FAQ Section | Frequently Asked Questions

Social trading refers to models in which the trading decisions of a third party - a so-called signal provider or strategy provider - are observed, subscribed to or automatically implemented by other market participants in their own trading accounts. 

This can be done: 

• manually (reading signals and executing them yourself) or 
• automatically (directly linked to your own account). 

The main attraction lies in the delegation: your own analysis, decision-making and market observation are replaced by trust in a third-party strategy.

Social trading addresses several basic human motives: 

• Complexity reduction: Markets do not need to be understood 
• Time saving: Decisions are outsourced 
• Transfer of authority: Someone more "experienced" acts 
• Social validation: Others also follow 
• Performance narratives: Success stories replace analysis 

Even automated signaling models reinforce this effect, as they give the impression that objective rules are implemented without emotion.

Following signals only answers one question: How does my account develop if I accept other people's decisions unfiltered? 

It does not answer: 

• why decisions are made 
• what assumptions are made 
• how risks are assessed internally 
• how adjustments are made 
• how exceptional situations are dealt with 

This makes social trading fundamentally different from proprietary trading: The subscriber experiences results, but not the creation process.

Signaling strategies are intransparent in nature, even if key figures are disclosed.

Typical black box characteristics: 

• Rules not fully known 
• Decision logic not verifiable 
• Adjustments are made without notice 
• Risk decisions are outside the subscriber's control 

Even detailed performance charts do not change this. They show what happened - not why. 

Thus, a signaling strategy is no different from a machine with unknown functionality: It provides outputs without revealing its internal states.

Track records of signaling strategies bundle several biases: 

• they show results, not decision stress 
• they abstract the human behind the strategy 
• they smooth out losing phases 
• they conceal discarded strategies 
• they are often selectively presented 

For the signal provider, drawdowns are part of his system. For the subscriber, they are real account stress. This asymmetry is structural - not moral.

Demo strategies combine several risk factors: 

• no real capital with the signal provider 
• no own loss aversion 
• no psychological feedback 
• different risk appetite 
• no real liability 

What works in the demo is not wrong, but it is optimized for a value-free reality. 

When such strategies are mirrored on real money accounts of third parties, a break occurs: decisions without real consequence become results with real consequences - but only for the subscriber.

The following errors in thinking often occur with automated following: 

• "Automatic = disciplined" 
• "Rule-based = safe" 
• "Past = future" 
• "Success of the signal provider = success of the subscriber" 

This overlooks the fact 

• that drawdowns are experienced differently by individuals 
• that risk parameters have different effects 
• that terminations usually occur at unfavorable times 

Automation does not replace responsibility – it merely shifts it.

Signaling strategies should not be regarded as investment solutions, but as external decision models. 

Central evaluation questions are: 

• In what knowledge space does the signal provider operate? 
• Does it itself bear real risk? 
• Is the strategy explainable or only observable? 
• How are loss phases communicated? 
• Who bears the psychological burden? 

The less insight there is, the closer the strategy comes to a black box risk.

Useful if: 

• it is used as a learning or observation tool 
• the context is transparent 
• risks are consciously accepted 
• there is no expectation of reproducibility 

Problematic if: 

• it serves as a substitute for own understanding 
• demo or backtest strategies are mirrored 
• track records are read as promises 
• responsibility is completely outsourced

1. cognitive space & capital commitment 

In which reality space does the signal strategy operate? 

☐ Real money trading by the signal provider 

☐ Demo account 

☐ Simulation / backtest 

☐ Mixed form (clearly explained) 

Classification: Strategies without real capital commitment by the signal provider are not subject to loss aversion - their results are not directly transferable to subscribers 

2. Alignment of interests 

Does the signal provider bear the same risk as the subscribers? 

☐ Own capital in the same structure 

☐ Own capital, but different parameters 

☐ No own capital 

☐ Unclear 

Classification: The lower the risk equality, the greater the structural asymmetry. 

