TradingSimuLab’s Macro Scenario Payoff Table connects the broader macro outlook with the historical behavior of the selected asset.
It answers three questions:
How likely is each macro scenario?
How did this asset historically perform after similar macro conditions?
How much does each scenario contribute to Macro Expected Value?
This is important because a weak macro environment does not automatically produce a negative asset payoff, and a supportive macro environment does not guarantee a positive one.
The payoff is asset-specific and history-based.
That makes the scenario table one of the most useful parts of the Macro Model for understanding where the final Macro Expected Value actually comes from.
Educational disclaimer: TradingSimuLab is an educational research platform. Scenario payoffs are historical research estimates, not guaranteed future returns, investment advice, or price forecasts.
What Does the Macro Scenario Payoff Table Show?
The table is the bridge between:
the Macro Model’s view of the environment
and:
the selected asset’s historical behavior following similar conditions.
TradingSimuLab uses four macro states:
- P(-2): Severe Macro Pressure
- P(-1): Weak Macro Backdrop
- P(+1): Constructive Macro Backdrop
- P(+2): Strong Macro / Liquidity Backdrop
For each scenario, the table shows how much probability the model assigns to that state and the historical 12-month payoff associated with the selected asset.
It then calculates how much that scenario contributes to the final Macro Expected Value.
The Three Columns That Matter Most
| Column | What it means |
|---|---|
| Probability | How much weight the Macro Model currently assigns to the scenario |
| Scenario Payoff | How the selected asset historically behaved over the next 12 months after similar model-defined conditions |
| Contribution | How much that scenario adds to or subtracts from Macro Expected Value |
These three numbers should be read together.
Looking only at the payoff or only at the probability can produce a misleading interpretation.
1. Scenario Probability
The probability column shows the model’s current weighting across the four macro scenarios.
For example:
P(-2): 10%
P(-1): 20%
P(+1): 50%
P(+2): 20%
In this example, P(+1) has the greatest probability.
That means the Macro Model currently leans toward a constructive backdrop.
But probability alone says nothing about how the selected asset historically performed in that environment.
For that, we need the payoff column.
2. Scenario Payoff
The Scenario Payoff is asset-specific.
This is one of the most important concepts in the Macro Model.
Suppose the model classifies P(-1) as a weak macro backdrop.
That does not require the selected asset’s historical payoff to be negative.
The asset may historically have:
- recovered strongly after similar weak periods;
- benefited from changing expectations;
- rebounded after previous macro stress.
Likewise, a positive macro environment does not guarantee that every asset historically performed well.
The macro scenario describes the environment.
The payoff describes the asset’s historical response.
Why a Bearish-Looking Scenario Can Have a Positive Payoff
This can initially look strange.
Imagine:
Scenario: Severe Macro Pressure
Historical Asset Payoff: +18%
How can a negative macro state produce a positive payoff?
Because the table is looking at what happened after similar historical model states.
An asset may historically have experienced strong rebound periods following severe stress.
This is particularly possible in volatile assets where large declines can be followed by large recoveries.
The correct interpretation is not:
“Severe macro pressure is bullish.”
It is:
“This asset historically produced a positive subsequent payoff after similar model-defined stress periods.”
Those are very different statements.
3. Scenario Contribution
Contribution combines probability and payoff.
Conceptually:
Contribution = Scenario Probability × Scenario Payoff
Suppose:
Probability: 40%
Historical Payoff: +15%
The scenario contributes approximately:
+6%
to Macro Expected Value.
Contribution is extremely useful because it shows which scenarios are actually driving the final headline number.
A Simple Example
Consider this illustrative table:
| Scenario | Probability | Historical payoff | Contribution |
|---|---|---|---|
| P(-2) | 10% | -10% | -1.0% |
| P(-1) | 20% | +5% | +1.0% |
| P(+1) | 50% | +12% | +6.0% |
| P(+2) | 20% | +20% | +4.0% |
The total Macro Expected Value is approximately:
+10%
But the important information is not simply the +10% headline.
The table shows that most of the positive Macro EV comes from P(+1) and P(+2).
That tells us why the result is positive.
Broad-Based vs Concentrated Macro EV
One of the most useful checks is whether Macro EV is broad-based or concentrated.
Broad-Based Example
Several scenarios contribute positively.
The final Macro EV is supported by multiple parts of the scenario distribution.
Concentrated Example
One scenario contributes almost the entire Macro EV.
The headline may still look attractive, but the result depends heavily on one historical scenario payoff.
