Market Trend Classification Model

Is this a unique feature?

I have checked the "open" and "closed" issues, and this is not a duplicate.

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

The current implementation of the Market Trend Classification Model provides a strong framework for identifying and classifying market regimes based on historical stock price data. However, it lacks functionality for:

Real-Time Market Adaptation: The model does not account for dynamic updates based on real-time market conditions, leading to a delay in identifying emerging trends.
Incorporation of Alternative Indicators: The existing model primarily uses historical price data and basic technical indicators. It does not include alternative data sources such as sentiment analysis from news and social media or macroeconomic indicators, which could significantly enhance model accuracy.
Visualization and Interpretability: There is no interactive dashboard or visualization tool for users to easily interpret the model's output. This limitation can hinder understanding and reduce the model's usability for non-technical users.

Proposed Enhancements

To improve the Market Trend Classification Model, I propose the following enhancements:

Real-Time Data Integration: Integrate APIs (e.g., Alpha Vantage or Yahoo Finance) to fetch real-time stock price and economic data, allowing the model to update and adjust market classifications dynamically.
Sentiment Analysis Integration: Incorporate sentiment analysis using libraries like TextBlob or VADER to evaluate social media and news data, adding a layer of information that reflects market sentiment.
Advanced Visualization: Build an interactive dashboard using frameworks like Dash or Streamlit for users to visualize market trends, classification results, and sentiment analysis outcomes in real time. This will enhance user engagement and interpretability.

Proposed Solution

To technically implement the proposed enhancements, the approach will be as follows:

Real-Time Data Integration: Integrate APIs such as Alpha Vantage or Yahoo Finance to pull real-time stock price data. Automate this process using Python scripts to update the model at regular intervals, ensuring that market classifications reflect current conditions.
Sentiment Analysis: Use Python libraries like TextBlob or VADER to perform sentiment analysis on financial news and social media. Sentiment scores will be incorporated as features in the model, enhancing its ability to capture market sentiment.
Visualization Tool: Develop an interactive dashboard using Dash or Streamlit to provide real-time visualizations of market trends and classifications. Users will be able to interact with the dashboard to adjust parameters, view sentiment analysis, and monitor trend changes.

Do you want to work on this issue?

Yes

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

To technically implement the proposed enhancements for the Market Trend Classification Model, I would follow a structured approach:

Real-Time Data Integration: Set up API connections (e.g., Alpha Vantage, Yahoo Finance) to pull livestock and economic data. Use Python scripts to automate the process, ensuring that the model updates classifications dynamically.
Sentiment Analysis: Implement sentiment analysis using Python libraries (TextBlob or VADER) to analyze social media and financial news. Sentiment scores will be incorporated as input features in the model, improving its classification accuracy.
Interactive Dashboard: Develop a dashboard using Dash or Streamlit that allows users to visualize the model's output and interact with different features (e.g., adjusting periods, and viewing sentiment analysis). This will enhance the model's accessibility for users of all technical levels.

rohitinu6 / Stock-Price-Prediction