Stock split repair: bug fixes & more testing
ValueRaider
parent
d1ea402792
commit
e57647c1d7
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@ -26,11 +26,16 @@ class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
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from pyrate_limiter import Duration, RequestRate, Limiter
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history_rate = RequestRate(1, Duration.SECOND*2)
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limiter = Limiter(history_rate)
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cache_fp = os.path.join(_ad.user_cache_dir(), "py-yfinance", "unittests-cache")
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if os.path.isfile(cache_fp + '.sqlite'):
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# Delete local cache if older than 1 day:
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mod_dt = _dt.datetime.fromtimestamp(os.path.getmtime(cache_fp + '.sqlite'))
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if mod_dt.date() < _dt.date.today():
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os.remove(cache_fp + '.sqlite')
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session_gbl = CachedLimiterSession(
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limiter=limiter,
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bucket_class=MemoryQueueBucket,
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backend=SQLiteCache(os.path.join(_ad.user_cache_dir(), "py-yfinance", "unittests-cache"),
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expire_after=_dt.timedelta(hours=1)),
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backend=SQLiteCache(cache_fp, expire_after=_dt.timedelta(hours=1)),
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)
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# Use this instead if only want rate-limiting:
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# from requests_ratelimiter import LimiterSession
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@ -0,0 +1,23 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
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2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
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2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
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2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
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2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
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2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
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2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
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2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
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2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
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2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
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2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
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2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
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2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
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2023-03-28 00:00:00+09:00,4200,4207,4124,4142,4087.837109375,4505500,0,0
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2023-03-27 00:00:00+09:00,4196,4204,4151,4192,4137.183203125,5959500,0,0
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2023-03-24 00:00:00+09:00,4130,4187,4123,4177,4122.379296875,8961500,0,0
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2023-03-23 00:00:00+09:00,4056,4106,4039,4086,4032.569140625,5480000,0,0
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2023-03-22 00:00:00+09:00,4066,4128,4057,4122,4068.0984375,8741500,0,0
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2023-03-20 00:00:00+09:00,4000,4027,3980,3980,3927.95546875,7006500,0,0
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2023-03-17 00:00:00+09:00,4018,4055,4016,4031,3978.28828125,6961500,0,0
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2023-03-16 00:00:00+09:00,3976,4045,3972,4035,3982.236328125,5019000,0,0
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2023-03-15 00:00:00+09:00,4034,4050,4003,4041,3988.1578125,6122000,0,0
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@ -0,0 +1,23 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
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2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
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2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
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2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
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2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
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2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
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2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
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2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
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2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
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2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
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2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
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2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
