The Growth and Trend of Algorithmic Trading. Algorithmic Trading System for Equity and Currency Traders.

May 12, 2010 by · 6 Comments
Filed under: Stock Trading 

There have been great transformations in the structures of trading and exchanges over the years. The advancement of technology has been one of the key drivers for these transformations as electronic methods have become integral to the way trading is conducted on exchanges and over the counter.
The growth of the hedge fund industry and also the retail sector in trading (i.e. day traders, swing traders and so on) has also impacted on the trading industry and exchanges. The trading requirements of these groups are markedly different to the traditional requirements of the brokers and dealers of yesteryear. The hedge funds, in particular, pursue very complex and unique trading strategies and these drive the way they go about trading. They require far more sophisticated technology to achieve their investment and trading objectives, which usually involve identifying even the smallest movements in the prices of securities.
Retail investors (traders) have benefited from the growth of the internet over the years and this has allowed them to trade anything from stocks to indices and currency (with forex uk). Non-banking institutions have also proliferated due to the range of possibilities that can be achieved on the internet. Technology has allowed these players to compete on an even keel with traditional banking institutions in their service offerings with regard to stock and currency trading. In addition, technology has enabled these players to improve on their service levels in a bid to differentiate their offerings.

The Growth of Algorithmic Trading
The era of algorithmic trading has begun. Algorithmic trading promises to cut costs, eliminate human error, and boost trading efficiency and productivity. The use of algorithms to make complex decisions and place thousands of orders in milliseconds has grown in popularity, particularly among equity and currency traders in recent times. Algorithmic trading, also known as algo trading, is defined as the placement of a buy or sell order of a defined quantity into a quantitative model that can automatically generate the timing of orders and the size of the orders on the basis of the parameters and constraints of the algorithm specified to achieve a certain trading objective.
The following is a definition of an algorithmic trading system by Investopedia:
A trading system that utilises very advanced mathematical models for making transaction decisions in the financial markets. The strict rules built into the model attempt to determine the optimal time for an order to be placed that will cause the least amount of impact on a stock’s price.

Within the equities markets, large blocks of shares are typically purchased by dividing a large share block into smaller lots and the decision as to timing of the purchase of the smaller blocks is taken by the complex algorithm. Large institutional investors are some of the major users of algorithmic trading, given the large amount of shares they purchase on a daily basis. These investors are able to achieve the best possible price without significantly affecting the stock’s price and escalating purchasing costs owing to these complex algorithms.

In the financial markets, the computerisation of order flow was first witnessed in the early 1970s with significant milestones such as the introduction of the New York Stock Exchange’s “designated order turnaround” system (DOT, and later SuperDOT), which routed orders electronically to the proper trading post to be executed manually, and the “opening automated reporting system” (OARS), which aided the specialist in determining the market clearing opening price.
Financial markets with fully electronic execution and similar electronic communication networks grew in the late 1980s and 1990s. In the USA, the advent of decimalisation, which resulted in the change of the minimum tick size from 1/16th of a dollar ($0.0625) to $0.01 per share, could have been partly responsible for the rise of algorithmic trading as it changed the market microstructure by allowing smaller differences between the bid and offer prices, decreasing the market-makers’ trading advantage, therefore decreasing market liquidity. In addition, regulations such as Reg-ATS changed the way in which the sell side and the buy side thought about trading.
This reduction in market liquidity prompted institutional traders to split up orders in accordance with computer algorithms so as to execute their orders at a better average price. These average price benchmarks are calibrated and calculated by computers by applying the time weighted (i.e. unweighted) average price (TWAP) or more usually the volume weighted average price (VWAP).
The opening of more electronic markets has made the creation of other algorithmic trading strategies possible. These strategies are more easily implemented by computer systems because the systems can react more rapidly to temporary mispricing and examine prices from several markets simultaneously.
In the early 2000s, algorithmic trading really started to take off as the execution product for the sell side. It soon became a product that was mass delivered to the buy side as the buy side became interested in taking on the trading of some of its order flow on its own instead of sending all its orders to the broker—dealers.
In recent years, the biggest growth area in the algorithmic trading universe is in customisation of algorithms. As the buy side has become more sophisticated in understanding trading and how it wants to trade in today’s marketplace, the more it desires customised strategies tuned to exactly the way it wants the strategies to work.
Algorithmic trading has spread across multiple asset classes — from equities to futures and options to foreign exchange — as people look for cross-asset trading to hedge their positions.

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