Technical Indicators — What’s Useful & What Isn’t | SMRT Algo

The Development of Technical Indicators

Technical indicators have evolved in tandem with advancements in digital signal processing and time-series analysis, primarily analyzing historical price data as a digital signal. Most traditional indicators are based on rolling statistics like moving averages, rolling variance/standard deviation, and momentum oscillators. The evolution of computing technology and the widespread accessibility to historical price data have simplified the creation and application of these indicators.

The integration of basic technical indicators into trading platforms marked a significant democratization, making these tools readily available to a broader range of traders.

A pivotal moment in the evolution of technical indicators was the introduction of programming languages tailored for developing technical trading tools, such as Pinescript. This development empowered traders to design their custom indicators with greater ease and facilitated the sharing of these tools within the trading community.

As a result, we now witness the emergence of more sophisticated technical indicators. These advanced tools incorporate intricate calculations and graphical elements, reflecting a significant leap in the capabilities and complexity of technical indicators compared to their earlier counterparts.

Technical Indicators Performance

The vast array of technical indicators available today highlights both the active development of new tools and the lack of a definitive set with proven performance. Commonly used indicators among traders include traditional ones like simple/exponential moving averages, momentum oscillators, stochastics, the Relative Strength Index (RSI), and Bollinger Bands. However, most of these fail to account for the dynamic nature of market price conditions, leading to the development of adaptive indicators such as the Kaufman Adaptive Moving Average (KAMA) and Fractal Adaptive Moving Average (FRAMA). Despite this innovation, adaptivity does not necessarily translate into significant profitability gains over fixed-length moving averages, potentially due to the need for user-defined settings.

The reliance on user settings in technical indicators poses a considerable challenge. These settings require optimization and, given that past performance is not indicative of future results, optimal settings may vary over time. For an indicator to be considered optimal, it should yield positive outcomes across a wide range of setting combinations, which is seldom the case. An interesting facet of user settings is that different indicators might produce similar signals with varied settings, potentially rendering some indicators redundant.

Given the fluctuating nature of market conditions, it is extremely challenging, perhaps even impossible, to definitively answer the question, “Is there a profitable technical indicator?” A more practical, albeit complex, question would be, “What is the best technical indicator under specific market conditions?” This approach acknowledges the complexities and variabilities inherent in market dynamics and the application of technical indicators.

Are Popular Indicators Better Than Less Popular Ones?

The popularity of a technical indicator might not always be a reliable measure of its effectiveness. While one might logically assume that more popular indicators are better, various factors can influence their popularity. These include external aspects such as marketing efforts, the renown of the indicator’s creator, and consumer behavior. An interesting consideration is the visual appeal of technical indicators. Since these indicators are entirely digital, their design and color scheme significantly impact user attraction.

Studies in the psychology of color demonstrate that colors significantly influence consumer behavior and perceptions. Products with more vibrant colors might be perceived as more complex, suggesting a higher information density, which can appeal to users aiming for better trading outcomes. Therefore, for authors or vendors of technical indicators, focusing on aesthetically appealing yet technically simple indicators might be a more viable strategy to enhance popularity than emphasizing performance.

Advertising plays a crucial role too. Indicators promoted by well-known publishers or in reputable journals may not necessarily demonstrate superior performance but can still significantly impact the trading community. This suggests that while popularity can indicate a level of acceptance or interest in the trading community, it is not a definitive gauge of the indicator’s ability to improve trading results. Traders should evaluate indicators based on their applicability to specific trading strategies and market conditions, rather than solely on their popularity.

Repainting and Non-Causality in Technical Indicators

The visual appeal of technical indicators significantly influences their popularity. While indicators with attractive designs often garner user interest, those that appear to offer excellent entry points for trades tend to attract the most attention from traders.

Over time, a specific category of technical indicators, known as “repainting” indicators, has been observed to produce particularly impressive results. These repainting indicators are characterized by their past values changing over time. This change can occur for various reasons, such as the indicator using future price data as input or the removal of historical data, which necessitates recalculating the indicator, potentially altering its previous values.

However, the utility of repainting indicators is primarily confined to non-real-time applications. They often track price movements or signal a trading opportunity after a delay, based on past data. As a result, repainting indicators are generally not effective for determining real-time entry points in trading. Their nature often leads to delayed decision-making, similar to the limitations found in other lagging indicators, such as those based on horizontal support and resistance levels that rely on pivot points.

This characteristic of repainting indicators highlights the importance of understanding the underlying mechanisms and limitations of technical tools used in trading. While they may appear attractive due to their ability to adjust to new data, their practical application in real-time trading scenarios is limited, necessitating caution and a comprehensive understanding by traders who choose to use them.

Conclusion

The effectiveness of strategies based on automated technical indicators can be challenging to validate, particularly when considering profitability. Trend-following strategies typically excel in clear, directional market trends, while contrarian strategies tend to perform better in stationary or range-bound market conditions. Given the market’s propensity to oscillate between these two states, it’s understandable why consistently profitable results from indicators can be elusive.

The real value of a technical indicator is often not found in its direct ability to generate profit. The complexity and variability of market price movements make this a difficult task, as technical indicators lack the intelligence and adaptability to navigate such volatile conditions. However, this does not necessarily apply to experienced traders who may use these indicators as supplementary tools in their decision-making process.

The utility of a technical indicator, therefore, is better assessed by the amount and quality of non-redundant, useful information it provides. This is more significant than its capacity to offer early and precise entry points, which is a more challenging objective to achieve.

Developing an indicator capable of delivering such a breadth of information remains a complex endeavor, considering the diverse methodologies employed by traders. A “universal” indicator would need to accommodate user interaction, and ease of use becomes a critical factor in its overall usefulness. Such indicators should ideally strike a balance between providing actionable insights and being user-friendly to cater to the varying needs and skill levels of different traders.

References

(1) Kaufman, P.J., 1995. Smarter Trading. McGraw-Hill, New York.

(2) Ehlers, John. “FRAMA–Fractal Adaptive Moving Average.” Technical Analysis of Stocks & Commodities (2005).

(3) Ellis, Craig A., and Simon A. Parbery. “Is smarter better? A comparison of adaptive, and simple moving average trading strategies.” Research in International Business and Finance 19.3 (2005): 399–411.