That deserves more scrutiny.
Two assets can therefore have the same Macro EV while having very different underlying scenario structures.
Why Concentration Matters
Imagine two assets both show:
Macro EV: +12%
For Asset A:
+3% comes from P(-1)
+4% from P(+1)
+5% from P(+2)
For Asset B:
+1% comes from the first three scenarios combined
+11% comes from P(+2)
The headline number is identical.
But Asset B’s Macro EV is much more dependent on one scenario.
The table reveals that difference immediately.
Watch for Unusually Large Payoffs
The recovered TradingSimuLab guide also recommends paying attention when one Scenario Payoff appears unusually large.
An extreme historical payoff can have a major effect on Macro EV, particularly when the scenario also receives meaningful probability.
That does not mean the value should automatically be rejected.
It means the headline deserves more context.
Ask:
- Is this payoff much larger than the others?
- Is it driving most of Macro EV?
- Is the asset historically volatile?
- Do Risk Simulation and Trend tools support the same conclusion?
The larger the historical payoff, the more important these checks become.
Scenario Payoff Is Not a Forecast
Suppose the P(+1) payoff is:
+17%
That does not mean TradingSimuLab predicts the asset will rise 17% if P(+1) occurs.
The payoff represents the asset’s historical 12-month behavior after similar model-labeled macro states.
Future markets can behave differently.
The scenario table is therefore a historical scenario-analysis tool, not a deterministic return forecast.
Why Confidence Matters
The scenario payoff table should also be read alongside Macro Model Confidence.
Imagine a strong-looking Macro EV while probabilities are widely distributed across all four scenarios.
The result may have less directional clarity than a Macro EV supported by a more concentrated probability distribution.
This is another reason the table should not be separated from the rest of the Macro Model.
Compare the Table With Risk Simulation
A positive historical scenario payoff can still come with substantial risk.
For example:
Macro EV: Positive
Scenario Payoffs: Strong
Risk Simulation: High Average Max Drawdown
CVaR: Severe
The macro scenario analysis may look attractive.
The simulated downside does not.
Both should remain visible.
Compare the Table With Trend and Timing
The same principle applies to market structure.
A positive Macro EV can coexist with:
weak Trend Strength
or:
high Fakeout Risk
or:
weakening Trend Persistence.
That does not make Macro EV wrong.
It means the different models are answering different questions.
The useful conclusion is the disagreement itself.
A Practical Way to Read the Table
Use this sequence:
1. Check Scenario Probabilities
Which macro states currently receive the most weight?
2. Review the Historical Payoffs
How did this specific asset historically behave after similar states?
3. Examine Contribution
Which scenarios actually drive Macro EV?
4. Check for Concentration
Is the final result supported across several scenarios or mostly one?
5. Inspect Extreme Values
Does one payoff look unusually large?
6. Review Confidence
How clear is the underlying macro probability distribution?
7. Compare Other Models
Check Trend Detector, Trend Persistence, Timing Model, and Risk Simulation.
That gives the payoff table its proper role inside the full research framework.
Frequently Asked Questions
What is a macro scenario payoff?
It is the selected asset’s historical 12-month payoff after similar TradingSimuLab model-defined macro conditions.
What does the probability column mean?
It shows how much probability the Macro Model currently assigns to each macro scenario.
What does contribution mean?
Contribution shows how much each scenario’s probability and payoff add to or subtract from the final Macro Expected Value.
Can a negative macro scenario have a positive payoff?
Yes. An asset may historically have rebounded after similar periods of macro pressure.
Does a positive scenario payoff predict a positive future return?
No. It is based on historical behavior following similar model-defined conditions.
Why should I check whether Macro EV is concentrated?
Because a strong headline Macro EV may sometimes depend heavily on one unusually large scenario contribution.
Should the payoff table be used alone?
No. It should be compared with Macro Confidence, Trend Detector, Trend Persistence, Timing Model, and Risk Simulation.
Final Takeaway
The Macro Scenario Payoff Table explains where Macro Expected Value comes from.
Read the table in three steps:
Probability = how likely the macro scenario appears.
Payoff = how the selected asset historically performed after similar conditions.
Contribution = how much that scenario affects Macro EV.
Then ask one more question:
Is the final result broad-based, or is one scenario doing most of the work?
That question can completely change how a strong Macro Expected Value should be interpreted.
The scenario table is therefore not just supporting detail.
It is the best place to understand whether the Macro Model’s headline result is diversified across several scenarios or concentrated in one historical outcome.