What is an algorithmic trader?

What is an algorithmic trader?

Algorithmic trader
Photo by @g_dezigner

Algorithmic trading is an automated approach to trading forex and financial markets. Before you set out to trade with this method, we’d recommend that you decide on a set of instructions. These instructions would be programmed into a computer model.

This is a trading process that usually takes into consideration price, time and volume. You’d typically involve complex formulas, mathematical models, and, potentially, even include some human interaction.

The object of algorithmic trading is to be able to make accurate decisions about forex trading and trading financial markets. These decisions include: 

  • Whether to buy or to sell
  • When to enter the trade
  • At which time or at which price to trade
  • Where to take profit
  • Where to place a stop-loss

Essentially, algorithmic trading is a rule-based strategy. The definition of the rules is the critical input as to whether the approach is profitable for you or not. This strategy moves on from our previous lesson, ‘What is Price Action Trading?’ — where you’d trade according to the ‘action’ of the price.

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How does algorithmic trading work?

Algorithmic trading works by first defining the objective of your strategy. This doesn’t necessarily mean that your only objective is to achieve a profit. For example, many algorithmic trading programs are used to execute large orders on behalf of institutional investors. These investors may be looking to achieve the best overall price to enter or exit a position in the market. 

But as an individual trader, you’d certainly be looking to make a profit. With this as your objective, the next step would be to define over what time period you’d be looking to achieve this profit.

Generally speaking, algorithmic trading is done on a short-term basis. Your trades may be held for days, but more likely for hours or less — maybe minutes or even for seconds.

Once you’ve decided on the timeframe of your strategy, you’ll then need to decide upon a set of rules to suit your strategy. These rules and the overall strategy would need to be vigorously back tested to ensure that the algorithmic trading strategy is at least profitable.

We’ll look at this process in more depth below in the section, ‘What are the best algorithmic trading strategies?’

What is an algorithmic trader?

Put simply, an algorithmic trader uses sequences of computer-based instructions in their trading approach. As an algorithmic trader, you’d decide the rules and processes that you’d use to define your algorithmic trading strategy. We’ll look into this in more detail below. 

Usually, an algorithmic trader would have the following characteristics:

  • A strong knowledge of markets and stocks
  • A preference for the technical analysis side of trading 
  • An interest in markets from a mathematical standpoint 
  • Some programming knowledge and abilities (although, this is not essential)

Since the surge in this type of trading at the start of the 21st century, algorithmic trading and the development of automated algorithmic trading strategies has continued to expand. Alongside this expansion, there’s also been a rise in the number of individuals who see themselves as algorithmic traders.

What is the best algorithmic trading software?

The best algorithmic trading software can be defined by the programming language and interface used. It’s not easy to determine the best algorithmic trading software. However, the best and the most common programming languages used to write trading software are: 

  • Matlab
  • Python 
  • C++ 
  • JAVA 
  • Perl 

Given that you’re not likely to be programming directly in these languages, there are many software interfaces on offer for the individual trader. The most commonly used, and arguably the best for the individual trader is the MetaTrader suite. This includes: 

  • MT4 
  • MT5 
  • MQL5 / MQL4 programming languages

We’d suggest giving these a try in your first move into algorithmic trading.

Why is algorithmic trading important?

Algorithmic trading is important because it’s been in and ascendancy since the 1980s. But it had a particular explosion at the start of the 21st century. This means that the strategy now makes up a significant percentage of global trading volumes each day. 

Algorithmic trading software provides significant liquidity to markets. But it can also create heightened volatility. At times, they can trigger aggressive plunges or surges in markets.

What are the best algorithmic trading strategies?

The best algorithmic trading strategies are numerous, and any strategy is only as good as the results it generates. Although algorithmic trading strategies come and go, there are some more commonly used starting points that you can make use of in your trading.

These include:

  • Trend-following strategy
  • Mean reversion strategy
  • Implementation shortfall strategy
  • Mathematical model-based strategy
  • Volume weighted average price (VWAP) and time weighted average price (TWAP) strategy

Let’s look at these in more depth. 

