The complete guide to algorithmic trading

You’ve used programming and algorithms to dictate how your machine needs to function. You realize that artificial intelligence (AI) and machine learning (ML) can be used to output beyond “Hello World”. Perhaps, you’re wondering if you can use AI to understand and predict stock market movements so that you have an assured shot at profitable investments.

Yes, you can, and it’s called algorithmic trading.

However, it’s not as easy as running a few lines of code to find the right stock to invest in. It requires the right mix of programming and finance expertise to be successful in algorithmic trading. Or is it? Let’s find out.

Here’s what we will cover:

The basics

What is algorithmic trading?

What are algorithms?

Algorithmic trading strategies

Methods of algorithmic trading

The Basics

Before we get started, you need to be familiar with three important terms or types of trading.

Discretionary trading

Discretionary trading is also known as decision-based trading. This means the trader decides which trades to make based on the market conditions and the available information and various factors. Though they follow a trading plan, they use their ‘discretion’ in managing trades. For instance, a discretionary trader might see that all their criteria for a long trade is met and yet decide against making the trade.

Black box trading

Black box trading is also known by a few other names – mechanical trading, rule-based trading, algorithmic trading, and algo trading. In this, all the buy-sell rules are well defined and strictly adhered to. Rules are what computers run on, making black-box trading ideal for machines.

The computer makes trades within the framework for the buy-sell rules and needs no human intervention. A huge benefit of using this method of trading is that rules can be historically tested, and you can be sure of where you want to put your money.

Gray box trading

Gray box trading is the third type of trading that combines discretionary trading and algo trading. It is also known as Hybrid trading.

What is algorithmic trading?

Algorithmic trading as the name suggests is a type of trading that is done using machines and algorithms. In definition, it is the process of using computer programs and machine learning algorithms to automate the buy-sell process of various financial instruments such as stocks, currencies, cryptocurrencies, etc. The computer programs are trained to trade based on the trading rules and input which are pre-defined.

What used to closely resemble scenes from the movie ‘The Pursuit of Happyness’, trading has come a long way due to digital transformation. Today, you don’t need as many people shouting at the top of their voices in the stock exchange building. All you need is a computer with a bunch of codes that will help you execute your trading decisions.

According to J.P. Morgan, discretionary traders account for only 10% of trading volume in stocks. 80% of the daily traders across the US are done by algorithmic trading and machines. There is a 41% growth in the number of users executing their trades algorithmically. If we dig deep into the analysis, AI and ML are expected to influence the future of trading by 57% and 61% respectively, in the next three years.

The machine never sleeps

AI owns its ever-rising popularity in the trading industry to the machines that never sleep. Most markets are open through the night and perhaps close only for a short time before they open again. To monitor market movements tirelessly, round the clock, is a mammoth task. However, with machines employed to do the trading for you, you can be sure that you are always ready to make a trade even if you are asleep. AI algorithms are largely useful to monitor and handle market volatility with ease.

Benefits of algorithmic trading

Algo trading or algorithmic trading has plenty of benefits. Some of them include:

• They enable execution of trades at the best prices

• The order placement is immediate and accurate to avoid drastic price changes

• They reduce transaction costs

• They can monitor multiple market conditions simultaneously

• Algorithms reduce the risk of manual errors when placing an order

• They can be backtested using historical and real-time data if ensure the trading strategy is successful.

• Removes emotional and psychological factors that influence human traders

Algorithmic trading strategies

Of course, just like algorithms, algorithmic strategies are in plenty. Some of the more common ones include

Momentum strategy

Momentum strategy is one where the algorithm starts executing trades based on a particular spike in the market or at a specific moment.

Mean-Reversion strategy

The mean-reversion algorithm strategy assumes that the prices usually deviate back to its average and execute the trade accordingly.

Market-making strategy

The market-making strategy is a slightly sophisticated type of algorithm strategy. They are also known as liquidity providers. This algorithm is built to supply buy-sell orders to fill the order book and to make a particular financial instrument within a market more liquid.

Arbitrage algorithm strategy

Arbitrage strategy allows you to buy a dual-listed stock at a lower price in one market while simultaneously selling it at a higher price in another market. The price difference is a risk-free profit or ‘arbitrage’.

Index Fund Rebalancing strategy

Index funds have specific periods of rebalancing. This creates a window of profitable opportunities for traders. An index fund rebalancing strategy uses algorithms to make trades based on this strategy on time and for the best prices.

Methods of algorithmic trading

Algo trading has got you interested and you’re wondering how to get started? Well, there are only two ways to do this.

Method #1

You can either take the longer, more complex route. One of the first requirements for this method of algorithmic trading is programming skills and expertise in programming languages such as python, java, r, etc. This will allow you to create and run the required algorithms.

The next crucial requirement in this method is access to high quality historical and real-time data of various markets. Once you have created the algorithms and trained them on the data, you need to backtest the strategy. This will ensure that there are no creeks in your strategy, and you don’t end up losing money. All of this is expensive and time-consuming.

Method #2

You can use an AI platform that is pre-programmed to create and run algorithms. This platform usually comes with tight integrations with financial databases. What’s more, you don’t require programming expertise to use such a platform. This reduces operational costs that you might have otherwise incurred to hire data scientists or set up a data team.

Brainalyzed Insight is one such platform that allows you to do all of this at just a fraction of the cost. It uses artificial swarm intelligence technology to run models and make accurate predictions. Don’t believe me? Give it a try for free.

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