FAQ Section | Frequently Asked Questions

Classification means putting numbers in relation to each other: 

• Results and risk 
• Depth and duration of loss phases 
• Frequency and magnitude of trades 
• Structure and behavior of a strategy 

Only this multidimensionality allows a realistic evaluation.

Performance describes the target point, not the path to it 
Two strategies with identical performance can differ fundamentally: 

• in drawdown 
• in recovery time 
• in stability 
• in psychological stress 

Without classification, these differences remain invisible.

Thinking errors do not arise from ignorance, but from intuitive shortcuts in our thinking. When dealing with numbers in particular, we often: 

• Look-back errors ("That was obvious") 
• Overweighting current phases 
• Confusing frequency and quality 
• Looking for simple rankings 

These patterns are human - but risky.

Even with identical KPIs, strategies can differ greatly in: 

• Time distribution of results 
• Order of gains and losses 
• Duration of drawdowns 
• Frequency of new highs 

These factors do not necessarily change the KPIs - but they do massively change the experience.

Example: 
Same performance, different experience 

Strategy A: 

• Smooth rise 
• Short, shallow setbacks 

Strategy B: 

• long sideways phases 
• few strong breakouts 

Both end with +20% performance. 
Subjectively, strategy A often feels "stable", while strategy B feels "exhausting" - despite identical figures.

Time acts as an amplifier 

• Long drawdowns feel more severe than short ones, even with less depth 
• Sideways phases without new highs are often interpreted as a standstill or failure 

T3 KPIs such as Time to Recovery or Time under Water make precisely these effects visible - classic performance indicators do not.

Because our brain does not calculate linearly, but compares, weighs and remembers: 

• Losses are perceived more strongly than gains 
• Long negative phases remain more emotionally present 
• Current events overlay previous successes 

These mechanisms are not a mistake, but part of human decision-making logic.

Because decisions are rarely made purely rationally. A strategy can make sense from a mathematical point of view - but be difficult to sustain in practice 

Without this differentiation, typical misjudgments arise:

• Rapidly abandoning strategies that work 
• Overweighting "pleasant" but fragile models 
• Searching for supposedly more stable alternatives

KPIs should not only be read, but also interpreted: 

• What do they say? 
• What do they conceal? 
• How do they work over time? 

Only this classification connects figures with reality.

Many users do not see losses as the biggest problem, but rather phases in which nothing seems to happen. Objectively speaking, sideways phases are often harmless. Subjectively, they are among the most stressful phases of a strategy.

What is meant by a sideways phase? A sideways phase describes a period in which:

• no new highs are reached 
• gains and losses balance out 
• performance stagnates over a longer period of time 

Important: A sideways phase is not a loss, but a state without progress 

Why are sideways phases experienced as particularly frustrating? 
Because they trigger several psychological effects simultaneously: 

• Lack of confirmation ("Nothing is working") 
• Time pressure ("I'm losing time") 
• Doubt about the strategy ("Maybe it's broken") 

In contrast to losses, there is no clear event - the stress arises gradually.

Why are losses often easier to accept? 
Losses are: 

• clearly recognizable, limited in time 
• emotionally intense, but clear 
• They generate a concrete reaction. 

Sideways phases, on the other hand, create uncertainty - and this is more difficult to endure 

What role does time play in this? 
Time works against the user in sideways phases: 

• Capital is tied up 
• Expectations remain unfulfilled 
• Alternatives appear more attractive 

KPIs such as time under water or drawdown duration make these effects visible, classic performance indicators do not. 

Why do sideways phases often lead to wrong decisions? 
Typical reactions are: 

• Breaking off a strategy that is basically working 
• Switching just before a recovery phase 
• Over-optimization or breaking the rules 

These decisions are not the result of analysis, but of mental fatigue. 

Why are sideways phases nevertheless unavoidable? 
Because markets do not constantly deliver trends. Even robust strategies need: 

• Adjustment phases 
• Consolidation 
• Patience 

A strategy without sideways phases does not exist - only representations that hide them. 

What helps to classify such phases? 
It is helpful to know in advance: 

• that sideways phases are part of it 
• how long they lasted historically 
• how the strategy reacted afterwards 

Classification replaces hope here. 

Short conclusion 
Sideways phases are rarely dangerous - but often crucial for discipline and stamina. Those who understand them make better decisions.

A widespread expectation is: 
If a strategy is good, it should work as consistently as possible. 

This expectation is understandable - but technically wrong. 

What is meant by "bad phases" anyway? 
Bad phases are not necessarily losses. 
They can manifest themselves as: 

• longer sideways movements 
• increased volatility without progress 
• below-average performance compared to the past 
• temporal deviation from the expected pattern 

Important: Bad phases do not mean that a strategy is defective. 

