FAQ Section | Frequently Asked Questions

Risk management only shows its quality where losses are real, painful and existentially relevant. Demo and micro accounts eliminate precisely those stressors for which professional risk management was developed in the first place. 

1. Capital dimension: Risk is a function of relevance, not of percentages Risk management is often mistakenly understood as a pure percentage calculation (e.g. "1% risk per trade"). In practice, however, risk is relational: 

• 1% of €500 → €5 
• 1% of €250,000 → €2,500 

The mathematical logic is identical, but the psychological and operational consequence is not. Consequence: 

• Drawdowns in demo/micro setups are reversible without meaning 
• Drawdowns in real setups create pressure, self-doubt, decision stress 

Risk management is designed precisely for this second situation 

2. Psychological risk:  The biggest risk factor is completely missing 

In demo and micro accounts, key stressors are missing: 

• no real capital loss 
• no opportunity cost 
• no social, family or financial pressure 
• no existential relevance 

Problem: 

Risk management under real-world conditions primarily addresses behavioral risk, not market volatility:

• Overtrading 
• Rule breaking 
• Revenge trading 
• Increasing positions after losses 
• Ignoring stops 

All of these effects only occur reliably when there is real pain. 

3. Margin & Leverage: Asymmetries that appear distorted in the demo context 
As soon as leverage or margin come into play, the rules of the game change: 

• Liquidation risks 
• Margin calls 
• Forced deleveraging 
• Slippage during volatility 
• Liquidity bottlenecks in stress phases 

In demo environments: 

• no real liquidation chains 
• no broker-side escalations 
• no realistic execution quality under stress 

Result: A supposedly "cleanly managed" risk only exists under laboratory conditions. 

4. Time dimension: risk accumulates - demo accounts isolate time 
Risk management does not work trade-by-trade, but over time: 

• Serial losses 
• Volatility clusters 
• Phases of structural underperformance 
• mental fatigue 

Micro accounts become frequent: 

• reset more quickly 
• emotionally faded 
• without consequences 

In reality, however:
:: You carry your losses further - mentally, financially and structurally. 

5. disincentive structure: demo setups reward wrong behaviour 
Demo and micro accounts create systematic distortions: 

The seemingly good risk management is often just an artifact of the environment. 

6. Professional benchmark: risk management is stress engineering 
In professional contexts (prop trading, asset management, family offices): 

:: Risk management is not protection against markets - but against your own behavior under stress.

Demo setups remove precisely this stress. They do not test risk management, but strategy mechanics. 

7. Precise delimitation
Demo & micro accounts are useful for: 

• Order mechanics 
• Platform understanding 
• Strategy logic rule training 

They are unsuitable for: 

• Reliable risk management 
• Drawdown discipline 
• Leverage and margin stress 
• Behavioral stability under loss pressure 

Those who only test risk management in the demo are testing the system - not themselves. And that is where the real risk lies.

Laboratory test vs. Ocean shipping

The lab 
A demo or micro account is like a lab experiment: 

• controlled conditions 
• known variables 
• can be aborted at any time 
• no real damage in the event of wrong decisions 

You test there: 

• Technique 
• Procedures 
• Hypotheses 

But not: 

• Resilience 
• Responsibility 
• Consequentiality 

The high seas 
A real-world, capital-relevant trading setup is a lot like the high seas: 

• changing weather patterns 
• unpredictable storms 
• limited reaction time 
• real damage in the event of a wrong course 

Here it shows: 

• whether navigation works even in storms 
• whether rules are adhered to when the pressure mounts 
• whether decisions remain stable when fear, fatigue and doubt set in 

The crucial difference Seamanship is not proven in port. And risk management not in the laboratory. A ship that is perfectly maneuvered in port is not automatically seaworthy. In the same way, a risk concept that works in a demo is no proof of resilience under real conditions.

Transfer to trading

q.e.d: 
Risk management is not a swimming test in the pool - but survival skills in the open sea.

At first glance, there is hardly any difference between demo accounts and real money accounts. Prices, charts, order types, trading interfaces and evaluations follow the same rules. In terms of technology and market technology, both environments operate almost identically.

However, the decisive difference lies not in the system, but outside of it.

While a demo account works exclusively with abstract figures, a real money account is directly linked to a real asset. This asset stands for value that has actually been created or can be used in the future. It is not neutral, but carries meaning.

