Pandas：在复函数上应用滚动窗口（赫斯特指数）

def HurstEXP( ts = [ None, ] ): # TESTED: HurstEXP() Hurst exponent ( Browninan Motion & other observations measure ) 100+ BARs back(!) """ __doc__ USAGE: HurstEXP( ts = [ None, ] ) Returns the Hurst Exponent of the time series vector ts[] PARAMETERS: ts[,] a time-series, with 100+ elements ( or [ None, ] that produces a demo run ) RETURNS: float - a Hurst Exponent approximation, as a real value or an explanatory string on an empty call THROWS: n/a EXAMPLE: >>> HurstEXP() # actual numbers will vary, as per np.random.randn() generator used HurstEXP( Geometric Browian Motion ): 0.49447454 HurstEXP( Mean-Reverting Series ): -0.00016013 HurstEXP( Trending Series ): 0.95748937 'SYNTH series demo ( on HurstEXP( ts == [ None, ] ) ) # actual numbers vary, as per np.random.randn() generator' >>> HurstEXP( rolling_window( aDSEG[:,idxC], 100 ) ) REF.s: >>> www.quantstart.com/articles/Basics-of-Statistical-Mean-Reversion-Testing """ #---------------------------------------------------------------------------------------------------------------------------<self-reflective> if ( ts[0] == None ): # DEMO: Create a SYNTH Geometric Brownian Motion, Mean-Reverting and Trending Series: gbm = np.log( 1000 + np.cumsum( np.random.randn( 100000 ) ) ) # a Geometric Brownian Motion[log(1000 + rand), log(1000 + rand + rand ), log(1000 + rand + rand + rand ),... log( 1000 + rand + ... )] mr = np.log( 1000 + np.random.randn( 100000 ) ) # a Mean-Reverting Series [log(1000 + rand), log(1000 + rand ), log(1000 + rand ),... log( 1000 + rand )] tr = np.log( 1000 + np.cumsum( 1 + np.random.randn( 100000 ) ) ) # a Trending Series [log(1001 + rand), log(1002 + rand + rand ), log(1003 + rand + rand + rand ),... log(101000 + rand + ... )] # Output the Hurst Exponent for each of the above SYNTH series print ( "HurstEXP( Geometric Browian Motion ): {0: > 12.8f}".format( HurstEXP( gbm ) ) ) print ( "HurstEXP( Mean-Reverting Series ): {0: > 12.8f}".format( HurstEXP( mr ) ) ) print ( "HurstEXP( Trending Series ): {0: > 12.8f}".format( HurstEXP( tr ) ) ) return ( "SYNTH series demo ( on HurstEXP( ts == [ None, ] ) ) # actual numbers vary, as per np.random.randn() generator" ) """ # FIX: =================================================================================================================== | |>>> QuantFX.HurstEXP( QuantFX.DATA[ :1000,QuantFX.idxH].tolist() ) 0.47537688039105963 | |>>> QuantFX.HurstEXP( QuantFX.DATA[ :101,QuantFX.idxH].tolist() ) -0.31081076640420308 | |>>> QuantFX.HurstEXP( QuantFX.DATA[ :100,QuantFX.idxH].tolist() ) nan | |>>> QuantFX.HurstEXP( QuantFX.DATA[ :99,QuantFX.idxH].tolist() ) Intel MKL ERROR: Parameter 6 was incorrect on entry to DGELSD. C:\Python27.anaconda\lib\site-packages\numpy\lib\polynomial.py:594: RankWarning: Polyfit may be poorly conditioned warnings.warn(msg, RankWarning) 0.026867491053098096 """ pass; too_short_list = 101 - len( ts ) # MUST HAVE 101+ ELEMENTS if ( 0 < too_short_list ): # IF NOT: ts = too_short_list * ts[:1] + ts # PRE-PEND SUFFICIENT NUMBER of [ts[0],]-as-list REPLICAS TO THE LIST-HEAD #--------------------------------------------------------------------------------------------------------------------------- lags = range( 2, 100 ) # Create the range of lag values tau = [ np.sqrt( np.std( np.subtract( ts[lag:], ts[:-lag] ) ) ) for lag in lags ] # Calculate the array of the variances of the lagged differences #oly = np.polyfit( np.log( lags ), np.log( tau ), 1 ) # Use a linear fit to estimate the Hurst Exponent #eturn ( 2.0 * poly[0] ) # Return the Hurst exponent from the polyfit output """ ********************************************************************************************************************************************************************* DONE:[MS]:ISSUE / FIXED ABOVE |>>> QuantFX.HurstEXP( QuantFX.DATA[ : QuantFX.aMinPTR,QuantFX.idxH] ) C:\Python27.anaconda\lib\site-packages\numpy\core\_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning) C:\Python27.anaconda\lib\site-packages\numpy\core\_methods.py:94: RuntimeWarning: invalid value encountered in true_divide arrmean, rcount, out=arrmean, casting='unsafe', subok=False) C:\Python27.anaconda\lib\site-packages\numpy\core\_methods.py:114: RuntimeWarning: invalid value encountered in true_divide ret, rcount, out=ret, casting='unsafe', subok=False) QuantFX.py:23034: RuntimeWarning: divide by zero encountered in log return ( 2.0 * np.polyfit( np.log( lags ), np.log( tau ), 1 )[0] ) # Return the Hurst exponent from the polyfit output ( a linear fit to estimate the Hurst Exponent ) Intel MKL ERROR: Parameter 6 was incorrect on entry to DGELSD. C:\Python27.anaconda\lib\site-packages\numpy\lib\polynomial.py:594: RankWarning: Polyfit may be poorly conditioned warnings.warn(msg, RankWarning) 0.028471879418359915 | | |# DATA: | |>>> QuantFX.DATA[ : QuantFX.aMinPTR,QuantFX.idxH] memmap([ 1763.31005859, 1765.01000977, 1765.44995117, 1764.80004883, 1765.83996582, 1768.91003418, 1771.04003906, 1769.43994141, 1771.4699707 , 1771.61999512, 1774.76000977, 1769.55004883, 1773.4699707 , 1773.32995605, 1770.08996582, 1770.20996094, 1768.34997559, 1768.02001953, 1767.59997559, 1767.23999023, 1768.41003418, 1769.06994629, 1769.56994629, 1770.7800293 , 1770.56994629, 1769.7800293 , 1769.90002441, 1770.44995117, 1770.9699707 , 1771.04003906, 1771.16003418, 1769.81005859, 1768.76000977, 1769.39001465, 1773.23999023, 1771.91003418, 1766.92004395, 1765.56994629, 1762.65002441, 1760.18005371, 1755. , 1756.67004395, 1753.48999023, 1753.7199707 , 1751.92004395, 1745.44995117, 1745.44995117, 1744.54003906, 1744.54003906, 1744.84997559, 1744.84997559, 1744.34997559, 1744.34997559, 1743.75 , 1743.75 , 1745.23999023, 1745.23999023, 1745.15002441, 1745.31005859, 1745.47998047, 1745.47998047, 1749.06994629, 1749.06994629, 1748.29003906, 1748.29003906, 1747.42004395, 1747.42004395, 1746.98999023, 1747.61999512, 1748.79003906, 1748.79003906, 1748.38000488, 1748.38000488, 1744.81005859, 1744.81005859, 1736.80004883, 1736.80004883, 1735.43005371, 1735.43005371, 1737.9699707 ], dtype=float32 ) | | | # CONVERTED .tolist() to avoid .memmap-type artifacts: | |>>> QuantFX.DATA[ : QuantFX.aMinPTR,QuantFX.idxH].tolist() [1763.31005859375, 1765.010009765625, 1765.449951171875, 1764.800048828125, 1765.8399658203125, 1768.9100341796875, 1771.0400390625, 1769.43994140625, 1771.469970703125, 1771.6199951171875, 1774.760 859375, 1743.75, 1743.75, 1745.239990234375, 1745.239990234375, 1745.1500244140625, 1745.31005859375, 1745.47998046875, 1745.47998046875, 1749.0699462890625, 1749.0699462890625, 1748.2900390625, 174 | |>>> QuantFX.HurstEXP( QuantFX.DATA[ : QuantFX.aMinPTR,QuantFX.idxH].tolist() ) C:\Python27.anaconda\lib\site-packages\numpy\core\_methods.py:116: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) Intel MKL ERROR: Parameter 6 was incorrect on entry to DGELSD. C:\Python27.anaconda\lib\site-packages\numpy\lib\polynomial.py:594: RankWarning: Polyfit may be poorly conditioned warnings.warn(msg, RankWarning) 0.028471876494884543 =================================================================================================================== | |>>> QuantFX.HurstEXP( QuantFX.DATA[ :1000,QuantFX.idxH].tolist() ) 0.47537688039105963 | |>>> QuantFX.HurstEXP( QuantFX.DATA[ :101,QuantFX.idxH].tolist() ) -0.31081076640420308 | |>>> QuantFX.HurstEXP( QuantFX.DATA[ :100,QuantFX.idxH].tolist() ) nan | |>>> QuantFX.HurstEXP( QuantFX.DATA[ :99,QuantFX.idxH].tolist() ) Intel MKL ERROR: Parameter 6 was incorrect on entry to DGELSD. C:\Python27.anaconda\lib\site-packages\numpy\lib\polynomial.py:594: RankWarning: Polyfit may be poorly conditioned warnings.warn(msg, RankWarning) 0.026867491053098096 """ return ( 2.0 * np.polyfit( np.log( lags ), np.log( tau ), 1 )[0] )

