The coefficient values have been revised for:

• 30-minute chart, NQ instrument;

• 4-hour chart, ES instrument;

• 4-hour chart, NQ instrument;

• 4-hour chart, RTY instrument.

Kind regards

Preview

The post is divided into the following sections:

1. Preliminary Remark

2. Optimisation

3. Better Entry Point

4. Monte Carlo

Details

• Instrument: MNQ

• session: RTH

• model: extreme (aggressive)

• type: long/short

• exploited conditions: LC01, SC01, LC02, SC02, LC03, SC03.

• approach: systematic

• number of charts: 1

• indicators: no

Reference code

• Number: 28082026

Read more

The coefficient values have been revised for:

• 2-hour chart, RTY instrument.

An update to the LC05 matrix covering the three-year period has been published.

The coefficient values have been revised for:

• 2-hour chart, YM instrument;

• 4-hour chart, ES instrument;

• 4-hour chart, YM instrument.

Kind regards

The coefficient values have been revised for:

• 30-minute chart, ES instrument;

• 30-minute chart, NQ instrument;

• 1-hour chart, NQ instrument.

An update to the SC03 matrix covering the three-year period has been published.

The coefficient values have been revised for:

• 30-minute chart, YM instrument;

• 1-hour chart, NQ instrument;

• 1-hour chart, YM instrument;

• 4-hour chart, YM instrument.

Kind regards

The coefficient values have been revised for:

• 30-minute chart, ES instrument;

• 30-minute chart, YM instrument;

• 1-hour chart, RTY instrument;

• 2-hour chart, ES instrument;

• 4-hour chart, ES instrument;

• 4-hour chart, NQ instrument;

• 4-hour chart, RTY instrument;

• 4-hour chart, YM instrument.

Kind regards

The coefficient values have been revised for:

• 30-minute chart, RTY instrument;

• 30-minute chart, YM instrument;

• 2-hour chart, ES instrument;

• 2-hour chart, YM instrument;

• 4-hour chart, ES instrument.

Kind regards

The coefficient values have been revised for:

• 2-hour chart, ES instrument;

• 2-hour chart, NQ instrument.

Kind regards

Preview

The post is divided into the following sections:

1. Preliminary Remark

2. Optimisation

3. Better Entry Point

4. Monte Carlo

5. Safety Cloud

6. Guard Rail

Details

• Instrument: MES

• session: RTH

• model: extreme (aggressive)

• type: long/short

• exploited conditions: LC01, SC01, LC02, SC02, LC03, SC03.

• approach: systematic

• number of charts: 1

• indicators: no

Reference code

• Number: 04072026

Read more

A time horizon has been added, along with an update to some of its operating conditions.

Specifically:

• an 8-year analysis window has been added, with the aim of providing further options and offering a different perspective;

• the relevant conditions have been updated: LC01, SC01, LC02, SC02, LC03, SC03, LC04, SC04, LC05, SC05, LC06 and SC06.

I would like to thank all readers for their interest and for their ongoing support for the research and development activities published on the website.

As always, any comments or feedback are welcome.

Kind regards

Every era has had its own information revolution.

• Writing allowed knowledge to outlive individual memory;

• the printing press multiplied its distribution;

• telecommunications eliminated distance;

• the internet reduced the time required to access information to a matter of seconds;

• more recently, systems capable of synthesising vast amounts of content have accelerated this process even further.

Viewed from a broader historical perspective, these innovations reveal a striking pattern. For centuries, we have invested extraordinary effort in making information more accessible, less expensive and faster to transmit. We have become remarkably efficient at transporting information, yet we have devoted far less attention to the system ultimately responsible for giving it meaning.

This distinction may seem subtle, but it leads to a fundamental question: if access to information is no longer the problem, why does our sense of genuinely understanding the world not appear to have grown at the same pace?

From Information to Understanding

To address this question, we must first clarify what we actually mean by information.

In 1948, Claude Shannon published A Mathematical Theory of Communication, one of the most influential scientific works of the twentieth century. Within Shannon’s mathematical framework, information is neither knowledge nor meaning. Information is defined as the reduction of uncertainty.

Although developed in the context of telecommunications, this definition offers a remarkably useful perspective on the relationship between human beings and knowledge. Receiving information reduces uncertainty, it does not necessarily deepen understanding.