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2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
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2023-03-28 00:00:00+09:00,21000,21035,20620,20710,20439.185546875,901100,0,0
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2023-03-27 00:00:00+09:00,20980,21020,20755,20960,20685.916015625,1191900,0,0
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2023-03-24 00:00:00+09:00,20650,20935,20615,20885,20611.896484375,1792300,0,0
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2023-03-23 00:00:00+09:00,20280,20530,20195,20430,20162.845703125,1096000,0,0
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2023-03-22 00:00:00+09:00,20330,20640,20285,20610,20340.4921875,1748300,0,0
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2023-03-20 00:00:00+09:00,20000,20135,19900,19900,19639.77734375,1401300,0,0
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2023-03-17 00:00:00+09:00,20090,20275,20080,20155,19891.44140625,1392300,0,0
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2023-03-16 00:00:00+09:00,19880,20225,19860,20175,19911.181640625,1003800,0,0
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2023-03-15 00:00:00+09:00,20170,20250,20015,20205,19940.7890625,1224400,0,0
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@ -0,0 +1,30 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2023-04-20 00:00:00+02:00,3,3,2,3,3,2076,0,0
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2023-04-21 00:00:00+02:00,3,3,2,3,3,2136,0,0
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2023-04-24 00:00:00+02:00,3,3,1,1,1,77147,0,0
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2023-04-25 00:00:00+02:00,1,2,1,2,2,9625,0,0
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2023-04-26 00:00:00+02:00,2,2,1,2,2,5028,0,0
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2023-04-27 00:00:00+02:00,2,2,1,1,1,3235,0,0
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2023-04-28 00:00:00+02:00,2,2,1,2,2,10944,0,0
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2023-05-02 00:00:00+02:00,2,2,2,2,2,12220,0,0
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2023-05-03 00:00:00+02:00,2,2,2,2,2,4683,0,0
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2023-05-04 00:00:00+02:00,2,2,1,2,2,3368,0,0
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2023-05-05 00:00:00+02:00,2,2,1,2,2,26069,0,0
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2023-05-08 00:00:00+02:00,1,2,1,1,1,70540,0,0
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2023-05-09 00:00:00+02:00,1,2,1,1,1,14228,0,0
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2023-05-10 00:00:00+02:00,1.08000004291534,1.39999997615814,0.879999995231628,1,1,81012,0,0.0001
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2023-05-11 00:00:00+02:00,1.03999996185303,1.03999996185303,0.850000023841858,1,1,40254,0,0
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2023-05-12 00:00:00+02:00,0.949999988079071,1.10000002384186,0.949999988079071,1.01999998092651,1.01999998092651,35026,0,0
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2023-05-15 00:00:00+02:00,0.949999988079071,1.01999998092651,0.860000014305115,0.939999997615814,0.939999997615814,41486,0,0
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2023-05-16 00:00:00+02:00,0.899999976158142,0.944000005722046,0.800000011920929,0.800000011920929,0.800000011920929,43583,0,0
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2023-05-17 00:00:00+02:00,0.850000023841858,0.850000023841858,0.779999971389771,0.810000002384186,0.810000002384186,29984,0,0
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2023-05-18 00:00:00+02:00,0.779999971389771,0.78600001335144,0.740000009536743,0.740000009536743,0.740000009536743,24679,0,0
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2023-05-19 00:00:00+02:00,0.78600001335144,0.78600001335144,0.649999976158142,0.65200001001358,0.65200001001358,26732,0,0
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2023-05-22 00:00:00+02:00,0.8299999833107,1.05999994277954,0.709999978542328,0.709999978542328,0.709999978542328,169538,0,0
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2023-05-23 00:00:00+02:00,0.899999976158142,1.60800004005432,0.860000014305115,1.22000002861023,1.22000002861023,858471,0,0
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2023-05-24 00:00:00+02:00,1.19400000572205,1.25999999046326,0.779999971389771,0.779999971389771,0.779999971389771,627823,0,0
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2023-05-25 00:00:00+02:00,0.980000019073486,1.22000002861023,0.702000021934509,0.732999980449677,0.732999980449677,1068939,0,0
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2023-05-26 00:00:00+02:00,0.660000026226044,0.72000002861023,0.602999985218048,0.611999988555908,0.611999988555908,631580,0,0
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2023-05-29 00:00:00+02:00,0.620000004768372,0.75,0.578999996185303,0.600000023841858,0.600000023841858,586150,0,0
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2023-05-30 00:00:00+02:00,0.610000014305115,0.634999990463257,0.497000008821487,0.497000008821487,0.497000008821487,552308,0,0
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2023-05-31 00:00:00+02:00,0.458999991416931,0.469999998807907,0.374000012874603,0.379999995231628,0.379999995231628,899067,0,0
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@ -0,0 +1,30 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2023-04-20 00:00:00+02:00,3.0,3.0,2.0,3.0,3.0,2076,0.0,0.0
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2023-04-21 00:00:00+02:00,3.0,3.0,2.0,3.0,3.0,2136,0.0,0.0
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2023-04-24 00:00:00+02:00,3.0,3.0,1.0,1.0,1.0,77147,0.0,0.0
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2023-04-25 00:00:00+02:00,1.0,2.0,1.0,2.0,2.0,9625,0.0,0.0
