Publications by Intelligent Trading
Practical Implementation of Neural Network based time series (stock) prediction – PART 1
The following introduction is to allow viewers to understand the basic concepts and practical implementation of neural nets towards a financial time series. I will not go too deep into detail about the mathematics behind the neural net at the moment. My goal is to get you to understand practical details about how to actually implemen...
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Practical Implementation of Neural Network based time series (stock) prediction – PART 2
As a brief follow up to the series, I want to take a moment to describe a bit about Weka, which is the machine learning tool that we will be using to implement the neural network. It is a fantastic open source JAVA based tool that was developed at the University of Waikato, New Zealand. Users who are not all that experienced with pr...
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Practical Implementation of Neural Network based Time Series (Stock) Prediction – PART 3
Ok, now that we have seen how well the perfect sine wave signal was learned, let’s turn it up a notch and see how well the complex sine wave was learned.Fig 1. Summary of Actual Vs. Predicted out of sample complex sine waveformUh Oh. What happened, the out of sample data does not look quite as good. But, let’s take a look at the ...
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Practical Implementation of Neural Network based time series (stock) prediction -PART 4
Consider this an introduction to how we need to pre-process the data.I mentioned earlier that a financial time series is typically a unit root or non-stationary signal, what this means is that if you sample statistical properties over time, they will obviously change.Fig 1. S&P 500 non-stationary signalYou can see that as we sample th...
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Practical Implementation of Neural Network based time series (stock) prediction -PART 5
Following is an example of what it looks like to predict an actual univariate price series. The period of the signal that was sampled was already in stationary form, so not much massaging was needed other than normalization (described earlier).What’s important to notice when you see these kinds of neural network predictions (partic...
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Classification for stock directional prediction
The neural network tutorial focused on a type of method known as regression. The other common method utilized in machine learning is called classification. The two approaches are somewhat similar in that they identify the best possible curve to learn from a set of data. The difference lies in how they use the curve to learn from the...
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Using J48 Decision Tree Classifier to Dynamically Allocate Next Day Position in Stocks or Bonds
The prior introduction using a simple model to determine next weeks change based on the S&P 500 index and VIX did not look very promising, although hopefully it served to familiarize yourself with how classification is used in augmenting trading decisions. Wouldn’t it be nice if we had something that performed a little better?Well,...
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Artificial Immune Systems and Financial Applications?
One of the buzzwords that seems to be common these days is AIS or Artificial Immune Systems. It is a biologically inspired classification type system that essentially tries to replicate some of our own natural immune system algorithms. Our bodies have various defense mechanisms for recognizing foreign invaders. One such defense mec...
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Genetic Algorithm Systematic Trading Development — Part 1
I want to start with a brief introduction to what I consider one of the most powerful learning methodologies to come out of Artificial Intelligence in the last several decades– the Genetic Algorithm. Although it was originally developed to model evolutionary biology in the late 50s, most give credit to John Holland for his detailed...
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Genetic Algorithm Systematic Trading Development– Part 2
We started by discussing the goal of a genetic algorithm, which is to optimally find the candidate pool of rules that are superior to other potential rules. In our example of moving averages, we are seeking the values of parameters of the rule :if ma(m) binop ma(n) then action.*Note: binop is short for binary operator; in this case t...
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