381 lines
13 KiB
Python
381 lines
13 KiB
Python
# -*- encoding:utf-8 -*-
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"""
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abupy中使用的金融时间序列分析模块, 模块真的方法真的参数都为abupy中格式化好的kl如下
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eg:
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close high low p_change open pre_close volume date date_week key atr21 atr14
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2016-07-20 228.36 229.800 225.00 1.38 226.47 225.26 2568498 20160720 2 499 9.1923 8.7234
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2016-07-21 220.50 227.847 219.10 -3.44 226.00 228.36 4428651 20160721 3 500 9.1711 8.7251
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2016-07-22 222.27 224.500 218.88 0.80 221.99 220.50 2579692 20160722 4 501 9.1858 8.7790
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2016-07-25 230.01 231.390 221.37 3.48 222.27 222.27 4490683 20160725 0 502 9.2669 8.9298
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2016-07-26 225.93 228.740 225.63 -1.77 227.34 230.01 41833 20160726 1 503 9.1337 8.7541
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from collections import Iterable
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import logging
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import numpy as np
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import pandas as pd
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from ..CoreBu import ABuEnv
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from ..CoreBu.ABuPdHelper import pd_resample
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__author__ = '阿布'
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__weixin__ = 'abu_quant'
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log_func = logging.info if ABuEnv.g_is_ipython else print
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def _df_dispatch(df, dispatch_func):
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"""
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根据df的类型分发callable的执行方法，
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:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
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:param dispatch_func: 分发的可执行的方法
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"""
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if isinstance(df, pd.DataFrame):
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# 参数只是pd.DataFrame
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return dispatch_func(df)
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elif isinstance(df, dict) and all([isinstance(_df, pd.DataFrame) for _df in df.values()]):
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# 参数只是字典形式
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return [dispatch_func(df[df_key], df_key) for df_key in df]
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elif isinstance(df, Iterable) and all([isinstance(_df, pd.DataFrame) for _df in df]):
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# 参数只是可迭代序列
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return [dispatch_func(_df) for _df in df]
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else:
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log_func('df type is error! {}'.format(type(df)))
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def _df_dispatch_concat(df, dispatch_func):
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"""
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根据df的类型分发callable的执行方法，如果是字典或者可迭代类型的返回值使用
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pd.concat连接起来
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:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
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:param dispatch_func: 分发的可执行的方法
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"""
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if isinstance(df, pd.DataFrame):
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# 参数只是pd.DataFrame
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return dispatch_func(df)
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elif isinstance(df, dict) and all([isinstance(_df, pd.DataFrame) for _df in df.values()]):
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# 参数只是字典形式
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return pd.concat([dispatch_func(df[df_key], df_key) for df_key in df], axis=1)
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elif isinstance(df, Iterable) and all([isinstance(_df, pd.DataFrame) for _df in df]):
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# 参数只是可迭代序列
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return pd.concat([dispatch_func(_df) for _df in df], axis=1)
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else:
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log_func('df type is error! {}'.format(type(df)))
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def resample_close_mean(df, bins=None):
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"""
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对金融时间序列进行变换周期重新采样，对重新采样的结果进行pct_change处理
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，对pct_change序列取abs绝对值，对pct_change绝对值序列取平均，即算出
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重新采样的周期内的平均变化幅度
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eg:
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tsla = ABuSymbolPd.make_kl_df('usTSLA')
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ABuKLUtil.resample_close_mean(tsla)
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out:
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resample
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5D 0.0340
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10D 0.0468
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21D 0.0683
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42D 0.0805
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60D 0.1002
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90D 0.0931
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120D 0.0939
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:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
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:param bins: 默认eg: ['5D', '10D', '21D', '42D', '60D', '90D', '120D']
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:return: pd.DataFrame
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"""
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def _resample_close_mean(p_df, df_name=''):
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resample_dict = {}
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for _bin in bins:
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change = abs(pd_resample(p_df.close, _bin, how='mean').pct_change()).mean()
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"""
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eg: pd_resample(p_df.close, bin, how='mean')
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2014-07-23 249.0728
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2014-09-03 258.3640
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2014-10-15 240.8663
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2014-11-26 220.1552
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2015-01-07 206.0070
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2015-02-18 198.0932
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2015-04-01 217.9791
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2015-05-13 251.3640
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2015-06-24 266.4511
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2015-08-05 244.3334
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2015-09-16 236.2250
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2015-10-28 222.0441
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2015-12-09 222.0574
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2016-01-20 177.2303
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2016-03-02 226.8766
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2016-04-13 230.6000
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2016-05-25 216.7596
