data-intelligence-fabric

name	Data Intelligence & Fabric
description	Institutional-grade data cleaning, tick physics auditing, and gap reconstruction.

Data Intelligence Skill

This skill ensures the "Fuel" (Data) for all other skills is pure. Follow these protocols for any OHLCV or Tick data processing:

1. Tick Physics Protocol

Before any strategy validation or feature engineering:

• Call scripts/data_auditor.py on the target OHLCV dataset.
• Identify Spikes: Flag candles where volatility exceeds the 20-period standard deviation by more than 4x (potential "fake" broker spikes).
• Audit Wicks: Check if High/Low spreads are realistic compared to the peer-group (other symbols in the same group).

2. Time-Series Integrity (Gap Recon)

Institutional models cannot handle missing time steps.

• Use scripts/gap_reconstructor.py to audit for missing bars in MT5 history.
• Interpolate: Fill gaps using forward-fill or linear interpolation to maintain a continuous time index.
• Alert: If a gap exceeds 4 bars (M15+) or 1 bar (H1+), log a "Data Quality Warning" in the Audit Trail.

3. Macro Filter Protocol

Institutional execution ignores technicals during extreme macro uncertainty.

• Call scripts/macro_context.py before opening any new position.
• BLOCK: If an "ULTRA" or "CRITICAL" event is detected within the current session, HALT all execution for that currency pair.
• CAUTION: Increase stop-loss buffers by 50% during "HIGH" impact event windows.

Core Tools:

• scripts/data_auditor.py: Spike and anomaly detector.
• scripts/gap_reconstructor.py: Gap detector and time-series filler.
• scripts/feature_factory.py: High-dimensional feature generator.
• scripts/macro_context.py: Fundamental impact scout.