🧭 Market Study Hierarchy

Understanding, Prediction, Action.

This document formalizes the three primary layers of empirical market study within QLIR-style research pipelines.
Each layer has distinct epistemic goals, data requirements, and output forms.
Together, they define a reproducible progression from exploration → inference → simulation.

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Level 1 — Non-Path-Dependent Characterization

“What does the landscape look like?”

Purpose
Quantify unconditional or quasi-conditional statistical features of the data — no sequencing, no temporal dependency, just structural description.

Examples

• Candle-size distributions by day-of-week or session.
• Bollinger-band width percentiles.
• Average wick-to-body ratio for volume-filtered bars.
• Volatility vs. time-of-day curves anchored on session open.

Properties

• Bar order is irrelevant (no temporal dependency).
• Output is distributional: histograms, percentiles, heatmaps.
• Used for calibration, feature scaling, or visualization (e.g., Tableau dashboards).

Typical Outputs

summary_table
distribution_plot
p(X | filter)

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Level 2 — Path-Dependent Characterization

“Given that this sequence happened, what tends to happen next?”

Purpose
Describe conditional or sequential behavior — transition probabilities without trade logic.

Examples

• “70 % of the time when Bollinger width < threshold for ≥ 5 bars, expansion follows within 15 bars.”
• “After three consecutive up-closes with declining volume, median next-bar return = -0.12 %.”
• “Volatility spikes decay to baseline within N bars 80 % of the time.”

Properties

• Preserves chronological order.
• No exposure or capital logic; introduces temporal conditioning.
• Produces persistence curves, hit-rate tables, or event-anchored averages.

Typical Outputs

event_summary
transition_matrix
p(next_state | pattern)

Role in Pipeline
This is the bridge layer that converts descriptive structure (Level 1) into parameterized hypotheses for backtesting.

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Level 3 — Backtests (Decision Simulations)

“If I acted on those patterns with real rules and capital, what happens?”

Purpose
Evaluate full trading logic under execution and capital constraints.

Adds New Dimensions

• Entry / exit definitions
• Position sizing and leverage
• Fees, slippage, funding
• Portfolio aggregation and risk metrics

Typical Outputs

equity_curve
PnL_distribution
drawdown_stats
Sharpe/Sortino

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Conceptual Flow

raw_market_data
   ↓
Level 1:  Descriptive statistics → what the terrain looks like
   ↓
Level 2:  Conditional characterization → what tends to happen next
   ↓
Level 3:  Simulation (backtest) → what happens if I act on it

Each layer feeds the next:

• L1 → L2: supply priors and filters
• L2 → L3: supply candidate conditions and parameters
• L3 → feedback: validate or falsify intuition, feeding back into new L1/L2 refinements

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Why Maintain This Separation

Dimension	Level 1	Level 2	Level 3
Goal	Understanding	Prediction	Action
Data handling	Shufflable	Ordered	Causal simulation
Validation	Statistical significance	Conditional probability	Economic utility
Time budget	Interactive	Exploratory	Batch/offline
Output	Distributions	Conditional metrics	Performance curves

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Key Takeaway

A characterization study (Levels 1 & 2) transforms intuition into reproducible statistics. A backtest (Level 3) transforms those statistics into actionable performance under constraints.

Maintaining these layers separately keeps your research stack modular, falsifiable, and conceptually clean.