ValueRaider
GitHub
commit
b6372c0945
2
setup.py
2
setup.py
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@ -39,7 +39,7 @@ setup(
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'License :: OSI Approved :: Apache Software License',
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# 'Development Status :: 3 - Alpha',
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'Development Status :: 4 - Beta',
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#'Development Status :: 5 - Production/Stable',
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# 'Development Status :: 5 - Production/Stable',
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'Operating System :: OS Independent',
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@ -0,0 +1,6 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2023-05-30 00:00:00+02:00,19.5900001525879,19.7999992370605,19.2700004577637,19.3500003814697,18.6291382416581,196309,0,0
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2023-05-31 00:00:00+02:00,19.1200008392334,19.1399993896484,18.7000007629395,18.7900009155273,18.0900009155273,156652,0,0
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2023-06-02 00:00:00+02:00,18.5499992370605,19,18.5100002288818,18.8999996185303,18.8999996185303,83439,0.7,0
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2023-06-05 00:00:00+02:00,18.9300003051758,19.0900001525879,18.8400001525879,19,19,153167,0,0
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2023-06-06 00:00:00+02:00,18.9099998474121,18.9500007629395,18.5100002288818,18.6599998474121,18.6599998474121,104352,0,0
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@ -0,0 +1,6 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2023-05-30 00:00:00+02:00,19.59000015258789,19.799999237060547,19.270000457763672,19.350000381469727,19.350000381469727,196309,0.0,0.0
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2023-05-31 00:00:00+02:00,19.1200008392334,19.139999389648438,18.700000762939453,18.790000915527344,18.790000915527344,156652,0.0,0.0
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2023-06-02 00:00:00+02:00,18.549999237060547,19.0,18.510000228881836,18.899999618530273,18.899999618530273,83439,0.7,0.0
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2023-06-05 00:00:00+02:00,18.93000030517578,19.09000015258789,18.84000015258789,19.0,19.0,153167,0.0,0.0
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2023-06-06 00:00:00+02:00,18.90999984741211,18.950000762939453,18.510000228881836,18.65999984741211,18.65999984741211,104352,0.0,0.0
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@ -0,0 +1,24 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2022-06-06 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-06-01 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-31 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-30 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-27 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-26 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-25 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-24 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-23 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-20 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-19 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
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2022-05-18 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,532454,0,0
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2022-05-17 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
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2022-05-16 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
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2022-05-13 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
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2022-05-12 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
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2022-05-11 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
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2022-05-10 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
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2022-05-09 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
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2022-05-06 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-05 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-04 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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2022-05-03 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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@ -0,0 +1,24 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2022-06-06 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-06-01 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-31 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-30 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-27 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-26 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-24 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-23 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-20 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-19 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
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2022-05-18 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,532454,0.0,0.0
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2022-05-17 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
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2022-05-16 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
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2022-05-13 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
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2022-05-12 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
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2022-05-11 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
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2022-05-10 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
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2022-05-09 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
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2022-05-06 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-05 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-04 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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2022-05-03 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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@ -0,0 +1,37 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2022-05-30 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-05-23 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-05-16 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,532454,0,0
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2022-05-09 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-05-02 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-04-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-04-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-04-11 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-04-04 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-03-28 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-03-21 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-03-14 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-03-07 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-02-28 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-02-21 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-02-14 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-02-07 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-01-31 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-01-24 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-01-17 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-01-10 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-01-03 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-12-27 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-12-20 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-12-13 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-12-06 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-11-29 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-11-22 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-11-15 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-11-08 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-11-01 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-10-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-10-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2021-10-11 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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||||
2021-10-04 00:00:00+01:00,14.8000,15.3400,14.4000,14.5500,14.5500,2171373,0,0
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2021-09-27 00:00:00+01:00,15.6000,16.0000,14.9000,15.0500,15.0500,3860549,0,0
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@ -0,0 +1,25 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2022-08-15 00:00:00+01:00,27.6000,28.2000,26.2000,27.6000,27.6000,3535668,0,0
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2022-08-12 00:00:00+01:00,27.3000,29.8000,26.4030,27.0000,27.0000,7223353,0,0
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2022-08-11 00:00:00+01:00,26.0000,29.8000,24.2000,27.1000,27.1000,12887933,0,0
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2022-08-10 00:00:00+01:00,25.0000,29.2000,22.5000,25.0000,25.0000,26572680,0,0
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2022-08-09 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-08-08 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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||||
2022-08-05 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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||||
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
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||||
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
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||||
2022-07-28 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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||||
2022-07-27 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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||||
2022-07-26 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-07-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-07-22 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-07-21 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-07-20 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-07-19 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-07-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-07-15 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-07-14 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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2022-07-13 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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@ -0,0 +1,37 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2022-05-30 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-05-23 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-05-16 00:00:00+01:00,14.550000190734863,14.550000190734863,0.14550000429153442,0.14550000429153442,0.14550000429153442,532454,0.0,0.0
|
||||
2022-05-09 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
|
||||
2022-05-02 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-04-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-04-18 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-04-11 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-04-04 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-03-28 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-03-21 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-03-14 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-03-07 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-02-28 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-02-21 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-02-14 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-02-07 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-01-31 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-01-24 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-01-17 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-01-10 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2022-01-03 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-12-27 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-12-20 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-12-13 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-12-06 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-11-29 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-11-22 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-11-15 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-11-08 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-11-01 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-10-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-10-18 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-10-11 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
|
||||
2021-10-04 00:00:00+01:00,14.800000190734863,15.34000015258789,0.14399999380111694,0.14550000429153442,0.14550000429153442,2171373,0.0,0.0
|
||||
2021-09-27 00:00:00+01:00,15.600000381469727,16.0,14.899999618530273,15.050000190734863,15.050000190734863,3860549,0.0,0.0
|
||||
|
|
|
@ -0,0 +1,25 @@
|
|||
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
|
||||
|
|
@ -0,0 +1,85 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2021-12-13 00:00:00+00:00,393.999975585938,406.6,391.4,402.899916992188,291.232287597656,62714764.4736842,0,0
|
||||
2021-12-20 00:00:00+00:00,393.999975585938,412.199990234375,392.502983398438,409.899997558594,296.292243652344,46596651.3157895,0,0
|
||||
2021-12-27 00:00:00+00:00,409.899997558594,416.550971679688,408.387001953125,410.4,296.653642578125,10818482.8947368,0,0
|
||||
2022-01-03 00:00:00+00:00,410.4,432.199995117188,410.4,432.099985351563,312.339265136719,44427327.6315789,0,0
|
||||
2022-01-10 00:00:00+00:00,431.3,439.199982910156,429.099970703125,436.099912109375,315.230618896484,29091400,0,0
|
||||
2022-01-17 00:00:00+00:00,437.999912109375,445.199965820313,426.999997558594,431.999975585938,312.267017822266,43787351.3157895,0,0
|
||||
2022-01-24 00:00:00+00:00,430.099975585938,440.999973144531,420.999968261719,433.499982910156,313.351237792969,58487296.0526316,0,0
|
||||
2022-01-31 00:00:00+00:00,436.199968261719,443.049987792969,432.099985351563,435.199916992188,314.580045166016,43335806.5789474,0,0
|
||||
2022-02-07 00:00:00+00:00,437.899995117188,448.799992675781,436.051994628906,444.39998046875,321.230207519531,39644061.8421053,0,0
|
||||
2022-02-14 00:00:00+00:00,437.699975585938,441.999978027344,426.699968261719,432.199995117188,312.411558837891,49972693.4210526,0,0
|
||||
2022-02-21 00:00:00+00:00,435.499992675781,438.476999511719,408.29998046875,423.399970703125,306.050571289063,65719596.0526316,0,0
|
||||
2022-02-28 00:00:00+00:00,415.099995117188,427.999909667969,386.199932861328,386.799945068359,279.594578857422,94057936.8421053,4.1875,0
|
||||
2022-03-07 00:00:00+00:00,374.999952392578,417.299978027344,361.101981201172,409.599968261719,298.389248046875,71269101.3157895,0,0
|
||||
2022-03-14 00:00:00+00:00,413.099985351563,426.699968261719,408.899992675781,422.399965820313,307.713929443359,55431927.6315789,0,0
|
||||
2022-03-21 00:00:00+00:00,422.699995117188,442.7,422.399965820313,437.799985351563,318.932696533203,39896352.6315789,0,0
|
||||
2022-03-28 00:00:00+01:00,442.49998046875,460.999978027344,440.097983398438,444.6,323.886403808594,56413515.7894737,0,0
|
||||
2022-04-04 00:00:00+01:00,439.699985351563,445.399985351563,421.999973144531,425.799973144531,310.190817871094,49415836.8421053,19.342106,0
|
||||
2022-04-11 00:00:00+01:00,425.39998046875,435.599909667969,420.799995117188,434.299968261719,327.211427001953,29875081.5789474,0,0
|
||||
2022-04-18 00:00:00+01:00,434.299968261719,447.799987792969,433.599992675781,437.799985351563,329.848419189453,49288272.3684211,0,0
|
||||
2022-04-25 00:00:00+01:00,430.699987792969,438.799990234375,423.999982910156,433.299916992188,326.457967529297,44656776.3157895,0,0
|
||||
2022-05-02 00:00:00+01:00,433.299916992188,450.999975585938,414.499982910156,414.899975585938,312.595018310547,29538167.1052632,0,0
|
||||
2022-05-09 00:00:00+01:00,413.199995117188,417.449992675781,368.282923583984,408.199970703125,307.547099609375,73989611.8421053,0,0
|
||||
2022-05-16 00:00:00+01:00,384,423.600006103516,384,412.100006103516,310.485473632813,81938261,101.69,0.76
|
||||
2022-05-23 00:00:00+01:00,416.100006103516,442.399993896484,341.915008544922,440.899993896484,409.764678955078,45432941,0,0
|
||||
2022-05-30 00:00:00+01:00,442.700012207031,444.200012207031,426.600006103516,428.700012207031,398.426239013672,37906659,0,0
|
||||
2022-06-06 00:00:00+01:00,425.299987792969,434.010009765625,405.200012207031,405.399993896484,376.771606445313,40648810,0,0
|
||||
2022-06-13 00:00:00+01:00,402.5,420,399.799987792969,411.200012207031,382.162048339844,74196958,0,0
|
||||
