Conditional vs. Non-Conditional Studies
Note: This is a first draft and will evolve as new examples and visualizations are added.
Overview:
This document introduces how QLIR treats every measurable feature — price, ATR, slope, funding rate, etc. — as a distribution of statistical likelihoods.
Conditional studies slice this distribution into contextual subsets, while non-conditional studies describe the global shape.
The framework below defines how to interpret, combine, and use those distributions to make context-aware decisions.
⚖️ Conditional vs. Non-Conditional Studies
One of the most common and reasonable critiques of any quantitative study or strategy is:
“That might have worked in that environment — but does it still work now?”
This question motivates the distinction between non-conditional and conditional studies.
Perfect — here’s your fully integrated Section 1 of conditionality.md, now including the new “Events and Contextual Weighting” material.
This version preserves your operational clarity (“rerun study on filtered raw data”), adds the bitmask + DAG explanation, and then closes with the event-based reasoning layer you just described.
🧩 1. The Core Idea
Every study describes a statistical landscape — a mapping of how likely certain outcomes are across all observations.
The first step is to run the study on the entire raw dataset, producing a global distribution. This gives us the baseline shape of the phenomenon across all available contexts.
When we apply conditions — temporal, structural, or contextual — we’re not modifying that landscape; we’re rerunning the same study on a filtered version of the raw data. Each filtered subset produces its own independent distribution, built using the same computational process but limited to data that meets the condition.
| Mode | What it Represents | Description |
|---|---|---|
| Global (Non-Conditional) | The study applied to the entire raw dataset. | Produces the baseline landscape of statistical likelihoods — the “world map.” |
| Conditional | The same study applied to a filtered subset of the raw data. | Produces an alternate landscape that reflects how the phenomenon behaves under specific conditions — the “regional view.” |
🖼️ Placeholder: Global vs. Conditional Distribution Diagram Visual: bell-curve-like probability density with a shaded region representing a conditional slice (e.g., UTC Monday 09:00–10:00).
Bitmask Interpretation
Each condition acts as a bitmask on the raw dataset — a Boolean selector that defines which observations participate in the study. Multiple masks can be combined to produce narrower subsets:
[ C = C_1 \land C_2 \land \ldots \land C_n ]
Each unique mask combination represents an independent run of the study on that subset of data. Removing or relaxing a condition simply widens the mask, restoring excluded observations.
🖼️ Placeholder: Bitmask Filtering Illustration Visual: binary inclusion diagram showing overlapping bitmasks and resulting intersections.
Combining vs. Merging Distributions
There are two main operational patterns:
- Combine Conditions Before Running the Study
- Apply all bitmasks first, then run the study once on that subset.
- Produces a single, tightly defined conditional distribution.
- Ideal when you want to analyze a specific regime directly.
- Run Separate Studies, Then Compare or Merge Results
- Execute the same study independently on each condition (
A,B, etc.). - Compare shapes, calculate divergence, or merge distributions post-hoc.
- Useful when you want to measure how contexts differ rather than conflate them.
🧭 Placeholder: Mermaid DAG Diagram Visual: DAG showing two branches — one combining bitmasks before study execution, one running parallel studies and merging outputs afterward.
Example (conceptual DAG):
Events and Contextual Weighting
Both global and conditional studies operate over the same underlying list of events — each event is a discrete observation (a candle, a trade, a feature measurement).
- The global distribution visualizes all events.
- A conditional distribution highlights the subset of events that meet one or more conditions.
If visualized in a scatter or box plot (e.g., Tableau), the global distribution can serve as a backdrop while conditional events are highlighted in color. This lets you see where those events land within the larger landscape — how far they sit from the global median, how many σ away they typically fall, and what range they occupy.
🖼️ Placeholder: Event Highlight Visualization Visual: two aligned box plots or dot clouds — the global distribution (gray) with conditional events highlighted in color.
Sometimes the global distribution offers little direct insight — the feature’s structure only becomes meaningful under certain contexts. In those cases, the conditional distribution carries more informational weight.
| Study Type | Informational Focus | Typical Weight |
|---|---|---|
| Global | Baseline reference; useful for normalization and z-scoring | Lower |
| Conditional | Contextual insight; used for regime detection or predictive tuning | Higher |
Practically, you maintain both:
- the global event list as the universal reference frame, and
- multiple conditional event sets to illuminate context-specific behavior.
Switching between them — analytically or interactively — shows how a condition doesn’t change the data itself but changes which parts of the data are emphasized in your reasoning.
🖼️ Placeholder: Highlighted-Subset Tableau Mockup Visual: interactive representation where toggling a condition highlights subset events within a global distribution.
🧮 2. Feature-Agnostic Principle
This framework applies to any measurable column — not only price.
Each field defines its own probability landscape.
| Example Feature | Possible Conditional Axis |
|---|---|
ATR | high- vs. low-volatility sessions |
Funding Rate | positive vs. negative |
Slope | direction or magnitude thresholds |
Volume Delta | market-buy imbalance regimes |
OI Ratio | growth vs. contraction phases |
🖼️ Placeholder: Multi-Feature Illustration
Visual: stacked histograms (ATR, funding, slope) each with shaded conditional slices.
