Machine Learning In Python for Trading

#artificialintelligence

Machine Learning has many advantages. It is the hot topic right now. For a trader or a fund manager, the pertinent question is "How can I apply this new tool to generate more alpha?". I will explore one such model that answers this question in a series of blogs. "How can I apply this new tool to generate more alpha?"Click


Machine Learning In Python for Trading

#artificialintelligence

Machine Learning has many advantages. It is the hot topic right now. For a trader or a fund manager, the pertinent question is "How can I apply this new tool to generate more alpha?". I will explore one such model that answers this question in a series of blogs. "How can I apply this new tool to generate more alpha?"Click


Distribution Assertive Regression

arXiv.org Machine Learning

In regression modelling approach, the main step is to fit the regression line as close as possible to the target variable. In this process most algorithms try to fit all of the data in a single line and hence fitting all parts of target variable in one go. It was observed that the error between predicted and target variable usually have a varying behavior across the various quantiles of the dependent variable and hence single point diagnostic like MAPE has its limitation to signify the level of fitness across the distribution of Y(dependent variable). To address this problem, a novel approach is proposed in the paper to deal with regression fitting over various quantiles of target variable. Using this approach we have significantly improved the eccentric behavior of the distance (error) between predicted and actual value of regression. Our proposed solution is based on understanding the segmented behavior of the data with respect to the internal segments within the data and approach for retrospectively fitting the data based on each quantile behavior. We believe exploring and using this approach would help in achieving better and more explainable results in most settings of real world data modelling problems.


Using the Mean Absolute Percentage Error for Regression Models

arXiv.org Machine Learning

We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We show that universal consistency of Empirical Risk Minimization remains possible using the MAPE instead of the MAE.


Understanding Regression Error Metrics

#artificialintelligence

Human brains are built to recognize patterns in the world around us. For example, we observe that if we practice our programming everyday, our related skills grow. But how do we precisely describe this relationship to other people? How can we describe how strong this relationship is? Luckily, we can describe relationships between phenomena, such as practice and skill, in terms of formal mathematical estimations called regressions. Regressions are one of the most commonly used tools in a data scientist's kit. When you learn Python or R, you gain the ability to create regressions in single lines of code without having to deal with the underlying mathematical theory. But this ease can cause us to forget to evaluate our regressions to ensure that they are a sufficient enough representation of our data. We can plug our data back into our regression equation to see if the predicted output matches corresponding observed value seen in the data.