial analysis Python libraries, are intended for use as general analytical aids. Any financial decisions made using these tools are done at the user's own risk. Alvestor assumes no liability for any losses or damages resulting from the use or misuse of these tools, nor does it claim ownership of the libraries therein.
For professional advice, users should consult a licensed financial advisor or other qualified professionals.
By downloading any of these tools, you acknowledge that you have read, understood, and agreed to be bound by the terms
of this disclaimer.
Volatility Surface Generator
Volatility Surface Generator
Input a ticker to terminal, and the script will return graphs of the IV surface, as well as the IV vs. price. The IV Surface Graph displays volatility across strike prices and expiration dates, helping traders understand how volatility changes with respect to strike price and time to expiration. The IV vs. Price Graph plots volatility against the underlying asset's price, illustrating how volatility changes as the asset's price fluctuates.
Created by: Calvin J. Lomax
Terminal Command for Installing Libraries:
pip install tkinter matplotlib yfinance pandas numpy scikit-learn plotly Pillow
Portfolio Optimizer
Portfolio Optimizer
Input a comma-seperated list of tickers and the total portfolio value to terminal, and the script will return the optimal weights, shares to buy, and acquisition price for each entered security, as well as a graph of the portfolio along the efficient frontier. The script uses Harry Marowitz's widely-accepted Modern Portfolio Theory formulas to calculate the optimal portfolio from the given ticker.
Created by: Calvin J. Lomax
Terminal Command for Installing Libraries:
pip install yfinance numpy pandas matplotlib scipy
Covered Call P/L Model
Covered Call P/L Model
Input a stock ticker, current price, strike price, option premium, and time to expiration, this tool calculates the stock’s return and volatility, then models potential future prices. Essentially, it simulates the payoff of a covered call strategy using historical stock data and Monte Carlo simulations. It provides a payoff diagram to visualize profits or losses, helping users assess the risks and rewards of the covered call strategy.
Created by: Calvin J. Lomax
Terminal Command for Installing Libraries:
pip install yfinance numpy matplotlib
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