OIPD
Probabilities of future stock price, based on options theory
Listed in categories:
AnalyticsGitHubInvesting
Description
This Python project generates probability density function (PDF) PDFs and cumulative distribution functions (CDF) for the future prices of stocks as implied by call options prices. The generated probability distributions reflect market expectations and serve as a useful tool for understanding market-implied uncertainty, skewness, and tail risks.
How to use OIPD?
To use the project, install it via pip, prepare a CSV file with options data, and specify the required parameters in the provided example notebook. The tool will generate the PDFs and CDFs based on the input data.
Core features of OIPD:
1️⃣
Generates probability density functions (PDFs) for stock prices based on call options data.
2️⃣
Calculates cumulative distribution functions (CDFs) for future stock prices.
3️⃣
Utilizes the Black-Scholes formula to convert strike prices into implied volatilities.
4️⃣
Fits a kernel density estimator (KDE) to improve the edge behavior of the PDF.
5️⃣
Allows for customization of solver methods for numerical calculations.
Why could be used OIPD?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Analyzing market expectations for stock price movements based on options data. | ✅ | |
| # 2 | Risk management and assessment of potential stock price volatility. | ✅ | |
| # 3 | Investment strategy development using implied probabilities from options pricing. | ✅ | |
Who developed OIPD?
The project is developed by Tyrneh, who welcomes feedback and contributions from users to enhance the tool's functionality.