🧾 Study Status Template

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📍 Overview

Study:

Category:

Core Question:

Primary Input:
SMA (starting baseline)

Status:
in-progress / validated / deprecated / experimental

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🧪 Current Progress

Phase	Description	Status
Study design	Conceptual and mathematical definition complete	✅
Implementation	Functions and tests in QLIR	⏳
Validation	Empirical verification against reference data	⏳
Visualization	Distributions or plots generated	⏳
Documentation	Markdown and supporting notes complete	✅

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🧭 Notes & Observations

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🧮 Data & Methodology Details

Parameter	Description	Value / Notes
Timeframe	Base candle frequency	e.g. 1min
Horizon	Lookahead window(s)	e.g. 1–5 bars
Conditions	Conditional subsets applied	e.g. RSI > 70, UTC 09:00–10:00
Features	Derived columns or metrics	e.g. slope_persist_prob, slope_decay_rate

🧩 Reminder: all distributions should follow the same event-based structure used in the global/conditional studies.

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📊 Outputs

Output Type	Location / Link	Description
Distribution Plot	(link or relative path)	Conditional likelihood visualization
Tableau Export	(if available)	Dashboard version of slope metrics
Raw Data Export	(CSV path)	Direct study output for validation
QLIR Notebook	(relative path)	Experiment notebook or Python script

🖼️ Placeholder: Visualization Reference
Visual: screenshot or description of primary output figure.

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🧩 Integration & Dependencies

• Depends on: slope_utils.py, rolling_window.py, timefreq.py
• Feeds into: feature_engineering/slope_features.md, distributions/global_conditional_analysis.md
• Relevant Framework: Conditional vs. Non-Conditional Studies, Event List Schema

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🧠 Next Steps

1. Validate study on multiple timeframes (1m, 3m, 5m).
2. Run conditional tests (e.g., ATR high vs. low volatility).
3. Compare persistence vs. rate-of-change alignment.
4. Prepare Tableau/Matplotlib comparative overlays.
5. Decide on which distributions to promote to feature engineering.

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