head 1.8; access; symbols pkgsrc-2023Q4:1.7.0.16 pkgsrc-2023Q4-base:1.7 pkgsrc-2023Q3:1.7.0.14 pkgsrc-2023Q3-base:1.7 pkgsrc-2023Q2:1.7.0.12 pkgsrc-2023Q2-base:1.7 pkgsrc-2023Q1:1.7.0.10 pkgsrc-2023Q1-base:1.7 pkgsrc-2022Q4:1.7.0.8 pkgsrc-2022Q4-base:1.7 pkgsrc-2022Q3:1.7.0.6 pkgsrc-2022Q3-base:1.7 pkgsrc-2022Q2:1.7.0.4 pkgsrc-2022Q2-base:1.7 pkgsrc-2022Q1:1.7.0.2 pkgsrc-2022Q1-base:1.7 pkgsrc-2021Q4:1.6.0.2 pkgsrc-2021Q4-base:1.6 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 @# @; 1.8 date 2024.01.20.16.10.37; author wiz; state Exp; branches; next 1.7; commitid eg1Eyrn4wES8NcVE; 1.7 date 2022.02.05.14.50.00; author adam; state Exp; branches; next 1.6; commitid lzdbJBzK8NvkirrD; 1.6 date 2021.10.26.10.26.03; author nia; state Exp; branches; next 1.5; commitid Sx37QeYJ6gZ27jeD; 1.5 date 2021.10.07.13.53.52; author nia; state Exp; branches; next 1.4; commitid ZW512wDymtKhSSbD; 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 @@ 1.8 log @py-alphalens: fix build with Python 3.12 @ text @$NetBSD: distinfo,v 1.7 2022/02/05 14:50:00 adam Exp $ BLAKE2s (alphalens-0.4.0.tar.gz) = f5a9843706a15824715d4311693c1ee0a275b4685a2c125bef55fc62330fbd6c SHA512 (alphalens-0.4.0.tar.gz) = ab749a200ee2c1585e1c6ac50598a9c7166af970d17edc714e750b79e03c75a40bca0ca8e812cefd35a07f09d88fdf0298a8f64e7ad2b21037f656361bd2a16c Size (alphalens-0.4.0.tar.gz) = 24037878 bytes SHA1 (patch-versioneer.py) = 1d76058af3871a26f0740e8392c60f7c8b6d36ae @ 1.7 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: distinfo,v 1.6 2021/10/26 10:26:03 nia Exp $ d6 1 @ 1.6 log @finance: Replace RMD160 checksums with BLAKE2s checksums All checksums have been double-checked against existing RMD160 and SHA512 hashes @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.5 2021/10/07 13:53:52 nia Exp $ d3 3 a5 3 BLAKE2s (alphalens-0.3.6.tar.gz) = 8db406b8df4fcfc523a257a06acca2b059643c674daf356d48f64f804a693222 SHA512 (alphalens-0.3.6.tar.gz) = 5d9ef5190691f68956a802b00c4eb1585d754c9510b54aa72f79e31de52d6c62cecf1bdc56d328805b327656c99523146ab781f0d0000725dfebb5b1babd6729 Size (alphalens-0.3.6.tar.gz) = 18910866 bytes @ 1.5 log @finance: Remove SHA1 hashes for distfiles @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.4 2019/06/17 05:31:49 adam Exp $ d3 1 a3 1 RMD160 (alphalens-0.3.6.tar.gz) = 165bda8727aa93e7cd4f1a6d4ebc03d984cf1c07 @ 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: distinfo,v 1.3 2018/07/05 09:21:29 minskim Exp $ a2 1 SHA1 (alphalens-0.3.6.tar.gz) = c39a9178e58eb84eb51a4eb7a1db7919ebd82539 @ 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: distinfo,v 1.2 2018/02/02 20:17:54 minskim Exp $ d3 4 a6 4 SHA1 (alphalens-0.3.2.tar.gz) = be0e5faeab9e6b9886c0fbd75be6d325b78aed7f RMD160 (alphalens-0.3.2.tar.gz) = fde900aee6f696bbed6a548b664ce27a9f7973b9 SHA512 (alphalens-0.3.2.tar.gz) = db633d9978d73426f4f673fff312ee27050ff2bd1895ab2b88437f3577e350b9ce141e08f7872489b7f37c738cd041905efd887859ed5434567e460802320275 Size (alphalens-0.3.2.tar.gz) = 18907452 bytes @ 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 $NetBSD$ d3 4 a6 4 SHA1 (alphalens-0.2.1.tar.gz) = 820dbf3cad9896db011881c76a82d80ba696ded0 RMD160 (alphalens-0.2.1.tar.gz) = 0851766003f7718f9a04e982eb6537c0d623497f SHA512 (alphalens-0.2.1.tar.gz) = 9b12cfb0564b9e10f756a785c98bc5952dadfa5c872fc211ad56fff15d1abd163ec8684d965d7964f913cf192dd9b7c1f477f2423ee3c450a30f7f33749d21ec Size (alphalens-0.2.1.tar.gz) = 9396744 bytes @ 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 @d3 4 a6 4 SHA1 (alphalens-0.1.1.tar.gz) = 1d48a690c2fa740194ea1d496bc646108cabef7b RMD160 (alphalens-0.1.1.tar.gz) = b3613b353d7703a607e738db2eb4bb15bc0e14a3 SHA512 (alphalens-0.1.1.tar.gz) = 08dcc25061afe05e0fe9da1aae1cbcf7f9782d9c392f05cc3304e02a750e1270f2612c1eb857db6d754774eb4e18cc1de9a9117b8210aed2af7ac9107dacd0ac Size (alphalens-0.1.1.tar.gz) = 12304162 bytes @