2022-06-20 00:00:00+01:00,412.5,421.899993896484,398.399993896484,411.5,382.440826416016,28679717,0,0
|
||||
2022-06-27 00:00:00+01:00,413.100006103516,422.399993896484,397.399993896484,401.600006103516,373.239959716797,35468994,0,0
|
||||
2022-07-04 00:00:00+01:00,405.399993896484,406.600006103516,382.299987792969,401.299987792969,372.961120605469,35304748,0,0
|
||||
2022-07-11 00:00:00+01:00,394.799987792969,405.850006103516,383.399993896484,396.600006103516,368.593048095703,42308459,0,0
|
||||
2022-07-18 00:00:00+01:00,392.5,399.700012207031,384.799987792969,391.700012207031,364.039093017578,36656839,0,0
|
||||
2022-07-25 00:00:00+01:00,392.200012207031,400.799987792969,388.700012207031,396,368.035430908203,33124660,0,0
|
||||
2022-08-01 00:00:00+01:00,396.399993896484,405.5,390.415008544922,402,373.611724853516,21753121,0,0
|
||||
2022-08-08 00:00:00+01:00,406.600006103516,473.700012207031,403.299987792969,467.899993896484,434.858032226563,59155709,0,0
|
||||
2022-08-15 00:00:00+01:00,468.100006103516,470.5,434,437,406.140106201172,36989620,10.3,0
|
||||
2022-08-22 00:00:00+01:00,436.100006103516,436.869995117188,419.299987792969,420.5,399.780303955078,36492572,0,0
|
||||
2022-08-29 00:00:00+01:00,420.5,426.600006103516,408.600006103516,426.600006103516,405.579742431641,29573657,0,0
|
||||
2022-09-05 00:00:00+01:00,418.5,444.4169921875,416.100006103516,443.100006103516,421.266723632813,34375126,0,0
|
||||
2022-09-12 00:00:00+01:00,444.649993896484,448.899993896484,435.200012207031,440.100006103516,418.414520263672,39085960,0,0
|
||||
2022-09-19 00:00:00+01:00,440.100006103516,447.200012207031,419.299987792969,422.899993896484,402.062042236328,27982081,0,0
|
||||
2022-09-26 00:00:00+01:00,421.200012207031,421.200012207031,373.31201171875,388.200012207031,369.071868896484,70408935,0,0
|
||||
2022-10-03 00:00:00+01:00,382.899993896484,409.875,380.555999755859,400.700012207031,380.955932617188,37581751,0,0
|
||||
2022-10-10 00:00:00+01:00,395.799987792969,404.470001220703,366.700012207031,394.299987792969,374.871276855469,52952323,0,0
|
||||
2022-10-17 00:00:00+01:00,394.299987792969,414.799987792969,393,406.5,386.470123291016,26441475,0,0
|
||||
2022-10-24 00:00:00+01:00,407.100006103516,418.227996826172,407.100006103516,413.299987792969,392.93505859375,26239756,0,0
|
||||
2022-10-31 00:00:00+00:00,413.899993896484,430.200012207031,412,429.299987792969,408.146667480469,23168047,0,0
|
||||
2022-11-07 00:00:00+00:00,427.299987792969,445.899993896484,420.652008056641,438.399993896484,416.798278808594,36709117,0,0
|
||||
2022-11-14 00:00:00+00:00,438.299987792969,458.489990234375,435,455.100006103516,432.675415039063,29106506,0,0
|
||||
2022-11-21 00:00:00+00:00,454.399993896484,461,450,456.600006103516,434.101501464844,21667730,0,0
|
||||
2022-11-28 00:00:00+00:00,453.799987792969,456.899993896484,435.100006103516,444.799987792969,422.882934570313,33326204,0,0
|
||||
2022-12-05 00:00:00+00:00,442.899993896484,450.25,441.299987792969,448,425.925262451172,29147089,0,0
|
||||
2022-12-12 00:00:00+00:00,445.100006103516,451.299987792969,431.200012207031,436.100006103516,414.611633300781,46593233,0,0
|
||||
2022-12-19 00:00:00+00:00,436,452.600006103516,433.600006103516,444,422.122344970703,20982140,0,0
|
||||
2022-12-26 00:00:00+00:00,444,452.058013916016,442.399993896484,442.799987792969,420.981475830078,8249664,0,0
|
||||
2023-01-02 00:00:00+00:00,445.899993896484,458.149993896484,443.299987792969,456,433.531066894531,28687622,0,0
|
||||
2023-01-09 00:00:00+00:00,456,461.066009521484,435.799987792969,444.200012207031,422.3125,39237336,0,0
|
||||
2023-01-16 00:00:00+00:00,444.299987792969,447.200012207031,434.399993896484,439,417.368713378906,35267336,0,0
|
||||
2023-01-23 00:00:00+00:00,440,459.299987792969,439.5,457.399993896484,434.862091064453,37495012,0,0
|
||||
2023-01-30 00:00:00+00:00,454.399993896484,459.399993896484,447.799987792969,450.299987792969,428.111907958984,48879358,0,0
|
||||
2023-02-06 00:00:00+00:00,448,449.200012207031,436.299987792969,440,418.319458007813,38799772,0,0
|
||||
2023-02-13 00:00:00+00:00,441.200012207031,450.299987792969,440,447.600006103516,425.544982910156,30251441,0,0
|
||||
2023-02-20 00:00:00+00:00,448.5,450.799987792969,434.299987792969,440,418.319458007813,26764528,0,0
|
||||
2023-02-27 00:00:00+00:00,442.899993896484,450.5,441.608001708984,447.200012207031,425.164703369141,29895454,0,0
|
||||
2023-03-06 00:00:00+00:00,447.399993896484,467.299987792969,443.100006103516,449.700012207031,427.54150390625,82322819,0,0
|
||||
2023-03-13 00:00:00+00:00,450,451.417999267578,400.68701171875,402.200012207031,382.382019042969,85158023,0,0
|
||||
2023-03-20 00:00:00+00:00,396.200012207031,425.399993896484,383.496002197266,408.299987792969,388.181427001953,60152666,0,0
|
||||
2023-03-27 00:00:00+01:00,416,422.049987792969,399.549987792969,404.200012207031,384.283477783203,81534829,20.7,0
|
||||
2023-04-03 00:00:00+01:00,405,434.100006103516,404.399993896484,417.100006103516,417.100006103516,43217151,0,0
|
||||
2023-04-10 00:00:00+01:00,419.100006103516,426.700012207031,419.100006103516,421.700012207031,421.700012207031,32435695,0,0
|
||||
2023-04-17 00:00:00+01:00,423.700012207031,427.635009765625,415.399993896484,420.299987792969,420.299987792969,37715986,0,0
|
||||
2023-04-24 00:00:00+01:00,418.100006103516,423,415.299987792969,423,423,34331974,0,0
|
||||
2023-05-01 00:00:00+01:00,423.399993896484,426.100006103516,406.399993896484,414.600006103516,414.600006103516,40446519,0,0
|
||||
2023-05-08 00:00:00+01:00,414.600006103516,419.100006103516,408,412.700012207031,412.700012207031,36950836,0,0
|
||||
2023-05-15 00:00:00+01:00,414,418.399993896484,407.399993896484,413.5,413.5,53109487,0,0
|
||||
2023-05-22 00:00:00+01:00,413.600006103516,424,394.700012207031,401.299987792969,401.299987792969,64363368,0,0
|
||||
2023-05-29 00:00:00+01:00,401.299987792969,409.477996826172,392.700012207031,409.100006103516,409.100006103516,47587959,0,0
|
||||
2023-06-05 00:00:00+01:00,406.299987792969,410.700012207031,400.100006103516,400.899993896484,400.899993896484,22494985,0,0
|
||||
2023-06-12 00:00:00+01:00,404.100006103516,406,394.5,396,396,41531163,0,0
|
||||
2023-06-19 00:00:00+01:00,394,399.899993896484,380.720001220703,386.200012207031,386.200012207031,40439880,0,0
|
||||
2023-06-26 00:00:00+01:00,387.200012207031,397,382.899993896484,395.200012207031,395.200012207031,27701915,0,0
|
||||
2023-07-03 00:00:00+01:00,396.5,399.799987792969,380.100006103516,381.799987792969,381.799987792969,26005305,0,0
|
||||
2023-07-10 00:00:00+01:00,380,392.299987792969,379.403991699219,386,386,29789300,0,0
|
||||
2023-07-17 00:00:00+01:00,385,389.5,384.251007080078,387.100006103516,387.100006103516,0,0,0
|
||||
|
|
|
@ -0,0 +1,85 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2021-12-13 00:00:00+00:00,518.4210205078125,535.0,515.0,530.1314697265625,383.20037841796875,47663221,0.0,0.0
|
||||
2021-12-20 00:00:00+00:00,518.4210205078125,542.368408203125,516.4512939453125,539.3421020507812,389.85821533203125,35413455,0.0,0.0
|
||||
2021-12-27 00:00:00+00:00,539.3421020507812,548.0933837890625,537.351318359375,540.0,390.333740234375,8222047,0.0,0.0
|
||||
2022-01-03 00:00:00+00:00,540.0,568.6842041015625,540.0,568.5526123046875,410.97271728515625,33764769,0.0,0.0
|
||||
2022-01-10 00:00:00+00:00,567.5,577.8947143554688,564.605224609375,573.815673828125,414.7771301269531,22109464,0.0,0.0
|
||||
2022-01-17 00:00:00+00:00,576.315673828125,585.7894287109375,561.8421020507812,568.4210205078125,410.8776550292969,33278387,0.0,0.0
|
||||
2022-01-24 00:00:00+00:00,565.9210205078125,580.2631225585938,553.9473266601562,570.3947143554688,412.30426025390625,44450345,0.0,0.0
|
||||
2022-01-31 00:00:00+00:00,573.9473266601562,582.9605102539062,568.5526123046875,572.6314697265625,413.9211120605469,32935213,0.0,0.0
|
||||
2022-02-07 00:00:00+00:00,576.1842041015625,590.5263061523438,573.7526245117188,584.73681640625,422.67132568359375,30129487,0.0,0.0
|
||||
2022-02-14 00:00:00+00:00,575.9210205078125,581.5789184570312,561.4473266601562,568.6842041015625,411.0678405761719,37979247,0.0,0.0
|
||||
2022-02-21 00:00:00+00:00,573.0263061523438,576.9434204101562,537.23681640625,557.105224609375,402.6981201171875,49946893,0.0,0.0
|
||||
2022-02-28 00:00:00+00:00,546.1842041015625,563.1577758789062,508.1578063964844,508.9472961425781,367.8876037597656,71484032,4.1875,0.0
|
||||
2022-03-07 00:00:00+00:00,493.4209899902344,549.0789184570312,475.1341857910156,538.9473266601562,392.617431640625,54164517,0.0,0.0
|
||||
2022-03-14 00:00:00+00:00,543.5526123046875,561.4473266601562,538.0263061523438,555.7894287109375,404.8867492675781,42128265,0.0,0.0
|
||||
2022-03-21 00:00:00+00:00,556.1842041015625,582.5,555.7894287109375,576.0526123046875,419.6482849121094,30321228,0.0,0.0
|
||||
2022-03-28 00:00:00+01:00,582.23681640625,606.5789184570312,579.0762939453125,585.0,426.16632080078125,42874272,0.0,0.0
|
||||
2022-04-04 00:00:00+01:00,578.5526123046875,586.0526123046875,555.2631225585938,560.2631225585938,408.14581298828125,37556036,19.342106,0.0
|
||||
2022-04-11 00:00:00+01:00,559.73681640625,573.1577758789062,553.6842041015625,571.4473266601562,430.5413513183594,22705062,0.0,0.0
|
||||
2022-04-18 00:00:00+01:00,571.4473266601562,589.2105102539062,570.5263061523438,576.0526123046875,434.0110778808594,37459087,0.0,0.0
|
||||
2022-04-25 00:00:00+01:00,566.7105102539062,577.368408203125,557.8947143554688,570.1314697265625,429.5499572753906,33939150,0.0,0.0
|
||||
2022-05-02 00:00:00+01:00,570.1314697265625,593.4210205078125,545.3947143554688,545.9210205078125,411.3092346191406,22449007,0.0,0.0
|
||||
2022-05-09 00:00:00+01:00,543.6842041015625,549.2763061523438,484.5827941894531,537.105224609375,404.667236328125,56232105,0.0,0.0
|
||||
2022-05-16 00:00:00+01:00,384.0,423.6000061035156,384.0,412.1000061035156,310.4854736328125,81938261,101.69,0.76
|
||||
2022-05-23 00:00:00+01:00,416.1000061035156,442.3999938964844,341.9150085449219,440.8999938964844,409.7646789550781,45432941,0.0,0.0
|
||||
2022-05-30 00:00:00+01:00,442.70001220703125,444.20001220703125,426.6000061035156,428.70001220703125,398.4262390136719,37906659,0.0,0.0
|
||||
2022-06-06 00:00:00+01:00,425.29998779296875,434.010009765625,405.20001220703125,405.3999938964844,376.7716064453125,40648810,0.0,0.0
|
||||
2022-06-13 00:00:00+01:00,402.5,420.0,399.79998779296875,411.20001220703125,382.16204833984375,74196958,0.0,0.0
|
||||
2022-06-20 00:00:00+01:00,412.5,421.8999938964844,398.3999938964844,411.5,382.4408264160156,28679717,0.0,0.0
|
||||
2022-06-27 00:00:00+01:00,413.1000061035156,422.3999938964844,397.3999938964844,401.6000061035156,373.2399597167969,35468994,0.0,0.0
|
||||
2022-07-04 00:00:00+01:00,405.3999938964844,406.6000061035156,382.29998779296875,401.29998779296875,372.96112060546875,35304748,0.0,0.0
|
||||
2022-07-11 00:00:00+01:00,394.79998779296875,405.8500061035156,383.3999938964844,396.6000061035156,368.5930480957031,42308459,0.0,0.0
|
||||
2022-07-18 00:00:00+01:00,392.5,399.70001220703125,384.79998779296875,391.70001220703125,364.0390930175781,36656839,0.0,0.0
|
||||
2022-07-25 00:00:00+01:00,392.20001220703125,400.79998779296875,388.70001220703125,396.0,368.0354309082031,33124660,0.0,0.0
|
||||
2022-08-01 00:00:00+01:00,396.3999938964844,405.5,390.4150085449219,402.0,373.6117248535156,21753121,0.0,0.0
|
||||
2022-08-08 00:00:00+01:00,406.6000061035156,473.70001220703125,403.29998779296875,467.8999938964844,434.8580322265625,59155709,0.0,0.0
|
||||
2022-08-15 00:00:00+01:00,468.1000061035156,470.5,434.0,437.0,406.1401062011719,36989620,10.3,0.0
|
||||
2022-08-22 00:00:00+01:00,436.1000061035156,436.8699951171875,419.29998779296875,420.5,399.7803039550781,36492572,0.0,0.0
|
||||
2022-08-29 00:00:00+01:00,420.5,426.6000061035156,408.6000061035156,426.6000061035156,405.5797424316406,29573657,0.0,0.0
|
||||
2022-09-05 00:00:00+01:00,418.5,444.4169921875,416.1000061035156,443.1000061035156,421.2667236328125,34375126,0.0,0.0
|
||||
2022-09-12 00:00:00+01:00,444.6499938964844,448.8999938964844,435.20001220703125,440.1000061035156,418.4145202636719,39085960,0.0,0.0
|
||||
2022-09-19 00:00:00+01:00,440.1000061035156,447.20001220703125,419.29998779296875,422.8999938964844,402.0620422363281,27982081,0.0,0.0
|
||||
2022-09-26 00:00:00+01:00,421.20001220703125,421.20001220703125,373.31201171875,388.20001220703125,369.0718688964844,70408935,0.0,0.0
|
||||
2022-10-03 00:00:00+01:00,382.8999938964844,409.875,380.5559997558594,400.70001220703125,380.9559326171875,37581751,0.0,0.0
|
||||
2022-10-10 00:00:00+01:00,395.79998779296875,404.4700012207031,366.70001220703125,394.29998779296875,374.87127685546875,52952323,0.0,0.0
|
||||
2022-10-17 00:00:00+01:00,394.29998779296875,414.79998779296875,393.0,406.5,386.4701232910156,26441475,0.0,0.0
|
||||
2022-10-24 00:00:00+01:00,407.1000061035156,418.2279968261719,407.1000061035156,413.29998779296875,392.93505859375,26239756,0.0,0.0
|
||||
2022-10-31 00:00:00+00:00,413.8999938964844,430.20001220703125,412.0,429.29998779296875,408.14666748046875,23168047,0.0,0.0
|
||||
2022-11-07 00:00:00+00:00,427.29998779296875,445.8999938964844,420.6520080566406,438.3999938964844,416.79827880859375,36709117,0.0,0.0
|
||||
2022-11-14 00:00:00+00:00,438.29998779296875,458.489990234375,435.0,455.1000061035156,432.6754150390625,29106506,0.0,0.0
|
||||
2022-11-21 00:00:00+00:00,454.3999938964844,461.0,450.0,456.6000061035156,434.10150146484375,21667730,0.0,0.0
|
||||
2022-11-28 00:00:00+00:00,453.79998779296875,456.8999938964844,435.1000061035156,444.79998779296875,422.8829345703125,33326204,0.0,0.0
|
||||
2022-12-05 00:00:00+00:00,442.8999938964844,450.25,441.29998779296875,448.0,425.9252624511719,29147089,0.0,0.0
|
||||
2022-12-12 00:00:00+00:00,445.1000061035156,451.29998779296875,431.20001220703125,436.1000061035156,414.61163330078125,46593233,0.0,0.0
|
||||
2022-12-19 00:00:00+00:00,436.0,452.6000061035156,433.6000061035156,444.0,422.1223449707031,20982140,0.0,0.0
|
||||
2022-12-26 00:00:00+00:00,444.0,452.0580139160156,442.3999938964844,442.79998779296875,420.9814758300781,8249664,0.0,0.0
|
||||
2023-01-02 00:00:00+00:00,445.8999938964844,458.1499938964844,443.29998779296875,456.0,433.53106689453125,28687622,0.0,0.0
|
||||
2023-01-09 00:00:00+00:00,456.0,461.0660095214844,435.79998779296875,444.20001220703125,422.3125,39237336,0.0,0.0
|
||||
2023-01-16 00:00:00+00:00,444.29998779296875,447.20001220703125,434.3999938964844,439.0,417.36871337890625,35267336,0.0,0.0
|
||||
2023-01-23 00:00:00+00:00,440.0,459.29998779296875,439.5,457.3999938964844,434.8620910644531,37495012,0.0,0.0
|
||||
2023-01-30 00:00:00+00:00,454.3999938964844,459.3999938964844,447.79998779296875,450.29998779296875,428.1119079589844,48879358,0.0,0.0
|
||||
2023-02-06 00:00:00+00:00,448.0,449.20001220703125,436.29998779296875,440.0,418.3194580078125,38799772,0.0,0.0
|
||||
2023-02-13 00:00:00+00:00,441.20001220703125,450.29998779296875,440.0,447.6000061035156,425.54498291015625,30251441,0.0,0.0
|
||||
2023-02-20 00:00:00+00:00,448.5,450.79998779296875,434.29998779296875,440.0,418.3194580078125,26764528,0.0,0.0
|
||||
2023-02-27 00:00:00+00:00,442.8999938964844,450.5,441.6080017089844,447.20001220703125,425.1647033691406,29895454,0.0,0.0
|
||||
2023-03-06 00:00:00+00:00,447.3999938964844,467.29998779296875,443.1000061035156,449.70001220703125,427.54150390625,82322819,0.0,0.0
|
||||
2023-03-13 00:00:00+00:00,450.0,451.4179992675781,400.68701171875,402.20001220703125,382.38201904296875,85158023,0.0,0.0
|
||||
2023-03-20 00:00:00+00:00,396.20001220703125,425.3999938964844,383.4960021972656,408.29998779296875,388.1814270019531,60152666,0.0,0.0
|
||||
2023-03-27 00:00:00+01:00,416.0,422.04998779296875,399.54998779296875,404.20001220703125,384.2834777832031,81534829,20.7,0.0
|
||||
2023-04-03 00:00:00+01:00,405.0,434.1000061035156,404.3999938964844,417.1000061035156,417.1000061035156,43217151,0.0,0.0
|
||||
2023-04-10 00:00:00+01:00,419.1000061035156,426.70001220703125,419.1000061035156,421.70001220703125,421.70001220703125,32435695,0.0,0.0
|
||||
2023-04-17 00:00:00+01:00,423.70001220703125,427.635009765625,415.3999938964844,420.29998779296875,420.29998779296875,37715986,0.0,0.0
|
||||
2023-04-24 00:00:00+01:00,418.1000061035156,423.0,415.29998779296875,423.0,423.0,34331974,0.0,0.0
|
||||
2023-05-01 00:00:00+01:00,423.3999938964844,426.1000061035156,406.3999938964844,414.6000061035156,414.6000061035156,40446519,0.0,0.0
|
||||
2023-05-08 00:00:00+01:00,414.6000061035156,419.1000061035156,408.0,412.70001220703125,412.70001220703125,36950836,0.0,0.0
|
||||
2023-05-15 00:00:00+01:00,414.0,418.3999938964844,407.3999938964844,413.5,413.5,53109487,0.0,0.0
|
||||
2023-05-22 00:00:00+01:00,413.6000061035156,424.0,394.70001220703125,401.29998779296875,401.29998779296875,64363368,0.0,0.0
|
||||
2023-05-29 00:00:00+01:00,401.29998779296875,409.4779968261719,392.70001220703125,409.1000061035156,409.1000061035156,47587959,0.0,0.0
|
||||
2023-06-05 00:00:00+01:00,406.29998779296875,410.70001220703125,400.1000061035156,400.8999938964844,400.8999938964844,22494985,0.0,0.0
|
||||
2023-06-12 00:00:00+01:00,404.1000061035156,406.0,394.5,396.0,396.0,41531163,0.0,0.0
|
||||
2023-06-19 00:00:00+01:00,394.0,399.8999938964844,380.7200012207031,386.20001220703125,386.20001220703125,40439880,0.0,0.0
|
||||
2023-06-26 00:00:00+01:00,387.20001220703125,397.0,382.8999938964844,395.20001220703125,395.20001220703125,27701915,0.0,0.0
|
||||
2023-07-03 00:00:00+01:00,396.5,399.79998779296875,380.1000061035156,381.79998779296875,381.79998779296875,26005305,0.0,0.0
