Will algorithmic strategies consistently outperform index funds, buy and hold, and nearly risk free lending services? If yes/no, what is their risk profile and potential losses?
Here at Trading Strategy, some of the most asked questions we get when speaking to people less familiar with algorithmic trading or automated trading are; Does algorithmic trading work? How profitable are these strategies? What are the potential returns?
We take a deep dive into the different ways a trader would utilise algorithmic trading strategies, different types of strategies, and whether or not they are profitable in an overall trading strategy.
What is algorithmic trading?
We first need to understand “what is algorithmic trading?” and how to use it effectively. We recommend reading our knowledge base article here for a primer. Fundamentally, algorithmic trading (also known as automated or algo trading) is a method of trading where an individual uses a computer program (an algorithm) that has predefined rules and instructions to place a trade on an exchange. These predefined rules can be based on timing, price, quantity, action or any mathematical model. A trader or strategy developer will create a trading algorithm with specific trade rules and then the owner will deploy this algorithm for it then to execute trades automatically on the traders behalf.
Understanding different types of algorithmic trading
To analyse the profitability of algorithmic trading, we must understand the different types of trading and how you intend to use an automated trading strategy. Here are a few common types of algorithmic trading approaches.
Momentum investing is one of the most common and basic algorithmic trading strategy approaches. This type of algorithm analyses the market trend that moves significantly in one direction at high velocity, meaning if the asset is rising, the algorithm places buy orders as it rises and sells when they look like they have peaked, or lose momentum, hence the name. The execution of the trading can vary based on the setup from the developer ranging from very simple to very complex.
A simple momentum investing strategy might invest in the best performing cryptocurrencies over a 6-month period. On the other hand, a complex algorithm might blend momentum over time, have more advanced signals for buying and selling meaning they can have more control over specific market movement or identify false indicators.
The mean reversion algorithmic strategy is set up to exploit the sudden price movement of an asset that is in the process of short term fluctuation. As a tendency many assets revert to the mean after periods where the asset has become oversold or overbought. This means that there can be a sudden influx in buy orders over a short period of time, but once that period has ended the asset price will revert back to its long-time average price. A mean reversion algorithm aims to capitalise on this fluctuation by purchasing at the low and selling when the asset price moves upwards towards the moving average.
Dollar cost averaging
One of the most simple algorithmic trading strategies and is widely used by many investors. The idea of this algorithm is to invest a fixed amount of money into an individual cryptocurrency periodically. This is a popular algorithmic approach as it is simple to set up and save a lot of time having this process automated than manually executing
Why would anyone use an algorithmic trading strategy?
Now we are familiar with the concept of algorithmic trading and some of the different approaches, we can look at why a trader would opt to use algorithmic trading in their strategy.
The technological boom over the past 20 years has meant we are much more efficient in our everyday lives and this is no exception when it comes to trading. Computers are designed to handle large sets of data and operate in a mathematical way, meaning they are faster and more accurate than humans. This means computers can make faster decisions and action on them much quicker than we can. Computers can analyse a chart based on market data, draw charts and then execute on any trade orders within a small amount of time. In comparison imagine you are monitoring a 1-minute chart for bitcoin and ethereum, using technical analysis to make your trade decision then placing the trade order, this is a much more labour approach especially when trading on a fast-moving volatile market. The computer wins that race 10 out of 10 times. This automated process is one of the many reasons why a trader would opt to use a trading algorithm opposed to manually trading, for a more in-depth look visit our “Algorithmic Trading vs Manual Trading” article here.
How much does it cost to set up a trading algorithm?
In order to identify how profitable algorithmic trading is, we need to first look into the cost of developing an automated trading system for decentralised markets from a financial and resources standpoint.
Setting up your trading strategy - To begin with you will need to decide what strategy type you are going to create, then research and analyse the market to set the parameters in which your algorithm will execute. This will include conducting technical analysis, setting up technical indicators and trading rule sets, this will be one of the most time consuming steps as this usually needs to be meticulous which can take anywhere from 150 to 200 hours, whether it is your own or you contract an individual to create this for you.
Backtesting - Once the trading strategy has been created the developer will need to test and optimise the algorithm using historical market data in a simulation environment rather than launching the strategy once developed. By not backtesting you run the risk of losing your funds by trading real money on unintended trade orders. This process can take anywhere between 80 - 120 hours.
Accessing real-time and historical market data - In order to backtest and create a trading algorithm you will need to have access to a large quantity of market data in order to conduct your market analysis and backtest. Accessing this data is time consuming and can come at a cost. Implementing market data sets into your algorithmic trading system could take between 40-100 hours along with any additional licensing fees from the data provider
Integration with exchanges - In order to place trade orders on a decentralised exchange you will need to have an integration to the decentralised exchange and set up a smart contract. This requires blockchain development knowledge and working with APIs, which can take anywhere from 80-120 hours
These are the very basic requirements in order to conduct algorithmic trading for decentralised markets. If we were to multiply the average amount of hours for each requirement by the average US hourly earning it would have an estimated cost of $4,872.75.
