Stock Trading System

The average stock is bought and sold within a matter of seconds, with few investments held for longer than a minute. How is this possible? It’s all very simple: the number one trader on the stock exchange isn’t human, it’s a machine.

Automated trading is nothing new; however, given the exponential growth in processing power and the proliferation of the personal computer it is certain that more traders have the ability to trade with robots now than at any point in the future. While the automated market is very much still in the hands of the institutional investors, a growth in technical oriented retail brokers means individuals will soon have the capacity (if they do not already) to create robots within even the most basic brokerage software. Recently, such companies as E-Trade and Charles Schwab have expanded their services to allow for automated stock scanning of the overall market, and signal services help bridge the information gap between brokers and individual traders.

Crafting a Stock Trading System
Unfortunately, computers still haven’t yet solved creativity, though it is certain many developers are attempting to harness creative thought within logical-thinking computers. For the investor, this means that crafting a stock trading system requires human input and thought, and greatly limits the overall abilities of a robot to execute trades.

The execution of automated trades requires some kind of input, usually a technical indicator, to make a buy or sell order. In many ways, designing these systems is related to using Microsoft Excel; traders should input a logical code in “if, then” statements to automate a trading robot. For example: A robot might be told to buy and sell on alternating crosses of the 50 and 100 day moving averages. As another example, a robot may be given a third variable: buy and sell on alternating crosses of the 50 and 100 day moving averages but only when volume exceeds 10 million shares per day.

Investors who trade with automated systems have a leg up on those who don’t: automation allows for investors to back-test their strategies to grade performance. If, for example, a trader were capable of testing a system into history, he or she could possibly determine how such strategy would perform in the future. Likewise, investors who see failing systems can tweak their algorithms on the fly to compare how minor adjustments may affect profitability.

These tweaks, though small, often result in changes in profitability by several orders of magnitude. Expanding a stop loss variable from $1 per share to $1.5 per share, for example, may result in greater profits as short-term dips are out-flanked by a deeper stop loss. Alternatively, such a change may mean that more losses are accumulated as stocks that plunged by $1 in the past are just as likely today to fall the additional $.50 before reversing.

Trading with Your Own Eyes, Ears, and Fingers
Stock trading systems don’t necessarily require automation, though it sure does make operating a system far easier. And while computers have had their heyday making millions of dollars in high-frequency trading pools, there have been plenty faults with the new reliance on trading programs.

Most recently, automated systems are have said to triggered the “Flash crash,” a market event that happened when hundreds of computers were selling stocks to one another, while no other computers were buying. Without human intervention, the automated programs sold some stocks into the ground, with a collection of exchange-traded funds plunging more than 80% toward new market lows.

Were the securities truly worth 80% less that day? Absolutely not, and following the rare event, new systems were designed to suspend trading when computers overwhelm the market. Today, declines of greater than 10% trigger “circuit breakers” to stop any automated market freefall.

No matter how you trade, it is certain that your system is already automated in at least some way. Take the time to evaluate the fastest growing careers on Wall Street. Wall Street isn’t seeking finance majors, nor MBAs, but instead those with an understanding of IT who can build investment banks powerful new algorithmic trading models.

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