Why are bad phases not a quality defect? 
Because every strategy is based on certain market assumptions: 

• Trend-following strategies require trends 
• Mean reversion strategies require exaggerations 
• Volatility strategies require movement 

If these conditions are temporarily missing, the strategy is not working optimally - but correctly. 

Why would a strategy without bad phases be suspicious? 
A strategy that: 

• completely produces new highs 
• shows no drawdowns 
• has no prolonged periods of weakness 

would either: 

• take unrealistic risks
• report extremely selectively 
• or rely on data that is not complete 

Phases of weakness are not a flaw, but a proof of reality. 

What role do statistics play in this?  
Strategies are not one-off events, but statistical processes. Even with positive expectations: 

• Results fluctuate 
• Sequences can be unfavorable 
• Periods can deviate from the mean 

Short-term weakness does not contradict long-term quality 

Why are bad phases nevertheless so often misinterpreted? 
Typical errors in thinking are: 

• Equating the current phase with the overall statement 
• Inferring from short-term weakness to structural failure 
• Comparing with other strategies in inappropriate market phases 

The problem is not the phase - but its overvaluation. 

What helps with the correct classification? 
It is helpful to clarify in advance: 

• which market conditions the strategy prefers
• how long previous phases of weakness lasted 
• how deep typical drawdowns were 
• how recoveries have gone historically 

If you know these questions, you will be less surprised. 

Why is patience not a moral appeal here, but logic? 

Patience does not mean "endure at all costs". Patience means: 

• not interrupting statistical processes 
• adjusting expectations to reality 
• not deriving decisions from snapshots 

Patience is therefore an analytical factor, not a test of character 

Short conclusion 
Good strategies have bad phases. Not despite their quality - but because of the way they work. Those who understand this separate temporary weakness from structural risk.

Many users are looking for stability. Even progressions, low fluctuations and calm phases have a calming effect - and are intuitively perceived as "safe". 

This intuition is understandable, but deceptive. 

What do we mean by "stability"? 
Stability is often equated with: 

• smooth performance 
• few fluctuations 
• seldom losing phases 
• smooth progression charts 

In short: It feels controlled. 

But stability initially only describes the surface, not the substance. 

Why is stability so convincing? 
Because it serves several psychological needs: 

• Predictability ("I know what to expect") 
• Sense of control ("Things are going smoothly") 
• Confirmation ("This feels right") 

The brain prefers patterns that trigger little alarm - even if they are fragile in the long term. 

Why can apparent stability be risky? 
Because stability often results from: 

• seldom but big risks 
• long phases of little movement 
• postponed adjustments 

Such strategies seem calm - until they have to react suddenly and strongly. Risk does not disappear. It shifts 

Why is risk often underestimated  
Because risk is rarely visible until it occurs. Typical misjudgements are: 

• low volatility = low risk 
• few losses = safe strategy 
• long calm = robust structure 

Risk often only becomes apparent with a time delay - and then in concentrated form. 

Why are more volatile strategies not automatically riskier? 
Because volatility and risk are not identical. A strategy can: 

• fluctuate frequently 
• but limit losses 
• and recover quickly

Such models feel more volatile, but can be structurally more robust than seemingly stable approaches. 

What role do KPIs play in this misperception? 
Individual KPIs reinforce the stability bias: 

• low volatility has a calming effect 
• consistent performance appears reliable 
• low drawdowns suggest security 

Only in combination with time, structural and behavioral key figures does it become visible how risk is actually distributed. 

What helps with a more realistic classification 
It is helpful to ask yourself: 

• Where is risk borne - visible or hidden 
• How does the strategy react under stress? 
• What do the extreme phases look like, not just the average? 

Stability should not be viewed in isolation, but contextualized. 

Short conclusion 
Stability feels good. Risk seems abstract. However, long-term decisions do not benefit from emotion, but from an understanding of distribution, time and structure.

Rankings have a calming effect. They organize, compare and pretend to provide clarity at a glance. This is precisely why they are dangerous if they are read without context.

Why are rankings so attractive? 
Rankings serve several basic human needs:

• Reduction of complexity ("Just tell me what's good") 
• Orientation ("Where do I stand in comparison?") 
• Delegation of responsibility ("The list will be right") 

They give the feeling of having made a well-founded decision - often without real analysis. 

What do rankings do objectively - and what don't they do? 
Objectively, rankings only do one thing: they sort according to one or a few criteria. What they don't do: 

• No representation of risk 
• No statement on stability 
• No information on distribution over time 
• No fit with individual goals 

A ranking is always a snapshot with blinkers on 

Why are rankings particularly problematic in trading? 

• Because trading results are path-dependent: 
• Start time market phase 
• Sequence of gains and losses 
• Individual implementation 

Two strategies can be ranked far apart - and still be equivalent in the long term.