This is an estimated cost if you have the resources and technical ability at your disposal. If you were to outsource this to a developer which averages $32 per hour it would cost $14,240.
As you can see, setting up an algorithmic trading system does not come cheap. There’s a lot of work needed in order to set up the most basic form of algorithmic trading, let alone a system that is reliable and has advanced capabilities. All of these elements become a factor when evaluating if algorithmic trading on decentralised markets is profitable. However, there is a way to avoid this setup cost entirely by leveraging technology solutions out built for this specific reason.
There are protocols out there that have already created an automated trading system and infrastructure that allows traders and investors to invest in trading algorithms that have been created by strategy developers and quants. This allows you to skip the time-consuming and costly process of setting up an automated trading system. For individuals looking to create a strategy, it enables them to have a platform for them to be able to research, create and deploy trading algorithms that they create.
- Identifying the use of algorithmic trading into your overall portfolio, how do you plan to leverage this technology into different parts of your strategy, are you using it for arbitrage, to capitalise on market movements outside of your “available trading hours”, to capitalise on specific price action based on prior historical market tends
- Using it as a trade executor to avoid any human errors
Trading Strategy Results and Data Quality
We analysed some of the strategies of different automated trading venues within a report we published titled “The Evolving Landscape of Automated Cryptocurrency Trading: A Market Study of Trading Strategy Solutions” The number one question investors are asking is if one can make risk-adjusted profits in automated trading and whether there exists “alpha”, or is the long term performance better than the markets overall. Based on the research conducted in the mentioned report, there exists successful trading strategies.
- One can find strategies with ~20%-40% yearly returns with limited drawdown
- Largest individual strategies can have $10M+ Assets Under Management
Cryptocurrency markets, especially DeFi markets, are still quite inefficient. We can expect automated opportunities to still be there for a long period of time. When the markets mature, we expect to see an increase in quantitative trading. Currently US stock markets are more than 50% automated trading and this figure excludes high-frequency trading activities.
We also see that many of the automated trading venues prefer misleading advertisements and dark user experience patterns. Because of misaligned incentive models, users are tricked to invest in badly performing strategies (performance manipulation tricks), as the strategy developer gets rewarded by the number of users, not by the strategy performance.
Outside cherry picked numbers, there are several reports of conflict of interest (platform trading against its users) and rampart security violations (3Commas stealing their user assets using their API keys). We expect a lot of the bad practices being cleaned up when more and more cryptocurrency trading is moving to DeFi: Better transparency and fairer market access will make it difficult to mislead users.
Are algorithmic trading strategies for decentralised markets profitable?
Algorithmic trading is a modern approach to a diverse trading strategy and many traders have found great success with this approach. Algorithmic trading allows computers to execute trades at near instant speeds. It also allows more variability when trading on decentralised markets, such as trading multiple currency pairs across a wide range of exchanges, the ability to set up order types found on traditional CEXs such as stop-loss, limit orders, buy-stop orders, etc.
The most effective way to get involved with algorithmic trading and give yourself the best probability of having a profitable trading strategy is to find a trading software, application or protocol that has all of the tool setup needed for you to create or invest in trading algorithms. This allows you to test and experiment with algorithmic trading without having to invest large amounts of funds or time into creating an automated trading system and infrastructure on your own.
- Algorithmic trading gives your trading on the go
- Less risk of manual human error
- New algorithms are continually being developed and optimised
- Allows you to trade more than one market at once
- Gives you a variety of trading strategies
- Computer never sleeps, meaning you are able to trade 24/7
For an in-depth look into the benefits, check out our algorithmic trading vs manual trading article
Algorithmic trading on decentralised markets increases your chances of becoming a profitable trader, if executed correctly. If you are looking for a data-driven trading strategy, algorithmic trading allows you to bring your trading strategy concept to life. The fact that these algorithmic trading strategies have been validated based on historical data, as well as programmed to execute on specific price action or movements that you have defined, illustrates the efficiency of trading.
For certain individuals, trading full time isn’t an option, however algorithmic trading gives traders an opportunity for them to simulate the activity of trading full-time. They essentially become an active trader who capitalises on market movements without having to dedicate the time to do so.
If you are interested in learning more about the benefits of algorithmic trading on decentralised markets, get in touch with us at [email protected] and join our Trading Strategy community Discord to connect with likeminded traders, strategy developers and quantitative finance experts.