Algorithmic Strategies in Technical Analysis
Technical analysis is a trading discipline employed to evaluate investments and identify trading opportunities by analyzing statistical trends gathered from trading activity, such as price movement and volume. Unlike fundamental analysts, who attempt to evaluate a security’s intrinsic value, technical analysts focus on charts of price movement and various analytical tools to evaluate a security’s strength or weakness. One of the most significant developments in technical analysis over the past few decades has been the introduction of algorithmic strategies.
Understanding Algorithmic Strategies
Algorithmic trading, also known as automated trading or black-box trading, is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders.
Types of Algorithmic Strategies
There are various types of algorithmic strategies employed in technical analysis. These include:
1. Trend-following Strategies: These algorithms follow trends, which are often caused by fundamentals far more substantial than the day-to-day news. Such strategies may involve using moving averages, channel breakouts, price level movements and related technical indicators.
2. Arbitrage Opportunities: Buying a dual-listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage.
3. Index Fund Rebalancing: Index funds have defined periods of rebalancing to bring their holdings to par with their respective benchmark indices. This creates profitable opportunities for algorithmic traders who capitalize on expected trades that offer 20-80 basis points profits depending upon the number of stocks in the index fund.
4. Mathematical Model-based Strategies: Proven mathematical models, like the delta-neutral trading strategy, allow trading on a combination of options and its underlying security.
Benefits of Algorithmic Strategies in Technical Analysis
Accuracy
Algorithmic trading strategies are known for their precision. By predefining the rules for entry, exit, and money management, they eliminate the role of human emotion and indecisiveness in trading decisions.
Speed
Algorithmic strategies can process complex calculations and execute trade orders at a speed far beyond human capability. This gives them a considerable advantage in a market where speed is everything.
Reduced Transaction Costs
Algorithmic strategies help reduce the cost of trading. Since the trades are executed by a computer, there is no need for a human trader, which significantly reduces the cost of trading.
Backtesting
Backtesting, the process of testing a strategy on historical data, is a significant advantage of algorithmic trading. It helps traders understand the potential and limitations of a strategy before implementing it.
Challenges of Algorithmic Strategies in Technical Analysis
While algorithmic trading offers numerous advantages, it also presents some challenges. For instance, system failures can lead to substantial losses. Additionally, monitoring is necessary to ensure the ‘algo’ is performing as expected. Lastly, over-optimization, the excessive curve-fitting that produces a trading plan unreliable in live trading, is a significant risk.
Conclusion
Algorithmic strategies have revolutionized the field of technical analysis, offering benefits like accuracy, speed, reduced costs, and the ability to backtest strategies. However, like any trading method, they come with their own set of challenges and risks. Therefore, traders must understand these strategies thoroughly before implementing them.