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2023-04-26 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,5028,0.0,0.0
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2023-04-27 00:00:00+02:00,2.0,2.0,1.0,1.0,1.0,3235,0.0,0.0
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2023-04-28 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,10944,0.0,0.0
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2023-05-02 00:00:00+02:00,2.0,2.0,2.0,2.0,2.0,12220,0.0,0.0
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2023-05-03 00:00:00+02:00,2.0,2.0,2.0,2.0,2.0,4683,0.0,0.0
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2023-05-04 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,3368,0.0,0.0
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2023-05-05 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,26069,0.0,0.0
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2023-05-08 00:00:00+02:00,9.999999747378752e-05,0.00019999999494757503,9.999999747378752e-05,9.999999747378752e-05,9.999999747378752e-05,705399568,0.0,0.0
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2023-05-09 00:00:00+02:00,1.0,2.0,1.0,1.0,1.0,14228,0.0,0.0
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2023-05-10 00:00:00+02:00,1.0800000429153442,1.399999976158142,0.8799999952316284,1.0,1.0,81012,0.0,0.0001
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2023-05-11 00:00:00+02:00,1.0399999618530273,1.0399999618530273,0.8500000238418579,1.0,1.0,40254,0.0,0.0
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2023-05-12 00:00:00+02:00,0.949999988079071,1.100000023841858,0.949999988079071,1.0199999809265137,1.0199999809265137,35026,0.0,0.0
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2023-05-15 00:00:00+02:00,0.949999988079071,1.0199999809265137,0.8600000143051147,0.9399999976158142,0.9399999976158142,41486,0.0,0.0
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2023-05-16 00:00:00+02:00,0.8999999761581421,0.9440000057220459,0.800000011920929,0.800000011920929,0.800000011920929,43583,0.0,0.0
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2023-05-17 00:00:00+02:00,0.8500000238418579,0.8500000238418579,0.7799999713897705,0.8100000023841858,0.8100000023841858,29984,0.0,0.0
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2023-05-18 00:00:00+02:00,0.7799999713897705,0.7860000133514404,0.7400000095367432,0.7400000095367432,0.7400000095367432,24679,0.0,0.0
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2023-05-19 00:00:00+02:00,0.7860000133514404,0.7860000133514404,0.6499999761581421,0.6520000100135803,0.6520000100135803,26732,0.0,0.0
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2023-05-22 00:00:00+02:00,0.8299999833106995,1.059999942779541,0.7099999785423279,0.7099999785423279,0.7099999785423279,169538,0.0,0.0
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2023-05-23 00:00:00+02:00,0.8999999761581421,1.6080000400543213,0.8600000143051147,1.2200000286102295,1.2200000286102295,858471,0.0,0.0
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2023-05-24 00:00:00+02:00,1.194000005722046,1.2599999904632568,0.7799999713897705,0.7799999713897705,0.7799999713897705,627823,0.0,0.0
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2023-05-25 00:00:00+02:00,0.9800000190734863,1.2200000286102295,0.7020000219345093,0.7329999804496765,0.7329999804496765,1068939,0.0,0.0
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2023-05-26 00:00:00+02:00,0.6600000262260437,0.7200000286102295,0.6029999852180481,0.6119999885559082,0.6119999885559082,631580,0.0,0.0
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2023-05-29 00:00:00+02:00,0.6200000047683716,0.75,0.5789999961853027,0.6000000238418579,0.6000000238418579,586150,0.0,0.0
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2023-05-30 00:00:00+02:00,0.6100000143051147,0.6349999904632568,0.4970000088214874,0.4970000088214874,0.4970000088214874,552308,0.0,0.0
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2023-05-31 00:00:00+02:00,0.45899999141693115,0.4699999988079071,0.37400001287460327,0.3799999952316284,0.3799999952316284,899067,0.0,0.0
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@ -0,0 +1,11 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2023-05-18 00:00:00+01:00,193.220001220703,200.839996337891,193.220001220703,196.839996337891,196.839996337891,653125,0,0
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2023-05-17 00:00:00+01:00,199.740005493164,207.738006591797,190.121994018555,197.860000610352,197.860000610352,822268,0,0
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2023-05-16 00:00:00+01:00,215.600006103516,215.600006103516,201.149993896484,205.100006103516,205.100006103516,451009,243.93939,0.471428571428571
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2023-05-15 00:00:00+01:00,215.399955531529,219.19995640346,210.599967302595,217.399987792969,102.39998147147,1761679.3939394,0,0
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2023-05-12 00:00:00+01:00,214.599988664899,216.199965558733,209.599965558733,211.399977329799,99.573855808803,1522298.48484849,0,0
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2023-05-11 00:00:00+01:00,219.999966430664,219.999966430664,212.199987357003,215.000000871931,101.269541277204,3568042.12121213,0,0
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2023-05-10 00:00:00+01:00,218.199954659598,223.000000435965,212.59995640346,215.399955531529,101.457929992676,5599908.78787879,0,0
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2023-05-09 00:00:00+01:00,224,227.688003540039,218.199996948242,218.399993896484,102.87100982666,1906090,0,0
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2023-05-05 00:00:00+01:00,220.999968174526,225.19996686663,220.799976457868,224.4,105.697140066964,964523.636363637,0,0
