We study some tech stock price through data visualization and some financial technique, focusing on those which are intended to give a sort of reliable prevision to permit brokers have a basis on which they could decide when it is the best moment to sell or buy stocks. We first analyze a year of data about the biggest companies as Amazon, Google, Apple and Microsoft but right after that we focus on Google stocks.
Next we leave the financial tools for supervised learning analysis. These machine learning processes learn a function from an input type to an output type using data comprising examples. Furthermore we'll talk specifically of regression supervised learning, meaning that we're interested in inferring a real valued function whose values corresponds to the mean of a dependant variable (stock prices).
We first applied linear regression on the last 6 years of Google Trends about the word 'google' specifically searched in the financial news domain, versus the last 6 years Google stock prices. From now on we change our feature domain with a multivariate input, i.e. we use other stock prices (AAPL, MSFT, TWTR, AMZN) to study the accuracy of others algorithms such as a multivariate linear regression, a SVR and a Random Forest.
keywords : Finance, Stock Price Analysis, MACD, Machine Learning, Linear Regression, SVR, Random Forest, Data Visualization, Python, R
import pandas as pd from pandas import Series,DataFrame import numpy as np # For Visualization import matplotlib.pyplot as plt import seaborn as sns sns.set_style('whitegrid') %matplotlib inline # For reading stock data from yahoo or google from pandas.io.data import DataReader # For time stamps from datetime import datetime # suppressing warnings import warnings warnings.filterwarnings('ignore')
# interactive plots import plotly.plotly as py import cufflinks as cf import plotly.tools as tls tls.set_credentials_file(username='affinito', api_key='') #from plotly import __version__ from plotly.offline import download_plotlyjs, init_notebook_mode, iplot init_notebook_mode()