Alex Tradingrunde Visualization of market data analysis for risk-conscious investors

Evidence-based decisions for getting started with digital assets

Alex Tradingrunde processes market data in real time and derives risk-adjusted recommendations for action. Each recommendation is documented and clearly disclosed in the transparency report the following day.

View sample report Methodology in detail

No promises of winnings. Every information is based on documented data processing.

Schematic sequence
Market data → Risk filter → Recommendation

Simplified representation of the analysis process, without real market values. See methodology section for details.

Initial situation

Why manual market observation has its limits

Students and young professionals rarely have the time to follow price trends, order book depth and volatility indicators in parallel. Without structured criteria, decisions are often based on short-term impressions instead of comprehensible analysis. Alex Tradingrunde replaces this process not with forecasts, but with consistently applied data criteria.

It is not the amount of available data that determines the quality of an investment decision, but rather the consistency with which risk criteria are applied.
  • Price movements without a recognizable pattern make it difficult to make a consistent assessment.
  • Manual chart analysis takes a lot of time apart from studying or working.
  • Short-term price fluctuations encourage emotional rather than rule-based decisions.
  • Past decisions are rarely documented and can hardly be verified.
background

A process that is designed for traceability

Alex Tradingrunde's analytical models were developed to evaluate market data in a structured rather than intuitive manner. Every processing step – from data collection to risk assessment – ​​follows fixed, documented rules.

These rules do not change depending on the situation. Deviations from the usual course, such as unusual volatility, are noted separately in the daily report so that users can see the basis of each recommendation.

Alex Tradingrunde Representation of the analytical decision-making process
Methodology

How the models come to a recommendation

The process is divided into three fixed steps. Each step is documented independently and can be traced independently.

01

Real-time data collection

Price data, order book depth, volatility indices and selected on-chain metrics are continuously recorded and structured for further processing.

02

Risk filtering according to fixed criteria

Each asset is tested against fixed thresholds for liquidity, volatility bands and correlation to other positions before being considered for a recommendation.

03

Recommendation logic

The remaining values are used to create a prioritized list with a suggested position size and a classification of the data situation - with no guarantee of success.

Core performance

The daily transparency report

Every recommendation is logged. The following day you can see what data was used and how the assessment developed.

Example excerpt from a daily log – without real market values
DateObservationClassification
Day 1Increase in volatility at an observed valueRisk filter reduces recommended position size
Day 2Stable liquidity ratiosValue remains on the watchlist
Day 3Increased correlation to existing positionDiversification rule excludes new admissions

How the development is presented

The accompanying history shows how the published recommendations have developed since they were first mentioned - regardless of whether the result is positive or negative. The display is updated daily and remains visible for past periods.

Mandatory disclosure

The report will be published regardless of the outcome. This consequence is part of the methodology, not an exception to good results.

Risk focus

Capital protection as a starting point, not an afterthought

The models are designed for careful entry. This particularly affects users with limited investment capital.

Position sizes

Capital protection logic

For each recommendation there is a fixed upper limit for each individual position, derived from liquidity and historical fluctuation range of the respective value.

Correlation

Diversification algorithms

New positions are checked against existing recommendations for correlation to avoid inadvertent focus on similarly reacting values.

Market phases

Volatility protection

In the event of unusually high short-term fluctuations, the system automatically reduces the recommended position size and notes this in the daily log.

Questions about the decision

Frequently asked questions

What data is included in the analysis?

Price trends, order book data, volatility indicators and selected on-chain metrics of publicly tradable digital assets are taken into account. All sources are cited in the methodology section.

How is access to the daily report organized?

Access is via registration. The scope and conditions are presented transparently in the registration process before a decision is required.

Do I need previous knowledge of trading?

No. The reports are prepared in such a way that the underlying data remains understandable even without previous experience in securities or crypto trading. Technical terms are briefly explained in the report.

Does the report replace a separate audit?

No. The report provides a data-based classification as a basis for decision-making. The final investment decision remains with the individual.

Comprehensible analysis instead of gut feeling

Request access to the daily transparency report and review the methodology with documented examples.

Request access

The request is non-binding. There is no obligation to trade specific assets.