651 lines
30 KiB
Python
651 lines
30 KiB
Python
# -*- encoding:utf-8 -*-
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"""
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相关系数上层技术线应用模块
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"""
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from __future__ import division
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from __future__ import print_function
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from __future__ import absolute_import
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import copy
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import logging
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from statsmodels.tsa.stattools import coint
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from ..CoreBu.ABuEnv import EMarketDataSplitMode
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from ..CoreBu import ABuEnv
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from ..MarketBu import ABuSymbolPd
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from ..MarketBu.ABuSymbol import code_to_symbol
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from ..SimilarBu import ABuSimilar
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from ..SimilarBu import ECoreCorrType
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from ..TradeBu.ABuBenchmark import AbuBenchmark
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from ..UtilBu import ABuScalerUtil
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from ..UtilBu.ABuProgress import do_clear_output
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from ..SimilarBu.ABuSimilar import from_local
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from ..TLineBu.ABuTLine import AbuTLine
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from ..UtilBu.ABuDTUtil import plt_show
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__author__ = '阿布'
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__weixin__ = 'abu_quant'
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g_top_corr_cnt = 300
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g_coint_threshold = 0.38
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g_coint_show_max = 10
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def rank_corr_sum(corr_df_dict, symbol):
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"""
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使用corr_df_dict各个相关性矩阵中symbol的rank值进行sum合并：
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eg：rank_pd
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pears sperm
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usBIDU 5.0 5.0
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usFB 8.0 8.0
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usGOOG 6.0 6.0
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usNOAH 1.0 1.0
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usSFUN 7.0 7.0
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usTSLA 9.0 9.0
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usVIPS 3.0 3.0
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usWUBA 4.0 4.0
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eg: rank_pd.sum(axis=1)
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usBIDU 10.0
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usFB 16.0
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usGOOG 12.0
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usNOAH 2.0
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usSFUN 14.0
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usTSLA 18.0
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usVIPS 6.0
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usWUBA 8.0
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:param corr_df_dict: 相关性矩阵组成的字典对象
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:param symbol: eg: 'usTSLA'
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:return: rank_pd.sum(axis=1)，pd.Series对象
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"""
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rank_pd = pd.DataFrame()
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for corr_df_key in corr_df_dict:
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corr_df = corr_df_dict[corr_df_key]
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"""
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eg：corr_df
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usBIDU usFB usGOOG usNOAH usSFUN usTSLA usVIPS usWUBA
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usBIDU 1.0000 0.3013 0.3690 0.4015 0.3680 0.3015 0.3706 0.4320
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usFB 0.3013 1.0000 0.6609 0.2746 0.1978 0.4080 0.2856 0.2438
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usGOOG 0.3690 0.6609 1.0000 0.3682 0.1821 0.3477 0.3040 0.2917
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usNOAH 0.4015 0.2746 0.3682 1.0000 0.3628 0.2178 0.4645 0.4488
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usSFUN 0.3680 0.1978 0.1821 0.3628 1.0000 0.2513 0.2843 0.4883
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usTSLA 0.3015 0.4080 0.3477 0.2178 0.2513 1.0000 0.2327 0.3340
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usVIPS 0.3706 0.2856 0.3040 0.4645 0.2843 0.2327 1.0000 0.4189
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usWUBA 0.4320 0.2438 0.2917 0.4488 0.4883 0.3340 0.4189 1.0000
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"""
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if symbol not in corr_df:
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# TODO 在这里处理有点晚
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# print(corr_df.columns)
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# print(code_to_symbol(symbol).symbol_code)
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return None
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corr_rank = corr_df[symbol].rank(ascending=False, method='first')
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"""
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eg： corr_rank
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usBIDU 5.0
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usFB 8.0
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usGOOG 6.0
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usNOAH 1.0
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usSFUN 7.0
