stockit

stockit is a python module that aids in easy price estimation and analysis of stock prices

Stockit is a python object that on initialization requires a Pandas dataframe containing historic stock prices.
Stockit is designed to work with the close price and searches for a close column in the dataframe

stockit has the ability to download stock information directly from yahoo finance.

Install:

Method 1 (pip):

pip3 install stockit

Method 2 (github clone)

step 1: clone the github repo

step 2: within the cloned directory, run the following command:

pip3 install .

from stockit import downloadData, returnData
# downloads data from yahoo finance and stores it as a CSV
downloaded = downloadData("NVDA")

# downloads data from yahoo finance and returns it as a pandas dataframe
data = returnData("NVDA")

stockits regression usage can be demoed here: [bencaunt1232.pythonanywhere.com] type /stockit-app/[stock name] to make the estimation

stockits regression algorithms are implemented using sci-kit learn. More information can be found here: www.scikit-learn.org

stockit object usage :

get stock data with Close/close column from a csv or other file as a pandas dataframe

import pandas as pd
data = pd.read('example.csv')

import the stockit class

from stockit import stockit_class

OR download the data directly from yahoo finance using stockit:

from stockit import returnData
# gets stock price history from yahoo finance from the past 5 years
data = returnData("TSLA", years = 5)

then lets create an instance of stockit_class, passing it our pandas dataframe

stockit = stockit_class(data)

from here we can do a few things:

1. Simple linear Regression
2. Support Vector Regression
3. Plot Moving Average
4. Simply Plot data

Plot Data:

stockit.plotData(name="TSLA")

Regression analysis:

 #next day that we will estimate the price of
 next = len(data)+1

 # fit the model to the dataset
 stockit.train(index = 300)

 #make estimation on the next day
 print(stockit.predict(next))

Stockit can also use support vector regression to achieve a tighter fit to the data:

 stockit.train(index = 300, SVRbool = True)
 print(stockit.predict(next))

stockit also has a custom regression model known as SSRR that is a modified version of the linear regression algorithim with randomness.

stockit.train(SSRRbool = True)

Moving Average Analysis:

 #simply call the moving_avg() method of stockit
 #index specifies the length of each window, lower = closer fit to live data, higher = smoother line, your choice
 stockit.moving_avg(index = 35)

Regression and Moving Average Analysis

import pandas as pd
from stockit import stockit_class
data = pd.read_csv('example.csv')
stockit = stockit_class(data)
stockit.train()
print(stockit.predict(100))
stockit.moving_avg(index = 35)

Please use stockit for educational purposes only. Ben Caunt is not liable for damaged caused by the usage of this product. Use at your own risk.