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2023-05-04 00:00:00+01:00,216.999989972796,222.799965558733,216.881988961356,221.399965994698,104.284055655343,880983.93939394,0,0
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@ -0,0 +1,11 @@
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Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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2023-05-18 00:00:00+01:00,193.220001220703,200.839996337891,193.220001220703,196.839996337891,196.839996337891,653125,0,0
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2023-05-17 00:00:00+01:00,199.740005493164,207.738006591797,190.121994018555,197.860000610352,197.860000610352,822268,0,0
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2023-05-16 00:00:00+01:00,215.600006103516,215.600006103516,201.149993896484,205.100006103516,205.100006103516,451009,243.93939,0.471428571428571
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2023-05-15 00:00:00+01:00,456.908996582031,464.969604492188,446.727203369141,461.151489257813,217.21208190918,830506,0,0
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2023-05-12 00:00:00+01:00,455.212097167969,458.605987548828,444.605987548828,448.424194335938,211.217269897461,717655,0,0
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2023-05-11 00:00:00+01:00,466.666595458984,466.666595458984,450.121185302734,456.060607910156,214.814178466797,1682077,0,0
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2023-05-10 00:00:00+01:00,462.848388671875,473.030303955078,450.969604492188,456.908996582031,215.213790893555,2639957,0,0
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2023-05-09 00:00:00+01:00,224,227.688003540039,218.199996948242,218.399993896484,102.87100982666,1906090,0,0
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2023-05-05 00:00:00+01:00,468.787811279297,477.696899414063,468.363586425781,476,224.2060546875,454704,0,0
|
||||
2023-05-04 00:00:00+01:00,460.303009033203,472.605987548828,460.052703857422,469.636291503906,221.208602905273,415321,0,0
|
||||
|
|
|
@ -0,0 +1,24 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-31 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-30 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0.4406
|
||||
2023-05-29 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-26 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-25 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-24 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-23 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-22 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-19 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-18 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-17 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-16 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-15 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-12 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-11 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-10 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-09 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-08 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-05 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-04 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-03 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-02 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-01 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
|
|
|
@ -0,0 +1,24 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-31 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-30 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0.4406
|
||||
2023-05-29 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-26 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-25 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-24 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-23 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-22 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-19 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-18 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-17 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-16 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-15 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-12 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-11 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-10 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-09 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-08 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-05 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-04 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-03 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-02 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-01 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
|
|
|
@ -0,0 +1,17 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-08 00:00:00+02:00,24.8999996185303,24.9500007629395,24.1000003814697,24.75,24.75,7187,0,0
|
||||
2023-05-09 00:00:00+02:00,25,25.5,23.1499996185303,24.1499996185303,24.1499996185303,22753,0,0
|
||||
2023-05-10 00:00:00+02:00,24.1499996185303,24.1499996185303,22,22.9500007629395,22.9500007629395,62727,0,0
|
||||
2023-05-11 00:00:00+02:00,22.9500007629395,25,22.9500007629395,23.3500003814697,23.3500003814697,19550,0,0
|
||||
2023-05-12 00:00:00+02:00,23.3500003814697,24,22.1000003814697,23.8500003814697,23.8500003814697,17143,0,0
|
||||
2023-05-15 00:00:00+02:00,23,25.7999992370605,22.5,23,23,43709,0,0
|
||||
2023-05-16 00:00:00+02:00,22.75,24.0499992370605,22.5,22.75,22.75,16068,0,0
|
||||
2023-05-17 00:00:00+02:00,23,23.8500003814697,22.1000003814697,23.6499996185303,23.6499996185303,19926,0,0
|
||||
2023-05-19 00:00:00+02:00,23.6499996185303,23.8500003814697,22.1000003814697,22.2999992370605,22.2999992370605,41050,0,0
|
||||
2023-05-22 00:00:00+02:00,22.0000004768372,24.1499996185303,21.5499997138977,22.7500009536743,22.7500009536743,34022,0,0
