Clean Energy Gets Dumped, but There's Hope Ahead!
Clean Energy Gets Dumped, but There's Hope Ahead!
Time for a turn? When cycles align, you should pay attention.
In the realm of clean energy stock market analysis, understanding market cycles and their potential impact on future directions can be helpful for investors.
In our current analysis, we have observed that the dominant cyclic phases of weekly and daily cycles for the clean energy sector are coming to an end. Over the past one to two years, there has been a downward trend accompanied by a dominant cycle in both the long-term weekly and short-term daily periods. However, it is expected that this downward phase might now switch to an uptrend. The current weekly and daily cycles are now synchronized into an upward-trending cyclic outlook.
This potential bullish setup can be seen through the analysis of various cycles and their relationship to the cyclic-tuned technical indicator.
As cycles analysts, we are looking for timing when long- and short-term cycles align with their projections. This seems to happen right now in the context of the clean energy sector as shown in the following charts.
The first chart displays the long-term weekly cycle, which indicates a bottoming phase followed by a projected upward move.
I. Weekly Cycle - Global Clean Energy ETF
The swings of the 209 days daily cycle align with the current changes in trend in this asset class.
Now, reading the daily cycle has changed. Up to now, the daily cycle had to be seen in the context of the longer cycle downward phase. So each time the daily cycle topped, we can see a significant downward move in the asset afterwards, as the long- and short-term cycles have aligned.
Now we need to change our view. The interesting time will come when the daily cycles model now points in the direction of the longer cycle, which now is upwards. We should now look for opportunities to join the upswing phase of both cycles.
Daily Cycle - Global Clean Energy ETF
It is important to conduct your own research, perform technical analysis, and implement risk management strategies before making any trades. In this case, we have developed a favorite technical indicator construction that we would like to share.
Our indicator is called the cyclic tuned RSI indicator which takes into account the length of dominant cycles to determine smoothness, adjusted bands, and length. When the longer-term RSI is in oversold territory (indicated by green shading), we look for classical setups where the signal line crosses from below the lower band to the upside. This crossover serves as a classic bullish signal and is further confirmed by divergence between price and the indicator.
Additionally, there is support for this bullish setup from short-term daily cycles, which indicate a bottoming phase with an anticipated upward move. This combination of the long-term weekly cycle and short-term daily cycles aligning with their projections suggests a potential bullish setup for clean energy asset class.
III. Technical Analysis “cyclic tuned RSI” - Global Clean Energy ETF
Links to the interactive cycle analysis workbooks and an audio commentary are added below for subscribers. Please scroll down.
Regards,
Lars
© 2023 lars.cycles.org, Lars von Thienen, All Rights Reserved.
Any reproduction, copying, or distribution, in whole or in part, is prohibited without permission from the publisher.
Information contained on this site is obtained from sources believed to be reliable, but its accuracy cannot be guaranteed. Lars von Thienen “lars.cycles.org” is a publisher of scientific cycle analysis results for global markets and is not an investment advisor. The published analysis is not designed to meet your personal circumstances – we are not financial advisors and do not give personalized financial advice. The opinions and statements contained herein are the sole opinion of the author and are subject to change without notice. It may become outdated and there is no obligation to update any such information.
Any projections, market outlooks or estimates herein are forward looking statements and are inherently unreliable. They are based upon certain assumptions and should not be construed to be indicative of the events that will occur.
Investments should be made only after consulting with your financial advisor and only after reviewing the prospectus or financial statements of the company or companies in question. You shouldn’t make any decision based solely on what you read here.
Neither the publisher, the author nor any of its affiliates accepts any liability whatsoever for any direct or consequential loss howsoever arising, directly or indirectly, from any use of the information contained herein.
Links to interactive cycle analysis workbooks with full cycle spectrum & personal audio podcast commentary:
Personal comment:
Daily analysis workbook:
https://app.cycles.org/workbook/L2yojoO73v
Weekly analysis workbook:
https://app.cycles.org/workbook/LwYMWMxyly
Introduction: In financial trading, the comprehension of cycles and the judicious application of statistical analysis are critical. This article not only explores the intersection of these elements through various metaphors but also delves into the human psychological biases that affect our interpretation of data. We will look at how the natural predisposition towards linearity, immediate causality, and ignoring feedback loops, influenced by hormonal balances like dopamine and cortisol, impacts financial decision-making.
1. Cycles in Financial Markets and the Law of Large Numbers: Just as one might seek to understand the overarching theme of a complex narrative, traders use the LLN to discern trends in market data. However, the human tendency to perceive patterns where there are none, a bias towards linearity and immediate causality, can lead to misinterpretations of market cycles, much like misreading a story's plot.
2. The Central Limit Theorem and Human Bias: The role of the CLT in financial trading parallels our inclination to find order in chaos. This statistical principle, likened to finding patterns in a lengthy novel, can be skewed by our innate biases. We often expect market behavior to follow a straightforward, linear path, akin to a well-laid narrative, which is not always the case.
3. Climate Science Metaphor and Immediate Causality Bias: In trading, differentiating correlation from causation is complex and often clouded by our inherent bias for immediate causality. This is similar to interpreting climate change data: the correlation between CO2 and temperature rise is clear, but the underlying causal mechanisms are intricate, much like the multifaceted factors driving market movements.
4. The Bathtub Curve: Market Volatility and Stability Perception: Our cognitive biases can misinterpret market phases, represented metaphorically by the bathtub curve. We may overestimate the stability of a market during its 'flat' phase or underestimate the significance of volatility at the beginning and end, akin to misunderstanding a story's climax or resolution.
5. Statistical Misinterpretations and Human Psychology: Just as biases in literary interpretation can lead to varied understandings of a text, human biases in statistical analysis can lead to diverse market predictions. The dopamine-driven reward system can reinforce the pursuit of apparent patterns, while cortisol may influence risk-averse or fear-driven trading decisions.
6. Practical Implications for Traders: Traders must navigate not only the statistical landscape but also their own psychological biases. Recognizing the difference between genuine market signals and those perceived through the lens of linear thinking or immediate causality is crucial for effective trading strategies.
7. High Variance and Psychological Impacts in Trading: High variance in financial data, symbolizing a narrative with numerous plot twists, denotes unpredictability. This can trigger a hormonal response – dopamine seeking the thrill of the chase, cortisol heightening stress – leading to biased decision-making processes.
8. Concluding Thoughts: The Human Element in Trading: Understanding financial trading requires more than just a grasp of cycles and statistical principles; it necessitates an awareness of our inherent psychological biases. As traders, we must strive to interpret market data with an understanding of our natural inclinations towards linearity, immediate causality, and the hormonal influences that drive our decision-making processes. It’s about balancing the narrative of the markets with the story our minds often seek to tell.
Great addition; pristine work. Trying to add it to my tradingview.com it gives me a red lock on the indicator, telling me to contact the author (you) what needs to be done? You have my email as paid suscriber, also part of working group cycles