Missing Stock Prices

A time series of a stock's highest price during a trading day (at the New York Stock Exchange), is provided. In each test case, the day's highest prices is missing for certain days. By analyzing the data, I will try to identify the missing price for those particular days.

Directories:

• my_hackerrank_solution: shows my accepted code on Hackerrank.
• model: is a broad implementation of the project.
• jupy_note: is a Jupyter notebook version of the project.
• unit_test: for testing some of the models.

Models are:

• Spline Interpolation
• Polynomial Interpolation
• Ridge(regularised) Linear Regression
• Ridge(regularised) Linear Regression (PolynomialFeatures)
• Times Series Analysis: FaceBook Prophet Library
      - https://facebook.github.io/prophet/docs/quick_start.html
      - https://github.com/facebook/prophet

To easily replicate the project:

Run "pipenv shell" from the project root directory, this will create a VIRTUAL ENVIRONMENT and install all dependencies in it.

Model Deployment

A RESTful API with OpenAPI 2.0 specifications was developed to interface the best trained model for price prediction. The API allows HTTP GET, with x as query string.

Parameters:
x - Array or comma separated numerical value(s) corresponding to the date(s). This value is expected to be within the date range used during model training.

• e.g [11, 23] or
• 11, 23

Response Body
{
     "price": [
          28.699501416164807,
          30.373399115774085
     ]
}

Run wsgi.py to initiate the RESTfull API. Default port is 8080. OR run with gunicorn --bind 0.0.0.0:8080 wsgi:API