【发布时间】：2021-08-21 09:50:59
【问题描述】：
我正在尝试实施资产管理书标签后的趋势。我找到了以下我想要实现的代码，但是我收到了一个错误
TypeError：不再支持带时间戳的整数和整数数组的加法/减法。不要加/减n，而是使用n * obj.freq
!pip install yfinance
!pip install mplfinance
import yfinance as yf
import mplfinance as mpf
import numpy as np
import pandas as pd
# get the data from yfiance
df=yf.download('BTC-USD',start='2008-01-04',end='2021-06-3',interval='1d')
#code snippet 5.1
# Fit linear regression on close
# Return the t-statistic for a given parameter estimate.
def tValLinR(close):
#tValue from a linear trend
x = np.ones((close.shape[0],2))
x[:,1] = np.arange(close.shape[0])
ols = sm1.OLS(close, x).fit()
return ols.tvalues[1]
#code snippet 5.2
'''
#search for the maximum absolutet-value. To identify the trend
# - molecule - index of observations we wish to labels.
# - close - which is the time series of x_t
# - span - is the set of values of L (look forward period) that the algorithm will #try (window_size)
# The L that maximizes |tHat_B_1| (t-value) is choosen - which is the look-forward #period
# with the most significant trend. (optimization)
'''
def getBinsFromTrend(molecule, close, span):
#Derive labels from the sign of t-value of trend line
#output includes:
# - t1: End time for the identified trend
# - tVal: t-value associated with the estimated trend coefficient
#- bin: Sign of the trend (1,0,-1)
#The t-statistics for each tick has a different look-back window.
#- idx start time in look-forward window
#- dt1 stop time in look-forward window
#- df1 is the look-forward window (window-size)
#- iloc ?
out = pd.DataFrame(index=molecule, columns=['t1', 'tVal', 'bin', 'windowSize'])
hrzns = range(*span)
windowSize = span[1] - span[0]
maxWindow = span[1]-1
minWindow = span[0]
for idx in close.index:
idx += maxWindow
if idx >= len(close):
break
df_tval = pd.Series(dtype='float64')
iloc0 = close.index.get_loc(idx)
if iloc0+max(hrzns) > close.shape[0]:
continue
for hrzn in hrzns:
dt1 = close.index[iloc0-hrzn+1]
df1 = close.loc[dt1:idx]
df_tval.loc[dt1] = tValLinR(df1.values) #calculates t-statistics on period
dt1 = df_tval.replace([-np.inf, np.inf, np.nan], 0).abs().idxmax() #get largest t-statistics calculated over span period
print(df_tval.index[-1])
print(dt1)
print(abs(df_tval.values).argmax() + minWindow)
out.loc[idx, ['t1', 'tVal', 'bin', 'windowSize']] = df_tval.index[-1], df_tval[dt1], np.sign(df_tval[dt1]), abs(df_tval.values).argmax() + minWindow #prevent leakage
out['t1'] = pd.to_datetime(out['t1'])
out['bin'] = pd.to_numeric(out['bin'], downcast='signed')
#deal with massive t-Value outliers - they dont provide more confidence and they ruin the scatter plot
tValueVariance = out['tVal'].values.var()
tMax = 20
if tValueVariance < tMax:
tMax = tValueVariance
out.loc[out['tVal'] > tMax, 'tVal'] = tMax #cutoff tValues > 20
out.loc[out['tVal'] < (-1)*tMax, 'tVal'] = (-1)*tMax #cutoff tValues < -20
return out.dropna(subset=['bin'])
if __name__ == '__main__':
#snippet 5.3
idx_range_from = 3
idx_range_to = 10
df1 = getBinsFromTrend(df.index, df['Close'], [idx_range_from,idx_range_to,1]) #[3,10,1] = range(3,10) This is the issue
tValues = df1['tVal'].values #tVal
doNormalize = False
#normalise t-values to -1, 1
if doNormalize:
np.min(tValues)
minusArgs = [i for i in range(0, len(tValues)) if tValues[i] < 0]
tValues[minusArgs] = tValues[minusArgs] / (np.min(tValues)*(-1.0))
plus_one = [i for i in range(0, len(tValues)) if tValues[i] > 0]
tValues[plus_one] = tValues[plus_one] / np.max(tValues)
#+(idx_range_to-idx_range_from+1)
plt.scatter(df1.index, df0.loc[df1.index].values, c=tValues, cmap='viridis') #df1['tVal'].values, cmap='viridis')
plt.plot(df0.index, df0.values, color='gray')
plt.colorbar()
plt.show()
plt.savefig('fig5.2.png')
plt.clf()
plt.df['Close']()
plt.scatter(df1.index, df0.loc[df1.index].values, c=df1['bin'].values, cmap='vipridis')
#Test methods
ols_tvalue = tValLinR( np.array([3.0, 3.5, 4.0]) )
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