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Automated trading worlds


We try to understand the big picture when getting to grips with a financial market topic, but with automated trading the picture’s multi-faceted shape presents us with a challenge. And within that big picture, defining pre- cisely what algorithmic trading is (and is not) involves further complexity. The understanding of this term depends heavily on who you are talking to and on where in the world they are located.

For big-picture purposes, automated trading can be defined as “any auto- mated action on an order”, which may take place at any stage of the trade execution process order creation, sending, modification or matching. There are a number of dierent automated trading worlds to be considered. The challenges that participants face in different areas of the markets are widely varied, and deeply impacted by a range of factors.

Automated and algorithmic trading

Few terms in financial markets cause more confusion than the apparently simple label algorithmic trading. It may be applied by different people to quite diverse areas: anything from VWAP algorithms to index arbitrage, ta- king in smart routing, matching engines, basket trading and more along the way.
Names often don’t help much: market Hide & Pounce, VWAP, statistical arbitrage, ticker tape trading... Fortunately, while there are many imagina- tively titled algorithmic strategies, these can be grouped into a small number of categories, with more or less well defined boundaries.

Decision-making algorithms

Lets´ look at the major types of automated trading that, in the strictly purist view, are not truly algorithmic trading. All share the basic characteristic that they apply some combination of market data and algorithmically coded judgment to make fundamental decisions about what to buy or sell, in what quantity and at what price: hence ‘decision-making algorithms’. They are also sometimes referred to as “systematic algorithms”. The decisions are often triggered by prices in the market data stream breaking a limit, or a combination of limits.

The algorithms applied can be highly complex and individual, and have high potential return on investment. They are usually built and/or custo- mized by the institution that uses them, and their details are always highly confidential. The quantitative analysts ”quants” who create these algos usu- ally have strong math/econ academic backgrounds, and their winning strategies may be deployed and refined over significant periods of time.

Extracto de Algorithmic Trading: A complex map elaborado por David Morgan de SUNGARD, para Trading & Risk Magazine, en el número de junio 2015.


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