This creates a fundamental difference:

Decisions made in real money trading have real consequences. Decisions in demo trading do not.

 This consequence is not technical, but psychological. It changes perception, motivation and behavior - even if all external conditions appear identical. The difference between demo and real money trading is therefore not a detail or a fine adjustment, but the transition between two different levels of reality that look the same on the outside but have a completely different effect on the inside.

Yes. 
Modern simulation environments (DEMO, paper, sandbox trading) generally reproduce the technical and market conditions of trading very realistically. Price positions, chart movements, order types, trading logic and evaluations follow the same mechanisms as in real money trading. 

However, it is precisely this technical proximity that is the reason why demo accounts are often perceived as completely comparable. The realistic depiction creates the impression that identical decisions under identical conditions should also lead to identical results. This assumption is the basis for the widespread notion that demo trading is a reliable dress rehearsal for real money trading. Technical realism does not automatically mean behavioral realism. A system can function objectively correctly and still produce different decision-making behavior if a central active component is missing. Demo accounts are therefore not unrealistic - they are incomplete in relation to the human decision-making level.

The deviation is not caused by incorrect market mapping, but by the lack of a real value link, which only becomes effective in real money trading.

Demo accounts do not lack market reality, but rather the reference to a real asset. 

In demo trading, figures move exclusively within a closed system. Profits and losses remain abstract. They can be observed, compared and evaluated - but they do not represent any value actually created or lost outside the trading surface. 

Real money, on the other hand, is always more than just a number. It is the result of value creation or the prerequisite for future uses. It is related to real alternatives: consumption, security, freedom, reserves or time. This relationship exists independently of trading itself. It is precisely this connection that is completely missing in the demo account. 

This eliminates a central impact factor: the decision is not linked back to a real loss or gain. Without this feedback, the meaning of an action changes. A decision can be formally correct without becoming subjectively relevant. Risks remain theoretical, consequences inconsequential. Demo accounts therefore simulate decisions, but they do not simulate their meaning. 

This lack of value linkage is not a technical deficit, but a structural property. It explains why identical market conditions can lead to different behavior - even with identical knowledge and experience.

Money is not a neutral mathematical value. It acts as a carrier of real meaning. 

Every real amount of money represents either value that has already been created or concrete future possibilities. It is changeable and connectable to the real world - independent of the trade itself. It is precisely this connectivity that gives decisions with real money a different weight. 

The integration of real money creates a mental relationship that goes beyond the pure result. Decisions are no longer viewed in isolation, but evaluated in the context of possible consequences. This not only changes the result, but also the decision-making process itself. This effect is not learned and cannot be consciously controlled. It arises automatically as soon as a real value is involved. This does not make trading more emotional, but more binding. 

This binding nature is missing in demo trading. Decisions remain correct or incorrect, successful or unsuccessful - but they remain inconsequential. The action ends on the surface of the system. In real money trading, on the other hand, every decision has an effect beyond the system. It is embedded in a real value chain and therefore has a psychological effect. Money therefore does not change decisions because people act irrationally, but because real values structure their actions.

Loss aversion is not a personal trait or a weakness. It is a fundamental mechanism of human decision-making. People systematically weigh potential losses more heavily than gains of equal value. This effect is not abstract, but immediate - but only when a real value is affected. 

This condition is fulfilled in real money trading. A loss does not just mean a negative figure, but the actual loss of an asset. This loss is irreversible and is in direct competition with real alternatives. This is precisely what creates pressure to make a decision. 

This condition is completely absent in demo trading. A loss has no consequences outside the system. It changes no possibilities, no security, no options for action. Accordingly, loss aversion also has no effect 

This has far-reaching consequences: 

• Risks are assessed differently 
• Losses are tolerated for longer
•  Profits are realized differently 
• Discipline is demanded less consistently 

These differences are not random and cannot be explained individually. They arise from the absence of real loss consequences. Loss aversion therefore not only determines the result of a trade, but also the entire structure of decision-making behavior. If it is absent, the internal logic of trading shifts - even with identical knowledge, identical experience and identical market access. Demo trading therefore reflects decisions without the central control mechanism that is effective in real money trading.

Demo accounts provide valid information - but only within their own frame of reference. 

They show how decisions are made under technically correct market conditions, without real value commitment and without effective loss aversion. They thus represent a specific form of trading, but not trading in its complete reality. 

