Stock Price Prediction with Genetic Algorithms

Problem

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

Constraints

• • Timeframe: Jan 2019 - May 2019
• • Role: ML Engineer
• • 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 optimization
• Euclidean distance evaluation

Lessons Learned

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