Built for INDOvateAI Sprint 2025 | Secured Second Prize š
This project integrates FinBERT-based sentiment analysis with an LSTM-based stock price prediction model to provide a comprehensive market analysis. It dynamically assigns weightage to sentiment and price forecasts to improve investment decision-making.
You can check out our GitHub Repository for codes and models.
š Investors face an overwhelming volume of real-time data, leading to delayed decisions and missed opportunities.
ā Extracting accurate sentiment from unstructured sources is complex and error-prone, posing high-stakes risks.
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Custom Nifty50 database (2014ā2025, 129,377 rows) ā Cleaned & preprocessed for time-series forecasting.
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FinBERT-based Sentiment Extraction ā Trained on 1.4M financial headlines to extract bullish, bearish, or neutral sentiment.
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LSTM-based Time-Series Prediction ā Forecasts stock price trends based on historical market data.
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User-Friendly Dashboard ā Displays prediction charts, source citations, analytics, and investment recommendations.
1ļøā£ Data Acquisition & Reliability
yfinance
for accurate real-time data.2ļøā£ NLP & Sentiment Analysis
3ļøā£ Forecasting & Dynamic Analysis
4ļøā£ Real-Time Processing & Scalability
5ļøā£ Visualization & User Empowerment
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LSTM & FinBERT Integration ā Combines deep learning & NLP for robust stock forecasting.
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Sentiment Analysis from Financial News ā Extracts real-time news sentiment.
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Custom Dynamic Weight Assignment ā Adjusts importance of sentiment vs. prediction confidence.
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Real-Time Market Predictions ā Generates buy/hold/sell signals.
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Market Analysis Dashboard ā Displays real-time sentiment, trend predictions, and historical analysis.
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Multi-Market Adaptability ā Can be extended to crypto, forex, and commodities.
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Research & Analytics Tool ā Useful for financial researchers & institutions.
There are no datasets linked
There are no datasets linked