Algorithmic trading strategies are generally used by financial institutions or hedge funds to profit from the buying and selling of securities in the stock market. Algorithmic trading strategies are designed to give an edge to traders who can forecast market trends accurately. This has made them extremely popular with both institutional and retail investors. However, this form of trading can be risky because it relies on the inherent mathematical algorithms that are used to make the decisions. In most cases, an investor will use one of two types of algorithmic trading strategies: Price drove and Time-dependent. In price-driven strategies, the trader relies on the ability of the program to analyze past and current market data to determine which securities are overpriced and undervalued. Once the program determines the value of the security, the trader purchases the security that gives the highest return. The time period over which the analysis is conducted may also depend on the program used. In most cases, the price-driven strategy is considered less risky than the time-dependent strategy. However, once the software makes a good analysis, the risk of loss may increase significantly because of the computer's inability to remember past decisions.
The second type of algorithmic trading strategy uses a different algorithm to calculate the optimal quantity or rate of purchases and sales. This second type of algorithm is called the time-independent approach. With this approach, a computer analyzes historical data in order to make a more accurate estimate of future market conditions. The advantage of time-independent algorithms is that they do not require the trader to stay in front of the computer screen all the time. However, this system is less efficient during times when the market is volatile. Another popular algorithmic trading strategy is the range-bound algorithms. These algorithms take into account the size of a range before calculating the best bid amount to buy a stock. Range bound algorithms are widely used by professional traders because it reduces the possibility of serious trading losses. The drawback of range-bound algorithms is that they may not necessarily be implemented for all types of markets.
The last major category of algorithmic trading strategies is hybrid or complex algorithms. These algorithms combine the efficiency of the range bound and the time-independence aspects of the time-dependent algorithms. However, these types of strategies are very complicated and cannot be easily developed by a novice. They also have high transaction costs. The difficulty of implementing certain algorithmic trading strategies is partly because some of them require advanced programming skills and knowledge of mathematical algorithms. Experienced traders can develop their strategies by following instructions. However, beginners may not be able to implement these sophisticated algorithms. A beginner needs to understand how an algorithmic trading strategy works in order for him to implement it correctly.
One of the most popular algorithmic trading strategies is the moving averages technique. This is based on the principle of trend-following or range-settling. Algorithms that use trend-following strategies send alerts when the price of security crosses a defined line, which is called the trend-line. Once the line is crossed, the closing price is adjusted to get closer to the mid-point of the drawn trend. Traders use moving averages to indicate the commencement of a reversal in the price. For this reason, this type of algorithm is commonly used in futures, and forex trading as well as in binary options.