Now let me show you how we use these very same algorithms
in a real application.
This is a cloud-based application called QuantDesk.
It was developed by a company I cofounded called Lucena Research.
Now, the way it works is over on the left here,
there are various lists of stocks that you can choose from.
I've chosen Dow Jones, and when you click on that,
you can see the list of stocks that make up that group of stocks.
There are any number of ways that you can list particular stocks you're
interested in looking at.
Now, over here are our forecasting options, and
this is how we tell the machine learning algorithm which
factors we want to use, how far in the future do we want to predict, and so on.
We're using right now the default model, and what that is is that's the list of
factors that we think are important for making a future price prediction.
So we can click here and see what those factors are.
This is the list of factors that we're using now.
So it turns out that these factors are determined
using another machine learning algorithm.
We use a genetic algorithm for discovering these.
That's a subject for a different lecture.
But anyhow, these are the list of factors considered at present.
Now, let's do a one week forecast.
Let's make it a one month forecast.
And let's use three months of data.
So we're going to be looking back three months at all these factors.
When we roll back time, we're able to see the future price of these stocks.
So we can see how those factors presumably affected the future price.
So let's do a forecast and see what the result looks like.
So, this area here is the forecast for Apple.
This is the historical price, looking back the last three months, and
this is the forecast future price.
So that line is indicating that we think it's going to go up,
and this arc line above and below Is our confidence interval.
We also report that data in a tabular format up here.
So this is the current price, this is the forecast change in price, and
the forecast percentage change.
So, our system thinks that from today Apple is going to go up about 2.5%.
Now, we also report some other information over here.
We report what we call confidence and back tests score.
Confidence refers to when we find those k nearest neighbors,
how diverse are the ys that comes back?
So, k is the number of neighbors, and when we look at all those neighbors,
are the closest to the values of the quantities that Apple has today, we find
the 30 closest ones, and we look at the standard deviation among all those 30.
If they're very close, we're confident in our prediction.
If they're spread apart, we're less confident.
So you can see here, American Express is a more confident prediction, and,
we rate this by a number of stars, by the way, where five stars represent our
most confident estimates and one star, of course, our least confident.
So our system thinks that American Express is going to go down by
0.9% over the next month.
It's got a high confidence, and it's also got a high back test score.
So what's this back test score?
What we do is we roll back time, and
we look over all this last three months and look forward one month.
And we see how accurate all those predictions were
over the last three months.
The more accurate those were, the higher ranking we get there.
So this is just an example of how we can use these same tools
to make predictions in a live real system.
And this is using, behind the scenes,
the same software that you're learning in this class.