Algorithm Trading

We talk a lot about how machines are being used more and more in finance. However, some high-frequency traders have found legal ways to scrape data from over-the-counter trading forums called Dark Pools.” These types of trading forums don't have to submit their order data in real time like an exchange, and so their movements tend to have a delayed effect on the market.
Algorithms essentially work as middlemen between buyers and sellers, with high-frequency-trading and ultra high-frequency-trading being a way for traders to capitalize on infinitesimal price discrepancies that might exist only for a miniscule period of time.



Nowadays, the securities trading landscape is characterized by a high level of automation, for example, enabling complex basket portfolios to be traded and executed on a single click or finding best execution via smart order-routing algorithms on international markets.
Richard Balarkas, CEO of Instinet Europe, an institutional brokerage firm, draws a dark future for human intermediaries: It algorithmic trading signaled the death of the dealer that just outsourced all risk and responsibility for the trade to the broker and heralded the arrival of the buy-side trader that could take full control of the trade and be a more discerning buyer of sell-side services” ( Trade News 2009 ).

Further, there are also a lot of proven mathematical models, like the delta-neutral trading strategy, which allows trading on combination of options and its underlying security, where trades are placed to offset positive and negative deltas so that the portfolio delta is maintained at zero.
Systematic and Robust: The main advantage of algorithmic trading is that it enables the user to convert an idea” about a trading strategy to a robust set of un-ambiguous rules that can be verified for effectiveness under a wide variety of instruments and historical market conditions.

In addition to these models, there are a number of other decision making models which can be used in the context of algorithmic trading (and markets in general) to make predictions regarding the direction of security prices or, for quantitative readers, to make predictions regarding the probability of any given move in a securities price.
In today's era, where more and more traditional traders follow technical charting for their trading calls, an algo trader finds it risky to depend merely on the findings gathered from an examination of charts and, thus, tries to reply on pure arithmetic.
As they focus on the lifetimes of the so-called no-fill deletion orders, that is, orders that are inserted and subsequently cancelled without being executed, they find algorithm-specific characteristics concerning the insertion limit of an order compared to ordinary trading by humans.

Therefore, it is central to enable algorithmic trading and HFT to unfold their benefits in times of quiet trading and to have mechanisms algo-trading (like circuit breakers) in place to control potential errors at both the level of the users of algorithms and at the market level.
In addition, with the help of new market access models, the buy side has gained more control over the actual trading and order allocation processes and is able to develop and implement its own trading algorithms or use standard software solutions from independent vendors.