Stock Trend Prediction & Financial Analytics
AI-powered financial forecasting web app utilizing LSTM neural networks for deep learning stock price prediction with moving average fallback and VADER sentiment analysis.
Problem and Solution Breakdown
🎯 The Engineering Problem
Traditional stock forecasting systems suffer from data leakage and look-ahead bias while failing to incorporate real-time market sentiment into price movements.
💡 Architecture Solution
Trained a multi-layer LSTM neural network on scaled historical OHLC price sequences paired with VADER NLP sentiment scoring from real-time financial news feeds.
System Architecture & Data Pipeline
Deep Learning Time-Series Forecasting & News Sentiment Pipeline
Yahoo Finance API
OHLC historical price & news data stream
MinMax Scaling
Sequence windowing & feature scaling
Keras LSTM Network
Multi-timeframe (1–90 day) price forecast
Plotly + Streamlit
Candlestick overlays & VADER sentiment score
Key Features & Measured Impact
- ✦Predicts stock market trends using pre-trained TensorFlow/Keras LSTM neural networks.
- ✦Includes dynamic fallback trend analysis using 50-day & 200-day moving averages.
- ✦Real-time market news integration with VADER sentiment scoring and interactive Plotly candlestick charts.
📊 Measured Outcome:
Provided interactive multi-timeframe price projection charts with technical moving average overlays (50-day & 200-day) and walk-forward validation.
🛠️Technologies & Frameworks
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