Algorithmic Crypto Trading Strategies

So, what is algorithmic trading? Its aim is to eliminate humans from the trading process, utilizing predetermined strategies built on statistical data. These can be operated by computers round the clock with virtually no potentially-harmful oversights to worry about.

The reason behind the shift towards algorithmic trading is obvious enough: computers offer a number of benefits over humans in the world of trading. First and foremost, they don’t need to sleep, eat, or spend time with family. Their 24/7 availability is a huge advantage that reduces the risk of missing valuable opportunities.

Another benefit is that computers have the power to evaluate data with the highest standard of accuracy and react to changes far faster than even the most savvy human.

Finally, emotion is never a factor when computers take charge of trading.

These are three excellent reasons why investors have continued to recognize the importance of machines, and how algorithmic trading has taken shape. It may have started with computers being able to trade within traditional trading markets, but the growth of digital assets and round-the-clock exchanges has elevated algorithmic trading to new heights.

It’s fair to say that cryptocurrencies and automated trading are a perfect match. While users still have to employ their own strategies, algorithmic trading gives traders an opportunity to let mathematical systems handle the heavy lifting instead.

The Primary Algorithmic Trading Strategies Explained

The core ethos behind algorithmic trading is based on utilizing cutting-edge software to identify opportunities which could be profitable and take advantage of them sooner than a human trader could. Various key practices are involved, including arbitrage, mean reversion, and momentum trading. Other strategies revolving around machine learning factor in this approach, too.

The majority of algorithmic crypto trading strategies focus on spotting opportunities within the relevant market, based on the analysis of statistics. With momentum trading, the goal is to follow the latest trends. Mean reversion takes advantage of statistical divergences, while arbitrage looks for spot-price differences throughout multiple exchanges. Finally, strategies hinging on machine learning aim to automate particularly complicated philosophies.

However, impressive as these appear, none of them offer a guarantee of making profit. Traders need to pinpoint the proper time and circumstance for implementing algorithms (AKA bots).

In general, these bots are tested against data pulled from previous market activities. This process is known as backtesting, and it enables users to trial strategies in the specific market they plan to target with movements which have already been established.

This process carries its own risks, including overfitting. This may occur when a bot built on a foundation of historical data is unable to reflect the present market conditions, and the strategy can struggle to generate the profits the user expects.

We’ll look at each of the primary strategies in turn below.

How Does Momentum Trading Work?

Momentum trading is built around the idea that if one predominant trend has started to emerge in a particular market, it’s possible that it will continue until signs start to suggest it has come to a close.

Momentum trading revolves around the concept that if a specific asset has moved primarily in a single direction for a number of months (or years), it’s safe to assume that movement will be sustained until statistical data reveals that’s no longer the case.

So, a trader would intend to buy on each dip and lock at every single pump (or the reverse, if they were shorting). Still, it’s vital that traders stay aware of the point at which a market demonstrates signs of reversals in trends. Otherwise, this approach might start to backfire fairly quickly.

It’s also key to remember that traders should avoid implementing strategies that try to buy and sell on highs and lows that occur (AKA the “catching the knife” method). Instead, they should lock in their profits and buy back in a way that they consider fairly safe.

For this type of tactic, algorithmic trading is perfect. Users have the chance to establish the percentages at which they feel comfortable, and allow the code to continue with the rest. But this can be an ineffective approach on its own if the market’s movement is sideways or is so volatile that there are no obvious trends appearing yet.

When monitoring trends, one fantastic thing to watch out for is moving averages — a line on a price chart which reveals an asset’s average price across a set period of time. Generally, amounts may be in 50s, 100s, or 200s, though different timeframes can be used for predicting trades.

Trends are considered to be strong when they maintain a level over or under a specific moving average. They’re viewed as weak, though, when they get near to — or cross — the moving average line. Those moving averages which are based on longer periods tend to have more weight than others.

How Does Mean Reversion Work?

Mean reversion is based on the idea that an asset’s price should tend back to that of its historical average, and if it deviates from this strongly, there’s a risk of overbuying, overselling, and even reversal.

Even a cryptocurrency such as Bitcoin has shown visible highs and lows which deviate from its historical movement. In most cases, markets trend back to the mean price eventually. So, keeping watch for long-term averages helps algorithms to bet on substantial deviations from such prices being temporary. As a result, they can set their trade orders accordingly.

As an example, a particular form of this process is known as standard deviation reversion. This is measured using Bollinger Bands, which serve as descending and ascending limits for deviations from a core moving average. As the relevant price action shifts towards one extreme, there’s a high chance that there will soon be a reversal towards the center again.

