From ce530b0855aea77cdaf5bb0878292e874f141fca Mon Sep 17 00:00:00 2001 From: ValueRaider Date: Sun, 29 Sep 2024 16:30:26 +0100 Subject: [PATCH] Improve dividend repair Improve dividend repair: - detect wrong date - detect split missing from coincident dividend - improve detecting adjust contradicting price action - improve detecting when Close has adjustment (remove phantoms before, then use AdjClose -> AdjLow deltas) Stop negative 'Adj Close' triggering a 'sudden change' repair. --- tests/data/4063-T-1d-bad-div-fixed.csv | 668 ++++++++ tests/data/4063-T-1d-bad-div.csv | 668 ++++++++ tests/data/AET-L-1d-100x-error-fixed.csv | 46 +- tests/data/AET-L-1wk-100x-error-fixed.csv.old | 25 - tests/data/AET-L-1wk-100x-error.csv.old | 25 - tests/data/LSC-L-1d-bad-div-fixed.csv | 450 +++--- tests/data/LSC-L-1d-bad-div.csv | 450 +++--- tests/data/NPK-1d-no-bad-divs.csv | 663 -------- tests/data/NVT-L-1d-bad-div-fixed.csv | 1352 +++++++++-------- tests/data/NVT-L-1d-bad-div.csv | 1352 +++++++++-------- tests/data/SCR-TO-1d-bad-div-fixed.csv | 1350 ++++++++-------- tests/data/SCR-TO-1d-bad-div.csv | 1350 ++++++++-------- tests/data/TENT-L-1d-bad-div-fixed.csv | 686 +++++++++ tests/data/TENT-L-1d-bad-div.csv | 686 +++++++++ tests/test_price_repair.py | 47 +- yfinance/scrapers/history.py | 497 ++++-- 16 files changed, 6309 insertions(+), 4006 deletions(-) create mode 100644 tests/data/4063-T-1d-bad-div-fixed.csv create mode 100644 tests/data/4063-T-1d-bad-div.csv delete mode 100644 tests/data/AET-L-1wk-100x-error-fixed.csv.old delete mode 100644 tests/data/AET-L-1wk-100x-error.csv.old delete mode 100644 tests/data/NPK-1d-no-bad-divs.csv create mode 100644 tests/data/TENT-L-1d-bad-div-fixed.csv create mode 100644 tests/data/TENT-L-1d-bad-div.csv diff --git a/tests/data/4063-T-1d-bad-div-fixed.csv b/tests/data/4063-T-1d-bad-div-fixed.csv new file mode 100644 index 000000000..c7bae7f79 --- /dev/null +++ b/tests/data/4063-T-1d-bad-div-fixed.csv @@ -0,0 +1,668 @@ 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00:00:00+09:00,5916.0,5979.0,5823.0,5862.0,5862.0,10802700,0.0,0.0 diff --git a/tests/data/AET-L-1d-100x-error-fixed.csv b/tests/data/AET-L-1d-100x-error-fixed.csv index b1fa16e28..ed6c8e520 100644 --- a/tests/data/AET-L-1d-100x-error-fixed.csv +++ b/tests/data/AET-L-1d-100x-error-fixed.csv @@ -1,24 +1,24 @@ Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits -2022-06-06 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-06-01 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-05-31 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-05-30 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-05-27 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-05-26 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-05-25 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-05-24 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-05-23 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-05-20 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0 -2022-05-19 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0 -2022-05-18 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,532454,0,0 -2022-05-17 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0 -2022-05-16 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0 -2022-05-13 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0 -2022-05-12 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00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0 +2022-05-06 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0 +2022-05-05 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0 +2022-05-04 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0 +2022-05-03 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0 diff --git a/tests/data/AET-L-1wk-100x-error-fixed.csv.old b/tests/data/AET-L-1wk-100x-error-fixed.csv.old deleted file mode 100644 index 4e661b0bd..000000000 --- a/tests/data/AET-L-1wk-100x-error-fixed.csv.old +++ /dev/null @@ -1,25 +0,0 @@ -Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits -2022-08-15 00:00:00+01:00,27.6000,28.2000,26.2000,27.6000,27.6000,3535668,0,0 -2022-08-12 00:00:00+01:00,27.3000,29.8000,26.4030,27.0000,27.0000,7223353,0,0 -2022-08-11 00:00:00+01:00,26.0000,29.8000,24.2000,27.1000,27.1000,12887933,0,0 -2022-08-10 00:00:00+01:00,25.0000,29.2000,22.5000,25.0000,25.0000,26572680,0,0 -2022-08-09 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-08-08 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-08-05 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-08-04 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-08-03 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-08-02 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-08-01 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-29 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-28 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-27 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-26 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-22 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-21 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-20 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-19 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-15 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-14 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 -2022-07-13 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0 diff --git a/tests/data/AET-L-1wk-100x-error.csv.old b/tests/data/AET-L-1wk-100x-error.csv.old deleted file mode 100644 index e629e2644..000000000 --- a/tests/data/AET-L-1wk-100x-error.csv.old +++ /dev/null @@ -1,25 +0,0 @@ -Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits -2022-08-15 00:00:00+01:00,27.600000381469727,28.200000762939453,26.200000762939453,27.600000381469727,27.600000381469727,3535668,0.0,0.0 -2022-08-12 00:00:00+01:00,27.299999237060547,29.799999237060547,26.402999877929688,27.0,27.0,7223353,0.0,0.0 -2022-08-11 00:00:00+01:00,26.0,29.799999237060547,24.200000762939453,27.100000381469727,27.100000381469727,12887933,0.0,0.0 -2022-08-10 00:00:00+01:00,25.0,29.200000762939453,22.5,25.0,25.0,26572680,0.0,0.0 -2022-08-09 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0 -2022-08-08 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0 -2022-08-05 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0 -2022-08-04 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0 -2022-08-03 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0 -2022-08-02 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0 -2022-08-01 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0 -2022-07-29 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-28 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-27 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-26 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-22 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-21 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-20 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-19 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-18 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-15 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-14 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 -2022-07-13 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0 diff --git a/tests/data/LSC-L-1d-bad-div-fixed.csv b/tests/data/LSC-L-1d-bad-div-fixed.csv index a8e3d1ac6..114741dbd 100644 --- a/tests/data/LSC-L-1d-bad-div-fixed.csv +++ b/tests/data/LSC-L-1d-bad-div-fixed.csv @@ -12,70 +12,70 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired? 2022-01-18 00:00:00+00:00,0.275,0.275,0.275,0.275,0.2529500382534256,0,0.0,0.0,True 2022-01-19 00:00:00+00:00,0.275,0.275,0.275,0.275,0.2529500382534256,0,0.0,0.0,True 2022-01-20 00:00:00+00:00,0.275,0.27299999237060546,0.27299999237060546,0.275,0.2529500382534256,36,0.0,0.0,True -2022-01-21 00:00:00+00:00,0.275,0.29,0.29,0.28500000000000003,0.2621482159594605,206,0.0,0.0,True -2022-01-24 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-01-25 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-01-26 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-01-27 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-01-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-01-31 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-02-01 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-02-02 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-02-03 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-02-04 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-02-07 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-02-08 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-02-09 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-02-10 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True -2022-02-11 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True -2022-02-14 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True -2022-02-15 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True -2022-02-16 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True -2022-02-17 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True -2022-02-18 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True -2022-02-21 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True -2022-02-22 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True -2022-02-23 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True -2022-02-24 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True -2022-02-25 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True -2022-02-28 00:00:00+00:00,0.295,0.3,0.29,0.305,0.28054459028871354,119,0.0,0.0,True -2022-03-01 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28054459028871354,0,0.0,0.0,True -2022-03-02 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28054459028871354,0,0.0,0.0,True -2022-03-03 00:00:00+00:00,0.305,0.32,0.32,0.315,0.28974278691193167,100,0.0,0.0,True -2022-03-04 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True -2022-03-07 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True -2022-03-08 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True -2022-03-09 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True -2022-03-10 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True -2022-03-11 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True -2022-03-14 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True -2022-03-15 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True -2022-03-16 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True -2022-03-17 00:00:00+00:00,0.315,0.3036000061035156,0.3036000061035156,0.315,0.28974278691193167,9,0.0,0.0,True -2022-03-18 00:00:00+00:00,0.315,0.3036000061035156,0.3036000061035156,0.315,0.28974278691193167,6,0.0,0.0,True -2022-03-21 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True +2022-01-21 00:00:00+00:00,0.275,0.29,0.29,0.28500000000000003,0.2621482348766438,206,0.0,0.0,True +2022-01-24 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-01-25 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-01-26 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-01-27 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-01-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-01-31 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-02-01 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-02-02 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-02-03 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-02-04 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-02-07 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-02-08 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-02-09 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-02-10 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True +2022-02-11 00:00:00+00:00,0.29,0.29,0.29,0.29,0.26674732372966126,0,0.0,0.0,True +2022-02-14 00:00:00+00:00,0.29,0.29,0.29,0.29,0.26674732372966126,0,0.0,0.0,True +2022-02-15 00:00:00+00:00,0.29,0.29,0.29,0.29,0.26674732372966126,0,0.0,0.0,True +2022-02-16 00:00:00+00:00,0.29,0.29,0.29,0.29,0.26674732372966126,0,0.0,0.0,True +2022-02-17 00:00:00+00:00,0.29,0.29,0.29,0.29,0.26674732372966126,0,0.0,0.0,True +2022-02-18 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00:00:00+01:00,0.245,0.245,0.245,0.245,0.2279080847547342,0,0.0,0.0,True +2022-10-26 00:00:00+01:00,0.245,0.245,0.245,0.245,0.2279080847547342,0,0.0,0.0,True +2022-10-27 00:00:00+01:00,0.245,0.245,0.245,0.245,0.2279080847547342,0,0.0,0.0,True 2022-10-28 00:00:00+01:00,0.245,0.26,0.26,0.26,0.2418616203490871,200,0.0,0.0,True 2022-10-31 00:00:00+00:00,0.26,0.26,0.26,0.26,0.2418616203490871,0,0.0,0.0,True 2022-11-01 00:00:00+00:00,0.26,0.26,0.26,0.26,0.2418616203490871,0,0.0,0.0,True @@ -226,7 +226,7 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired? 2022-11-23 00:00:00+00:00,0.255,0.22670000076293945,0.215,0.23,0.21753748848254773,1258,0.0,0.0,True 2022-11-24 00:00:00+00:00,0.225,0.225,0.225,0.225,0.21280840949387678,0,0.0,0.0,True 2022-11-25 00:00:00+00:00,0.225,0.225,0.225,0.225,0.21280840949387678,0,0.0,0.0,True -2022-11-28 00:00:00+00:00,0.225,0.24760000228881837,0.23450000762939455,0.24700000762939453,0.23361637154366044,912,0.0,0.0,True +2022-11-28 00:00:00+00:00,0.225,0.24760000228881837,0.23450000762939455,0.24700000762939453,0.2336163473776079,912,0.0,0.0,True 2022-11-29 00:00:00+00:00,0.24700000762939453,0.2670000076293945,0.24,0.26,0.24591193824852078,201,0.0,0.0,True 2022-11-30 00:00:00+00:00,0.26,0.26,0.26,0.26,0.24591193824852078,0,0.0,0.0,True 2022-12-01 00:00:00+00:00,0.26,0.26,0.26,0.26,0.24591193824852078,0,0.0,0.0,True @@ -242,8 +242,8 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired? 2022-12-15 00:00:00+00:00,0.27,0.27,0.27,0.27,0.25537009622586265,0,0.0,0.0,True 2022-12-16 00:00:00+00:00,0.27,0.27,0.27,0.27,0.25537009622586265,0,0.0,0.0,True 2022-12-19 00:00:00+00:00,0.27,0.27,0.27,0.27,0.25537009622586265,0,0.0,0.0,True -2022-12-20 00:00:00+00:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True -2022-12-21 00:00:00+00:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True +2022-12-20 00:00:00+00:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True +2022-12-21 00:00:00+00:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True 2022-12-22 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.26955733319187547,0,0.0,0.0,True 2022-12-23 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.26955733319187547,0,0.0,0.0,True 2022-12-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.26955733319187547,0,0.0,0.0,True @@ -251,77 +251,77 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired? 2022-12-30 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.26955733319187547,0,0.0,0.0,True 2023-01-03 00:00:00+00:00,0.28500000000000003,0.2720000076293945,0.2720000076293945,0.28500000000000003,0.26955733319187547,15,0.0,0.0,True 2023-01-04 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00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-02-28 00:00:00+00:00,0.305,0.2929999923706055,0.2929999923706055,0.305,0.28847362498050666,127,0.0,0.0,True +2023-03-01 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-02 00:00:00+00:00,0.305,0.2929999923706055,0.2929999923706055,0.305,0.28847362498050666,5,0.0,0.0,True +2023-03-03 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-06 00:00:00+00:00,0.305,0.2929999923706055,0.2929999923706055,0.305,0.28847362498050666,4,0.0,0.0,True +2023-03-07 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-08 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-09 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-10 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-13 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-14 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-15 00:00:00+00:00,0.305,0.2929999923706055,0.2929999923706055,0.305,0.28847362498050666,25,0.0,0.0,True +2023-03-16 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-17 00:00:00+00:00,0.315,0.31,0.31,0.305,0.28847362498050666,15,0.0,0.0,True +2023-03-20 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-21 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-22 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-23 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-24 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-27 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-28 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-29 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-30 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-03-31 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-04-03 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-04-04 00:00:00+01:00,0.305,0.31,0.31,0.305,0.28847362498050666,4,0.0,0.0,True +2023-04-05 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-04-06 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-04-11 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-04-12 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-04-13 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-04-14 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True +2023-04-17 00:00:00+01:00,0.315,0.31,0.2929999923706055,0.305,0.28847362498050666,35,0.0,0.0,True 2023-04-18 00:00:00+01:00,0.305,0.30950000762939456,0.30950000762939456,0.31,0.2932027039691775,24,0.0,0.0,True 2023-04-19 00:00:00+01:00,0.31,0.31,0.31,0.31,0.2932027039691775,0,0.0,0.0,True 2023-04-20 00:00:00+01:00,0.31,0.31,0.31,0.31,0.2932027039691775,0,0.0,0.0,True @@ -343,12 +343,12 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired? 2023-05-16 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2600991752145336,0,0.0,0.0,True 2023-05-17 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2600991752145336,0,0.0,0.0,True 2023-05-18 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2600991752145336,0,0.0,0.0,True -2023-05-19 00:00:00+01:00,0.275,0.29649999618530276,0.29649999618530276,0.28,0.264828230037152,33,0.0,0.0,True -2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True -2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True -2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True -2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True -2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True +2023-05-19 00:00:00+01:00,0.275,0.29649999618530276,0.29649999618530276,0.28,0.26482825420320455,33,0.0,0.0,True +2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True +2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True +2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True +2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True +2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True 2023-05-30 00:00:00+01:00,0.29,0.29,0.29,0.29,0.27428638801449384,0,0.0,0.0,True 2023-05-31 00:00:00+01:00,0.29,0.29,0.29,0.29,0.27428638801449384,0,0.0,0.0,True 2023-06-01 00:00:00+01:00,0.29,0.29,0.29,0.29,0.27428638801449384,0,0.0,0.0,True @@ -662,4 +662,10 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired? 2024-08-16 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0,False 2024-08-19 00:00:00+01:00,37.5,38.1,38.1,39.0,39.0,14,0.0,0.0,False 2024-08-20 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0,False -2024-08-21 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0,False +2024-08-21 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False +2024-08-22 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False +2024-08-23 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False +2024-08-27 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False +2024-08-28 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False +2024-08-29 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False +2024-08-30 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False diff --git a/tests/data/LSC-L-1d-bad-div.csv b/tests/data/LSC-L-1d-bad-div.csv index dc5f5bcc1..2e0923a74 100644 --- a/tests/data/LSC-L-1d-bad-div.csv +++ b/tests/data/LSC-L-1d-bad-div.csv @@ -12,70 +12,70 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits 2022-01-18 00:00:00+00:00,0.275,0.275,0.275,0.275,0.01594000220298767,0,0.0,0.0 2022-01-19 00:00:00+00:00,0.275,0.275,0.275,0.275,0.01594000220298767,0,0.0,0.0 2022-01-20 00:00:00+00:00,0.275,0.27299999237060546,0.27299999237060546,0.275,0.01594000220298767,36,0.0,0.0 -2022-01-21 00:00:00+00:00,0.275,0.29,0.29,0.28500000000000003,0.016519638299942015,206,0.0,0.0 -2022-01-24 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-01-25 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-01-26 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-01-27 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-01-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-01-31 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-02-01 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-02-02 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-02-03 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-02-04 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-02-07 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-02-08 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-02-09 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-02-10 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0 -2022-02-11 00:00:00+00:00,0.29,0.29,0.29,0.29,0.01680945634841919,0,0.0,0.0 -2022-02-14 00:00:00+00:00,0.29,0.29,0.29,0.29,0.01680945634841919,0,0.0,0.0 -2022-02-15 00:00:00+00:00,0.29,0.29,0.29,0.29,0.01680945634841919,0,0.0,0.0 -2022-02-16 00:00:00+00:00,0.29,0.29,0.29,0.29,0.01680945634841919,0,0.0,0.0 -2022-02-17 00:00:00+00:00,0.29,0.29,0.29,0.29,0.01680945634841919,0,0.0,0.0 -2022-02-18 00:00:00+00:00,0.295,0.295,0.295,0.295,0.017099275588989257,0,0.0,0.0 -2022-02-21 00:00:00+00:00,0.295,0.295,0.295,0.295,0.017099275588989257,0,0.0,0.0 -2022-02-22 00:00:00+00:00,0.295,0.295,0.295,0.295,0.017099275588989257,0,0.0,0.0 -2022-02-23 00:00:00+00:00,0.295,0.295,0.295,0.295,0.017099275588989257,0,0.0,0.0 -2022-02-24 00:00:00+00:00,0.295,0.295,0.295,0.295,0.017099275588989257,0,0.0,0.0 -2022-02-25 00:00:00+00:00,0.295,0.295,0.295,0.295,0.017099275588989257,0,0.0,0.0 -2022-02-28 00:00:00+00:00,0.295,0.3,0.29,0.305,0.017678911685943603,119,0.0,0.0 -2022-03-01 00:00:00+00:00,0.305,0.305,0.305,0.305,0.017678911685943603,0,0.0,0.0 -2022-03-02 00:00:00+00:00,0.305,0.305,0.305,0.305,0.017678911685943603,0,0.0,0.0 -2022-03-03 00:00:00+00:00,0.305,0.32,0.32,0.315,0.018258548974990844,100,0.0,0.0 -2022-03-04 00:00:00+00:00,0.315,0.315,0.315,0.315,0.018258548974990844,0,0.0,0.0 -2022-03-07 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00:00:00+01:00,0.245,0.245,0.245,0.245,-0.11834244728088379,0,0.0,0.0 +2022-10-26 00:00:00+01:00,0.245,0.245,0.245,0.245,-0.11834244728088379,0,0.0,0.0 +2022-10-27 00:00:00+01:00,0.245,0.245,0.245,0.245,-0.11834244728088379,0,0.0,0.0 2022-10-28 00:00:00+01:00,0.245,0.26,0.26,0.26,-0.12558789253234864,200,0.0,0.0 2022-10-31 00:00:00+00:00,0.26,0.26,0.26,0.26,-0.12558789253234864,0,0.0,0.0 2022-11-01 00:00:00+00:00,0.26,0.26,0.26,0.26,-0.12558789253234864,0,0.0,0.0 @@ -226,7 +226,7 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits 2022-11-23 00:00:00+00:00,0.255,0.22670000076293945,0.215,0.23,0.17169532775878907,1258,0.0,0.0 2022-11-24 00:00:00+00:00,0.225,0.225,0.225,0.225,0.16796281814575195,0,0.0,0.0 2022-11-25 00:00:00+00:00,0.225,0.225,0.225,0.225,0.16796281814575195,0,0.0,0.0 -2022-11-28 00:00:00+00:00,0.225,0.24760000228881837,0.23450000762939455,0.24700000762939453,0.18438587188720704,912,0.0,0.0 +2022-11-28 00:00:00+00:00,0.225,0.24760000228881837,0.23450000762939455,0.24700000762939453,0.18438585281372072,912,0.0,0.0 2022-11-29 00:00:00+00:00,0.24700000762939453,0.2670000076293945,0.24,0.26,0.1940903663635254,201,0.0,0.0 2022-11-30 00:00:00+00:00,0.26,0.26,0.26,0.26,0.1940903663635254,0,0.0,0.0 2022-12-01 00:00:00+00:00,0.26,0.26,0.26,0.26,0.1940903663635254,0,0.0,0.0 @@ -242,8 +242,8 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits 2022-12-15 00:00:00+00:00,0.27,0.27,0.27,0.27,0.20155538558959962,0,0.0,0.0 2022-12-16 00:00:00+00:00,0.27,0.27,0.27,0.27,0.20155538558959962,0,0.0,0.0 2022-12-19 00:00:00+00:00,0.27,0.27,0.27,0.27,0.20155538558959962,0,0.0,0.0 -2022-12-20 00:00:00+00:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0 -2022-12-21 00:00:00+00:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0 +2022-12-20 00:00:00+00:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0 +2022-12-21 00:00:00+00:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0 2022-12-22 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0 2022-12-23 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0 2022-12-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0 @@ -251,77 +251,77 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits 2022-12-30 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0 2023-01-03 00:00:00+00:00,0.28500000000000003,0.2720000076293945,0.2720000076293945,0.28500000000000003,0.21275291442871094,15,0.0,0.0 2023-01-04 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0 -2023-01-05 00:00:00+00:00,0.3,0.3,0.3,0.3,0.22395040512084963,0,0.0,0.0 -2023-01-06 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-09 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-10 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-11 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-12 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-13 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-16 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-17 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-18 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-19 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-20 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-23 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-24 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-25 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-26 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-27 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-30 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-01-31 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-01 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-02 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-03 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-06 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-07 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-08 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-09 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-10 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-13 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-14 00:00:00+00:00,0.305,0.3139999961853027,0.3139999961853027,0.305,0.22768295288085938,127,0.0,0.0 -2023-02-15 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-16 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-17 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-20 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-21 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-22 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-23 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-24 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-27 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-02-28 00:00:00+00:00,0.305,0.2929999923706055,0.2929999923706055,0.305,0.22768295288085938,127,0.0,0.0 -2023-03-01 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-02 00:00:00+00:00,0.305,0.2929999923706055,0.2929999923706055,0.305,0.22768295288085938,5,0.0,0.0 -2023-03-03 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-06 00:00:00+00:00,0.305,0.2929999923706055,0.2929999923706055,0.305,0.22768295288085938,4,0.0,0.0 -2023-03-07 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-08 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-09 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-10 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-13 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-14 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-15 00:00:00+00:00,0.305,0.2929999923706055,0.2929999923706055,0.305,0.22768295288085938,25,0.0,0.0 -2023-03-16 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-17 00:00:00+00:00,0.315,0.31,0.31,0.305,0.22768295288085938,15,0.0,0.0 -2023-03-20 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-21 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-22 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-23 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-24 00:00:00+00:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-27 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-28 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-29 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-30 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-03-31 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-04-03 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-04-04 00:00:00+01:00,0.305,0.31,0.31,0.305,0.22768295288085938,4,0.0,0.0 -2023-04-05 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-04-06 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-04-11 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-04-12 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-04-13 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-04-14 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768295288085938,0,0.0,0.0 -2023-04-17 00:00:00+01:00,0.315,0.31,0.2929999923706055,0.305,0.22768295288085938,35,0.0,0.0 +2023-01-05 00:00:00+00:00,0.3,0.3,0.3,0.3,0.22395042419433595,0,0.0,0.0 +2023-01-06 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00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0 +2023-03-31 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0 +2023-04-03 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0 +2023-04-04 00:00:00+01:00,0.305,0.31,0.31,0.305,0.22768293380737306,4,0.0,0.0 +2023-04-05 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0 +2023-04-06 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0 +2023-04-11 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0 +2023-04-12 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0 +2023-04-13 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0 +2023-04-14 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0 +2023-04-17 00:00:00+01:00,0.315,0.31,0.2929999923706055,0.305,0.22768293380737306,35,0.0,0.0 2023-04-18 00:00:00+01:00,0.305,0.30950000762939456,0.30950000762939456,0.31,0.23141544342041015,24,0.0,0.0 2023-04-19 00:00:00+01:00,0.31,0.31,0.31,0.31,0.23141544342041015,0,0.0,0.0 2023-04-20 00:00:00+01:00,0.31,0.31,0.31,0.31,0.23141544342041015,0,0.0,0.0 @@ -343,12 +343,12 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits 2023-05-16 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2052878952026367,0,0.0,0.0 2023-05-17 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2052878952026367,0,0.0,0.0 2023-05-18 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2052878952026367,0,0.0,0.0 -2023-05-19 00:00:00+01:00,0.275,0.29649999618530276,0.29649999618530276,0.28,0.2090203857421875,33,0.0,0.0 -2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0 -2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0 -2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0 -2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0 -2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0 +2023-05-19 00:00:00+01:00,0.275,0.29649999618530276,0.29649999618530276,0.28,0.20902040481567383,33,0.0,0.0 +2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0 +2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0 +2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0 +2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0 +2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0 2023-05-30 00:00:00+01:00,0.29,0.29,0.29,0.29,0.2164854049682617,0,0.0,0.0 2023-05-31 00:00:00+01:00,0.29,0.29,0.29,0.29,0.2164854049682617,0,0.0,0.0 2023-06-01 00:00:00+01:00,0.29,0.29,0.29,0.29,0.2164854049682617,0,0.0,0.0 @@ -662,4 +662,10 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits 2024-08-16 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0 2024-08-19 00:00:00+01:00,37.5,38.1,38.1,39.0,39.0,14,0.0,0.0 2024-08-20 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0 -2024-08-21 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0 +2024-08-21 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0 +2024-08-22 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0 +2024-08-23 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0 +2024-08-27 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0 +2024-08-28 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0 +2024-08-29 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0 +2024-08-30 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0 diff --git a/tests/data/NPK-1d-no-bad-divs.csv b/tests/data/NPK-1d-no-bad-divs.csv deleted file mode 100644 index 294cdd0d8..000000000 --- a/tests/data/NPK-1d-no-bad-divs.csv +++ /dev/null @@ -1,663 +0,0 @@ -Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits -2022-01-03 00:00:00-05:00,82.0,83.0999984741211,81.93000030517578,82.7699966430664,69.97517395019531,31600,0.0,0.0 -2022-01-04 00:00:00-05:00,83.11000061035156,83.93000030517578,83.11000061035156,83.58000183105469,70.65996551513672,53300,0.0,0.0 -2022-01-05 00:00:00-05:00,83.93000030517578,84.66999816894531,83.16999816894531,83.80000305175781,70.84595489501953,39600,0.0,0.0 -2022-01-06 00:00:00-05:00,83.75,83.75,82.77999877929688,83.38999938964844,70.49932861328125,25400,0.0,0.0 -2022-01-07 00:00:00-05:00,83.36000061035156,83.87999725341797,83.16000366210938,83.5199966430664,70.60923767089844,19700,0.0,0.0 -2022-01-10 00:00:00-05:00,82.98999786376953,84.05999755859375,82.87000274658203,83.25,70.38097381591797,30300,0.0,0.0 -2022-01-11 00:00:00-05:00,83.41999816894531,83.58000183105469,81.9000015258789,82.55000305175781,69.78919219970703,25300,0.0,0.0 -2022-01-12 00:00:00-05:00,82.41999816894531,84.2699966430664,81.95999908447266,83.6500015258789,70.71914672851562,41300,0.0,0.0 -2022-01-13 00:00:00-05:00,83.4800033569336,86.26000213623047,83.44000244140625,85.31999969482422,72.13098907470703,82200,0.0,0.0 -2022-01-14 00:00:00-05:00,85.31999969482422,87.70999908447266,84.48999786376953,87.55000305175781,74.01627349853516,59100,0.0,0.0 -2022-01-18 00:00:00-05:00,87.73999786376953,89.05000305175781,87.02999877929688,87.05999755859375,73.60200500488281,37000,0.0,0.0 -2022-01-19 00:00:00-05:00,87.25,87.25,84.55000305175781,84.7300033569336,71.63219451904297,24500,0.0,0.0 -2022-01-20 00:00:00-05:00,85.2699966430664,85.76000213623047,82.5,82.72000122070312,69.93290710449219,18300,0.0,0.0 -2022-01-21 00:00:00-05:00,82.06999969482422,84.12000274658203,82.06999969482422,82.62000274658203,69.84835815429688,23000,0.0,0.0 -2022-01-24 00:00:00-05:00,82.48999786376953,83.9000015258789,82.0,83.52999877929688,70.61769104003906,40000,0.0,0.0 -2022-01-25 00:00:00-05:00,83.11000061035156,83.8499984741211,81.87999725341797,83.36000061035156,70.47396850585938,31200,0.0,0.0 -2022-01-26 00:00:00-05:00,84.11000061035156,84.47000122070312,81.44999694824219,82.0199966430664,69.34111022949219,34500,0.0,0.0 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00:00:00-04:00,71.7300033569336,72.27999877929688,71.55000305175781,71.98999786376953,71.98999786376953,22000,0.0,0.0 -2024-07-11 00:00:00-04:00,73.0199966430664,74.23999786376953,73.0199966430664,73.80000305175781,73.80000305175781,33600,0.0,0.0 -2024-07-12 00:00:00-04:00,74.48999786376953,74.8499984741211,73.80999755859375,74.30999755859375,74.30999755859375,27500,0.0,0.0 -2024-07-15 00:00:00-04:00,74.94999694824219,75.86000061035156,74.44999694824219,74.54000091552734,74.54000091552734,33800,0.0,0.0 -2024-07-16 00:00:00-04:00,75.05999755859375,77.75,75.05999755859375,77.5999984741211,77.5999984741211,32600,0.0,0.0 -2024-07-17 00:00:00-04:00,76.91000366210938,78.93000030517578,76.91000366210938,77.68000030517578,77.68000030517578,53400,0.0,0.0 -2024-07-18 00:00:00-04:00,77.26000213623047,78.7699966430664,76.2300033569336,76.33999633789062,76.33999633789062,35200,0.0,0.0 -2024-07-19 00:00:00-04:00,76.51000213623047,77.20999908447266,74.66000366210938,75.08999633789062,75.08999633789062,33800,0.0,0.0 -2024-07-22 00:00:00-04:00,74.77999877929688,76.80000305175781,74.5999984741211,76.6500015258789,76.6500015258789,29400,0.0,0.0 -2024-07-23 00:00:00-04:00,76.47000122070312,78.7300033569336,76.4000015258789,78.43000030517578,78.43000030517578,30000,0.0,0.0 -2024-07-24 00:00:00-04:00,78.05999755859375,79.0,76.87000274658203,77.06999969482422,77.06999969482422,42200,0.0,0.0 -2024-07-25 00:00:00-04:00,77.37000274658203,79.0999984741211,77.37000274658203,78.0999984741211,78.0999984741211,31600,0.0,0.0 -2024-07-26 00:00:00-04:00,79.26000213623047,79.26000213623047,77.12999725341797,77.91000366210938,77.91000366210938,34200,0.0,0.0 -2024-07-29 00:00:00-04:00,78.0,78.0,76.43000030517578,77.16999816894531,77.16999816894531,24500,0.0,0.0 -2024-07-30 00:00:00-04:00,77.08000183105469,77.7699966430664,76.80000305175781,77.22000122070312,77.22000122070312,18600,0.0,0.0 -2024-07-31 00:00:00-04:00,76.91999816894531,78.23999786376953,76.04000091552734,76.47000122070312,76.47000122070312,32300,0.0,0.0 -2024-08-01 00:00:00-04:00,76.0,76.0,74.63999938964844,75.45999908447266,75.45999908447266,24200,0.0,0.0 -2024-08-02 00:00:00-04:00,74.41000366210938,75.54000091552734,74.01000213623047,74.41000366210938,74.41000366210938,31100,0.0,0.0 -2024-08-05 00:00:00-04:00,72.97000122070312,73.29000091552734,71.83000183105469,72.83000183105469,72.83000183105469,32100,0.0,0.0 -2024-08-06 00:00:00-04:00,72.72000122070312,73.48999786376953,72.33000183105469,72.87999725341797,72.87999725341797,25500,0.0,0.0 -2024-08-07 00:00:00-04:00,73.27999877929688,73.27999877929688,72.18000030517578,72.44999694824219,72.44999694824219,16800,0.0,0.0 -2024-08-08 00:00:00-04:00,72.69000244140625,73.41999816894531,72.41000366210938,73.01000213623047,73.01000213623047,24000,0.0,0.0 -2024-08-09 00:00:00-04:00,73.11000061035156,73.68000030517578,72.31999969482422,72.8499984741211,72.8499984741211,27300,0.0,0.0 -2024-08-12 00:00:00-04:00,73.05000305175781,73.22000122070312,70.87999725341797,71.11000061035156,71.11000061035156,25300,0.0,0.0 -2024-08-13 00:00:00-04:00,71.2300033569336,72.69000244140625,70.44999694824219,72.43000030517578,72.43000030517578,27200,0.0,0.0 -2024-08-14 00:00:00-04:00,71.83999633789062,72.5199966430664,71.62000274658203,71.86000061035156,71.86000061035156,20800,0.0,0.0 -2024-08-15 00:00:00-04:00,72.91999816894531,73.55999755859375,72.1500015258789,72.87000274658203,72.87000274658203,28900,0.0,0.0 -2024-08-16 00:00:00-04:00,72.87000274658203,73.58000183105469,72.4000015258789,73.20999908447266,73.20999908447266,37000,0.0,0.0 -2024-08-19 00:00:00-04:00,72.94999694824219,73.98999786376953,72.87000274658203,73.5,73.5,25200,0.0,0.0 -2024-08-20 00:00:00-04:00,73.38999938964844,73.4000015258789,72.70999908447266,73.20999908447266,73.20999908447266,21400,0.0,0.0 -2024-08-21 00:00:00-04:00,74.0,74.18000030517578,72.79000091552734,74.12000274658203,74.12000274658203,31200,0.0,0.0 diff --git a/tests/data/NVT-L-1d-bad-div-fixed.csv b/tests/data/NVT-L-1d-bad-div-fixed.csv index c86b08d88..6af186ea3 100644 --- a/tests/data/NVT-L-1d-bad-div-fixed.csv +++ b/tests/data/NVT-L-1d-bad-div-fixed.csv @@ -1,666 +1,686 @@ -Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired? -2022-01-04 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.5880745263310649,14877,0.0,0.0,True -2022-01-05 00:00:00+00:00,0.6875,0.6751000213623047,0.675,0.6875,0.5880745263310649,15180,0.0,0.0,True -2022-01-06 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-01-07 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-01-10 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-01-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-01-12 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-01-13 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-01-14 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.5880745263310649,10268,0.0,0.0,True -2022-01-17 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880745263310649,1004,0.0,0.0,True -2022-01-18 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.5880745263310649,15202,0.0,0.0,True -2022-01-19 00:00:00+00:00,0.6875,0.675,0.67,0.6875,0.5880745263310649,26258,0.0,0.0,True -2022-01-20 00:00:00+00:00,0.6875,0.675,0.675,0.6875,0.5880745263310649,11286,0.0,0.0,True -2022-01-21 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880745263310649,653,0.0,0.0,True -2022-01-24 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880745263310649,300,0.0,0.0,True -2022-01-25 00:00:00+00:00,0.6875,0.67,0.67,0.6825,0.5837975216144157,18000,0.0,0.0,True -2022-01-26 00:00:00+00:00,0.6825,0.6940000152587891,0.6940000152587891,0.6825,0.5837975216144157,1560,0.0,0.0,True -2022-01-27 00:00:00+00:00,0.6825,0.67,0.67,0.6825,0.5837975216144157,1000,0.0,0.0,True -2022-01-28 00:00:00+00:00,0.6825,0.67,0.67,0.6825,0.5837975216144157,5588,0.0,0.0,True -2022-01-31 00:00:00+00:00,0.6825,0.6825,0.6825,0.6825,0.5837975216144157,0,0.0,0.0,True -2022-02-01 00:00:00+00:00,0.6825,0.66,0.65,0.66,0.5645515349551748,30612,0.0,0.0,True -2022-02-02 00:00:00+00:00,0.66,0.65,0.65,0.66,0.5645515349551748,9000,0.0,0.0,True -2022-02-03 00:00:00+00:00,0.67,0.68,0.67,0.6775,0.579520664364161,19301,0.0,0.0,True -2022-02-04 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.579520664364161,29455,0.0,0.0,True -2022-02-07 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.579520664364161,3125,0.0,0.0,True -2022-02-08 00:00:00+00:00,0.6775,0.685,0.67,0.6775,0.579520664364161,15202,0.0,0.0,True -2022-02-09 00:00:00+00:00,0.6775,0.6775,0.6775,0.6775,0.579520664364161,0,0.0,0.0,True -2022-02-10 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.579520664364161,3506,0.0,0.0,True -2022-02-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-02-14 00:00:00+00:00,0.6875,0.68,0.68,0.6875,0.5880745263310649,2461,0.0,0.0,True -2022-02-15 00:00:00+00:00,0.6875,0.68,0.68,0.6875,0.5880745263310649,19756,0.0,0.0,True -2022-02-16 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-02-17 00:00:00+00:00,0.6875,0.6825,0.6825,0.6875,0.5880745263310649,7275,0.0,0.0,True -2022-02-18 00:00:00+00:00,0.6875,0.68,0.675,0.6875,0.5880745263310649,45627,0.0,0.0,True -2022-02-21 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-02-22 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-02-23 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-02-24 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880745263310649,14323,0.0,0.0,True -2022-02-25 00:00:00+00:00,0.6875,0.6827999877929688,0.6827999877929688,0.6875,0.5880745263310649,28473,0.0,0.0,True -2022-02-28 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-03-01 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-03-02 00:00:00+00:00,0.6875,0.6825,0.6825,0.6875,0.5880745263310649,1559,0.0,0.0,True -2022-03-03 00:00:00+00:00,0.6875,0.67,0.67,0.6875,0.5880745263310649,1524,0.0,0.0,True -2022-03-04 00:00:00+00:00,0.6875,0.6809999847412109,0.6809999847412109,0.6875,0.5880745263310649,718,0.0,0.0,True -2022-03-07 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True -2022-03-08 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00:00:00+00:00,0.585,0.5906999969482422,0.5906999969482422,0.585,0.5344191366283723,8300,0.0,0.0,True -2022-12-13 00:00:00+00:00,0.585,0.5700000000000001,0.5700000000000001,0.585,0.5344191366283723,4797,0.0,0.0,True -2022-12-14 00:00:00+00:00,0.585,0.5712799835205078,0.5711999893188476,0.585,0.5344191366283723,7000,0.0,0.0,True -2022-12-15 00:00:00+00:00,0.585,0.585,0.585,0.585,0.5344191366283723,0,0.0,0.0,True -2022-12-16 00:00:00+00:00,0.585,0.5712799835205078,0.5700000000000001,0.585,0.5344191366283723,31125,0.0,0.0,True -2022-12-19 00:00:00+00:00,0.585,0.5712799835205078,0.5712799835205078,0.585,0.5344191366283723,8500,0.0,0.0,True -2022-12-20 00:00:00+00:00,0.585,0.5793000030517578,0.5793000030517578,0.585,0.5344191366283723,94265,0.0,0.0,True -2022-12-21 00:00:00+00:00,0.585,0.5712799835205078,0.5712799835205078,0.585,0.5344191366283723,8475,0.0,0.0,True -2022-12-22 00:00:00+00:00,0.585,0.5906999969482422,0.5906999969482422,0.585,0.5344191366283723,320,0.0,0.0,True -2022-12-23 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00:00:00+00:00,0.58,0.555,0.555,0.58,0.5298514756474588,2500,0.0,0.0,True -2023-02-08 00:00:00+00:00,0.58,0.58,0.58,0.58,0.5298514756474588,0,0.0,0.0,True -2023-02-09 00:00:00+00:00,0.58,0.58,0.58,0.58,0.5298514756474588,0,0.0,0.0,True -2023-02-10 00:00:00+00:00,0.58,0.58,0.58,0.58,0.5298514756474588,0,0.0,0.0,True -2023-02-13 00:00:00+00:00,0.58,0.58,0.58,0.5750000000000001,0.5252837777999467,12276,0.0,0.0,True -2023-02-14 00:00:00+00:00,0.5750000000000001,0.5602999877929687,0.5602999877929687,0.5750000000000001,0.5252837777999467,4049,0.0,0.0,True -2023-02-15 00:00:00+00:00,0.5750000000000001,0.5602999877929687,0.5602999877929687,0.5750000000000001,0.5252837777999467,5000,0.0,0.0,True -2023-02-16 00:00:00+00:00,0.5750000000000001,0.5750000000000001,0.5750000000000001,0.5750000000000001,0.5252837777999467,0,0.0,0.0,True -2023-02-17 00:00:00+00:00,0.5750000000000001,0.5750000000000001,0.5750000000000001,0.5750000000000001,0.5252837777999467,0,0.0,0.0,True -2023-02-20 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+2024-09-20 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False diff --git a/tests/data/NVT-L-1d-bad-div.csv b/tests/data/NVT-L-1d-bad-div.csv index 701299180..64fc266a1 100644 --- a/tests/data/NVT-L-1d-bad-div.csv +++ b/tests/data/NVT-L-1d-bad-div.csv @@ -1,666 +1,686 @@ -Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits -2022-01-04 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.608498306274414,14877,0.0,0.0 -2022-01-05 00:00:00+00:00,0.6875,0.6751000213623047,0.675,0.6875,0.608498306274414,15180,0.0,0.0 -2022-01-06 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.608498306274414,0,0.0,0.0 -2022-01-07 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.608498306274414,0,0.0,0.0 -2022-01-10 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.608498306274414,0,0.0,0.0 -2022-01-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.608498306274414,0,0.0,0.0 -2022-01-12 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.608498306274414,0,0.0,0.0 -2022-01-13 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00:00:00+01:00,0.56,0.5750000000000001,0.5750000000000001,0.56,0.56,1,0.0,0.0,0.0 +2024-08-02 00:00:00+01:00,0.56,0.56,0.56,0.56,0.56,0,0.0,0.0,0.0 +2024-08-05 00:00:00+01:00,0.56,0.56,0.56,0.56,0.56,0,0.0,0.0,0.0 +2024-08-06 00:00:00+01:00,0.56,0.545,0.545,0.56,0.56,204,0.0,0.0,0.0 +2024-08-07 00:00:00+01:00,0.56,0.56,0.56,0.56,0.56,0,0.0,0.0,0.0 +2024-08-08 00:00:00+01:00,0.56,0.56,0.56,0.56,0.56,0,0.0,0.0,0.0 +2024-08-09 00:00:00+01:00,0.56,0.56,0.56,0.56,0.56,0,0.0,0.0,0.0 +2024-08-12 00:00:00+01:00,0.56,0.545099983215332,0.545099983215332,0.56,0.56,149031,0.0,0.0,0.0 +2024-08-13 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,12789,0.0,0.0,0.0 +2024-08-14 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,14657,0.0,0.0,0.0 +2024-08-15 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,8766,0.0,0.0,0.0 +2024-08-16 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 +2024-08-19 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,40292,0.0,0.0,0.0 +2024-08-20 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 +2024-08-21 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,5000,0.0,0.0,0.0 +2024-08-22 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,10356,0.0,0.0,0.0 +2024-08-23 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 +2024-08-27 00:00:00+01:00,0.555,0.5700000000000001,0.5700000000000001,0.555,0.555,2,0.0,0.0,0.0 +2024-08-28 00:00:00+01:00,0.555,0.5700000000000001,0.54,0.555,0.555,19432,0.0,0.0,0.0 +2024-08-29 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,18546,0.0,0.0,0.0 +2024-08-30 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 +2024-09-02 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,6478,0.0,0.0,0.0 +2024-09-03 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,9736,0.0,0.0,0.0 +2024-09-04 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,21232,0.0,0.0,0.0 +2024-09-05 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,42413,0.0,0.0,0.0 +2024-09-06 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,4797,0.0,0.0,0.0 +2024-09-09 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 +2024-09-10 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 +2024-09-11 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 +2024-09-12 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 +2024-09-13 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 +2024-09-16 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,1030,0.0,0.0,0.0 +2024-09-17 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,10000,0.0,0.0,0.0 +2024-09-18 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,9547,0.0,0.0,0.0 +2024-09-19 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,10580,0.0,0.0,0.0 +2024-09-20 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0 diff --git a/tests/data/SCR-TO-1d-bad-div-fixed.csv b/tests/data/SCR-TO-1d-bad-div-fixed.csv index 210b01d25..ca77be86c 100644 --- a/tests/data/SCR-TO-1d-bad-div-fixed.csv +++ b/tests/data/SCR-TO-1d-bad-div-fixed.csv @@ -1,663 +1,689 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired? -2022-01-04 00:00:00-05:00,3.8299999237060547,4.269999980926514,3.8299999237060547,4.0,3.979404256584549,559600,0.0,0.0,True -2022-01-05 00:00:00-05:00,4.050000190734863,4.199999809265137,4.039999961853027,4.050000190734863,4.029146783672725,467700,0.0,0.0,True -2022-01-06 00:00:00-05:00,4.150000095367432,4.179999828338623,4.050000190734863,4.090000152587891,4.068941056086924,393300,0.0,0.0,True -2022-01-07 00:00:00-05:00,4.130000114440918,4.230000019073486,4.070000171661377,4.159999847412109,4.13858051042915,860600,0.0,0.0,True -2022-01-10 00:00:00-05:00,4.199999809265137,4.199999809265137,4.119999885559082,4.199999809265137,4.178374364937252,281900,0.0,0.0,True -2022-01-11 00:00:00-05:00,4.199999809265137,4.460000038146973,4.159999847412109,4.420000076293945,4.397241818450103,695900,0.0,0.0,True -2022-01-12 00:00:00-05:00,4.449999809265137,4.639999866485596,4.449999809265137,4.579999923706055,4.556417654388606,452500,0.0,0.0,True -2022-01-13 00:00:00-05:00,4.590000152587891,4.639999866485596,4.420000076293945,4.46999979019165,4.44698392763218,373700,0.0,0.0,True -2022-01-14 00:00:00-05:00,4.480000019073486,4.590000152587891,4.429999828338623,4.559999942779541,4.5365207271345565,339600,0.0,0.0,True -2022-01-17 00:00:00-05:00,4.570000171661377,4.760000228881836,4.519999980926514,4.679999828338623,4.655902708564958,317900,0.0,0.0,True -2022-01-18 00:00:00-05:00,4.75,4.78000020980835,4.650000095367432,4.659999847412109,4.6360057813109075,317400,0.0,0.0,True -2022-01-19 00:00:00-05:00,4.71999979019165,4.730000019073486,4.489999771118164,4.519999980926514,4.496726454720356,642600,0.0,0.0,True -2022-01-20 00:00:00-05:00,4.400000095367432,4.639999866485596,4.320000171661377,4.539999961853027,4.516623799880505,780900,0.0,0.0,True -2022-01-21 00:00:00-05:00,4.460000038146973,4.579999923706055,4.369999885559082,4.420000076293945,4.397241818450103,426200,0.0,0.0,True -2022-01-24 00:00:00-05:00,4.309999942779541,4.400000095367432,4.070000171661377,4.380000114440918,4.357447963942002,391900,0.0,0.0,True -2022-01-25 00:00:00-05:00,4.400000095367432,4.639999866485596,4.309999942779541,4.579999923706055,4.556417654388606,461500,0.0,0.0,True -2022-01-26 00:00:00-05:00,4.699999809265137,4.75,4.550000190734863,4.570000171661377,4.54646939971463,229400,0.0,0.0,True -2022-01-27 00:00:00-05:00,4.590000152587891,4.699999809265137,4.429999828338623,4.480000019073486,4.456932600212255,1782700,0.0,0.0,True -2022-01-28 00:00:00-05:00,4.539999961853027,4.699999809265137,4.53000020980835,4.690000057220459,4.665851381145033,340300,0.0,0.0,True -2022-01-31 00:00:00-05:00,4.670000076293945,4.960000038146973,4.670000076293945,4.889999866485596,4.864821489497735,341600,0.0,0.0,True -2022-02-01 00:00:00-05:00,4.900000095367432,4.900000095367432,4.75,4.809999942779541,4.7852337804815335,245000,0.0,0.0,True -2022-02-02 00:00:00-05:00,4.789999961853027,4.880000114440918,4.659999847412109,4.75,4.725542580813283,335900,0.0,0.0,True -2022-02-03 00:00:00-05:00,4.71999979019165,4.800000190734863,4.579999923706055,4.730000019073486,4.705645235653133,567100,0.0,0.0,True -2022-02-04 00:00:00-05:00,4.760000228881836,4.980000019073486,4.760000228881836,4.880000114440918,4.854873234823759,728600,0.0,0.0,True -2022-02-07 00:00:00-05:00,4.800000190734863,4.849999904632568,4.639999866485596,4.670000076293945,4.645954453890982,509100,0.0,0.0,True -2022-02-08 00:00:00-05:00,4.650000095367432,4.650000095367432,4.360000133514404,4.460000038146973,4.437035672958205,520500,0.0,0.0,True -2022-02-09 00:00:00-05:00,4.46999979019165,4.619999885559082,4.449999809265137,4.550000190734863,4.52657247246058,225400,0.0,0.0,True -2022-02-10 00:00:00-05:00,4.519999980926514,4.610000133514404,4.449999809265137,4.510000228881836,4.4867782000463805,225300,0.0,0.0,True -2022-02-11 00:00:00-05:00,4.5,4.630000114440918,4.489999771118164,4.630000114440918,4.60616059938288,380600,0.0,0.0,True -2022-02-14 00:00:00-05:00,4.5,4.550000190734863,4.400000095367432,4.510000228881836,4.4867782000463805,507600,0.0,0.0,True -2022-02-15 00:00:00-05:00,4.420000076293945,4.71999979019165,4.420000076293945,4.690000057220459,4.665851381145033,342800,0.0,0.0,True -2022-02-16 00:00:00-05:00,4.699999809265137,4.800000190734863,4.539999961853027,4.579999923706055,4.556417654388606,508700,0.0,0.0,True -2022-02-17 00:00:00-05:00,4.599999904632568,4.659999847412109,4.519999980926514,4.570000171661377,4.54646939971463,309900,0.0,0.0,True -2022-02-18 00:00:00-05:00,4.510000228881836,4.559999942779541,4.380000114440918,4.420000076293945,4.397241818450103,191700,0.0,0.0,True -2022-02-22 00:00:00-05:00,4.460000038146973,4.599999904632568,4.429999828338623,4.53000020980835,4.506675545206529,1063700,0.0,0.0,True -2022-02-23 00:00:00-05:00,4.590000152587891,4.739999771118164,4.559999942779541,4.679999828338623,4.655902708564958,256600,0.0,0.0,True -2022-02-24 00:00:00-05:00,4.699999809265137,4.820000171661377,4.559999942779541,4.670000076293945,4.645954453890982,392200,0.0,0.0,True -2022-02-25 00:00:00-05:00,4.659999847412109,4.880000114440918,4.630000114440918,4.849999904632568,4.8250272170835355,264400,0.0,0.0,True -2022-02-28 00:00:00-05:00,4.880000114440918,5.099999904632568,4.869999885559082,5.079999923706055,5.05384376108256,543200,0.0,0.0,True -2022-03-01 00:00:00-05:00,5.159999847412109,5.28000020980835,5.03000020980835,5.179999828338623,5.153328397352814,481700,0.0,0.0,True -2022-03-02 00:00:00-05:00,5.239999771118164,5.309999942779541,5.079999923706055,5.179999828338623,5.153328397352814,232200,0.0,0.0,True -2022-03-03 00:00:00-05:00,5.21999979019165,5.230000019073486,4.949999809265137,4.96999979019165,4.944409616420036,234500,0.0,0.0,True -2022-03-04 00:00:00-05:00,5.03000020980835,5.389999866485596,5.03000020980835,5.360000133514404,5.332401996357564,1110400,0.0,0.0,True -2022-03-07 00:00:00-05:00,5.570000171661377,5.849999904632568,5.440000057220459,5.650000095367432,5.620908068400445,738400,0.0,0.0,True -2022-03-08 00:00:00-05:00,5.829999923706055,5.889999866485596,5.46999979019165,5.679999828338623,5.650754086140669,733300,0.0,0.0,True -2022-03-09 00:00:00-05:00,5.300000190734863,5.639999866485596,5.199999809265137,5.21999979019165,5.193121833954816,796500,0.0,0.0,True -2022-03-10 00:00:00-05:00,5.360000133514404,5.449999809265137,5.239999771118164,5.309999942779541,5.282658633457191,454800,0.0,0.0,True -2022-03-11 00:00:00-05:00,5.289999961853027,5.329999923706055,5.110000133514404,5.25,5.22296785169504,222100,0.0,0.0,True -2022-03-14 00:00:00-04:00,5.050000190734863,5.099999904632568,4.559999942779541,4.789999961853027,4.765336435321384,642400,0.0,0.0,True -2022-03-15 00:00:00-04:00,4.53000020980835,4.889999866485596,4.420000076293945,4.610000133514404,4.5862632542227315,594600,0.0,0.0,True -2022-03-16 00:00:00-04:00,4.579999923706055,4.829999923706055,4.579999923706055,4.679999828338623,4.655902708564958,583800,0.0,0.0,True -2022-03-17 00:00:00-04:00,4.789999961853027,4.960000038146973,4.739999771118164,4.800000190734863,4.775285525807557,654300,0.0,0.0,True -2022-03-18 00:00:00-04:00,4.71999979019165,4.920000076293945,4.710000038146973,4.71999979019165,4.695696980979157,284100,0.0,0.0,True -2022-03-21 00:00:00-04:00,4.829999923706055,5.010000228881836,4.820000171661377,4.980000019073486,4.954357871094012,344500,0.0,0.0,True -2022-03-22 00:00:00-04:00,4.96999979019165,4.96999979019165,4.820000171661377,4.940000057220459,4.914564016585911,374000,0.0,0.0,True -2022-03-23 00:00:00-04:00,5.010000228881836,5.099999904632568,4.940000057220459,4.940000057220459,4.914564016585911,535800,0.0,0.0,True -2022-03-24 00:00:00-04:00,5.0,5.0,4.840000152587891,4.900000095367432,4.874770579983908,385800,0.0,0.0,True -2022-03-25 00:00:00-04:00,4.849999904632568,5.21999979019165,4.840000152587891,5.179999828338623,5.153328397352814,821200,0.0,0.0,True -2022-03-28 00:00:00-04:00,4.900000095367432,5.110000133514404,4.900000095367432,5.070000171661377,5.043894670596387,338100,0.0,0.0,True -2022-03-29 00:00:00-04:00,4.940000057220459,5.230000019073486,4.809999942779541,5.210000038146973,5.183173997186938,628200,0.0,0.0,True -2022-03-30 00:00:00-04:00,5.269999980926514,5.400000095367432,5.269999980926514,5.369999885559082,5.342349833125442,448200,0.0,0.0,True -2022-03-31 00:00:00-04:00,5.300000190734863,5.409999847412109,5.260000228881836,5.309999942779541,5.282658633457191,308000,0.0,0.0,True -2022-04-01 00:00:00-04:00,5.210000038146973,5.400000095367432,5.210000038146973,5.28000020980835,5.252813451529165,279000,0.0,0.0,True -2022-04-04 00:00:00-04:00,5.349999904632568,5.429999828338623,5.260000228881836,5.300000190734863,5.2727107966893145,298100,0.0,0.0,True -2022-04-05 00:00:00-04:00,5.329999923706055,5.420000076293945,5.199999809265137,5.21999979019165,5.193121833954816,308800,0.0,0.0,True -2022-04-06 00:00:00-04:00,5.179999828338623,5.309999942779541,5.090000152587891,5.119999885559082,5.093637197684563,395100,0.0,0.0,True -2022-04-07 00:00:00-04:00,5.159999847412109,5.230000019073486,5.03000020980835,5.179999828338623,5.153328397352814,277200,0.0,0.0,True -2022-04-08 00:00:00-04:00,5.230000019073486,5.400000095367432,5.190000057220459,5.349999904632568,5.322453323777489,281000,0.0,0.0,True -2022-04-11 00:00:00-04:00,5.389999866485596,5.389999866485596,5.210000038146973,5.309999942779541,5.282658633457191,474300,0.0,0.0,True -2022-04-12 00:00:00-04:00,5.400000095367432,5.5,5.300000190734863,5.329999923706055,5.302556396523439,440400,0.0,0.0,True -2022-04-13 00:00:00-04:00,5.400000095367432,5.46999979019165,5.309999942779541,5.360000133514404,5.332401996357564,553200,0.0,0.0,True -2022-04-14 00:00:00-04:00,5.369999885559082,5.510000228881836,5.349999904632568,5.429999828338623,5.402041032793692,399900,0.0,0.0,True -2022-04-18 00:00:00-04:00,5.460000038146973,5.639999866485596,5.400000095367432,5.550000190734863,5.5214234321301925,412700,0.0,0.0,True -2022-04-19 00:00:00-04:00,5.489999771118164,5.489999771118164,5.21999979019165,5.329999923706055,5.302556396523439,375600,0.0,0.0,True -2022-04-20 00:00:00-04:00,5.329999923706055,5.400000095367432,5.25,5.28000020980835,5.252813451529165,245400,0.0,0.0,True -2022-04-21 00:00:00-04:00,5.289999961853027,5.389999866485596,5.0,5.059999942779541,5.033945998016313,441300,0.0,0.0,True -2022-04-22 00:00:00-04:00,5.059999942779541,5.059999942779541,4.820000171661377,4.829999923706055,4.805130707735584,444800,0.0,0.0,True -2022-04-25 00:00:00-04:00,4.610000133514404,4.800000190734863,4.5,4.739999771118164,4.715593908233208,598100,0.0,0.0,True -2022-04-26 00:00:00-04:00,4.78000020980835,4.929999828338623,4.730000019073486,4.820000171661377,4.795182035155509,362000,0.0,0.0,True -2022-04-27 00:00:00-04:00,4.820000171661377,4.909999847412109,4.710000038146973,4.880000114440918,4.854873234823759,306800,0.0,0.0,True -2022-04-28 00:00:00-04:00,4.920000076293945,4.989999771118164,4.800000190734863,4.949999809265137,4.924512271259887,337000,0.0,0.0,True -2022-04-29 00:00:00-04:00,4.960000038146973,5.0,4.769999980926514,4.820000171661377,4.795182035155509,312400,0.0,0.0,True -2022-05-02 00:00:00-04:00,4.710000038146973,4.829999923706055,4.630000114440918,4.730000019073486,4.705645235653133,438800,0.0,0.0,True -2022-05-03 00:00:00-04:00,4.710000038146973,5.03000020980835,4.710000038146973,4.96999979019165,4.944409616420036,675000,0.0,0.0,True -2022-05-04 00:00:00-04:00,5.0,5.079999923706055,4.900000095367432,5.050000190734863,5.023998579154534,1268500,0.0,0.0,True -2022-05-05 00:00:00-04:00,5.099999904632568,5.150000095367432,4.860000133514404,5.03000020980835,5.004100816088287,356000,0.0,0.0,True -2022-05-06 00:00:00-04:00,4.96999979019165,5.139999866485596,4.880000114440918,4.940000057220459,4.914564016585911,250600,0.0,0.0,True -2022-05-09 00:00:00-04:00,4.78000020980835,4.78000020980835,4.460000038146973,4.579999923706055,4.556417654388606,587200,0.0,0.0,True -2022-05-10 00:00:00-04:00,4.650000095367432,4.75,4.440000057220459,4.539999961853027,4.516623799880505,359400,0.0,0.0,True -2022-05-11 00:00:00-04:00,4.670000076293945,4.670000076293945,4.21999979019165,4.329999923706055,4.307705018947728,709200,0.0,0.0,True -2022-05-12 00:00:00-04:00,4.349999904632568,4.489999771118164,4.25,4.380000114440918,4.357447963942002,564300,0.0,0.0,True -2022-05-13 00:00:00-04:00,4.429999828338623,4.840000152587891,4.429999828338623,4.769999980926514,4.745439508067333,800600,0.0,0.0,True -2022-05-16 00:00:00-04:00,4.769999980926514,5.010000228881836,4.760000228881836,4.920000076293945,4.89466708933186,430900,0.0,0.0,True -2022-05-17 00:00:00-04:00,5.0,5.159999847412109,4.989999771118164,5.130000114440918,5.103585870264637,491800,0.0,0.0,True -2022-05-18 00:00:00-04:00,5.239999771118164,5.28000020980835,4.949999809265137,5.059999942779541,5.033945998016313,526000,0.0,0.0,True -2022-05-19 00:00:00-04:00,4.940000057220459,5.28000020980835,4.940000057220459,5.230000019073486,5.203071342347087,440200,0.0,0.0,True -2022-05-20 00:00:00-04:00,5.179999828338623,5.400000095367432,5.179999828338623,5.349999904632568,5.322453323777489,510600,0.0,0.0,True -2022-05-24 00:00:00-04:00,5.320000171661377,5.579999923706055,5.300000190734863,5.570000171661377,5.541320777290341,522100,0.0,0.0,True -2022-05-25 00:00:00-04:00,5.599999904632568,5.760000228881836,5.550000190734863,5.690000057220459,5.660702758720744,634300,0.0,0.0,True -2022-05-26 00:00:00-04:00,5.849999904632568,5.849999904632568,5.610000133514404,5.610000133514404,5.581114213892345,492900,0.0,0.0,True -2022-05-27 00:00:00-04:00,5.619999885559082,5.78000020980835,5.590000152587891,5.78000020980835,5.750239558223119,746000,0.0,0.0,True -2022-05-30 00:00:00-04:00,5.840000152587891,6.139999866485596,5.840000152587891,6.139999866485596,6.108385920420423,430100,0.0,0.0,True -2022-05-31 00:00:00-04:00,6.150000095367432,6.170000076293945,5.710000038146973,5.840000152587891,5.809930757891369,3694200,0.0,0.0,True -2022-06-01 00:00:00-04:00,5.96999979019165,6.099999904632568,5.849999904632568,5.949999809265137,5.9193632309295,478200,0.0,0.0,True -2022-06-02 00:00:00-04:00,5.949999809265137,6.070000171661377,5.860000133514404,6.0,5.9691065938298715,243400,0.0,0.0,True -2022-06-03 00:00:00-04:00,5.960000038146973,6.210000038146973,5.889999866485596,6.190000057220459,6.1581280296025005,758200,0.0,0.0,True -2022-06-06 00:00:00-04:00,6.300000190734863,6.369999885559082,6.039999961853027,6.369999885559082,6.337201210701153,489200,0.0,0.0,True -2022-06-07 00:00:00-04:00,6.369999885559082,6.679999828338623,6.269999980926514,6.570000171661377,6.536171736959953,647300,0.0,0.0,True -2022-06-08 00:00:00-04:00,6.699999809265137,6.71999979019165,6.380000114440918,6.460000038146973,6.42673759229743,727300,0.0,0.0,True -2022-06-09 00:00:00-04:00,6.46999979019165,6.46999979019165,6.28000020980835,6.28000020980835,6.247664829104876,508200,0.0,0.0,True -2022-06-10 00:00:00-04:00,6.28000020980835,6.309999942779541,6.050000190734863,6.170000076293945,6.13823110234845,448700,0.0,0.0,True -2022-06-13 00:00:00-04:00,6.0,6.079999923706055,5.710000038146973,6.070000171661377,6.038745630266,462500,0.0,0.0,True -2022-06-14 00:00:00-04:00,6.199999809265137,6.199999809265137,5.670000076293945,5.690000057220459,5.660702758720744,506000,0.0,0.0,True -2022-06-15 00:00:00-04:00,5.75,5.789999961853027,5.449999809265137,5.519999980926514,5.491577414389969,632600,0.0,0.0,True -2022-06-16 00:00:00-04:00,5.260000228881836,5.409999847412109,5.119999885559082,5.130000114440918,5.103585870264637,745300,0.0,0.0,True -2022-06-17 00:00:00-04:00,5.150000095367432,5.25,4.5,4.599999904632568,4.576314999548756,2540000,0.0,0.0,True -2022-06-20 00:00:00-04:00,4.579999923706055,4.739999771118164,4.519999980926514,4.690000057220459,4.665851381145033,273900,0.0,0.0,True -2022-06-21 00:00:00-04:00,4.800000190734863,4.949999809265137,4.710000038146973,4.769999980926514,4.745439508067333,592700,0.0,0.0,True -2022-06-22 00:00:00-04:00,4.449999809265137,4.460000038146973,4.300000190734863,4.309999942779541,4.287808091693677,809900,0.0,0.0,True -2022-06-23 00:00:00-04:00,4.329999923706055,4.389999866485596,3.740000009536743,3.8499999046325684,3.8301762574139233,1175400,0.0,0.0,True -2022-06-24 00:00:00-04:00,3.9100000858306885,4.170000076293945,3.880000114440918,3.9700000286102295,3.949558656750424,708700,0.0,0.0,True -2022-06-27 00:00:00-04:00,4.070000171661377,4.159999847412109,3.930000066757202,4.099999904632568,4.0788893107609,638900,0.0,0.0,True -2022-06-28 00:00:00-04:00,4.199999809265137,4.400000095367432,4.179999828338623,4.309999942779541,4.287808091693677,998100,0.0,0.0,True -2022-06-29 00:00:00-04:00,4.349999904632568,4.400000095367432,4.090000152587891,4.099999904632568,4.0788893107609,623400,0.0,0.0,True -2022-06-30 00:00:00-04:00,4.0,4.110000133514404,3.8499999046325684,3.9800000190734863,3.9595073293304988,788400,0.0,0.0,True -2022-07-04 00:00:00-04:00,4.019999980926514,4.050000190734863,3.890000104904175,4.03000020980835,4.009249856418673,501500,0.0,0.0,True -2022-07-05 00:00:00-04:00,3.9000000953674316,3.930000066757202,3.680000066757202,3.799999952316284,3.7804341482318455,1068800,0.0,0.0,True -2022-07-06 00:00:00-04:00,3.7100000381469727,3.8499999046325684,3.380000114440918,3.4700000286102295,3.452133176915617,785800,0.0,0.0,True -2022-07-07 00:00:00-04:00,3.640000104904175,3.7899999618530273,3.640000104904175,3.7300000190734863,3.7107942759835213,537500,0.0,0.0,True -2022-07-08 00:00:00-04:00,3.75,3.880000114440918,3.640000104904175,3.799999952316284,3.7804341482318455,380000,0.0,0.0,True -2022-07-11 00:00:00-04:00,3.740000009536743,3.890000104904175,3.640000104904175,3.8299999237060547,3.8102797480659705,1113200,0.0,0.0,True -2022-07-12 00:00:00-04:00,3.7100000381469727,3.7899999618530273,3.5199999809265137,3.5399999618530273,3.521772840210893,693900,0.0,0.0,True -2022-07-13 00:00:00-04:00,3.4600000381469727,3.5799999237060547,3.440000057220459,3.509999990463257,3.4919272403767683,537100,0.0,0.0,True -2022-07-14 00:00:00-04:00,3.380000114440918,3.4600000381469727,3.259999990463257,3.440000057220459,3.4222875770814922,877700,0.0,0.0,True -2022-07-15 00:00:00-04:00,3.5199999809265137,3.609999895095825,3.390000104904175,3.5299999713897705,3.5118241676308184,565800,0.0,0.0,True -2022-07-18 00:00:00-04:00,3.5799999237060547,3.809999942779541,3.559999942779541,3.7699999809265137,3.7505885483977206,1215000,0.0,0.0,True -2022-07-19 00:00:00-04:00,3.759999990463257,3.880000114440918,3.740000009536743,3.8499999046325684,3.8301762574139233,861800,0.0,0.0,True -2022-07-20 00:00:00-04:00,3.819999933242798,3.890000104904175,3.7300000190734863,3.859999895095825,3.8401253479000967,397100,0.0,0.0,True -2022-07-21 00:00:00-04:00,3.740000009536743,3.799999952316284,3.619999885559082,3.7100000381469727,3.690897766635569,481600,0.0,0.0,True -2022-07-22 00:00:00-04:00,3.740000009536743,3.7899999618530273,3.630000114440918,3.630000114440918,3.61130922180717,547400,0.0,0.0,True -2022-07-25 00:00:00-04:00,3.6500000953674316,3.890000104904175,3.609999895095825,3.8399999141693115,3.8202280027399476,617400,0.0,0.0,True -2022-07-26 00:00:00-04:00,3.9000000953674316,3.9800000190734863,3.7899999618530273,3.869999885559082,3.8500736025740725,538400,0.0,0.0,True -2022-07-27 00:00:00-04:00,3.8499999046325684,4.03000020980835,3.8499999046325684,4.0,3.979404256584549,607300,0.0,0.0,True -2022-07-28 00:00:00-04:00,4.059999942779541,4.190000057220459,4.019999980926514,4.090000152587891,4.068941056086924,850200,0.0,0.0,True -2022-07-29 00:00:00-04:00,4.190000057220459,4.369999885559082,4.130000114440918,4.289999961853027,4.267911164439627,953100,0.0,0.0,True -2022-08-02 00:00:00-04:00,4.239999771118164,4.239999771118164,4.03000020980835,4.059999942779541,4.039095038346701,471000,0.0,0.0,True -2022-08-03 00:00:00-04:00,4.090000152587891,4.110000133514404,3.8399999141693115,3.930000066757202,3.9097643843362238,677200,0.0,0.0,True -2022-08-04 00:00:00-04:00,3.859999895095825,3.880000114440918,3.680000066757202,3.680000066757202,3.661052166801444,809500,0.0,0.0,True -2022-08-05 00:00:00-04:00,3.609999895095825,3.890000104904175,3.609999895095825,3.8499999046325684,3.8301762574139233,380700,0.0,0.0,True -2022-08-08 00:00:00-04:00,3.799999952316284,3.940000057220459,3.759999990463257,3.940000057220459,3.919713056916298,429300,0.0,0.0,True -2022-08-09 00:00:00-04:00,4.0,4.03000020980835,3.950000047683716,4.010000228881836,3.9893529291646237,328300,0.0,0.0,True -2022-08-10 00:00:00-04:00,4.039999961853027,4.039999961853027,3.9000000953674316,3.990000009536743,3.9694555840044745,848100,0.0,0.0,True -2022-08-11 00:00:00-04:00,4.03000020980835,4.190000057220459,3.990000009536743,4.159999847412109,4.13858051042915,4875600,0.0,0.0,True -2022-08-12 00:00:00-04:00,4.150000095367432,4.420000076293945,4.110000133514404,4.300000190734863,4.277859837019701,1629200,0.0,0.0,True -2022-08-15 00:00:00-04:00,4.110000133514404,4.369999885559082,4.03000020980835,4.300000190734863,4.277859837019701,712300,0.0,0.0,True -2022-08-16 00:00:00-04:00,4.369999885559082,4.489999771118164,4.21999979019165,4.289999961853027,4.267911164439627,596600,0.0,0.0,True -2022-08-17 00:00:00-04:00,4.269999980926514,4.389999866485596,4.21999979019165,4.28000020980835,4.25796290976565,404100,0.0,0.0,True -2022-08-18 00:00:00-04:00,4.349999904632568,4.639999866485596,4.349999904632568,4.550000190734863,4.52657247246058,1213700,0.0,0.0,True -2022-08-19 00:00:00-04:00,4.610000133514404,4.650000095367432,4.489999771118164,4.5,4.476829945372404,348900,0.0,0.0,True -2022-08-22 00:00:00-04:00,4.460000038146973,4.519999980926514,4.349999904632568,4.510000228881836,4.4867782000463805,589300,0.0,0.0,True -2022-08-23 00:00:00-04:00,4.550000190734863,4.78000020980835,4.550000190734863,4.699999809265137,4.675799635819009,526400,0.0,0.0,True -2022-08-24 00:00:00-04:00,4.730000019073486,4.960000038146973,4.730000019073486,4.929999828338623,4.904615761911935,472800,0.0,0.0,True -2022-08-25 00:00:00-04:00,4.96999979019165,5.090000152587891,4.960000038146973,5.059999942779541,5.033945998016313,453900,0.0,0.0,True -2022-08-26 00:00:00-04:00,5.059999942779541,5.170000076293945,5.010000228881836,5.010000228881836,4.984203888834236,342300,0.0,0.0,True -2022-08-29 00:00:00-04:00,4.949999809265137,5.199999809265137,4.909999847412109,5.130000114440918,5.103585870264637,319600,0.0,0.0,True -2022-08-30 00:00:00-04:00,5.070000171661377,5.070000171661377,4.900000095367432,4.940000057220459,4.914564016585911,335300,0.0,0.0,True -2022-08-31 00:00:00-04:00,4.849999904632568,5.050000190734863,4.78000020980835,4.880000114440918,4.854873234823759,517200,0.0,0.0,True -2022-09-01 00:00:00-04:00,4.880000114440918,4.880000114440918,4.570000171661377,4.619999885559082,4.596211926802806,587000,0.0,0.0,True -2022-09-02 00:00:00-04:00,4.840000152587891,4.869999885559082,4.690000057220459,4.699999809265137,4.675799635819009,392000,0.0,0.0,True -2022-09-06 00:00:00-04:00,4.800000190734863,4.860000133514404,4.579999923706055,4.670000076293945,4.645954453890982,545600,0.0,0.0,True -2022-09-07 00:00:00-04:00,4.539999961853027,4.559999942779541,4.400000095367432,4.449999809265137,4.427087000378131,412100,0.0,0.0,True -2022-09-08 00:00:00-04:00,4.449999809265137,4.539999961853027,4.409999847412109,4.480000019073486,4.456932600212255,369200,0.0,0.0,True -2022-09-09 00:00:00-04:00,4.559999942779541,4.809999942779541,4.559999942779541,4.78000020980835,4.755388180647407,513000,0.0,0.0,True -2022-09-12 00:00:00-04:00,4.789999961853027,4.880000114440918,4.739999771118164,4.829999923706055,4.805130707735584,285000,0.0,0.0,True -2022-09-13 00:00:00-04:00,4.800000190734863,4.849999904632568,4.71999979019165,4.760000228881836,4.735491253393357,225800,0.0,0.0,True -2022-09-14 00:00:00-04:00,4.809999942779541,4.989999771118164,4.78000020980835,4.869999885559082,4.844924980149783,1021100,0.0,0.0,True -2022-09-15 00:00:00-04:00,4.809999942779541,4.920000076293945,4.739999771118164,4.75,4.725542580813283,352500,0.0,0.0,True -2022-09-16 00:00:00-04:00,4.730000019073486,4.730000019073486,4.550000190734863,4.659999847412109,4.6360057813109075,702500,0.0,0.0,True -2022-09-19 00:00:00-04:00,4.5,4.659999847412109,4.389999866485596,4.639999866485596,4.616108436150758,537500,0.0,0.0,True -2022-09-20 00:00:00-04:00,4.639999866485596,4.639999866485596,4.429999828338623,4.480000019073486,4.456932600212255,400900,0.0,0.0,True -2022-09-21 00:00:00-04:00,4.519999980926514,4.559999942779541,4.309999942779541,4.320000171661377,4.297756764273752,364600,0.0,0.0,True -2022-09-22 00:00:00-04:00,4.389999866485596,4.449999809265137,4.099999904632568,4.130000114440918,4.108734910595025,515800,0.0,0.0,True -2022-09-23 00:00:00-04:00,4.0,4.0,3.5999999046325684,3.700000047683716,3.680949094055494,960400,0.0,0.0,True -2022-09-26 00:00:00-04:00,3.690000057220459,3.75,3.3299999237060547,3.369999885559082,3.352648122739266,683500,0.0,0.0,True -2022-09-27 00:00:00-04:00,3.440000057220459,3.4700000286102295,3.3399999141693115,3.380000114440918,3.36259679531934,931200,0.0,0.0,True -2022-09-28 00:00:00-04:00,3.380000114440918,3.6600000858306885,3.359999895095825,3.6600000858306885,3.641154821641295,541000,0.0,0.0,True -2022-09-29 00:00:00-04:00,3.5899999141693115,3.75,3.4800000190734863,3.740000009536743,3.7207433664696943,640300,0.0,0.0,True -2022-09-30 00:00:00-04:00,3.6700000762939453,3.7699999809265137,3.569999933242798,3.700000047683716,3.680949094055494,536300,0.0,0.0,True -2022-10-03 00:00:00-04:00,3.809999942779541,3.940000057220459,3.7699999809265137,3.9000000953674316,3.879918784502099,656400,0.0,0.0,True -2022-10-04 00:00:00-04:00,3.9800000190734863,4.119999885559082,3.9800000190734863,4.010000228881836,3.9893529291646237,638800,0.0,0.0,True -2022-10-05 00:00:00-04:00,4.019999980926514,4.269999980926514,3.990000009536743,4.170000076293945,4.148529183009225,615400,0.0,0.0,True -2022-10-06 00:00:00-04:00,4.139999866485596,4.409999847412109,4.139999866485596,4.380000114440918,4.357447963942002,418100,0.0,0.0,True -2022-10-07 00:00:00-04:00,4.360000133514404,4.440000057220459,4.21999979019165,4.269999980926514,4.248013819279477,673400,0.0,0.0,True -2022-10-11 00:00:00-04:00,4.090000152587891,4.099999904632568,3.859999895095825,3.990000009536743,3.9694555840044745,307800,0.0,0.0,True -2022-10-12 00:00:00-04:00,3.940000057220459,3.9800000190734863,3.8399999141693115,3.9700000286102295,3.949558656750424,371100,0.0,0.0,True -2022-10-13 00:00:00-04:00,3.9100000858306885,4.099999904632568,3.9100000858306885,4.039999961853027,4.019198528998748,625900,0.0,0.0,True -2022-10-14 00:00:00-04:00,4.010000228881836,4.070000171661377,3.8299999237060547,3.8399999141693115,3.8202280027399476,353500,0.0,0.0,True -2022-10-17 00:00:00-04:00,3.890000104904175,3.9700000286102295,3.8499999046325684,3.9000000953674316,3.879918784502099,546400,0.0,0.0,True -2022-10-18 00:00:00-04:00,3.930000066757202,3.990000009536743,3.8299999237060547,3.9100000858306885,3.889867874988272,377200,0.0,0.0,True -2022-10-19 00:00:00-04:00,3.9000000953674316,4.0,3.869999885559082,3.990000009536743,3.9694555840044745,379500,0.0,0.0,True -2022-10-20 00:00:00-04:00,4.0,4.130000114440918,3.9600000381469727,3.990000009536743,3.9694555840044745,453900,0.0,0.0,True -2022-10-21 00:00:00-04:00,3.9800000190734863,4.039999961853027,3.930000066757202,3.950000047683716,3.9296621474024716,396900,0.0,0.0,True -2022-10-24 00:00:00-04:00,3.9800000190734863,4.059999942779541,3.9100000858306885,4.0,3.979404256584549,496100,0.0,0.0,True -2022-10-25 00:00:00-04:00,3.9700000286102295,4.079999923706055,3.9700000286102295,4.059999942779541,4.039095038346701,532500,0.0,0.0,True -2022-10-26 00:00:00-04:00,4.050000190734863,4.230000019073486,4.050000190734863,4.210000038146973,4.188323037517326,877200,0.0,0.0,True -2022-10-27 00:00:00-04:00,4.21999979019165,4.300000190734863,4.130000114440918,4.170000076293945,4.148529183009225,474000,0.0,0.0,True -2022-10-28 00:00:00-04:00,4.119999885559082,4.199999809265137,4.03000020980835,4.070000171661377,4.049044128832874,363900,0.0,0.0,True -2022-10-31 00:00:00-04:00,4.010000228881836,4.230000019073486,4.010000228881836,4.110000133514404,4.088837983340975,628500,0.0,0.0,True -2022-11-01 00:00:00-04:00,4.230000019073486,4.25,4.139999866485596,4.179999828338623,4.158477019777103,319700,0.0,0.0,True -2022-11-02 00:00:00-04:00,4.170000076293945,4.340000152587891,4.110000133514404,4.21999979019165,4.198271292191302,481300,0.0,0.0,True -2022-11-03 00:00:00-04:00,4.190000057220459,4.340000152587891,4.179999828338623,4.289999961853027,4.267911164439627,279400,0.0,0.0,True -2022-11-04 00:00:00-04:00,4.360000133514404,4.590000152587891,4.340000152587891,4.550000190734863,4.52657247246058,741800,0.0,0.0,True -2022-11-07 00:00:00-05:00,4.559999942779541,4.599999904632568,4.5,4.579999923706055,4.556417654388606,366700,0.0,0.0,True -2022-11-08 00:00:00-05:00,4.590000152587891,4.599999904632568,4.460000038146973,4.510000228881836,4.4867782000463805,426500,0.0,0.0,True -2022-11-09 00:00:00-05:00,4.099999904632568,4.130000114440918,3.509999990463257,3.5799999237060547,3.561566694718994,3261400,0.0,0.0,True -2022-11-10 00:00:00-05:00,3.609999895095825,3.609999895095825,3.240000009536743,3.5199999809265137,3.501875495050744,2489900,0.0,0.0,True -2022-11-11 00:00:00-05:00,3.549999952316284,3.549999952316284,3.369999885559082,3.4800000190734863,3.462081640542642,1531900,0.0,0.0,True -2022-11-14 00:00:00-05:00,3.4100000858306885,3.619999885559082,3.380000114440918,3.5199999809265137,3.501875495050744,1636500,0.0,0.0,True -2022-11-15 00:00:00-05:00,3.549999952316284,3.6500000953674316,3.509999990463257,3.619999885559082,3.6013609671331936,613200,0.0,0.0,True -2022-11-16 00:00:00-05:00,3.619999885559082,3.619999885559082,3.440000057220459,3.450000047683716,3.432236458614616,1095300,0.0,0.0,True -2022-11-17 00:00:00-05:00,3.4200000762939453,3.4200000762939453,3.2200000286102295,3.359999895095825,3.34269965911224,1058500,0.0,0.0,True -2022-11-18 00:00:00-05:00,3.299999952316284,3.3499999046325684,3.2300000190734863,3.3299999237060547,3.312854059278115,1005000,0.0,0.0,True -2022-11-21 00:00:00-05:00,3.2899999618530273,3.3299999237060547,3.130000114440918,3.2899999618530273,3.273059995816965,1701700,0.0,0.0,True -2022-11-22 00:00:00-05:00,3.309999942779541,3.450000047683716,3.309999942779541,3.4100000858306885,3.392442395153465,668000,0.0,0.0,True -2022-11-23 00:00:00-05:00,3.3399999141693115,3.559999942779541,3.299999952316284,3.4600000381469727,3.442184713288592,626600,0.0,0.0,True -2022-11-24 00:00:00-05:00,3.450000047683716,3.4600000381469727,3.380000114440918,3.430000066757202,3.412339113454467,213400,0.0,0.0,True -2022-11-25 00:00:00-05:00,3.450000047683716,3.4800000190734863,3.369999885559082,3.380000114440918,3.36259679531934,404100,0.0,0.0,True -2022-11-28 00:00:00-05:00,3.309999942779541,3.359999895095825,3.259999990463257,3.299999952316284,3.28300845944399,472800,0.0,0.0,True -2022-11-29 00:00:00-05:00,3.3299999237060547,3.4800000190734863,3.309999942779541,3.4000000953674316,3.382493513620342,782700,0.0,0.0,True -2022-11-30 00:00:00-05:00,3.5899999141693115,3.5899999141693115,3.4000000953674316,3.4000000953674316,3.382493513620342,1510300,0.0,0.0,True -2022-12-01 00:00:00-05:00,3.4600000381469727,3.549999952316284,3.359999895095825,3.369999885559082,3.352648122739266,570700,0.0,0.0,True -2022-12-02 00:00:00-05:00,3.369999885559082,3.450000047683716,3.3299999237060547,3.3499999046325684,3.332750777579117,319600,0.0,0.0,True -2022-12-05 00:00:00-05:00,3.4000000953674316,3.4700000286102295,3.259999990463257,3.299999952316284,3.28300845944399,1100300,0.0,0.0,True -2022-12-06 00:00:00-05:00,3.2899999618530273,3.3499999046325684,3.0799999237060547,3.0799999237060547,3.064141214884188,1268000,0.0,0.0,True -2022-12-07 00:00:00-05:00,3.0799999237060547,3.180000066757202,2.9800000190734863,3.0899999141693115,3.074089678511213,769100,0.0,0.0,True -2022-12-08 00:00:00-05:00,3.190000057220459,3.190000057220459,2.990000009536743,3.0299999713897705,3.0143988967490616,618300,0.0,0.0,True -2022-12-09 00:00:00-05:00,3.0199999809265137,3.059999942779541,2.950000047683716,3.059999942779541,3.044244078677088,779100,0.0,0.0,True -2022-12-12 00:00:00-05:00,3.059999942779541,3.0899999141693115,2.950000047683716,2.9700000286102295,2.954707697080811,1015200,0.0,0.0,True -2022-12-13 00:00:00-05:00,3.059999942779541,3.130000114440918,2.930000066757202,3.049999952316284,3.0342956150500626,1585100,0.0,0.0,True -2022-12-14 00:00:00-05:00,3.0799999237060547,3.119999885559082,3.009999990463257,3.0999999046325684,3.084038142138238,508600,0.0,0.0,True -2022-12-15 00:00:00-05:00,3.049999952316284,3.0799999237060547,2.9700000286102295,3.0799999237060547,3.064141214884188,802900,0.0,0.0,True -2022-12-16 00:00:00-05:00,3.009999990463257,3.0299999713897705,2.890000104904175,2.940000057220459,2.9248623061997345,1059300,0.0,0.0,True -2022-12-19 00:00:00-05:00,2.9100000858306885,2.930000066757202,2.680000066757202,2.740000009536743,2.7258919888939834,2117800,0.0,0.0,True -2022-12-20 00:00:00-05:00,2.740000009536743,2.859999895095825,2.7100000381469727,2.8299999237060547,2.815428579443309,988400,0.0,0.0,True -2022-12-21 00:00:00-05:00,2.8399999141693115,3.059999942779541,2.8399999141693115,3.0299999713897705,3.0143988967490616,796100,0.0,0.0,True -2022-12-22 00:00:00-05:00,3.049999952316284,3.059999942779541,2.890000104904175,2.9200000762939453,2.9049653789456844,1115000,0.0,0.0,True -2022-12-23 00:00:00-05:00,2.9700000286102295,3.190000057220459,2.950000047683716,3.1700000762939453,3.153677805433514,1357700,0.0,0.0,True -2022-12-28 00:00:00-05:00,3.109999895095825,3.119999885559082,2.9200000762939453,2.940000057220459,2.9248623061997345,1075500,0.0,0.0,True -2022-12-29 00:00:00-05:00,2.9200000762939453,3.009999990463257,2.9200000762939453,2.990000009536743,2.9746046243348614,471300,0.0,0.0,True -2022-12-30 00:00:00-05:00,2.9600000381469727,3.0299999713897705,2.9600000381469727,3.0,2.9845532969149358,573100,0.0,0.0,True -2023-01-03 00:00:00-05:00,2.9700000286102295,3.0,2.75,2.7699999809265137,2.7557375887281084,766300,0.0,0.0,True -2023-01-04 00:00:00-05:00,2.7699999809265137,2.7799999713897705,2.680000066757202,2.7100000381469727,2.696046598012907,735100,0.0,0.0,True -2023-01-05 00:00:00-05:00,2.690000057220459,2.7699999809265137,2.680000066757202,2.7100000381469727,2.696046598012907,716200,0.0,0.0,True -2023-01-06 00:00:00-05:00,2.7300000190734863,2.809999942779541,2.7300000190734863,2.799999952316284,2.7855829796091838,333100,0.0,0.0,True -2023-01-09 00:00:00-05:00,2.859999895095825,2.950000047683716,2.819999933242798,2.880000114440918,2.8651711065314847,491400,0.0,0.0,True -2023-01-10 00:00:00-05:00,2.859999895095825,2.9100000858306885,2.809999942779541,2.890000104904175,2.8751197791115595,418900,0.0,0.0,True -2023-01-11 00:00:00-05:00,2.890000104904175,2.990000009536743,2.890000104904175,2.9200000762939453,2.9049653789456844,808900,0.0,0.0,True -2023-01-12 00:00:00-05:00,2.9700000286102295,3.0799999237060547,2.950000047683716,3.0399999618530273,3.0243473603760864,900700,0.0,0.0,True -2023-01-13 00:00:00-05:00,3.0399999618530273,3.0999999046325684,2.9800000190734863,3.0299999713897705,3.0143988967490616,625000,0.0,0.0,True -2023-01-16 00:00:00-05:00,3.0299999713897705,3.0399999618530273,3.0,3.009999990463257,2.9945017605419615,360400,0.0,0.0,True -2023-01-17 00:00:00-05:00,3.059999942779541,3.1700000762939453,3.0,3.1500000953674316,3.133780878179463,699200,0.0,0.0,True -2023-01-18 00:00:00-05:00,3.190000057220459,3.2899999618530273,3.130000114440918,3.130000114440918,3.1138837419723635,707500,0.0,0.0,True -2023-01-19 00:00:00-05:00,3.130000114440918,3.140000104904175,3.0299999713897705,3.119999885559082,3.103935278345338,291600,0.0,0.0,True -2023-01-20 00:00:00-05:00,3.109999895095825,3.1600000858306885,3.0799999237060547,3.119999885559082,3.103935278345338,191000,0.0,0.0,True -2023-01-23 00:00:00-05:00,3.140000104904175,3.180000066757202,3.069999933242798,3.109999895095825,3.093986814718313,329700,0.0,0.0,True -2023-01-24 00:00:00-05:00,3.059999942779541,3.0899999141693115,3.009999990463257,3.0199999809265137,3.0044500152159377,496300,0.0,0.0,True -2023-01-25 00:00:00-05:00,3.009999990463257,3.049999952316284,2.930000066757202,3.049999952316284,3.0342956150500626,415700,0.0,0.0,True -2023-01-26 00:00:00-05:00,3.049999952316284,3.0899999141693115,2.990000009536743,3.0399999618530273,3.0243473603760864,243700,0.0,0.0,True -2023-01-27 00:00:00-05:00,3.0299999713897705,3.069999933242798,2.9600000381469727,3.0199999809265137,3.0044500152159377,432400,0.0,0.0,True -2023-01-30 00:00:00-05:00,2.9800000190734863,2.9800000190734863,2.869999885559082,2.890000104904175,2.8751197791115595,427200,0.0,0.0,True -2023-01-31 00:00:00-05:00,2.859999895095825,2.9600000381469727,2.809999942779541,2.9600000381469727,2.9447590245007365,428900,0.0,0.0,True -2023-02-01 00:00:00-05:00,2.9600000381469727,3.0,2.880000114440918,2.940000057220459,2.9248623061997345,800000,0.0,0.0,True -2023-02-02 00:00:00-05:00,2.9700000286102295,2.9700000286102295,2.8499999046325684,2.880000114440918,2.8651711065314847,552400,0.0,0.0,True -2023-02-03 00:00:00-05:00,2.869999885559082,2.950000047683716,2.859999895095825,2.9000000953674316,2.8850680337855357,468600,0.0,0.0,True -2023-02-06 00:00:00-05:00,2.9000000953674316,2.9200000762939453,2.8299999237060547,2.8399999141693115,2.8253770430703344,214400,0.0,0.0,True -2023-02-07 00:00:00-05:00,2.869999885559082,3.0899999141693115,2.859999895095825,3.0899999141693115,3.074089678511213,736000,0.0,0.0,True -2023-02-08 00:00:00-05:00,3.0999999046325684,3.109999895095825,3.0,3.0,2.9845532969149358,293100,0.0,0.0,True -2023-02-09 00:00:00-05:00,2.9700000286102295,3.0399999618530273,2.9700000286102295,3.0199999809265137,3.0044500152159377,290000,0.0,0.0,True -2023-02-10 00:00:00-05:00,3.009999990463257,3.0899999141693115,3.009999990463257,3.049999952316284,3.0342956150500626,319600,0.0,0.0,True -2023-02-13 00:00:00-05:00,3.0899999141693115,3.0899999141693115,2.9700000286102295,3.0,2.9845532969149358,494600,0.0,0.0,True -2023-02-14 00:00:00-05:00,2.990000009536743,3.009999990463257,2.950000047683716,3.0,2.9845532969149358,354700,0.0,0.0,True -2023-02-15 00:00:00-05:00,2.9700000286102295,2.990000009536743,2.890000104904175,2.9700000286102295,2.954707697080811,301400,0.0,0.0,True -2023-02-16 00:00:00-05:00,2.940000057220459,3.0,2.9200000762939453,2.990000009536743,2.9746046243348614,212600,0.0,0.0,True -2023-02-17 00:00:00-05:00,2.930000066757202,2.930000066757202,2.759999990463257,2.7799999713897705,2.7656860523551337,823900,0.0,0.0,True -2023-02-21 00:00:00-05:00,2.809999942779541,2.8299999237060547,2.7100000381469727,2.7200000286102295,2.705994852686883,352700,0.0,0.0,True -2023-02-22 00:00:00-05:00,2.740000009536743,2.740000009536743,2.640000104904175,2.6600000858306885,2.646303861971682,343700,0.0,0.0,True -2023-02-23 00:00:00-05:00,2.700000047683716,2.7799999713897705,2.6600000858306885,2.75,2.7358406614740574,292200,0.0,0.0,True -2023-02-24 00:00:00-05:00,2.700000047683716,2.799999952316284,2.700000047683716,2.7799999713897705,2.7656860523551337,322100,0.0,0.0,True -2023-02-27 00:00:00-05:00,2.809999942779541,2.9100000858306885,2.75,2.880000114440918,2.8651711065314847,268200,0.0,0.0,True -2023-02-28 00:00:00-05:00,2.880000114440918,2.9200000762939453,2.819999933242798,2.8499999046325684,2.8353255066973597,917800,0.0,0.0,True -2023-03-01 00:00:00-05:00,2.859999895095825,2.9800000190734863,2.859999895095825,2.9800000190734863,2.9646563696608856,327600,0.0,0.0,True -2023-03-02 00:00:00-05:00,3.0,3.0299999713897705,2.9200000762939453,2.9600000381469727,2.9447590245007365,287600,0.0,0.0,True -2023-03-03 00:00:00-05:00,2.9100000858306885,3.0799999237060547,2.9100000858306885,3.049999952316284,3.0342956150500626,289700,0.0,0.0,True -2023-03-06 00:00:00-05:00,3.059999942779541,3.059999942779541,2.9700000286102295,3.009999990463257,2.9945017605419615,232100,0.0,0.0,True -2023-03-07 00:00:00-05:00,2.9800000190734863,3.0,2.880000114440918,2.9100000858306885,2.8950167063656096,279700,0.0,0.0,True -2023-03-08 00:00:00-05:00,2.9700000286102295,3.059999942779541,2.9000000953674316,2.9600000381469727,2.9447590245007365,455000,0.0,0.0,True -2023-03-09 00:00:00-05:00,3.0,3.180000066757202,2.990000009536743,3.009999990463257,2.9945017605419615,336300,0.0,0.0,True -2023-03-10 00:00:00-05:00,3.009999990463257,3.059999942779541,2.9100000858306885,2.950000047683716,2.9348107698267607,350400,0.0,0.0,True -2023-03-13 00:00:00-04:00,2.8299999237060547,2.9100000858306885,2.75,2.9000000953674316,2.8850680337855357,435800,0.0,0.0,True -2023-03-14 00:00:00-04:00,2.869999885559082,2.950000047683716,2.8399999141693115,2.880000114440918,2.8695347040886388,231900,0.00441,0.0,True -2023-03-15 00:00:00-04:00,2.75,2.759999990463257,2.4700000286102295,2.630000114440918,2.6204429810925967,1133800,0.0,0.0,True -2023-03-16 00:00:00-04:00,2.6500000953674316,2.7300000190734863,2.569999933242798,2.7200000286102295,2.7101159516858417,420000,0.0,0.0,True -2023-03-17 00:00:00-04:00,2.680000066757202,2.75,2.619999885559082,2.630000114440918,2.6204429810925967,403800,0.0,0.0,True -2023-03-20 00:00:00-04:00,2.640000104904175,2.7300000190734863,2.619999885559082,2.7200000286102295,2.7101159516858417,251300,0.0,0.0,True -2023-03-21 00:00:00-04:00,2.7699999809265137,2.8399999141693115,2.7200000286102295,2.7699999809265137,2.759934367264093,311100,0.0,0.0,True -2023-03-22 00:00:00-04:00,2.759999990463257,2.7899999618530273,2.6600000858306885,2.6600000858306885,2.650334030439547,162000,0.0,0.0,True -2023-03-23 00:00:00-04:00,2.690000057220459,2.740000009536743,2.5899999141693115,2.640000104904175,2.630406664208247,190900,0.0,0.0,True -2023-03-24 00:00:00-04:00,2.569999933242798,2.6600000858306885,2.569999933242798,2.630000114440918,2.6204429810925967,301600,0.0,0.0,True -2023-03-27 00:00:00-04:00,2.630000114440918,2.7200000286102295,2.559999942779541,2.7100000381469727,2.7001524460177984,226600,0.0,0.0,True -2023-03-28 00:00:00-04:00,2.740000009536743,2.740000009536743,2.6600000858306885,2.7100000381469727,2.7001524460177984,161900,0.0,0.0,True -2023-03-29 00:00:00-04:00,2.700000047683716,2.759999990463257,2.690000057220459,2.7200000286102295,2.7101159516858417,202700,0.0,0.0,True -2023-03-30 00:00:00-04:00,2.700000047683716,2.75,2.6700000762939453,2.7200000286102295,2.7101159516858417,118100,0.0,0.0,True -2023-03-31 00:00:00-04:00,2.75,2.799999952316284,2.75,2.7699999809265137,2.759934367264093,201700,0.0,0.0,True -2023-04-03 00:00:00-04:00,2.7699999809265137,2.9600000381469727,2.7699999809265137,2.9600000381469727,2.949243814118627,876600,0.0,0.0,True -2023-04-04 00:00:00-04:00,2.990000009536743,2.990000009536743,2.880000114440918,2.9200000762939453,2.909389436551239,151100,0.0,0.0,True -2023-04-05 00:00:00-04:00,2.940000057220459,2.940000057220459,2.819999933242798,2.8399999141693115,2.8296799716260375,90400,0.0,0.0,True -2023-04-06 00:00:00-04:00,2.880000114440918,2.880000114440918,2.7799999713897705,2.799999952316284,2.7898252391634366,123900,0.0,0.0,True -2023-04-10 00:00:00-04:00,2.7899999618530273,2.9000000953674316,2.7799999713897705,2.7899999618530273,2.7798615560477864,205200,0.0,0.0,True -2023-04-11 00:00:00-04:00,2.7699999809265137,2.8299999237060547,2.7699999809265137,2.809999942779541,2.7997890997266937,345000,0.0,0.0,True -2023-04-12 00:00:00-04:00,2.8299999237060547,2.8499999046325684,2.799999952316284,2.809999942779541,2.7997890997266937,210200,0.0,0.0,True -2023-04-13 00:00:00-04:00,2.7899999618530273,2.809999942779541,2.7699999809265137,2.7899999618530273,2.7798615560477864,234700,0.0,0.0,True -2023-04-14 00:00:00-04:00,2.799999952316284,2.8299999237060547,2.740000009536743,2.75,2.7400071784803988,545200,0.0,0.0,True -2023-04-17 00:00:00-04:00,2.7899999618530273,2.7899999618530273,2.7200000286102295,2.75,2.7400071784803988,171800,0.0,0.0,True -2023-04-18 00:00:00-04:00,2.75,2.75,2.680000066757202,2.7100000381469727,2.7001524460177984,194200,0.0,0.0,True -2023-04-19 00:00:00-04:00,2.7100000381469727,2.7100000381469727,2.619999885559082,2.6600000858306885,2.650334030439547,269500,0.0,0.0,True -2023-04-20 00:00:00-04:00,2.640000104904175,2.640000104904175,2.569999933242798,2.619999885559082,2.610479297976946,833900,0.0,0.0,True -2023-04-21 00:00:00-04:00,2.619999885559082,2.6500000953674316,2.5999999046325684,2.6500000953674316,2.6403705247715035,174500,0.0,0.0,True -2023-04-24 00:00:00-04:00,2.609999895095825,2.6600000858306885,2.569999933242798,2.6600000858306885,2.650334030439547,255300,0.0,0.0,True -2023-04-25 00:00:00-04:00,2.619999885559082,2.6500000953674316,2.569999933242798,2.5899999141693115,2.5805882486299954,406500,0.0,0.0,True -2023-04-26 00:00:00-04:00,2.569999933242798,2.619999885559082,2.4800000190734863,2.4800000190734863,2.4709882667006626,293400,0.0,0.0,True -2023-04-27 00:00:00-04:00,2.5,2.5299999713897705,2.450000047683716,2.4800000190734863,2.4709882667006626,251700,0.0,0.0,True -2023-04-28 00:00:00-04:00,2.5299999713897705,2.549999952316284,2.4800000190734863,2.5,2.4909154554843567,405600,0.0,0.0,True -2023-05-01 00:00:00-04:00,2.4800000190734863,2.5799999237060547,2.4800000190734863,2.5399999618530273,2.5307701879469575,138100,0.0,0.0,True -2023-05-02 00:00:00-04:00,2.549999952316284,2.549999952316284,2.299999952316284,2.3299999237060547,2.3215331974135163,846200,0.0,0.0,True -2023-05-03 00:00:00-04:00,2.309999942779541,2.3499999046325684,2.240000009536743,2.3499999046325684,2.34146038619721,555600,0.0,0.0,True -2023-05-04 00:00:00-04:00,2.3499999046325684,2.380000114440918,2.299999952316284,2.3499999046325684,2.34146038619721,359300,0.0,0.0,True -2023-05-05 00:00:00-04:00,2.4200000762939453,2.5199999809265137,2.4100000858306885,2.5199999809265137,2.5108426442680503,321700,0.0,0.0,True -2023-05-08 00:00:00-04:00,2.509999990463257,2.549999952316284,2.5,2.5199999809265137,2.5108426442680503,226500,0.0,0.0,True -2023-05-09 00:00:00-04:00,2.549999952316284,2.549999952316284,2.490000009536743,2.5299999713897705,2.5208063273837005,120400,0.0,0.0,True -2023-05-10 00:00:00-04:00,2.549999952316284,2.549999952316284,2.430000066757202,2.4800000190734863,2.4709882667006626,236300,0.0,0.0,True -2023-05-11 00:00:00-04:00,2.4800000190734863,2.5199999809265137,2.380000114440918,2.380000114440918,2.3713516129917673,433000,0.0,0.0,True -2023-05-12 00:00:00-04:00,2.430000066757202,2.549999952316284,2.4000000953674316,2.5299999713897705,2.5208063273837005,510700,0.0,0.0,True -2023-05-15 00:00:00-04:00,2.5399999618530273,2.630000114440918,2.5299999713897705,2.619999885559082,2.610479297976946,230800,0.0,0.0,True -2023-05-16 00:00:00-04:00,2.619999885559082,2.6700000762939453,2.440000057220459,2.4800000190734863,2.4709882667006626,579000,0.0,0.0,True -2023-05-17 00:00:00-04:00,2.4800000190734863,2.509999990463257,2.4100000858306885,2.4800000190734863,2.4709882667006626,196000,0.0,0.0,True -2023-05-18 00:00:00-04:00,2.4600000381469727,2.5399999618530273,2.440000057220459,2.5,2.4909154554843567,233000,0.0,0.0,True -2023-05-19 00:00:00-04:00,2.559999942779541,2.5799999237060547,2.4700000286102295,2.509999990463257,2.500879138600007,229000,0.0,0.0,True -2023-05-23 00:00:00-04:00,2.4600000381469727,2.609999895095825,2.4600000381469727,2.559999942779541,2.5506973767306516,240100,0.0,0.0,True -2023-05-24 00:00:00-04:00,2.5199999809265137,2.559999942779541,2.440000057220459,2.4700000286102295,2.461024406137406,199100,0.0,0.0,True -2023-05-25 00:00:00-04:00,2.5,2.5,2.380000114440918,2.4000000953674316,2.3912789792230678,287100,0.0,0.0,True -2023-05-26 00:00:00-04:00,2.4000000953674316,2.4700000286102295,2.369999885559082,2.4000000953674316,2.3912789792230678,150100,0.0,0.0,True -2023-05-29 00:00:00-04:00,2.4000000953674316,2.4800000190734863,2.4000000953674316,2.4600000381469727,2.451060723021756,58700,0.0,0.0,True -2023-05-30 00:00:00-04:00,2.440000057220459,2.440000057220459,2.3299999237060547,2.390000104904175,2.3813151186598107,281300,0.0,0.0,True -2023-05-31 00:00:00-04:00,2.3299999237060547,2.4600000381469727,2.259999990463257,2.430000066757202,2.4211698511224116,708800,0.0,0.0,True -2023-06-01 00:00:00-04:00,2.390000104904175,2.4100000858306885,2.299999952316284,2.3299999237060547,2.3215331974135163,490100,0.0,0.0,True -2023-06-02 00:00:00-04:00,2.3299999237060547,2.549999952316284,2.3299999237060547,2.549999952316284,2.5407335161673945,1196900,0.0,0.0,True -2023-06-05 00:00:00-04:00,2.509999990463257,2.619999885559082,2.509999990463257,2.549999952316284,2.5407335161673945,317400,0.0,0.0,True -2023-06-06 00:00:00-04:00,2.569999933242798,2.5799999237060547,2.4700000286102295,2.490000009536743,2.4809515949211,401600,0.0,0.0,True -2023-06-07 00:00:00-04:00,2.4800000190734863,2.5199999809265137,2.4600000381469727,2.4800000190734863,2.4709882667006626,176900,0.0,0.0,True -2023-06-08 00:00:00-04:00,2.5,2.609999895095825,2.430000066757202,2.559999942779541,2.5506973767306516,510900,0.0,0.0,True -2023-06-09 00:00:00-04:00,2.549999952316284,2.5999999046325684,2.5,2.5,2.4909154554843567,178700,0.0,0.0,True -2023-06-12 00:00:00-04:00,2.4800000190734863,2.4800000190734863,2.390000104904175,2.4100000858306885,2.401242484891111,205900,0.0,0.0,True -2023-06-13 00:00:00-04:00,2.4200000762939453,2.5299999713897705,2.4200000762939453,2.4200000762939453,2.411206168006762,201500,0.0,0.0,True -2023-06-14 00:00:00-04:00,2.4200000762939453,2.440000057220459,2.3499999046325684,2.3499999046325684,2.3457350319797525,158000,0.00441,0.0,True -2023-06-15 00:00:00-04:00,2.430000066757202,2.430000066757202,2.3399999141693115,2.4100000858306885,2.4056263080628395,92600,0.0,0.0,True -2023-06-16 00:00:00-04:00,2.4100000858306885,2.440000057220459,2.3399999141693115,2.369999885559082,2.3656987906741147,407100,0.0,0.0,True -2023-06-19 00:00:00-04:00,2.4100000858306885,2.4100000858306885,2.359999895095825,2.369999885559082,2.3656987906741147,69900,0.0,0.0,True -2023-06-20 00:00:00-04:00,2.359999895095825,2.359999895095825,2.2699999809265137,2.2899999618530273,2.285844046648658,404500,0.0,0.0,True -2023-06-21 00:00:00-04:00,2.2899999618530273,2.2899999618530273,2.2100000381469727,2.259999990463257,2.2558984812951124,234900,0.0,0.0,True -2023-06-22 00:00:00-04:00,2.25,2.259999990463257,2.119999885559082,2.130000114440918,2.1261345585977436,366900,0.0,0.0,True -2023-06-23 00:00:00-04:00,2.1600000858306885,2.200000047683716,2.0799999237060547,2.0999999046325684,2.0961887024922055,175300,0.0,0.0,True -2023-06-26 00:00:00-04:00,2.1600000858306885,2.1600000858306885,2.0899999141693115,2.0999999046325684,2.0961887024922055,109500,0.0,0.0,True -2023-06-27 00:00:00-04:00,2.0899999141693115,2.130000114440918,2.059999942779541,2.059999942779541,2.0562613304794772,165900,0.0,0.0,True -2023-06-28 00:00:00-04:00,2.0999999046325684,2.1600000858306885,2.0199999809265137,2.1600000858306885,2.156080123951289,287900,0.0,0.0,True -2023-06-29 00:00:00-04:00,2.1600000858306885,2.2100000381469727,2.1500000953674316,2.2100000381469727,2.205989302623201,113900,0.0,0.0,True -2023-06-30 00:00:00-04:00,2.190000057220459,2.299999952316284,2.180000066757202,2.2699999809265137,2.265880287954295,354000,0.0,0.0,True -2023-07-04 00:00:00-04:00,2.319999933242798,2.380000114440918,2.25,2.359999895095825,2.3557168386389353,245600,0.0,0.0,True -2023-07-05 00:00:00-04:00,2.4600000381469727,2.4600000381469727,2.2100000381469727,2.2100000381469727,2.205989302623201,475400,0.0,0.0,True -2023-07-06 00:00:00-04:00,2.2100000381469727,2.2699999809265137,2.190000057220459,2.200000047683716,2.1960074959640177,240200,0.0,0.0,True -2023-07-07 00:00:00-04:00,2.2100000381469727,2.380000114440918,2.2100000381469727,2.3299999237060547,2.3257714186613865,222100,0.0,0.0,True -2023-07-10 00:00:00-04:00,2.309999942779541,2.3499999046325684,2.299999952316284,2.299999952316284,2.295825853307841,55700,0.0,0.0,True -2023-07-11 00:00:00-04:00,2.2899999618530273,2.4000000953674316,2.2799999713897705,2.4000000953674316,2.3956445014036567,238500,0.0,0.0,True -2023-07-12 00:00:00-04:00,2.450000047683716,2.4600000381469727,2.390000104904175,2.440000057220459,2.4355718734163854,241500,0.0,0.0,True -2023-07-13 00:00:00-04:00,2.4000000953674316,2.4600000381469727,2.390000104904175,2.440000057220459,2.4355718734163854,153200,0.0,0.0,True -2023-07-14 00:00:00-04:00,2.3499999046325684,2.390000104904175,2.2799999713897705,2.2899999618530273,2.285844046648658,232100,0.0,0.0,True -2023-07-17 00:00:00-04:00,2.2899999618530273,2.309999942779541,2.2200000286102295,2.240000009536743,2.2359348679767463,144600,0.0,0.0,True -2023-07-18 00:00:00-04:00,2.2300000190734863,2.3499999046325684,2.2300000190734863,2.309999942779541,2.305807659967024,146700,0.0,0.0,True -2023-07-19 00:00:00-04:00,2.299999952316284,2.4700000286102295,2.299999952316284,2.450000047683716,2.445553680075568,443000,0.0,0.0,True -2023-07-20 00:00:00-04:00,2.4800000190734863,2.569999933242798,2.4800000190734863,2.5199999809265137,2.5154266174418423,270100,0.0,0.0,True -2023-07-21 00:00:00-04:00,2.5799999237060547,2.5799999237060547,2.450000047683716,2.4800000190734863,2.47549939080511,222600,0.0,0.0,True -2023-07-24 00:00:00-04:00,2.5,2.5299999713897705,2.4700000286102295,2.490000009536743,2.485481197464293,197200,0.0,0.0,True -2023-07-25 00:00:00-04:00,2.4700000286102295,2.490000009536743,2.4100000858306885,2.440000057220459,2.4355718734163854,188700,0.0,0.0,True -2023-07-26 00:00:00-04:00,2.4000000953674316,2.450000047683716,2.4000000953674316,2.450000047683716,2.445553680075568,128100,0.0,0.0,True -2023-07-27 00:00:00-04:00,2.4800000190734863,2.4800000190734863,2.4000000953674316,2.4100000858306885,2.4056263080628395,381600,0.0,0.0,True -2023-07-28 00:00:00-04:00,2.450000047683716,2.549999952316284,2.380000114440918,2.5299999713897705,2.5254084241010255,424500,0.0,0.0,True -2023-07-31 00:00:00-04:00,2.5,2.7300000190734863,2.5,2.7200000286102295,2.7150636228814813,516500,0.0,0.0,True -2023-08-01 00:00:00-04:00,2.740000009536743,2.759999990463257,2.3399999141693115,2.450000047683716,2.445553680075568,3980500,0.0,0.0,True -2023-08-02 00:00:00-04:00,2.4600000381469727,2.4600000381469727,2.3299999237060547,2.369999885559082,2.3656987906741147,2111700,0.0,0.0,True -2023-08-03 00:00:00-04:00,2.359999895095825,2.450000047683716,2.359999895095825,2.440000057220459,2.4355718734163854,814300,0.0,0.0,True -2023-08-04 00:00:00-04:00,2.4700000286102295,2.5399999618530273,2.4200000762939453,2.5399999618530273,2.5353902307602083,1363900,0.0,0.0,True -2023-08-08 00:00:00-04:00,2.509999990463257,2.549999952316284,2.4700000286102295,2.5299999713897705,2.5254084241010255,776900,0.0,0.0,True -2023-08-09 00:00:00-04:00,2.549999952316284,2.559999942779541,2.5,2.5199999809265137,2.5154266174418423,932100,0.0,0.0,True -2023-08-10 00:00:00-04:00,2.5199999809265137,2.5299999713897705,2.4700000286102295,2.490000009536743,2.485481197464293,389700,0.0,0.0,True -2023-08-11 00:00:00-04:00,2.4800000190734863,2.509999990463257,2.4800000190734863,2.509999990463257,2.505444810782659,280800,0.0,0.0,True -2023-08-14 00:00:00-04:00,2.509999990463257,2.509999990463257,2.4000000953674316,2.430000066757202,2.425590066757202,361600,0.0,0.0,True -2023-08-15 00:00:00-04:00,2.4200000762939453,2.440000057220459,2.2699999809265137,2.319999933242798,2.315789466626207,1139100,0.0,0.0,True -2023-08-16 00:00:00-04:00,2.2899999618530273,2.359999895095825,2.2300000190734863,2.259999990463257,2.2558984812951124,474700,0.0,0.0,True -2023-08-17 00:00:00-04:00,2.259999990463257,2.309999942779541,2.25,2.309999942779541,2.305807659967024,1188900,0.0,0.0,True -2023-08-18 00:00:00-04:00,2.2699999809265137,2.390000104904175,2.240000009536743,2.359999895095825,2.3557168386389353,554900,0.0,0.0,True -2023-08-21 00:00:00-04:00,2.380000114440918,2.4000000953674316,2.3299999237060547,2.3299999237060547,2.3257714186613865,211200,0.0,0.0,True -2023-08-22 00:00:00-04:00,2.3499999046325684,2.369999885559082,2.25,2.2699999809265137,2.265880287954295,336200,0.0,0.0,True -2023-08-23 00:00:00-04:00,2.240000009536743,2.259999990463257,2.200000047683716,2.2200000286102295,2.2159711092823837,368100,0.0,0.0,True -2023-08-24 00:00:00-04:00,2.2100000381469727,2.2100000381469727,2.130000114440918,2.1600000858306885,2.156080123951289,270700,0.0,0.0,True -2023-08-25 00:00:00-04:00,2.1600000858306885,2.190000057220459,2.0,2.109999895095825,2.106170654527385,706100,0.0,0.0,True -2023-08-28 00:00:00-04:00,2.1500000953674316,2.190000057220459,2.130000114440918,2.1500000953674316,2.1460981719161096,655500,0.0,0.0,True -2023-08-29 00:00:00-04:00,2.1600000858306885,2.200000047683716,2.1500000953674316,2.190000057220459,2.1860255439288383,245600,0.0,0.0,True -2023-08-30 00:00:00-04:00,2.180000066757202,2.2200000286102295,2.1700000762939453,2.180000066757202,2.176043737269655,211200,0.0,0.0,True -2023-08-31 00:00:00-04:00,2.190000057220459,2.299999952316284,2.190000057220459,2.2799999713897705,2.2758620946134784,1133800,0.0,0.0,True -2023-09-01 00:00:00-04:00,2.299999952316284,2.369999885559082,2.299999952316284,2.359999895095825,2.3557168386389353,761300,0.0,0.0,True -2023-09-05 00:00:00-04:00,2.4200000762939453,2.4200000762939453,2.2699999809265137,2.390000104904175,2.3856626947444735,1434000,0.0,0.0,True -2023-09-06 00:00:00-04:00,2.380000114440918,2.4000000953674316,2.3299999237060547,2.4000000953674316,2.3956445014036567,352700,0.0,0.0,True -2023-09-07 00:00:00-04:00,2.359999895095825,2.390000104904175,2.319999933242798,2.3499999046325684,2.3457350319797525,501700,0.0,0.0,True -2023-09-08 00:00:00-04:00,2.3499999046325684,2.359999895095825,2.3299999237060547,2.359999895095825,2.3557168386389353,405100,0.0,0.0,True -2023-09-11 00:00:00-04:00,2.390000104904175,2.4200000762939453,2.3299999237060547,2.3499999046325684,2.3457350319797525,740800,0.0,0.0,True -2023-09-12 00:00:00-04:00,2.3499999046325684,2.440000057220459,2.3499999046325684,2.430000066757202,2.425590066757202,696100,0.0,0.0,True -2023-09-13 00:00:00-04:00,2.440000057220459,2.4600000381469727,2.4000000953674316,2.430000066757202,2.425590066757202,328600,0.0,0.0,True -2023-09-14 00:00:00-04:00,2.450000047683716,2.5199999809265137,2.430000066757202,2.5199999809265137,2.5199999809265137,553500,0.00441,0.0,False -2023-09-15 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00:00:00-04:00,26.56999969482422,28.06999969482422,26.56999969482422,27.989999771118164,27.989999771118164,35600,0.0,0.0,False +2024-09-25 00:00:00-04:00,27.8700008392334,27.8700008392334,27.0,27.489999771118164,27.489999771118164,30200,0.0,0.0,False +2024-09-26 00:00:00-04:00,27.299999237060547,27.479999542236328,26.559999465942383,26.610000610351562,26.610000610351562,51800,0.0,0.0,False +2024-09-27 00:00:00-04:00,26.989999771118164,26.989999771118164,26.25,26.579999923706055,26.579999923706055,20600,0.0,0.0,False diff --git a/tests/data/SCR-TO-1d-bad-div.csv b/tests/data/SCR-TO-1d-bad-div.csv index 0daff1558..211d1fcce 100644 --- a/tests/data/SCR-TO-1d-bad-div.csv +++ b/tests/data/SCR-TO-1d-bad-div.csv @@ -1,663 +1,689 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits -2022-01-04 00:00:00-05:00,3.8299999237060547,4.269999980926514,3.8299999237060547,4.0,2.270280122756958,559600,0.0,0.0 -2022-01-05 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00:00:00+01:00,0.72,0.72,0.712979965209961,0.7180000305175781,0.46800003051757816,634981,0.0,0.0 +2024-09-16 00:00:00+01:00,0.7219999694824218,0.7219999694824218,0.7140000152587891,0.7169999694824218,0.46734817504882814,1095043,0.0,0.0 +2024-09-17 00:00:00+01:00,0.7180000305175781,0.7219999694824218,0.7138200378417969,0.7190000152587891,0.46865180969238285,667418,0.0,0.0 +2024-09-18 00:00:00+01:00,0.7219999694824218,0.7219999694824218,0.7140000152587891,0.7180000305175781,0.46800003051757816,1771156,0.0,0.0 +2024-09-19 00:00:00+01:00,0.4640000152587891,0.4909999847412109,0.4520000076293945,0.4640000152587891,0.4640000152587891,794932,0.25,0.0 +2024-09-20 00:00:00+01:00,0.4458200073242188,0.480999984741211,0.4458200073242188,0.46950000762939453,0.46950000762939453,328898,0.0,0.0 diff --git a/tests/test_price_repair.py b/tests/test_price_repair.py index 514230a1a..33ed2a6f0 100644 --- a/tests/test_price_repair.py +++ b/tests/test_price_repair.py @@ -562,37 +562,6 @@ def test_repair_bad_stock_splits(self): print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)]) raise - # pass - def test_repair_missing_div_adjust(self): - tkr = '8TRA.DE' - - dat = yf.Ticker(tkr, session=self.session) - tz_exchange = dat.fast_info["timezone"] - hist = dat._lazy_load_price_history() - - df_bad = _pd.read_csv(os.path.join(self.dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust.csv"), index_col="Date") - df_bad.index = _pd.to_datetime(df_bad.index) - - repaired_df = hist._fix_missing_div_adjust(df_bad, "1d", tz_exchange) - - correct_df = _pd.read_csv(os.path.join(self.dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust-fixed.csv"), index_col="Date") - correct_df.index = _pd.to_datetime(correct_df.index) - - repaired_df = repaired_df.sort_index() - correct_df = correct_df.sort_index() - for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]: - try: - self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all()) - except Exception: - print(f"tkr={tkr} COLUMN={c}") - print("- repaired_df") - print(repaired_df) - print("- correct_df[c]:") - print(correct_df[c]) - print("- diff:") - print(repaired_df[c] - correct_df[c]) - raise - def test_repair_bad_div_adjusts(self): interval = '1d' bad_tkrs = [] @@ -606,11 +575,11 @@ def test_repair_bad_div_adjusts(self): # These tickers were exceptionally bad bad_tkrs += ['LSC.L'] bad_tkrs += ['TEM.L'] - bad_tkrs += ['CLC.L'] # Other special sits bad_tkrs += ['KME.MI'] # 2023 dividend paid to savings share, not common/preferred bad_tkrs += ['REL.L'] # 100x div also missing adjust + bad_tkrs.append('4063.T') # Div with same-day split not split adjusted # Adj too small bad_tkrs += ['ADIG.L'] @@ -623,8 +592,14 @@ def test_repair_bad_div_adjusts(self): bad_tkrs += ['ELCO.L'] bad_tkrs += ['KWS.L'] bad_tkrs += ['PSH.L'] + + # Div 100x and adjust too big bad_tkrs += ['SCR.TO'] + # Div 0.01x + bad_tkrs += ['NVT.L'] + bad_tkrs += ['TENT.L'] + # Missing div adjusts: bad_tkrs += ['1398.HK'] bad_tkrs += ['3988.HK'] @@ -652,12 +627,13 @@ def test_repair_bad_div_adjusts(self): hist = dat._lazy_load_price_history() hist.history(period='1mo') # init metadata for currency currency = hist._history_metadata['currency'] + tz = hist._history_metadata['exchangeTimezoneName'] fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-no-bad-divs.csv") if not os.path.isfile(fp): continue df = _pd.read_csv(fp, index_col='Datetime') - df.index = _pd.to_datetime(df.index, utc=True) + df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz) repaired_df = hist._fix_bad_div_adjust(df, interval, currency) @@ -683,15 +659,16 @@ def test_repair_bad_div_adjusts(self): hist = dat._lazy_load_price_history() hist.history(period='1mo') # init metadata for currency currency = hist._history_metadata['currency'] + tz = hist._history_metadata['exchangeTimezoneName'] fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-bad-div.csv") if not os.path.isfile(fp): continue df_bad = _pd.read_csv(fp, index_col='Datetime') - df_bad.index = _pd.to_datetime(df_bad.index, utc=True) + df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz) fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-bad-div-fixed.csv") correct_df = _pd.read_csv(fp, index_col='Datetime') - correct_df.index = _pd.to_datetime(correct_df.index, utc=True) + correct_df.index = _pd.to_datetime(correct_df.index, utc=True).tz_convert(tz) repaired_df = hist._fix_bad_div_adjust(df_bad, interval, currency) diff --git a/yfinance/scrapers/history.py b/yfinance/scrapers/history.py index 00f454f38..832f7c723 100644 --- a/yfinance/scrapers/history.py +++ b/yfinance/scrapers/history.py @@ -1297,14 +1297,6 @@ def _fix_bad_div_adjust(self, df, interval, currency): # Split df2 into: nan data, and non-nan data f_nan = df2['Close'].isna().to_numpy() - f_no_change_inter = df2['Close'].iloc[1:].to_numpy() == df2['Close'].iloc[:-1].to_numpy() - f_no_change_inter = np.append([False], f_no_change_inter) - f_no_change_intra = (df2['High'] == df2['Low']).to_numpy() - f_no_change = f_no_change_intra & f_no_change_inter - f_nan = f_nan | f_no_change - # Sometimes a dividend occurs on day with zero volume, and sometimes they are bad dividends that need correction! - # So don't discard volume=0 - f_nan = f_nan & (df2['Dividends']==0.0).to_numpy() df2_nan = df2[f_nan].copy() df2 = df2[~f_nan].copy() @@ -1325,8 +1317,11 @@ def _fix_bad_div_adjust(self, df, interval, currency): if div_idx == 0: continue prices_before = df2.iloc[div_idx-1] - if prices_before['Close'] < prices_before['Low']: - div = df2['Dividends'].iloc[div_idx] + diff = prices_before['Low'] - prices_before['Close'] + div = df2['Dividends'].iloc[div_idx] + if diff > 0 and (diff/div-1)<0.01: + # Close dividend then something else caused problem. dt_before = df2.index[div_idx-1] new_close = prices_before['Close'] + div if new_close >= prices_before['Low'] and new_close <= prices_before['High']: @@ -1353,8 +1348,6 @@ def _fix_bad_div_adjust(self, df, interval, currency): div_pct = div / df2['Close'].iloc[div_idx-1] # Check if dividend is 100x market movement. - div_too_big = False - div_too_small = False div_too_small_improvement_threshold = 1 # div_too_big_improvement_threshold = 1 div_too_big_improvement_threshold = 2 @@ -1375,6 +1368,7 @@ def _fix_bad_div_adjust(self, df, interval, currency): drops = df2['Close'].iloc[div_idx-1:div_idx+1].to_numpy() - df2['Low'].iloc[div_idx:div_idx+2].to_numpy() drop_2Dmax = np.max(drops) else: + drops = np.array([drop]) drop_2Dmax = drop if (len(df2)-div_idx) < 4: @@ -1390,42 +1384,93 @@ def _fix_bad_div_adjust(self, df, interval, currency): diffs = df2['Close'].iloc[start:end-1].to_numpy() - df2['Low'].iloc[start+1:end].to_numpy() typical_volatility = np.median(np.abs(diffs)) - if drop == 0.0 and df2['Volume'].iloc[div_idx]==0: + possibilities = [] + if (drops==0.0).all() and df2['Volume'].iloc[div_idx]==0: # Can't analyse price action so use crude heuristics - # if div_pct*100 < 0.1: - if div_pct*100 < 0.08: # lowered for $AG and their tiny divs - div_too_small = True # Could be a 0.01x error - elif div_pct > 1.0: - div_too_big = True # Could be a 100x error - if not (div_too_small or div_too_big): + pct_zero_vol = np.sum(df2['Volume']==0.0)/len(df2) + if div_pct*100 < 0.1: + # Could be a 0.01x error + possibilities.append({'state':'div-too-small', 'diff':0.0}) + # elif div_pct > 1.0: + # Update: lower threshold for illiquid stocks, because why paying mega dividends? + elif (pct_zero_vol > 0.75 and div_pct > 0.25) or (div_pct > 1.0): + # Could be a 100x error + possibilities.append({'state':'div-too-big', 'diff':0.0}) + else: + split = df2['Stock Splits'].loc[dt] + if split == 0.0: + div_postSplit = None + else: + # Maybe Yahoo has not applied coincident split to dividend + div_postSplit = div / split + + if div_postSplit > div: + # Use volatility-adjusted drop + _drop = drop - typical_volatility + else: + _drop = drop_2Dmax + if _drop > 0: + diff = abs(div-_drop) + diff_postSplit = abs(div_postSplit-_drop) + if (diff_postSplit * div_too_big_improvement_threshold) <= diff: + possibilities.append({'state':'div-pre-split', 'diff':diff_postSplit}) + # Check for div-too-big if div_pct > too_big_check_threshold: if drop_2Dmax <= 0.0: - div_too_big = True + possibilities.append({'state':'div-too-big', 'diff':0.0}) else: diff = abs(div-drop_2Dmax) - diff2 = abs((div/currency_divide)-drop_2Dmax) - if (diff2 * div_too_big_improvement_threshold) <= diff: - # div exceeds market move AND 0.01x div fits drop better - div_too_big = True + diff_fx = abs((div/currency_divide)-drop_2Dmax) + if div_postSplit is None: + if (diff_fx * div_too_big_improvement_threshold) <= diff: + possibilities.append({'state':'div-too-big', 'diff':diff_fx}) + else: + diff_fxPostSplit = abs((div_postSplit/currency_divide)-drop_2Dmax) + if diff_fx < diff_fxPostSplit: + if (diff_fx * div_too_big_improvement_threshold) <= diff: + possibilities.append({'state':'div-too-big', 'diff':diff_fx}) + else: + if (diff_fxPostSplit * div_too_big_improvement_threshold) <= diff: + possibilities.append({'state':'div-too-big-and-pre-split', 'diff':diff_fxPostSplit}) # Check for div-too-small - can be tricked by normal price volatility - if not div_too_big and not np.isnan(typical_volatility): - drop_wo_vol = drop_2Dmax - typical_volatility + if not np.isnan(typical_volatility): + # drop_wo_vol = drop_2Dmax - typical_volatility + # Update: only use same-day change for too-small, to reduce false-positives + drop_wo_vol = drop - typical_volatility if drop_wo_vol > 0: diff = abs(div-drop_wo_vol) - diff2 = abs((div*currency_divide)-drop_wo_vol) - if (diff2 * div_too_small_improvement_threshold) <= diff: - # market move exceeds div AND 100x div fits drop better - div_too_small = True + diff_fx = abs((div*currency_divide)-drop_wo_vol) + if div_postSplit is None: + if (diff_fx * div_too_small_improvement_threshold) <= diff: + possibilities.append({'state':'div-too-small', 'diff':diff_fx}) + else: + diff_fxPostSplit = abs((div_postSplit*currency_divide)-drop_wo_vol) + if diff_fx < diff_fxPostSplit: + if (diff_fx * div_too_big_improvement_threshold) <= diff: + possibilities.append({'state':'div-too-small', 'diff':diff_fx}) + else: + if (diff_fxPostSplit * div_too_big_improvement_threshold) <= diff: + possibilities.append({'state':'div-too-small-and-pre-split', 'diff':diff_fxPostSplit}) div_status = {'date': dt, 'idx':div_idx, 'div': div, '%': div_pct} div_status['drop'] = drop div_status['drop_2Dmax'] = drop_2Dmax div_status['volume'] = df2['Volume'].iloc[div_idx] div_status['vol'] = typical_volatility - div_status['div_too_big'] = div_too_big - div_status['div_too_small'] = div_too_small + + div_status['div_too_big'] = False + div_status['div_too_small'] = False + div_status['div_pre_split'] = False + div_status['div_too_big_and_pre_split'] = False + div_status['div_too_small_and_pre_split'] = False + if len(possibilities) > 0: + # Something is wrong with dividend - pick the best correction + possibilities = sorted(possibilities, key=lambda k: k['diff']) + p = possibilities[0] + div_status[p['state'].replace('-', '_')] = True + row = pd.DataFrame([div_status]).set_index('date') if div_status_df is None: div_status_df = row @@ -1434,9 +1479,10 @@ def _fix_bad_div_adjust(self, df, interval, currency): if div_status_df is None and not df_modified: return df + checks = [c for c in div_status_df.columns if c.startswith('div_')] div_status_df = div_status_df.sort_index() - def cluster_dividends(df, column='div', threshold=10): + def cluster_dividends(df, column='div', threshold=7): n = len(df) sorted_df = df.sort_values(column) clusters = [] @@ -1465,6 +1511,8 @@ def cluster_dividends(df, column='div', threshold=10): return cluster_labels # Check if the present div-adjustment is too big/small, or missing + # - too-big determined from Adj Close movement vs Close + # - too-small compares Adj Close vs dividends for i in range(len(div_status_df)): div_idx = div_status_df['idx'].iloc[i] dt = div_status_df.index[i] @@ -1480,28 +1528,6 @@ def cluster_dividends(df, column='div', threshold=10): post_adj = df2['Adj Close'].iloc[div_idx] / df2['Close'].iloc[div_idx] div_missing_from_adjclose = post_adj == pre_adj - # Adj Close should drop by LESS than Close on ex-div, at least for big dividends. - # Update: Yahoo might be reporting dividend slightly early, meaning - # Mr Market's price drop happens tomorrow e.g. UNTC in december 2023 - lookahead_date = dt+_datetime.timedelta(days=20) - lookahead_idx = bisect.bisect_left(df2.index, lookahead_date) - lookahead_idx = min(lookahead_idx, len(df2)-1) - # In rare cases, the price dropped 1 day before dividend (DVD.OL @ 2024-05-15) - lookback_idx = div_idx-2 if div_idx > 1 else div_idx-1 - div_adj_exceeds_prices = False - if lookahead_idx > lookback_idx: - close_deltas = np.diff(df2['Close'].iloc[lookback_idx:lookahead_idx+1].to_numpy()) - adjClose_deltas = np.diff(df2['Adj Close'].iloc[lookback_idx:lookahead_idx+1].to_numpy()) - close_chgs_pct = close_deltas / df2['Close'].iloc[lookback_idx:lookahead_idx].to_numpy() - adjClose_chgs_pct = adjClose_deltas / df2['Adj Close'].iloc[lookback_idx:lookahead_idx].to_numpy() - # Check if adjustment is too much. Need a big dividend % to be sure. - if div_pct > 0.15: - adjClose_chg_pct = np.max(adjClose_chgs_pct) - close_chg_pct = np.max(close_chgs_pct) - if adjClose_chg_pct > 0.1 and adjClose_chg_pct > 1.0001*close_chg_pct: - # Bigger drop - div_adj_exceeds_prices = True - # Check if adjustment too small present_adj = pre_adj / post_adj implied_div_yield = 1.0 - present_adj @@ -1510,39 +1536,33 @@ def cluster_dividends(df, column='div', threshold=10): # ... and use same method for adjustment too big: div_adj_exceeds_div = implied_div_yield > (10*div_pct) - # Have done 4 different checks that can interact, so handle very carefully. # Can prune the space: if div_missing_from_adjclose: div_adj_is_too_small = False # redundant information - if div_adj_exceeds_prices and div_adj_is_too_small: - # Contradiction. Assume former tricked by low-liquidity price action - div_adj_exceeds_prices = False - div_status = {'present adj': present_adj} div_status['adj_missing'] = div_missing_from_adjclose - div_status['adj_exceeds_prices'] = div_adj_exceeds_prices div_status['adj_exceeds_div'] = div_adj_exceeds_div div_status['div_exceeds_adj'] = div_adj_is_too_small for k,v in div_status.items(): if k not in div_status_df: - if isinstance(v, (bool, np.bool)): + if isinstance(v, (bool, np.bool_)): div_status_df[k] = False elif isinstance(v, int): div_status_df[k] = 0 elif isinstance(v, float): div_status_df[k] = 0.0 + # elif k == 'div_true_date': + # div_status_df[k] = pd.Series(dtype='datetime64[ns, UTC]') else: raise Exception(k,v,type(v)) div_status_df.loc[dt, k] = v - - checks = ['adj_missing', 'adj_exceeds_div', 'adj_exceeds_prices', 'div_exceeds_adj', 'div_too_big', 'div_too_small', 'fx_mixup'] - checks = [c for c in checks if c in div_status_df.columns] + checks += ['adj_missing', 'adj_exceeds_div', 'div_exceeds_adj'] div_status_df['phantom'] = False phantom_proximity_threshold = _datetime.timedelta(days=7) - f = div_status_df['div_too_big'] | div_status_df['div_exceeds_adj'] + f = div_status_df[['div_too_big', 'div_exceeds_adj']].any(axis=1) if f.any(): # One/some of these may be phantom dividends. Clue is if another correct dividend is very close indices = np.where(f)[0] @@ -1617,13 +1637,146 @@ def cluster_dividends(df, column='div', threshold=10): div_status_df.loc[phantom_div_dt, c] = False checks.append('phantom') + if not div_status_df[checks].any().any(): + # Perfect + return df + + # Remove phantoms early + if 'phantom' in div_status_df.columns: + f_phantom = div_status_df['phantom'] + # ... but only if no other problems + f_phantom = f_phantom & (~div_status_df[[c for c in checks if c != 'phantom']].any(axis=1)) + if f_phantom.any(): + div_dts = div_status_df.index[f_phantom] + msg = f'Removing phantom div(s): {[str(dt.date()) for dt in div_dts]}' + logger.info(msg, extra=log_extras) + phantom_div_dts = div_status_df.index[f_phantom] + for dt in phantom_div_dts: + enddt = dt-_datetime.timedelta(seconds=1) + df2.loc[ :enddt, 'Adj Close'] /= div_status_df['present adj'].loc[dt] + df2.loc[ :enddt, 'Repaired?'] = True + df2_nan.loc[:enddt, 'Adj Close'] /= div_status_df['present adj'].loc[dt] + df2_nan.loc[:enddt, 'Repaired?'] = True + df2.loc[dt, 'Dividends'] = 0 + df_modified = True + div_status_df = div_status_df.drop(dt) + div_status_df.loc[f_phantom, 'phantom'] = False + div_status_df = div_status_df.drop('phantom', axis=1) + if 'phantom' in checks: + checks.remove('phantom') + + # Check if the present div-adjustment contradicts price action + for i in range(len(div_status_df)): + div_idx = div_status_df['idx'].iloc[i] + dt = div_status_df.index[i] + div = div_status_df['div'].iloc[i] + if div_idx == 0: + continue + div_pct = div / df2['Close'].iloc[div_idx-1] + + # Adj Close should drop by LESS than Close on ex-div, at least for big dividends. + # Update: Yahoo might be reporting dividend slightly early, meaning + # Mr Market's price drop happens tomorrow e.g. UNTC in december 2023. + # Or worse, Yahoo is 1 month early e.g. GWI.L ex-div was mid-April not mid-March + lookahead_date = dt+_datetime.timedelta(days=35) + lookahead_idx = bisect.bisect_left(df2.index, lookahead_date) + lookahead_idx = min(lookahead_idx, len(df2)-1) + # In rare cases, the price dropped 1 day before dividend (DVD.OL @ 2024-05-15) + lookback_idx = div_idx-2 if div_idx > 1 else div_idx-1 + # Check for bad stock splits in the lookahead period - + # if present, reduce lookahead to before. + future_changes = df2['Close'].iloc[div_idx:lookahead_idx+1].pct_change() + f_big_change = (future_changes > 2).to_numpy() | (future_changes < -0.9).to_numpy() + if f_big_change.any(): + lookahead_idx = div_idx + np.where(f_big_change)[0][0]-1 + lookahead_date = df2.index[lookahead_idx] + + div_adj_exceeds_prices = False + div_date_wrong = False + div_true_date = pd.NaT + if lookahead_idx > lookback_idx: + x = df2.iloc[lookback_idx:lookahead_idx+1].copy() + x['Adj'] = x['Adj Close'] / x['Close'] + x['Adj Low'] = x['Adj'] * x['Low'] + deltas = x['Low'].iloc[1:].to_numpy() - x['Close'].iloc[:-1].to_numpy() + deltas = np.append([0.0], deltas) + x['delta'] = deltas + adjDeltas = x['Adj Low'].iloc[1:].to_numpy() - x['Adj Close'].iloc[:-1].to_numpy() + adjDeltas = np.append([0.0], adjDeltas) + x['adjDelta'] = adjDeltas + deltas = x[['delta', 'adjDelta']] + if div_pct > 0.15 and div_pct < 1.0: # avoid analysing impossibly-big dividends here + adjDiv = div * x['Adj'].iloc[0] + f = deltas['adjDelta'] > (adjDiv*0.6) + if f.any(): + for idx in np.where(f)[0]: + adjDelta_max_drop_idx = idx + adjDelta_max_drop = deltas['adjDelta'].iloc[idx] + if adjDelta_max_drop > 1.001*deltas['delta'].iloc[adjDelta_max_drop_idx]: + # Adjusted price has risen by more than unadjusted, should not happen. + # See if Adjusted price later falls by a similar amount. This would mean + # dividend has been applied too early. + ratios = (-1*deltas['adjDelta'])/adjDelta_max_drop + f_near1_or_above = ratios>=0.8 + if f_near1_or_above.any(): + near_indices = np.where(f_near1_or_above)[0] + if len(near_indices) > 1: + penalties = np.zeros(len(near_indices)) + for i in range(len(near_indices)): + idx = near_indices[i] + dti = ratios.index[idx] + if dti < dt: + penalties[i] += (dt-dti).days + else: + penalties[i] += 0.1*(dti-dt).days + i = np.argmin(penalties) + reversal_idx = near_indices[i] + else: + reversal_idx = near_indices[0] + div_date_wrong = True + div_true_date = ratios.index[reversal_idx] + break + elif adjDelta_max_drop > 0.39*adjDiv: + # Still true that applied adjustment exceeds price action, + # just not clear what solution is (if any). + div_adj_exceeds_prices = True + break + + # Can prune the space: + div_adj_is_too_small = div_status_df.loc[dt, 'div_exceeds_adj'] + if div_adj_exceeds_prices and div_adj_is_too_small: + # Contradiction. Assume former tricked by low-liquidity price action + div_adj_exceeds_prices = False + + div_status = {} + div_status['adj_exceeds_prices'] = div_adj_exceeds_prices + div_status['div_date_wrong'] = div_date_wrong + div_status['div_true_date'] = div_true_date + + for k,v in div_status.items(): + if k not in div_status_df: + if isinstance(v, (bool, np.bool_)): + div_status_df[k] = False + elif isinstance(v, int): + div_status_df[k] = 0 + elif isinstance(v, float): + div_status_df[k] = 0.0 + elif k == 'div_true_date': + div_status_df[k] = pd.Series(dtype='datetime64[ns, UTC]') + else: + raise Exception(k,v,type(v)) + div_status_df.loc[dt, k] = v + if 'div_too_big' in div_status_df.columns and 'div_date_wrong' in div_status_df.columns: + # Where div_date_wrong = True, discard div_too_big. Helps with false-positive handling later. + div_status_df.loc[div_status_df['div_date_wrong'].to_numpy(), 'div_too_big'] = False + + checks += ['adj_exceeds_prices', 'div_date_wrong'] + if not div_status_df[checks].any().any(): # Maybe failed to detect a too-small div. If div is ~0.01x of previous and next, then # treat as a 0.01x error if len(div_status_df) > 1: for i in range(0, len(div_status_df)): - if div_status_df['phantom'].iloc[i]: - continue r_pre, r_post = None, None if i > 0: r_pre = div_status_df['%'].iloc[i-1] / div_status_df['%'].iloc[i] @@ -1635,39 +1788,26 @@ def cluster_dividends(df, column='div', threshold=10): div_dt = div_status_df.index[i] div_status_df.loc[div_dt, 'div_too_small'] = True - if not div_status_df[checks].any().any(): - # Perfect - return df for c in checks: if not div_status_df[c].any(): div_status_df = div_status_df.drop(c, axis=1) + c = 'div_true_date' + if c in div_status_df.columns and div_status_df[c].isna().all(): + div_status_df = div_status_df.drop(c, axis=1) checks = [c for c in checks if c in div_status_df.columns] # With small dividends e.g. < 10%, my error detecting logic can be tricked by price volatility. # But by looking at all the dividends, can find errors that previous logic missed. div_status_df = div_status_df.sort_values('%') - div_status_df['cluster'] = cluster_dividends(div_status_df, column='%', ) - - # Discard columns with no problems - for c in checks: - if (~div_status_df[c]).all(): - div_status_df = div_status_df.drop(c, axis=1) - checks = [c for c in checks if c in div_status_df.columns] - if len(checks) == 0: - return df + div_status_df['cluster'] = cluster_dividends(div_status_df, column='%') + # Check for inconsistencies cluster_ids = div_status_df['cluster'].unique() for cid in cluster_ids: fc = div_status_df['cluster'] == cid cluster = div_status_df[fc].sort_index() - - if 'phantom' in cluster.columns: - cluster = cluster[~cluster['phantom']] n = len(cluster) - if n == 0: - # this cluster is just phantom - continue div_pcts = cluster[['%']].copy() if len(div_pcts) > 1: time_diffs = div_pcts['%'].index.to_series().diff().dt.total_seconds() / (365.25 * 24 * 60 * 60) @@ -1685,6 +1825,13 @@ def cluster_dividends(df, column='div', threshold=10): true_threshold = 1.0 fals_threshold = 0.2 + if 'div_date_wrong' in cluster.columns and (cluster[c] == cluster['div_date_wrong']).all(): + continue + + if 'adj_exceeds_prices' in cluster.columns and (cluster[c] == cluster['adj_exceeds_prices']).all(): + # More likely that true-positive. Maybe the div never happened + continue + if 'div_exceeds_adj' in cluster.columns and cluster['div_exceeds_adj'].all(): # Dividend too big for prices AND the present adjustment, # more likely the dividends are too big. @@ -1693,15 +1840,14 @@ def cluster_dividends(df, column='div', threshold=10): fals_threshold = 2/3 else: # Relax thresholds - true_threshold = 2/5 + true_threshold = 0.25 - elif 'adj_exceeds_prices' in cluster.columns and cluster['adj_exceeds_prices'].all(): + elif 'adj_exceeds_prices' in cluster.columns and (cluster[c]==cluster['adj_exceeds_prices']).all(): # Both dividend and present adjust too big for prices, # more likely the dividends are too big. true_threshold = 1/2 - elif not ('div_exceeds_adj' in cluster.columns or 'adj_exceeds_prices' in cluster.columns): - # Present adjustment seems correct, so more likely that 'div_too_big' are false positives: NOT too big + else: fals_threshold = 1/2 if pct_fail >= true_threshold: @@ -1716,7 +1862,8 @@ def cluster_dividends(df, column='div', threshold=10): fals_threshold = 0.1 if 'adj_exceeds_div' not in cluster.columns: # Adjustment confirms dividends => more likely that 'div_too_small' are false positives: NOT too small - fals_threshold = 2/3 + true_threshold = 6/11 + fals_threshold = 1/2 if pct_fail >= true_threshold: div_status_df.loc[fc, c] = True continue @@ -1731,53 +1878,40 @@ def cluster_dividends(df, column='div', threshold=10): if c == 'div_exceeds_adj': continue + if c == 'adj_exceeds_prices': + continue + if c == 'phantom' and self.ticker in ['KAP.IL', 'SAND']: # Manually approve, but these are probably safe to assume ok continue + if c == 'div_date_wrong': + # Fine, these should be rare + continue + if c == 'div_pre_split': + # Fine, these should be rare + continue + div_status_df = div_status_df.sort_index() # Discard dividends with no problems div_status_df = div_status_df[div_status_df[checks].any(axis=1)] if div_status_df.empty: - return df - - # Process phantoms first - if 'phantom' in div_status_df.columns: - f_phantom = div_status_df['phantom'] - # ... but only if no other problems - f_phantom = f_phantom & (~div_status_df[[c for c in checks if c != 'phantom']].any(axis=1)) - if f_phantom.any(): - div_dts = div_status_df.index[f_phantom] - msg = f'Removing phantom div(s): {[str(dt.date()) for dt in div_dts]}' - logger.info(msg, extra=log_extras) - for i in np.where(f_phantom)[0]: - dt = div_status_df.index[i] - enddt = dt-_datetime.timedelta(seconds=1) - df2.loc[ :enddt, 'Adj Close'] /= div_status_df['present adj'].iloc[i] - df2.loc[ :enddt, 'Repaired?'] = True - df2_nan.loc[:enddt, 'Adj Close'] /= div_status_df['present adj'].iloc[i] - df2_nan.loc[:enddt, 'Repaired?'] = True - df2.loc[dt, 'Dividends'] = 0 - df_modified = True - div_status_df.loc[f_phantom, 'phantom'] = False - # Discard dividends with no problems - div_status_df = div_status_df[div_status_df[checks].any(axis=1)] - - if div_status_df.empty: - if not df2_nan.empty: - df2 = pd.concat([df2, df2_nan]).sort_index() - return df2 + if not df2_nan.empty: + df2 = pd.concat([df2, df2_nan]).sort_index() + return df2 # These arrays track changes for constructing compact log messages div_repairs = {} - for cid in div_status_df['cluster'].unique(): + for cid in list(div_status_df['cluster'].unique()): cluster = div_status_df[div_status_df['cluster']==cid] cluster = cluster.sort_index(ascending=False) cluster['Fixed?'] = False - for i in range(len(cluster)): + # Reverse order because may delete false-positives + for i in range(len(cluster)-1, -1, -1): row = cluster.iloc[i] dt = row.name + enddt = dt-_datetime.timedelta(seconds=1) adj_missing = 'adj_missing' in row and row['adj_missing'] div_exceeds_adj = 'div_exceeds_adj' in row and row['div_exceeds_adj'] @@ -1785,15 +1919,38 @@ def cluster_dividends(df, column='div', threshold=10): adj_exceeds_prices = 'adj_exceeds_prices' in row and row['adj_exceeds_prices'] div_too_small = 'div_too_small' in row and row['div_too_small'] div_too_big = 'div_too_big' in row and row['div_too_big'] - + div_pre_split = 'div_pre_split' in row and row['div_pre_split'] + # div_too_small_and_pre_split = 'div_too_small_and_pre_split' in row and row['div_too_small_and_pre_split'] # not happened yet + # div_too_big_and_pre_split = 'div_too_big_and_pre_split' in row and row['div_too_big_and_pre_split'] # not happened yet + div_date_wrong = 'div_date_wrong' in row and row['div_date_wrong'] n_failed_checks = np.sum([row[c] for c in checks if c in row]) + + if div_too_big and adj_exceeds_prices and n_failed_checks == 2: + # adj_exceeds_prices is redundant information, fixing div-too-big + # will fix adjustment + adj_exceeds_prices = False + n_failed_checks -= 1 + + if div_date_wrong: + if div_too_big: + # redundant information + div_too_big = False + cluster.loc[dt, 'div_too_big'] = False + n_failed_checks -= 1 + + if div_pre_split: + if adj_exceeds_prices: + # redundant information + adj_exceeds_prices = False + cluster.loc[dt, 'adj_exceeds_prices'] = False + n_failed_checks -= 1 + if n_failed_checks == 1: if div_exceeds_adj or adj_exceeds_div: # Simply recalculate Adj Close k = 'too-small div-adjust' if div_exceeds_adj else 'too-big div-adjust' div_repairs.setdefault(k, []).append(dt) adj_correction = (1.0 - row['%']) / row['present adj'] - enddt = dt-_datetime.timedelta(seconds=1) df2.loc[ :enddt, 'Adj Close'] *= adj_correction df2.loc[ :enddt, 'Repaired?'] = True df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction @@ -1812,7 +1969,6 @@ def cluster_dividends(df, column='div', threshold=10): k += ' & div-adjust' target_adj = 1.0 - ((1.0 - row['present adj']) * correction) adj_correction = target_adj / row['present adj'] - enddt = dt-_datetime.timedelta(seconds=1) df2.loc[ :enddt, 'Adj Close'] *= adj_correction df2.loc[ :enddt, 'Repaired?'] = True df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction @@ -1827,13 +1983,11 @@ def cluster_dividends(df, column='div', threshold=10): correct_div = row['div'] * correction df2.loc[dt, 'Dividends'] = correct_div - # Also correct adjustment to match corrected dividend - target_div_pct = row['%']/currency_divide + target_div_pct = row['%'] * correction target_adj = 1.0 - target_div_pct present_adj = row['present adj'] k += ' & div-adjust' - adj_correction = target_adj / row['present adj'] - enddt = dt-_datetime.timedelta(seconds=1) + adj_correction = target_adj / present_adj df2.loc[ :enddt, 'Adj Close'] *= adj_correction df2.loc[ :enddt, 'Repaired?'] = True df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction @@ -1845,7 +1999,6 @@ def cluster_dividends(df, column='div', threshold=10): elif adj_missing: k = 'missing div-adjust' div_repairs.setdefault(k, []).append(dt) - enddt = dt-_datetime.timedelta(seconds=1) adj_correction = 1.0-row['%'] df2.loc[ :enddt, 'Adj Close'] *= adj_correction df2.loc[ :enddt, 'Repaired?'] = True @@ -1853,6 +2006,69 @@ def cluster_dividends(df, column='div', threshold=10): df2_nan.loc[:enddt, 'Repaired?'] = True cluster.loc[dt, 'Fixed?'] = True + elif div_date_wrong: + k = 'wrong ex-div date' + div_repairs.setdefault(k, []).append(dt) + + # First rollback the present adj + adj_correction = 1.0/row['present adj'] + df2.loc[ :enddt, 'Adj Close'] *= adj_correction + df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction + + # Apply correct adj from correct date + div_true_date = row['div_true_date'] + close_before = df2['Close'].iloc[row['idx']] + div = row['div'] + true_adj = 1.0 - div/close_before + enddt2 = div_true_date-_datetime.timedelta(seconds=1) + df2.loc[ :enddt2, 'Adj Close'] *= true_adj + df2_nan.loc[:enddt2, 'Adj Close'] *= true_adj + + # Move div to correct date + df2.loc[div_true_date, 'Dividends'] += div + df2.loc[dt, 'Dividends'] = 0 + + df2.loc[ :enddt, 'Repaired?'] = True + df2_nan.loc[:enddt, 'Repaired?'] = True + cluster.loc[dt, 'Fixed?'] = True + + elif adj_exceeds_prices: + # Nothing else wrong => probably false positive, + # but no harm checking the adjustment + target_adj = 1.0 - row['%'] + present_adj = row['present adj'] + if abs((target_adj/present_adj)-1) > 0.05: + # Also correct adjustment to match corrected dividend + k += ' & div-adjust' + adj_correction = target_adj / present_adj + df2.loc[ :enddt, 'Adj Close'] *= adj_correction + df2.loc[ :enddt, 'Repaired?'] = True + df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction + df2_nan.loc[:enddt, 'Repaired?'] = True + cluster.loc[dt, 'Fixed?'] = True + else: + div_status_df = div_status_df.drop(dt) + cluster = cluster.drop(dt) + + elif div_pre_split: + k = 'pre-split div' + correction = 1.0/df2['Stock Splits'].loc[dt] + correct_div = row['div'] * correction + df2.loc[dt, 'Dividends'] = correct_div + + target_div_pct = row['%'] * correction + target_adj = 1.0 - target_div_pct + present_adj = row['present adj'] + # Also correct adjustment to match corrected dividend + k += ' & div-adjust' + adj_correction = target_adj / present_adj + df2.loc[ :enddt, 'Adj Close'] *= adj_correction + df2.loc[ :enddt, 'Repaired?'] = True + df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction + df2_nan.loc[:enddt, 'Repaired?'] = True + cluster.loc[dt, 'Fixed?'] = True + div_repairs.setdefault(k, []).append(dt) + elif n_failed_checks == 2: if div_too_big and adj_missing: # A currency unit mixup AND adjustment missing @@ -1860,7 +2076,6 @@ def cluster_dividends(df, column='div', threshold=10): div_repairs.setdefault(k, []).append(dt) adj_correction = 1.0 - row['%']/currency_divide df2.loc[dt, 'Dividends'] /= currency_divide - enddt = dt-_datetime.timedelta(seconds=1) df2.loc[ :enddt, 'Adj Close'] *= adj_correction df2.loc[ :enddt, 'Repaired?'] = True df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction @@ -1879,7 +2094,6 @@ def cluster_dividends(df, column='div', threshold=10): msg = None div_adj = 1.0 - (row['%']/currency_divide) adj_correction = div_adj / row['present adj'] - enddt = dt-_datetime.timedelta(seconds=1) df2.loc[ :enddt, 'Adj Close'] *= adj_correction df2.loc[ :enddt, 'Repaired?'] = True df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction @@ -1899,13 +2113,15 @@ def cluster_dividends(df, column='div', threshold=10): target_adj = 1.0 - target_div_pct adj_correction = target_adj / row['present adj'] df2.loc[dt, 'Dividends'] /= currency_divide - enddt = dt-_datetime.timedelta(seconds=1) df2.loc[ :enddt, 'Adj Close'] *= adj_correction df2.loc[ :enddt, 'Repaired?'] = True df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction df2_nan.loc[:enddt, 'Repaired?'] = True cluster.loc[dt, 'Fixed?'] = True + if cluster.empty: + continue + for k in div_repairs: msg = f"Repaired {k}: {[str(dt.date()) for dt in sorted(div_repairs[k])]}" logger.info(msg, extra=log_extras) @@ -2158,6 +2374,12 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v r = _1d_change_x / split_rcp f_down = _1d_change_x < 1.0 / threshold + if f_down.any(): + # Discard where triggered by negative Adj Close after dividend + f_neg = _1d_change_x < 0.0 + f_div = (df2['Dividends']>0).to_numpy() + f_div_before = np.roll(f_div, 1) + f_down = f_down & ~(f_neg + f_div_before) f_up = _1d_change_x > threshold f_up_ndims = len(f_up.shape) f_up_shifts = f_up if f_up_ndims==1 else f_up.any(axis=1) @@ -2219,13 +2441,13 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v logger.info('100x changes are too soon after stock split events, aborting', extra=log_extras) return df - # if logger.isEnabledFor(logging.DEBUG): + if logger.isEnabledFor(logging.DEBUG): df_debug['i'] = list(range(0, df_debug.shape[0])) df_debug['i_rev'] = df_debug.shape[0]-1 - df_debug['i'] if correct_columns_individually: f_change = df_debug[[c+'_down' for c in debug_cols]].any(axis=1) | df_debug[[c+'_up' for c in debug_cols]].any(axis=1) else: - f_change = df_debug['f_down'] | df_debug['f_up'] + f_change = df_debug['down'] | df_debug['up'] f_change = f_change | np.roll(f_change, -1) | np.roll(f_change, 1) | np.roll(f_change, -2) | np.roll(f_change, 2) with pd.option_context('display.max_rows', None, 'display.max_columns', 10, 'display.width', 1000): # more options can be specified also logger.debug("price-repair-split: my workings:" + '\n' + str(df_debug[f_change])) @@ -2457,7 +2679,12 @@ def map_signals_to_ranges(f, f_up, f_down): logger.debug(msg, extra=log_extras) n_corrected += r[1] - r[0] - msg = f"Corrected: {n_corrected}x" + if len(ranges) <= 2: + msg = "Corrected:" + for r in ranges: + msg += f" {df2.index[r[1]-1].date()} -> {df2.index[r[0]].date()}" + else: + msg = f"Corrected: {n_corrected}x" logger.info(msg, extra=log_extras) if correct_volume: