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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 same algorithm can be used in other situations, for example: [2] suppose there are 7 doctors that are assigned to night-shifts; each doctor is assigned to a night-shift in one day of the week. Some doctors prefer the shifts given to other doctors. The TTC algorithm can be used here to attain a maximal mutually-beneficial exchange.
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.
To tackle these issues, FIX Protocol Limited established the Algorithmic Trading Working Group in Q3 2004. [1] The initial focus of the group was to solve the first of these issues, which it did by defining a new group of fields, the StrategyParametersGrp, made up of FIX tags 957 through 960 – these tags were formally introduced with the release of FIX 5.0 in Q4 2006.
Systematic trading (also known as mechanical trading) is a way of defining trade goals, risk controls and rules that can make investment and trading decisions in a methodical way. [ 1 ] Systematic trading includes both manual trading of systems, and full or partial automation using computers.
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.
Example of pair trade graphical representation. A pairs trade or pair trading is a market neutral trading strategy enabling traders to profit from virtually any market conditions: uptrend, downtrend, or sideways movement. This strategy is categorized as a statistical arbitrage and convergence trading strategy. [1]
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 ...