|
||||
2023-07-10 00:00:00+01:00,380.0,392.29998779296875,379.40399169921875,386.0,386.0,29789300,0.0,0.0
|
||||
2023-07-17 00:00:00+01:00,385.0,389.5,384.2510070800781,387.1000061035156,387.1000061035156,0,0.0,0.0
|
||||
|
|
|
@ -0,0 +1,42 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2020-09-30 00:00:00-04:00,4.40000009536743,4.44999980926514,4.01999998092651,4.44999980926514,4.44999980926514,22600,0,0
|
||||
2020-09-29 00:00:00-04:00,4.3899998664856,4.40000009536743,4.13000011444092,4.30000019073486,4.30000019073486,10800,0,0
|
||||
2020-09-28 00:00:00-04:00,4.09000015258789,4.25,4.09000015258789,4.25,4.25,8000,0,0
|
||||
2020-09-25 00:00:00-04:00,3.95000004768372,4.09999990463257,3.95000004768372,4.05000019073486,4.05000019073486,13500,0,0
|
||||
2020-09-24 00:00:00-04:00,3.84999990463257,4,3.84999990463257,4,4,8800,0,0
|
||||
2020-09-23 00:00:00-04:00,3.99000000953674,4,3.99000000953674,4,4,5900,0,0
|
||||
2020-09-22 00:00:00-04:00,3.90000009536743,4.09999990463257,3.84999990463257,4.09999990463257,4.09999990463257,3100,0,0
|
||||
2020-09-21 00:00:00-04:00,4.09999990463257,4.09999990463257,4.09999990463257,4.09999990463257,4.09999990463257,1200,0,0
|
||||
2020-09-18 00:00:00-04:00,3.92000007629395,4.09999990463257,3.92000007629395,4.09999990463257,4.09999990463257,27200,0,0
|
||||
2020-09-17 00:00:00-04:00,3.90000009536743,3.99000000953674,3.8199999332428,3.99000000953674,3.99000000953674,3300,0,0
|
||||
2020-09-16 00:00:00-04:00,3.79999995231628,4,3.79999995231628,4,4,3300,0,0
|
||||
2020-09-15 00:00:00-04:00,3.95000004768372,4,3.95000004768372,4,4,2400,0,0
|
||||
2020-09-14 00:00:00-04:00,3.96000003814697,4,3.96000003814697,4,4,800,0,0
|
||||
2020-09-11 00:00:00-04:00,3.95000004768372,3.97000002861023,3.72000002861023,3.97000002861023,3.97000002861023,5700,0,0
|
||||
2020-09-10 00:00:00-04:00,4,4.09999990463257,4,4.09999990463257,4.09999990463257,7100,0,0
|
||||
2020-09-09 00:00:00-04:00,3.5699999332428,4,3.5699999332428,4,4,18100,0,0
|
||||
2020-09-08 00:00:00-04:00,3.40000009536743,3.59999990463257,3.40000009536743,3.59999990463257,3.59999990463257,19500,0,0
|
||||
2020-09-04 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,400,0,0
|
||||
2020-09-03 00:00:00-04:00,3.58999991416931,3.58999991416931,3.58999991416931,3.58999991416931,3.58999991416931,0,0,0
|
||||
2020-09-02 00:00:00-04:00,3.5,3.58999991416931,3.5,3.58999991416931,3.58999991416931,2000,0,0
|
||||
2020-09-01 00:00:00-04:00,3.5,3.59999990463257,3.5,3.59999990463257,3.59999990463257,1200,0,0
|
||||
2020-08-31 00:00:00-04:00,3.15000009536743,3.70000004768372,3.15000009536743,3.70000004768372,3.70000004768372,26500,0,0
|
||||
2020-08-28 00:00:00-04:00,3.76999998092651,3.76999998092651,3.70000004768372,3.70000004768372,3.70000004768372,1600,0,0
|
||||
2020-08-27 00:00:00-04:00,3.65000009536743,3.65000009536743,3.65000009536743,3.65000009536743,3.65000009536743,0,0,0
|
||||
2020-08-26 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0.1
|
||||
2020-08-25 00:00:00-04:00,3.40000009536743,3.70000004768372,3.40000009536743,3.70000004768372,3.70000004768372,2900,0,0
|
||||
2020-08-24 00:00:00-04:00,3.29999995231628,3.5,3.29999995231628,3.5,3.5,10000,0,0
|
||||
2020-08-21 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,150,0,0
|
||||
2020-08-20 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-19 00:00:00-04:00,3.40000009536743,3.5,3.40000009536743,3.5,3.5,9050,0,0
|
||||
2020-08-18 00:00:00-04:00,3.5,3.79999995231628,3.5,3.5,3.5,2250,0,0
|
||||
2020-08-17 00:00:00-04:00,2.79999995231628,3.70000004768372,2.79999995231628,3.70000004768372,3.70000004768372,5050,0,0
|
||||
2020-08-14 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-13 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-12 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-11 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-10 00:00:00-04:00,3.5,3.70000004768372,3.5,3.5,3.5,3300,0,0
|
||||
2020-08-07 00:00:00-04:00,3.5,3.79999995231628,3.5,3.79999995231628,3.79999995231628,2500,0,0
|
||||
2020-08-06 00:00:00-04:00,3.5,3.70000004768372,3.40000009536743,3.70000004768372,3.70000004768372,3000,0,0
|
||||
2020-08-05 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0
|
||||
2020-08-04 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0
|
||||
|
|
|
@ -0,0 +1,42 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2020-09-30 00:00:00-04:00,4.40000009536743,4.44999980926514,4.01999998092651,4.44999980926514,4.44999980926514,22600,0,0
|
||||
2020-09-29 00:00:00-04:00,4.3899998664856,4.40000009536743,4.13000011444092,4.30000019073486,4.30000019073486,10800,0,0
|
||||
2020-09-28 00:00:00-04:00,4.09000015258789,4.25,4.09000015258789,4.25,4.25,8000,0,0
|
||||
2020-09-25 00:00:00-04:00,3.95000004768372,4.09999990463257,3.95000004768372,4.05000019073486,4.05000019073486,13500,0,0
|
||||
2020-09-24 00:00:00-04:00,3.84999990463257,4,3.84999990463257,4,4,8800,0,0
|
||||
2020-09-23 00:00:00-04:00,3.99000000953674,4,3.99000000953674,4,4,5900,0,0
|
||||
2020-09-22 00:00:00-04:00,3.90000009536743,4.09999990463257,3.84999990463257,4.09999990463257,4.09999990463257,3100,0,0
|
||||
2020-09-21 00:00:00-04:00,4.09999990463257,4.09999990463257,4.09999990463257,4.09999990463257,4.09999990463257,1200,0,0
|
||||
2020-09-18 00:00:00-04:00,3.92000007629395,4.09999990463257,3.92000007629395,4.09999990463257,4.09999990463257,27200,0,0
|
||||
2020-09-17 00:00:00-04:00,3.90000009536743,3.99000000953674,3.8199999332428,3.99000000953674,3.99000000953674,3300,0,0
|
||||
2020-09-16 00:00:00-04:00,3.79999995231628,4,3.79999995231628,4,4,3300,0,0
|
||||
2020-09-15 00:00:00-04:00,3.95000004768372,4,3.95000004768372,4,4,2400,0,0
|
||||
2020-09-14 00:00:00-04:00,3.96000003814697,4,3.96000003814697,4,4,800,0,0
|
||||
2020-09-11 00:00:00-04:00,3.95000004768372,3.97000002861023,3.72000002861023,3.97000002861023,3.97000002861023,5700,0,0
|
||||
2020-09-10 00:00:00-04:00,4,4.09999990463257,4,4.09999990463257,4.09999990463257,7100,0,0
|
||||
2020-09-09 00:00:00-04:00,3.5699999332428,4,3.5699999332428,4,4,18100,0,0
|
||||
2020-09-08 00:00:00-04:00,3.40000009536743,3.59999990463257,3.40000009536743,3.59999990463257,3.59999990463257,19500,0,0
|
||||
2020-09-04 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,400,0,0
|
||||
2020-09-03 00:00:00-04:00,3.58999991416931,3.58999991416931,3.58999991416931,3.58999991416931,3.58999991416931,0,0,0
|
||||
2020-09-02 00:00:00-04:00,3.5,3.58999991416931,3.5,3.58999991416931,3.58999991416931,2000,0,0
|
||||
2020-09-01 00:00:00-04:00,3.5,3.59999990463257,3.5,3.59999990463257,3.59999990463257,1200,0,0
|
||||
2020-08-31 00:00:00-04:00,3.15000009536743,3.70000004768372,3.15000009536743,3.70000004768372,3.70000004768372,26500,0,0
|
||||
2020-08-28 00:00:00-04:00,3.76999998092651,3.76999998092651,3.70000004768372,3.70000004768372,3.70000004768372,1600,0,0
|
||||
2020-08-27 00:00:00-04:00,3.65000009536743,3.65000009536743,3.65000009536743,3.65000009536743,3.65000009536743,0,0,0
|
||||
2020-08-26 00:00:00-04:00,0.370000004768372,0.370000004768372,0.370000004768372,0.370000004768372,0.370000004768372,0,0,0.1
|
||||
2020-08-25 00:00:00-04:00,3.40000009536743,3.70000004768372,3.40000009536743,3.70000004768372,3.70000004768372,2900,0,0
|
||||
2020-08-24 00:00:00-04:00,3.29999995231628,3.5,3.29999995231628,3.5,3.5,10000,0,0
|
||||
2020-08-21 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,150,0,0
|
||||
2020-08-20 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-19 00:00:00-04:00,3.40000009536743,3.5,3.40000009536743,3.5,3.5,9050,0,0
|
||||
2020-08-18 00:00:00-04:00,3.5,3.79999995231628,3.5,3.5,3.5,2250,0,0
|
||||
2020-08-17 00:00:00-04:00,2.79999995231628,3.70000004768372,2.79999995231628,3.70000004768372,3.70000004768372,5050,0,0
|
||||
2020-08-14 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-13 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-12 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-11 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
|
||||
2020-08-10 00:00:00-04:00,3.5,3.70000004768372,3.5,3.5,3.5,3300,0,0
|
||||
2020-08-07 00:00:00-04:00,3.5,3.79999995231628,3.5,3.79999995231628,3.79999995231628,2500,0,0
|
||||
2020-08-06 00:00:00-04:00,3.5,3.70000004768372,3.40000009536743,3.70000004768372,3.70000004768372,3000,0,0
|
||||
2020-08-05 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0
|
||||
2020-08-04 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0
|
||||
|
|
|
@ -0,0 +1,30 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-06-09 00:00:00+02:00,34.7000,34.7100,33.2400,33.6200,33.6200,7148409,0,0
|
||||
2023-06-08 00:00:00+02:00,34.9000,34.9900,34.0400,34.3600,34.3600,10406999,0,0
|
||||
2023-06-07 00:00:00+02:00,34.5500,35.6400,34.3200,35.0900,35.0900,10118918,0,0
|
||||
2023-06-06 00:00:00+02:00,34.5000,34.8200,34.0500,34.4600,34.4600,9109709,0,0
|
||||
2023-06-05 00:00:00+02:00,35.0000,35.3000,34.2000,34.7000,34.7000,8791993,0,0
|
||||
2023-06-02 00:00:00+02:00,35.6900,36.1800,34.6000,34.9700,34.9700,8844549,0,0
|
||||
2023-06-01 00:00:00+02:00,35.2300,35.3800,34.2400,35.3500,35.3500,6721030,0,0
|
||||
2023-05-31 00:00:00+02:00,34.8,35.48,34.26,35.01,35.01,32605833,0,0
|
||||
2023-05-30 00:00:00+02:00,34.39,35.37,33.85,34.23,34.23,8970804,0,0
|
||||
2023-05-29 00:00:00+02:00,34.66,35.06,34.02,34.32,34.32,3912803,0,0
|
||||
2023-05-26 00:00:00+02:00,34.75,35.99,34.33,34.53,34.53,6744718,0,0
|
||||
2023-05-25 00:00:00+02:00,35.4,36.09,34.63,35.07,35.07,16900221,0,0
|
||||
2023-05-24 00:00:00+02:00,36.2,36.5,35.26,35.4,35.4,9049505,0,0
|
||||
2023-05-23 00:00:00+02:00,36.9,36.67,35.56,36.1,36.1,10797373,0,0
|
||||
2023-05-22 00:00:00+02:00,37.05,37.36,36.09,36.61,36.61,7132641,0,0
|
||||
2023-05-19 00:00:00+02:00,36.2,37.15,36.25,36.9,36.9,12648518,0,0
|
||||
2023-05-18 00:00:00+02:00,36.57,36.99,35.84,36.46,36.46,10674542,0,0
|
||||
2023-05-17 00:00:00+02:00,36.87,37.31,36.56,36.71,36.71,9892791,0,0
|
||||
2023-05-16 00:00:00+02:00,37.15,37.73,36.96,37.03,37.03,4706789,0,0
|
||||
2023-05-15 00:00:00+02:00,37.74,38.05,36.96,37.27,37.27,7890969,0,0
|
||||
2023-05-12 00:00:00+02:00,37.5,38.44,36.71,37.74,37.74,8724303,0,0
|
||||
2023-05-11 00:00:00+02:00,38.8,38.88,37.01,37.32,37.32,14371855,0,0
|
||||
2023-05-10 00:00:00+02:00,38.93,38.8,36.42,38.1,38.1,30393389,0,0
|
||||
2023-05-09 00:00:00+02:00,44.41,44.41,39.39,39.66,39.66,19833428,0,0
|
||||
2023-05-08 00:00:00+02:00,44.63,45.78,44.56,44.71,44.71,11092519,0,0
|
||||
2023-05-05 00:00:00+02:00,42.99,44.9,42.87,44.58,44.58,28539048,0,0
|
||||
2023-05-04 00:00:00+02:00,41.49,43.3,41.23,42.83,42.83,15506868,0,0
|
||||
2023-05-03 00:00:00+02:00,39.75,40.98,39.68,40.95,40.95,14657028,0,0
|
||||
2023-05-02 00:00:00+02:00,40.37,40.32,39.17,39.65,39.65,11818133,0,0
|
||||
|
325
tests/prices.py
325
tests/prices.py
|
|
@ -59,7 +59,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
dt1 = df.index[-1]
|
||||
try:
|
||||
self.assertNotEqual(dt0.hour, dt1.hour)
|
||||
except:
|
||||
except AssertionError:
|
||||
print("Ticker = ", tkr)
|
||||
raise
|
||||
|
||||
|
|
@ -82,7 +82,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
dt1 = df.index[-1]
|
||||
try:
|
||||
self.assertNotEqual(dt0, dt1)
|
||||
except:
|
||||
except AssertionError:
|
||||
print("Ticker = ", tkr)
|
||||
raise
|
||||
|
||||
|
|
@ -106,7 +106,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
dt1 = df.index[-1]
|
||||
try:
|
||||
self.assertNotEqual(dt0.week, dt1.week)
|
||||
except:
|
||||
except AssertionError:
|
||||
print("Ticker={}: Last two rows within same week:".format(tkr))
|
||||
print(df.iloc[df.shape[0] - 2:])
|
||||
raise
|
||||
|
|
@ -172,18 +172,19 @@ class TestPriceHistory(unittest.TestCase):
|
|||
start_d = _dt.date(2022, 1, 1)
|
||||
end_d = _dt.date(2023, 1, 1)
|
||||
|
||||
tkr_div_dates = {}
|
||||
tkr_div_dates['BHP.AX'] = [_dt.date(2022, 9, 1), _dt.date(2022, 2, 24)] # Yahoo claims 23-Feb but wrong because DST
|
||||
tkr_div_dates['IMP.JO'] = [_dt.date(2022, 9, 21), _dt.date(2022, 3, 16)]
|
||||
tkr_div_dates['BP.L'] = [_dt.date(2022, 11, 10), _dt.date(2022, 8, 11), _dt.date(2022, 5, 12), _dt.date(2022, 2, 17)]
|
||||
tkr_div_dates['INTC'] = [_dt.date(2022, 11, 4), _dt.date(2022, 8, 4), _dt.date(2022, 5, 5), _dt.date(2022, 2, 4)]
|
||||
tkr_div_dates = {'BHP.AX': [_dt.date(2022, 9, 1), _dt.date(2022, 2, 24)], # Yahoo claims 23-Feb but wrong because DST
|
||||
'IMP.JO': [_dt.date(2022, 9, 21), _dt.date(2022, 3, 16)],
|
||||
'BP.L': [_dt.date(2022, 11, 10), _dt.date(2022, 8, 11), _dt.date(2022, 5, 12),
|
||||
_dt.date(2022, 2, 17)],
|
||||
'INTC': [_dt.date(2022, 11, 4), _dt.date(2022, 8, 4), _dt.date(2022, 5, 5),
|
||||
_dt.date(2022, 2, 4)]}
|
||||
|
||||
for tkr,dates in tkr_div_dates.items():
|
||||
for tkr, dates in tkr_div_dates.items():
|
||||
df = yf.Ticker(tkr, session=self.session).history(interval='1d', start=start_d, end=end_d)
|
||||
df_divs = df[df['Dividends']!=0].sort_index(ascending=False)
|
||||
df_divs = df[df['Dividends'] != 0].sort_index(ascending=False)
|
||||
try:
|
||||
self.assertTrue((df_divs.index.date == dates).all())
|
||||
except:
|
||||
except AssertionError:
|
||||
print(f'- ticker = {tkr}')
|
||||
print('- response:') ; print(df_divs.index.date)
|
||||
print('- answer:') ; print(dates)
|
||||
|
|
@ -201,7 +202,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
|
||||
try:
|
||||
self.assertTrue(df1.index.equals(df2.index))
|
||||
except:
|
||||
except AssertionError:
|
||||
missing_from_df1 = df2.index.difference(df1.index)
|
||||
missing_from_df2 = df1.index.difference(df2.index)
|
||||
print("{} missing these dates: {}".format(tkr1, missing_from_df1))
|
||||
|
|
@ -216,7 +217,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
|
||||
try:
|
||||
self.assertTrue(df1.index.equals(df2.index))
|
||||
except:
|
||||
except AssertionError:
|
||||
missing_from_df1 = df2.index.difference(df1.index)
|
||||
missing_from_df2 = df1.index.difference(df2.index)
|
||||
print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
|
||||
|
|
@ -298,7 +299,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
|
||||
try:
|
||||
self.assertTrue(df1.index.equals(df2.index))
|
||||
except:
|
||||
except AssertionError:
|
||||
missing_from_df1 = df2.index.difference(df1.index)
|
||||
missing_from_df2 = df1.index.difference(df2.index)
|
||||
print("{} missing these dates: {}".format(tkr1, missing_from_df1))
|
||||
|
|
@ -313,7 +314,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
|
||||
try:
|
||||
self.assertTrue(df1.index.equals(df2.index))
|
||||
except:
|
||||
except AssertionError:
|
||||
missing_from_df1 = df2.index.difference(df1.index)
|
||||
missing_from_df2 = df1.index.difference(df2.index)
|
||||
print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
|
||||
|
|
@ -331,7 +332,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
|
||||
try:
|
||||
self.assertTrue(df1.index.equals(df2.index))
|
||||
except:
|
||||
except AssertionError:
|
||||
missing_from_df1 = df2.index.difference(df1.index)
|
||||
missing_from_df2 = df1.index.difference(df2.index)
|
||||
print("{} missing these dates: {}".format(tkr1, missing_from_df1))
|
||||
|
|
@ -346,7 +347,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
|
||||
try:
|
||||
self.assertTrue(df1.index.equals(df2.index))
|
||||
except:
|
||||
except AssertionError:
|
||||
missing_from_df1 = df2.index.difference(df1.index)
|
||||
missing_from_df2 = df1.index.difference(df2.index)
|
||||
print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
|
||||
|
|
@ -358,15 +359,15 @@ class TestPriceHistory(unittest.TestCase):
|
|||
dfm = yf.Ticker("ABBV").history(period="max", interval="1mo")
|
||||
dfd = yf.Ticker("ABBV").history(period="max", interval="1d")
|
||||
dfd = dfd[dfd.index > dfm.index[0]]
|
||||
dfm_divs = dfm[dfm['Dividends']!=0]
|
||||
dfd_divs = dfd[dfd['Dividends']!=0]
|
||||
dfm_divs = dfm[dfm['Dividends'] != 0]
|
||||
dfd_divs = dfd[dfd['Dividends'] != 0]
|
||||
self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
|
||||
|
||||
dfm = yf.Ticker("F").history(period="50mo",interval="1mo")
|
||||
dfm = yf.Ticker("F").history(period="50mo", interval="1mo")
|
||||
dfd = yf.Ticker("F").history(period="50mo", interval="1d")
|
||||
dfd = dfd[dfd.index > dfm.index[0]]
|
||||
dfm_divs = dfm[dfm['Dividends']!=0]
|
||||
dfd_divs = dfd[dfd['Dividends']!=0]
|
||||
dfm_divs = dfm[dfm['Dividends'] != 0]
|
||||
dfd_divs = dfd[dfd['Dividends'] != 0]
|
||||
self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
|
||||
|
||||
def test_tz_dst_ambiguous(self):
|
||||
|
|
@ -397,7 +398,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
df = dat.history(start=start, end=end, interval=interval)
|
||||
try:
|
||||
self.assertTrue((df.index.weekday == 0).all())
|
||||
except:
|
||||
except AssertionError:
|
||||
print("Weekly data not aligned to Monday")
|
||||
raise
|
||||
|
||||
|
|
@ -449,18 +450,18 @@ class TestPriceHistory(unittest.TestCase):
|
|||
interval = "1h"
|
||||
interval_td = _dt.timedelta(hours=1)
|
||||
time_open = _dt.time(9)
|
||||
time_close = _dt.time(17,30)
|
||||
time_close = _dt.time(17, 30)
|
||||
special_day = _dt.date(2022, 12, 23)
|
||||
time_early_close = _dt.time(13, 2)
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
|
||||
# Half trading day Jan 5, Apr 14, May 25, Jun 23, Nov 4, Dec 23, Dec 30
|
||||
half_days = [_dt.date(special_day.year, x[0], x[1]) for x in [(1,5), (4,14), (5,25), (6,23), (11,4), (12,23), (12,30)]]
|
||||
half_days = [_dt.date(special_day.year, x[0], x[1]) for x in [(1, 5), (4, 14), (5, 25), (6, 23), (11, 4), (12, 23), (12, 30)]]
