In today's world, it's very easy to analyze data sets online, including financial data. This article shows how to compute standard statistical properties of a financial data set, like linear approximation and standard deviation. The example is using python based jupyter notebook, shared through Google Colab.
Here's a
Jupyter Notebook, which can be used for this exercise.
First, we start by creating an API key in AlphaVantage.
Next, we can display a downloaded financial dataset, which corresponds to MSFT historical data.
After this step, we run statistical analysis of dataset using Python stats library.
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
Then, we can display the resulting data using Matplotlib. The chart will be displayed in the result window of Jupyter Notebook:
Google Research Colab is a very popular and simple tool for running all sorts of data set analysis tasks. This also includes deep learning using tensorflow library. It is a step forward into simplifying data analysis in the research community.
