semvec/pypi-packages huggingface dataset:
quantlib-st 1.14.0
A quantitative finance library and cli for systematic trading
Requires Python: >=3.11

# quantlib

[![PyPI version](https://badge.fury.io/py/quantlib-st.svg)](https://badge.fury.io/py/quantlib-st)
[![CI](https://github.com/rodionlim/quantlib-st/actions/workflows/ci.yml/badge.svg)](https://github.com/rodionlim/quantlib-st/actions/workflows/ci.yml)
[![GHCR](https://img.shields.io/badge/ghcr-quantlib--st-blue?logo=github)](https://github.com/rodionlim/quantlib-st/pkgs/container/quantlib-st)

Minimal, self-contained CLI tools and library for quantitative finance.

## Subcommands

- **[corr](src/quantlib_st/correlation/README.md)**: Compute correlation matrices over time from returns.
- **[costs](src/quantlib_st/costs/README.md)**: Calculate Sharpe Ratio (SR) costs for instruments based on spread and fees.

## Modules

- **[estimators](src/quantlib_st/estimators/README.md)**: Volatility and Forecast Scalar estimators — contains `robust_vol_calc` and `mixed_vol_calc` for daily volatility estimation, and `forecast_scalar` for scaling forecasts.
- **[accounts](src/quantlib_st/systems/accounts/README.md)**: P&L calculation framework — contains `account_forecast` for generating P&L curves from forecasts and prices.

## Install (editable - for developers)

From the repo root:

- `cd quantlib`
- `python -m pip install -e .`

This installs the `quantlib` command.

- `uv sync --extra dev`

## Docker

Pull a published image from GitHub Container Registry:

- `docker pull ghcr.io/rodionlim/quantlib-st:latest`

Run a quick correlation query by piping a CSV into the container (one-liner):

- `cat sample_data/returns_10x4.csv | docker run --rm -i ghcr.io/rodionlim/quantlib-st:latest corr --min-periods 3 --ew-lookback 10`

When publishing the image the Makefile also tags and pushes `:latest` in addition to the versioned tag.

## Package Sample Usage

### Correlation

```python
import pandas as pd
import numpy as np

from quantlib_st import correlation

# Sample data
data = pd.DataFrame(
    np.random.randn(100, 3),
    columns=['Asset_A', 'Asset_B', 'Asset_C'],
    index=pd.date_range(start='2020-01-01', periods=100, freq='D')  # daily dates
)
# Compute correlation matrix
corr_matrix = correlation.correlation_over_time(
    data,
    is_price_series=False,
    frequency='D', # resampling purpose
    interval_frequency='7D',
    date_method='expanding',
    ew_lookback=50,
    min_periods=10
)
print(corr_matrix.as_("jsonable"))
print(corr_matrix.as_("long"))
```
allprs 2.1.1
Approve and merge PRs in bulk
Requires Python: >=3.14

# allprs

## Installation

```bash
# Using uv (will automatically install Python if necessary):
uv tool install allprs
# Using pipx:
pipx install allprs
# Using pip:
pip install allprs
```

## Python version support

This project will only ever support the latest released minor version of python, but will most likely work on older
versions as well. Change `requires-python` manually in `pyproject.toml` if necessary.
corecon 1.4.4
an open collection of constraints on the Epoch of Reionization
Requires Python: >=3.9
Keywords: constraints, reionization, cosmic, observations, data

![](logo.png)

[![DOI](https://zenodo.org/badge/224433919.svg)](https://zenodo.org/badge/latestdoi/224433919)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.05407/status.svg)](https://doi.org/10.21105/joss.05407)
![unit-tests](https://github.com/EGaraldi/corecon/actions/workflows/unit-tests.yml/badge.svg)


CoReCon: open collection of Reionization Constraints
====================================================

**CoReCon** is an open collection of constraints on various physical
quantities linked to the *Epoch of Reionization (EoR)*.

It is built to be easily complemented by contribution from the scientific
community, thanks to a simple data form that supports two different ways of
input data.

CoReCon takes care of loading and interpreting the data, and presenting them
in an organic and ready-to-use way. It also implement simple slicing capabilities,
which allow to perform simple data filtering.

See https://corecon.readthedocs.io/en/latest/ for the latest documentation.