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Technological advancement has enhanced the art of trading and helped reduce the barriers to entry. Unlike when trading was the domain of institutions or investors with huge financial backing, retail traders are also becoming key players in the business.
Likewise, it has given rise to unique innovations and solutions, making it easy to analyze the market. And scan for profitable trading opportunities. Consequently, gone are the days when traders had to spend hours glued to the screen in search of trading opportunities. Instead, algorithmic trading is the latest sensation sweeping the landscape.
Algorithmic trading is a process in which market analysis and order execution are carried out through automated and preprogrammed instructions. It involves using computer codes and software that analyze the market in search of high-probability trading opportunities. And open and close trades based on set rules.
Once the prevailing market conditions match the predetermined criteria, the trading algos execute buy or sell orders. Therefore, traders spend the least time on the screen as the trading algos do everything.
Algorithmic trading stands out by using complex formulas combined with mathematical formulas and trading indicators to gauge the direction in which the market is likely to move. The strategy is commonly used in high-frequency trading. It is allowing traders and large institutions to make thousands of trades per second. While taking advantage of the smallest price movements in the market.
Additionally, the trading practice can be used in various situations, including arbitrage, trend trading strategies, and order execution.
Contrary to perception, algorithmic trading is not the domain of institutional investors. Retail traders are also getting involved in the practice thanks to an influx of programmers able to write computer codes integrated into trading platforms to analyze and execute market orders.
Computer programmers are increasingly competing against one another in writing the best algorithmic trading codes. That lead to profitable outcomes most of the time. The practice continues to fuel the spread of algorithmic trading as most of the codes are now available for the mass market to access at a small fee.
Additionally, the emergence of advanced technologies such as artificial intelligence and machine learning allows programmers to create programs that improve themselves through iterative processes. The development of algorithmic trading codes that rely on deep learning has led to profitable trading in the sector.
Algorithmic trading involves using trading software and tools to make trading decisions based on pre-set rules. In this case, a trader or a programmer writes code that is integrated into a trading platform to execute trades on behalf of the traders when the pre-set conditions are met.
For instance, one can write a simple code that requires the trading algo to short 20 lots of GBPUSD once it rises above the 1.22540 level. In this case, the software will open a short position once GBPUSD rises to this level. In return, it could lock in profits once the price tanks to 1.22300. The algo could also avert the risk of incurring significant losses on price failing to drop after the short position is triggered and rises to the 1.22670 level.
Likewise, one can write a code requiring the computer program to Buy 100,000 shares of Apple stock once the price falls below the $200 a share level. In return, the program could buy an additional 1,000 shares on price increasing by 0.1% afterward and sell 1,000 shares for every 0.1% decrease in price.
Algorithmic trading leverages various tools to identify the ideal conditions and time to open and close positions. For example, one of the popular strategies involves the use of moving averages. That allows computer programs to open trades based on the prevailing trend.
In this case, a trading algorithm would be programmed to identify whenever the price of the underlying assets breaks below the moving average and moves above. It is expected that whenever the price breaks and closes below the 20-day moving average. It signals a buildup in selling pressure suggesting the likelihood of the price edging lower afterward.
Similarly, whenever the price rises and closes above the 20-day moving average. It implies a buildup in buying pressure suggesting the likelihood of price edging higher afterward.
As a result, programming an algorithmic trading software would involve identifying instances when prices cross above and below the 20-day moving average.
Likewise, the program would open a short position as soon as the price moves below the 20-day moving average. And lock in profits 20 to 30 pips after the slide. To implement a stop loss order, one would set it to trigger at a price level that is ten pips above the entry-level.
Similarly, the algorithmic trading solution would open a buy position when the price rises and closes above the 20-day moving average. The program will close the position when the price moves 10 to 20 pips above the entry-level to lock in profits.
It would also trigger a stop loss order ten pips below the entry level to prevent losses accumulation on price edging lower after moving above the moving average before the profit target order is triggered.
The Relative Strength Index can also be used in combination with the moving average to generate trading signals in algorithmic trading. While the MA hints at the direction price is likely to move, the RSI provides hints on prevailing market momentum, whether bullish or bearish.
