head 1.5; access; symbols pkgsrc-2023Q4:1.5.0.16 pkgsrc-2023Q4-base:1.5 pkgsrc-2023Q3:1.5.0.14 pkgsrc-2023Q3-base:1.5 pkgsrc-2023Q2:1.5.0.12 pkgsrc-2023Q2-base:1.5 pkgsrc-2023Q1:1.5.0.10 pkgsrc-2023Q1-base:1.5 pkgsrc-2022Q4:1.5.0.8 pkgsrc-2022Q4-base:1.5 pkgsrc-2022Q3:1.5.0.6 pkgsrc-2022Q3-base:1.5 pkgsrc-2022Q2:1.5.0.4 pkgsrc-2022Q2-base:1.5 pkgsrc-2022Q1:1.5.0.2 pkgsrc-2022Q1-base:1.5 pkgsrc-2021Q4:1.4.0.22 pkgsrc-2021Q4-base:1.4 pkgsrc-2021Q3:1.4.0.20 pkgsrc-2021Q3-base:1.4 pkgsrc-2021Q2:1.4.0.18 pkgsrc-2021Q2-base:1.4 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 @# @; 1.5 date 2022.02.05.14.50.00; author adam; state Exp; branches; next 1.4; commitid lzdbJBzK8NvkirrD; 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 @@ 1.5 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 @@@comment $NetBSD: PLIST,v 1.4 2019/06/17 05:31:49 adam Exp $ ${PYSITELIB}/${EGG_INFODIR}/PKG-INFO ${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt ${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt ${PYSITELIB}/${EGG_INFODIR}/requires.txt ${PYSITELIB}/${EGG_INFODIR}/top_level.txt ${PYSITELIB}/alphalens/__init__.py ${PYSITELIB}/alphalens/__init__.pyc ${PYSITELIB}/alphalens/__init__.pyo ${PYSITELIB}/alphalens/_version.py ${PYSITELIB}/alphalens/_version.pyc ${PYSITELIB}/alphalens/_version.pyo ${PYSITELIB}/alphalens/examples/alphalens_tutorial_on_quantopian.ipynb ${PYSITELIB}/alphalens/examples/daily_factor_synthetic_data.ipynb ${PYSITELIB}/alphalens/examples/event_study.ipynb ${PYSITELIB}/alphalens/examples/event_study_synthetic_data.ipynb ${PYSITELIB}/alphalens/examples/ic_tear.png ${PYSITELIB}/alphalens/examples/intraday_factor.ipynb ${PYSITELIB}/alphalens/examples/intraday_factor_synthetic_data.ipynb ${PYSITELIB}/alphalens/examples/predictive_vs_non-predictive_factor.ipynb ${PYSITELIB}/alphalens/examples/pyfolio_integration.ipynb ${PYSITELIB}/alphalens/examples/returns_tear.png ${PYSITELIB}/alphalens/examples/sector_tear.png ${PYSITELIB}/alphalens/examples/table_tear.png ${PYSITELIB}/alphalens/examples/tear_sheet_walk_through.ipynb ${PYSITELIB}/alphalens/performance.py ${PYSITELIB}/alphalens/performance.pyc ${PYSITELIB}/alphalens/performance.pyo ${PYSITELIB}/alphalens/plotting.py ${PYSITELIB}/alphalens/plotting.pyc ${PYSITELIB}/alphalens/plotting.pyo ${PYSITELIB}/alphalens/tears.py ${PYSITELIB}/alphalens/tears.pyc ${PYSITELIB}/alphalens/tears.pyo ${PYSITELIB}/alphalens/tests/__init__.py ${PYSITELIB}/alphalens/tests/__init__.pyc ${PYSITELIB}/alphalens/tests/__init__.pyo ${PYSITELIB}/alphalens/tests/test_performance.py ${PYSITELIB}/alphalens/tests/test_performance.pyc ${PYSITELIB}/alphalens/tests/test_performance.pyo ${PYSITELIB}/alphalens/tests/test_tears.py ${PYSITELIB}/alphalens/tests/test_tears.pyc ${PYSITELIB}/alphalens/tests/test_tears.pyo ${PYSITELIB}/alphalens/tests/test_utils.py ${PYSITELIB}/alphalens/tests/test_utils.pyc ${PYSITELIB}/alphalens/tests/test_utils.pyo ${PYSITELIB}/alphalens/utils.py ${PYSITELIB}/alphalens/utils.pyc ${PYSITELIB}/alphalens/utils.pyo @ 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 @@comment $NetBSD: PLIST,v 1.3 2018/07/05 09:21:29 minskim Exp $ d13 1 a25 1 ${PYSITELIB}/alphalens/examples/thomas data.ipynb @ 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 @@comment $NetBSD$ d25 1 @ 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 @d13 1 d15 1 d17 2 d20 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 @d8 1 d10 4 a13 9 ${PYSITELIB}/alphalens/__init__.pyc ${PYSITELIB}/alphalens/performance.pyo ${PYSITELIB}/alphalens/performance.pyc ${PYSITELIB}/alphalens/plotting.pyo ${PYSITELIB}/alphalens/plotting.pyc ${PYSITELIB}/alphalens/tears.pyo ${PYSITELIB}/alphalens/tears.pyc ${PYSITELIB}/alphalens/utils.pyo ${PYSITELIB}/alphalens/utils.pyc d21 2 d24 2 d27 2 d30 1 d32 2 a33 1 ${PYSITELIB}/alphalens/tests/__init__.pyc d35 5 a39 1 ${PYSITELIB}/alphalens/tests/test_performance.pyc a40 3 ${PYSITELIB}/alphalens/tests/test_utils.pyc ${PYSITELIB}/alphalens/tests/test_performance.py ${PYSITELIB}/alphalens/tests/test_utils.py d42 2 @