Algorithmic trading aims to get rid of all human factors in trading. In its place, it looks to follow predetermined, statistics-based strategies that can be operated 24/7 by automated machines and computers with minimal oversight.
The trading computers can give many benefits to human traders. First, they can remain active in the markets throughout the day without having to shut down like it is the case for humans since they need to sleep. Moreover, they analyze data accurately and respond to any changes that arise within milliseconds.
The computers are never affected by emotions while making decisions. Due to all these reasons, most of the investors have realized that machines have the capability of making excellent trading decisions since they use the correct strategies to execute transactions. This is how the space of algorithmic trading has changed.
While it started with computers trading in the traditional markets, the rapid increase of digital assets in the past decade and 24/7 exchanges has brought computer trading to a higher level. It almost appears like automated trading and cryptos were made for each other. Users will still need to create their customized strategies, but when applied correctly, these methods can help traders let the mathematics do the work.
What are the Primary Strategies?
The primary philosophies behind a majority of the algorithmic trading oscillate around software to detect profitable opportunities and exploit them faster than any human could. Most commonly, mean reversion, momentum trading, arbitrage, and several machine-learning methods are used.
The opportunities that algorithmic trading exploit in the market are identified based on statistics. Momentum trading follows current trends while arbitrage seeks the differences in spot prices across various exchanges. On the other hand, mean reversion looks for any existing statistical divergences in the market while the machine learning strategies aim to automate more difficult philosophies or blend several simultaneously.
None of these methods is a clear assurance that you will get profits from the trading process. Thus, traders must determine and understand when and where they can implement the correct algorithm or ‘bot.’ In general, bots are tested against historical market data that is called backtesting.
This enables users to test their strategy in the real market they want to introduce it on, but they do so with established movements from the past. Some of the risks that come with doing this include ‘overfitting’ which happens whenever a bot is devised around the historical data that never reflects on the current conditions. It may result in a trading method that fails to produce desired results.
A simple example would be that if you designed and tested a trading robot against the data from a bull market but started running it live in a bear market. It is not possible to get the returns that you were expecting.
Momentum Trading
This method of trading is based around the logic that in the case a predominant trade is already existing in the market, then this trend is expectedly going to continue until the signals start to come in that it has ended. The main idea about momentum trading is that if a specific asset has been moving in one direction, for several months, it is safe to assume that the trend will continue until data shows otherwise.
Thus, the plan will be to buy the asset on every dip and lock in the profits on every pump and vice versa if shorting. Traders need to remain aware of when the market shows signs of trend reversals or the same strategy may start to turn around rapidly.
Experts advise that traders should avoid setting strategies that aim to buy and sell on the real highs and lows, dubbed ‘catching the knife’, but they should instead lock in profits and purchase at reasonably safe levels. Algorithmic trading is perfect for such situations since the users can just set percentages they are comfortable with and let the bot code do the rest.
But, this technique on its own can be ineffective in the case that a market is choppy or highly volatile with no clear trend forming. One great indicator for watching these trends is the moving averages. A moving average, by description, is a line on a price chart that reveals the average price for any asset market over a certain number of hours, days, weeks, months, or even years.
Different strategies look at different periods to make trade predictions. A trend is always believed to be strong whenever it manages to stay above or below a moving average line and weak whenever it approaches or crosses over the MA line. Generally, the moving averages based upon longer periods are given more weight than those that watch the last 100 hours or a similar time frame.
Mean Reversion
Mean reversion is statistically to the fact that the price of an asset should tend back heading to the historical average price. Extreme deviations from that price suggest overbought or oversold conditions and the likelihood of a reversal.
Even for cryptos like Bitcoin that only ever been in a bear market, there can be significant highs and lows that stray from the price trajectory that has historically developed. In most cases, markets trend back toward the price mean before a long time. By analyzing the long-term averages, the algorithms can safely bet that massive deviations from the prices may not last long and set trade orders accordingly.
For instance, a particular form of this is known as standard deviation reversion. It is measured by an indicator that is referred to as Bollinger Bands. Generally, the bands act as downward and upward limits on deviations from a central moving average. Whenever the price action moves to one of the extremes, the odds become high suggesting that a reversal toward the center may be coming soon.
One of the major risks in this context is that the algorithm cannot be responsible for any changes in the fundamentals. If the particular market is crashing due to some problem in the underlying asset, then there is a possibility that the may never recover or may take time before recovery happens. Hence, traders need to monitor and account for various conditions that their algorithms cannot detect.