|
||||
2023-05-23 00:00:00+02:00,22.75,22.8999996185303,21.75,22.5,22.5,13992,0,0
|
||||
2023-05-24 00:00:00+02:00,21,24,21,22.0100002288818,22.0100002288818,18306,0,0.1
|
||||
2023-05-25 00:00:00+02:00,21.5699996948242,22.8899993896484,20,21.1599998474121,21.1599998474121,35398,0,0
|
||||
2023-05-26 00:00:00+02:00,21.1599998474121,22.4950008392334,20.5,21.0949993133545,21.0949993133545,8039,0,0
|
||||
2023-05-29 00:00:00+02:00,22.1000003814697,22.1000003814697,20.25,20.75,20.75,17786,0,0
|
||||
2023-05-30 00:00:00+02:00,20.75,21.6499996185303,20.1499996185303,20.4500007629395,20.4500007629395,10709,0,0
|
||||
|
|
|
@ -0,0 +1,17 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-08 00:00:00+02:00,24.899999618530273,24.950000762939453,24.100000381469727,24.75,24.75,7187,0.0,0.0
|
||||
2023-05-09 00:00:00+02:00,25.0,25.5,23.149999618530273,24.149999618530273,24.149999618530273,22753,0.0,0.0
|
||||
2023-05-10 00:00:00+02:00,24.149999618530273,24.149999618530273,22.0,22.950000762939453,22.950000762939453,62727,0.0,0.0
|
||||
2023-05-11 00:00:00+02:00,22.950000762939453,25.0,22.950000762939453,23.350000381469727,23.350000381469727,19550,0.0,0.0
|
||||
2023-05-12 00:00:00+02:00,23.350000381469727,24.0,22.100000381469727,23.850000381469727,23.850000381469727,17143,0.0,0.0
|
||||
2023-05-15 00:00:00+02:00,23.0,25.799999237060547,22.5,23.0,23.0,43709,0.0,0.0
|
||||
2023-05-16 00:00:00+02:00,22.75,24.049999237060547,22.5,22.75,22.75,16068,0.0,0.0
|
||||
2023-05-17 00:00:00+02:00,23.0,23.850000381469727,22.100000381469727,23.649999618530273,23.649999618530273,19926,0.0,0.0
|
||||
2023-05-19 00:00:00+02:00,23.649999618530273,23.850000381469727,22.100000381469727,22.299999237060547,22.299999237060547,41050,0.0,0.0
|
||||
2023-05-22 00:00:00+02:00,2.200000047683716,2.4149999618530273,2.1549999713897705,2.2750000953674316,2.2750000953674316,340215,0.0,0.0
|
||||
2023-05-23 00:00:00+02:00,22.75,22.899999618530273,21.75,22.5,22.5,13992,0.0,0.0
|
||||
2023-05-24 00:00:00+02:00,21.0,24.0,21.0,22.010000228881836,22.010000228881836,18306,0.0,0.1
|
||||
2023-05-25 00:00:00+02:00,21.56999969482422,22.889999389648438,20.0,21.15999984741211,21.15999984741211,35398,0.0,0.0
|
||||
2023-05-26 00:00:00+02:00,21.15999984741211,22.4950008392334,20.5,21.094999313354492,21.094999313354492,8039,0.0,0.0
|
||||
2023-05-29 00:00:00+02:00,22.100000381469727,22.100000381469727,20.25,20.75,20.75,17786,0.0,0.0
|
||||
2023-05-30 00:00:00+02:00,20.75,21.649999618530273,20.149999618530273,20.450000762939453,20.450000762939453,10709,0.0,0.0
|
||||
|
|
|
@ -0,0 +1,23 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2022-06-01 00:00:00+02:00,5.72999992370606,5.78199996948242,5.3939998626709,5.3939998626709,5.3939998626709,3095860,0,0
|
||||
2022-06-02 00:00:00+02:00,5.38600006103516,5.38600006103516,5.26800003051758,5.2939998626709,5.2939998626709,1662880,0,0
|
||||
2022-06-03 00:00:00+02:00,5.34599990844727,5.34599990844727,5.15800018310547,5.16800003051758,5.16800003051758,1698900,0,0
|
||||
2022-06-06 00:00:00+02:00,5.16800003051758,5.25200004577637,5.13800010681152,5.18800010681152,5.18800010681152,1074910,0,0
|
||||
2022-06-07 00:00:00+02:00,5.21800003051758,5.22200012207031,5.07400016784668,5.1560001373291,5.1560001373291,1850680,0,0
|
||||
2022-06-08 00:00:00+02:00,5.1560001373291,5.17599983215332,5.07200012207031,5.10200004577637,5.10200004577637,1140360,0,0
|
||||
2022-06-09 00:00:00+02:00,5.09799995422363,5.09799995422363,4.87599983215332,4.8939998626709,4.8939998626709,2025480,0,0
|
||||
2022-06-10 00:00:00+02:00,4.87999992370606,4.87999992370606,4.50400009155274,4.50400009155274,4.50400009155274,2982730,0,0
|
||||
2022-06-13 00:00:00+02:00,4.3,4.37599983215332,3.83600006103516,3.83600006103516,3.83600006103516,4568210,0,0.1
|
||||
2022-06-14 00:00:00+02:00,3.87750015258789,4.15999984741211,3.85200004577637,3.9439998626709,3.9439998626709,5354500,0,0
|
||||
2022-06-15 00:00:00+02:00,4.03400001525879,4.16450004577637,3.73050003051758,3.73050003051758,3.73050003051758,6662610,0,0
|
||||
2022-06-16 00:00:00+02:00,3.73050003051758,3.98499984741211,3.72400016784668,3.82550010681152,3.82550010681152,13379960,0,0
|
||||
2022-06-17 00:00:00+02:00,3.8,4.29949989318848,3.75,4.29949989318848,4.29949989318848,12844160,0,0
|
||||
2022-06-20 00:00:00+02:00,2.19422197341919,2.2295401096344,2.13992595672607,2.2295401096344,2.2295401096344,12364104,0,0
|
||||
2022-06-21 00:00:00+02:00,2.24719905853272,2.28515291213989,2.19712090492249,2.21557092666626,2.21557092666626,8434013,0,0
|
||||
2022-06-22 00:00:00+02:00,1.98679196834564,2.00365996360779,1.73798203468323,1.73798203468323,1.73798203468323,26496542,0,0
|
||||
2022-06-23 00:00:00+02:00,1.62411904335022,1.68526804447174,1.37320005893707,1.59776198863983,1.59776198863983,48720201,0,0
|
||||
2022-06-24 00:00:00+02:00,1.47599303722382,1.54610300064087,1.1739410161972,1.24932205677032,1.24932205677032,56877192,0,0
|
||||
2022-06-27 00:00:00+02:00,1.49899995326996,1.79849994182587,1.49899995326996,1.79849994182587,1.79849994182587,460673,0,0
|
||||
2022-06-28 00:00:00+02:00,2.15799999237061,3.05100011825562,2.12599992752075,3.05100011825562,3.05100011825562,3058635,0,0
|
||||
2022-06-29 00:00:00+02:00,2.90000009536743,3.73799991607666,2.85899996757507,3.26399993896484,3.26399993896484,6516761,0,0
|
||||
2022-06-30 00:00:00+02:00,3.24900007247925,3.28099989891052,2.5,2.5550000667572,2.5550000667572,4805984,0,0
|
||||
|
|
|
@ -0,0 +1,23 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2022-06-01 00:00:00+02:00,57.29999923706055,57.81999969482422,53.939998626708984,53.939998626708984,53.939998626708984,309586,0.0,0.0
|
||||
2022-06-02 00:00:00+02:00,53.86000061035156,53.86000061035156,52.68000030517578,52.939998626708984,52.939998626708984,166288,0.0,0.0
|
||||
2022-06-03 00:00:00+02:00,53.459999084472656,53.459999084472656,51.58000183105469,51.68000030517578,51.68000030517578,169890,0.0,0.0
