Quant trading is in fashion nowadays. This article may help you in separating the wheat from the chaff. Articles about trading strategies that demonstrate exceptional skill in programming in R or Python are published almost on a daily basis. However, a large percentage of the articles violate basic trading mechanics among other things. For example, a frequent error in some articles is that some indicators are calculated based on the daily closing price but positions are also initiated at the same closing price if there is a signal.
The trouble with developing'artificially intelligent' trading systems is that a mass market for the technology has yet to develop. "Potential clients that might want to use a system like ours usually would want it on an exclusive basis," said Guillaume Vidal, the CEO of Paris-based startup Walnut Algorithms. "They would want to own us or buy us out." San Francisco-Based Tech Trader also found a similar environment when it launched in 2012, according to CEO William Mok. Both firms found it was much easier to rely on the trading revenue generated by their AIs to fund their businesses.
GO Market has made the decision to include a-Quant's trading signals to selected clients. This means clients can use artificial intelligence (AI) to forecast the movement of their asset portfolios. AI has been utilized in the financial trading world for a while but has only recently seen more traction in the retail industry due to the demands of traders wanting tools to maximize their gains. GO Market has promoted this recent change to the public and state that they are happy that their clients can quickly deploy the signals a-Quant services provide, by using this cutting-edge technology. GO Market made the headlines earlier this year by adding stocks from the Australian Stock Exchange to be traded on MT5.
FIFTY YEARS ago investing was a distinctly human affair. "People would have to take each other out, and dealers would entertain fund managers, and no one would know what the prices were," says Ray Dalio, who worked on the trading floor of the New York Stock Exchange (NYSE) in the early 1970s before founding Bridgewater Associates, now the world's largest hedge fund. Kenneth Jacobs, the boss of Lazard, an investment bank, remembers using a pocket calculator to analyse figures gleaned from company reports. His older colleagues used slide rules. Even by the 1980s "reading the Wall Street Journal on your way into work, a television on the trading floor and a ticker tape" offered a significant information advantage, recalls one investor. Since then the role humans play in trading has diminished rapidly. In their place have come computers, algorithms and passive managers--institutions which offer an index fund that holds a basket of shares to match the return of the stockmarket, or sectors of it, rather than trying to beat it (see chart 1).
Technology moves at a startling speed and it has been the same case in the algorithmic and quantitative trading domain. Traders around the world are making use of Machine Learning, Artificial Intelligence, Blockchain, Neural Networks, Deep Learning and similar techniques to execute their trades. One of the key factors to benefit from the ever-changing trend in technology is to learn the impact of the evolving ways that other traders use and learn from their mistakes.