Trader Insights

A Guide to Algorithmic Trading

Significant technological advancements in trading and the financial markets have paved the way for a whole new type of trading: Algorithmic trading.

Around 80% of the daily moves made in both the United States Stock Exchange and Forex markets are made by machine-led algorithmic traders. At Alphachain Academy, we are one of the only trading firms offering a comprehensive Algorithmic trading programme. You can check out our course here 

In this guide to algorithmic trading, we’re going to explore the concepts of algorithmic trading as well as detail the potential steps new traders can take to forge their career in algorithmic trading.

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What is Algorithmic Trading?

Algorithmic trading, also colloquially known as algo trading, refers to a type of trading where trading instructions are automated through mathematical models of computer code. 

These lines of code detail trading orders such as when to enter and exit trades and which parameters to set, and can also include chart and volatility analysis, price arbitrage analysis as well as trend following price movements.

If market conditions align with the criteria the algorithmic trader has programmed, trading algorithms will automatically execute buy, sell, or hold orders on behalf of the trader without requiring input. This saves time, and provides maximum opportunity as the trade can occur without the trader needing to even be present.

Algorithmic trading is favoured so highly because of the number of different benefits it offers. These include:

  • The removal of human error

Algo trading takes care of decisions for you, meaning there’s no room for human error or for trades borne from emotional decisions over logical ones.

You can read more about trading psychology in our blog, found here.

  • Backtesting

Backtesting your algorithmic trading strategy using historical data is easier, quicker and more reliable than backtesting manually, which will establish more accurate buying and selling points  and more efficient strategy changes.

  • Supporting risk management strategies

Algorithms can implement stops and limits on your behalf, which better controls the amount of risk available to a trader.

  • Increasing opportunities

Algorithms are able to be created or tweaked in conjunction with a trading strategy. This potentially allows traders to maximise their exposure to opportunities in the market.

  • Low maintenance

For traders who want a better work/life balance, or who are making the transition to full time, algorithmic trading allows them to trade on their schedule because algorithms can run independently of the trader, whether that’s through the day or at night.

How to Become an Algorithmic Trader

Aspiring algorithmic traders must focus on excelling their skills in a number of different core areas and requirements. We’ve listed them below, as well as some helpful insider tips.

Core Areas:

There are three prominent domains which make up the core areas of algorithmic trading. These are:

  • Quantitative analysis and modelling
  • Coding or programming skills
  • Trading and financial market knowledge

Quantitative Analysis:

Quantitative analysis refers to utilising mathematical and statistical modelling, measurements and research methods to evaluate behaviour.

Quantitative analysts are usually seen as complex problem solvers, and it’s a skill that is highly valuable to any trading firm.

Quantitative analysts should enjoy working with softwares such as TradingView and PineEditor, as well as programming languages like Python and R, and should also be keen on statistical analysis.

If evaluating historical data and backtest performance  and using it to design a new algorithm fails to conjure any excitement within you – this won’t be the role for you. Additionally, traders who are used to trading using fundamental and technical analysis would find the quantitative analysis learning curve quite steep, so this is something to be aware of.

Trading Knowledge

Just like any aspiring trader, traders must possess trading knowledge – even if this is at a very basic level of understanding. 

Traders with little to no knowledge will find it difficult to create programmes and interact with trading indicators and review performance analysis. Algorithmic Traders in trading firms are at the very least expected to possess:

  • Knowledge of types of trading instruments, such as currencies, stocks and cryptocurrencies
  • Knowledge of types of basic trading strategies like Mean Reversion, Trend Following etc
  • Understanding of probability and good maths skills
  • Knowledge of risk management and good risk management techniques
  • Good problem solving skills

Programming Skills

Algorithmic trading relies on coding. However, it should not dissuade those who have never coded. 

Aspiring algorithmic traders can continue to enhance their skills and learn a coding language in their own time. Preferred languages algorithmic traders should have experience of include:

  • Python
  • C++
  • Java
  • R

Learn from Resources:

Any type of trading places emphasis on the continued importance of learning. A trader cannot excel at their profession without constantly learning, and being open to new ideas, strategies, and ways of thinking. Experimenting with strategies is part of the role, and just as aspiring traders should learn all they can whilst they are starting out in their careers, that should not stop once a trader enters a firm.

For algorithmic traders, there are a number of different learning resources readily available. Algorithmic trading books are great resources to begin learning. Highly recommended titles include:

  • Options, Futures and Derivatives by John Hull, which is considered a good read for beginners as they begin to learn derivatives.
  • Algorithmic Trading: Winning Strategies and their Rationale by Dr Ernest Chan

And

  • Python for Finance: Mastering Data-Driven Finance by Yves Hilpisch

Alongside books, algorithmic traders should also:

  • Read blogs on algorithmic trading and quantitative analysis
  • Watch YouTube videos 
  • Listen to trading and coding podcasts
  • Attend free online webinars
  • Register on coding platforms and learn to code
  • Register for free algorithmic trading courses

Learn from Professionals:

Whilst good knowledge in core areas of algorithmic trading like statistics and coding are sought after and recommended, it is also necessary to gain experience from professional algorithmic traders.

The experience and depth of knowledge that professional algorithmic traders can provide is invaluable to those just starting their career, and it is easier to create and implement strategies alongside a professional to receive feedback. 