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usTSLA 9.0
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usVIPS 3.0
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usWUBA 4.0
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"""
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rank_pd = rank_pd.join(pd.DataFrame(corr_rank.values, index=corr_rank.index,
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columns=[corr_df_key]), how='outer')
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"""
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eg：rank_pd
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pears sperm
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usBIDU 5.0 5.0
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usFB 8.0 8.0
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usGOOG 6.0 6.0
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usNOAH 1.0 1.0
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usSFUN 7.0 7.0
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usTSLA 9.0 9.0
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usVIPS 3.0 3.0
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usWUBA 4.0 4.0
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"""
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# TODO: 全是等权重的计算，需要可分配权重计算的参数
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"""
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eg: rank_pd.sum(axis=1)
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usBIDU 10.0
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usFB 16.0
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usGOOG 12.0
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usNOAH 2.0
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usSFUN 14.0
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usTSLA 18.0
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usVIPS 6.0
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usWUBA 8.0
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"""
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# 清一下输出，太乱
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do_clear_output()
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return rank_pd.sum(axis=1)
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@from_local
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def calc_similar(symbol, cmp_symbol, sum_rank=None, corr_jobs=(ECoreCorrType.E_CORE_TYPE_PEARS,
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ECoreCorrType.E_CORE_TYPE_SPERM), show=True):
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"""
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使用ABuTLSimilar.calc_similar()函数计算返回的相关性数值是以目标股票所在市场为观察者，
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它不关心某一个股票具体相关性的数值的大小，calc_similar(a, b) 的工作流程如下：
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1.计算a与市场中所有股票的相关性
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2.将所有相关性进行rank排序
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3.查询股票b在rank序列中的位置，此位置值即为结果
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即ABuTLSimilar.calc_similar返回值由0至1，这样的好处是通过计算usTSLA与usAAPL在所有股票中的相似度水平，会更全局客观的体现相关性
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:param symbol: eg: 'usTSLA'
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:param cmp_symbol: 与symbol进行整体市场相关对比的symbol eg: 'usTSLA'
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:param sum_rank: 已经缓存了的sum_rank数据，
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eg: sum_rank
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usBIDU 10.0
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usFB 16.0
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usGOOG 12.0
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usNOAH 2.0
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usSFUN 14.0
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usTSLA 18.0
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usVIPS 6.0
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usWUBA 8.0
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:param corr_jobs: 默认：corr_jobs=(ECoreCorrType.E_CORE_TYPE_PEARS, ECoreCorrType.E_CORE_TYPE_SPERM)
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可以再添加更多jobs
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eg：
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corr_jobs=(ECoreCorrType.E_CORE_TYPE_PEARS, ECoreCorrType.E_CORE_TYPE_SPERM,
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ECoreCorrType.E_CORE_TYPE_SIGN, ECoreCorrType.E_CORE_TYPE_ROLLING)
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注意每添加一种相关计算方法，耗时都会增加
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:param show: 是否进行可视化
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:return: rank_score (float: 0至1), sum_rank
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"""
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cs_symbol = code_to_symbol(symbol)
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cs_cmp_symbol = code_to_symbol(cmp_symbol)
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if cs_symbol.market != cs_cmp_symbol.market:
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# 必须在同一个市场
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logging.info('{} and {} in different market!!!'.format(symbol, cmp_symbol))
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return
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symbol = cs_symbol.value
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cmp_symbol = cs_cmp_symbol.value
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if sum_rank is None:
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tmp_market = ABuEnv.g_market_target
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# 强制把市场设置为一样的
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ABuEnv.g_market_target = cs_symbol.market
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corr_df_dict = ABuSimilar.multi_corr_df(corr_jobs)
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# 恢复之前的市场
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ABuEnv.g_market_target = tmp_market
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"""
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eg： corr_df_dict
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{'pears':
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usBIDU usFB usGOOG usNOAH usSFUN usTSLA usVIPS usWUBA
