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MOCHILAB / AI SKILL
Drawdown and Historical Risk
Analyze drawdowns, recoveries, rolling volatility and historical VaR and expected shortfall.
What it does
Analyze drawdowns, recoveries, rolling volatility and historical VaR and expected shortfall.
- Peak and trough of maximum drawdown
- Recovery to the prior peak
- Rolling and full-sample volatility
- Historical quantile and tail-average losses
Try saying
Use my supplied records to analyze drawdowns, recoveries, rolling volatility and historical VaR and expected shortfall.
Export a reproducible report with the assumptions, key metrics and detailed CSV. Ask me for missing facts first.
Calculation scope
Use a cash-flow-adjusted value series or total-return index, unique ordered dates and a stated equal-frequency annualization basis. Use sample standard deviation and linearly interpolated historical quantiles. VaR and expected shortfall describe observed returns and do not guarantee future losses.
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 market-data key is needed. Files stay in the active assistant workspace, with input and output hashes. Analysis only; no account access or automatic trading.
References