|
||||
|
||||
# Yahoo has incorrectly classified afternoon of 2022-04-13 as post-market.
|
||||
# Nothing yfinance can do because Yahoo doesn't return data with prepost=False.
|
||||
# But need to handle in this test.
|
||||
expected_incorrect_half_days = [_dt.date(2022,4,13)]
|
||||
expected_incorrect_half_days = [_dt.date(2022, 4, 13)]
|
||||
half_days = sorted(half_days+expected_incorrect_half_days)
|
||||
|
||||
# Run
|
||||
|
|
@ -477,7 +478,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
last_dts = _pd.Series(df.index).groupby(df.index.date).last()
|
||||
f_early_close = (last_dts+interval_td).dt.time < time_close
|
||||
early_close_dates = last_dts.index[f_early_close].values
|
||||
unexpected_early_close_dates = [d for d in early_close_dates if not d in half_days]
|
||||
unexpected_early_close_dates = [d for d in early_close_dates if d not in half_days]
|
||||
self.assertEqual(len(unexpected_early_close_dates), 0)
|
||||
self.assertEqual(len(early_close_dates), len(half_days))
|
||||
self.assertTrue(_np.equal(early_close_dates, half_days).all())
|
||||
|
|
@ -493,7 +494,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
interval = "1h"
|
||||
interval_td = _dt.timedelta(hours=1)
|
||||
time_open = _dt.time(10)
|
||||
time_close = _dt.time(16,12)
|
||||
time_close = _dt.time(16, 12)
|
||||
# No early closes in 2022
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
|
||||
|
|
@ -530,6 +531,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
|
||||
df = dat.history(start=start, end=end, interval=interval)
|
||||
|
||||
|
||||
class TestPriceRepair(unittest.TestCase):
|
||||
session = None
|
||||
|
||||
|
|
@ -573,12 +575,12 @@ class TestPriceRepair(unittest.TestCase):
|
|||
"High": [476, 476.5, 477, 480],
|
||||
"Low": [470.5, 470, 465.5, 468.26],
|
||||
"Close": [475, 473.5, 472, 473.5],
|
||||
"Adj Close": [475, 473.5, 472, 473.5],
|
||||
"Adj Close": [470.1, 468.6, 467.1, 468.6],
|
||||
"Volume": [2295613, 2245604, 3000287, 2635611]},
|
||||
index=_pd.to_datetime([_dt.date(2022, 10, 24),
|
||||
_dt.date(2022, 10, 17),
|
||||
_dt.date(2022, 10, 10),
|
||||
_dt.date(2022, 10, 3)]))
|
||||
index=_pd.to_datetime([_dt.date(2022, 10, 24),
|
||||
_dt.date(2022, 10, 17),
|
||||
_dt.date(2022, 10, 10),
|
||||
_dt.date(2022, 10, 3)]))
|
||||
df = df.sort_index()
|
||||
df.index.name = "Date"
|
||||
df_bad = df.copy()
|
||||
|
|
@ -590,18 +592,17 @@ class TestPriceRepair(unittest.TestCase):
|
|||
|
||||
# Run test
|
||||
|
||||
df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False, silent=True)
|
||||
df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
|
||||
|
||||
# First test - no errors left
|
||||
for c in data_cols:
|
||||
try:
|
||||
self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
|
||||
except:
|
||||
except AssertionError:
|
||||
print(df[c])
|
||||
print(df_repaired[c])
|
||||
raise
|
||||
|
||||
|
||||
# Second test - all differences should be either ~1x or ~100x
|
||||
ratio = df_bad[data_cols].values / df[data_cols].values
|
||||
ratio = ratio.round(2)
|
||||
|
|
@ -624,16 +625,16 @@ class TestPriceRepair(unittest.TestCase):
|
|||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
|
||||
df = _pd.DataFrame(data={"Open": [400, 398, 392.5, 417],
|
||||
"High": [421, 425, 419, 420.5],
|
||||
"Low": [400, 380.5, 376.5, 396],
|
||||
"Close": [410, 409.5, 402, 399],
|
||||
"Adj Close": [398.02, 397.53, 390.25, 387.34],
|
||||
df = _pd.DataFrame(data={"Open": [400, 398, 392.5, 417],
|
||||
"High": [421, 425, 419, 420.5],
|
||||
"Low": [400, 380.5, 376.5, 396],
|
||||
"Close": [410, 409.5, 402, 399],
|
||||
"Adj Close": [393.91, 393.43, 386.22, 383.34],
|
||||
"Volume": [3232600, 3773900, 10835000, 4257900]},
|
||||
index=_pd.to_datetime([_dt.date(2020, 3, 30),
|
||||
_dt.date(2020, 3, 23),
|
||||
_dt.date(2020, 3, 16),
|
||||
_dt.date(2020, 3, 9)]))
|
||||
index=_pd.to_datetime([_dt.date(2020, 3, 30),
|
||||
_dt.date(2020, 3, 23),
|
||||
_dt.date(2020, 3, 16),
|
||||
_dt.date(2020, 3, 9)]))
|
||||
df = df.sort_index()
|
||||
# Simulate data missing split-adjustment:
|
||||
df[data_cols] *= 100.0
|
||||
|
|
@ -648,13 +649,13 @@ class TestPriceRepair(unittest.TestCase):
|
|||
df.index = df.index.tz_localize(tz_exchange)
|
||||
df_bad.index = df_bad.index.tz_localize(tz_exchange)
|
||||
|
||||
df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False, silent=True)
|
||||
df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
|
||||
|
||||
# First test - no errors left
|
||||
for c in data_cols:
|
||||
try:
|
||||
self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
|
||||
except:
|
||||
except AssertionError:
|
||||
print("Mismatch in column", c)
|
||||
print("- df_repaired:")
|
||||
print(df_repaired[c])
|
||||
|
|
@ -688,10 +689,10 @@ class TestPriceRepair(unittest.TestCase):
|
|||
"Close": [475.5, 475.5, 474.5, 475],
|
||||
"Adj Close": [475.5, 475.5, 474.5, 475],
|
||||
"Volume": [436414, 485947, 358067, 287620]},
|
||||
index=_pd.to_datetime([_dt.date(2022, 11, 1),
|
||||
_dt.date(2022, 10, 31),
|
||||
_dt.date(2022, 10, 28),
|
||||
_dt.date(2022, 10, 27)]))
|
||||
index=_pd.to_datetime([_dt.date(2022, 11, 1),
|
||||
_dt.date(2022, 10, 31),
|
||||
_dt.date(2022, 10, 28),
|
||||
_dt.date(2022, 10, 27)]))
|
||||
df = df.sort_index()
|
||||
df.index.name = "Date"
|
||||
df_bad = df.copy()
|
||||
|
|
@ -701,7 +702,7 @@ class TestPriceRepair(unittest.TestCase):
|
|||
df.index = df.index.tz_localize(tz_exchange)
|
||||
df_bad.index = df_bad.index.tz_localize(tz_exchange)
|
||||
|
||||
df_repaired = dat._fix_unit_random_mixups(df_bad, "1d", tz_exchange, prepost=False, silent=True)
|
||||
df_repaired = dat._fix_unit_random_mixups(df_bad, "1d", tz_exchange, prepost=False)
|
||||
|
||||
# First test - no errors left
|
||||
for c in data_cols:
|
||||
|
|
@ -725,46 +726,55 @@ class TestPriceRepair(unittest.TestCase):
|
|||
# Some 100x errors are not sporadic.
|
||||
# Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
|
||||
|
||||
tkr = "SSW.JO"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
tkrs = ['AET.L', 'SSW.JO']
|
||||
for tkr in tkrs:
|
||||
for interval in ['1d', '1wk']:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
|
||||
_dp = os.path.dirname(__file__)
|
||||
df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-100x-error.csv"), index_col="Date")
|
||||
df_bad.index = _pd.to_datetime(df_bad.index)
|
||||
df_bad = df_bad.sort_index()
|
||||
data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
|
||||
_dp = os.path.dirname(__file__)
|
||||
fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error.csv")
|
||||
if not os.path.isfile(fp):
|
||||
continue
|
||||
df_bad = _pd.read_csv(fp, index_col="Date")
|
||||
df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz_exchange)
|
||||
df_bad = df_bad.sort_index()
|
||||
|
||||
df = df_bad.copy()
|
||||
for d in data_cols:
|
||||
df.loc[:'2023-05-31', d] *= 0.01 # fix error
|
||||
df = df_bad.copy()
|
||||
fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error-fixed.csv")
|
||||
df = _pd.read_csv(fp, index_col="Date")
|
||||
df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz_exchange)
|
||||
df = df.sort_index()
|
||||
|
||||
df_repaired = dat._fix_unit_switch(df_bad, "1d", tz_exchange)
|
||||
df_repaired = df_repaired.sort_index()
|
||||
df_repaired = dat._fix_unit_switch(df_bad, interval, tz_exchange)
|
||||
df_repaired = df_repaired.sort_index()
|
||||
|
||||
# First test - no errors left
|
||||
for c in data_cols:
|
||||
try:
|
||||
self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
|
||||
except:
|
||||
print(df_repaired[c])
|
||||
print(df[c])
|
||||
print(f"TEST FAIL on column '{c}")
|
||||
raise
|
||||
# First test - no errors left
|
||||
for c in data_cols:
|
||||
try:
|
||||
self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
|
||||
except:
|
||||
print("- repaired:")
|
||||
print(df_repaired[c])
|
||||
print("- correct:")
|
||||
print(df[c])
|
||||
print(f"TEST FAIL on column '{c}' (tkr={tkr} interval={interval})")
|
||||
raise
|
||||
|
||||
# Second test - all differences should be either ~1x or ~100x
|
||||
ratio = df_bad[data_cols].values / df[data_cols].values
|
||||
ratio = ratio.round(2)
|
||||
# - round near-100 ratio to 100:
|
||||
f = ratio > 90
|
||||
ratio[f] = (ratio[f] / 10).round().astype(int) * 10 # round ratio to nearest 10
|
||||
# - now test
|
||||
f_100 = ratio == 100
|
||||
f_1 = ratio == 1
|
||||
self.assertTrue((f_100 | f_1).all())
|
||||
# Second test - all differences should be either ~1x or ~100x
|
||||
ratio = df_bad[data_cols].values / df[data_cols].values
|
||||
ratio = ratio.round(2)
|
||||
# - round near-100 ratio to 100:
|
||||
f = ratio > 90
|
||||
ratio[f] = (ratio[f] / 10).round().astype(int) * 10 # round ratio to nearest 10
|
||||
# - now test
|
||||
f_100 = (ratio == 100) | (ratio == 0.01)
|
||||
f_1 = ratio == 1
|
||||
self.assertTrue((f_100 | f_1).all())
|
||||
|
||||
self.assertTrue("Repaired?" in df_repaired.columns)
|
||||
self.assertFalse(df_repaired["Repaired?"].isna().any())
|
||||
self.assertTrue("Repaired?" in df_repaired.columns)
|
||||
self.assertFalse(df_repaired["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_zeroes_daily(self):
|
||||
tkr = "BBIL.L"
|
||||
|
|
@ -777,9 +787,9 @@ class TestPriceRepair(unittest.TestCase):
|
|||
"Close": [103.03, 102.05, 102.08],
|
||||
"Adj Close": [102.03, 102.05, 102.08],
|
||||
"Volume": [560, 137, 117]},
|
||||
index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
|
||||
_dt.datetime(2022, 10, 31),
|
||||
_dt.datetime(2022, 10, 30)]))
|
||||
index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
|
||||
_dt.datetime(2022, 10, 31),
|
||||
_dt.datetime(2022, 10, 30)]))
|
||||
df_bad = df_bad.sort_index()
|
||||
df_bad.index.name = "Date"
|
||||
df_bad.index = df_bad.index.tz_localize(tz_exchange)
|
||||
|
|
@ -808,11 +818,11 @@ class TestPriceRepair(unittest.TestCase):
|
|||
"Adj Close": [28.12, 28.93, 28.57, 29.83, 29.70],
|
||||
"Volume": [36e6, 51e6, 49e6, 58e6, 62e6],
|
||||
"Dividends": [0, 0, 0.365, 0, 0]},
|
||||
index=_pd.to_datetime([_dt.datetime(2023, 2, 8),
|
||||
_dt.datetime(2023, 2, 7),
|
||||
_dt.datetime(2023, 2, 6),
|
||||
_dt.datetime(2023, 2, 3),
|
||||
_dt.datetime(2023, 2, 2)]))
|
||||
index=_pd.to_datetime([_dt.datetime(2023, 2, 8),
|
||||
_dt.datetime(2023, 2, 7),
|
||||
_dt.datetime(2023, 2, 6),
|
||||
_dt.datetime(2023, 2, 3),
|
||||
_dt.datetime(2023, 2, 2)]))
|
||||
df = df.sort_index()
|
||||
df.index.name = "Date"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
|
|
@ -853,7 +863,7 @@ class TestPriceRepair(unittest.TestCase):
|
|||
for c in ["Open", "Low", "High", "Close"]:
|
||||
try:
|
||||
self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-7).all())
|
||||
except:
|
||||
except AssertionError:
|
||||
print("COLUMN", c)
|
||||
print("- repaired_df")
|
||||
print(repaired_df)
|
||||
|
|
@ -867,35 +877,6 @@ class TestPriceRepair(unittest.TestCase):
|
|||
self.assertFalse(repaired_df["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_bad_stock_split(self):
|
||||
bad_tkrs = ['4063.T', 'ALPHA.PA', 'CNE.L', 'MOB.ST', 'SPM.MI']
|
||||
for tkr in bad_tkrs:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
_dp = os.path.dirname(__file__)
|
||||
df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-bad-stock-split.csv"), index_col="Date")
|
||||
df_bad.index = _pd.to_datetime(df_bad.index)
|
||||
|
||||
repaired_df = dat._fix_bad_stock_split(df_bad, "1d", tz_exchange)
|
||||
|
||||
correct_df = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-bad-stock-split-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:
|
||||
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
|
||||
|
||||
# Stocks that split in 2022 but no problems in Yahoo data,
|
||||
# so repair should change nothing
|
||||
good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
|
||||
|
|
@ -908,7 +889,7 @@ class TestPriceRepair(unittest.TestCase):
|
|||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
_dp = os.path.dirname(__file__)
|
||||
df_good = dat.history(period='2y', interval=interval, auto_adjust=False)
|
||||
df_good = dat.history(start='2020-01-01', end=_dt.date.today(), interval=interval, auto_adjust=False)
|
||||
|
||||
repaired_df = dat._fix_bad_stock_split(df_good, interval, tz_exchange)
|
||||
|
||||
|
|
@ -925,6 +906,100 @@ class TestPriceRepair(unittest.TestCase):
|
|||
print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
|
||||
raise
|
||||
|
||||
bad_tkrs = ['4063.T', 'ALPHA.PA', 'AV.L', 'CNE.L', 'MOB.ST', 'SPM.MI']
|
||||
bad_tkrs.append('LA.V') # special case - stock split error is 3 years ago! why not fixed?