The results from demo accounts are therefore not wrong, but context-bound. They allow conclusions to be drawn about understanding, system operation, knowledge of rules and basic decision-making logic. However, statements about behavior under real decision-making pressure can only be derived from them to a limited extent.

The decisive point here is not the accuracy of the simulation, but the lack of transferability of the internal decision-making dynamics. 

Demo trading answers the question: How would we trade if decisions had no real consequences? 

Real money trading answers a different question: How would we trade if every decision was linked to a real value? 

Both answers are legitimate - but they are not congruent. The informative value of demo accounts therefore lies not in the prediction of real action, but in the isolated consideration of individual capabilities under value-free conditions. Anyone who recognizes this limit can make good use of demo accounts. Those who ignore it risk drawing the wrong conclusions.

No. Demo accounts are not useless - their value depends on their intended purpose 

Whether a demo account is useful does not depend on its technical quality, but on what it is used for and with what motivation. Demo accounts are well suited for certain purposes: 

• to get to know trading interfaces 
• to understand how order types work 
• to train processes 
• to test strategies in a controlled, value-free environment 

In these contexts, demo accounts reliably fulfill their purpose. They allow learning without consequences and offer a protected framework for orientation. 

For other issues, however, demo accounts are unsuitable: 

• for evaluating one's own behavior under pressure 
• for assessing personal risk tolerance 
• for deriving emotional or disciplinary stability 
• for forecasting actions with real capital 

A misunderstanding often arises here. Not because demo accounts deliver incorrect results, but because they are used for a question that they cannot structurally answer 

• The motivation behind their use therefore determines their informative value: 
• Whoever wants to learn how a system works will find a suitable tool in a demo account. 

Those who want to understand how they themselves trade under real conditions need an environment with real value retention. 

Demo accounts and real money accounts are therefore not preliminary stages of the same process, but tools for different knowledge objectives. Only when the purpose is clear can their usefulness be meaningfully evaluated.

Simulated environments offer a controlled framework in which trading strategies can be developed, tested and adapted without real consequences. They allow complex market mechanisms to be analyzed, hypotheses to be tested and correlations to be made visible without risking real capital.

It is precisely this freedom from consequences that makes simulations attractive. They enable a high iteration speed, systematic variation of parameters and the direct comparison of different approaches. Errors are reversible, adjustments can be made at any time and results can be viewed in isolation.

Simulations therefore create a development space that is focused on functionality and logic. They answer the question of whether a strategy can basically work under given assumptions - regardless of whether and how it is later implemented in reality. 

This approach is neither unusual nor problematic. On the contrary: simulated environments are a legitimate and often indispensable tool for developing, understanding and structuring trading ideas. 

At the same time, there is a fundamental characteristic associated with this: Strategies emerge in an environment in which real consequences are deliberately excluded. This framework condition shapes the type of optimization, the weighting of risks and the criteria according to which a strategy is evaluated as "working".

This clearly defines the starting point: simulations answer an important but clearly defined question.

Simulated environments offer a number of clear advantages in the development of trading strategies that would not be available in real markets, or only to a limited extent. 

One key advantage lies in their controllability. Market conditions, parameters and assumptions can be varied in a targeted manner without external factors such as capital restrictions or emotional stress influencing the development process. This means that correlations can be viewed in isolation and systematically analyzed. 

In addition, there is the high iteration speed. Strategies can be adapted, discarded or further developed in a short space of time. Hypotheses can be tested quickly without incurring real losses or operational consequences. This rapid feedback cycle is particularly valuable in early development phases.

Simulations also enable a clear focus on results. Key figures such as hit rate, expected value, drawdown or volatility can be compared objectively. This creates transparency as to which approaches are mathematically superior under the selected assumptions.

Another advantage lies in the abstraction from implementation pressure. As no real capital is involved, attention can be focused entirely on the logic, structure and rules of the strategy. This makes it easier to understand complex mechanisms and promotes analytical clarity. 

In this function, simulations are a powerful tool: they create order, enable comparability and support the systematic development of trading ideas. 

At the same time, these advantages also define the framework within which simulations work. They optimize strategies within an environment that is deliberately decoupled from real consequences. This characteristic is not a disadvantage - it is the prerequisite for their strengths.

Simulated environments do not generate random distortions, but structural shifts that result directly from their freedom from consequences. These effects are unavoidable and also not person-related - they arise independently of experience, discipline or expertise.

A central shift concerns the risk structure.