It’s important to remember that a major risk in this area is that algorithms are unable to factor in fluctuations in the fundamentals. So, if a market crashes because of a flaw in the underlying asset, there’s a possibility that the price will be unable to reach a higher point again. Not for some time, at least. Traders have to watch out for specific conditions that an algorithm might struggle to notice.

Another type of mean reversion may impact a number of assets, in a technique known as pairs trading. This occurs when two assets are connected — one goes up or down, and so does the other. A trader may create an algorithm that watches for at least one of the assets to show signs of movement. It will then make a trade based on the chance that the connected asset will do the same. This can occur within a short period of time, which makes automation a major benefit to this strategy.

How Does Arbitrage Work?

Arbitrage is a strategy designed to act on the difference in price for a single asset across a number of markets.

Certain currencies or commodities may have a varying price across multiple exchanges, which gives traders a valuable chance to earn a profit. However, trades must be performed fast enough before the markets balance. Users can craft an algorithm designed to monitor specific assets across several markets and conduct trades when it spots discrepancies with potential.

This is a fairly simple technique to try, but traders able to respond the fastest have a clear competitive edge over their slower rivals. High frequency trading is particularly helpful here, as traders who take advantage of such market activities can contribute to a collapse in price gaps.

How Does Machine Learning Work in Algorithmic Trading?

AI and machine learning are set to take algorithmic trading to bold new heights. Traders can adopt strategies which are especially advanced and changed, while innovative techniques (such as Natural Language Processing for news updates) have the potential to open up more doors for in-depth insights into key movements within target markets.

Algorithms have the capacity to process complicated decisions and act on them, using data pulled from numerous resources and established strategies. However, machine learning allows these strategies to become updated according to what is shown to actually work best.

So, rather than relying on an “if/then” logic, a machine learning algorithm can evaluate a number of strategies and hone upcoming trades based on the biggest returns achieved. They can be difficult to configure, but traders have the luxury of relying on their bots even when market conditions shift outside of their original parameters.

One of the most common machine learning strategies is “naive Bayes”: algorithms conducting trades according to historical stats and probabilities. As an example, let’s say that historical data demonstrates Bitcoin increasing up to 60 percent after spending four days straight in the red. The “naive Bayes” algorithm would notice that the four days had been negative for Bitcoin, and place an automated order in the expectation that the price would rise on the fifth day.

These systems can be customized significantly, so each trader is responsible to establish their own unique parameters related to risk/reward. However, the algorithms can be left to operate independently with minimal human input.

Machine learning offers another key advantage: machines can read and make interpretations of the latest news updates by scanning for specific keywords. With relevant strategies in place, machine learning bots have the power to conduct trades within a matter of moments based on news updates (good or bad).

These may not be as accurate as the logic fed into them, but they can still give traders an edge when implemented properly. However, as this is a state-of-the-art area of automatic trading, machine learning bots can be more expensive and harder to predict than more traditional options.

How Does Order Chasing Work?

In order chasing, traders watch out for specific large-scale orders and act quickly based on the belief that it will lead to ongoing movement in price.

Traditionally, the ability to anticipate a key player placing a large order would demand some sort of inside information. And as you probably know, making trades based on inside information is illegal. But some traders have discovered legal techniques for gathering data from certain trading-focused forums known as Dark Pools.

Here, users are not required to submit real-time order data as they would be at a conventional exchange. As a result, their movements can make a delayed market impact. Pulling and acting on data sooner than average traders offers users the opportunity to gain a major advantage over others.

So, let’s imagine you spot a huge order being conducted in a Dark Pool, and realize that a lot of smaller sellers will make their own orders once this information reaches the wider market. As you can anticipate this, you’re able to move ahead of the curve and be one of the first to make a sale. You can buy back in with ease when the dip starts to show signs of cooling.

This might seem illegal, but it’s not if you gather the data via the right channels. A high number of algorithmic traders utilize this strategy.

How can I Start Algorithmic Trading for Cryptocurrencies?

A number of services are available to help you start algorithmic trading, including Zignaly. You can use various account types (though prices fluctuate) depending on the tools available. A free account is usually the best for newcomers, but upgrading to paid options is useful when graduating to a more professional level. Gain some experience and confidence before you start paying.

These sites tend to provide new members with tutorials and additional helpful resources, to help you find the right bots and techniques for your goals. Not all of these services are compatible with all of the exchanges, but most of these products can support the biggest exchanges. On occasion, promotions will be available for using their bots with certain platforms.

You’ll find more services and techniques to explore beyond those we’ve covered here, but this guide is intended to give you the details you need to dip your toes into the algorithmic trading pool. Take your time, learn from traders with experience, and you’ll be able to find an automated strategy that works.