|
||||
for tkr in bad_tkrs:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
_dp = os.path.dirname(__file__)
|
||||
interval = '1d'
|
||||
fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
|
||||
if not os.path.isfile(fp):
|
||||
interval = '1wk'
|
||||
fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
|
||||
df_bad = _pd.read_csv(fp, index_col="Date")
|
||||
df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
|
||||
|
||||
repaired_df = dat._fix_bad_stock_split(df_bad, "1d", tz_exchange)
|
||||
|
||||
fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
|
||||
correct_df = _pd.read_csv(fp, 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 AssertionError:
|
||||
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
|
||||
|
||||
# Had very high price volatility in Jan-2021 around split date that could
|
||||
# be mistaken for missing stock split adjustment. And old logic did think
|
||||
# column 'High' required fixing - wrong!
|
||||
sketchy_tkrs = ['FIZZ']
|
||||
intervals = ['1wk']
|
||||
for tkr in sketchy_tkrs:
|
||||
for interval in intervals:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
_dp = os.path.dirname(__file__)
|
||||
df_good = dat.history(start='2020-11-30', end='2021-04-01', interval=interval, auto_adjust=False)
|
||||
|
||||
repaired_df = dat._fix_bad_stock_split(df_good, interval, tz_exchange)
|
||||
|
||||
# Expect no change from repair
|
||||
df_good = df_good.sort_index()
|
||||
repaired_df = repaired_df.sort_index()
|
||||
for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
|
||||
try:
|
||||
self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
|
||||
except AssertionError:
|
||||
print(f"tkr={tkr} interval={interval} COLUMN={c}")
|
||||
df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
|
||||
f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
|
||||
print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
|
||||
raise
|
||||
|
||||
def test_repair_missing_div_adjust(self):
|
||||
tkr = '8TRA.DE'
|
||||
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
_dp = os.path.dirname(__file__)
|
||||
df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust.csv"), index_col="Date")
|
||||
df_bad.index = _pd.to_datetime(df_bad.index)
|
||||
|
||||
repaired_df = dat._fix_missing_div_adjust(df_bad, "1d", tz_exchange)
|
||||
|
||||
correct_df = _pd.read_csv(os.path.join(_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:
|
||||
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
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
|
|
|||
770
yfinance/base.py
770
yfinance/base.py
File diff suppressed because it is too large
Load Diff
|
|
@ -1,10 +1,118 @@
|
|||
_BASE_URL_ = 'https://query2.finance.yahoo.com'
|
||||
_ROOT_URL_ = 'https://finance.yahoo.com'
|
||||
|
||||
fundamentals_keys = {}
|
||||
|
||||
fundamentals_keys['financials'] = ["TaxEffectOfUnusualItems","TaxRateForCalcs","NormalizedEBITDA","NormalizedDilutedEPS","NormalizedBasicEPS","TotalUnusualItems","TotalUnusualItemsExcludingGoodwill","NetIncomeFromContinuingOperationNetMinorityInterest","ReconciledDepreciation","ReconciledCostOfRevenue","EBITDA","EBIT","NetInterestIncome","InterestExpense","InterestIncome","ContinuingAndDiscontinuedDilutedEPS","ContinuingAndDiscontinuedBasicEPS","NormalizedIncome","NetIncomeFromContinuingAndDiscontinuedOperation","TotalExpenses","RentExpenseSupplemental","ReportedNormalizedDilutedEPS","ReportedNormalizedBasicEPS","TotalOperatingIncomeAsReported","DividendPerShare","DilutedAverageShares","BasicAverageShares","DilutedEPS","DilutedEPSOtherGainsLosses","TaxLossCarryforwardDilutedEPS","DilutedAccountingChange","DilutedExtraordinary","DilutedDiscontinuousOperations","DilutedContinuousOperations","BasicEPS","BasicEPSOtherGainsLosses","TaxLossCarryforwardBasicEPS","BasicAccountingChange","BasicExtraordinary","BasicDiscontinuousOperations","BasicContinuousOperations","DilutedNIAvailtoComStockholders","AverageDilutionEarnings","NetIncomeCommonStockholders","OtherunderPreferredStockDividend","PreferredStockDividends","NetIncome","MinorityInterests","NetIncomeIncludingNoncontrollingInterests","NetIncomeFromTaxLossCarryforward","NetIncomeExtraordinary","NetIncomeDiscontinuousOperations","NetIncomeContinuousOperations","EarningsFromEquityInterestNetOfTax","TaxProvision","PretaxIncome","OtherIncomeExpense","OtherNonOperatingIncomeExpenses","SpecialIncomeCharges","GainOnSaleOfPPE","GainOnSaleOfBusiness","OtherSpecialCharges","WriteOff","ImpairmentOfCapitalAssets","RestructuringAndMergernAcquisition","SecuritiesAmortization","EarningsFromEquityInterest","GainOnSaleOfSecurity","NetNonOperatingInterestIncomeExpense","TotalOtherFinanceCost","InterestExpenseNonOperating","InterestIncomeNonOperating","OperatingIncome","OperatingExpense","OtherOperatingExpenses","OtherTaxes","ProvisionForDoubtfulAccounts","DepreciationAmortizationDepletionIncomeStatement","DepletionIncomeStatement","DepreciationAndAmortizationInIncomeStatement","Amortization","AmortizationOfIntangiblesIncomeStatement","DepreciationIncomeStatement","ResearchAndDevelopment","SellingGeneralAndAdministration","SellingAndMarketingExpense","GeneralAndAdministrativeExpense","OtherGandA","InsuranceAndClaims","RentAndLandingFees","SalariesAndWages","GrossProfit","CostOfRevenue","TotalRevenue","ExciseTaxes","OperatingRevenue"]
|
||||
|
||||
fundamentals_keys['balance-sheet'] = ["TreasurySharesNumber","PreferredSharesNumber","OrdinarySharesNumber","ShareIssued","NetDebt","TotalDebt","TangibleBookValue","InvestedCapital","WorkingCapital","NetTangibleAssets","CapitalLeaseObligations","CommonStockEquity","PreferredStockEquity","TotalCapitalization","TotalEquityGrossMinorityInterest","MinorityInterest","StockholdersEquity","OtherEquityInterest","GainsLossesNotAffectingRetainedEarnings","OtherEquityAdjustments","FixedAssetsRevaluationReserve","ForeignCurrencyTranslationAdjustments","MinimumPensionLiabilities","UnrealizedGainLoss","TreasuryStock","RetainedEarnings","AdditionalPaidInCapital","CapitalStock","OtherCapitalStock","CommonStock","PreferredStock","TotalPartnershipCapital","GeneralPartnershipCapital","LimitedPartnershipCapital","TotalLiabilitiesNetMinorityInterest","TotalNonCurrentLiabilitiesNetMinorityInterest","OtherNonCurrentLiabilities","LiabilitiesHeldforSaleNonCurrent","RestrictedCommonStock","PreferredSecuritiesOutsideStockEquity","DerivativeProductLiabilities","EmployeeBenefits","NonCurrentPensionAndOtherPostretirementBenefitPlans","NonCurrentAccruedExpenses","DuetoRelatedPartiesNonCurrent","TradeandOtherPayablesNonCurrent","NonCurrentDeferredLiabilities","NonCurrentDeferredRevenue","NonCurrentDeferredTaxesLiabilities","LongTermDebtAndCapitalLeaseObligation","LongTermCapitalLeaseObligation","LongTermDebt","LongTermProvisions","CurrentLiabilities","OtherCurrentLiabilities","CurrentDeferredLiabilities","CurrentDeferredRevenue","CurrentDeferredTaxesLiabilities","CurrentDebtAndCapitalLeaseObligation","CurrentCapitalLeaseObligation","CurrentDebt","OtherCurrentBorrowings","LineOfCredit","CommercialPaper","CurrentNotesPayable","PensionandOtherPostRetirementBenefitPlansCurrent","CurrentProvisions","PayablesAndAccruedExpenses","CurrentAccruedExpenses","InterestPayable","Payables","OtherPayable","DuetoRelatedPartiesCurrent","DividendsPayable","TotalTaxPayable","IncomeTaxPayable","AccountsPayable","TotalAssets","TotalNonCurrentAssets","OtherNonCurrentAssets","DefinedPensionBenefit","NonCurrentPrepaidAssets","NonCurrentDeferredAssets","NonCurrentDeferredTaxesAssets","DuefromRelatedPartiesNonCurrent","NonCurrentNoteReceivables","NonCurrentAccountsReceivable","FinancialAssets","InvestmentsAndAdvances","OtherInvestments","InvestmentinFinancialAssets","HeldToMaturitySecurities","AvailableForSaleSecurities","FinancialAssetsDesignatedasFairValueThroughProfitorLossTotal","TradingSecurities","LongTermEquityInvestment","InvestmentsinJointVenturesatCost","InvestmentsInOtherVenturesUnderEquityMethod","InvestmentsinAssociatesatCost","InvestmentsinSubsidiariesatCost","InvestmentProperties","GoodwillAndOtherIntangibleAssets","OtherIntangibleAssets","Goodwill","NetPPE","AccumulatedDepreciation","GrossPPE","Leases","ConstructionInProgress","OtherProperties","MachineryFurnitureEquipment","BuildingsAndImprovements","LandAndImprovements","Properties","CurrentAssets","OtherCurrentAssets","HedgingAssetsCurrent","AssetsHeldForSaleCurrent","CurrentDeferredAssets","CurrentDeferredTaxesAssets","RestrictedCash","PrepaidAssets","Inventory","InventoriesAdjustmentsAllowances","OtherInventories","FinishedGoods","WorkInProcess","RawMaterials","Receivables","ReceivablesAdjustmentsAllowances","OtherReceivables","DuefromRelatedPartiesCurrent","TaxesReceivable","AccruedInterestReceivable","NotesReceivable","LoansReceivable","AccountsReceivable","AllowanceForDoubtfulAccountsReceivable","GrossAccountsReceivable","CashCashEquivalentsAndShortTermInvestments","OtherShortTermInvestments","CashAndCashEquivalents","CashEquivalents","CashFinancial"]
|
||||
|
||||
fundamentals_keys['cash-flow'] = ["ForeignSales","DomesticSales","AdjustedGeographySegmentData","FreeCashFlow","RepurchaseOfCapitalStock","RepaymentOfDebt","IssuanceOfDebt","IssuanceOfCapitalStock","CapitalExpenditure","InterestPaidSupplementalData","IncomeTaxPaidSupplementalData","EndCashPosition","OtherCashAdjustmentOutsideChangeinCash","BeginningCashPosition","EffectOfExchangeRateChanges","ChangesInCash","OtherCashAdjustmentInsideChangeinCash","CashFlowFromDiscontinuedOperation","FinancingCashFlow","CashFromDiscontinuedFinancingActivities","CashFlowFromContinuingFinancingActivities","NetOtherFinancingCharges","InterestPaidCFF","ProceedsFromStockOptionExercised","CashDividendsPaid","PreferredStockDividendPaid","CommonStockDividendPaid","NetPreferredStockIssuance","PreferredStockPayments","PreferredStockIssuance","NetCommonStockIssuance","CommonStockPayments","CommonStockIssuance","NetIssuancePaymentsOfDebt","NetShortTermDebtIssuance","ShortTermDebtPayments","ShortTermDebtIssuance","NetLongTermDebtIssuance","LongTermDebtPayments","LongTermDebtIssuance","InvestingCashFlow","CashFromDiscontinuedInvestingActivities","CashFlowFromContinuingInvestingActivities","NetOtherInvestingChanges","InterestReceivedCFI","DividendsReceivedCFI","NetInvestmentPurchaseAndSale","SaleOfInvestment","PurchaseOfInvestment","NetInvestmentPropertiesPurchaseAndSale","SaleOfInvestmentProperties","PurchaseOfInvestmentProperties","NetBusinessPurchaseAndSale","SaleOfBusiness","PurchaseOfBusiness","NetIntangiblesPurchaseAndSale","SaleOfIntangibles","PurchaseOfIntangibles","NetPPEPurchaseAndSale","SaleOfPPE","PurchaseOfPPE","CapitalExpenditureReported","OperatingCashFlow","CashFromDiscontinuedOperatingActivities","CashFlowFromContinuingOperatingActivities","TaxesRefundPaid","InterestReceivedCFO","InterestPaidCFO","DividendReceivedCFO","DividendPaidCFO","ChangeInWorkingCapital","ChangeInOtherWorkingCapital","ChangeInOtherCurrentLiabilities","ChangeInOtherCurrentAssets","ChangeInPayablesAndAccruedExpense","ChangeInAccruedExpense","ChangeInInterestPayable","ChangeInPayable","ChangeInDividendPayable","ChangeInAccountPayable","ChangeInTaxPayable","ChangeInIncomeTaxPayable","ChangeInPrepaidAssets","ChangeInInventory","ChangeInReceivables","ChangesInAccountReceivables","OtherNonCashItems","ExcessTaxBenefitFromStockBasedCompensation","StockBasedCompensation","UnrealizedGainLossOnInvestmentSecurities","ProvisionandWriteOffofAssets","AssetImpairmentCharge","AmortizationOfSecurities","DeferredTax","DeferredIncomeTax","DepreciationAmortizationDepletion","Depletion","DepreciationAndAmortization","AmortizationCashFlow","AmortizationOfIntangibles","Depreciation","OperatingGainsLosses","PensionAndEmployeeBenefitExpense","EarningsLossesFromEquityInvestments","GainLossOnInvestmentSecurities","NetForeignCurrencyExchangeGainLoss","GainLossOnSaleOfPPE","GainLossOnSaleOfBusiness","NetIncomeFromContinuingOperations","CashFlowsfromusedinOperatingActivitiesDirect","TaxesRefundPaidDirect","InterestReceivedDirect","InterestPaidDirect","DividendsReceivedDirect","DividendsPaidDirect","ClassesofCashPayments","OtherCashPaymentsfromOperatingActivities","PaymentsonBehalfofEmployees","PaymentstoSuppliersforGoodsandServices","ClassesofCashReceiptsfromOperatingActivities","OtherCashReceiptsfromOperatingActivities","ReceiptsfromGovernmentGrants","ReceiptsfromCustomers"]
|
||||
fundamentals_keys = {
|
||||
'financials': ["TaxEffectOfUnusualItems", "TaxRateForCalcs", "NormalizedEBITDA", "NormalizedDilutedEPS",
|
||||
"NormalizedBasicEPS", "TotalUnusualItems", "TotalUnusualItemsExcludingGoodwill",
|
||||
"NetIncomeFromContinuingOperationNetMinorityInterest", "ReconciledDepreciation",
|
||||
"ReconciledCostOfRevenue", "EBITDA", "EBIT", "NetInterestIncome", "InterestExpense",
|
||||
"InterestIncome", "ContinuingAndDiscontinuedDilutedEPS", "ContinuingAndDiscontinuedBasicEPS",
|
||||
"NormalizedIncome", "NetIncomeFromContinuingAndDiscontinuedOperation", "TotalExpenses",
|
||||
"RentExpenseSupplemental", "ReportedNormalizedDilutedEPS", "ReportedNormalizedBasicEPS",
|
||||
"TotalOperatingIncomeAsReported", "DividendPerShare", "DilutedAverageShares", "BasicAverageShares",
|
||||
"DilutedEPS", "DilutedEPSOtherGainsLosses", "TaxLossCarryforwardDilutedEPS",
|
||||
"DilutedAccountingChange", "DilutedExtraordinary", "DilutedDiscontinuousOperations",
|
||||
"DilutedContinuousOperations", "BasicEPS", "BasicEPSOtherGainsLosses", "TaxLossCarryforwardBasicEPS",
|
||||
"BasicAccountingChange", "BasicExtraordinary", "BasicDiscontinuousOperations",
|
||||
"BasicContinuousOperations", "DilutedNIAvailtoComStockholders", "AverageDilutionEarnings",
|
||||
"NetIncomeCommonStockholders", "OtherunderPreferredStockDividend", "PreferredStockDividends",
|
||||
"NetIncome", "MinorityInterests", "NetIncomeIncludingNoncontrollingInterests",
|
||||
"NetIncomeFromTaxLossCarryforward", "NetIncomeExtraordinary", "NetIncomeDiscontinuousOperations",
|
||||
"NetIncomeContinuousOperations", "EarningsFromEquityInterestNetOfTax", "TaxProvision",
|
||||
"PretaxIncome", "OtherIncomeExpense", "OtherNonOperatingIncomeExpenses", "SpecialIncomeCharges",
|
||||
"GainOnSaleOfPPE", "GainOnSaleOfBusiness", "OtherSpecialCharges", "WriteOff",
|
||||
"ImpairmentOfCapitalAssets", "RestructuringAndMergernAcquisition", "SecuritiesAmortization",
|
||||
"EarningsFromEquityInterest", "GainOnSaleOfSecurity", "NetNonOperatingInterestIncomeExpense",
|
||||
"TotalOtherFinanceCost", "InterestExpenseNonOperating", "InterestIncomeNonOperating",
|
||||
"OperatingIncome", "OperatingExpense", "OtherOperatingExpenses", "OtherTaxes",
|
||||
"ProvisionForDoubtfulAccounts", "DepreciationAmortizationDepletionIncomeStatement",
|
||||
"DepletionIncomeStatement", "DepreciationAndAmortizationInIncomeStatement", "Amortization",
|