In the simulation, losses remain inconsequential. Drawdowns are mathematical values, not real losses. As a result, risk parameters appear acceptable that would not be psychologically or operationally viable under real conditions. 

The time horizon also changes. 
Persistence phases cost nothing. Long series of losses require no justification, no endurance, no decision under pressure. Strategies can be optimized for patience without checking whether this patience can be realistically maintained. 

In addition, there is a shift in the optimization logic. 
Simulations favour strategies that are mathematically convincing. Key figures come to the fore, while aspects such as mental resilience, decision-making discipline or routine actions play no role. The strategy is optimized for stability in the model, not for stability in people.

Another effect relates to selection pressure. 
In simulated environments, strategies that look good under idealized conditions prevail. Strategies that are more robust but less spectacular are less likely to be pursued. The selection is based on computational attractiveness, not practical viability.

These distortions are not a fault of the simulation. They are the logical consequence of a development space in which real consequences are deliberately excluded. Simulations therefore do not form strategies neutrally. They form strategies for a certain reality - a reality without real stress, without psychological pressure and without irreversible consequences.

A functioning strategy is a strategy that achieves a positive mathematical result under defined conditions. It fulfills formal criteria, follows clear rules and shows statistically reliable results in simulations or backtests.

A viable strategy goes beyond this. Viability does not describe whether a strategy works, but whether it can be implemented in the long term under real conditions. This distinction is key because it addresses two different levels. 

A functioning strategy is system-related. 
A sustainable strategy is people-related. 

In real trading, a strategy is not just executed, but endured. It must be implemented over phases in which results fail to materialize, losses occur or uncertainty dominates. In these phases, it is not the logic of the strategy that is decisive, but the ability to follow it consistently. 

Simulations test functionality. 
They do not check whether a strategy: 

• is mentally sustainable 
• remains emotionally stable 
• is compatible with real risk 
• fits personal risk tolerance 

A strategy can therefore be mathematically correct and still fail in practice because it overtaxes the people who are supposed to implement it. Sustainability only arises where rules, risk, time and human resilience are in harmony with each other. This dimension cannot be simulated, but can only be experienced under real value retention.

The difference between a functioning and a viable strategy is therefore not a judgment of quality, but a question of level: one answers a mathematical question, the other a real one.

In real trading, people are not an external influencing factor, but an integral part of the strategy. Any rule structure, however clearly defined, only becomes effective through consistent implementation - and this implementation is inextricably linked to human perception, resilience and decision-making ability.

While simulations consider strategies in isolation from their application, real-money trading inextricably links rules and actions. Decisions are not only made, but are the responsibility of the trader. Every deviation, every hesitation and every adjustment has real consequences. 

The human being does not act as a disruptive factor, but as the carrier of the strategy. They decide: 

• whether rules are adhered to 
• how phases of loss are dealt with 
• when discipline is maintained or abandoned 
• how uncertainty is processed 

These factors cannot be separated from the set of rules. They determine whether a strategy is sustainable in practice or not 

In simulated environments, this dimension remains invisible 
Rules are always followed, stops are always set, parameters are always precisely adhered to. The strategy exists detached from fatigue, doubt or pressure of expectation. 

In real trading, on the other hand, strategy and people merge into one unit. The quality of the result depends not only on the logic of the strategy, but also on the ability of the person to implement it consistently under real conditions. A strategy is therefore not just a technical construct. It is a stress model that includes people.

Simulated strategies provide reliable findings - but only within the framework in which they were created. 

They show whether a set of rules is mathematically consistent under defined assumptions, how it behaves under certain market conditions and what statistical properties it has. In this function, they are valuable and necessary.

However, their informative value ends where real feasibility begins.

A simulated strategy does not answer the question of whether it can be adhered to in the long term under real decision-making pressure. It makes no statement as to whether its risk structure is mentally sustainable, whether loss phases can be sustained or whether deviations from the rules are likely. 

For the evaluation of simulated strategies, this means: 

Good key figures are a starting point, not proof 

Stability in the model is no guarantee of stability in implementation 

Functionality is no substitute for sustainability 

Simulated strategies are therefore not forecasts of real results, but models under simplified conditions. They describe possibilities, not certainties 

Those who evaluate them should therefore not ask: How good does this strategy look 

but rather: Under what conditions did this result arise - and which of these are missing in reality 

Only with this classification do simulated results retain their value without creating false certainty.