Consequently, an algorithmic trading solution would be programmed to identify whenever the market is oversold and whenever it is overbought by monitoring the RSI indicator. The RSI indicator reading below or near 30 implies that the prevailing asset is oversold. The prospect of price edging lower is usually low.
Likewise, whenever the RSI reading is above 70, it implies the underlying asset is overbought. As a result, the prospect of a price increase is usually low as market participants exit by locking in profits resulting in price reversals.
Therefore, a trader could program an algorithmic trading solution to look to enter a long or buy position as soon as the RSI reading is below 30. The Buy order should be triggered when the price bounces back and moves above the 20-day moving average.
Look at the gold chart above. It is clear that gold was in a downtrend amid bearish momentum, with the RSI indicator nearing the 30 level. However, once the RSI started moving up from the oversold conditions, it signaled a change in momentum from oversold conditions.
Likewise, an algorithmic trading solution would have opened a buy position as soon as the RSI reading moved above the 50 level and the price closed above the 20-day moving average on the price chart.
The algorithmic trading solution will continue scanning for opportunities and look to enter a short or sell position on the RSI reading being above 70 and the price moving lower and closing below the 20-day moving average.
Algorithmic trading is a preferred trading strategy for institutions looking to place large positions that would distort market price. Therefore, instead of placing one large position at once, the strategy would open the trades in portions or tranches.
For instance, an institution looking to buy 1 million Apple shares would rely on algorithmic trading to buy 50,000 shares at a go weighing its impact on the market. The buying spree will continue until one buys the entire 1 million shares intended.
Humans are prone to emotions, often forcing them to make costly irrational decisions. However, with algorithmic trading, such practices are curtailed as trades are opened and closed based on predetermined rules.
With algorithmic trading, there is usually no room for emotions to take over and affect trading decisions. Instead, algorithms solve the problem by ensuring all market positions adhere to set out rules.
During periods of heightened volatility in the market, prices move rapidly, making it extremely difficult to take advantage of the smallest price changes on time. Nevertheless, algorithmic trading solves the issues as it allows faster and easier execution of orders. Consequently, traders can open and lock in profits within seconds or minutes before an opportunity dissipates.
Scalping is a popular trading strategy that relies on algorithms to enable rapid buying and selling while taking advantage of the slightest price changes.
One of the biggest downfalls of algorithmic trading is that the programmed solutions can miss out on trades because an opportunity needs to exhibit the signs programmed in the first place. Different opportunities manifest in different forms, which the trading algo might not detect.
The speed of order execution with algorithmic trading can also be challenging whenever several orders are executed simultaneously without intervention. Should the market change course with more orders than usual in play, the risk of capital being wiped out is usually high. For example, the market crash of 2010 was due to algorithmic trading.
Algorithmic trading, like any other trading style, can be profitable if one gets a couple of things right. First, one must deeply understand the markets to program trading rules that would generate high probable trading opportunities. One must also effectively back-test their trading system to ensure it generates profits most of the time.
Yes, algorithmic trading works. It is one of the reasons institutional traders with vast sums of money use it to take advantage of the smallest of price changes in the market. It works much better than manual trading, as most algorithm trading rules are quantifiable and retestable. In addition, high-frequency trading systems have since cropped, leveraging advanced technologies and enhancing algo trading effectiveness.
Algorithmic trading is legal as no rules or laws prevent traders from programming their trading rules and using them to squeeze profits from the market. Nevertheless, some people have objections to how automated trading impacts the overall impact, especially when used by institutions.
Like any other trading strategy, algo trading usually beats the market. Its high success rate is becoming increasingly popular in a world where people are looking to spend the least time scanning for opportunities. In addition, the strategy can consistently beat the market as it is based on rules rather than trading based on emotions.
Algorithmic trading is an emerging strategy revolutionizing how people trade and invest in financial markets. Using pre-set rules and computer programs makes it easy for people to scan the markets around the clock and take advantage of the smallest price changes or short-term opportunities.