https://github.com/fedesgh/parkinson_volatility_spread_on_cedears

Creating a function that returns a graph with the difference between Parkinson's volatility and regular volatility given a certain bounds
https://github.com/fedesgh/parkinson_volatility_spread_on_cedears

numpy pandas pickle seaborn

Last synced: 12 days ago
JSON representation

Creating a function that returns a graph with the difference between Parkinson's volatility and regular volatility given a certain bounds

• Host: GitHub
• URL: https://github.com/fedesgh/parkinson_volatility_spread_on_cedears
• Owner: Fedesgh
• License: apache-2.0
• Created: 2024-10-02T19:24:48.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-10-24T18:45:45.000Z (26 days ago)
• Last Synced: 2024-10-25T21:09:24.618Z (25 days ago)
• Topics: numpy, pandas, pickle, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 189 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
## Motivation

Calculating the difference between Parkinson's volatility and normal volatility is useful for trading low-liquidity financial assets.

**A high Parkinson's volatility with a low normal volatility tells us about a profitable trading opportunity with low risk.**

In this case, we create a function that returns a bar chart with the top-20 spreads given 2 limits: an upper limit for the volatility and a lower limit for the difference value.

Market data is from the last 3 months provided by **yfinance**

## Basic Functions 

Formulas for each type of volatility.

Normal volatility has as input the **Close** price serie.

Parkinson´s volatility has as input **Low** and **High** price series.

![images/SharedScreenshot.jpg](images/SharedScreenshot.jpg)

We then define our **v_df** dataset that we will work with, which contains both volatilities with their difference for each Ticker. 

![images/df.jpg](images/df.jpg)

## Final function

 The last function **find_ticker** with inputs: 

 

♦ **dif** which is the minimum difference between both volatilities that we want  

 

 ♦ **v** which is the maximum normal volatility (**Risk**) that we want to assume 

 

Search in **v_df** for all the stocks that meet those requirements and return a bar chart with the first 20 ordered by the difference between volatilities

![images/final_function.jpg](images/final_function.jpg)

![images/barchar.png](images/barchar.png)