head 1.10; access; symbols pkgsrc-2023Q4:1.10.0.4 pkgsrc-2023Q4-base:1.10 pkgsrc-2023Q3:1.10.0.2 pkgsrc-2023Q3-base:1.10 pkgsrc-2023Q2:1.9.0.2 pkgsrc-2023Q2-base:1.9 pkgsrc-2023Q1:1.8.0.10 pkgsrc-2023Q1-base:1.8 pkgsrc-2022Q4:1.8.0.8 pkgsrc-2022Q4-base:1.8 pkgsrc-2022Q3:1.8.0.6 pkgsrc-2022Q3-base:1.8 pkgsrc-2022Q2:1.8.0.4 pkgsrc-2022Q2-base:1.8 pkgsrc-2022Q1:1.8.0.2 pkgsrc-2022Q1-base:1.8 pkgsrc-2021Q4:1.5.0.6 pkgsrc-2021Q4-base:1.5 pkgsrc-2021Q3:1.5.0.4 pkgsrc-2021Q3-base:1.5 pkgsrc-2021Q2:1.5.0.2 pkgsrc-2021Q2-base:1.5 pkgsrc-2021Q1:1.4.0.16 pkgsrc-2021Q1-base:1.4 pkgsrc-2020Q4:1.4.0.14 pkgsrc-2020Q4-base:1.4 pkgsrc-2020Q3:1.4.0.12 pkgsrc-2020Q3-base:1.4 pkgsrc-2020Q2:1.4.0.10 pkgsrc-2020Q2-base:1.4 pkgsrc-2020Q1:1.4.0.6 pkgsrc-2020Q1-base:1.4 pkgsrc-2019Q4:1.4.0.8 pkgsrc-2019Q4-base:1.4 pkgsrc-2019Q3:1.4.0.4 pkgsrc-2019Q3-base:1.4 pkgsrc-2019Q2:1.4.0.2 pkgsrc-2019Q2-base:1.4 pkgsrc-2019Q1:1.3.0.6 pkgsrc-2019Q1-base:1.3 pkgsrc-2018Q4:1.3.0.4 pkgsrc-2018Q4-base:1.3 pkgsrc-2018Q3:1.3.0.2 pkgsrc-2018Q3-base:1.3 pkgsrc-2018Q2:1.2.0.4 pkgsrc-2018Q2-base:1.2 pkgsrc-2018Q1:1.2.0.2 pkgsrc-2018Q1-base:1.2 pkgsrc-2017Q4:1.1.0.6 pkgsrc-2017Q4-base:1.1 pkgsrc-2017Q3:1.1.0.4 pkgsrc-2017Q3-base:1.1; locks; strict; comment @# @; 1.10 date 2023.08.01.23.20.42; author wiz; state Exp; branches; next 1.9; commitid lyjXpsSeA6xpH8zE; 1.9 date 2023.05.04.17.53.49; author adam; state Exp; branches; next 1.8; commitid MUubh0SfNDlHKFnE; 1.8 date 2022.02.05.14.50.00; author adam; state Exp; branches; next 1.7; commitid lzdbJBzK8NvkirrD; 1.7 date 2022.01.04.20.53.52; author wiz; state Exp; branches; next 1.6; commitid CYyhdK9qtoffkmnD; 1.6 date 2021.12.30.13.05.32; author adam; state Exp; branches; next 1.5; commitid w23rFuQ4pTWhUFmD; 1.5 date 2021.04.07.08.16.37; author nia; state Exp; branches; next 1.4; commitid 7kyABYuEs3HfTkOC; 1.4 date 2019.06.17.05.31.49; author adam; state Exp; branches; next 1.3; commitid nWC7cKfbXlBObvrB; 1.3 date 2018.07.05.09.21.29; author minskim; state Exp; branches; next 1.2; commitid 8xnQ550bGWB1CVIA; 1.2 date 2018.02.02.20.17.54; author minskim; state Exp; branches; next 1.1; commitid QhgmyJLbcJpcakpA; 1.1 date 2017.09.16.21.31.35; author minskim; state Exp; branches; next ; commitid ib3Dy4ReJbFE2t7A; desc @@ 1.10 log @*: remove more references to Python 3.7 @ text @# $NetBSD: Makefile,v 1.9 2023/05/04 17:53:49 adam Exp $ DISTNAME= alphalens-0.4.0 PKGNAME= ${PYPKGPREFIX}-${DISTNAME} CATEGORIES= finance python MASTER_SITES= ${MASTER_SITE_PYPI:=a/alphalens/} MAINTAINER= minskim@@NetBSD.org HOMEPAGE= https://github.com/quantopian/alphalens COMMENT= Performance analysis of predictive stock factors LICENSE= apache-2.0 DEPENDS+= ${PYPKGPREFIX}-empyrical>=0.5.0:../../finance/py-empyrical DEPENDS+= ${PYPKGPREFIX}-ipython>=3.2.3:../../devel/py-ipython DEPENDS+= ${PYPKGPREFIX}-matplotlib>=1.4.0:../../graphics/py-matplotlib DEPENDS+= ${PYPKGPREFIX}-numpy>=1.9.1:../../math/py-numpy DEPENDS+= ${PYPKGPREFIX}-pandas>=0.18.0:../../math/py-pandas DEPENDS+= ${PYPKGPREFIX}-scipy>=0.14.0:../../math/py-scipy DEPENDS+= ${PYPKGPREFIX}-seaborn>=0.6.0:../../graphics/py-seaborn DEPENDS+= ${PYPKGPREFIX}-statsmodels>=0.6.1:../../math/py-statsmodels USE_LANGUAGES= # none PYTHON_VERSIONS_INCOMPATIBLE= 27 38 # py-ipython, py-matplotlib, py-scipy .include "../../lang/python/egg.mk" .include "../../mk/bsd.pkg.mk" @ 1.9 log @py-alphalens: not for Python 3.8 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.8 2022/02/05 14:50:00 adam Exp $ d24 1 a24 1 PYTHON_VERSIONS_INCOMPATIBLE= 27 37 38 # py-ipython, py-matplotlib, py-scipy @ 1.8 log @py-alphalens: updated to 0.4.0 v0.4.0 This is a minor release from 0.3.6 that includes bugfixes, performance improvements, and build changes. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.7 2022/01/04 20:53:52 wiz Exp $ d24 1 a24 1 PYTHON_VERSIONS_INCOMPATIBLE= 27 37 # py-ipython, py-matplotlib, py-scipy @ 1.7 log @*: bump PKGREVISION for egg.mk users They now have a tool dependency on py-setuptools instead of a DEPENDS @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.6 2021/12/30 13:05:32 adam Exp $ d3 1 a3 1 DISTNAME= alphalens-0.3.6 a4 1 PKGREVISION= 1 d13 1 d24 1 a24 1 PYTHON_VERSIONS_INCOMPATIBLE= 27 # py-matplotlib, py-scipy @ 1.6 log @Forget about Python 3.6 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.5 2021/04/07 08:16:37 nia Exp $ d5 1 @ 1.5 log @Unbreak bulk builds. A dependency became incompatible with Python 3.6. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.4 2019/06/17 05:31:49 adam Exp $ d23 1 a23 1 PYTHON_VERSIONS_INCOMPATIBLE= 36 27 # py-matplotlib, py-scipy @ 1.4 log @py-alphalens: updated to 0.3.6 v0.3.6 Add option to compute forward returns non-cumulatively v0.3.5 This is a minor release from 0.3.4 that includes bugfixes, speed enhancement and compatibility with more recent pandas versions. We recommend that all users upgrade to this version. v0.3.4 This is a minor release from 0.3.3 that includes bugfixes, small enhancements and backward compatibility breakages. We recommend that all users upgrade to this version. v0.3.3 TEST: added tests for perf.mean_return_by_quantile @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.3 2018/07/05 09:21:29 minskim Exp $ d23 1 a23 1 PYTHON_VERSIONS_INCOMPATIBLE= 27 # py-matplotlib, py-scipy @ 1.3 log @finance/py-alphalens: Update to 0.3.2 New features since 0.2.1: - Integration with Pyfolio. It is now possible to simulate a portfolio using the input alpha factor and analyze the performance with Pyfolio. - Added new API utils.get_clean_factor to run Alphalens with returns instead of prices - Changed color palette to improve the visual experience for colorblind users - Standard deviation bars optional in tears.create_event_returns_tear_sheet - Alphalens now properly handles intraday factors @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.2 2018/02/02 20:17:54 minskim Exp $ d3 1 a3 1 DISTNAME= alphalens-0.3.2 d5 2 a6 4 CATEGORIES= finance MASTER_SITES= ${MASTER_SITE_GITHUB:=quantopian/} GITHUB_PROJECT= alphalens GITHUB_TAG= v${PKGVERSION_NOREV} d9 1 a9 1 HOMEPAGE= https://github.com/quantopian/alphalens/ d21 4 @ 1.2 log @finance/py-alphalens: Update to 0.2.1 New features since 0.1.0: - Added event study analysis: an event study is a statistical method to assess the impact of a particular event on the value of equities and it is now possible to perform this analysis through the API alphalens.tears.create_event_study_tear_sheet. Check out the relative NoteBook in the example folder. - Added support for group neutral factor analysis (group_neutral argument): this affects the return analysis that is now able to compute returns statistics for each group independently and aggregate them together assuming a portfolio where each group has equal weight. - utils.get_clean_factor_and_forward_returns has a new parameter max_loss that controls how much data the function is allowed to drop due to not having enough price data or due to binning errors (pandas.qcut). This gives the users more control on what is happening and also avoid the function to raise an exception if the binning doesn't go well on some values. - Greatly improved API documentation @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.1 2017/09/16 21:31:35 minskim Exp $ d3 1 a3 1 DISTNAME= alphalens-0.2.1 @ 1.1 log @finance/py-alphalens: version 0.1.1 Alphalens is a Python Library for performance analysis of predictive (alpha) stock factors. Alphalens works great with the Zipline open source backtesting library, and Pyfolio which provides performance and risk analysis of financial portfolios. @ text @d1 1 a1 1 # $NetBSD$ d3 1 a3 1 DISTNAME= alphalens-0.1.1 d8 1 a8 1 GITHUB_TAG= v0.1.1 d15 7 a21 6 DEPENDS+= ${PYPKGPREFIX}-matplotlib-[0-9]*:../../graphics/py-matplotlib DEPENDS+= ${PYPKGPREFIX}-numpy-[0-9]*:../../math/py-numpy DEPENDS+= ${PYPKGPREFIX}-pandas-[0-9]*:../../math/py-pandas DEPENDS+= ${PYPKGPREFIX}-scipy-[0-9]*:../../math/py-scipy DEPENDS+= ${PYPKGPREFIX}-seaborn-[0-9]*:../../graphics/py-seaborn DEPENDS+= ${PYPKGPREFIX}-statsmodels-[0-9]*:../../math/py-statsmodels @