||||
"AmortizationOfIntangiblesIncomeStatement", "DepreciationIncomeStatement", "ResearchAndDevelopment",
|
||||
"SellingGeneralAndAdministration", "SellingAndMarketingExpense", "GeneralAndAdministrativeExpense",
|
||||
"OtherGandA", "InsuranceAndClaims", "RentAndLandingFees", "SalariesAndWages", "GrossProfit",
|
||||
"CostOfRevenue", "TotalRevenue", "ExciseTaxes", "OperatingRevenue"],
|
||||
'balance-sheet': ["TreasurySharesNumber", "PreferredSharesNumber", "OrdinarySharesNumber", "ShareIssued", "NetDebt",
|
||||
"TotalDebt", "TangibleBookValue", "InvestedCapital", "WorkingCapital", "NetTangibleAssets",
|
||||
"CapitalLeaseObligations", "CommonStockEquity", "PreferredStockEquity", "TotalCapitalization",
|
||||
"TotalEquityGrossMinorityInterest", "MinorityInterest", "StockholdersEquity",
|
||||
"OtherEquityInterest", "GainsLossesNotAffectingRetainedEarnings", "OtherEquityAdjustments",
|
||||
"FixedAssetsRevaluationReserve", "ForeignCurrencyTranslationAdjustments",
|
||||
"MinimumPensionLiabilities", "UnrealizedGainLoss", "TreasuryStock", "RetainedEarnings",
|
||||
"AdditionalPaidInCapital", "CapitalStock", "OtherCapitalStock", "CommonStock", "PreferredStock",
|
||||
"TotalPartnershipCapital", "GeneralPartnershipCapital", "LimitedPartnershipCapital",
|
||||
"TotalLiabilitiesNetMinorityInterest", "TotalNonCurrentLiabilitiesNetMinorityInterest",
|
||||
"OtherNonCurrentLiabilities", "LiabilitiesHeldforSaleNonCurrent", "RestrictedCommonStock",
|
||||
"PreferredSecuritiesOutsideStockEquity", "DerivativeProductLiabilities", "EmployeeBenefits",
|
||||
"NonCurrentPensionAndOtherPostretirementBenefitPlans", "NonCurrentAccruedExpenses",
|
||||
"DuetoRelatedPartiesNonCurrent", "TradeandOtherPayablesNonCurrent",
|
||||
"NonCurrentDeferredLiabilities", "NonCurrentDeferredRevenue",
|
||||
"NonCurrentDeferredTaxesLiabilities", "LongTermDebtAndCapitalLeaseObligation",
|
||||
"LongTermCapitalLeaseObligation", "LongTermDebt", "LongTermProvisions", "CurrentLiabilities",
|
||||
"OtherCurrentLiabilities", "CurrentDeferredLiabilities", "CurrentDeferredRevenue",
|
||||
"CurrentDeferredTaxesLiabilities", "CurrentDebtAndCapitalLeaseObligation",
|
||||
"CurrentCapitalLeaseObligation", "CurrentDebt", "OtherCurrentBorrowings", "LineOfCredit",
|
||||
"CommercialPaper", "CurrentNotesPayable", "PensionandOtherPostRetirementBenefitPlansCurrent",
|
||||
"CurrentProvisions", "PayablesAndAccruedExpenses", "CurrentAccruedExpenses", "InterestPayable",
|
||||
"Payables", "OtherPayable", "DuetoRelatedPartiesCurrent", "DividendsPayable", "TotalTaxPayable",
|
||||
"IncomeTaxPayable", "AccountsPayable", "TotalAssets", "TotalNonCurrentAssets",
|
||||
"OtherNonCurrentAssets", "DefinedPensionBenefit", "NonCurrentPrepaidAssets",
|
||||
"NonCurrentDeferredAssets", "NonCurrentDeferredTaxesAssets", "DuefromRelatedPartiesNonCurrent",
|
||||
"NonCurrentNoteReceivables", "NonCurrentAccountsReceivable", "FinancialAssets",
|
||||
"InvestmentsAndAdvances", "OtherInvestments", "InvestmentinFinancialAssets",
|
||||
"HeldToMaturitySecurities", "AvailableForSaleSecurities",
|
||||
"FinancialAssetsDesignatedasFairValueThroughProfitorLossTotal", "TradingSecurities",
|
||||
"LongTermEquityInvestment", "InvestmentsinJointVenturesatCost",
|
||||
"InvestmentsInOtherVenturesUnderEquityMethod", "InvestmentsinAssociatesatCost",
|
||||
"InvestmentsinSubsidiariesatCost", "InvestmentProperties", "GoodwillAndOtherIntangibleAssets",
|
||||
"OtherIntangibleAssets", "Goodwill", "NetPPE", "AccumulatedDepreciation", "GrossPPE", "Leases",
|
||||
"ConstructionInProgress", "OtherProperties", "MachineryFurnitureEquipment",
|
||||
"BuildingsAndImprovements", "LandAndImprovements", "Properties", "CurrentAssets",
|
||||
"OtherCurrentAssets", "HedgingAssetsCurrent", "AssetsHeldForSaleCurrent", "CurrentDeferredAssets",
|
||||
"CurrentDeferredTaxesAssets", "RestrictedCash", "PrepaidAssets", "Inventory",
|
||||
"InventoriesAdjustmentsAllowances", "OtherInventories", "FinishedGoods", "WorkInProcess",
|
||||
"RawMaterials", "Receivables", "ReceivablesAdjustmentsAllowances", "OtherReceivables",
|
||||
"DuefromRelatedPartiesCurrent", "TaxesReceivable", "AccruedInterestReceivable", "NotesReceivable",
|
||||
"LoansReceivable", "AccountsReceivable", "AllowanceForDoubtfulAccountsReceivable",
|
||||
"GrossAccountsReceivable", "CashCashEquivalentsAndShortTermInvestments",
|
||||
"OtherShortTermInvestments", "CashAndCashEquivalents", "CashEquivalents", "CashFinancial"],
|
||||
'cash-flow': ["ForeignSales", "DomesticSales", "AdjustedGeographySegmentData", "FreeCashFlow",
|
||||
"RepurchaseOfCapitalStock", "RepaymentOfDebt", "IssuanceOfDebt", "IssuanceOfCapitalStock",
|
||||
"CapitalExpenditure", "InterestPaidSupplementalData", "IncomeTaxPaidSupplementalData",
|
||||
"EndCashPosition", "OtherCashAdjustmentOutsideChangeinCash", "BeginningCashPosition",
|
||||
"EffectOfExchangeRateChanges", "ChangesInCash", "OtherCashAdjustmentInsideChangeinCash",
|
||||
"CashFlowFromDiscontinuedOperation", "FinancingCashFlow", "CashFromDiscontinuedFinancingActivities",
|
||||
"CashFlowFromContinuingFinancingActivities", "NetOtherFinancingCharges", "InterestPaidCFF",
|
||||
"ProceedsFromStockOptionExercised", "CashDividendsPaid", "PreferredStockDividendPaid",
|
||||
"CommonStockDividendPaid", "NetPreferredStockIssuance", "PreferredStockPayments",
|
||||
"PreferredStockIssuance", "NetCommonStockIssuance", "CommonStockPayments", "CommonStockIssuance",
|
||||
"NetIssuancePaymentsOfDebt", "NetShortTermDebtIssuance", "ShortTermDebtPayments",
|
||||
"ShortTermDebtIssuance", "NetLongTermDebtIssuance", "LongTermDebtPayments", "LongTermDebtIssuance",
|
||||
"InvestingCashFlow", "CashFromDiscontinuedInvestingActivities",
|
||||
"CashFlowFromContinuingInvestingActivities", "NetOtherInvestingChanges", "InterestReceivedCFI",
|
||||
"DividendsReceivedCFI", "NetInvestmentPurchaseAndSale", "SaleOfInvestment", "PurchaseOfInvestment",
|
||||
"NetInvestmentPropertiesPurchaseAndSale", "SaleOfInvestmentProperties",
|
||||
"PurchaseOfInvestmentProperties", "NetBusinessPurchaseAndSale", "SaleOfBusiness",
|
||||
"PurchaseOfBusiness", "NetIntangiblesPurchaseAndSale", "SaleOfIntangibles", "PurchaseOfIntangibles",
|
||||
"NetPPEPurchaseAndSale", "SaleOfPPE", "PurchaseOfPPE", "CapitalExpenditureReported",
|
||||
"OperatingCashFlow", "CashFromDiscontinuedOperatingActivities",
|
||||
"CashFlowFromContinuingOperatingActivities", "TaxesRefundPaid", "InterestReceivedCFO",
|
||||
"InterestPaidCFO", "DividendReceivedCFO", "DividendPaidCFO", "ChangeInWorkingCapital",
|
||||
"ChangeInOtherWorkingCapital", "ChangeInOtherCurrentLiabilities", "ChangeInOtherCurrentAssets",
|
||||
"ChangeInPayablesAndAccruedExpense", "ChangeInAccruedExpense", "ChangeInInterestPayable",
|
||||
"ChangeInPayable", "ChangeInDividendPayable", "ChangeInAccountPayable", "ChangeInTaxPayable",
|
||||
"ChangeInIncomeTaxPayable", "ChangeInPrepaidAssets", "ChangeInInventory", "ChangeInReceivables",
|
||||
"ChangesInAccountReceivables", "OtherNonCashItems", "ExcessTaxBenefitFromStockBasedCompensation",
|
||||
"StockBasedCompensation", "UnrealizedGainLossOnInvestmentSecurities", "ProvisionandWriteOffofAssets",
|
||||
"AssetImpairmentCharge", "AmortizationOfSecurities", "DeferredTax", "DeferredIncomeTax",
|
||||
"DepreciationAmortizationDepletion", "Depletion", "DepreciationAndAmortization",
|
||||
"AmortizationCashFlow", "AmortizationOfIntangibles", "Depreciation", "OperatingGainsLosses",
|
||||
"PensionAndEmployeeBenefitExpense", "EarningsLossesFromEquityInvestments",
|
||||
"GainLossOnInvestmentSecurities", "NetForeignCurrencyExchangeGainLoss", "GainLossOnSaleOfPPE",
|
||||
"GainLossOnSaleOfBusiness", "NetIncomeFromContinuingOperations",
|
||||
"CashFlowsfromusedinOperatingActivitiesDirect", "TaxesRefundPaidDirect", "InterestReceivedDirect",
|
||||
"InterestPaidDirect", "DividendsReceivedDirect", "DividendsPaidDirect", "ClassesofCashPayments",
|
||||
"OtherCashPaymentsfromOperatingActivities", "PaymentsonBehalfofEmployees",
|
||||
"PaymentstoSuppliersforGoodsandServices", "ClassesofCashReceiptsfromOperatingActivities",
|
||||
"OtherCashReceiptsfromOperatingActivities", "ReceiptsfromGovernmentGrants", "ReceiptsfromCustomers"]}
|
||||
|
||||
price_colnames = ['Open', 'High', 'Low', 'Close', 'Adj Close']
|
||||
|
|
|
|||
|
|
@ -22,14 +22,16 @@
|
|||
from __future__ import print_function
|
||||
|
||||
import logging
|
||||
import traceback
|
||||
import time as _time
|
||||
import traceback
|
||||
|
||||
import multitasking as _multitasking
|
||||
import pandas as _pd
|
||||
|
||||
from . import Ticker, utils
|
||||
from . import shared
|
||||
|
||||
|
||||
@utils.log_indent_decorator
|
||||
def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
|
||||
group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
|
||||
|
|
@ -181,7 +183,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
|
|||
for ticker in shared._ERRORS:
|
||||
err = shared._ERRORS[ticker]
|
||||
err = err.replace(f'{ticker}', '%ticker%')
|
||||
if not err in errors:
|
||||
if err not in errors:
|
||||
errors[err] = [ticker]
|
||||
else:
|
||||
errors[err].append(ticker)
|
||||
|
|
@ -193,7 +195,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
|
|||
for ticker in shared._TRACEBACKS:
|
||||
tb = shared._TRACEBACKS[ticker]
|
||||
tb = tb.replace(f'{ticker}', '%ticker%')
|
||||
if not tb in tbs:
|
||||
if tb not in tbs:
|
||||
tbs[tb] = [ticker]
|
||||
else:
|
||||
tbs[tb].append(ticker)
|
||||
|
|
|
|||
|
|
@ -1,14 +1,13 @@
|
|||
import datetime
|
||||
import logging
|
||||
import json
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
from yfinance import utils, const
|
||||
from yfinance.data import TickerData
|
||||
from yfinance.exceptions import YFinanceException, YFNotImplementedError
|
||||
|
||||
|
||||
class Fundamentals:
|
||||
|
||||
def __init__(self, data: TickerData, proxy=None):
|
||||
|
|
@ -76,9 +75,9 @@ class Financials:
|
|||
allowed_timescales = ["yearly", "quarterly"]
|
||||
|
||||
if name not in allowed_names:
|
||||
raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
|
||||
raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
|
||||
if timescale not in allowed_timescales:
|
||||
raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
|
||||
raise ValueError(f"Illegal argument: timescale must be one of: {allowed_names}")
|
||||
|
||||
try:
|
||||
statement = self._create_financials_table(name, timescale, proxy)
|
||||
|
|
@ -86,7 +85,7 @@ class Financials:
|
|||
if statement is not None:
|
||||
return statement
|
||||
except YFinanceException as e:
|
||||
utils.get_yf_logger().error("%s: Failed to create %s financials table for reason: %r", self._data.ticker, name, e)
|
||||
utils.get_yf_logger().error(f"{self._data.ticker}: Failed to create {name} financials table for reason: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def _create_financials_table(self, name, timescale, proxy):
|
||||
|
|
@ -106,15 +105,12 @@ class Financials:
|
|||
timescale = timescale_translation[timescale]
|
||||
|
||||
# Step 2: construct url:
|
||||
ts_url_base = \
|
||||
"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{0}?symbol={0}" \
|
||||
.format(self._data.ticker)
|
||||
|
||||
ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._data.ticker}?symbol={self._data.ticker}"
|
||||
url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
|
||||
# Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
|
||||
start_dt = datetime.datetime(2016, 12, 31)
|
||||
end = pd.Timestamp.utcnow().ceil("D")
|
||||
url += "&period1={}&period2={}".format(int(start_dt.timestamp()), int(end.timestamp()))
|
||||
url += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"
|
||||
|
||||
# Step 3: fetch and reshape data
|
||||
json_str = self._data.cache_get(url=url, proxy=proxy).text
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import pandas as pd
|
|||
|
||||
from yfinance.data import TickerData
|
||||
|
||||
|
||||
class Holders:
|
||||
_SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
|
||||
|
||||
|
|
@ -32,7 +33,7 @@ class Holders:
|
|||
return self._mutualfund
|
||||
|
||||
def _scrape(self, proxy):
|
||||
ticker_url = "{}/{}".format(self._SCRAPE_URL_, self._data.ticker)
|
||||
ticker_url = f"{self._SCRAPE_URL_}/{self._data.ticker}"
|
||||
try:
|
||||
resp = self._data.cache_get(ticker_url + '/holders', proxy=proxy)
|
||||
holders = pd.read_html(resp.text)
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
import datetime
|
||||
import logging
|
||||
import json
|
||||
import logging
|
||||
import warnings
|
||||
from collections.abc import MutableMapping
|
||||
|
||||
import pandas as pd
|
||||
import numpy as _np
|
||||
import pandas as pd
|
||||
|
||||
from yfinance import utils
|
||||
from yfinance.data import TickerData
|
||||
|
|
@ -22,7 +23,7 @@ info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_
|
|||
|
||||
_BASIC_URL_ = "https://query2.finance.yahoo.com/v6/finance/quoteSummary"
|
||||
|
||||
from collections.abc import MutableMapping
|
||||
|
||||
class InfoDictWrapper(MutableMapping):
|
||||
""" Simple wrapper around info dict, intercepting 'gets' to
|
||||
print how-to-migrate messages for specific keys. Requires
|
||||
|
|
@ -126,12 +127,12 @@ class FastInfo:
|
|||
|
||||
# Because released before fixing key case, need to officially support
|
||||
# camel-case but also secretly support snake-case
|
||||
base_keys = [k for k in _properties if not '_' in k]
|
||||
base_keys = [k for k in _properties if '_' not in k]
|
||||
|
||||
sc_keys = [k for k in _properties if '_' in k]
|
||||
|
||||
self._sc_to_cc_key = {k:utils.snake_case_2_camelCase(k) for k in sc_keys}
|
||||
self._cc_to_sc_key = {v:k for k,v in self._sc_to_cc_key.items()}
|
||||
self._sc_to_cc_key = {k: utils.snake_case_2_camelCase(k) for k in sc_keys}
|
||||
self._cc_to_sc_key = {v: k for k, v in self._sc_to_cc_key.items()}
|
||||
|
||||
self._public_keys = sorted(base_keys + list(self._sc_to_cc_key.values()))
|
||||
self._keys = sorted(self._public_keys + sc_keys)
|
||||
|
|
@ -139,37 +140,44 @@ class FastInfo:
|
|||
# dict imitation:
|
||||
def keys(self):
|
||||
return self._public_keys
|
||||
|
||||
def items(self):
|
||||
return [(k,self[k]) for k in self._public_keys]
|
||||
return [(k, self[k]) for k in self._public_keys]
|
||||
|
||||
def values(self):
|
||||
return [self[k] for k in self._public_keys]
|
||||
|
||||
def get(self, key, default=None):
|
||||
if key in self.keys():
|
||||
if key in self._cc_to_sc_key:
|
||||
key = self._cc_to_sc_key[key]
|
||||
return self[key]
|
||||
return default
|
||||
|
||||
def __getitem__(self, k):
|
||||
if not isinstance(k, str):
|
||||
raise KeyError(f"key must be a string")
|
||||
if not k in self._keys:
|
||||
if k not in self._keys:
|
||||
