Metadata-Version: 2.1
Name: pyindia-stock
Version: 0.0.1
Summary: Stock Prediction using FBProphet
Home-page: https://github.com/Shivananmn/pyindia-stock
Author: Shivanand
Author-email: shivanandnaduvin@gmail.com
License: MIT License
Description: # StockAnalysis
        
        ## Installation
        As ususal installation 
        ```
        $ pip install pyindia-stock
        ```
        It uses FBProphet for analysis and prediction. 
        
        ## Getting Started
        You can use command-line script. 
        `pyindia_stock -h` will give the following.
        
        ```
        usage: pyindia_stock [-h] --index INDEX --from_date FROM_DATE
                             [--to_date TO_DATE]
        
        Analyzing the Past Behavior of an index from Indian Stock Market
        
        required arguments:
          --index INDEX         NSE index name
          --from_date FROM_DATE
                                starting date to consider for evaluation. Date in
                                d/m/Y,H format, H: should be in 24hrs format
        
        optional arguments:
          --to_date TO_DATE     Specific/present date to consider for evaluation. Date
                                in d/m/Y,H format, H: should be in 24hrs format.
                                Default: Sets to present date and time.
        ```
        
        You can use it in scripts.
        ```
        # import pyindia_stock
        $ from pyindia_stock import StockAnalysis
        
        # run StockAnalysis with index and period_from as arguments.
        $ StockAnalysis("SBIN",period_from="01/01/2000,15")
        
        # StockAnalysis has the following arguments:
        # - index: Only NSE index name
        # - period_from: starting date to consider for evaluation. Date in "%d/%m/%Y,%H" format, H: should be in 24hrs format.
        # - period_to: Specific/present date to consider for evaluation. Date in "%d/%m/%Y,%H" format, H: should be in 24hrs 				format.Default: Sets present date and time.
        #StockAnalysis has attributes 
        # - read_data: Read stock Dataframe
        # - fbprophet: Instance of class FBProphet with daily seasonality.
        # - is_data_available: if data has loaded to dataframe and has suitable format, then in is true
        
        ```
        
        ## How to use it?
        Colab starter notebook: 
        
        ## About FBProphet:
        Prophet is a forecasting procedure implementation in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts. \
        Prophet follows the sklearn model API. We create an instance of the Prophet class and then call its fit and predict methods.\
        The input to Prophet is always a dataframe with two columns: ds and y. The ds (datestamp) column should be of a format expected by Pandas, ideally YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. The y column must be numeric, and represents the measurement we wish to forecast.
        
        
        
Platform: UNKNOWN
Requires-Python: >3.5.2
Description-Content-Type: text/markdown
