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Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. [1] This type of trading attempts to leverage the speed and computational resources of computers relative to human traders.
The effects of algorithmic and high-frequency trading are the subject of ongoing research. High frequency trading causes regulatory concerns as a contributor to market fragility. [ 56 ] Regulators claim these practices contributed to volatility in the May 6, 2010, Flash Crash [ 62 ] and find that risk controls are much less stringent for faster ...
It is important to note that alpha generation platforms differ from low latency algorithmic trading systems. Alpha generation platforms focus solely on quantitative investment research rather than the rapid trading of investments. While some of these platforms do allow analysts to take their strategies to market, others focus solely on the ...
Around 2005, copy trading and mirror trading emerged as forms of automated algorithmic trading. These systems allowed traders to share their trading histories and strategies, which other traders could replicate in their accounts. One of the first companies to offer an auto-trading platform was Tradency in 2005 with its "Mirror Trader" software.
EasyLanguage is a proprietary programming language that was developed by TradeStation and built into its electronic trading platform. [2] It is used to create custom indicators for financial charts and also to create algorithmic trading strategies for the markets.
Many bank proprietary operations now center to varying degrees around statistical arbitrage trading. As a trading strategy, statistical arbitrage is a heavily quantitative and computational approach to securities trading. It involves data mining and statistical methods, as well as the use of automated trading systems.
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