raise KeyError(f"'{k}' not valid key. Examine 'FastInfo.keys()'")
|
||||
if k in self._cc_to_sc_key:
|
||||
k = self._cc_to_sc_key[k]
|
||||
return getattr(self, k)
|
||||
|
||||
def __contains__(self, k):
|
||||
return k in self.keys()
|
||||
|
||||
def __iter__(self):
|
||||
return iter(self.keys())
|
||||
|
||||
def __str__(self):
|
||||
return "lazy-loading dict with keys = " + str(self.keys())
|
||||
|
||||
def __repr__(self):
|
||||
return self.__str__()
|
||||
|
||||
def toJSON(self, indent=4):
|
||||
d = {k:self[k] for k in self.keys()}
|
||||
return _json.dumps({k:self[k] for k in self.keys()}, indent=indent)
|
||||
d = {k: self[k] for k in self.keys()}
|
||||
return json.dumps({k: self[k] for k in self.keys()}, indent=indent)
|
||||
|
||||
def _get_1y_prices(self, fullDaysOnly=False):
|
||||
if self._prices_1y is None:
|
||||
|
|
@ -183,7 +191,7 @@ class FastInfo:
|
|||
self._today_open = pd.to_datetime(ctp["regular"]["start"], unit='s', utc=True).tz_convert(self.timezone)
|
||||
self._today_close = pd.to_datetime(ctp["regular"]["end"], unit='s', utc=True).tz_convert(self.timezone)
|
||||
self._today_midnight = self._today_close.ceil("D")
|
||||
except:
|
||||
except Exception:
|
||||
self._today_open = None
|
||||
self._today_close = None
|
||||
self._today_midnight = None
|
||||
|
|
@ -588,9 +596,7 @@ class Quote:
|
|||
return
|
||||
self._already_fetched = True
|
||||
modules = ['financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
|
||||
params_dict = {}
|
||||
params_dict["modules"] = modules
|
||||
params_dict["ssl"] = "true"
|
||||
params_dict = {"modules": modules, "ssl": "true"}
|
||||
result = self._data.get_raw_json(
|
||||
_BASIC_URL_ + f"/{self._data.ticker}", params=params_dict, proxy=proxy
|
||||
)
|
||||
|
|
@ -612,13 +618,14 @@ class Quote:
|
|||
if v1
|
||||
}
|
||||
# recursively format but only because of 'companyOfficers'
|
||||
|
||||
def _format(k, v):
|
||||
if isinstance(v, dict) and "raw" in v and "fmt" in v:
|
||||
v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
|
||||
elif isinstance(v, list):
|
||||
v2 = [_format(None, x) for x in v]
|
||||
elif isinstance(v, dict):
|
||||
v2 = {k:_format(k, x) for k, x in v.items()}
|
||||
v2 = {k: _format(k, x) for k, x in v.items()}
|
||||
elif isinstance(v, str):
|
||||
v2 = v.replace("\xa0", " ")
|
||||
else:
|
||||
|
|
@ -663,8 +670,7 @@ class Quote:
|
|||
# pass
|
||||
#
|
||||
# For just one/few variable is faster to query directly:
|
||||
url = "https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{}?symbol={}".format(
|
||||
self._data.ticker, self._data.ticker)
|
||||
url = f"https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._data.ticker}?symbol={self._data.ticker}"
|
||||
for k in keys:
|
||||
url += "&type=" + k
|
||||
# Request 6 months of data
|
||||
|
|
|
|||
|
|
@ -22,10 +22,10 @@
|
|||
from __future__ import print_function
|
||||
|
||||
import datetime as _datetime
|
||||
import pandas as _pd
|
||||
|
||||
from collections import namedtuple as _namedtuple
|
||||
|
||||
import pandas as _pd
|
||||
|
||||
from .base import TickerBase
|
||||
|
||||
|
||||
|
|
@ -33,25 +33,29 @@ class Ticker(TickerBase):
|
|||
def __init__(self, ticker, session=None):
|
||||
super(Ticker, self).__init__(ticker, session=session)
|
||||
self._expirations = {}
|
||||
self._underlying = {}
|
||||
|
||||
def __repr__(self):
|
||||
return 'yfinance.Ticker object <%s>' % self.ticker
|
||||
return f'yfinance.Ticker object <{self.ticker}>'
|
||||
|
||||
def _download_options(self, date=None, proxy=None):
|
||||
if date is None:
|
||||
url = "{}/v7/finance/options/{}".format(
|
||||
self._base_url, self.ticker)
|
||||
url = f"{self._base_url}/v7/finance/options/{self.ticker}"
|
||||
else:
|
||||
url = "{}/v7/finance/options/{}?date={}".format(
|
||||
self._base_url, self.ticker, date)
|
||||
url = f"{self._base_url}/v7/finance/options/{self.ticker}?date={date}"
|
||||
|
||||
r = self._data.get(url=url, proxy=proxy).json()
|
||||
if len(r.get('optionChain', {}).get('result', [])) > 0:
|
||||
for exp in r['optionChain']['result'][0]['expirationDates']:
|
||||
self._expirations[_datetime.datetime.utcfromtimestamp(
|
||||
exp).strftime('%Y-%m-%d')] = exp
|
||||
|
||||
self._underlying = r['optionChain']['result'][0].get('quote', {})
|
||||
|
||||
opt = r['optionChain']['result'][0].get('options', [])
|
||||
return opt[0] if len(opt) > 0 else []
|
||||
|
||||
return dict(**opt[0],underlying=self._underlying) if len(opt) > 0 else {}
|
||||
return {}
|
||||
|
||||
def _options2df(self, opt, tz=None):
|
||||
data = _pd.DataFrame(opt).reindex(columns=[
|
||||
|
|
@ -84,15 +88,15 @@ class Ticker(TickerBase):
|
|||
self._download_options()
|
||||
if date not in self._expirations:
|
||||
raise ValueError(
|
||||
"Expiration `%s` cannot be found. "
|
||||
"Available expiration are: [%s]" % (
|
||||
date, ', '.join(self._expirations)))
|
||||
f"Expiration `{date}` cannot be found. "
|
||||
f"Available expirations are: [{', '.join(self._expirations)}]")
|
||||
date = self._expirations[date]
|
||||
options = self._download_options(date, proxy=proxy)
|
||||
|
||||
return _namedtuple('Options', ['calls', 'puts'])(**{
|
||||
return _namedtuple('Options', ['calls', 'puts', 'underlying'])(**{
|
||||
"calls": self._options2df(options['calls'], tz=tz),
|
||||
"puts": self._options2df(options['puts'], tz=tz)
|
||||
"puts": self._options2df(options['puts'], tz=tz),
|
||||
"underlying": options['underlying']
|
||||
})
|
||||
|
||||
# ------------------------
|
||||
|
|
|
|||
|
|
@ -22,19 +22,21 @@
|
|||
from __future__ import print_function
|
||||
|
||||
from . import Ticker, multi
|
||||
|
||||
|
||||
# from collections import namedtuple as _namedtuple
|
||||
|
||||
|
||||
class Tickers:
|
||||
|
||||
def __repr__(self):
|
||||
return 'yfinance.Tickers object <%s>' % ",".join(self.symbols)
|
||||
return f"yfinance.Tickers object <{','.join(self.symbols)}>"
|
||||
|
||||
def __init__(self, tickers, session=None):
|
||||
tickers = tickers if isinstance(
|
||||
tickers, list) else tickers.replace(',', ' ').split()
|
||||
self.symbols = [ticker.upper() for ticker in tickers]
|
||||
self.tickers = {ticker:Ticker(ticker, session=session) for ticker in self.symbols}
|
||||
self.tickers = {ticker: Ticker(ticker, session=session) for ticker in self.symbols}
|
||||
|
||||
# self.tickers = _namedtuple(
|
||||
# "Tickers", ticker_objects.keys(), rename=True
|
||||
|
|
|
|||
|
|
@ -21,29 +21,32 @@
|
|||
|
||||
from __future__ import print_function
|
||||
|
||||
from yfinance import const
|
||||
import atexit as _atexit
|
||||
|
||||
import datetime as _datetime
|
||||
import dateutil as _dateutil
|
||||
import logging
|
||||
import os as _os
|
||||
import re as _re
|
||||
import sqlite3 as _sqlite3
|
||||
import sys as _sys
|
||||
import threading
|
||||
from functools import lru_cache
|
||||
from inspect import getmembers
|
||||
from threading import Lock
|
||||
from types import FunctionType
|
||||
from typing import Dict, Union, List, Optional
|
||||
|
||||
import appdirs as _ad
|
||||
import numpy as _np
|
||||
import pandas as _pd
|
||||
import pytz as _tz
|
||||
import requests as _requests
|
||||
import re as _re
|
||||
import pandas as _pd
|
||||
import numpy as _np
|
||||
import sys as _sys
|
||||
import os as _os
|
||||
import appdirs as _ad
|
||||
import sqlite3 as _sqlite3
|
||||
import atexit as _atexit
|
||||
from functools import lru_cache
|
||||
import logging
|
||||
|
||||
from threading import Lock
|
||||
|
||||
from dateutil.relativedelta import relativedelta
|
||||
from pytz import UnknownTimeZoneError
|
||||
|
||||
from yfinance import const
|
||||
from .const import _BASE_URL_
|
||||
|
||||
try:
|
||||
import ujson as _json
|
||||
except ImportError:
|
||||
|
|
@ -54,14 +57,12 @@ user_agent_headers = {
|
|||
|
||||
|
||||
# From https://stackoverflow.com/a/59128615
|
||||
from types import FunctionType
|
||||
from inspect import getmembers
|
||||
def attributes(obj):
|
||||
disallowed_names = {
|
||||
name for name, value in getmembers(type(obj))
|
||||
name for name, value in getmembers(type(obj))
|
||||
if isinstance(value, FunctionType)}
|
||||
return {
|
||||
name: getattr(obj, name) for name in dir(obj)
|
||||
name: getattr(obj, name) for name in dir(obj)
|
||||
if name[0] != '_' and name not in disallowed_names and hasattr(obj, name)}
|
||||
|
||||
|
||||
|
|
@ -72,7 +73,7 @@ def print_once(msg):
|
|||
print(msg)
|
||||
|
||||
|
||||
## Logging
|
||||
# Logging
|
||||
# Note: most of this logic is adding indentation with function depth,
|
||||
# so that DEBUG log is readable.
|
||||
class IndentLoggerAdapter(logging.LoggerAdapter):
|
||||
|
|
@ -84,20 +85,26 @@ class IndentLoggerAdapter(logging.LoggerAdapter):
|
|||
msg = '\n'.join([i + m for m in msg.split('\n')])
|
||||
return msg, kwargs
|
||||
|
||||
import threading
|
||||
|
||||
_indentation_level = threading.local()
|
||||
|
||||
|
||||
class IndentationContext:
|
||||
def __init__(self, increment=1):
|
||||
self.increment = increment
|
||||
|
||||
def __enter__(self):
|
||||
_indentation_level.indent = getattr(_indentation_level, 'indent', 0) + self.increment
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
_indentation_level.indent -= self.increment
|
||||
|
||||
|
||||
def get_indented_logger(name=None):
|
||||
# Never cache the returned value! Will break indentation.
|
||||
return IndentLoggerAdapter(logging.getLogger(name), {'indent': getattr(_indentation_level, 'indent', 0)})
|
||||
|
||||
|
||||
def log_indent_decorator(func):
|
||||
def wrapper(*args, **kwargs):
|
||||
logger = get_indented_logger('yfinance')
|
||||
|
|
@ -111,6 +118,7 @@ def log_indent_decorator(func):
|
|||
|
||||
return wrapper
|
||||
|
||||
|
||||
class MultiLineFormatter(logging.Formatter):
|
||||
# The 'fmt' formatting further down is only applied to first line
|
||||
# of log message, specifically the padding after %level%.
|
||||
|
|
@ -138,8 +146,11 @@ class MultiLineFormatter(logging.Formatter):
|
|||
formatted.extend(padding + line for line in lines[1:])
|
||||
return '\n'.join(formatted)
|
||||
|
||||
|
||||
yf_logger = None
|
||||
yf_log_indented = False
|
||||
|
||||
|
||||
def get_yf_logger():
|
||||
global yf_logger
|
||||
if yf_logger is None:
|
||||
|
|
@ -149,6 +160,7 @@ def get_yf_logger():
|
|||
yf_logger = get_indented_logger('yfinance')
|
||||
return yf_logger
|
||||
|
||||
|
||||
def setup_debug_formatting():
|
||||
global yf_logger
|
||||
yf_logger = get_yf_logger()
|
||||
|
|
@ -167,24 +179,21 @@ def setup_debug_formatting():
|
|||
global yf_log_indented
|
||||
yf_log_indented = True
|
||||
|
||||
|
||||
def enable_debug_mode():
|
||||
get_yf_logger().setLevel(logging.DEBUG)
|
||||
setup_debug_formatting()
|
||||
|
||||
##
|
||||
|
||||
|
||||
def is_isin(string):
|
||||
return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))
|
||||
return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9])$", string))
|
||||
|
||||
|
||||
def get_all_by_isin(isin, proxy=None, session=None):
|
||||
if not (is_isin(isin)):
|
||||
raise ValueError("Invalid ISIN number")
|
||||
|
||||
from .base import _BASE_URL_
|
||||
session = session or _requests
|
||||
url = "{}/v1/finance/search?q={}".format(_BASE_URL_, isin)
|
||||
url = f"{_BASE_URL_}/v1/finance/search?q={isin}"
|
||||
data = session.get(url=url, proxies=proxy, headers=user_agent_headers)
|
||||
try:
|
||||
data = data.json()
|
||||
|
|
@ -236,7 +245,7 @@ def empty_earnings_dates_df():
|
|||
|
||||
|
||||
def build_template(data):
|
||||
'''
|
||||
"""
|
||||
build_template returns the details required to rebuild any of the yahoo finance financial statements in the same order as the yahoo finance webpage. The function is built to be used on the "FinancialTemplateStore" json which appears in any one of the three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
|
||||
|
||||
Returns:
|
||||
|
|
@ -245,95 +254,80 @@ def build_template(data):
|
|||
- template_order: The order that quarterlies should be in (note that quarterlies have no pre-fix - hence why this is required).
|
||||
- level_detail: The level of each individual line item. E.g. for the "/financials" webpage, "Total Revenue" is a level 0 item and is the summation of "Operating Revenue" and "Excise Taxes" which are level 1 items.
|
||||
|
||||
'''
|
||||
"""
|
||||
template_ttm_order = [] # Save the TTM (Trailing Twelve Months) ordering to an object.
|
||||
template_annual_order = [] # Save the annual ordering to an object.
|
||||
template_order = [] # Save the ordering to an object (this can be utilized for quarterlies)
|
||||
level_detail = [] # Record the level of each line item of the income statement ("Operating Revenue" and "Excise Taxes" sum to return "Total Revenue" we need to keep track of this)
|
||||
for key in data['template']:
|
||||
# Loop through the json to retreive the exact financial order whilst appending to the objects
|
||||
template_ttm_order.append('trailing{}'.format(key['key']))
|
||||
template_annual_order.append('annual{}'.format(key['key']))
|
||||
template_order.append('{}'.format(key['key']))
|
||||
level_detail.append(0)
|
||||
if 'children' in key:
|
||||
for child1 in key['children']: # Level 1
|
||||
template_ttm_order.append('trailing{}'.format(child1['key']))
|
||||
template_annual_order.append('annual{}'.format(child1['key']))
|
||||
template_order.append('{}'.format(child1['key']))
|
||||
level_detail.append(1)
|
||||
if 'children' in child1:
|
||||
for child2 in child1['children']: # Level 2
|
||||
template_ttm_order.append('trailing{}'.format(child2['key']))
|
||||
template_annual_order.append('annual{}'.format(child2['key']))
|
||||
template_order.append('{}'.format(child2['key']))
|
||||
level_detail.append(2)
|
||||
if 'children' in child2:
|
||||
for child3 in child2['children']: # Level 3
|
||||
template_ttm_order.append('trailing{}'.format(child3['key']))
|
||||
template_annual_order.append('annual{}'.format(child3['key']))
|
||||
template_order.append('{}'.format(child3['key']))
|
||||
level_detail.append(3)
|
||||
if 'children' in child3:
|
||||
for child4 in child3['children']: # Level 4
|
||||
template_ttm_order.append('trailing{}'.format(child4['key']))
|
||||
template_annual_order.append('annual{}'.format(child4['key']))
|
||||
template_order.append('{}'.format(child4['key']))
|
||||
level_detail.append(4)
|
||||
if 'children' in child4:
|
||||
for child5 in child4['children']: # Level 5
|
||||
template_ttm_order.append('trailing{}'.format(child5['key']))
|
||||
template_annual_order.append('annual{}'.format(child5['key']))
|
||||
template_order.append('{}'.format(child5['key']))
|
||||
level_detail.append(5)
|
||||
|
||||
def traverse(node, level):
|
||||
"""
|
||||
A recursive function that visits a node and its children.
