Financial Stock Price Prediction using Deep Learning

I have a ML code written in python, already working, that uses the past stock value prices to train, predict and validate the stock values for a given share (e.g APPLE). I need to modify the code to train the model with all the information up to date and predict the stock values for the specified time period. *** First Milestone *** 1. Code shall be able to predict any given SYMBOL manually typed, for the given time window. The program shall do the following: a. Prompt to input which SYMBOL to predict b. Prompt to input if the model is going to take all the historical data (Y/n). c. If answer to point b. is "Y" (default), then skip point d. and take all the historical data until current date. d. If answer to point b. is "n", then prompt to input start_date (format DD/MM/YY). Then prompt to input end_date (default current date) e. Prompt to input until which date the model is going to predict (predict_until_date) e. Graph a daily based line chart with the data f. Train the model based on the specified time window g. Make the prediction, graph the result in a daily based line chart h. Save the results in .csv and .json files, both into my Google Drive (I'm going to run this code from Google Colab) 2. Python code in .ipynb format (Google Colab) shall be sent to me. I will make the First Milestone payment (50% of project budget) after succesfully testing the code in Google Colab. --- *** Second Milestone *** 1. Code shall be able to do all the same, but the input will be a .csv file with a list of SYMBOLs and the code shall make the predictions for all of them. a. Input file .csv (provided by me) will be obtained from a specified directory in Google Drive b. Prompt to input points b. to e. from First Milestone, and the code shall use that information for all SYMBOLs in the list c. Print how many SYMBOLs were found in the list d. Graph a daily based line chart for every SYMBOL in the list e. Train the model for ALL of the symbols in the list, SYMBOL by SYMBOL, taking into account the inputs of point b. f. Make the predictions for ALL of the symbols in the list g. Save the results in .csv and .json files, both into my Google Drive 2. Python code in .ipynb format (Google Colab) shall be sent to me. I will make the Second Milestone payment (50% of project budget) after succesfully testing the code in Google Colab. *Other details to be agreed during the project. Potentially, this could grow into a larger project like an app.