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usBIDU 1.0000 0.3013 0.3690 0.4015 0.3680 0.3015 0.3706 0.4320
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usFB 0.3013 1.0000 0.6609 0.2746 0.1978 0.4080 0.2856 0.2438
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usGOOG 0.3690 0.6609 1.0000 0.3682 0.1821 0.3477 0.3040 0.2917
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usNOAH 0.4015 0.2746 0.3682 1.0000 0.3628 0.2178 0.4645 0.4488
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usSFUN 0.3680 0.1978 0.1821 0.3628 1.0000 0.2513 0.2843 0.4883
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usTSLA 0.3015 0.4080 0.3477 0.2178 0.2513 1.0000 0.2327 0.3340
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usVIPS 0.3706 0.2856 0.3040 0.4645 0.2843 0.2327 1.0000 0.4189
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usWUBA 0.4320 0.2438 0.2917 0.4488 0.4883 0.3340 0.4189 1.0000
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'sperm':
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usBIDU usFB usGOOG usNOAH usSFUN usTSLA usVIPS usWUBA
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usBIDU 1.0000 0.3888 0.4549 0.4184 0.3747 0.3623 0.4333 0.4396
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usFB 0.3888 1.0000 0.7013 0.2927 0.2379 0.4200 0.3123 0.2216
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usGOOG 0.4549 0.7013 1.0000 0.3797 0.2413 0.3871 0.3922 0.3035
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usNOAH 0.4184 0.2927 0.3797 1.0000 0.3581 0.2066 0.4643 0.4382
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usSFUN 0.3747 0.2379 0.2413 0.3581 1.0000 0.2645 0.3890 0.4693
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usTSLA 0.3623 0.4200 0.3871 0.2066 0.2645 1.0000 0.2540 0.2801
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usVIPS 0.4333 0.3123 0.3922 0.4643 0.3890 0.2540 1.0000 0.4080
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usWUBA 0.4396 0.2216 0.3035 0.4382 0.4693 0.2801 0.4080 1.0000 }
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"""
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sum_rank = rank_corr_sum(corr_df_dict, symbol)
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"""
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eg: sum_rank
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usBIDU 10.0
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usFB 16.0
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usGOOG 12.0
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usNOAH 2.0
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usSFUN 14.0
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usTSLA 18.0
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usVIPS 6.0
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usWUBA 8.0
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"""
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if sum_rank is None:
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logging.info('{} not in corr df!!!'.format(symbol))
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return None, None
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if cmp_symbol not in sum_rank.index:
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logging.info('{} not in sum_rank.index'.format(cmp_symbol))
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return None, None
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# sum_rank.sort_values(ascending=True)之后的结果index即是对比的排序结果值cmp_rank
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cmp_rank = sum_rank.sort_values(ascending=True).index.tolist().index(cmp_symbol)
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"""
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eg: sum_rank.sort_values(ascending=True)
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usNOAH 2.0
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us_NYSE:.IXIC 4.0
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usVIPS 6.0
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usWUBA 8.0
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usBIDU 10.0
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usGOOG 12.0
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usSFUN 14.0
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usFB 16.0
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usTSLA 18.0
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"""
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# 计算cmp_rank在整体sum_rank的比例位置rank_score
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rank_score = 1 - cmp_rank / sum_rank.shape[0]
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if show:
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log_func = logging.info if ABuEnv.g_is_ipython else print
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log_func('{} similar rank score {} : {}'.format(symbol, cmp_symbol, rank_score))
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# 通过make_kl_df序列的接口获取两个金融时间序列
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mul_pd = ABuSymbolPd.make_kl_df([symbol, cmp_symbol], n_folds=2)
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kl_pd = mul_pd[symbol]
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kl_pd_cmp = mul_pd[cmp_symbol]
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# 缩放到同一个数量级type_look='look_max'
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kl_pd, kl_pd_cmp = ABuScalerUtil.scaler_xy(kl_pd.close,
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kl_pd_cmp.close,
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type_look='look_max')
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with plt_show():
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# 首先可视化已经缩放到一个级别的两个金融序列
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kl_pd.plot()
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kl_pd_cmp.plot()
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plt.legend([symbol, cmp_symbol])
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plt.title('similar draw')
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distance = (kl_pd - kl_pd_cmp)
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# 通过distance构造技术线对象AbuTLine，可视化几个技术线
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line = AbuTLine(distance, line_name='{} distance {}'.format(symbol, cmp_symbol))
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line.show()
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# 可视化技术线拟合曲线及上下拟合通道曲线
