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MOCHILAB / AI SKILL
ETF Tracking Quality
Calculate active-return tracking error and geometric return differences from aligned total returns.
What it does
Calculate active-return tracking error and geometric return differences from aligned total returns.
- Aligned total returns
- Sample active-return dispersion
- Geometric return differences
Try saying
Use my supplied records to calculate active-return tracking error and geometric return differences from aligned total returns.
Export a reproducible report with the assumptions, key metrics and detailed CSV. Ask me for missing facts first.
Calculation scope
returns are matched same-period total returns, dates increasing; missing days must be reviewed, never filled with zero. periods_per_year is explicit. Tracking error uses sample SD*sqrt(periods_per_year); cumulative/annualized return differences are separately computed geometric differences, not tracking error.
Deliverables and runtime
Includes executable Python 3.10+ standard-library calculations, independent numerical tests, fictional examples and an input reference. Supported flat collections accept CSV. No extra key is needed for supplied data. Live data needs an available permitted source; no all-market real-time feed is bundled. Fictional rule profiles are not current legal/broker defaults. Files stay in the active assistant workspace, with input and output hashes. Analysis only; no account access or automatic trading.
References