💡[FEATURE]:

[Idk507]

Is your feature request related to a problem? Please describe. The primary challenge is accurately forecasting the stock prices of Google, Microsoft, and Apple using historical data. Key issues include:

Data Acquisition: Ensuring the historical data is up-to-date and accurately fetched from Yahoo Finance. Model Accuracy: Building robust time series forecasting models using the Prophet library that can accurately predict future stock prices. Visualization: Creating clear and interactive visualizations for both historical and forecasted data to make the information easily interpretable. User Interaction: Allowing users to input specific dates or time periods to get custom stock price forecasts, enhancing the utility of the forecasting tool.

Describe the solution you'd like The project should include the following features:

Automated Data Fetching: Implement a script to automatically fetch and update historical stock price data for Google, Microsoft, and Apple from Yahoo Finance. Forecasting Model: Use the Prophet library to build and fine-tune time series forecasting models for each stock. Evaluate the models for accuracy and reliability. Data Visualization: Develop interactive visualizations using libraries like Plotly or Bokeh. These should display both historical and forecasted stock prices. User Input Feature: Create a user interface (UI) where users can input specific dates or time periods and receive forecasted stock prices for those inputs. This can be a simple web-based dashboard or command-line interface.

Describe alternatives you've considered Different Forecasting Methods: Explore other time series forecasting techniques like ARIMA, LSTM, or Holt-Winters to compare performance with Prophet. Alternative Data Sources: Use other data providers like Alpha Vantage or Google Finance to fetch historical stock price data.

Additional context Technical Requirements: The solution involves time series data handling, model training and evaluation, and interactive data visualization. Scalability: Ensure the system can scale to include more stocks or handle multiple user requests efficiently. User Experience: Focus on making the UI intuitive and responsive for a better user experience. Performance Metrics: Use cross-validation, Mean Absolute Error (MAE), or Root Mean Square Error (RMSE) to evaluate the forecasting models' performance.

Linkedin : https://www.linkedin.com/in/dhanushkumar-r-datascience/ Github: https://github.com/Idk507

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[Akshat111111]

similar work has been already done, so whats unique in your idea??

[Idk507]

Develop a web-based dashboard using Flask Use Plotly or Bokeh to create interactive plots that allow users to zoom in, hover over data points for more information, and toggle between different views Enable users to compare the forecasted performance of multiple stocks on the same graph Combine Prophet with other machine learning models (e.g., LSTM, Random Forest) to improve prediction accuracy and robustness.

On Tue, 2 Jul 2024 at 15:02, Akshat Sharma @.***> wrote:

similar work has been already done, so whats unique in your idea??

— Reply to this email directly, view it on GitHub https://github.com/Akshat111111/Hedging-of-Financial-Derivatives/issues/435#issuecomment-2202555354, or unsubscribe https://github.com/notifications/unsubscribe-auth/AWIXCOIH3U7SKTSGVSAG4DTZKJXTZAVCNFSM6AAAAABKGY3EMSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMBSGU2TKMZVGQ . You are receiving this because you authored the thread.Message ID: <Akshat111111/Hedging-of-Financial-Derivatives/issues/435/2202555354@ github.com>