As I touched on in a previous blog, algorithmic trading provides countless benefits that complement and enhance those of traditional trading approaches — the main ones being an increase in speed, a general consolidation of time otherwise spent managing numerous trade aspects, and a removal of potential folly rooted in human emotion. As a result of these perks, the technology has become prominent in many different financial markets.
There are a variety of effective strategies contained in algorithmic trading, whether you are starting from scratch or striving to improve upon past forays into the algorithmic cycle. Here now are several of those strategies at a glance.
Moving Average Crossover Strategy
If you are experienced in trading, you likely already know quite a bit about Simple Moving Average (SMA); it is the most basic of moving averages used in trading. SMA is calculated via any predfined or fixed number of days, and when implemented in a quantitative trading strategy, it can be broken down into a simple algorithmic formula sometimes referred to as Moving Average Crossover Strategy:
- Calculate SMA of five days
- Calculate SMA of 20 days
- Finally, “take a long position when the five days SMA is larger than or equal to the 20 days SMA” (and a short position if it is smaller).
If, however, you are new to algorithmic trading, it is important to recognize that the aforementioned is a relatively simple strategic example. As you continue to delve into the process, it will become increasingly evident that more complex algorithms stand as the norm within trading strategy. Still, it is never illogical to start small as you learn the language.
Trend Following Strategy
Trends are key in the trading landscape — the process of monitoring which directions a market is moving in and why. A trend following algorithmic strategy can help traders “produce, buy, and sell signals” following the emergence of new trends; this is a tried-and-true technique amongst seasoned traders, but one that, in this case, is put on the shoulders of trading automatons. The resulting strategy is an effective blend of traditional and modern trading ideologies, one that is both fluid and comparatively easy to implement. Technical analysis and market patterns are automatically utilized to make key trade decisions both quickly and efficiently.
Carry Trade Strategy
An asset to new and experienced traders alike, carry trade strategy is one of many strategies that has been considerably fine-tuned when paired with automation; it traditionally focuses primarily on “the difference in yield between two currencies.” Many carry trade systems have implemented algorithmic functionality — mainly to mitigate the strategy’s consistent drawbacks — namely increased volatility and susceptibility to interest rate shocks — while supplementing its benefits. Algorithms can select ideal currencies based on crucial factors like low volatility and potential profitability.