TradingSimuLab’s Macro Model reduces a complicated economic backdrop into four scenario states:
- P(-2): Severe Macro Pressure
- P(-1): Weak Macro Backdrop
- P(+1): Constructive Macro Backdrop
- P(+2): Strong Macro / Liquidity Backdrop
These scenarios summarize the model’s view of conditions such as monetary policy, inflation, the yield curve, credit spreads, consumer sentiment, and broader liquidity.
They are not direct recession, stagflation, or soft-landing forecasts.
Instead, they provide a structured way to answer:
How supportive or stressful does the current macro environment appear?
The probabilities assigned to the four scenarios show how the model distributes uncertainty across those possible macro states.
Educational disclaimer: TradingSimuLab is an educational research platform. This article is for informational and educational purposes only and does not provide investment advice, personalized recommendations, or guaranteed forecasts.
What Are the Four Macro Scenarios?
The Macro Model does not try to reduce the economy to a single bullish or bearish label.
Instead, it distributes probability across four states ranging from severe macro pressure to a strongly supportive macro and liquidity environment.
| Scenario | TradingSimuLab interpretation |
|---|---|
| P(-2) | Severe Macro Pressure |
| P(-1) | Weak Macro Backdrop |
| P(+1) | Constructive Macro Backdrop |
| P(+2) | Strong Macro / Liquidity Backdrop |
The probabilities matter because economic conditions are uncertain.
If the model assigns meaningful weight to both positive and negative scenarios, the correct interpretation is not to pick whichever scenario looks most attractive.
It is to recognize that the macro outlook itself is uncertain.
What Inputs Drive the Macro Scenarios?
The Macro Model is connected to several important areas of the economic environment.
These include:
- policy rates;
- inflation;
- yield-curve conditions;
- credit spreads;
- consumer sentiment;
- broader macro and liquidity conditions.
TradingSimuLab does not disclose the proprietary weighting of those inputs.
But the interpretation is straightforward.
Macro pressure generally increases when several conditions become less supportive at the same time—for example, restrictive monetary policy, persistent inflation, wider credit spreads, weaker sentiment, or stressed yield-curve conditions.
The backdrop generally becomes more constructive when inflation pressure cools, financing conditions improve, credit stress remains contained, sentiment strengthens, and the broader feature mix becomes more supportive.
The scenario label is therefore a summary of several macro signals, not an economic diagnosis based on one indicator.
P(-2): Severe Macro Pressure
P(-2) is the most defensive scenario in the framework.
It can represent an environment where several macro pressures are appearing together.
Examples can include:
- restrictive policy conditions;
- persistent inflation pressure;
- wider credit spreads;
- weak sentiment;
- stressed yield-curve conditions;
- poor liquidity;
- a strongly negative overall macro score.
In plain English:
financial conditions are difficult and several macro indicators are pointing toward stress at the same time.
A recession-like or strong risk-off environment could potentially resemble P(-2), but P(-2) should not automatically be renamed “recession.”
The model is identifying a combination of features, not officially declaring an economic recession.
P(-1): Weak Macro Backdrop
P(-1) represents a weaker environment without necessarily reaching the severity associated with P(-2).
Possible characteristics include:
- elevated inflation;
- restrictive monetary policy;
- softer consumer sentiment;
- moderately wider credit spreads;
- weaker liquidity;
- less supportive growth conditions.
A stagflation-like environment may sometimes push more probability toward P(-1), particularly when inflation and policy pressure remain elevated while growth or sentiment weakens.
But again, the correct interpretation is:
weak macro conditions
rather than automatically:
stagflation.
The labels describe what the model sees in its input features.
P(+1): Constructive Macro Backdrop
P(+1) represents a generally supportive macro environment.
Conditions might include:
- moderating inflation pressure;
- contained credit spreads;
- improving sentiment;
- less restrictive monetary conditions;
- healthier liquidity;
- a positive overall macro score.
This is the scenario most easily compared with a soft-landing or improving-growth environment.
However, TradingSimuLab does not formally equate P(+1) with a soft landing.
The better interpretation is:
the overall macro feature mix is constructive without being exceptionally strong.
That distinction prevents the model from making a stronger economic claim than its inputs support.
P(+2): Strong Macro / Liquidity Backdrop
P(+2) is the most supportive scenario.
It can reflect conditions such as:
- favorable liquidity;
- tight or contained credit spreads;
- stronger sentiment;
- less restrictive policy pressure;
- easing inflation concerns;
- a clearly positive macro score.
In simple terms:
several important macro conditions are supportive at the same time.
This may resemble a strong growth, reflation, or highly favorable liquidity environment depending on the underlying feature mix.
But P(+2) does not mean:
stocks must rise.
It describes the macro environment.
Individual assets can still perform poorly because of company-specific weakness, poor timing, trend deterioration, or unfavorable risk characteristics.
Why TSL Does Not Simply Use “Recession” and “Soft Landing”
Terms such as:
- recession;
- soft landing;
- stagflation;
- expansion;
- risk-on;
- risk-off
are useful ways for people to discuss macroeconomics.
But they can also imply more certainty than the data supports.
TradingSimuLab therefore uses its own scenario labels.
