akshat111111/hedging-of-financial-derivatives

This strategy works for every market condition irrespective of the movement

Hedging of Financial Derivative 💼

Welcome to Hedging of Financial Derivative! 📈

This project focuses on implementing a robust trading strategy using statistical arbitrage and convergence techniques for hedging financial derivatives.

Video Description of the Project and Repository Overview

Hedging.of.Financial.Derivatives.-.Made.with.Clipchamp.mp4

Overview 📊

The project utilizes:

• Financial Programming 💻
• Deep learning 🧠
• Machine learning 🤖

Getting Started 🚀

To contribute to this project, follow these steps:

1. Fork the repository on GitHub.
2. Clone the forked project to your local machine: git clone <forked_repo_url>
3. Create a new branch for your work: git checkout -b your-branch-name
4. Make changes and improvements in your branch.
5. Commit your changes: git commit -m 'Add your descriptive commit message'
6. Push your changes to your forked repository: git push origin your-branch-name
7. Submit a Pull Request (PR) to the main repository for review.

Ways to Contribute 🛠️

We welcome contributions in various forms, such as:

• Reporting bugs or issues 🐞
• Providing feedback on the existing codebase 💬
• Submitting fixes for identified issues ✅
• Proposing new features or enhancements 🚀
• Improving documentation 📝
• Adding code snippets, algorithms, or techniques related to financial programming 💼

Our Contributors

Thanks to These Amazing People 😀

• Kiran Mishra (misskiran)
• PRIYANSHU TIWARI (PRIYANSHU2026)
• Diptarup Chakravorty (diptarup794)
• Bharti Joshi (bhartijoshi04)
• Shivani Sharma (Shivani-Sharma-23)
• G Richard (gratusrichard)
• Sanmarg Sandeep Paranjpe (sanmarg)
• Mansi Yadav (FreeSpirit11)
• Saswat Susmoy (Saswatsusmoy)
• Shubha Ruidas (shubha987)
• Alisha Singh (alishasingh06)
• Amarta Waghani (Amarta113)
• Tejasvini Goel (tejasvinigoel)
• Apoorv (apooyadv)
• amishhaa (amishhaa)
• aryan1165 (aryan1165)
• Divyanshi1002 (Divyanshi1002)
• Pradnya (PradnyaGaitonde)
• michaelfred533 (michaelfred533)
• mdutta1234 (mdutta1234)
• adi271001 (adi271001)
• Su-creator-spec (Su-creator-spec)
• Pranshu-jais (Pranshu-jais)
• Abirami Gurushanker (Abirami Gurushanker)
• Titiksha Agrawal (AgrawalTitiksha)
• Jagjeet Singh Chauhan (JagjeetChauhan)
• Srijan Sarkar (SrijanSarkar)

Code Guidelines 📝

Please adhere to proper coding standards and conventions:

• Follow clear and descriptive commit messages.
• Provide adequate comments within the code for readability.
• Thoroughly test your changes before submitting a PR.

Issue Tracking 🔍

We use GitHub issues to manage tasks. Feel free to open an issue for bugs, suggestions, or discussions related to the project.

Code of Conduct 🤝

We maintain a Code of Conduct to ensure a welcoming environment for all contributors. Please review and follow our Code of Conduct.

Thank you for your interest in contributing to the Financial Derivative Hedging Project! 🙌

Example Strategy 📊

Hedging is a market-neutral trading strategy that enables 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.

How It Works:

1. Cointegration Analysis: Identify cointegrated pairs of stocks within a specified time interval.
2. Spread Calculation: Calculate the spread of the cointegrated pairs using linear regression.
3. Signal Generation: Generate trading signals based on Z-score normalization.
   • 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. Backtesting: Test the strategy on historical data to evaluate performance.
5. Portfolio Returns: Calculate and analyze the returns of the portfolio based on the strategy.

💪 Thanks to all Contributors

Thanks to all the contributors for helping this project grow! 🍻

🙏 Support

Don't forget to leave a star ⭐ for this project!

Crafted with ♥ by @Akshat111111

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hedging-of-financial-derivatives's Issues

OCR implementation of financial documents

The Optical character recognition or optical character reader (OCR) is the conversion of images of typed, handwritten or printed text into machine-encoded text, from a scanned documents

Sentimental Analysis in Hedging

Its all about the behavior of hedged market based on sentimental analysis

Building a cryptography algo for securing AI model

You are free to use any algo like DES,AES or other

Adding a reinforcement learning model

It will involve a basic implementation of self learning model

Evaluation metrices RMSE, MSE and MAE

Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean absolute Error (MAE) are a standard way to measure the error of a model in predicting quantitative data.

Enhancing the representation

You can use anything you want in order to improve the graphical representation of the given things.

Implementing using Reinforcement Learning

Using reinforcement learning to implement hedging entails developing a trading environment in which the (the learner) makes decisions based on market observations. The purpose is to maximize profit while minimizing loss (negative profit).