Another form associated with mean reversion may happen across several assets and using this trading process is known as pairs trading. It usually happens where two assets are traditionally correlated. When one goes up, the other is also statistically expected to rise as well.
An algorithm may be designed to monitor one of these assets awaiting a move and then places a trade based on the probability that the other commodity will soon follow. The timeframes for such discrepancies can at times be quite short which makes the automated nature of this trading method highly valuable.
What is Arbitrage?
Arbitrage is described as a strategy that exploits price difference on the same asset across many markets. At times the same product including currency or commodity can temporarily have different prices when trading on different exchanges. That provides a great opportunity to make profits for those fast enough to trade between the markets before they eventually balance out.
To this end, an algorithm can be developed to watch different assets across various markets and open trades as soon as these inconsistencies and discrepancies are found. The technique is not overly complex. Nonetheless, the traders who can respond the quickest have a unique edge over those who are slower to react.
While using this method, high-frequency trading offers significant advantages. Traders take advantage of these market conditions that may cause a gap in prices to collapse.
Machine Learning Strategies
Artificial intelligence and machine learning strive to push algorithmic trading to new levels. Not only can the more advanced trading methods be employed and adapted in instant times, but new techniques like Natural Language Processing of news articles may provide more ways for getting special insight into market movements.
Algorithms can make complex decisions and make them according to several predetermined strategies and data. However, with machine learning, the strategies can update themselves based on what is working. Instead of working with the ‘if/then’ logic, a machine learning algorithm can assess many strategies and refine the next trades according to the highest returns.
Even though they take a lot of time and effort to set up, it means that traders can have faith in their bot even as market conditions evolve beyond the original parameters. A popular type of machine learning strategy is known as naive Bayes. In this trading method, learning algorithms make trades based on previous statistics and probability.
For instance, historical market data indicates that Bitcoin rises 70% after having three consecutive days in the red. A naive Bayes algorithm may see that the past three days have all been down and it automatically places an order based on the likelihood it may surge today.
These systems are majorly customizable and it might be up to every trader to set their parameter for things like risk and reward ratios. However, once you are happy with a balance, you may let it run with reduced interference.
Another significant benefit of machine learning is the capability of machines to read and interpret news reports. The machines scan for keywords and create appropriate strategies. These bots execute trades within seconds when negative or positive news breaks.
For obvious reasons, the bots are as accurate and the amount of logic that goes into them. Hence, they are highly tricky to implement although they have an edge over traders when they are properly set up.
This technology is the cutting edge of a new branch in automated trading. Hence, bots designed to work this way may prove harder to find, they cost more to access or simply be less predictable than some of the more time-tested techniques.
What is Order Chasing?
This is the practice of watching out for various, very large, orders; and then trying to work swiftly based on the assumption that this will lead to further price movement. Normally, having the ability to anticipate a large order from a major trader would need inside information of some kind.
But, some of the high-frequency traders have discovered legal methods of scrapping data from over-the-counter trading forums that are known as ‘Dark Pools.’ These trading forums never have to submit their order data in real-time like an exchange. Therefore, their movements tend to have a delayed effect on the market.
Through gathering and implementing this type of data faster than the average trader, the users of this trading method can have a major advantage over the others who do not have such information.
For instance, you may see a major sell order that is being executed on a Dark Pool. This means that soon when the data is published to the rest of the market, many other small sellers will maybe respond with their orders. Thus, it is possible to get ahead of the wave and be among the first traders to sell since the market movements can be anticipated.
You can also buy in easily when the dip cools down. Again, this strategy is not illegal so long as the data is collected via the correct channels and many algorithmic traders have made this their strategy of choice.
Where Can I Start Algorithmic Trading With Cryptocurrency?
Many sites offer various trading algorithms. You can use them to connect to the digital asset exchange of your choice. Although they exist, they are few of them that can enable you to quickly set up your activities with algorithmic trading. Sites like Bitsgap, TradeSanta, and Cryptohopper offer you many types of accounts that can range from the free to somewhat pricey options. But, it all depends on the tools that are made available.
A free account provides many options for beginners to get started. Nonetheless, the paid accounts come in handy if you want to become a professional trader. The sites generally offer tutorials and other materials to enable you to become educated on finding the bots and strategies that will work for you perfectly.
Please note that not every service is compatible with every exchange. But, you will discover that most of their products will support nearly all of the biggest and most popular exchanges. Some of them even have special promotions that encourage traders to use their bots in connection with a particular platform. Therefore, users should have plenty of options to select from.
There are many techniques and services that you can explore. But, these basics highlighted above can help you start implementing algorithmic trading successfully.
