For educational purposes only. Not investment advice. MAY — POTENTIAL — EDUCATIONAL.
Given Analytics is built on a single belief: serious investors make better decisions when they understand the environment they are operating in. These materials explain the core concepts behind Atlas Terminal — the frameworks, the language, and the historical context — so you can interpret the outputs independently and in your own way.
All environment labels and symbol conditions in Atlas are outputs of a rules-based model applied to specific data. They are analytical views under this framework, not complete descriptions of reality, and they may differ from other sources or from how actual conditions are defined or experienced by others.
1. How to Read Environment States
An environment state is this framework’s classification of current conditions based on how growth-, inflation-, and related data are behaving at the same time. It is a model-based view, not a statement of fact about the economy, and other providers or frameworks may classify the same period differently.
The idea of organizing conditions this way is not new — institutional macro investors have used similar concepts for decades. Atlas makes this type of framework explicit, rules-based, and accessible in a consistent way.
Example environment labels
Expansion
The framework currently reads growth-related data as improving while inflation-related measures are easing. Historically, similar configurations in the Atlas data have often coincided with broader participation across risk assets, but not universally and not in every instance. This reflects how the Atlas model groups those episodes, not a claim that they are the only correct classification, and many important drivers of markets lie outside this model. MAY — POTENTIAL — EDUCATIONAL.
Acceleration
The framework currently reads both growth- and inflation-related data as picking up. Historically, similar configurations in the Atlas data have often coincided with stronger behavior in certain real assets and commodity-linked exposures under this framework. This is a description of how the model has grouped past episodes, not a forecast or exclusive view of reality. MAY — POTENTIAL — EDUCATIONAL.
Stagflation
The framework currently reads growth-related data as weakening while inflation-related measures remain elevated or firm. Historically, similar readings in the Atlas data have often coincided with more challenging conditions for broad risk-taking under the model’s lens. Other models may describe the same periods differently, and many real-world forces are not captured here. MAY — POTENTIAL — EDUCATIONAL.
Contraction
The framework currently reads both growth- and inflation-related data as slowing. Historically, similar configurations in the Atlas data have often coincided with more defensive behavior in some assets versus more cyclical exposures, under this framework’s classification. This is one model’s way of organizing history, not a guarantee or exclusive description. MAY — POTENTIAL — EDUCATIONAL.
How to use environment readings
- Use the environment as context, not as a trade trigger. A label tells you which historical episodes are relevant for comparison under this framework, not what to buy or sell.
- Pay attention to confirmation: higher internal confirmation means more of the framework’s inputs agree with the label; lower confirmation means more dispersion and a higher chance the label changes.
- Watch directional pressure: when internal measures lean toward another label, it signals that the underlying math is shifting, even if the published label has not yet changed. This is early context, not a “go” signal.
2. Understanding Historical Base Rates
Everything in Atlas is framed as a historical base rate. A base rate is about what has happened in the past under this framework, not a prediction about what will happen next.
What a base rate is
A base rate is a statement about the past within the Atlas data set, such as:
“In X of Y historical instances where condition A occurred under the framework, behavior B followed within Z time.”
It is a frequency count inside this model’s history, not a causal claim and not a guarantee. Different datasets, time windows, or models may produce different counts or patterns.
Example
“In 14 of 18 historical instances where a given volatility reading occurred during a particular environment label, volatility remained above a specified level for at least 8 more sessions.”
This tells you what has happened before under similar model readings. It does not tell you what will happen now.
How to use base rates
- As context: Base rates help you understand whether current readings are historically common or unusual under this framework.
- Not as strict probabilities: A 70% historical base rate in our sample does not mean there is a 70% chance of the outcome now. It is a summary of past observations in this data and framework, not a universal probability.
- With skepticism: All models face non-stationarity — relationships that held in the past may not hold in the future. Use base rates to ask better questions, not to answer them definitively.
3. Five-Layer Condition Logic (Conceptual)
The Atlas condition engine evaluates each symbol through multiple independent mathematical lenses. All of the framework’s required lenses must align before a full Primary Condition is logged. These lenses reflect how this framework chooses to look at the data. They do not capture every relevant factor, and other approaches may produce different classifications from the same raw prices.
This section explains the design logic at a high level; exact formulas are proprietary.
Why multiple layers?
Single-factor views (for example, only trend or only momentum) tend to produce many false positives because markets generate noise that can look like structure. By requiring agreement across several independent perspectives, the framework reduces false positives at the cost of logging fewer conditions. For an educational tool, that tradeoff is intentional.
Why agreement at the same time?
If each lens confirms at different times, the combined picture can change before all of them line up together. The framework focuses on moments where its required lenses agree concurrently, which it treats as more demanding and historically more meaningful under its own rules.
What the lenses represent conceptually
While implementation details are proprietary, the lenses conceptually reflect areas such as:
- Structural direction: How price has been behaving over an intermediate window.
- Recent force: How fast and persistently price has been moving.
- Risk/noise environment: Whether behavior looks stable or erratic under the model.
- Participation: Whether moves appear to have broad or thin participation.
Each lens captures something the others miss. Together they provide a more complete mathematical picture than any single factor alone, within the limits of the model.
What happens when one lens does not align
If the framework’s criteria across lenses are not fully met, no Primary Condition is logged. The symbol can still be monitored internally, but Atlas does not surface “almost” conditions. Only full agreement under the rules generates a logged condition.
4. Case Study: Reading a Transition (Educational)
This is a historical, simplified educational example. It is not investment advice. Past conditions are not predictive of future results. MAY — POTENTIAL — EDUCATIONAL.
It illustrates how the Atlas framework behaved in one period. Other models might describe the same period differently, and real-time experience of that period included forces that do not show up in this simplified view.
In a past period such as 2022, the framework at one stage classified the environment in a way that reflected strong growth- and inflation-related readings at the same time. Under that label, certain commodity-linked and related symbols showed multiple-lens agreement and generated conditions under the model.
Over subsequent months, under this framework’s lens:
- Growth-related indicators in the data began to weaken.
- Inflation-related measures remained firm.
- Internal confirmation measures declined as more inputs diverged.
- Directional pressure measures began leaning toward a different environment label.
The published environment label did not change overnight. Internal pressure built first; confirmation evolved over time; then the label updated once the framework’s criteria were met. During that transition:
- The mix of symbol conditions shifted as the environment label changed.
- Fewer conditions aligned with the earlier label; more aligned with the new label as defined by the framework.
Key educational takeaways under this model:
- Environment changes are often a process, not a single event.
- Internal confirmation and pressure metrics are designed to surface that process while it is underway.
- The symbol engine adapts mechanically to the environment classification; no manual overrides are required.
Nothing about this example is a recommendation. It is an illustration of how the framework behaved in one historical period and does not capture every factor that mattered in real time.
More Resources
- How Atlas Works — High-level overview of Atlas Terminal
- Methodology — Deep dive into data sources, environment logic, and condition engine
- Glossary — Definitions of Atlas terms
- FAQ — Answers to common questions
- Educational Disclaimer — Full legal disclosures
For educational and informational purposes only. Not investment advice. Given Analytics is not a registered investment adviser. Nothing here constitutes a recommendation or solicitation to buy, sell, or hold any security. All observations from any models or tools are readings based on historical data and rules. Past conditions are not predictive of future results. You are solely responsible for your investment decisions. MAY — POTENTIAL — EDUCATIONAL.
Is there any place in this draft where you still feel it sounds too close to a “how to trade” guide rather than a “how to read the model” guide?