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Microsoft’s stock (NASDAQ: MSFT) has lost approximately 16% YTD, What’s Next?
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Microsoft’s stock (NASDAQ: MSFT) has lost approximately 16% YTD as compared to the 14% drop in the S&P500 index over the same period. The weekly price broke yearly Pivot level to below for the primary weekly bearish reversal: the price was bounced from the first pivot support level to above for the secondary rally to be started. The key level for the possible bullish trend to be resumed is 299.50 as the yearly pivot level now.
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Learn how to design a trading system by Alligator
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Here is a new article from our series about how to design a trading system based on the most popular technical indicators. In this article, we will learn in detail about the Alligator indicator by learning what it is, it measures, how we can calculate it, and how we can read and use it. Then we will create a trading system based on some simple strategies based on the main objective of this indicator.
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Population optimization algorithms: Ant Colony Optimization (ACO)
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Belgian researcher Marco Dorigo has created a mathematical model that scientifically describes the process of collective intelligence in an ant colony. He published it in his doctoral dissertation in 1992 and implemented it as an algorithm. Ant colony optimization (ACO) is a population-based stochastic search method for solving a wide range of combinatorial optimization problems. ACO is based on the concept of stigmergy. In 1959, Pierre-Paul Grasset invented the theory of stigmergy to explain the nest-building behavior of termites. Stigmergy consists of two Greek words: stigma - sign and ergon - action.
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Population optimization algorithms: Artificial Bee Colony (ABC)
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For many years, the bee search methods were researched exclusively by biologists. However, the interest in applying swarm behavior in the development of new optimization algorithms was growing. In 2005, professor Dervis Karaboga became interested in the research results. He published a scientific article and was the first to describe the model of swarm intelligence mostly inspired by bee dance. The model was called the artificial bee colony.
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Population optimization algorithms: Grey Wolf Optimizer (GWO)
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The gray wolf algorithm is a metaheuristic stochastic swarm intelligence algorithm developed in 2014. Its idea is based on the gray wolf pack hunting model. There are four types of wolves: alpha, beta, delta and omega. Alpha has the most "weight" in decision making and managing the pack. Next come the beta and the delta, which obey the alpha and have power over the rest of the wolves. The omega wolf always obeys the rest of the dominant wolves.
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Population optimization algorithms: Cuckoo Optimization Algorithm (COA)
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The cuckoo is a fascinating bird, not only because of its singing, but also because of its aggressive breeding strategy, which consists in laying eggs into nests of other birds.
Cuckoo search is one of the latest nature-inspired heuristic algorithms developed by Yang and Deb in 2009. It is based on the parasitism of some cuckoo species. This algorithm has been further improved by so-called Levy flights rather than simple isotropic random walk methods.
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The Cuckoo Optimization Algorithm (COA) is used for continuous non-linear optimization. COA is inspired by the lifestyle of this bird. The optimization algorithm is based on the features of the species' egg-laying and reproduction. Like other evolutionary approaches, COA starts with an initial population. The basis of the algorithm is an attempt to survive. While competing for survival, some of the birds die. Surviving cuckoos move to better places and begin to breed and lay eggs. Finally, the surviving cuckoos converge in such a way that a cuckoo society with similar fitness values is obtained.
The main advantage of this method is its simplicity: cuckoo search requires only four understandable parameters, so tuning becomes a no-brainer.
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Population optimization algorithms: Firefly Algorithm (FA)
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Nature has always been an inspiration for many metaheuristic algorithms. It managed to find solutions to problems without prompting, based on individual experience. Natural selection and survival of the fittest were the main motivation for the creation of early metaheuristic algorithms. In nature, animals communicate with each other in many ways. Fireflies use their ability to blink to communicate. There are about 2000 species of fireflies with their own special flash patterns.
There are two variants of population optimization algorithms inspired by the behavior of fireflies: the Firefly Algorithm and the Glowworm Swarm Optimization (GSO) algorithm. The main difference between firefly and glowworms is that the latter are wingless. In this article, we will consider the first type of the optimization algorithm.
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Population optimization algorithms: ElectroMagnetism-like algorithm (ЕМ)
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Over the past few decades, researchers around the world have come up with many metaheuristic search methods to solve complex global optimization problems and ways to improve them.
The ElectroMagnetism-like (ЕМ) algorithm is a relatively new metaheuristic search algorithm based on the simulation of the behavior of electromagnetic particles in physical space, first introduced by I. Birbil and S.С. Fang in 2003. It is described as an evolutionary algorithm with random noise and a population based on the electromagnetic force of the interaction between charged particles.
This algorithm is inspired by the mechanism of attraction and repulsion of charges in the theory of electromagnetism for solving non-linear optimization problems without restrictions in a continuous domain. Due to its ability to solve complex global optimization problems, EM is widely used as an optimization tool in many areas.
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Rebuy algorithm: Multicurrency trading simulation
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previous article I showed you a lot of useful features that you probably did not know, but the most interesting thing is ahead - research or trading simulation. Sometimes a strategy tester is not enough. Although this is a very convenient tool for getting to know the market, but this is only the first step. If you carefully read the previous article, then you most likely know the reason already.
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Estimate future performance with confidence intervals
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Creation of profitable automated trading systems is no easy task. Even if one happens to make a profitable expert advisor, there are still questions about whether it is worth the risk. We may be satisfied that our strategy will not blow through all capital allocated to it, but this is no reason to immediately enable live trading. Ultimately, profit is the motive and if we later find that our strategy is indeed profitable, but not profitable enough to justify the risk, or generates poor returns relative to other investment opportunities we will no doubt have serious regrets.
Therefore, in this article we will explore techniques borrowed from the field of statistics that can help us estimate the future performance of an automated trading system, using data collected from out of sample tests.
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