Financial Analysis - CallPut Skew

This project aims to analyze the skewness of call and put options in the financial market. By examining the skewness, we can gain insights into the market sentiment and potential price movements.

This project is still in active development with only rudimentary functionality currently.

Table of Contents

• Introduction
• Data Collection
• Data Preprocessing
• Skewness Analysis
• Results
• Conclusion

Introduction

Options are derivative contracts that give the holder the right, but not the obligation, to buy (call option) or sell (put option) an underlying asset at a predetermined price within a specific time period. The skewness of call and put options refers to the asymmetry in the distribution of their implied volatility.

Planned functionality includes:

Data Collection

To perform the analysis, historical options data will be collected from reliable financial data sources. The data will include the prices and other relevant information of call and put options for a specific set of underlying assets.

Data Preprocessing

The collected options data will undergo preprocessing to clean and transform it into a suitable format for analysis. This may involve removing outliers, handling missing values, and normalizing the data.

Skewness Analysis

The skewness analysis will involve calculating the skewness of call and put options for different underlying assets. Various statistical techniques and visualizations will be used to analyze and interpret the skewness values.

Results

The results of the skewness analysis will be presented and discussed in this section. The findings will provide insights into the market sentiment and potential price movements based on the skewness of call and put options.

Output Explanation

The output of the main.py script provides a summary of the skewness analysis results. It includes the skewness values for call and put options of different underlying assets, along with any relevant statistical measures. This information helps us understand the asymmetry in the distribution of implied volatility and provides insights into the market sentiment and potential price movements.

Key Greeks: Delta: Sensitivity to changes in the underlying asset's price. Gamma: Sensitivity of Delta to changes in the underlying asset's price. Vega: Sensitivity to changes in volatility. Theta: Sensitivity to the passage of time (time decay). Rho: Sensitivity to changes in the risk-free interest rate.

Future Extensions and Ideas

1. Custom Curve building for options pricing
2. Using blackschoels or binomials for options pricing
3. Historical Price charting
4. Use of ib_async to pull optiosn chain and historical