|
||||
|
||||
Args:
|
||||
node: The current node in the data structure.
|
||||
level: The depth of the current node in the data structure.
|
||||
"""
|
||||
if level > 5: # Stop when level is above 5
|
||||
return
|
||||
template_ttm_order.append(f"trailing{node['key']}")
|
||||
template_annual_order.append(f"annual{node['key']}")
|
||||
template_order.append(f"{node['key']}")
|
||||
level_detail.append(level)
|
||||
if 'children' in node: # Check if the node has children
|
||||
for child in node['children']: # If yes, traverse each child
|
||||
traverse(child, level + 1) # Increment the level by 1 for each child
|
||||
|
||||
for key in data['template']: # Loop through the data
|
||||
traverse(key, 0) # Call the traverse function with initial level being 0
|
||||
|
||||
return template_ttm_order, template_annual_order, template_order, level_detail
|
||||
|
||||
|
||||
def retreive_financial_details(data):
|
||||
'''
|
||||
retreive_financial_details returns all of the available financial details under the "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
|
||||
def retrieve_financial_details(data):
|
||||
"""
|
||||
retrieve_financial_details returns all of the available financial details under the
|
||||
"QuoteTimeSeriesStore" for any of the following three yahoo finance webpages:
|
||||
"/financials", "/cash-flow" and "/balance-sheet".
|
||||
|
||||
Returns:
|
||||
- TTM_dicts: A dictionary full of all of the available Trailing Twelve Month figures, this can easily be converted to a pandas dataframe.
|
||||
- Annual_dicts: A dictionary full of all of the available Annual figures, this can easily be converted to a pandas dataframe.
|
||||
'''
|
||||
"""
|
||||
TTM_dicts = [] # Save a dictionary object to store the TTM financials.
|
||||
Annual_dicts = [] # Save a dictionary object to store the Annual financials.
|
||||
for key in data['timeSeries']: # Loop through the time series data to grab the key financial figures.
|
||||
|
||||
for key, timeseries in data.get('timeSeries', {}).items(): # Loop through the time series data to grab the key financial figures.
|
||||
try:
|
||||
if len(data['timeSeries'][key]) > 0:
|
||||
time_series_dict = {}
|
||||
time_series_dict['index'] = key
|
||||
for each in data['timeSeries'][key]: # Loop through the years
|
||||
if each == None:
|
||||
if timeseries:
|
||||
time_series_dict = {'index': key}
|
||||
for each in timeseries: # Loop through the years
|
||||
if not each:
|
||||
continue
|
||||
else:
|
||||
time_series_dict[each['asOfDate']] = each['reportedValue']
|
||||
# time_series_dict["{}".format(each['asOfDate'])] = data['timeSeries'][key][each]['reportedValue']
|
||||
time_series_dict[each.get('asOfDate')] = each.get('reportedValue')
|
||||
if 'trailing' in key:
|
||||
TTM_dicts.append(time_series_dict)
|
||||
elif 'annual' in key:
|
||||
Annual_dicts.append(time_series_dict)
|
||||
except Exception as e:
|
||||
pass
|
||||
except KeyError as e:
|
||||
print(f"An error occurred while processing the key: {e}")
|
||||
return TTM_dicts, Annual_dicts
|
||||
|
||||
|
||||
def format_annual_financial_statement(level_detail, annual_dicts, annual_order, ttm_dicts=None, ttm_order=None):
|
||||
'''
|
||||
"""
|
||||
format_annual_financial_statement formats any annual financial statement
|
||||
|
||||
Returns:
|
||||
- _statement: A fully formatted annual financial statement in pandas dataframe.
|
||||
'''
|
||||
"""
|
||||
Annual = _pd.DataFrame.from_dict(annual_dicts).set_index("index")
|
||||
Annual = Annual.reindex(annual_order)
|
||||
Annual.index = Annual.index.str.replace(r'annual', '')
|
||||
|
||||
# Note: balance sheet is the only financial statement with no ttm detail
|
||||
if (ttm_dicts not in [[], None]) and (ttm_order not in [[], None]):
|
||||
TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index")
|
||||
TTM = TTM.reindex(ttm_order)
|
||||
if ttm_dicts and ttm_order:
|
||||
TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index").reindex(ttm_order)
|
||||
# Add 'TTM' prefix to all column names, so if combined we can tell
|
||||
# the difference between actuals and TTM (similar to yahoo finance).
|
||||
TTM.columns = ['TTM ' + str(col) for col in TTM.columns]
|
||||
|
|
@ -351,12 +345,12 @@ def format_annual_financial_statement(level_detail, annual_dicts, annual_order,
|
|||
|
||||
|
||||
def format_quarterly_financial_statement(_statement, level_detail, order):
|
||||
'''
|
||||
"""
|
||||
format_quarterly_financial_statements formats any quarterly financial statement
|
||||
|
||||
Returns:
|
||||
- _statement: A fully formatted quarterly financial statement in pandas dataframe.
|
||||
'''
|
||||
"""
|
||||
_statement = _statement.reindex(order)
|
||||
_statement.index = camel2title(_statement.T)
|
||||
_statement['level_detail'] = level_detail
|
||||
|
|
@ -407,7 +401,7 @@ def camel2title(strings: List[str], sep: str = ' ', acronyms: Optional[List[str]
|
|||
|
||||
# Apply str.title() to non-acronym words
|
||||
strings = [s.split(sep) for s in strings]
|
||||
strings = [[j.title() if not j in acronyms else j for j in s] for s in strings]
|
||||
strings = [[j.title() if j not in acronyms else j for j in s] for s in strings]
|
||||
strings = [sep.join(s) for s in strings]
|
||||
|
||||
return strings
|
||||
|
|
@ -437,14 +431,14 @@ def _parse_user_dt(dt, exchange_tz):
|
|||
|
||||
def _interval_to_timedelta(interval):
|
||||
if interval == "1mo":
|
||||
return _dateutil.relativedelta.relativedelta(months=1)
|
||||
return relativedelta(months=1)
|
||||
elif interval == "3mo":
|
||||
return _dateutil.relativedelta.relativedelta(months=3)
|
||||
return relativedelta(months=3)
|
||||
elif interval == "1y":
|
||||
return _dateutil.relativedelta.relativedelta(years=1)
|
||||
return relativedelta(years=1)
|
||||
elif interval == "1wk":
|
||||
return _pd.Timedelta(days=7)
|
||||
else:
|
||||
else:
|
||||
return _pd.Timedelta(interval)
|
||||
|
||||
|
||||
|
|
@ -544,8 +538,7 @@ def parse_actions(data):
|
|||
splits.set_index("date", inplace=True)
|
||||
splits.index = _pd.to_datetime(splits.index, unit="s")
|
||||
splits.sort_index(inplace=True)
|
||||
splits["Stock Splits"] = splits["numerator"] / \
|
||||
splits["denominator"]
|
||||
splits["Stock Splits"] = splits["numerator"] / splits["denominator"]
|
||||
splits = splits[["Stock Splits"]]
|
||||
|
||||
if dividends is None:
|
||||
|
|
@ -624,7 +617,7 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
|
|||
elif interval == "3mo":
|
||||
last_rows_same_interval = dt1.year == dt2.year and dt1.quarter == dt2.quarter
|
||||
else:
|
||||
last_rows_same_interval = (dt1-dt2) < _pd.Timedelta(interval)
|
||||
last_rows_same_interval = (dt1 - dt2) < _pd.Timedelta(interval)
|
||||
|
||||
if last_rows_same_interval:
|
||||
# Last two rows are within same interval
|
||||
|
|
@ -680,12 +673,12 @@ def safe_merge_dfs(df_main, df_sub, interval):
|
|||
df_main = df_main.drop('_date', axis=1)
|
||||
df_sub = df_sub.drop('_date', axis=1)
|
||||
else:
|
||||
indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1]+td), df_sub.index, side='right')
|
||||
indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
|
||||
indices -= 1 # Convert from [[i-1], [i]) to [[i], [i+1])
|
||||
# Numpy.searchsorted does not handle out-of-range well, so handle manually:
|
||||
for i in range(len(df_sub.index)):
|
||||
dt = df_sub.index[i]
|
||||
if dt < df_main.index[0] or dt >= df_main.index[-1]+td:
|
||||
if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
|
||||
# Out-of-range
|
||||
indices[i] = -1
|
||||
|
||||
|
|
@ -707,7 +700,7 @@ def safe_merge_dfs(df_main, df_sub, interval):
|
|||
next_interval_end_dt = next_interval_start_dt + td
|
||||
for i in _np.where(f_outOfRange)[0]:
|
||||
dt = df_sub.index[i]
|
||||
if dt >= next_interval_start_dt and dt < next_interval_end_dt:
|
||||
if next_interval_start_dt <= dt < next_interval_end_dt:
|
||||
new_dt = next_interval_start_dt
|
||||
get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
|
||||
empty_row = _pd.DataFrame(data=empty_row_data, index=[dt])
|
||||
|
|
@ -715,12 +708,12 @@ def safe_merge_dfs(df_main, df_sub, interval):
|
|||
df_main = df_main.sort_index()
|
||||
|
||||
# Re-calculate indices
|
||||
indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1]+td), df_sub.index, side='right')
|
||||
indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
|
||||
indices -= 1 # Convert from [[i-1], [i]) to [[i], [i+1])
|
||||
# Numpy.searchsorted does not handle out-of-range well, so handle manually:
|
||||
for i in range(len(df_sub.index)):
|
||||
dt = df_sub.index[i]
|
||||
if dt < df_main.index[0] or dt >= df_main.index[-1]+td:
|
||||
if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
|
||||
# Out-of-range
|
||||
indices[i] = -1
|
||||
|
||||
|
|
@ -749,10 +742,11 @@ def safe_merge_dfs(df_main, df_sub, interval):
|
|||
df = df.groupby("_NewIndex").prod()
|
||||
df.index.name = None
|
||||
else:
|
||||
raise Exception("New index contains duplicates but unsure how to aggregate for '{}'".format(data_col_name))
|
||||
raise Exception(f"New index contains duplicates but unsure how to aggregate for '{data_col_name}'")
|
||||
if "_NewIndex" in df.columns:
|
||||
df = df.drop("_NewIndex", axis=1)
|
||||
return df
|
||||
|
||||
new_index = df_main.index[indices]
|
||||
df_sub = _reindex_events(df_sub, new_index, data_col)
|
||||
|
||||
|
|
@ -811,7 +805,7 @@ def format_history_metadata(md, tradingPeriodsOnly=True):
|
|||
|
||||
if "tradingPeriods" in md:
|
||||
tps = md["tradingPeriods"]
|
||||
if tps == {"pre":[], "post":[]}:
|
||||
if tps == {"pre": [], "post": []}:
|
||||
# Ignore
|
||||
pass
|
||||
elif isinstance(tps, (list, dict)):
|
||||
|
|
@ -827,8 +821,8 @@ def format_history_metadata(md, tradingPeriodsOnly=True):
|
|||
post_df = _pd.DataFrame.from_records(_np.hstack(tps["post"]))
|
||||
regular_df = _pd.DataFrame.from_records(_np.hstack(tps["regular"]))
|
||||
|
||||
pre_df = pre_df.rename(columns={"start":"pre_start", "end":"pre_end"}).drop(["timezone", "gmtoffset"], axis=1)
|
||||
post_df = post_df.rename(columns={"start":"post_start", "end":"post_end"}).drop(["timezone", "gmtoffset"], axis=1)
|
||||
pre_df = pre_df.rename(columns={"start": "pre_start", "end": "pre_end"}).drop(["timezone", "gmtoffset"], axis=1)
|
||||
post_df = post_df.rename(columns={"start": "post_start", "end": "post_end"}).drop(["timezone", "gmtoffset"], axis=1)
|
||||
regular_df = regular_df.drop(["timezone", "gmtoffset"], axis=1)
|
||||
|
||||
cols = ["pre_start", "pre_end", "start", "end", "post_start", "post_end"]
|
||||
|
|
@ -845,6 +839,7 @@ def format_history_metadata(md, tradingPeriodsOnly=True):
|
|||
|
||||
return md
|
||||
|
||||
|
||||
class ProgressBar:
|
||||
def __init__(self, iterations, text='completed'):
|
||||
self.text = text
|
||||
|
|
@ -877,19 +872,16 @@ class ProgressBar:
|
|||
def update_iteration(self, val=None):
|
||||
val = val if val is not None else self.elapsed / float(self.iterations)
|
||||
self.__update_amount(val * 100.0)
|
||||
self.prog_bar += ' %s of %s %s' % (
|
||||
self.elapsed, self.iterations, self.text)
|
||||
self.prog_bar += f" {self.elapsed} of {self.iterations} {self.text}"
|
||||
|
||||
def __update_amount(self, new_amount):
|
||||
percent_done = int(round((new_amount / 100.0) * 100.0))
|
||||
all_full = self.width - 2
|
||||
num_hashes = int(round((percent_done / 100.0) * all_full))
|
||||
self.prog_bar = '[' + self.fill_char * \
|
||||
num_hashes + ' ' * (all_full - num_hashes) + ']'
|
||||
self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'
|
||||
pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
|
||||
pct_string = '%d%%' % percent_done
|
||||
self.prog_bar = self.prog_bar[0:pct_place] + \
|
||||
(pct_string + self.prog_bar[pct_place + len(pct_string):])
|
||||
pct_string = f'{percent_done}%%'
|
||||
self.prog_bar = self.prog_bar[0:pct_place] + (pct_string + self.prog_bar[pct_place + len(pct_string):])
|
||||
|
||||
def __str__(self):
|
||||
return str(self.prog_bar)
|
||||
|
|
@ -900,7 +892,7 @@ class ProgressBar:
|
|||
# ---------------------------------
|
||||
|
||||
class _KVStore:
|
||||
"""Simpel Sqlite backed key/value store, key and value are strings. Should be thread safe."""
|
||||
"""Simple Sqlite backed key/value store, key and value are strings. Should be thread safe."""
|
||||
|
||||
def __init__(self, filename):
|
||||
self._cache_mutex = Lock()
|
||||
|
|
@ -967,8 +959,7 @@ class _TzCache:
|
|||
try:
|
||||
self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
|
||||
except _sqlite3.DatabaseError as err:
|
||||
raise _TzCacheException("Error creating TzCache folder: '{}' reason: {}"
|
||||
.format(self._db_dir, err))
|
||||
raise _TzCacheException(f"Error creating TzCache folder: '{self._db_dir}' reason: {err}")
|
||||
self._migrate_cache_tkr_tz()
|
||||
|
||||
def _setup_cache_folder(self):
|
||||
|
|
@ -976,12 +967,10 @@ class _TzCache:
|
|||
try:
|
||||
_os.makedirs(self._db_dir)
|
||||
except OSError as err:
|
||||
raise _TzCacheException("Error creating TzCache folder: '{}' reason: {}"
|
||||
.format(self._db_dir, err))
|
||||
raise _TzCacheException(f"Error creating TzCache folder: '{self._db_dir}' reason: {err}")
|
||||
|
||||
elif not (_os.access(self._db_dir, _os.R_OK) and _os.access(self._db_dir, _os.W_OK)):
|
||||
raise _TzCacheException("Cannot read and write in TzCache folder: '{}'"
|
||||
.format(self._db_dir, ))
|
||||
raise _TzCacheException(f"Cannot read and write in TzCache folder: '{self._db_dir}'")
|
||||
|
||||
def lookup(self, tkr):
|
||||
return self.tz_db.get(tkr)
|
||||
|
|
@ -1022,7 +1011,7 @@ class _TzCache:
|
|||
else:
|
||||
# Discard corrupt data:
|
||||
df = df[~df["Tz"].isna().to_numpy()]
|
||||
df = df[~(df["Tz"]=='').to_numpy()]
|
||||
df = df[~(df["Tz"] == '').to_numpy()]
|
||||
df = df[~df.index.isna()]
|
||||
if not df.empty:
|
||||
try:
|
||||
|
|
@ -1061,10 +1050,9 @@ def get_tz_cache():
|
|||
try:
|
||||
_tz_cache = _TzCache()
|
||||
except _TzCacheException as err:
|
||||
get_yf_logger().info("Failed to create TzCache, reason: %s. "
|
||||
"TzCache will not be used. "
|
||||
"Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'",
|
||||
err)
|
||||
get_yf_logger().info(f"Failed to create TzCache, reason: {err}. "
|
||||
"TzCache will not be used. "
|
||||
"Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
|
||||
_tz_cache = _TzCacheDummy()
|
||||
|
||||
return _tz_cache
|
||||
|
|
|
|||
Loading…
Reference in New Issue