For example:
A combination of high inflation, restrictive policy, and weak sentiment might resemble a stagflationary environment.
But the model still reports that condition through its probability distribution across P(-2), P(-1), P(+1), and P(+2).
That keeps the model tied to the features it actually measures.
How to Read the Scenario Probabilities
Suppose the Macro Model showed:
P(-2): 10%
P(-1): 20%
P(+1): 50%
P(+2): 20%
The largest probability is P(+1).
The correct interpretation would be:
The model currently leans toward a constructive macro backdrop, although weaker and stronger scenarios still retain meaningful probability.
It would be incorrect to say:
The model guarantees a soft landing.
Scenario probabilities describe uncertainty.
They should be read as a distribution, not as a single prediction.
Why Probability Distribution Matters
Imagine two model outputs.
Example A
P(-2): 5%
P(-1): 10%
P(+1): 70%
P(+2): 15%
The model has a relatively clear constructive lean.
Example B
P(-2): 25%
P(-1): 25%
P(+1): 30%
P(+2): 20%
P(+1) is still the largest scenario.
But the model is far less decisive.
A reader looking only at the highest probability would miss that difference.
The spread of probabilities tells you how confident or divided the macro picture appears.
Macro Scenario Is Not the Same as Asset Outlook
This is one of the most important concepts in the Macro Model.
A positive macro scenario does not guarantee that a selected stock, ETF, crypto asset, or forex pair will perform well.
Likewise, a negative macro scenario does not guarantee poor asset performance.
The macro scenario describes the environment.
The selected asset has its own:
- trend;
- persistence;
- timing;
- valuation or fundamentals;
- volatility;
- and risk characteristics.
That is why TradingSimuLab combines the Macro Model with the other four models.
Scenario Probabilities vs Scenario Payoffs
The Macro Model separates two ideas:
Scenario probability:
How likely does each macro state appear according to the model?
Scenario payoff:
How did the selected asset historically behave after similar model-defined macro conditions?
These should not be confused.
A negative macro state can still have a positive historical asset payoff.
For example, an asset may historically rebound strongly after periods of severe macro pressure.
That would not turn P(-2) into a positive macro environment.
It simply means the selected asset historically behaved favorably after similar conditions.
Why This Matters for Macro Expected Value
TradingSimuLab can combine scenario probabilities and historical scenario payoffs into a broader expected-value framework.
A strong Macro Expected Value can arise because:
- several scenarios have supportive historical payoffs;
- the most likely scenarios have favorable payoffs;
- or one unusually large payoff materially influences the result.
That is why users should inspect the scenario table, not just the final Macro EV number.
The components tell you where the result comes from.
A Better Way to Read the Macro Model
Use this sequence.
1. Check the Macro Net Score
Determine whether the overall feature mix currently leans supportive or defensive.
2. Review Scenario Probabilities
Look at the distribution across P(-2), P(-1), P(+1), and P(+2).
3. Check How Concentrated the Distribution Is
A dominant scenario suggests a clearer model lean.
A widely distributed probability set signals more uncertainty.
4. Review Scenario Payoffs
See how the selected asset historically behaved after similar macro states.
5. Compare the Macro Model With the Other Models
Check:
- Trend Detector;
- Trend Persistence;
- Timing Model;
- Risk Simulation.
The Macro Model should provide context—not replace the rest of the research process.
Frequently Asked Questions
What are the four TradingSimuLab macro scenarios?
They are P(-2) Severe Macro Pressure, P(-1) Weak Macro Backdrop, P(+1) Constructive Macro Backdrop, and P(+2) Strong Macro / Liquidity Backdrop.
Is P(-2) a recession forecast?
No. P(-2) represents severe macro pressure based on the model’s features. A recessionary environment could resemble it, but the label itself is not an official recession call.
Is P(+1) a soft landing?
Not necessarily. P(+1) represents a constructive macro backdrop. A soft-landing environment is one possible real-world analogy.
Does P(+2) mean stocks will rise?
No. P(+2) indicates a strongly supportive macro/liquidity feature mix. Individual asset performance remains uncertain.
Can a negative macro scenario have a positive asset payoff?
Yes. Scenario probabilities describe the macro environment, while scenario payoffs describe how the selected asset historically behaved after similar conditions.
Why use four scenarios instead of one forecast?
A probability distribution better reflects economic uncertainty than pretending one economic outcome is guaranteed.
Final Takeaway
TradingSimuLab’s four Macro Model scenarios turn a complicated set of economic indicators into an easier framework:
P(-2): Severe Macro Pressure
P(-1): Weak Macro Backdrop
P(+1): Constructive Macro Backdrop
P(+2): Strong Macro / Liquidity Backdrop
The scenarios summarize conditions involving policy, inflation, yield curves, credit, sentiment, and liquidity.
They should not be treated as literal recession, stagflation, or soft-landing predictions.
Instead, use them to answer:
How supportive or stressful is the current macro environment, and how much uncertainty exists around that conclusion?
Then examine how the selected asset historically behaved under similar states and compare the result with Trend Detector, Trend Persistence, Timing Model, and Risk Simulation.
That is a more useful way to read macro conditions than reducing the entire economy to one bullish or bearish forecast.