ALGORITHMIC TRADING

For years now, algorithmic trading has been popular in global financial markets with over 80% of daily trading volume in the stock market currently attributed to it. There are also projections that its market will continue to experience growth. In this discussion, we shall describe what algorithmic trading is, how it works, and its strategies.

Algorithmic Trading Definition

Also called automated trading, algo-trading, or black-box trading, algorithm trading involves using computer programs to execute trades automatically depending on a predefined set of rules/conditions (algorithms). The two main types of this trading are rule-based and machine learning based where the former relies on a specific set of criteria or technical indicators to make trading decisions, while the latter uses historical data to “learn’’ and adapt its strategies over time. It’s easier to grasp rule-based algorithms making them more commonly used than machine learning-based algorithms, which require a deep essential understanding of statistics and programming.

How Algorithmic Trading Works

Theoretically, algorithmic trading can generate profits at a speed and regularity that is impossible for a human trader. The algorithms (defined set of rules/instructions) depend on price, quantity, timing, and any mathematical model. Besides the profit opportunities for a trader, automated trading renders markets more liquid and makes trading more systematic by eliminating the impact of human emotions on trading affairs. How does this trade type work? Let’s use a case example; suppose you follow below simple trade criteria:

·??????You purchase 50 shares of a stock when its 50-day moving average (an algorithmic trading indicator) moves above the 200-day moving average. A moving average is an indicator that calculates the average price of a security over a particular period, smoothing out price fluctuations and identifying the underlying trend.

·??????You sell shares of the stock when its 50-day moving average moves below the 200-day moving average.

By using the above two simple instructions, a computer program will instantly monitor the stock price (including the moving average indicators) and place the buy and sell orders when the defined conditions are reached. You will no longer need to monitor live prices and graphs or place the orders manually – the algo-trading system does this automatically by correctly recognizing the trading opportunity.

Algorithmic Trading Strategies

Below are the most common types of strategies used in algorithmic trading:

·??????Arbitrage Strategies. They exploit price differences between financial instruments or markets. They can involve trading across various exchanges, securities, or currencies. Arbitrage traders utilize real-time data feeds and advanced algorithms to detect and capitalize on temporary price inefficiencies.

·??????Sentiment Analysis Strategies. These use natural language processing and machine learning techniques to gauge investor sentiment from multiple data sources, like news articles, analyst reports, and social media posts. Traders then utilize such sentiment data to make informed decisions, expecting possible price movements based on the ongoing market sentiment.

·??????Swing Trading Strategies. They aim to get gains in a type of security over a short to medium-term time horizon. They depend on technical analysis to recognize trend reversals, support and resistance levels, and other chart patterns that suggest possible price movements. Swing traders normally retain positions for several days and weeks.

·??????Scalping Strategies. These entail entering and exiting trades swiftly, seizing small price movements, or exploiting temporary price inefficiencies. Algorithm scalp traders use high-frequency techniques and low-latency infrastructure to perform trades quickly and reduce market impact.

·??????News-Based Strategies. These strategies utilize algorithms to analyze social media sentiment, news releases, and other data sources to create trading signals. They can react rapidly to market-moving events, like economic data releases or corporate earnings announcements, and conduct trades in response to the identified sentiment or anticipated market impact.

·??????Market-Making Strategies. They involve offering liquidity to the market by putting both buy and sell orders simultaneously, profiting from the bid-ask spread. Market-making traders use high-frequency trading techniques and sophisticated order management systems to manage inventory risk and lower exposure to extreme price movements.

·??????Trend Following Strategies. They aim to seize gains through trading in the direction of the prevailing market trend. They use varying technical indicators like moving averages to detect and confirm trends. Algorithmic trend followers basically enter trades when a trend is generated and exit when it weakens or is reversed.

·??????Mean Reversion Strategies. They assume that prices will undoubtedly be reversed to their historical averages. Traders using this strategy identify overbought or oversold securities and take positions in expectation of a price reversal.

·??????Buying the Dips. It refers to buying a security when its price experiences a temporary decline, with the anticipation that it will rebound and resume its upward trend. Those using the strategy utilize technical analysis to recognize support levels, oversold conditions, and other signals that propose an appropriate entry point for a long position.

·??????Momentum Strategies. These capitalize on the continuance of an existing market trend and identify securities with strong price movements and trade in the direction of the trend. Technical indicators like moving averages can be used to confirm the strength and direction of the trend.

Conclusion

Algorithmic trading aims to strip emotions out of trades, making sure of the most efficient execution of a trade by placing orders automatically. It may also lower trading fees. Since it combines computer programming and financial markets to carry out trade, traders or those who wish to get started with it must have computer and network access, financial market knowledge, and to some extent coding skills.?