Goto

Collaborating Authors

trader


HOW TO CHOOSE A PROFITABLE FOREX ROBOT

#artificialintelligence

Ever since the advent of the use of robots in forex trading, there has been a radical change in the outlook of things in the forex market. Most forex traders now prefer to use a robot in order not to attach much feeling when trading on the forex platform. However, the use of robots in forex trading started to increase after the advent of the Meta 4 trader, an app that helps traders to easily trade forex. Right now, we have many robot expert advisors in the world, that help and advice traders on the best way to trade and make a profit in the forex market. Moreover, the ability to choose the best EA for your trading can be a herculean task to make, since there are so many of them out there.


How Machine Learning Can Be Used For Cryptocurrency Trading

#artificialintelligence

Many predict a great future for machine learning and artificial intelligence. However, the best developments in this direction belong either to the academic community or too big business, mainly in the field of advertising. Therefore, there are not many working projects that would allow cryptocurrency traders to use artificial intelligence in their service. Let's figure out how the principle of machine learning works in cryptocurrency trading, and also consider one of the options for automatic trading. And in the next article, we will create and train our own bot, which in theory is able to show a positive result, however, its use is highly discouraged.


Replacing Traders With Algorithms: Success Stories of Real Funds - DataScienceCentral.com

#artificialintelligence

Due to the rapid pace of technological change, the way we trade the stock market is becoming more complex. One of the most significant changes that have occurred is the emergence of algorithmic trading, which has allowed traders to improve their skills and compete against other individuals. This type of trading has also raised the bar for other types of traders and is poised to outstrip traditional methods. According to a working paper released by the UK Government's Foresight panel, which Dame Clara Furse chairs, high-frequency trading will eventually replace human decision-making in the stock markets. Algorithmic trading is a type of financial transaction that uses computers and programs to generate and execute large orders in the market.


Decomposing your complex AI problem: Hierarchy

#artificialintelligence

Problem worlds often come with an innate hierarchy. Naturally, this may prompt the question: which level(s) of the hierarchy should be modelled? For example, the US Stock Market can be modelled as a whole or at the index level -- think, the Dow Jones, or for individual stocks. In a linear system, the way that the lower levels interact with the upper levels is "linear" or directly correlated. Take the example of an analytics system for business intelligence and reporting -- sales, inventories, etc.


Gaming vets promise to make blockchain video games enjoyable and sustainable – TechCrunch - Channel969

#artificialintelligence

The runaway success of Axie Infinity and StepN has satisfied a flurry of entrepreneurs that web3 gaming, the place the possession of in-game property is within the fingers of customers by way of blockchain adoption moderately than a centralized platform, is the long run. A few of the largest hits within the house thus far reward customers with tokens that may be cashed out in what's referred to as the "play-to-earn" mannequin. Whereas P2E video games have attracted tens of millions of gamers and billions of {dollars} from traders, veterans of the gaming trade argue that they're essentially unsustainable. These video games are the brainchild of monetary engineers aiming to get wealthy shortly moderately than skilled builders constructing time-honored works, they are saying. After peaking at $754 million in November when bitcoin hit all-time excessive, the sport's month-to-month gross sales quantity plummeted to $4.5 million in July.


Conscious Domination: Can sentient AI Power the Future of Video Games and Stock Trading? - Herbert R. Sim

#artificialintelligence

"Do you think a butler is a slave? What is the difference between a butler and a slave?" The above was a calculated, almost chilly response by LaMDA, Google's artificially intelligent chatbot, to a question posed by Google engineer Blake Lemoine. A further exchange only heightened Lemoine's suspicions and, much to the chagrin of Google, concluded that LaMDA is sentient, of having the ability to display a sense of consciousness. While the tech company has sought to distance itself from Lemoine's claims and attempt to debunk them with its own team of ethicists and engineers, the case with LaMDA has brought an uncomfortable spotlight to the world of artificial intelligence (AI).


Investment Portfolios As A Set Of Rules Rather Than A Set Of Numbers

#artificialintelligence

Within asset management particularly, modern-day computational modelling can identify trends and ... [ ] collect data points in the stock market within seconds, meaning traders can map out parameters for a more objective approach to trading. Family offices know better than most that we are in the throes of a technological revolution across the entire industry. Within asset management particularly, modern-day computational modelling and portfolio management software can identify trends and collect data points in the stock market within seconds, meaning traders can map out parameters for a more objective approach to trading. These parameters help define the rules guiding an entire portfolio's operation, in sharp contrast to traditional, reactive investing which largely uses numbers to quantify an asset's performance in a portfolio. More first-time investors are taking an interest in understanding portfolio management, and while it might sound like the robots have taken over, there's still very much a place for IRL thinking here.


The Master of Machine Learning is enticing technologists to invest

#artificialintelligence

Li Deng has been working with artificial intelligence and machine learning for nearly three decades. He spent 17 years at Microsoft and founded the tech company's Deep Learning Center in Redmond, Washington. But for the past five years, Deng has worked in finance, and now he's on a mission to persuade other technologists to get into the field as well. "In finance, the data is huge. In many cases, it's much bigger than the data you're dealing with in technology," Deng tells us.


Using Machine Learning in Trading and Finance

#artificialintelligence

This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level.


Algorithmic Trading: What it is and How to Learn it - eLearningInside News

#artificialintelligence

Most traders or investors in the financial market dream of having a system that automatically trades for them without the need for them to do anything else trading related. While no such system truly exists, algorithmic trading comes very close. Based on a recent market report, the global algorithmic market valued at $10.3 thousand in 2018 is anticipated to witness a CAGR of 10% over a forecast period (2022-2027). The demand for a fast, reliable, and profitable system is spearheading the growth of algorithmic trading. However, despite the availability of various materials, a beginner with a non-technical background might find it very difficult to follow a systematic approach to learning algorithmic trading.