Trading can be a complicated yet rewarding activity. You can trade many types of assets; stocks, bonds, currencies, commodities, cryptocurrencies, derivatives, etc. The trading sector is enormous, leaving a lot of room for different types of trading strategies to exist, of which algorithmic trading is one of the most common.
Algorithmic trading refers to trading based on pre-programmed, automated instructions given to a computer, accounting for variables such as price, time, and volume. The objective of algorithmic trading is to leverage the speed of computers relative to human traders to get better results.
The rise of algorithmic trading began in the 1980s, as the advancing sophistication of computers permitted the computerization of the order flow in financial markets. Concurrently, there was a rapid expansion of access to computers, so traders could comfortably use them as never before.
The rise of algorithmic trading prompted traders and financial experts to develop various strategies. To date, they have successfully conceptualized and implemented numerous techniques that people around the globe can imitate. The notable ones include;
- Index Fund Rebalancing
- Time Weighted Average Price (TWAP) strategy
- Volume-weighted Average Price (VWAP) strategy
- Trend following
Arbitrage implies taking advantage of price differences of the same asset in two or more markets to make a profit. In this strategy, you score a profit from the positive difference between the price you buy an asset and the price you sell it within a short period.
Here’s a simple example of arbitrage; a company named Example A, Inc is listed on two stock markets, the London Stock Exchange (LSE) and the New York Stock Exchange (NYSE). London and New York are two different cities over 3,000 miles apart, hence a significant time difference.
London is 5 hours ahead of New York. Hence, the markets in the latter open much earlier than the former. As a New York resident, I can take advantage of the time difference by connecting to the London markets and buying shares of Example A early. Five or six hours later, when the New York markets open, I can sell those shares for a profit if there is any price increase within that short period.
In the example above, I capitalized on the time difference to buy an asset in one location and sell it within a short period in another location.
Another arbitrage example, a real-world scenario, is taking advantage of the price differences between cryptocurrencies in different regions. Alameda Research is a crypto trading firm currently managing hundreds of millions of dollars. In its early days, the firm exploited what’s known as the kimchi premium, the price gap between digital tokens on South Korean exchanges and those outside of Korea.
Alameda bought Bitcoin for specific amounts in the US, offloaded them on Korean exchanges within the same day, and pocketed the profits. On some days, the realized profit was up to 50%.
But, arbitrage isn’t as easy as it sounds. Alameda to tackle significant challenges, such as finding the right platform to buy and sell Bitcoin at scale, getting approval to use foreign exchanges and accounts, and frequently moving significant sums of money between different countries.
Arbitrage also carries its risks. You may buy an asset and watch its price fall in the other market you planned to sell it in. Nonetheless, it can be a winning strategy if you play your cards right.
Index Fund Rebalancing
Index funds are mutual funds or exchange-traded funds (ETFs) that track a specified pool of underlying investments. For example, you can invest in an ETF tracking renewable energy companies or a mutual fund tracking the S&P 500 index.
Index fund managers periodically buy or sell the assets underlying their funds to manage risks and ensure the fund maintains its target asset allocation. This process, known as “rebalancing,” opens up opportunities for algorithmic traders to capitalize on the expected trades.
It isn’t out of the norm for index fund managers to direct enormous sums of capital. Hence, if you can predict their expected rebalancing trades, you can implement algorithms to capitalize on them.
Time-Weighted Average Price (TWAP) Strategy
Time-Weighted Average Price is the average price of an asset over a specified time. You can use this metric to direct your trades with the goal of buying or selling assets at the best prices.
Let’s say, you want to buy 1,000 shares of Example A, Inc. Instead of acquiring it in a single trade, you can divide it into 10 trades of 100 shares each. You can implement an algorithm to buy 100 shares each at a specified time interval (e.g., every 2 hours). In the end, due to price fluctuations, you may have paid significantly less to acquire the shares than if you had purchased them in one stretch.
Likewise, you can set an algorithm to sell 100 shares of Example A at the same time interval. In the end, you may get more money for your shares than if you had sold them in a single trade.
Volume-weighted Average Price (VWAP) Strategy
The volume-weighted average price is the average price of an asset-weighted by the total trading volume. Similarly, you can use this metric to direct your trades to buy or sell assets at the best prices.
As the name suggests, trend following implies pursuing trends. It’s a trading strategy that dictates buying an asset when its price trend rises and selling it when its price trend decreases for a specified period. In this strategy, you’re betting that a price trend will continue and allow you to take advantage of it.
Scalping is a trading strategy that entails profiting from small price changes, with profits taken as quickly as possible. The aim is to rake in a lot of small profits that add up to significant amounts, rather than placing your hopes on a few winning trades.
Implementing the scalping strategy is easy. You just need to set an algorithm to sell an asset once its current price rises to a specific level above the purchase price.
There are numerous algorithmic investment strategies, such that we can’t list all of them. But, we’ve identified and explained some of the best that you can follow.
Requirements for Algorithmic Trading
You need some critical resources to become a successful algorithmic trader. They include:
Computer Programming Knowledge
Algorithmic trading is all about using computer programs to direct your trades. Hence, you must have sufficient knowledge and understanding of computer programs and how to write them if you want to be an effective algorithmic trader.
We can’t overemphasize this requirement. Computer programs are sensitive, such that little mistakes can cause big losses. You must know how to write accurate instructions that a computer can understand and implement correctly.
Alongside computer programming, you must also have sufficient knowledge of financial processes to implement profitable trading strategies. Likewise, you must understand the sector you’re trading to a great extent. Let’s say, you’re trading oil, you should be familiar with the business atmosphere of the industry and how political decisions of the top oil-producing countries affect prices.
If you think you aren’t knowledgeable enough to formulate your trading strategies, don’t give up. Some platforms that allow you to buy trading bots built by professionals and imitate their strategies, e.g., MQL5.community Market.
If you’re an expert and looking to make money building trading bots for others, you can also do that on MQL5.community Freelance.
The trading platform you select is another critical factor affecting your chances of success in algorithmic trading. Picking the wrong platform can frustrate your efforts and prevent you from working productively.
Many platforms support algorithmic trading, each with its strengths and weaknesses. One example is MetaTrader 5, which offers a user-friendly interface and mutual funds, prop trading firms, and investment companies. For example, PWE Capital, a multi-million dollar investment firm, uses MetaTrader 5 to handle all its trades.
Benefits of Algorithmic Trading
- Reduced transaction costs
With algorithmic trading, you don’t have to spend much time monitoring the markets compared to a manual trader. Hence, you’re likely to place fewer trades and save money on transaction costs.
- Less prone to errors
Humans are vulnerable to making trading mistakes compared to computers, which carry out instructions perfectly. Algorithmic trading reduces the risk of errors, which can cause devastating losses in some cases.
When you build a trading algorithm, you can test it on historical data “back-testing” or live data “forward-testing” to see how it’ll fare. Manual traders don’t have this privilege and thus are at a disadvantage compared to algorithmic traders.