Algorithmic trading is a concept that involves the automation of your trading decisions. Instead of waiting for an asset to reach certain conditions (value, for instance), to place a manual trade, you set up the algorithm that will do so automatically when these conditions are met.
While it may sound quite complicated, the truth is that trading becomes a lot simpler and more reliable once you set up the algorithm. Still, this doesn’t mean that you’re devoid of any agency as a trader. It’s your job to decide when you are entering or exiting the market.
Naturally, the type of algorithmic trading that suits you the best heavily depends on your skill level and even your preference. With that in mind, some of these strategies will be riskier than others, while some are best left to seasoned traders.
Also, instead of just giving you a list of specific strategies, we’ll give our best to cover each trading strategy category.
Momentum-Based Strategies
Momentum-based strategies are also known as trend strategies. If something new happens in the market, you notice it and assume whether the trend will continue in that direction. If you believe that it will, you may just have an idea of how to trade.
Keep in mind that this is not a “buy low, sell high” but more of a “buy high, sell higher.” Sure, the most profitable thing to do is to recognize a trend before it becomes a trend. There are a lot of people who have bought Bitcoin while it was still $8, but even those who bought it while it was $8,000 or $11,000 have managed to profit greatly (provided they bought at the right time and held).
There are three steps through which this is achieved:
- Short-term positions
- Value Investing
- Momentum (hence the name)
Seeing as how these three are the basics of trading, momentum-based strategies (although a bit intimidating) may be the best way to learn algorithmic trading. They’re also a great opportunity for you to learn how to set up your algorithmic tools.
Arbitrage Algorithmic Trading
Another way to describe arbitrage algorithmic trading is to refer to it as event trading. What does this mean? Well, certain events will have an impact on the market—a major merger or acquisition, for instance. Major political occurrences are another example (for instance, how GBP lost value immediately after BREXIT).
Your job is to be on the lookout for these events and set up your algorithm to trade at the right time. Even this is not as simple as it sounds, seeing as how, with the right approach, you can place trades before, during, or after an event (depending on what you’re trying to achieve).
There’s also the concept of statistical arbitrage to consider. For instance, if you find a connection between two stocks. For instance, if stock X gains $1 in value (almost) every time stock Y gains $10 in value, you could set up your algorithm to track one of those stocks’ changes in value.
The key thing to understand when considering arbitrage algorithmic trading is that it is based on the principle that the relative price of the market has an equilibrium. Therefore, every time there’s a deviation, it is likely that it will eventually be corrected.
Machine Learning in Trading
One of the biggest advantages of AI is that it has insane computing power, capable of processing a lot of data. The speed at which this data is processed is just as important, which is essential for rapid decisions.
With enough data and computing power, AI should be able to predict market trends. Even a human trader takes a look at historical data (or at least should). The biggest problem with this is that it’s incredibly time-consuming. It could take you hours and days to fully examine the historical performance of a trading asset, but with the concept of machine learning, this can be done in seconds.
AI can also be used for scouting for potential investments and trades. Manually, you would be able to search a small portion of the market, which means that your potential can be limited by something as unpredictable and unreliable as luck.
Now, keep in mind that machine learning depends on the programming language used. For instance, using machine learning in Python is different than with JavaScript (for instance). Here, Python would have a clear advantage. You would also have to find the right learning resources, as well as learn a thing or two about portfolio construction using machine learning.
Options-Trading Strategies
An option is a contract under which the stock option buyer holds the right to buy shares at a predetermined price. Now, it’s worth mentioning that while the buyer holds the right, they don’t have to obligation to do so. In other words, if you believe that the price will go up drastically, you can buy an option and later buy the stock at the previous price.
Lastly, the options-trading strategies might just be the most numerous groups on the list to date. Seeing as how options are derivative, it’s possible to evaluate the option based on the price of the underlying stock. As we’ve mentioned in the previous section, with the help of AI and machine learning, making this type of analysis becomes simpler and more reliable.
The list of strategies is quite lengthy:
- Diagonal spread
- Calendar spread
- Bear spread
- Collar option
- Long strangle
- Broken wing butterfly
Keep in mind that while you don’t have to know all of these strategies in order to use them, it’s best if you’re familiar with all of them in order to choose which of them you like best.
Wrap Up
The more strategies you’re familiar with, the higher the odds that you’ll find something that suits your trading style. Overall, algorithmic trading just seems intimidating from the outside. In reality, it’s quite easy. Seeing as how you’re optimizing your use of time, effort, and resources, it also has the potential to become incredibly profitable. The key thing is that you master the concept and the only way to do so is to start learning as much as you can about specific algorithmic trading strategies.