Trend-following strategy

A trend following strategy is probably the most common of the algorithmic trading strategies. This strategy would look to follow trends by using moving averages and channels (as covered in our lesson ‘What are channels?’). 

You can also use a trend following strategy to identify trend lines. Plus, you can use various other technical analysis indicators that signal trends. These include trend momentum indicators, like an RSI. 

With a trend following strategy, there’s no need to make future price calculations. All that’s required is to enter trades in the direction of trends, on any defined time frame. When these trends are deemed by the strategy to have ended, you’d then exit and maybe reverse the position. 

You can also consider a trend that follows an algorithmic trading strategy as a momentum-following algorithmic trading strategy.

Algorithmic trading
Screenshot from TradingView

Mean reversion strategy

A mean reversion strategy is based around the idea that market prices will revert to an average or mean price level over any time period. This is based on the mathematical concept of ‘regression to the mean’. 

You’ll find that mean reversion strategies work best in situations where a particular market experiences significant price changes. These changes move away from an average level, assuming that it’ll revert to its previous state. 

An algorithmic mean reversion trading strategy is simply one that uses the above concept with defined rules. It packages it with an automated program. Much like the trend following strategies that you’ll have seen in this lesson, technical indicators such a Bollinger Bands or momentum indicators like Stochastics could be used with this strategy.

Algorithmic trading 2
Screenshot from TradingView

Implementation shortfall strategy

The implementation shortfall strategy is an algorithmic trading strategy that aims to minimise the cost of executing an order in the market.

It isn’t necessarily a strategy that you’d look to use to profit from directly. But it’s a way to achieve better execution prices and potentially improve bottom-line performance. 

Implementation shortfall is when you receive a different execution price than intended on any trade. You can also categorise this strategy under the broader term of ‘slippage’. Essentially, this is due to the cost of execution and the difference between the time you would’ve made the trade execution after the trade decision.

By using an algorithmic implementation shortfall strategy, it may be possible for you to achieve better price execution and improve the overall profitability of your trading strategy

Mathematical model-based strategy

You’ll find that most algorithmic trading strategies are, in some ways, mathematical model-based. Even algorithmic trading strategies based on fundamentals, such as macroeconomic data, geopolitical events and news, still have a mathematical basis. 

There are some algorithmic trading strategies that are based around specific mathematical models. These include:

  • Delta-neutral strategies
  • Arbitrage strategies
  • Pairs trading strategies
  • Index fund rebalancing strategies
  • ‘Fixing’ strategies

Volume weighted average price (VWAP) and time weighted average price (TWAP) strategy

Volume weighted average price (VWAP) and time weighted average price (TWAP) strategies come under the same umbrella as algorithmic trading strategies. These trading strategies are generally used by institutional brokers when they execute orders for their large institutional clients. These could include hedge funds or pension funds.

Volume weighted average price strategies look to execute large market orders to be close to the VWAP

But what is the VWAP exactly? It’s the average market price, traded over a certain time period, and weighted by the volume of trades, often over a trading day. The algorithmic strategy aims to achieve an average price close to the VWAP, over the same period of time.

VWAP
Screenshot from TradingView

On the other hand, the TWAP is the average price over a certain time period, maybe over a trading day, but not weighted by the volume of trades. It’s simply the average price between two times.

TWAP
Screenshot from TradingView

Algorithmic trading summary

In this lesson on algorithmic trading strategies, we’ve looked at how algorithmic trading works and why it’s an important part of your trading strategy. We’ve also explored the characteristics of the algorithmic trader and various algorithmic trading approaches that you can use. 

The key takeaways from this lesson are:

  • To be an algorithmic trader, you need a strong understanding of technical analysis and mathematical models
  • Programming knowledge would be a big advantage too

In our next lesson, we’re going to explore the answer to the question ‘what is events trading?’ looking at what it is, how to do it and why it’s important. 

Alternatively, for more trading strategies, explore our trading strategies module and strategies blog.

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