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MOCHILAB / AI SKILL
Retirement Cash-Flow Lab
Run reproducible retirement cash-flow scenarios with inflation, annual spending and sequence-of-return variation.
What it does
Run reproducible retirement cash-flow scenarios with inflation, annual spending and sequence-of-return variation.
- Withdraw at the beginning of each year
- Inflation-index the net spending gap
- Reproduce paths with a fixed seed
- Shortfall years and terminal wealth quantiles
Try saying
Use my supplied records to run reproducible retirement cash-flow scenarios with inflation, annual spending and sequence-of-return variation.
Export a reproducible report with the assumptions, key metrics and detailed CSV. Ask me for missing facts first.
Calculation scope
mean_log_return is the mean log return, not an arithmetic return; log_return_volatility is its annual standard deviation. Withdraw max(spending minus income, zero) at year-start, index that gap by inflation, then apply independent annual lognormal returns. The survival fraction belongs only to the declared model, not real-world retirement success or a guaranteed withdrawal rate.
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