Stock Price Prediction with Genetic Algorithms

Problem

LSTM model enhanced with evolutionary algorithms for financial time-series forecasting. This project addressed the challenge of ml, finance, researchin the context of ml researcher work.

Constraints

• • Timeframe: Jan–May 2019
• • Role: ML Researcher
• • Stack: Python, Keras, TensorFlow, LSTM, Genetic Algorithms

Process

The development process involved iterative design, implementation, and testing phases. Key focus areas included ml, finance, research considerations and ensuring measurable outcomes.

Results

• 74% reduction in processing time (180→46 min)
• Genetic algorithm hyperparameter optimization
• Euclidean distance evaluation framework

Lessons Learned

This project reinforced the importance of ml, finance, researchbest practices and the value of iterative development approaches.