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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Blog RSS</title><link>delecta.co/blog/</link><pubDate>Fri, 07 Dec 2018 01:39:57 +0000</pubDate><item><title><![CDATA[Are the markets random?]]></title><link>delecta.co/blog/are-the-markets-random/</link><description><![CDATA[Legend has it that Burton Malkiel once created some fake market prices based on coin flips, and then showed it to a chartist. The chartist did some eye inspection and chanted enthusiastically that now is the perfect time to buy the stock! This visual confusion persuaded Burton about his random-walk hypothesis, so much so that he]]></description><pubDate>Wed, 05 Dec 2018 00:00:00 +0000</pubDate></item><item><title><![CDATA[Straight Non-Linear Thinking:]]></title><link>delecta.co/blog/straight-non-linear-thinking/</link><description><![CDATA[In this post we want to share a method of thinking instead of a machine-learning technique or quantitative tool. Building a machine-learning algorithm that can systematically profit in the markets is like riding on a long and bumpy road full of surprises and sudden turns.]]></description><pubDate>Fri, 23 Nov 2018 00:00:00 +0000</pubDate></item><item><title><![CDATA[Detecting a Simple Pattern Using Neural Networks (part-2):]]></title><link>delecta.co/blog/detecting-a-simple-pattern-using-neural-networks-part-2/</link><description><![CDATA[In this post, we first examine the difference between the baseline accuracies of market-data compared to a random-normal sequence.Then turn to the three-consecutive-drop decision boundary. Combining the observations made in the first two steps, we reformulate the classification problem, and compare the performances of the neural networks before and after this new formulation. The highlights of the findings are listed at the end.]]></description><pubDate>Fri, 16 Nov 2018 00:00:00 +0000</pubDate></item><item><title><![CDATA[Detecting a Simple Pattern Using Neural Networks (part-1)]]></title><link>delecta.co/blog/detecting-a-simple-pattern-using-neural-networks-part-1/</link><description><![CDATA[In this post we aim at detecting a simple rule-based pattern using neural networks (NN):

We start by looking at samples drawn from a normal distribution to find the proper depth of the NN for this task. Then, we move to market data, and tackle the difficulties that arise when considering market data. The results of this post are summarized at the end and we finish with a surprise!]]></description><pubDate>Fri, 09 Nov 2018 00:00:00 +0000</pubDate></item></channel></rss>
