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MOCHILAB / AI SKILL
Fund Rolling Holding Experience
Replay overlapping holding windows to compare historical positive returns, excess returns and worst experiences.
What it does
Replay overlapping holding windows to compare historical positive returns, excess returns and worst experiences.
- Multiple historical entry points
- Matched benchmark windows
- Correlated historical outcomes
Try saying
Use my supplied records to replay overlapping holding windows to compare historical positive returns, excess returns and worst experiences.
Export a reproducible report with the assumptions, key metrics and detailed CSV. Ask me for missing facts first.
Calculation scope
window_periods counts supplied aligned observations, not calendar months/years. Series gaps/frequency require review. Overlapping windows are correlated; historical positive-window fraction is not a future success probability. No automated interpolation or backtest trading occurs.
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