HomeProjectsData Science & Big Data AnalyticsAlgorithmic Stock Trend Forecasting with FinBERT Sentiment & LSTM
Data Science & Big Data AnalyticsB.Tech • M.Tech • MCAComplexity: AdvancedIEEE Verified
Algorithmic Stock Trend Forecasting with FinBERT Sentiment & LSTM
Dual-pipeline financial forecasting engine combining Bi-directional LSTM time-series predictions with live financial news sentiment analysis via FinBERT.
Core Stack:PythonFinBERTPyTorch / TensorFlowPandasStreamlitPlotlyFastAPI
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Full Source Code & Database SQL
60-Page IEEE Project Report (.docx & .pdf)
25-Slide Seminar Presentation Deck (.pptx)
50+ Viva Voce Questions & Answers Guide
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Detailed Project Overview
An institutional-style quantitative analytics platform. It consumes live OHLCV price histories from Yahoo Finance and combines them with NLP sentiment polarity scores scraped from Reuters and Bloomberg headlines. Visualizes predicted future candlesticks alongside SHAP feature importance charts.
Academic Problem Statement
Traditional stock prediction models rely solely on technical price indicators and fail to account for abrupt market shifts triggered by breaking economic news and company disclosures.
Proposed Methodology & Flow
1. Financial news headlines vectorized using pre-trained FinBERT transformer.
2. Daily sentiment score combined with technical indicators (RSI, MACD, Bollinger Bands).
3. Multivariate Bi-LSTM network trained to output next 7-day probabilistic price distribution.