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6 changes: 4 additions & 2 deletions CHANGELOG.md
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@ All notable changes to this project are documented here. The format follows

## Unreleased

## 0.2.0 - 2026-11-05

### Added

- `quantkit.var`, VaR model validation:
Expand Down Expand Up @@ -48,8 +50,8 @@ All notable changes to this project are documented here. The format follows

- The README quick start installed the v0.1.0 release wheel, which predates
`quantkit.var`: the VaR doctests failed with `ModuleNotFoundError` and
`var-backtest` was not a command. The quick start now installs from GitHub
and says what the release lacks.
`var-backtest` was not a command. The quick start now installs the v0.2.0
wheel.
- `quantkit var-backtest` printed its table and then exited with an error when
the sample was too long for `null_rejection_rates` to enumerate, for example
20 years of daily returns at 95%. It now only leaves out the exact size of
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6 changes: 2 additions & 4 deletions README.md
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Expand Up @@ -15,12 +15,10 @@ quantkit is not on PyPI. Install the library and the `quantkit` command from Git
(Python 3.11 or newer):

```bash
pip install "git+https://github.com/ult666666/quantkit.git"
pip install "https://github.com/ult666666/quantkit/releases/download/v0.2.0/quantkit-0.2.0-py3-none-any.whl"
```

The wheel attached to the [v0.1.0 release](https://github.com/ult666666/quantkit/releases/tag/v0.1.0)
predates VaR model validation, so it has no `quantkit.var` and no `var-backtest`. To
work on the code, clone the repository and run `uv sync` with [uv](https://docs.astral.sh/uv/),
To work on the code, clone the repository and run `uv sync` with [uv](https://docs.astral.sh/uv/),
or `pip install ".[plot]"` in a virtual environment for the charts.

## Example
Expand Down
2 changes: 1 addition & 1 deletion pyproject.toml
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
[project]
name = "quantkit"
version = "0.1.0"
version = "0.2.0"
description = "Options pricing, portfolio optimization, backtesting and VaR model validation in Python, with every model checked against closed-form results, textbook values or an independent method."
readme = "README.md"
license = "MIT"
Expand Down
2 changes: 1 addition & 1 deletion uv.lock

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