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2016-07-06 222.6420
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abs(pd_resample(p_df.close, bin, how='mean').pct_change())
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2014-09-03 0.037
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2014-10-15 0.068
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2014-11-26 0.086
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2015-01-07 0.064
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2015-02-18 0.038
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2015-04-01 0.100
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2015-05-13 0.153
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2015-06-24 0.060
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2015-08-05 0.083
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2015-09-16 0.033
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2015-10-28 0.060
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2015-12-09 0.000
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2016-01-20 0.202
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2016-03-02 0.280
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2016-04-13 0.016
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2016-05-25 0.060
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2016-07-06 0.027
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abs(pd_resample(p_df.close, bin, how='mean').pct_change()).mean():
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0.080
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"""
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resample_dict[_bin] = change
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resample_df = pd.DataFrame.from_dict(resample_dict, orient='index')
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resample_df.columns = ['{}resample'.format(df_name)]
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return resample_df
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if bins is None:
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bins = ['5D', '10D', '21D', '42D', '60D', '90D', '120D']
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return _df_dispatch_concat(df, _resample_close_mean)
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def bcut_change_vc(df, bins=None):
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"""
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eg:
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tsla = ABuSymbolPd.make_kl_df('usTSLA')
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ABuKLUtil.bcut_change_vc(tsla)
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out:
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p_change rate
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(0, 3] 209 0.4147
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(-3, 0] 193 0.3829
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(3, 7] 47 0.0933
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(-7, -3] 44 0.0873
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(-10, -7] 6 0.0119
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(7, 10] 3 0.0060
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(10, inf] 1 0.0020
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(-inf, -10] 1 0.0020
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:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
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:param bins: 默认eg：[-np.inf, -10, -7, -3, 0, 3, 7, 10, np.inf]
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:return: pd.DataFrame
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"""
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def _bcut_change_vc(p_df, df_name=''):
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dww = pd.DataFrame(pd.cut(p_df.p_change, bins=bins).value_counts())
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# 计算各个bin所占的百分比
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dww['{}rate'.format(df_name)] = dww.p_change.values / dww.p_change.values.sum()
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if len(df_name) > 0:
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dww.rename(columns={'p_change': '{}'.format(df_name)}, inplace=True)
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return dww
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if bins is None:
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bins = [-np.inf, -10, -7, -3, 0, 3, 7, 10, np.inf]
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return _df_dispatch_concat(df, _bcut_change_vc)
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def qcut_change_vc(df, q=10):
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"""
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eg:
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tsla = ABuSymbolPd.make_kl_df('usTSLA')
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ABuKLUtil.qcut_change_vc(tsla)
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out:
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change
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0 [-10.45, -3.002]
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1 (-3.002, -1.666]
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2 (-1.666, -0.93]
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3 (-0.93, -0.396]
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4 (-0.396, 0.065]
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5 (0.065, 0.48]
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6 (0.48, 1.102]
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7 (1.102, 1.922]
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8 (1.922, 3.007]
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9 (3.007, 11.17]
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:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
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:param q: 透传qcut使用的q参数，默认10，10等分
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:return: pd.DataFrame
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"""
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def _qcut_change_vc(p_df, df_name=''):
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dww = pd.qcut(p_df.p_change, q).value_counts().index.values
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# 构造Categories使用DataFrame套Series
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dww = pd.Series(dww)
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# 涨跌从负向正开始排序
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dww.sort_values(inplace=True)
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dww = pd.DataFrame(dww)
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# 排序后index重新从0开始排列
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dww.index = np.arange(0, q)
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dww.columns = ['{}change'.format(df_name)]
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return dww
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return _df_dispatch_concat(df, _qcut_change_vc)
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def date_week_mean(df):
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"""
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eg:
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tsla = ABuSymbolPd.make_kl_df('usTSLA')
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ABuKLUtil.date_week_mean(tsla)
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out:
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周一 0.0626
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周二 0.0475
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周三 0.0881
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周四 0.2691
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周五 -0.2838
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:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
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:return: pd.Series或者pd.DataFrame
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"""
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def _date_week_win(p_df, df_name=''):
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dww = p_df.groupby('date_week')['p_change'].mean()