|
||||
2022-06-06 00:00:00+02:00,51.68000030517578,52.52000045776367,51.380001068115234,51.880001068115234,51.880001068115234,107491,0.0,0.0
|
||||
2022-06-07 00:00:00+02:00,52.18000030517578,52.220001220703125,50.7400016784668,51.560001373291016,51.560001373291016,185068,0.0,0.0
|
||||
2022-06-08 00:00:00+02:00,51.560001373291016,51.7599983215332,50.720001220703125,51.02000045776367,51.02000045776367,114036,0.0,0.0
|
||||
2022-06-09 00:00:00+02:00,50.97999954223633,50.97999954223633,48.7599983215332,48.939998626708984,48.939998626708984,202548,0.0,0.0
|
||||
2022-06-10 00:00:00+02:00,48.79999923706055,48.79999923706055,45.040000915527344,45.040000915527344,45.040000915527344,298273,0.0,0.0
|
||||
2022-06-13 00:00:00+02:00,43.0,43.7599983215332,38.36000061035156,38.36000061035156,38.36000061035156,456821,0.0,0.1
|
||||
2022-06-14 00:00:00+02:00,38.775001525878906,41.599998474121094,38.52000045776367,39.439998626708984,39.439998626708984,535450,0.0,0.0
|
||||
2022-06-15 00:00:00+02:00,40.34000015258789,41.64500045776367,37.30500030517578,37.30500030517578,37.30500030517578,666261,0.0,0.0
|
||||
2022-06-16 00:00:00+02:00,37.30500030517578,39.849998474121094,37.2400016784668,38.255001068115234,38.255001068115234,1337996,0.0,0.0
|
||||
2022-06-17 00:00:00+02:00,38.0,42.994998931884766,37.5,42.994998931884766,42.994998931884766,1284416,0.0,0.0
|
||||
2022-06-20 00:00:00+02:00,2.1942219734191895,2.2295401096343994,2.139925956726074,2.2295401096343994,2.2295401096343994,12364104,0.0,0.0
|
||||
2022-06-21 00:00:00+02:00,2.247199058532715,2.2851529121398926,2.1971209049224854,2.2155709266662598,2.2155709266662598,8434013,0.0,0.0
|
||||
2022-06-22 00:00:00+02:00,1.986791968345642,2.003659963607788,1.7379820346832275,1.7379820346832275,1.7379820346832275,26496542,0.0,0.0
|
||||
2022-06-23 00:00:00+02:00,1.6241190433502197,1.6852680444717407,1.3732000589370728,1.5977619886398315,1.5977619886398315,48720201,0.0,0.0
|
||||
2022-06-24 00:00:00+02:00,1.475993037223816,1.5461030006408691,1.1739410161972046,1.2493220567703247,1.2493220567703247,56877192,0.0,0.0
|
||||
2022-06-27 00:00:00+02:00,1.4989999532699585,1.7984999418258667,1.4989999532699585,1.7984999418258667,1.7984999418258667,460673,0.0,0.0
|
||||
2022-06-28 00:00:00+02:00,2.1579999923706055,3.0510001182556152,2.125999927520752,3.0510001182556152,3.0510001182556152,3058635,0.0,0.0
|
||||
2022-06-29 00:00:00+02:00,2.9000000953674316,3.73799991607666,2.8589999675750732,3.2639999389648438,3.2639999389648438,6516761,0.0,0.0
|
||||
2022-06-30 00:00:00+02:00,3.249000072479248,3.2809998989105225,2.5,2.555000066757202,2.555000066757202,4805984,0.0,0.0
|
||||
|
|
|
@ -662,36 +662,27 @@ class TestPriceRepair(unittest.TestCase):
|
|||
self.assertFalse(repaired_df["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_bad_stock_split(self):
|
||||
# Setup:
|
||||
|
||||
# import logging
|
||||
# logging.getLogger('yfinance').setLevel(logging.DEBUG)
|
||||
|
||||
tkrs = ['4063.T', 'CNE.L', 'DEX.AX', 'MOB.ST']
|
||||
for tkr in tkrs:
|
||||
bad_tkrs = ['4063.T', 'ALPHA.PA', 'CNE.L', 'DEX.AX', '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)
|
||||
# print(df_bad.index)
|
||||
# print(df_bad[['Close', 'Adj Close', 'Stock Splits']])
|
||||
# return
|
||||
|
||||
repaired_df = dat._fix_bad_stock_split(df_bad, "1d")
|
||||
# print(repaired_df[['Close', 'Adj Close', 'Stock Splits']])
|
||||
# return
|
||||
|
||||
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)
|
||||
correct_df = correct_df.sort_index(ascending=False)
|
||||
|
||||
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("COLUMN", c)
|
||||
print(f"tkr={tkr} COLUMN={c}")
|
||||
print("- repaired_df")
|
||||
print(repaired_df)
|
||||
print("- correct_df[c]:")
|
||||
|
|
@ -700,6 +691,36 @@ class TestPriceRepair(unittest.TestCase):
|
|||
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']
|
||||
good_tkrs += ['AEI', 'CHRA', 'GHI', 'IRON', 'LXU', 'NUZE', 'RSLS', 'TISI']
|
||||
good_tkrs += ['BOL.ST', 'TUI1.DE']
|
||||
for tkr in good_tkrs:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
_dp = os.path.dirname(__file__)
|
||||
df_good = dat.history(period='2y', auto_adjust=False)
|
||||
|
||||
repaired_df = dat._fix_bad_stock_split(df_good, "1d")
|
||||
|
||||
# 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:
|
||||
print(f"tkr={tkr} COLUMN={c}")
|
||||
print("- repaired_df")
|
||||
print(repaired_df)
|
||||
print("- df_good[c]:")
|
||||
print(df_good[c])
|
||||
print("- diff:")
|
||||
print(repaired_df[c] - df_good[c])
|
||||
raise
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
|
|
|||
137
yfinance/base.py
137
yfinance/base.py
|
|
@ -414,6 +414,7 @@ class TickerBase:
|
|||
df = self._fix_unit_mixups(df, interval, tz_exchange, prepost, silent=(repair=="silent"))
|
||||
df = self._fix_missing_div_adjust(df, interval)
|
||||
df = self._fix_bad_stock_split(df, interval)
|
||||
df = df.sort_index()
|
||||
|
||||
# Auto/back adjust
|
||||
try:
|
||||
|
|
@ -952,11 +953,12 @@ class TickerBase:
|
|||
|
||||
# If stock split occurred, then trading must have happened.
|
||||
# I should probably rename the function, because prices aren't zero ...
|
||||
f_split = (df2['Stock Splits'] != 0.0).to_numpy()
|
||||
if f_split.any():
|
||||
f_change_expected_but_missing = f_split & ~f_change
|
||||
if f_change_expected_but_missing.any():
|
||||
f_prices_bad[f_change_expected_but_missing] = True
|
||||
if 'Stock Splits' in df2.columns:
|
||||
f_split = (df2['Stock Splits'] != 0.0).to_numpy()
|
||||
if f_split.any():
|
||||
f_change_expected_but_missing = f_split & ~f_change
|
||||
if f_change_expected_but_missing.any():
|
||||
f_prices_bad[f_change_expected_but_missing] = True
|
||||
|
||||
# Check whether worth attempting repair
|
||||
f_prices_bad = f_prices_bad.to_numpy()
|
||||
|
|
@ -1021,6 +1023,8 @@ class TickerBase:
|
|||
# Sometimes, if a dividend occurred today, then Yahoo has not adjusted historic data.
|
||||
# Easy to detect and correct.
|
||||
|
||||
if df is None or df.empty:
|
||||
return df
|
||||
interday = interval in ['1d', '1wk', '1mo', '3mo']
|
||||
if not interday:
|
||||
return df
|
||||
|
|
@ -1044,7 +1048,7 @@ class TickerBase:
|
|||
# No other divs in data
|
||||
start_idx = 0
|
||||
else:
|
||||
start_idx = div_indices[-2] + 1
|
||||
start_idx = div_indices[-2]
|
||||
start_dt = df.index[start_idx]
|
||||
f_no_adj = (df['Close']==df['Adj Close']).to_numpy()[start_idx:last_div_idx]
|
||||
threshold_pct = 0.5
|
||||
|
|
@ -1085,90 +1089,106 @@ class TickerBase:
|
|||
most_recent_split_day = df.index[split_f].max()
|
||||
split = df.loc[most_recent_split_day, 'Stock Splits']
|
||||
split_rcp = 1.0/split
|
||||
|
||||
if most_recent_split_day == df.index[0]:
|
||||
logger.info("split-repair: Need 1+ day of price data after split to determine true price. Won't repair")
|
||||
return df
|
||||
|
||||
logger.debug(f'split-repair: Most recent split = {split}')
|
||||
logger.debug(f'split-repair: Most recent split = {split:.4f} @ {most_recent_split_day.date()}')
|
||||
|
||||
price_col = 'Close'
|
||||
price_cols = ['Open', 'Low', 'High', 'Close']
|
||||
|
||||
# Do not attempt repair of the split is small,
|
||||
# could be mistaken for normal price variance
|
||||
if split > 0.9 and split < 1.1:
|
||||
if split > 0.8 and split < 1.25:
|
||||
logger.info("split-repair: Split ratio too close to 1. Won't repair")
|
||||
return df
|
||||
|
||||
df_debug = df.copy()
|
||||
if logger.level == logging.DEBUG:
|
||||
df_debug = df.copy()
|
||||
df_debug = df_debug.drop(['Adj Close', 'Low', 'High', 'Volume', 'Dividends', 'Repaired?'], axis=1)
|
||||
|
||||
# Calculate daily price % change.
|
||||
_1d_change_x = _np.full(df.shape[0], 1.0)
|
||||
_1d_change_x[1:] = df[price_col].to_numpy()[1:] / df[price_col].to_numpy()[:-1]
|
||||
df_debug['1D change X'] = _1d_change_x
|
||||
# Calculate daily price % change. To reduce effect of price volatility,
|
||||
# calculate change for each OHLC column and select value nearest 1.0.
|
||||
_1d_change_x = _np.full((df.shape[0], 4), 1.0)
|
||||
_1d_change_x[1:] = df[price_cols].to_numpy()[1:,] / df[price_cols].to_numpy()[:-1,]
|
||||
diff = _np.abs(_1d_change_x - 1.0)
|
||||
j_indices = _np.argmin(diff, axis=1)
|
||||
_1d_change_x = _1d_change_x[_np.arange(_1d_change_x.shape[0]), j_indices]
|
||||
x = pd.DataFrame(_1d_change_x, index=df.index)
|
||||
if logger.level == logging.DEBUG:
|
||||
df_debug['1D change X'] = _1d_change_x
|
||||
|
||||
# Calculate the true price variance, i.e. remove effect of bad split-adjustments
|
||||
avg = _np.mean(_1d_change_x)
|
||||
if split < 1.0:
|
||||
logger.debug("split-repair: Expect true prices to be the smaller cluster")
|
||||
# Calculate the variance of changes, excluding changes ABOVE mean which may be changes from bad split adjustment
|
||||
f_inclusion = _1d_change_x < avg
|
||||
else:
|
||||
logger.debug("split-repair: Expect true prices to be the larger cluster")
|
||||
# Calculate the variance of changes, excluding changes BELOW mean which may be changes from bad split adjustment
|
||||
f_inclusion = _1d_change_x > avg
|
||||
_1d_change_x_inclusion = _1d_change_x[f_inclusion]
|
||||
variance = _np.var(_1d_change_x_inclusion)
|
||||
sd = _np.sqrt(variance)
|
||||
logger.debug(f"split-repair: Estimation of StdDev = {sd:.2f}")
|
||||
# Next step is a better guess at identifying all good changes.
|
||||
# 1) calculate mean of _1d_change_x_inclusion
|
||||
avg = _np.mean(_1d_change_x_inclusion)
|
||||
logger.debug(f"split-repair: Naive estimation of avg change = {avg:.2f}")
|
||||
# 2) identify changes from original data within 3 SDs of mean
|
||||
f = _np.abs(_1d_change_x - avg) <= 3*sd
|
||||
if not f.any():
|
||||
logger.debug(f'split-repair: Fault in logic identifying true price variance')
|
||||
# If all 1D changes are closer to 1.0 than split, exit
|
||||
split_max = max(split, split_rcp)
|
||||
if _np.max(_1d_change_x) < (split_max-1)*0.5+1 and _np.min(_1d_change_x) > 1.0/((split_max-1)*0.5 +1):
|
||||
logger.info(f"split-repair: No bad splits detected")
