How to Create a Trading Strategy for Average Online Traders

Author: Janice Wilson
Author Bio: Janice Wilson is a financial expert who enjoys trying to find the best ways to use technology to help her financial life. When she is not trading stocks online, she spends her time trying to find lower car insurance rates online.

Over the past several years, a revolution has been taking place in online stock trading. With the continued advancement of computing power and internet speed, high-frequency and algorithmic trading have become very popular methods by which to profit from online trading. Although high-frequency and algorithmic trading are not precisely the same thing, they are generally thought of as two sides of the same coin. High-frequency trading describes the use of computers to make split-second trading decision, often employing strategies that require a position for only minutes or even seconds. Alternatively, algorithmic trading employs the use of sophisticated programming models to profit from market inefficiencies. Both institutions and individual traders have embraced such trading strategies to such an extent that high-frequency trading is now believed to account for three-quarters of all stock trades on the various exchanges. As such, it is very difficult to develop a profitable trading method without the use of high-powered computers with sophisticated programs. Of course, a computer is only a tool; in order to profit from online trading, a trader must develop a strategy in order to exploit profitable opportunities that exist in the market. There are many such possibilities of such strategies. However, two of the most popular strategies are the following:

1. Technical Analysis
2. Arbitrage

Technical Analysis

Technical analysis is a term used to describe a range of trading strategies designed to analyze price movements in an attempt to speculate on future price movements. This is the antithesis of fundamental analysis, which is used to analyze financial statements in order to determine the intrinsic value of a company, often with the use of a discounted cash flow analysis. The most basic of all tools used by technical analysts is the moving average, which is simply the average historical price of some asset over some period of time. A moving average can be constructed for almost any period of time; the choice is often dependent on the time frame of the investor using them. Longer-term investors may use a three-month moving average while high-frequency investors may look at a 15-minute moving average. Moving averages are typically analyzed on graphs in real-time, giving investors a continuous stream of data concerning the pricing action of some asset. Typically, a stock moving above its moving average is considering a bullish sign, indicating positive momentum in the stock. Conversely, a stock moving below its moving average is considered a bearish sign, indicating a sell-off may be occurring. Of course, there are many more technical indicators one could possibly employ in order to profit for particular price movements. Some investors look at relative strength indicators, designed to measure both the size and volume of different price movements. At its most basic, these traders consider upward price movements to be more impressive when they are associated with strong underlying volume. Alternatively, a price increase with light volume may indicate a sucker's rally that will soon stall. It would be impossible to describe all the possible strategies employed by stock technicians in anything less than a reference book:

• Cup and Handle Patterns
• Head and Shoulder Patterns
• Support and Resistance Levels

These are just some of the many indicators a trader could use to predict future price movements. The important thing to realize here is that such trades are speculative in nature: you are attempting to use past price movements in order to predict future price movements.

Arbitrage

As an alternative to outright speculation, some traders attempt to employ the strategy of arbitrage to make a risk-less profit in the market. There are many possible arbitrage strategies, but they are all designed to take advantage of inefficient pricing in either the same of similar securities. Typically, arbitrage trading is first described with examples where a given asset is trading at different prices in different exchanges. For instance, if gold is trading for $1500 in the United States but has a price of $2000 in England, an arbitrageur would buy gold in America and simultaneously sell it in England, thereby earning a risk-less profit of $500 per ounce of gold. Although this type of arbitrage happens on occasion, trading markets have become so efficient that it is very difficult to find such opportunities. Even when such opportunities do arise, they are typically very small price differentials that exist for only seconds at a time. As such, any trading wishing to take advantage of such arbitrage opportunities needs to have access to large amount of capital and very fast computers. Generally, statistical arbitrage in modern markets takes a somewhat more sophisticated approach. Instead of buying and selling the same asset simultaneously, statistical arbitrageurs attempt to study the correlations between similar assets using historical data. When such correlations become disjointed, the statistical arbitrageur will jump into action in the hopes that the historical correlations will come back into line. For example, a computer program using historical price data may show that the historical correlation between the S&P 500 index and the NASDAQ is 90 percent, meaning that a $1.00 move in one market is associated with a $0.90 move in another. However, if a trading program indicated that this correlation has changed in recent trading, a trader may take a long position in one market and a short position in the other, hoping to take advantage of the temporary divergence of the two assets.

Conclusion

Online trading has become far more sophisticated over the past decade as traders compete against one another in the attempt to create profitable trading strategies. However, the individual online trader is also benefiting from many trends, including falling trading commissions, thanks especially to deep-discount brokerage like Interactive Brokers. For those traders interested in algorithmic trading, open-source programming languages and databases give traders unprecedented control over their data. For those willing to work very hard at this craft, success is a very real possibility.