Types of Algorithmic Trading Strategies

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Types of Algorithmic Trading Strategies2017-10-03T21:00:00+00:00

algorithmic trading

The term algorithmic trading refers to a number of approaches to both trading and investing. While the approaches can differ, they all share certain traits.

Firstly, they can be reduced to a set of rules. And secondly, they are almost always based on hard data rather than forecasts or opinions.

This article briefly describes some of the most noteworthy approaches for investors.

 

 

 

 

Types of Algorithmic Trading Strategies

Long-Term Algorithmic Trading Strategies

Rules-based investing covers a wide range of strategies. It also goes by a wide range of names. There is a lot of overlap between the following strategies and the terms are often used interchangeably too.

Factor-Based Investing

Factor-based systems use factors associated with high returns, based on historical data. Noteworthy factors include market capitalization, free cash flow, momentum, earnings momentum, and beta. Investors combine factors using a static weighing system, or a dynamic allocation.

Smart Beta

Smart Beta is a strategy that attempts to bridge the gap between passive and active investing. A market capitalization based index can be reweighed using fundamental metrics, or other factors. As a result, the risks inherent in an index dominated by a few very large stocks can be reduced. Smart Beta is appealing as the strategies can be packaged as ETFs which are rebalanced every three months.

ETF Rotation Strategies

Investors can use ETF rotation strategies to optimise return for a given level of risk. This is done in two ways. The strategies rotate into ETFs with strong momentum to maximise return. They also move capital into uncorrelated ETFs to control risk during periods of volatility. ETF rotation strategies take full advantage of patterns uncovered by quantitative research, as well as the low fees charged by ETFs.

Momentum Investing

One of the most basic algorithmic trading systems one can follow is a momentum investing strategy. These systems can vary from very simple to quite complex. A very simple strategy might invest in the top five performing shares in an index based on 12-month performance. More complex systems make use of both absolute and relative momentum, and blend momentum over time. Furthermore, investors can rebalance momentum systems weekly, monthly, quarterly or even only annually.

Trend Following

Trend following is one of the oldest forms of algorithmic trading systems. The goal is to buy assets when prices break significant resistance levels and sell short assets which break below significant support. Trendlines, moving averages or 10-weeks highs and lows make good triggers for these systems. Trend-following systems work well with currencies and commodities which tend to develop strong trends.

Mean Reversion

Mean reversion exploits the tendency of many asset prices to revert to the mean after periods when they become overbought or oversold. Investors will buy assets when they trade at the lower end of a trading range and sell them when they approach a moving average or the center of the trading range. This is very much the opposite of trend-following and performs well in rangebound markets.

Other approaches

There are numerous other systems, many of which focus on shorter time periods. These include statistical arbitrage, sentiment based systems, high-frequency trading, and systems based on seasonality.

Logical Invest’s approach to Algorithmic Trading

Logical Invest has developed a range of ETF rotation strategies with differing risk and return profiles. These strategies make use of a very wide range of ETFs covering multiple regions and sectors. This gives investors the opportunity to enhance returns by moving to wherever the money is flowing and companies are growing.

These strategies also make use of hedging instruments, leveraged exposure, and short exposure. This maximises potential returns and reduces volatility. They also use hedging instruments like gold and bond ETFs that have a low correlation to equities. So, they can reduce drawdowns during periods of volatility.

Summary

Algorithmic trading and investing includes numerous strategies. Some of these focus on the short term, while others are focus on generating longer term returns. Stratgies like smart beta and ETF rotation can effectivly bridge the gap between active and passive investing. These strategies are ideal for investors looking to enhance long term portfolio returns, while reducing risk.