See also, Orange Part I: Building Predictive Models.

In this blogpost, we will explore the versatility of Orange through a Monte Carlo simulation of Apple’s stock price.  For an explanation on Monte Carlo simulation for stocks, visit Investopedia

See also, Orange Part I: Building Predictive Models.

In this blogpost, we will explore the versatility of Orange through a Monte Carlo simulation of Apple’s stock price.  For an explanation on Monte Carlo simulation for stocks, visit Investopedia

Let’s take a look at our schema:

We start off by grabbing AAPL stock data off of Yahoo! Finance and loading it into our canvas. This gives us all of AAPL’s data starting from 1980, but we only want to look at relatively recent data. Fortunately, Orange comes with a variety of data management and preprocessing techniques. Here, we can use the “Purge Domain” widget to simply remove the excess data.
 
After doing so, we can see what AAPL’s closing stock price is post-2008 through a scatter plot.

In order to run our simulation, we need certain inputs, including daily returns. Our data does not come with AAPL’s daily returns, but, fortunately, daily returns can easily be calculated via pandas. We can save our current data, add on our daily returns and then load up the modified dataset back into our canvas. After saving the data to AAPL.tab, we run the following script on our data: https://anaconda.org/rahuljain/monte-carlo-with-orange/notebook. After doing so, we simply load up the new data. Here is what our daily returns look like: 

Now, we need to use our daily returns to run the Monte Carlo simulation. We can again use a Python script for this task; this time let’s use the built-in Python script widget. Note that we could have used the built-in widget for the last script as well, but we wanted to see how we could save/load our data within the canvas. For our Monte Carlo simulation, we will need four parameters: starting stock price, number of days we want to simulate and the standard deviation and mean of AAPL’s daily returns. We can find these inputs with the following script: 

We go ahead and run our simulation 1000 times with the starting stock price of $125.04. The script takes in our stock data and outputs a dataset containing 1000 price points 365 days later. 
We can visualize these prices via a box plot and histograms: 

With this simulated data, we can make various calculations; a common calculation is Value at Risk (VaR). Here, we can say with 99% confidence that our stock’s price will be above $116.41 in 365 days, so we are putting $8.63 (starting price – 116.41) at risk 99% of the time. 

We have successfully built a monte carlo simulation via Orange; this task demonstrated how we can use Orange outside of its machine learning tools. 

Summary

These three demos in this Orange blogpost series showed how Orange users can quickly and intuitively work with data sets.  Because of component-based design and integration with Python, Orange should appeal to machine learning researchers for the speed of execution and ease of prototyping of new methods. Graphical user’s interface is provided through visual programming and a large toolbox of widgets that support interactive data exploration. Component-based design, both on the level of procedural and visual programming, flexibility in combining components to design new machine learning methods and data mining applications and user-friendly environment are also the most significant attributes of Orange and where Orange can make its biggest contribution to the community. 


About the Author

Rahul Jain is currently pursuing a B.S. in Computer Engineering and Mathematics at The University of Texas at Austin. 

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