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Machine Learning
Advanced Quantitative Analysis of Equity Price Dynamics
Overview
This project dives deep into the mathematical models governing stock price movements. It employs advanced statistical methods and machine learning algorithms to predict future price trends and volatility, providing a robust framework for algorithmic trading strategies.
The Problem
Financial markets are notoriously volatile and non-linear, making traditional prediction models insufficient. Investors need more sophisticated tools to understand underlying patterns and manage risk effectively.
The Solution
We implemented a hybrid approach combining classical time-series models (ARIMA, GARCH) with modern deep learning architectures (LSTM, GRU). The system processes historical minute-level data to forecast short-term price movements with improved accuracy.
Key Features
- Time-series forecasting with ARIMA & LSTM
- Volatility modeling using GARCH
- Sentiment analysis integration
- Backtesting engine for strategy validation
- Interactive data visualization dashboards
Tech Stack
PythonPandasScikit-learnTensorFlow/KerasMatplotlibJupyter Notebooks