🧾 Study Status Template
📍 Overview
Study:
Category:
Core Question:
Primary Input:
SMA (starting baseline)
Status:
in-progress / validated / deprecated / experimental
🧪 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 | ✅ |
🧭 Notes & Observations
🧮 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.
📊 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.
🧩 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
🧠 Next Steps
- Validate study on multiple timeframes (1m, 3m, 5m).
- Run conditional tests (e.g., ATR high vs. low volatility).
- Compare persistence vs. rate-of-change alignment.
- Prepare Tableau/Matplotlib comparative overlays.
- Decide on which distributions to promote to feature engineering.