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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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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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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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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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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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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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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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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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In the 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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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.
Without any prior programming experience, learning MQL5 can be difficult but not impossible. Understanding MQL5, a specialized language created for algorithmic trading, necessitates having both programming and financial market expertise.
In this article, we will cover the following topics:
- Introduction to Programming
- Types of MQL5 Programs
- MetaEditor IDE
- MQL5 Language Basics
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Welcome back, fellow traders and aspiring algorithmic enthusiasts! As we step into the third chapter of our MQL5 journey, we stand at the crossroads of theory and practice, poised to unravel the secrets behind arrays, custom functions, preprocessors, and event handling. Our mission is to empower every reader, regardless of their programming background, with a profound understanding of these fundamental MQL5 elements.
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Bill Williams has a doctorate in Psychology, this is important because some traders use markets sentiment. This is very used and can be done with a sentiment script with an IA to score the sentiment of traders feelings with web markets scrapping in a for example social web. At the end of the article, there will be an example of Sentiment Analysis of market news.
Bill Williams psychology formation helped him adapt psychological concepts to trading. He studied market from a human perspective and how emotions deal with traders and its trading. Hi had a holistic view of the market, and he thought the market was chaotic and markets must be studied and traded taking this into account for our benefit. This will be discussed with the Indicators.
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This is a unique research, the idea for which came to me while answering questions that arose during the discussion of one of my articles. I am hopeful that readers will appreciate the value and originality of this work.
To carry out the experiment, we need to first initialize the coordinates of the agents forcibly outside the algorithm, using the coordinates of the global minimum, before measuring the fitness function at the first epoch.
Such an experiment will allow us to evaluate resistance to extremely difficult conditions and the ability to overcome limitations.
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Get ready to be introduced into the wonderful world of auto-optimizing forex trading algorithms. It can allow your Expert Advisor (EA) to adjust itself for the next iteration of trading based on how the market conditions are after a trade is done.
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Welcome back to our MQL5 series! We'll discuss a fascinating subject in this chapter that I believe you'll find very useful and fascinating. We looked at how to use MQL5's built-in indicators in the last chapter, paying particular attention to the RSI. Not only that, but we even worked on a practical project that showed you how to incorporate the RSI into your trading approach with ease. We're going one step further this time by including not one but three potent indications in our project (RSI, the Stochastic Oscillator, and the Moving Average). That's not all; you’ll also learn about the intriguing idea of hidden divergence detection through our project. We will specifically discuss how to spot hidden bullish and bearish divergences.