3. transparency of the decision-making logic 

Is it clear how decisions are made? 

☐ Rules roughly described 

☐ Decision logic explainable 

☐ Adjustments communicated 

☐ Black box (only signals visible) 

Classification: A strategy that is only observable but not explainable remains a black box - regardless of its performance 

4. Origin of the performance 

What is the performance shown based on 

☐ Real money track record 

☐ Demo performance 

☐ Backtest

 ☐ Combination (clearly separated) 

Classification: Outwardly identical curves can originate from completely different areas of knowledge. 

5. Presentation of risk & loss phases Are risks presented realistically and completely? 

☐ Maximum drawdown transparent 

☐ Duration of loss phases visible 

☐ Volatility comprehensible 

☐ Losses smoothed or shortened 

Classification: It is not the profit that determines sustainability, but the question of whether loss phases can be endured. 

6. adaptations & strategy changes 

How are changes dealt with? 

☐ Rule adjustments announced 

☐ Reasons explained 

☐ Changes retroactively visible 

☐ Adjustments not transparent 

Classification: It is not adjustments that are problematic, but their invisibility 

7. Degree of automation & loss of control 

How much control remains with the subscriber 

☐ Manual execution 

☐ Partially automated 

☐ Fully automated 

☐ No possibility of intervention 

Classification: Automation does not replace responsibility - it shifts it. 

8. Psychological burden 

Who bears the mental pressure of real losses?

☐ Signal provider and subscriber alike 

☐ Only the subscriber 

☐ Not addressed 

Classification: Drawdowns are key figures for the provider - a reality for the subscriber 

9. Expectation management 

How is the strategy communicated?

☐ As a historical example 

☐ As a learning or observation tool 

☐ As an ongoing trading solution 

☐ Implicit promise of success 

Classification: The greatest danger lies not in the strategy, but in the expectations. 

10. transferability 

Who is the strategy realistically suitable for? 

☐ Only for this specific setup 

☐ For similar risk profiles

☐ For all 

☐ Unclear 

Classification: Performance is not universal - it is context-dependent.

Information asymmetry describes a situation in which the parties involved do not have the same level of knowledge - especially when one side has structurally more information about processes, incentives and consequences than the other. 

In the context of trading and investment platforms, this means: 

• Users see products, results and interfaces 
• Providers also see: user behaviour, monetization, abandonment rates, activation rates and economic effects 

This knowledge gap is not impermissible per se. However, it becomes relevant when decisions are made on the basis of incomplete transparency.

Many platforms present themselves to the outside world as a technical infrastructure or neutral intermediary. It often remains unclear: 

• who supports the business model economically 
• where revenues are generated 
• which players benefit from user activity 
• how interests are distributed 

The surface shows functionality - not the economic architecture behind it. The more intuitive and playful a platform appears, the less often people ask who benefits from which behavior.

A common misconception is the assumption: "If there are many providers, there must be high demand." 

In fact, popularity can also be generated by supply incentives. 

Certain models are particularly attractive because they: 

• activate inactive users 
• motivate insecure users to take action 
• reduce barriers to decision-making 
• increase transactions 

not because they deliver better results - but because they trigger behavior. This shifts the focus: from quality of results to activation effect.

In many business models, value is not created through long-term user success, but through:

• trading volume 
• frequency 
• engagement 
• dwell time 

interaction models such as: 

• signal subscriptions 
• social trading 
• automated following 
• gamified interfaces 

are particularly suitable for bridging doubts and triggering activity. The economic benefit comes before the result.

A conflict of interest arises where: 

• Users would have to exercise rational restraint 
• But providers benefit from activity 

In such constellations, there is a tension between: 

• user interest (reflection, risk assessment, non-action) 
• provider interest (action, commitment, sales) 

This conflict is not necessarily openly visible - it is systemic. The less transparently incentives are communicated, the greater the information asymmetry.