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Algorithmic trading is a way of executing orders using automated pre-programmed trading instructions or strategies. It attempts to leverage the speed and computational resources of computers relative to human traders. The overall trading volume generated through algorithmic trading in the U.S. stock market and many other developed financial markets is about 70-80 percent.
What is an Algorithm?
The algorithm is a set of directions given to the computer for solving a problem. It is made up of complex formulas and calculations combined with mathematical models.
Understanding the Mechanics of Algorithmic Trading
A trader or investor writes code that defines the criteria and conditions, which, when met, will lead to the automatic execution of the trade.
For example, the criteria is:
Buy 500,000 shares of Microsoft (MSFT) if the price falls below 210. For every 0.5% increase in price beyond 210, buy 2,000 shares. For every 0.5% decrease in price below 210, sell 2,000 shares.
Algorithmic trading is more of a rule-based use of strategies to execute trades automatically and reduces the chance of errors.
Machine learning and Artificial intelligence developments have enabled computer programmers to develop programs that can improve themselves through an iterative process.
Algorithmic Trading Strategies
- Trend Identification
Investors can identify the trend or early reversal of the trend with the help of algorithmic trading strategies. These strategies are based on price, volume, support, resistance, moving averages, channel breakout, or any related technical indicators which the investor has confidence in and is comfortable with. Algo uses data for analyzing trends, and it has more chances of detecting the correct trend.
Traders can initiate trades based on the occurrence of desirable trends, which are simple and easy to execute through algorithms without getting into the intricacy of predictive analysis.
Traders attempt to earn the bid-ask spread by attempting to act like traditional market makers or specialists. The difference between the bid-ask price is the profit. Traditional market makers are the ones who stand ready to buy and sell stock on a continuous and regular basis at a publicly quoted price. Scalping is usually considered as the shortest time frame in trading and exploits small price changes.
Example: If the price of a stock, say eBay Inc (NASDAQ: EBAY) rises from $61.02 to $61.12, the difference is negligible to most investors, but scalpers capitalize on this by buying 100,000 shares at $61.02 and selling at $61.12 for a profit of $10,000. If this price change takes place in the course of just one trading day, which is entirely possible, the trader can make $10,000 in a day. Often, the price changes are even smaller than $0.10 a share for scalpers to profit.
With the help of Algo trading, investors can script a strategy that buys or sells a stock as many times as possible, making a small profit as often as possible. However, there needs to be some indicator of when to buy and sell the stock.
Delta neutral is a strategy consisting of numerous positions by offsetting positive and negative deltas. Delta is a change in the price of the derivative with respect to the change in the underlying asset. This strategy typically contains options and their related underlying securities such that the overall delta totals zero. Since the overall effect is offset, these strategies are relatively insensitive to changes in the value of the underlying security.
Example: Assume that you own 100 shares of Company A, which is trading at $50 per share. Since the underlying stock’s delta is 1, your current position has a delta of positive 100. To obtain a delta-neutral position, you need to enter into a position that has a total delta of -100. Assume then you find at-the-money put options on Company A that are trading with a delta of -0.5. You could purchase 2 of these put options, which would have a total delta of (200 x -0.5), or -100. With this combined position of 100 Company A shares and 4 long at-the-money put options on Company A, your overall position is delta neutral.
However, delta neutral strategies are manually impossible to manage because of the continuous movement of an asset. With the help of Algorithmic Trading, it becomes easy to manage your delta positions and is calculated automatically by the computer system.
Arbitrage is the process of simultaneous purchase and sale of securities to take advantage of price inefficiencies in two markets and earn a riskless profit. However, these arbitrage opportunities can be absorbed very quickly. Hence, implementing an algorithm to identify such opportunities and placing the orders quickly before it disappears can be profitable for traders.
Example: Assume that Alibaba (NYSE: BABA) is trading at $210.06 on the New York Stock Exchange (NYSE) while, at the same moment, it is trading for $211 on the Hong Kong Stock Exchange (HKEX). A trader can buy the stock on the NYSE and immediately sell the same shares on the (HKEX), earning a profit.
- Mean Reversion
Mean reversion is based on the sense that both a stock’s high and low prices are temporary and that a stock’s price tends to have an average price over time. The first step is identifying the trading range for a stock and then computing the average price.
Deviations from the average price are expected to revert to the average; that is, when the stock is trading above its average price, it is expected to fall. By identifying a defined price range, traders can implement an algorithm that automatically places trades when an asset’s price breaks in and out of its defined range.
High Frequency Trading
High-frequency trading is an extension of algorithmic trading identified by high speeds, turnover rates, and order-to-trade ratios that leverages high-frequency financial data and electronic trading tools.
It manages small-sized trade orders to be sent to the market at high speeds, often in milliseconds or microseconds, which are managed by high-speed algorithms that replicate a market maker’s role.
It uses proprietary trading strategies achieved by computers to exploit market conditions that can’t be detected by the human eye and find profit potential in the ultra-short time duration.
Advantages of Algorithmic Trading
With the help of Algorithmic trading, the trades can be executed at the best possible prices, and orders can be timed well and help to avoid any losses from significant price changes. These algorithms also help place the order instantly and efficiently and reduce the risk of manual errors while placing orders.
One of the other biggest advantages of algorithmic trading is removing human emotion and following a disciplined rule-based strategy. Human emotions can often play a significant role in making the right or wrong choice and making the correct or incorrect decision. Many times these emotions are affected by biases and other beliefs which may not let the traders stick to a strategy and prove to be unprofitable. Algorithmic trading keeps emotions in check and allows traders to stick to the plan since trades are automatically executed.
Disadvantages of Algorithmic Trading
Algorithmic trading majorly depends on technology. All the trades and orders reside on a computer, not a server. It means if the internet connection is lost, an order might not be placed in the market. In these scenarios, the traders may miss out on the opportunities and even lose money. There might also be the case of time lags if internet connectivity is not efficient.
Even if the strategies are integrated into the server, the trader still needs to monitor the system and ensure successful execution. Monitoring and changing the strategies in case of new information can also be a strenuous task. For example, when you enter an order for AAPL for $120 when price is $125, wanting just a little bit better price. And then some horrible news comes out about AAPL and the stock loses 20% in a few minutes. The algorithm bought at $120, but the price fell to $100. So you’re underwater already. If you had not used algos, you might have seen the news article and waited.
In order for traders to trade using algorithms, it requires them to learn and develop the algorithms. There are many restrictions on algorithmic trading and may be subject to many regulations in many countries.
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