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# 将周几这个信息变成中文
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dww.rename(index={6: '周日', 0: '周一', 1: '周二', 2: '周三', 3: '周四', 4: '周五', 5: '周六'},
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inplace=True)
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# p_change变成对应的pchange
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dww = pd.DataFrame(dww)
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dww.rename(columns={'p_change': '{}_p_change'.format(df_name)}, inplace=True)
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return dww
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return _df_dispatch_concat(df, _date_week_win)
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def date_week_win(df):
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"""
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eg:
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tsla = ABuSymbolPd.make_kl_df('usTSLA')
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ABuKLUtil.date_week_win(tsla)
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out：
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0 1 win
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date_week
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0 44 51 0.5368
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1 55 48 0.4660
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2 48 57 0.5429
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3 44 57 0.5644
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4 53 47 0.470
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:param df: bupy中格式化好的kl，或者字典，或者可迭代序列
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:return: pd.Series或者pd.DataFrame
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"""
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def _date_week_win(p_df, df_name=''):
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_df = p_df.copy()
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win_key = '{}win'.format(df_name)
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_df[win_key] = _df['p_change'] > 0
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_df[win_key] = _df[win_key].astype(int)
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dww = pd.concat([pd.crosstab(_df.date_week, _df[win_key]), _df.pivot_table([win_key], index='date_week')],
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axis=1)
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# 将周几这个信息变成中文
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# noinspection PyUnresolvedReferences
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dww.rename(index={6: '周日', 0: '周一', 1: '周二', 2: '周三', 3: '周四', 4: '周五', 5: '周六'}, inplace=True)
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return dww
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return _df_dispatch_concat(df, _date_week_win)
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def wave_change_rate(df):
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"""
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eg:
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tsla = ABuSymbolPd.make_kl_df('usTSLA')
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ABuKLUtil.wave_change_rate(tsla)
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out:
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日振幅涨跌幅比：1.794156
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:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
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"""
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def _wave_change_rate(p_df, df_name=''):
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wave = ((p_df.high - p_df.low) / p_df.pre_close) * 100
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# noinspection PyUnresolvedReferences
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wave_rate = wave.mean() / np.abs(p_df['p_change']).mean()
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print('{}日振幅涨跌幅比：{:2f}, {}日统计套利条件'.format(
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df_name, wave_rate, '具备' if wave_rate > 1.80 else '不具备'))
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_df_dispatch(df, _wave_change_rate)
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def p_change_stats(df):
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"""
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eg :
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tsla = ABuSymbolPd.make_kl_df('usTSLA')
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ABuKLUtil.p_change_stats(tsla)
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out:
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日涨幅平均值1.861, 共260个交易日上涨走势
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日跌幅平均值-1.906, 共244个交易日下跌走势
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日平均涨跌比0.977, 上涨下跌数量比:1.066
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:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
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"""
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def _p_change_stats(p_df, df_name=''):
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p_change_up = p_df[p_df['p_change'] > 0].p_change
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p_change_down = p_df[p_df['p_change'] < 0].p_change
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print('{}日涨幅平均值{:.3f}, 共{}个交易日上涨走势'.format(df_name, p_change_up.mean(), p_change_up.count()))
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print('{}日跌幅平均值{:.3f}, 共{}个交易日下跌走势'.format(df_name, p_change_down.mean(), p_change_down.count()))
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print('{}日平均涨跌比{:.3f}, 上涨下跌数量比:{:.3f}\n'.format(
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df_name, abs(p_change_up.mean() / p_change_down.mean()), p_change_up.count() / p_change_down.count()))
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_df_dispatch(df, _p_change_stats)
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def date_week_wave(df):
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"""
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根据周几分析金融时间序列中的日波动:
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eg:
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tsla = ABuSymbolPd.make_kl_df('usTSLA')
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ABuKLUtil.date_week_wave(tsla)
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out:
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usTSLAwave
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date_week
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周一 3.8144
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周二 3.3326
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周三 3.3932
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周四 3.3801
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周五 2.9923
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:param df: abupy中格式化好的kl，或者字典，或者可迭代序列
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:return: pd.Series或者pd.DataFrame
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"""
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def _date_week_wave(p_df, df_name=''):
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# 要改df所以copy
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df_copy = p_df.copy()
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wave_key = '{}wave'.format(df_name)
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# 计算波动: * 100目的是和金融序列中的p_change单位一致
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df_copy[wave_key] = ((df_copy.high - df_copy.low) / df_copy.pre_close) * 100
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dww = df_copy.groupby('date_week')[wave_key].mean()
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# 将周几这个信息变成中文
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dww.rename(index={6: '周日', 0: '周一', 1: '周二', 2: '周三', 3: '周四', 4: '周五', 5: '周六'}, inplace=True)
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return dww
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return _df_dispatch_concat(df, _date_week_wave)
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