Hedging Currency Exposure

We may utilize currency exposure, which is a significant component of the volatility of a global portfolio. Currency risk can be reduced by hedging currency exposure in overseas investments.

Building an web app

It can be implemented using any framework of python or javascript.

Trading bot implementation

Decentralized Finance (DeFi) Integration

You can implement using any of the existing technologies

Optimization of the code

This code can be optimized by integrating more technical indicators or other concepts of Machine learning or deep learning

Hedging using KNN

KNN is a fundamental machine learning approach that works with a database of categorized data. It may be used to categorize each form of investment in a database into high, moderate, and low profit potential groups.

2024 Indian election sentimental analysis

The task is to find the sentiments and corelation with financial market movement.

Automated and Algorithmic Trading

It involves comprehensive web research and data collection efforts related to automated trading and algorithmic trading.

Hedging using DBSCAN

Build a model using DBSCAN and compare it with the model built with KNN.

Building a basic python library

You can refer to this https://www.reddit.com/r/learnpython/comments/13ouob6/how_to_create_a_python_packagelibrary/?rdt=50001 for more clarity

Integrated Sentiment Analysis of Indian Political Elections and Time Series Forecasting for Financial Market Correlation

Sentiment Analysis:

Using transformer-based models like BERT or DistilBERT for sentiment analysis is a good choice, especially considering their effectiveness in capturing contextual information. I plan to fine-tune these models on Indian political news articles and Twitter posts, as these sources contain a wealth of information about public sentiment. Additionally, I'll utilize a 5-point rating scale (very good, good, moderate, bad, very bad) to provide granularity in sentiment analysis, which can be helpful for market analysis.

Financial Market Analysis:

Time series analysis techniques like ARIMA (AutoRegressive Integrated Moving Average) can be useful for analyzing financial market movements. ARIMA models are commonly used for forecasting future values based on past observations. If I'm not familiar with time series analysis yet, it's a good area to explore, especially for understanding and predicting market trends.

Correlation Analysis:

Once I have sentiment scores from the text data and market movements from the financial data, I can use statistical techniques like the Pearson correlation coefficient to identify any relationships between sentiment and market movements. The Pearson correlation coefficient measures the linear correlation between two variables, providing insights into how changes in sentiment relate to changes in financial market movements.

Overall, my approach integrates sentiment analysis with financial market analysis effectively. As I delve deeper into each component, gaining a solid understanding of time series analysis techniques like ARIMA will enhance my ability to uncover insights from financial data. Additionally, applying statistical methods like the Pearson correlation coefficient will help me quantify the relationship between sentiment and market movements accurately.

Incorporating backtesting library

Backtesting is a Python module for backtesting trading strategies against historical data. It is compatible with a wide range of financial products, including forex, cryptocurrency, stocks, and futures. For best usage, the library is built on top of ecosystem libraries such as Pandas, NumPy, and Bokeh.

Monte carlo simulation in hedging

Monte Carlo simulation provides a straightforward approach for numerically pricing stocks; but, it does not immediately produce the weights required to form a replicating portfolio.

Integrating a fintech model with broker

The task is to link the fintech application which uses any trading strategy to a broker like upstox or zerodha.

Automated Greeting Workflow

I propose to create a GitHub workflow named "Greetings". This workflow will automatically greet users who create new issues or pull requests in this repository. The Greeting will look like "Hi there! Thanks for opening this issue. We appreciate your contribution to this open-source project. We aim to respond or assign your issue as soon as possible."

Please assign this issue to me.

enhancement of SK-learn models

we can use hyperparamete method to get good accuracy and give better result and also use logistic regression especialy for binary digits and also apply ROC method to increase accuracy

there are other method like pipeline but it may not suitable for financial datasets.

give your openions

Adding Facebook prophet

Facebook Prophet is a strong time series forecasting tool that may be used for derivative hedging. It is an open-source program created by Facebook that forecasts time series data using a decomposable additive model. This model considers trends, seasonality, and holidays, making it especially useful for data with numerous seasonalities.

Add VIX (volatility index indicator)

The VIX index measures the implied volatility of S&P 500 index calls and options. It's known as the fear gauge since its value rises at times of increasing volatility. As a volatility-specific hedge, investors can utilize ETFs that track the VIX to go long on the VIX.

enhance readme

will add some contribution guidelines , pictures in it

Hedged Cryptocurrencies

Hedging in cryptocurrency works on the same principles as hedging in traditional financial markets. It entails investing in a linked asset that is predicted to move in the opposite way as the principal asset.

Creating CONTRIBUTING.md

want to create a separate contributing.md for proper instruction related to it
@Akshat111111 please assign me the issue

Real Estate Price Prediction Model

I would like to build a Real Estate Price Prediction Model, if this enhancement aligns with the goal of this project can i go ahead and start working on this ?

Mobile Application development

It can be implemented by using java,C++,kotlin or swift

Building Risk Assesment Model

This risk assesment model can use any machine learning algorithm to estimate past,current and future risk of the model.

Typo Error in README File

I noticed a minor typo error in the README file of the project.
There is spelling mistake in README file about the code of conduct link.

