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Examples of strategies used in algorithmic trading include systematic trading, market making, inter-market spreading, arbitrage, or pure speculation, such as trend following. Many fall into the category of high-frequency trading (HFT), which is characterized by high turnover and high order-to-trade ratios. [ 7 ]
The average quantitative strategy may take from 10 weeks to seven months to develop, code, test and launch. [6] 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 ...
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.
A separate, "naïve" class of high-frequency trading strategies relies exclusively on ultra-low latency direct market access technology. In these strategies, computer scientists rely on speed to gain minuscule advantages in arbitraging price discrepancies in some particular security trading simultaneously on disparate markets. [49]
In capital markets, low latency is the use of algorithmic trading to react to market events faster than the competition to increase profitability of trades. For example, when executing arbitrage strategies the opportunity to "arb" the market may only present itself for a few milliseconds before parity is achieved. To demonstrate the value that ...
The trading strategy is developed by the following methods: Automated trading; by programming or by visual development. Trading Plan Creation; by creating a detailed and defined set of rules that guide the trader into and through the trading process with entry and exit techniques clearly outlined and risk, reward parameters established from the outset.
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