To learn from professionals there are two options available to aspiring algorithmic traders:

  • Internships or Inhouse Courses: Some trading firms will offer internships which can give new algorithmic traders the environment they need in order to familiarise themselves with best market practices and risk management strategies.

At Alphachain Academy our Algorithmic Trading Programme allows students unprecedented access to a mentorship team and CEO, as well as access to an in-house trading psychologist with over 20 years experience, Ron William. You’ll also receive guaranteed funding for your strategy to begin your career as an Algorithmic Trader. What’s more, coding experience is not necessary! Find out more about our course here.

  • Online self-learning courses: Online learning portals such as QuantInsti, Udemy and edX all have professional teachers from mathematical and computer science backgrounds.

Education and Background:

Aspiring algorithmic traders will usually have an educational background in the following areas:

  • Knowledge of Python, R, and other programming languages like C++ (usually required for internships)
  • Domain knowledge in stock markets such as derivatives, fundamental, quant and macro, plus strong logical skills.
  • A Bachelors or Master’s in either mathematics, statistics, economics, or computer science

An Algorithmic Traders Job

Once an algorithmic trader lands a role in a trading firm, the onus is on them to apply, design, and implement their algorithmic trading knowledge across real markets on behalf of their firm. 

Firms which trade low latency strategies for example will tend to have their platforms built on a programming language such as C++. Whereas in trading firms that do use latency as a parameter, trading platforms may be built upon a programming language like Python. This is why it is a necessity for all aspiring algorithmic traders to have good knowledge of all types of programming languages.

New recruits will often be tasked with specific projects alongside access to training to ensure they have a firm grasp on the subject. They may also allow recruits to spend time across different trading desks, such as Quant Desks, Programming Desks and Risk Management Desks to educate them on the work processes followed by the firm.

Looking to make a career in trading? Read our complete guide and become a Top Proprietary Market Trader today.

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Algorithmic Trading Strategies

There are a number of algorithmic trading strategies, and new ones are constantly being created due to the advancements that coding allows traders to make. The core strategies of the most common algorithms can be categorised into the following however:

Index rebalancing strategies

Typically index funds refer to funds such as pension and retirement accounts. These accounts need to be rebalanced as prices fluctuate and tracked securities are moved by market capitalisation. The rebalancing then creates unique opportunities for algorithmic traders who can maximise the opportunities found in trades expected to take place before the fund is rebalanced.

This is a common strategy utilised by algorithmic traders as trades can be entered within nano-seconds at the best prices. Retail trading platforms do not support this type of strategy, which leaves it open for trading firms and traders who specialise in high frequency trading.

High-frequency arbitrage trading strategies

Arbitrage trading is the practice of finding opportunities in the price differences between two or more markets, which commonly occurs when the same market is traded across different exchanges. One example of this is the price of Bitcoin, which differs between the various cryptocurrency exchanges that it is traded on. 

Although the concept is simple in practice, algorithmic traders are best placed to take advantage of these differences because of the speed in which they can enter and exit the trade. The price differences are only available for a few seconds at most, which is why they rely on high internet speeds and good execution models.

Mean reversion trading strategies

Mean reversion refers to the effect of a market’s trading price reverting back to its original or historical average price. These types of strategies are usually based on mathematical models that assume a high or low fluctuation in an asset is temporary, and works on the basis that the asset will revert to its average price over a selected period of time.

Machine learning AI trading strategies

Machine learning artificial intelligence algorithms are relatively new in the world of algorithmic trading. The success and outcome of these strategies ultimately depends on how reliable the predetermined inputs are in the programming language created by the trader. 

One new and exciting aspect of machine learning AI strategies is that the trading robot itself can update its own algorithm based on what has had success, and what has not had desired outcomes, removing some of the work for a trader.

Trend-following momentum trading strategies

A popular algorithmic trading strategy, this strategy is commonly used by a variety of traders and trading firms, whether big or small.

Trend following strategies work on the idea that if a trend looks to be in place, the market could continue following the direction of the trend until there are evidential signals that are beginning to reverse.

Many experts agree that this is a primary reason why movements in the financial markets have changed drastically over the course of decade – price moves can go much further and much quicker because of the amount of algorithmic trades entering the move at significant speed.

Algorithmic traders make use of trend-following momentum strategies by using trading indicators to identify the long-term trend and then the subsequent overbought or oversold conditions. Then, instead of themselves needing to sit and analyse each movement in the trend, they would code conditions into an algo trading system which will place a trade once the predetermined conditions match up with the user parameters, saving time and working efficiently at the maximum time of opportunity. 

Conclusion

The popularity of algorithmic trading has surged considerably in the past decade because of its increased ability to promote market efficiency. Whereas human traders are at risk of poor risk management and questionable decision making due to lapses in trading psychology, algorithmic trading removes this issue and instead performs the best trades, at the best times, solely working on a defined set of instructions. 

It’s no surprise then that we at Alphachain Academy offer an algorithmic trading course. Students do not need to have prior experience in coding, but should have a background in a STEM, finance or quantitative discipline. 

Our programme will teach you to code and develop your own custom indicators, as well as enact your algorithmic strategies on a live Alphachain funded account. Towards the end of the programme you will also have the opportunity to present your findings to the Head of the Academy and/or the CEO and receive direct feedback on your work. To find out more about the Alphachain Academy Algorithmic Trading Course click here or alternatively book a free consultation with us.

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