What Is Algorithmic Trading?

Algorithmic trading, also referred to as algo and automated trading, is a method of executing preprogrammed orders to eliminate the need for manual trading. Its strategies include mathematical models and arbitrage opportunities.

But what is algorithmic trading used for, and how can you benefit from it? Read on to find out.

Algorithmic Trading in a Nutshell

Algo trading is based on computer programs that automatically make trades based on a set of conditions or inputs that have already been set. These conditions can be based on price, timing, quantity, etc.

This type of trading is meant to stop traders from acting on their impulses and make sure that buy and sell orders are carried out quickly. Institutional investors and brokerage houses, in particular, do this type of trading to lower costs. However, algorithmic trading works for anyone possessing relevant market knowledge and experience.

How Does Algorithmic Trading Work?

Essentially, an investor or a trader preprograms orders to execute when specific market conditions are met. Such a practice eliminates the room for human error and executes trades on that person’s behalf.

Now, let's get into the specifics.

Basic Algo Requirements

Make sure you meet the following algorithmic trading requirements to start black-box trading, which is another name for this practice.

1. Computer access
2. Network access
3. Financial market knowledge
4. Coding skills

Technical Requirements

The technical requirements for this trading option are:

• Computer-programing skills
• Access to market data feeds 
• Entry to trading platforms such as Pionex and MetaTrader 4
• The ability to test a system on historical data before using it on actual markets.

Algorithmic Trading Example

Before we start, let's briefly explain the term “moving average.”

Algorithmic traders typically use technical analysis to decide when to buy or sell a stock. They use moving averages (MA), among other stock indicators, to identify market trends and make trading decisions.

When coding in the relevant software, you can instruct the computer to buy 100 shares of a specific stock when its 50-day moving average goes above its 200-day moving average. Accordingly, you order the selling of stock shares when the 50-day moving average falls below the 200-day moving average.

Once you complete the code, you no longer have to monitor live market prices and analyze graphs. Instead, your program will scan the prices and moving average indicators on your behalf and execute the buy or sell orders when the conditions you set are met.

Note, however, that you may apply different strategies depending on various trends, formulas, results, and even software, which brings us to the next point.

Algorithmic Trading Strategies

If you are new to this type of trading, consider copy trading, i.e., mirroring the trading activities of other, experienced investors. You can try to do this manually or use one of the many fine copy trading platforms. These are some of the strategies you’ll encounter:

Trend-Following

Some traders attempt to profit from market trends by buying assets while they’re still increasing in value and selling them when the price begins to drop. This strategy, known as trend following, is based on the belief that market movements repeat over time and across different asset types. Rather than predicting when a new trend will start, trend followers use price action and technical indicators to identify when a trend has already begun.

Because of the lack of predictions, trend following is the simplest algorithmic trading strategy to implement. Aside from 50- and 200-day moving averages, channel breakouts and price level movements are the most common algorithmic indicators.

Mean Reversion

To profit from an asset's low and high prices, you need to know when prices will revert to their mean value. You can do this by implementing an algorithm that automatically places trades when the cost of an asset breaks a defined range. 

For example, if you rightly predict extreme price changes for a specific stock, this algorithm strategy would be a jackpot.

Index Fund Rebalancing

Index funds have preset periods during which their holdings are rebalanced to match the weightings of their respective benchmark indices. Just before these rebalancing periods, there is often an opportunity for algorithmic traders to profit from expected trades that offer 20 to 80 basis points in profits.

Arbitrage Opportunities

Arbitrage is common in algorithmic trading in stocks. Traders buy a dual-listed stock in one market for a lower price, selling it immediately in another for a higher price, thus earning a risk-free profit from the difference. You can replicate this same operation with stocks and futures where there are temporary price differentials.

Your algorithm can therefore track these price differences and place orders quicker than manual traders can respond.

Volume-Weighted Average Price (VWAP)

As its name suggests, this is the average price of a stock weighted by its total trading volume. The VWAP is used as a benchmark to compare the current price of a stock and make investment decisions about entering or exiting the market.

In addition, the VWAP can help investors determine their trading strategy for a particular stock (active or passive) before making a suitable algorithm for stock trading.

Time-Weighted Average Price (TWAP)

This type of order executes in evenly spaced chunks, whose size is determined based on the movement of the average price. This type of trading is meant to minimize market impact while still capitalizing on market changes.

Percentage of Volume (POV)

The total number of stocks, futures, cryptocurrencies, and other assets you traded within a trading day, or some other period, is volume. So, what is algorithmic trading based on volume, and how does it work?

Every trading platform updates the volume of successful transactions between sellers and buyers and reports it at day’s end.

Your algorithm records and sends partial orders based on the specified participation ratio and the traded volume for as long as it takes for your order to fill. Similarly, the "steps strategy" delivers orders with a pre-defined participation rate, which it lowers or raises when the asset reaches a price you set.

Implementation Shortfall

The implementation shortfall is an algo trading strategy that lowers the execution expenses by trading off the real-time market. Accordingly, traders resorting to this strategy can make savings on the order's cost and benefit from the delayed execution's opportunity cost. 

Furthermore, the implementation shortfall increases the targeted participation rate when the price of a stock is going in the right direction. Otherwise, the rate decreases.

Algo Trading Steps

Now that we answered the "What is algo trading?" question, let's define a few key steps you should stick to before you begin trading.

1. Strategy formulation: The effectiveness of trade largely determines how efficient the strategy will be.
2. Algorithm automation: You need to turn the strategy into an algorithm before automating it and sending it for approval.
3. Software development or acquisition: This step involves choosing trading software or creating your own.
4. Trade performing: With everything else in place, you only need to wait and respond to trading signals.

Algorithm Trading Benefits and Drawbacks

Let's review the key algorithmic trading pros and cons now. 

Advantages

• Executing multiple trades and strategies at the same time
• Simultaneous automated checks on various market conditions
• Perform a large number of trades in a brief period, reducing transaction costs.
• No impulse decisions: Once the required objectives have been met, the trade is executed automatically, preventing the trader from going against their original plan.
• Analyzing parameters and indicators very quickly and making near-instant trades allows traders to take advantage of price movements as soon as they happen.
• All algo trading strategies have low error rates, because all information is checked beforehand.

Disadvantages

• Most algorithms are usable only briefly, becoming obsolete when the market changes, which happens often.
• The lack of human control prevents reaction when a trader realizes the strategy will not work in a particular scenario. If the program runs into unfavorable conditions, the trader is powerless to remedy the situation.
• In many cases, trade orders are stored on personal computers rather than servers, so internet connection loss prevents the order from executing, which can lead to substantial losses.

Programming Languages for Algorithmic Trading

C++ and Python are commonly used algorithmic trading programming languages. While the former is faster and thus popular among traders, it's also more complex than the latter. Therefore, various finance professionals prefer Python since it caters to beginners and is easier to manage overall.

Bottom Line

Algorithmic trading is popular among those investing in the stock market. Algorithms perform preprogrammed actions as soon as the defined market conditions are met. 

It aims to take impulse decisions out of trading, which lowers the possibility of error. However, there are various obstacles investors can face when trading algorithmically, so an aspiring trader should acquire substantial financial market knowledge before starting algo trading.