🧭 3. Unequal and Irregular Slices
Conditions rarely yield slices with equal bar counts.
A filter such as RSI > 70 may produce:
- a 5-bar cluster here,
- a 25-bar stretch there,
- and long gaps of exclusion in between.
Each slice is still valid; it simply carries a different temporal density.
Sometimes that density itself has meaning — the duration or frequency of a condition can be treated as another feature.
| Property | Interpretation |
|---|---|
| Slice length | How long the condition persists (regime duration) |
| Slice frequency | How often the regime appears |
| Slice overlap | Interactions between multiple conditions |
🖼️ Placeholder: Irregular Slice Timeline
Visual: time axis showing shaded RSI>70 intervals of varying widths.
⚙️ 4. How These Distributions Are Used
Conditional and non-conditional studies aren’t only descriptive.
They form the reference distributions you consult when current conditions are known.
Think of it as:
“Given today’s market snapshot, which historical distributions are relevant?”
Example: Slope Persistence
Suppose you maintain historical slope-persistence distributions under various regimes:
| Condition | Description | Dataset Tag |
|---|---|---|
None | Global / Non-conditional | D_global |
VolHigh | ATR above 80th percentile | D_volHigh |
VolLow | ATR below 20th percentile | D_volLow |
RSI>70 | Overbought context | D_rsiHigh |
RSI>70 & VolHigh | Composite condition | D_rsiHigh_volHigh |
At runtime you input the current state:
{
"ATR_percentile": 0.84,
"RSI": 74,
"FundingRate": -0.0015
}
The system resolves this into applicable conditions (VolHigh, RSI>70),
then queries the corresponding conditional distributions (D_rsiHigh_volHigh)
to estimate the likelihood that slope persistence increases from here.
🖼️ Placeholder: Runtime Query Flow Visual: “Current Market State → Match Conditions → Fetch Conditional Distribution → Display Likelihood Range.”
Practical Interpretation
- Global distributions are your defaults.
- Conditional distributions are your contextual overrides.
- Combining or removing conditions is equivalent to adding or collapsing dimensions in your statistical space.
🖼️ Placeholder: Dimensionality Cube Diagram Visual: 3-axis cube (ATR, RSI, Funding) with highlighted slice representing current condition intersection.
🧮 6. Dimensionality Explosion and Reduction
As you introduce more conditions, the number of possible distribution combinations grows exponentially:
(where k = number of binary condition axes).
This is the dimensionality explosion problem — each new condition doubles the number of possible intersections.
In practice, you manage this by:
- Feature importance pruning → keep only conditions that materially deform distributions.
- Hierarchical reduction → merge similar conditional profiles back into parent distributions.
- Dynamic resolution → compute fine-grained distributions only when the system is in that region of state space.
🖼️ Placeholder: Dimensionality Reduction Illustration Visual: branching tree that prunes low-impact branches back toward the trunk.
Conceptual Loop
- Observe current state (inputs from live data).
- Locate matching conditional distributions.
- Evaluate feature likelihoods within those distributions.
- If conditions change, shift to the new slice.
- Periodically recompute or merge underutilized slices to control dimensionality.
This is how conditional studies evolve from historical analysis to decision-time inference engines.
🖼️ Placeholder: Adaptive Loop Diagram Visual: circular flow “Observe → Match → Evaluate → Update → Merge.”
🧭 7. Measuring Distance in Likelihood Space
Instead of describing “average” outcomes, we measure positions within the probability landscape.
A conditional result that lies several σ away from the global median indicates a distributional deformation — that regime produces outcomes that are globally rare.
🖼️ Placeholder: Z-Score Axis Diagram Visual: standard-deviation axis with markers showing where conditional results land.
🔁 8. Diagnostic Workflow
- Global baseline: establish the full distribution ( D ).
- Conditional slice: derive local ( D|C ).
- Map back: locate conditional outcomes on the global axis.
- Interpret: measure deviation (σ-distance) to quantify regime effect.
🖼️ Placeholder: Flow Diagram Visual: “Global Study → Apply Condition → Conditional Study → Map Back to Global.”
🧱 9. Design Philosophy
- Global studies define the coordinate system — the zero point.
- Conditional studies explore deviations from that system.
- The transition is reversible: remove conditions to recover the global view, or project global results into any conditional frame.
🔗 10. Related Topics
🔗 See also:
Summary
| Step | Question | Output | |
|---|---|---|---|
| 1 | What is the global statistical likelihood landscape? | ( D ) | |
| 2 | How does that landscape change under condition C? | ( D | C ) |
| 3 | How far does ( D | C ) drift from ( D )? | σ-distance, Δp(x) |
| 4 | What does that imply about regime sensitivity? | Contextual insight for decision-making |
🖼️ Placeholder: Combined Summary Illustration Visual: layered plot showing global curve, conditional slice, z-axis distances, and interpretation arrows.
In short:
Non-conditional studies define the global distribution of likelihoods. Conditional studies reveal how those likelihoods shift and deform under specific contexts. Together they describe not just whether a phenomenon “works,” but where in the statistical landscape it holds — and how far it drifts when real-world conditions change.