Algorithmic trading is one of the most effective intraday trading approaches in existence. As computer programs improve the ability to program increasingly complex and advanced algorithms, algorithmic trading continues to become more refined nd also generate healthy returns. But algorithmic trading has a relatively high barrier to entry. Because it's dependent on the instructions programmed into the algorithm a trader uses, this trading method needs careful strategizing.

Thankfully, several robust algorithmic trading strategies have emerged. These strategies tend to deliver consistent results. Investors interested in learning more about the possibilities of algorithmic trading can use these well-established strategies easily. Here, we've gathered some of the most common strategies along with concrete examples of their performance.

Algorithmic trading is an automated trading process. Split-second buy and sell orders are made according to algorithms, sets of computer-programmed directions used to solve problems. Algorithms can be built with high levels of complexity, which is why computer programming is required to write these algorithms and execute them. These automated systems process order executions according to their preprogrammed trading instructions. Algorithms make their decisions by monitoring market variables like timing, price and volume.

Algorithmic trading uses complex formulas, mathematical modeling and human oversight to automate the buy and sell decisions they make. Many algorithms have built-in, high-frequency trading technology to make hundreds or even thousands of executions in just a few seconds. This makes algorithmic trading incredibly efficient for intraday trading, as this speed would be almost impossible to achieve without it.

Momentum

Momentum trading is a classic day-trading strategy that has been delivering results for more than 80 years. It was only a matter of time before traders decided to leverage this investing method by combining it with algorithmic trading. The fundamental idea behind momentum trading is to make predictions on future values based on values that have been previously observed. Examples of momentum trading in action are straightforward. Investing activity literally follows the momentum of a specific stock. If the price is rising, momentum trading strategy calls for purchasing that stock to drive the price higher until it reaches a certain threshold. Then, the strategy calls for a sale. Momentum trading is most useful in highly controlled situations with very short holds, making it ideal for algorithmic trading. You can read more about momentum trading here.

Trend Following

Trend following is also known as time-series momentum. It's related to momentum trading in that it seeks to generate profit through expectations that future asset price returns will be in the same direction of that asset's historical returns. Strategies for trend-following use closely defined market situations like range breakouts, volume profile skews or volatility jumps. The "simple moving average crossover" is one of the most well-known strategies. It works by identifying stocks that have short-period moving average values that surpass their long-period moving-average value. This triggers a buy order. If the inverse happens, this triggers a sell order.

Inverse Volatility

Inverse volatility strategy is often used in conjunction with markets for exchange-traded funds (ETFs). This strategy involves buying inverse volatility ETFs to hedge against portfolio risk by gaining exposure to volatility. Doing so makes it no longer necessary to buy options. Investors can see substantial returns if volatility remains low. This is because an inverse volatility ETF bets on market stability being the prevailing condition. Practical use of this strategy includes using a specific metric: the Cboe Volatility Index (VIX). When an ETF's benchmark volatility rises, it loses value. Using algorithmic trading to monitor an ETF's volatility on the VIX can help automate buy and sell orders to limit losses and maximize gains.

Index Fund Rebalancing

Index funds are linked to benchmark indices. Each fund has a defined period where it goes through a rebalancing to bring its holdings in line with its index. When this occurs, algorithmic traders can capitalize on the event. The trades that this rebalancing brings can offer profits of anywhere between 20 to 80 basis points, depending on how many stocks are in the index fund prior to rebalancing. Algorithmic trading systems excel in these environments as they can make buy and sell decisions much more quickly than human beings. An algorithm can initiate rebalancing trades in the timeliest manner. This provides untold opportunities for the best and most advantageous prices, maximizing profit opportunities.

Market Timing

Market timing strategy is all about waiting until the perfect moment to buy or sell an asset. This strategy can be hit or miss, as an investor can wait for an asset to hit what they perceive to be an all-time low only to see the price drop even further after investing in it. Likewise, investors can miss out on profit if they sell an asset when a perceived high hits, only to watch its value climb higher after their sale. Algorithmic trading can help smooth out these issues with market timing. By analyzing current market trends and comparing them against historical activity, algorithms can aid in determining whether an investor's timing choices are accurate. While still not perfect, using algorithmic trading in this way can reduce false starts by a significant margin.