A Look into Algorithmic Trading Strategies – What You Need to Know

Beginner’s Guide / 26.09.2020

Algorithmic trading, also referred to as automated or black-box trading, is an emerging trend that has seen a massive surge in recent years. A study shows that it now accounts for up to 80% of all forex trading.

As the name implies, algorithmic trading involves the execution of orders using a defined set of instructions or algorithms on a computer program. The aim is to maximize speed and data processing beyond human capabilities, replacing the slower traditional human trading.

Opportunities at faster profitability drive the choice of strategies employed. This article shall comprehensively review the various strategies employed.

Algorithmic trading strategies

Trend-following Strategies

They are the most common and most straightforward of all strategies to implement, simply because it lacks the need to predict or forecast prices. They involve the following trends to estimate channel breakouts, movements in price levels, averages, and other technical indicators as applied. 

Trades are kick-started by the occurrence of a favorable or desired trend. Such a trend involves buying an asset as its price trend goes up and selling it as the trend goes down. Complex predictive analysis is, therefore, not required.

Trading before Index Fund Rebalancing

The nature of index funds requires a periodic rebalancing or adjustment of their portfolio within a set period. Rebalancing helps match the holdings or new prices of such funds with their benchmark indices. Thus, a profitable opportunity is created to active investors, many of whom are algorithmic traders, by capitalizing on the index rebalancing effect. 

The stake may be up to 80 basis points profits depending on the stock number. Algorithmic trading systems initiate the trade by timely executing it at the best prices.

Arbitrage

In a forex trade, there exist stocks that are dual-listed in various markets at different prices. Taking advantage of the price difference by buying such stock at a lower price from one market and selling it to the other market at a higher price is referred to as arbitrage. It allows for a risk-free profit with no negative outflows.

The term can also refer to current stocks vs. futures since prices vary from time to time. Algorithms play a crucial role in identifying the price differences and making the orders in time.

Delta-neutral Strategy

It is a type of mathematical model-based strategy. Such strategies use proven mathematical models to allow trading on a set of options and related security.

The delta-neutral strategies create a reference position unlikely to be affected by small changes in the stock prices. The overall “delta” value is made as close to zero as possible to ensure this.

Mean Reversion

Also referred to as range trading, it relies on the idea that both a stock’s highs and lows have a temporary aspect and should periodically move back to their mean prices (average price). A market price lower than the mean price means stocks are attractive to buy as they are speculated to rise. A market price above the mean is also expected to fall.

An algorithm automatically trades assets when a deviation from the defined average price is observed.

Percentage of Volume (POV)

An algorithm is set following a defined ratio of the traded market volume to continue trading until the trade order is met. There’s an automatic adjustment of participation rate, limiting it to a specified percentage of stocks in the total traded volume.

Implementation Shortfall

In trade, implementation shortfall refers to the difference between the trader’s decision price and the average trade prices, including taxes and commissions. The reference price quoted by the trader is used as a benchmark. 

The strategy aims to benefit from opportunity cost delays by trading off the real-time market and saving on the order’s cost. The algorithmic speed of executing the order capitalizes on this, increasing as stock prices become desirable and slowing when prices become untenable.

Volume-weighted Average Price (VWAP)

The strategy is effective with short-term time frames, usually a day. The VWAP strategy starts by first breaking up a large order. Using stock-specific profiles of historical volumes, reduced chunks of the order are then traded in the market. The action aims to keep the price within the average.

Time-weighted Average Price (TWAP)

The time-weighted average price starts with breaking down a larger order. These smaller chunks of the order are then traded as per evenly distributed time slots from the start to the end time. The aim is to keep the price close to the average.

High-tech Front-running

This strategy is also referred to as beyond the usual strategy. Some algorithms will try and spot out ongoings from the opposite side of the trade. For instance, a market maker algorithm from the sellers’ side has the ability to recognise an algorithm from the buyers’ side with a large order. Such knowledge enables the market maker to spot the massive order opportunity and fill these orders at a higher price.

Requirements for Algorithmic Trading

The trading strategy is only complete when the algorithm is implemented using a computer program. The requirements are:

  • Computer programming knowledge to craft the strategy
  • Access to stock trading platforms and a stable network connectivity
  • Access to data feeds from the markets that will be analyzed by the algorithm
  • Infrastructure and ability to carry a backtest of the system before real-life market usage
  • Access to historical data for backtesting as per the rules on complexity implemented by the algorithm

Takeaway

Algorithmic trading allows for correctly timed trades, with a reduced risk of manual errors when placed. In an article by Nasdaq states that algorithm trading’s main advantage is the elimination of human emotions, which causes irrational decisions during trading. The other advantages include the ability to backtest and reduced costs. 

The loss of human control and the need for constant monitoring of power loss and connectivity are otherwise key drawbacks to algorithmic trading. The need to know the programming language necessitates traders to learn the skill of developing the algorithms.

Adam is an outgoing young lad who likes adventures and discovering new things. Despite his boring life, he loves writing about cryptocurrencies and exploring what blockchain technology can do for the coming digital world where all adventures will be virtual.