Python-Based NASDAQ Stock Prediction Model using pytorch

I am looking for an expert in machine learning and deep learning to assist me in the forecasting of NASDAQ Composite from historical stock prices. The project involves applying Python programming language to timeline data for the development of an intelligent and efficient stock prediction model.

The ideal candidate for this project would:
- Have deep expertise in Machine Learning and Deep Learning concepts, specifically as applied to stock market predictions.
- Have proficiency in Python and associated libraries used for machine learning and data science such as Scikit-learn, TensorFlow, Keras, and Pandas.
- Have experience in working with time-series data and financial modelling.
- Have a sound understanding of the stock market, particularly the NASDAQ Composite index.
- Model should plot the graph for the last 2 months actual vs predicted price. Also should predict next 30 days data.
- As we get the actual data, model should learn from the actual data.
- I will provide the list of stocks, its around 30 in number.
Your role will involve:
- Cleaning, sorting, and presenting historical stock price data in a manner that can be used for model training.
- Creating a deep learning model using Python to predict future trends of the NASDAQ Composite index.
- Validating the prediction model against recent data to test its reliability and accuracy.

Please note that this model's efficiency will define its success, forecasting with both preciseness and speed. More details will be provided once we progress into the project.