The phrase "algorithmic trader" is correct and usable in written English.
It can be used to refer to a person or entity that uses algorithms to automate trading decisions in financial markets.
Example: "As an algorithmic trader, she relies on complex mathematical models to execute trades at high speeds."
Alternatives: "quantitative trader" or "automated trader".
Former math professor and code cracker James Simons founded algorithmic trader Renaissance Technologies in 1982.
The company was founded by Nancy Hua, an expert algorithmic trader in the finance industry, and Jeremy Orlow, an ex-Googler and previous 3LM mobile security engineer, who has first-hand startup experience building apps like DrawChat.
"I used to work as an algorithmic trader at a hedge fund and I wasn't allowed to trade normal assets, and wanted to trade something that you could still buy and sell on exchanges, because that's what I knew about, and wine was something that the Financial Conduct Authority are happy for people to trade without any registration".
As Fawcett explained to me recently, building such an algorithm is extraordinarily difficult, since there are real challenges in balancing how algorithmic traders use their own accounts with how they might trade with others' capital.
Algorithmic traders can use speed and technology to consistently "front-run" the fundamental trader.
Instead, algorithmic traders must register their formulae with regulators and introduce circuit-breakers.
It also set out additional steps, including tightening controls on the growing number of computerized high-frequency and algorithmic traders.
By free-riding on the intelligence of others, algorithmic traders save themselves time and money – and take home the winnings at the same time.
Where informed actors see their gains systematically reduced or wiped out by swifter algorithmic traders, investing in deeper information and analysis makes little business sense.
These biases make them an easy target for algorithmic traders, or "quants", who exploit such predictable behaviour.Fortunately for Taiwanese day-traders, the researchers find that digit prejudice abates over time.
By gaining information seconds or minutes before others, high-speed traders — sometimes known as high-frequency or algorithmic traders — can use the computerized nature of modern finance to make quick profits or, if a bet goes wrong, take large losses.
