Skip to content

This strategy works for every market condition irrespective of the movement

Notifications You must be signed in to change notification settings

michaelfred533/Hedging-of-Financial-Derivatives

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

16 Commits
 
 
 
 
 
 

Repository files navigation

Hedging of Financial Derivative

Work In Progress We are building a robust code snippet tool which is based on :

-Financial Programming

-Deep Learning

-Machine Learning

Contributing

Star the repo on GitHub. In general, we follow the "fork-and-pull" Git workflow.

  1. Fork the repo on GitHub
  2. Clone the project to your own machine
  3. Commit changes to your own branch
  4. Push the code
  5. Submit a Pull request so that we can review your changes

This project relies heavily on issues.

Ideas are an important source of contribution.

We appreciate your feedback! We encourage you to contribute to this repository in the most straightforward and transparent way possible, whether it's:

Reporting a bug Discussing the current state of the code Submitting a fix Proposing new features or Implementation

Hedging of Financial Derivative

A Hedging is a market neutral trading strategy enabling traders to profit from virtually any market conditions: uptrend, downtrend, or sideways movement. This strategy is categorized as a statistical arbitrage and convergence trading strategy.

  1. To explain what our strategy exactly does and how is it working, we have taken the example of the Indian Market, we have taken a few securities over a specified time interval. Then we have found all the cointegrated pairs of stocks from all the possible pairs of securities by considering all the stocks having p-value less than a certain cut-off. For further analysis, we have identified the security pair with minimum p-value.
  2. Next we have calculated the spread of the two series. In order to actually calculate the spread, we use a linear regression to get the coefficient for the linear combination to construct between our two securities. Since, the absolute spread isn't very useful in statistical terms. It is more helpful to normalize our signal by treating it as a z-score. A Z-score is nothing but a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean.
  3. Next we define our strategy and generate the trading signals during backtesting on another time period which we have defined-
    • Go "Long" the spread whenever the z-score is below -1.0
    • Go "Short" the spread when the z-score is above 1.0
    • Exit positions when the z-score approaches zero 4)Next we have created calculated the returns of our portfolio on the basis of strategy

About

This strategy works for every market condition irrespective of the movement

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%