|
||||
return df
|
||||
# 3) re-calculate the statistics
|
||||
|
||||
# Calculate the true price variance, i.e. remove effect of bad split-adjustments.
|
||||
# Key = ignore 1D changes outside of interquartile range
|
||||
q1, q3 = _np.percentile(_1d_change_x, [25, 75])
|
||||
iqr = q3 - q1
|
||||
lower_bound = q1 - 1.5 * iqr
|
||||
upper_bound = q3 + 1.5 * iqr
|
||||
f = (_1d_change_x >= lower_bound) & (_1d_change_x <= upper_bound)
|
||||
avg = _np.mean(_1d_change_x[f])
|
||||
sd = _np.std(_1d_change_x[f])
|
||||
logger.debug(f"split-repair: Improved estimation of avg change = {avg:.2f} and StdDev = {sd:.4f}")
|
||||
# Now can calculate SD as % of mean
|
||||
sd_pct = sd / avg
|
||||
logger.debug(f"split-repair: SD % mean = {sd_pct:.4f}")
|
||||
logger.debug(f"split-repair: Estimation of true 1D change stats: mean = {avg:.2f}, StdDev = {sd:.4f} ({sd_pct*100.0:.1f}% of mean)")
|
||||
|
||||
# Only proceed if split adjustment far exceeds normal 1D changes
|
||||
if (split < 1.0 and 100*split_rcp < 5*sd_pct) or \
|
||||
(split > 1.0 and 100*split < 5*sd_pct):
|
||||
largest_change_pct = 5*sd_pct
|
||||
if (max(split, split_rcp) < 1.0+largest_change_pct):
|
||||
logger.info("split-repair: Split ratio too close to normal price volatility. Won't repair")
|
||||
# if logger.level == logging.DEBUG:
|
||||
# logger.debug(f"split-repair: my workings:")
|
||||
# logger.debug('\n' + str(df_debug))
|
||||
return df
|
||||
|
||||
# Now can detect bad split adjustments
|
||||
# Set threshold to halfway between split ratio and largest expected normal price change
|
||||
r = _1d_change_x / split_rcp
|
||||
# - within 50% => more likely to be bad split adjustment than within normal price variance
|
||||
f1 = (r > 0.5) & (r < 1.5)
|
||||
r = _1d_change_x / split
|
||||
f2 = (r > 0.5) & (r < 1.5)
|
||||
split_max = max(split, split_rcp)
|
||||
threshold = (split_max + largest_change_pct) * 0.5
|
||||
logger.debug(f"split-repair: threshold={threshold:.3f}")
|
||||
f1 = _1d_change_x < 1.0/threshold
|
||||
f2 = _1d_change_x > threshold
|
||||
f = f1 | f2
|
||||
# df_debug['f'] = f
|
||||
# df_debug['f1'] = f1
|
||||
# df_debug['f2'] = f2
|
||||
if logger.level == logging.DEBUG:
|
||||
df_debug['r'] = r
|
||||
df_debug['f1'] = f1
|
||||
df_debug['f2'] = f2
|
||||
if not f.any():
|
||||
logger.info('split-repair: No bad split adjustments detected')
|
||||
return df
|
||||
|
||||
true_indices = _np.where(f)[0]
|
||||
ranges = []
|
||||
# mask = _np.zeros_like(f, dtype=bool)
|
||||
if logger.level == logging.DEBUG:
|
||||
bad = _np.zeros_like(f, dtype=bool)
|
||||
for i in range(len(true_indices) - 1):
|
||||
if i % 2 == 0:
|
||||
# mask[true_indices[i]:true_indices[i + 1]] = True
|
||||
adj = 'split' if f1[true_indices[i]] else '1.0/split'
|
||||
if logger.level == logging.DEBUG:
|
||||
bad[true_indices[i]:true_indices[i + 1]] = True
|
||||
if split > 1.0:
|
||||
adj = 'split' if f1[true_indices[i]] else '1.0/split'
|
||||
else:
|
||||
adj = '1.0/split' if f1[true_indices[i]] else 'split'
|
||||
ranges.append((true_indices[i], true_indices[i+1], adj))
|
||||
if len(true_indices) % 2 != 0:
|
||||
# mask[true_indices[-1]:] = True
|
||||
adj = 'split' if f1[true_indices[-1]] else '1.0/split'
|
||||
if logger.level == logging.DEBUG:
|
||||
bad[true_indices[-1]:] = True
|
||||
if split > 1.0:
|
||||
adj = 'split' if f1[true_indices[-1]] else '1.0/split'
|
||||
else:
|
||||
adj = '1.0/split' if f1[true_indices[-1]] else 'split'
|
||||
ranges.append((true_indices[-1], len(f), adj))
|
||||
# print("ranges:") ; pprint(ranges)
|
||||
# df_debug['mask'] = mask
|
||||
# if logger.level == logging.DEBUG:
|
||||
# from pprint import pprint ; print("ranges:") ; pprint(ranges)
|
||||
if logger.level == logging.DEBUG:
|
||||
df_debug['Bad?'] = bad
|
||||
|
||||
for r in ranges:
|
||||
if r[2] == 'split':
|
||||
|
|
@ -1177,6 +1197,7 @@ class TickerBase:
|
|||
else:
|
||||
m = split_rcp
|
||||
m_rcp = split
|
||||
# logger.debug(f"split-repair: range={r} m={m}")
|
||||
for c in ['Open', 'High', 'Low', 'Close', 'Adj Close']:
|
||||
df.iloc[r[0]:r[1], df.columns.get_loc(c)] *= m
|
||||
df.iloc[r[0]:r[1], df.columns.get_loc("Volume")] *= m_rcp
|
||||
|
|
@ -1194,9 +1215,11 @@ class TickerBase:
|
|||
else:
|
||||
msg = f"split-repair: Corrected bad split adjustment across intervals {start} -> {end} (inclusive)"
|
||||
logger.info(msg)
|
||||
df['Volume'] = df['Volume'].round(0).astype('int')
|
||||
|
||||
# print(df_debug.drop(['Close', 'Low', 'High', 'Volume', 'Dividends'], axis=1))
|
||||
# print(df.drop(['Close', 'Low', 'High', 'Volume', 'Dividends'], axis=1))
|
||||
# if logger.level == logging.DEBUG:
|
||||
# logger.debug(f"split-repair: my workings:")
|
||||
# logger.debug('\n' + str(df_debug))
|
||||
|
||||
return df
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue