Last Updated on 13 January, 2021 by Samuelsson
There are different categories of trading algorithms all over the internet and many vendors market them as an easy way to make money from trading. But we all know that no matter the approach — manual or algorithmic — trading is never a get-rich-quick business. So, it is not out of place if one may wondering whether algorithmic trading is legal.
Yes, algorithmic trading is legal, but some people do have their objections to how automated trading can impact the markets. While their concerns may be legitimate, there are no rules or laws in place that keep retail traders from making use of trading algorithms.
In this post, we will cover the following:
- What algorithmic trading is about
- Why algorithmic trading is legal
- How algorithmic trading can help your trading career
- The things you should know about algorithmic trading
- The steps for developing an algorithmic trading system
- Some tips that can help you get the best from algo trading
What is algorithmic trading?
Algorithmic trading, sometimes called automated trading, is a trading approach that uses a computer algorithm to create buy and sell orders (when the conditions are suitable for each) and automatically send the orders to the market via the brokerage platform. For a trading order to be generated, the market conditions must match the predefined criteria for a trade setup, based on the trading strategy used to create the trading algorithm.
In essence, computer algorithms search the markets for matching trade setups, and once they encounter the right setups, they execute the trades and manages them per the instructions written in the codes regarding the right position size and the conditions that must be met to for trade exit.
The entire process — from spotting the trade setups to executing and managing the trade — is automated. However, before the trading algorithm is launched to trade a live account, it has to be tested on the historical price action of many years’ duration. While this backtesting can show the strategy’s performance, it could be subject to curve-fitting because the data is already known. Curve fitting can make the result of backtesting misleading, as the system won’t be able to perform as well in live trading, which is why you have to be wary when buying a trading algorithm from a bot vendor.
To rule out curve fitting, the algo system has to be tested for robustness using the walk-forward methodology or forward testing, which actually tests the performance of the system in a real-time market. If a strategy performs well in the forward testing, it will likely do well when used to trade a live account if the market conditions do not drastically change.
Developing an algorithmic trading system is difficult and time-consuming — it might even take you up to a year or more, but if you enroll in a good algo trading course, you can learn it in a few months. You can then develop multiple systems to trade multiple markets at the same time, which can help you to diversify risks.
Why algorithmic trading is legal
Some people often ask whether algo trading is legal. Well, in some climes, that may be an important question. For example, in India, algorithmic trading is legal only for institutional traders/investors, as the Securities and Exchange Board of India (SEBI) approved its use by institutional traders/investors but has not approved it for retail traders and investors.
However, in the US and other western worlds, algo trading is seen as any other type of trading. It is basically a stage in the evolution of how trading is carried out, which is brought about by improvement in technology.
You know trading started as a gathering in coffee shops and progressed to trading pits at the exchanges. Then, with the advent of technology and the internet, electronic trading started, and the need for trading pit reduced, as most people prefer to send their orders via the electronic network. Pit traders who couldn’t adapt to online trading had to quit trading.
As technology improved, traders realized that computers can do more than just send orders electronically — computers can also execute trades based on preset rules. That was the birth of algorithmic trading, and it turned out that it can help traders to automate their trading strategies, removing the effects of trading emotions in trade execution.
The evolution in financial trading continues with the application of machine learning and Artificial Intelligence in trading. Now, we don’t just have simple algorithmic trading but also quantitative trading and high-frequency trading. However, in every revolution, there will be first movers and late adopters since not everyone can accept change. So, it is not uncommon to find people who are opposed to algorithmic trading.
Nevertheless, the constant investment in computing and other areas of technology indicates a wide acceptance of algo trading in the western world. So, there are really no grounds for algorithmic trading to be considered illegal. But, as it were, the legality of algorithmic trading is subject to jurisdictions and type of trader/investor. In some countries, only institutional traders can legally adopt algo trading.
But even in the US where algo trading has no restriction at all, the authorities have realized the effects of mass adoption of algo trading — like in the March 6, 2010 flash crash where the Dow 30 lost 1,000 points in 10 minutes due to numerous trading algorithms reacting to market aberration — and have set up circuit breakers to forestall such events in the future. Circuit breakers are regulatory measures put in place to temporarily halt trading on an exchange in the event of panic-selling. Other proposed regulatory measures have not been adopted. In the EU, however, algo traders are required to have a kill switch functionality in case of malfunction.
How algorithmic trading can improve your trading
Algorithmic trading can help your trading career if you can put in the effort to learn from a good algo trading course. Trading is getting more and more automated, and institutional traders are even bringing machine learning and AI to the game. So, the earlier you start planning to automate your trading, the better for your trading career. Having said that, these are some of the ways algorithmic trading can improve your trading:
1. Automated and systematic process
With algorithmic trading, the entire process of asset selection, identifying a trade setup, executing orders, and closing positions are automated. The trading algorithm has step-by-step instructions on what to do, and it does exactly that. So, your trading is objective and systematic.
2. Backtested strategies
In the process of creating your trading algorithm, you backtest it on the historical price and volume data to know how well the strategy used in the algorithm can perform. In addition to that, you get to know the odds of your trades so that you can better plan your capital allocation.
3. Fast and accurate execution
It is the computer algorithms that scan the market and trade the setups, so the process is a lot faster than any human can do. This speed is very important in fast-moving markets or intraday trading styles where any delay can lead to poor entry price and poor trade outcome. Moreover, the minimum human intervention in algo trading ensures accuracy and reduces the chance of dangerous trading mistakes, like placing abnormally large position sizes or entering trades unintentionally.
4. Less impact of human emotions
One of the most important benefits of algorithmic trading is that it reduces the impact of your trading emotions, such as greed and fear, on the outcome of your trades since you are not directly involved in the trade execution. You simply provide the computer with the instructions to trade, and it does just that if you don’t intervene.
5. Trading all the time
Computer algorithms can trade all the time without going on break or vacation. As long as the market is open, your trading algo is scanning it for trade setups and taking the trades. This is unlike in discretionary trading where you can only take the setups you see and miss the ones that form when you are not around your trading screen. With a good algo trading system, there is no missing any qualified trade setup.
6. Ease of diversification
You can set up your algo system with multiple strategies to trade different markets across different timeframes at the same time, which, in essence, is diversification. This would be nearly possible if you are manually analyzing each market and executing the trades, but trading algorithms can easily scan a range of markets, assets, and instruments and place multiple orders simultaneously.
7. Consistency in trading
When you are trading by yourself, it is always difficult to plan your trades and execute the plan flawlessly. You might have wonderful strategies and good trading plans, but adhering to them can be quite difficult because it is not everything that your eye can see. With trading algos, your trades are consistent at all times.
Things you should know about algorithmic trading
Although algo trading offers a lot of benefits, there are some things you need to know about it before delving into it. These are not limitations per se, but it is important you are aware of them and put them into consideration when planning to adopt this form of trading.
1. Algorithmic trading depends a lot on technology
As you would expect, algorithmic trading depends so much on technology — a fast-processing computer, fast internet connection, stable power supply, remote server, etc. When there is an issue with any aspect, the entire system will stop working, at least, for as long as the issue lasts.
For instance, if the internet connection is lost, your order will not be sent for execution. If it is an entry order, you will miss an opportunity to make money, but if it is an exit for an ongoing trade, you might end up losing money even for a trade that’s already in profit before the glitch.
2. Some trading strategies may be difficult to automate
There are many excellent and simple strategies that can easily be converted to trading algorithms, but not all strategies can be coded into an algorithm for automated trading. If you have a strategy that is difficult to code, you may have to keep trading it manually and look for a simpler strategy for your algorithmic trading.
3. Algo trading can be expensive to set up
Algorithmic trading can be expensive to set up. You will need high-end resources like a fast-processing computer and a high-speed internet connection. There is also a need to pay for a private server where you will host your algo system to prevent power issues. In addition, you will have to pay a programmer to create the algorithms if you can’t do it yourself, and there is also the cost of data feeds used in backtesting and forward-testing the strategies.
4. You may need to have programming skills
If you want to develop your trading algorithms yourself, you will need to have some programming skills. But it doesn’t mean that you will have to learn all programming languages; just learn the coding language of the trading platform you are using to trade.
Alternatively, you can hire a programmer to code your strategies for you, or you buy trading algos from reliable vendors if you don’t want to develop your own strategies.
The steps for developing an algorithmic trading system
Developing your own algorithmic trading system is quite a long and difficult process. These are the steps involved:
- Step 1: Search for trade ideas with reliable edges in the markets:
- Step 2: Convert the ideas into tradable strategies with specific criteria for trade entry, trade management, and trade exit
- Step 3: Code the strategies into trading algorithms by defining the strategy rules and writing the commands for each of the steps required in executing and managing the trades.
- Step 4: Backtest your trading algorithms using historical market data of up to 10 years to see how they perform, which would determine whether you go further to test them in the real-time market or modify them for better performance.
- Step 5: Test the system for robustness to be sure that the backtest results weren’t due to over-optimization (curve-fitting), which would make the system a flop in the real market environment. The best way to test for robustness is forward testing: a good result here shows that the system will do well when traded on a live account.