Enhancing Real-Time Data, Model Performance, and Scalability

[Keerthi421]

Is this a unique feature?

• [X] I have checked "open" AND "closed" issues and this is not a duplicate

Is your feature request related to a problem/unavailable functionality? Please describe.

The current reliance on historical data limits the predictive accuracy of the stock price models, making it challenging to respond to real-time market fluctuations. This situation undermines the effectiveness of our predictions and could lead to significant inaccuracies.

Proposed Solution

Real-Time Data Integration: Integrate live data feeds from APIs such as Alpha Vantage or Yahoo Finance to ensure predictions reflect current market conditions, enhancing accuracy and responsiveness.

Advanced Model Optimization: Leverage LSTM models and hyperparameter tuning to improve forecasting performance, along with advanced techniques like cross-validation to mitigate overfitting and ensure robust generalization.

Scalability Enhancements: Revamp the data pipelines to support larger datasets and implement cloud solutions (e.g., AWS Lambda) for dynamic scaling, ensuring the system remains efficient under increased load.

Screenshots

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Do you want to work on this issue?

Yes

If "yes" to above, please explain how you would technically implement this (issue will not be assigned if this is skipped)

To implement real-time data integration, I will research relevant APIs for seamless live data ingestion. I will apply LSTM frameworks to improve model architecture and utilize libraries for effective hyperparameter tuning. Additionally, I will redesign the data processing pipeline for scalability and deploy cloud services to enhance performance Please assign me this issue . @rohitinu6

[rohitinu6]

@Keerthi421 , please ensure you star this repo All the best

[Keerthi421]

@rohitinu6 ,Thanks for assigning .