Adding Mathematical methods for quantitative finance

You can add math models which helps in financial forcasting.

Crude Oil Analysis

It involves data analysis and prediction of crude oil

Data analysis on financial instruments

undertake web research,data collection and data analysis on it

we can use ANN or logistic regression

for better accuracy and better result we can use these two algorithms especially for financial prediction

Missing CONTRIBUTING.md File in Repository

The repository currently lacks a CONTRIBUTING.md file, which is an important resource for guiding potential contributors on how to engage with the project effectively. A CONTRIBUTING.md file outlines the contribution guidelines, coding standards, and procedures for submitting pull requests, thus streamlining the contribution process and ensuring consistency across contributions.

Can you assign this issue to me under GSSOC 24

text summarization model

Hi,i would like to add a model which can summarize the text using Python and the TextRank algorithm.I will use the Natural Language Toolkit (NLTK) library for text processing and Networkx for implementing TextRank.

Please assign this issue to me under GSSOC'24 .

Feature Extraction in hedging

In financial derivatives hedging, feature extraction is locating and extracting useful data or features from raw data that may be utilized to make forecasts or choices. These characteristics may include historical price data, volatility, interest rates, and other market indicators in the context of financial derivatives.

For hedging, you may utilize machine learning models such as Feedforward Neural Networks (FNN) and Long Short Term Memory (LSTM), which collect and analyze data to forecast effective hedging strategies.

Random Walk in python.

Random Walks are are the limiting case of Auto Regressive One.

Hedging in scalping

Working on the model with timeframe 5 min and 1 min.

compare the regions financial market growth increase with the financial index increase

I will be using GDP and Market Capital as Parameters to determine the relation between financial market growth index with the financial index increase and estimate the increase or decrease of GDP percentage with respect to Increase or Decrease in Market Capital.
I request to be assigned this with appropriate GSSOC tags.

ARIMA implementation

ARIMA (Autoregressive Integrated Moving Average) is a common time series forecasting technique, which is also used to hedge financial derivatives. The ARIMA model predicts future values of a time series based on past values. It's especially valuable for hedging since it can forecast future price fluctuations of a financial derivative, which can then be used to change the derivative's position to hedge against prospective losses.

You may use the following methods to create an ARIMA model for hedging financial derivatives:

1)Data Preparation: First, collect historical data for the financial derivative in question. Each trading day's opening, high, low, and closing prices should be included in this data.
2)Identify Parameters of the model: ARIMA models are defined.

3)Each trading day's opening, high, low, and closing prices should be included in this data.
Determine Model Parameters: Three parameters define ARIMA models: p (the order of the autoregressive portion), d (the order of differencing), and q (the order of the moving average part). You must determine appropriate values for these parameters. This is often accomplished via the use of approaches such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC). In some circumstances, a grid search or a random search may be used to determine the optimal settings.

4. Fit the Model: Once the parameters have been determined, you may apply the ARIMA model to your data. This entails determining the coefficients for the autoregressive and moving average components of the equation.
   Forecast Future Prices: Once the model has been fitted, you can use it to forecast future prices of financial derivatives. This is accomplished by employing the model's equations to determine anticipated prices based on previous prices.
   develop a Hedging Strategy: You may develop a hedging strategy based on expected future pricing. For instance, if the forecasted price is expected to rise, you may purchase the derivative to profit from the rise. If the forecasted price falls, you may want to sell the derivative to reduce your losses.

Implementing the same thing with different markets, indices or stocks

The same code can be implemented with different stocks exchange of the world or different pairs of stocks

Update README.md and adding code of conduct

updating command and in details.

@Akshat111111 please assign me the issue

Inclusion of Web Scraping

Implementing the web scraping using html or any API.

Developing Machine learning models for GOLD PRICE PREDICTION

@Akshat111111 Hey 👋 , I have checked the Gold price prediction Code that was trained over linear regression model in your code base.
I have some better solutions where we can use XG boost and Gradient Boosting Algorithms etc inorder to produce precise results as price prediction is a sensitive task.

My approach : First I would be doing Proper EDA over the I would be using and then i would be using Different Machine learning models and train them over different hyperparameters to obtain the best results , i would also provide the performance analysis and a proper conclusion of the performance.

I'm a Machine learning and Deep learning dev and have been constantly contributing , Can you please assign me this Issue with a GSSoC '24 contribution tag with appropriate level tag as it requires alot of efforts

Fraud Detection with Logistic Regression

Objective : This model would predict whether a transaction is fraudulent or not based on transaction features.

This model would understand data, preprocess, train logistic regression; assess metrics, interpret coefficients; deploy for real-time security .

0-Normal
1-Fraud

Value of risk calculation(Var)

VaR is a measure of the risk of investment loss. It calculates how much a group of assets would lose (with a specified probability) in a specific time period, such as a day, under typical market conditions.

Fix README.md

The README.md file is a bit unformatted and could be difficult for understanding for some people. It could be redone in proper markdown format.

Kindly assign this to me under gssoc.