import pandas as pd import yfinance as yf from datetime import datetime from dateutil.relativedelta import relativedelta years = 5 today = datetime.today().strftime('%Y-%m-%d') lastyeartoday = (datetime.today() - relativedelta(years=years)).strftime('%Y-%m-%d') df = yf.download('TSLA', start=lastyeartoday, end=today, progress=False) df = df.dropna() df = df[[u'Close']] df

Date Close 2016-02-16 31.034000 2016-02-17 33.736000 2016-02-18 33.354000 2016-02-19 33.316002 2016-02-22 35.548000 ... ... 2021-02-08 863.419983 2021-02-09 849.460022 2021-02-10 804.820007 2021-02-11 811.659973 2021-02-12 816.119995 1259 rows × 1 columns

TypeError: Cannot convert input [[-31.0340004 -33.73600006 -33.35400009 -33.31600189 -35.54800034 -35.44200134 -35.79999924 -37.48600006 -38.06800079 -38.38600159 -37.27000046 -37.66799927 -39.14799881 -40.20800018 -41.05799866 -40.52000046 -41.74399948 -41.0359993 -41.5 -43.02999878]] of type <class 'numpy.ndarray'> to Timestamp

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1楼 · 发布于 2024-06-26 00:26:00

我想你的问题是你的窗户太短了。它在docstring中说窗口长度必须是100+个元素，Hurst代码没有正确处理它，导致SVD失败

另外，您的测试实际上是切片所有元素，但是最后20个元素，因此实际上是一个长数组，这就是它没有失败的原因：

tmp = df[u'Close'][:-20].to_numpy()

print(tmp.shape, HurstEXP(ts = tmp))
(1239,) 0.5163981260143368

如果您测试一个窗口<；如果长度为100，则会引发LinAlg异常：

tmp = df[u'Close'][:20].to_numpy()

print(tmp.shape, HurstEXP(ts = tmp))
(fails)

如果增加滚动窗口长度，或者修复Hurst函数中的代码以填充数组（如果数组太短），则应该可以使用

window = 500
df.Close.rolling(window).apply(lambda x: HurstEXP(ts = x), raw=True)

HurstEXP函数中用于处理少于100个元素的列表的代码对于ts的值不起作用，这些值是np.ndarray对象，如.rolling(raw=True)提供的对象

您可以修改该函数以从以下内容开始，它将适用于100个元素以下的窗口：

def HurstEXP( ts= [ None, ] ):   
        if isinstance(ts, np.ndarray):
            ts = ts.tolist()

…或者，如果您总是使用numpy阵列，则可以更改修复该阵列的行：

     ts = too_short_list * ts[:1] + ts                      #    PRE-PEND SUFFICIENT NUMBER of [ts[0],]-as-list REPLICAS TO THE LIST-HEAD

到

     ts = np.pad(ts, pad_width=(too_short_list,0), mode='edge')

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