Or vice versa: 
A top ranking can be the result of a single exceptional situation. 

Typical error in thinking: 1st place = best strategy 

A high ranking often only means: 

• strong performance in a limited period of time 
• favorable market conditions 
• high concentration on a few trades 

Not infrequently, the strategies at the top are precisely those that carry the highest risk of disappointment. 

Why do rankings change so frequently? 
Because market conditions change. Strategies rotate through phases of: 

• superiority 
• mediocrity 
• underperformance 

Rankings reflect this rotation - but are often misunderstood as a judgment of quality. 

What happens when decisions are based on rankings? 
Typical consequences are: 

• Entry into strategies after strong phases 
• exit shortly before drawdowns 
• permanent reallocation 
• lack of commitment to an analysis 

The result is often: good strategies - poorly utilized. 

How should rankings be read instead? 
As a filter, not a decision: 

• Why is this strategy here? 
• Which metric dominates the ranking? 
• What risks are not shown? 
• Does this fit my time horizon? 

Rankings provide clues - not answers. 

What alternative makes more sense than rankings? 
A multidimensional view: 

• Performance and drawdown 
• Results and time 
• Efficiency and behavior 
• Structure and consistency 

In short: classification instead of ranking. 

Short conclusion 
Rankings suggest security because they create order. However, real security only arises where context, risk and time are taken into account.

Past performance is like proof: visible, measurable, concrete. This is precisely why it is so often overestimated. However, from a technical perspective:

Performance is a result of the past, not a promise for the future.

Why does performance seem so convincing? 
Because it provides a clear figure - and figures convey certainty. But performance combines many influencing factors that can change at any time: 

• Market regimes (trend, sideways, crisis phases) 
• Volatility and liquidity 
• Cost and execution effects 
• Risk behavior and position management 
• Random and distribution effects 

A single figure cannot "carry" these changes. 

What does "no forecast" mean in concrete terms? 
It does not mean that performance is worthless 
It means: 

• Performance explains what happened 
• Performance does not guarantee that it will happen again 
• Performance can even be misleading if it comes from special phases 

The difference is between description and prediction. 

Why does the market environment play such a big role 
Many strategies do not "always" work, but rather under certain conditions. When these conditions change, the result logic also changes. 

Example: 

• Trend strategies suffer in sideways markets 
• Mean reversion suffers in strong trend phases low 
• Volatility can "dry up" certain approaches V

past performance is often regime performance. 

Why are short periods particularly dangerous? 
Short periods are susceptible to: 

• Outliers (a few trades dominate the picture) 
• "Lucky windows" (a suitable market regime) 
• Random sequences (good series instead of good structure) 

This is where performance is particularly impressive - and at the same time the least resilient. 

Typical misconception 
"If it has worked for the last few months, it will probably continue to work." 

This logic is human, but risky: it assumes stability where markets reward and punish change. 

What is the better question to ask? 
Instead of "How high was the performance?", it is often more important: 

• How did it come about? 
• What was the risk along the way? (Drawdowns, duration, exposure) 
• Is the performance broad-based or concentrated? (T4 logic) 
• Does the pattern fit the strategy idea - or does it seem random? 

Short conclusion 
Past performance is a rear-view mirror, not a navigation device. It is important - but only as part of a classification, not as a forecast.

Comparisons appear objective: Strategy A vs. strategy B, numbers next to each other, ranking list done. The problem: without context, comparability is often simulated, not achieved 

Why are strategies not automatically comparable? 
Because they can differ in: 

• Trading style (trend, mean reversion, discretionary, systematic) 
• Time horizon (intraday vs. swing vs. long-term) 
• Risk and leverage profile 
• Asset class and market structure 
• Trade frequency and database 

A comparison without this categorization is like comparing vehicles without specifying the area of application. 

Why do "same KPIs" pretend to be comparable? 
Even if KPIs are similar, the paths to them can be different: 

• few big winners vs. many small winners 
• short hard drawdowns vs. long tough phases 
• stable returns vs. volatile returns with the same terminal value 

Identical KPIs can conceal different experience and risk profiles 

Typical misconception 
Higher performance = better
Without risk and time context, this is not a statement, but a reflex. 

Better is the question: 

• Better for what? 
• Better for whom? 
• Better under what conditions? 

What is a meaningful comparison? 
Comparisons become resilient when you consciously create context, e.g.: 

• similar observation periods 
• similar market phases (regimes) 
• consistent measurement dimensions (account/portfolio/strategy) 
• common risk limits or target profiles 

Only then do figures become statements. 

Short conclusion 
Comparison without context creates apparent clarity - and real wrong decisions. A good comparison is not quick, but clean.