We might think of our relationship with information as a process consisting of three stages:

1. information is produced;

2. it is transmitted;

3. it is interpreted.

Over the past centuries, we have transformed the first two. The third has remained remarkably unchanged.

The Receiver Has Not Changed

The history of innovation can largely be understood as a continuous effort to reduce the cost of information. Every technological breakthrough has made it easier, faster and cheaper to obtain. Today, we can access, within seconds, an amount of material that would have required days or even weeks of research only a few decades ago.

Yet this evolution has taken place almost entirely outside ourselves. The human brain remains the same biological system responsible for processing all of that information.

Neuroscience and cognitive psychology have been describing these limitations for decades. The work of George Miller, later refined by Nelson Cowan, showed that working memory can actively process only a surprisingly small amount of information at any given moment. Daniel Kahneman further demonstrated that attention itself is a finite cognitive resource: every new stimulus inevitably competes with those already demanding our mental effort.

These are not flaws in the brain, they are the consequence of its evolution.

For hundreds of thousands of years, our ancestors did not struggle with an excess of information. Their challenge was obtaining enough reliable information to survive. Human cognition evolved in an environment where information was scarce and acquiring it required time, effort and direct experience.

The informational environment has changed dramatically within only a few generations. The brain has not.

That is where the real bottleneck lies. Not in producing information. Not in distributing it. But in the biological capacity to transform it into a coherent understanding of reality.

When the Process Disappears

There is, however, another transformation that may be even more significant than the sheer abundance of information.

For most of history, obtaining an answer meant engaging with the process that produced it.

Understanding a mathematical theorem required following its proof. Interpreting a historical event meant comparing sources before reaching a conclusion. Reading a scientific paper meant examining its methods, evidence, limitations and reasoning. The final answer was only the last step in a much longer intellectual journey. But that relationship is gradually changing.

Increasingly, we encounter only the outputs:

• a summary;

• a list of key points;

• a ready-made explanation;

• an answer.

This shift is entirely understandable. Reducing the time required to obtain answers offers enormous practical advantages. Yet efficiency can easily become confused with understanding.

The brain does not build understanding by memorising conclusions. It builds understanding by recognising relationships. By establishing connections. By testing hypotheses. By correcting errors. In other words, by constructing models of reality.

An answer may be entirely correct without generating genuine understanding. Knowing what is true is not the same as understanding why it is true. The value of knowledge lies not only in its conclusions, but also in the reasoning that makes those conclusions meaningful.

It is precisely during that reasoning process that the brain develops the mental models that allow knowledge to be transferred to new situations. When the process is systematically replaced by its output, what we risk losing is not merely part of the learning experience. We risk losing the very mechanism through which understanding is formed.

The New Scarcity

This perspective also helps explain one of the best documented phenomena in cognitive psychology: cognitive overload. When the flow of information exceeds the capacity of working memory, information begins to compete for limited cognitive resources, reducing our ability to identify meaningful relationships and integrate new knowledge effectively.

And beyond a certain threshold, abundance ceases to be an advantage. It becomes noise.

In 1971, Herbert Simon wrote a sentence that has become increasingly relevant with time:

“A wealth of information creates a poverty of attention.”

Today, that observation may deserve to be extended.

“A wealth of answers can create a poverty of understanding.”

For centuries, humanity’s greatest challenge was obtaining information. Today, that challenge has largely been overcome. Technology has transformed information into something abundant, inexpensive and instantly accessible. Ironically, this success has exposed a limitation that had always existed.

The true constraint was never the availability of information. It is the biological time required for the brain to transform information into mental models, assign meaning to it, and integrate it into experience.

Perhaps this is the real scarcity of our age.

**Note on transparency** The ideas set out in this article, as well as the argument, stem from personal reflections that have developed over time through various readings. In drafting this text, I have used AI tools solely as an editorial aid, with the aim of improving clarity and minimising as far as possible the gap between what I intended to communicate and what the reader would understand.

Preview

The post is divided into the following sections:

1. Preliminary Remark

2. Optimisation

Details

• Instrument: RTY

• session: ETH

• exploited condition: LC06

• approach: systematic

• number of charts: 1

• indicators: no

Reference code

• Number: 26062026

Read more