pyindia-stock 0.0.1

Stock Prediction using FBProphet

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.