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line.show_regress_trend_channel()
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# 可视化技术线'路程位移比'
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# line.show_shift_distance()
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# 对技术线阻力位和支撑位进行绘制
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# line.show_support_resistance_trend(show=False)
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return rank_score, sum_rank
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@from_local
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def calc_similar_top(symbol, sum_rank=None, corr_jobs=(ECoreCorrType.E_CORE_TYPE_PEARS,
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ECoreCorrType.E_CORE_TYPE_SPERM), show=True, show_cnt=10):
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"""
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使用corr_jobs种相关算法在env所在的市场中寻找与symbol最相关的show_cnt个，可视化
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show_cnt个
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:param symbol: eg: 'usTSLA'
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:param sum_rank: 已经缓存了的sum_rank数据，
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eg: sum_rank
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usBIDU 10.0
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usFB 16.0
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usGOOG 12.0
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usNOAH 2.0
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usSFUN 14.0
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usTSLA 18.0
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usVIPS 6.0
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usWUBA 8.0
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:param corr_jobs: 默认：corr_jobs=(ECoreCorrType.E_CORE_TYPE_PEARS, ECoreCorrType.E_CORE_TYPE_SPERM)
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可以再添加更多jobs
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eg：
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corr_jobs=(ECoreCorrType.E_CORE_TYPE_PEARS, ECoreCorrType.E_CORE_TYPE_SPERM,
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ECoreCorrType.E_CORE_TYPE_SIGN, ECoreCorrType.E_CORE_TYPE_ROLLING)
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注意每添加一种相关计算方法，耗时都会增加
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:param show: 是否进行可视化
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:param show_cnt: 可视化最相关的top n的个数
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:return: 排序好的top 相关序列，pd.Series对象
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eg：
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us_NYSE:.IXIC 4.0
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usFB 6.0
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usGOOG 8.0
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usWUBA 11.0
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usBIDU 11.0
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usSFUN 14.0
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usVIPS 16.0
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usNOAH 18.0
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"""
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cs = code_to_symbol(symbol)
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symbol = cs.value
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if sum_rank is None:
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# TODO 重复代码太多，提前头装饰器
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tmp_market = ABuEnv.g_market_target
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# 强制把市场设置为一样的
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ABuEnv.g_market_target = cs.market
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corr_df_dict = ABuSimilar.multi_corr_df(corr_jobs)
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# 恢复之前的市场
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ABuEnv.g_market_target = tmp_market
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"""
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eg： corr_df_dict
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{'pears':
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usBIDU usFB usGOOG usNOAH usSFUN usTSLA usVIPS usWUBA
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usBIDU 1.0000 0.3013 0.3690 0.4015 0.3680 0.3015 0.3706 0.4320
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usFB 0.3013 1.0000 0.6609 0.2746 0.1978 0.4080 0.2856 0.2438
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usGOOG 0.3690 0.6609 1.0000 0.3682 0.1821 0.3477 0.3040 0.2917
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usNOAH 0.4015 0.2746 0.3682 1.0000 0.3628 0.2178 0.4645 0.4488
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usSFUN 0.3680 0.1978 0.1821 0.3628 1.0000 0.2513 0.2843 0.4883
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usTSLA 0.3015 0.4080 0.3477 0.2178 0.2513 1.0000 0.2327 0.3340
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usVIPS 0.3706 0.2856 0.3040 0.4645 0.2843 0.2327 1.0000 0.4189
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usWUBA 0.4320 0.2438 0.2917 0.4488 0.4883 0.3340 0.4189 1.0000
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'sperm':
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usBIDU usFB usGOOG usNOAH usSFUN usTSLA usVIPS usWUBA
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usBIDU 1.0000 0.3888 0.4549 0.4184 0.3747 0.3623 0.4333 0.4396
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usFB 0.3888 1.0000 0.7013 0.2927 0.2379 0.4200 0.3123 0.2216
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usGOOG 0.4549 0.7013 1.0000 0.3797 0.2413 0.3871 0.3922 0.3035
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usNOAH 0.4184 0.2927 0.3797 1.0000 0.3581 0.2066 0.4643 0.4382
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usSFUN 0.3747 0.2379 0.2413 0.3581 1.0000 0.2645 0.3890 0.4693
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usTSLA 0.3623 0.4200 0.3871 0.2066 0.2645 1.0000 0.2540 0.2801
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usVIPS 0.4333 0.3123 0.3922 0.4643 0.3890 0.2540 1.0000 0.4080
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usWUBA 0.4396 0.2216 0.3035 0.4382 0.4693 0.2801 0.4080 1.0000 }
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"""
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sum_rank = rank_corr_sum(corr_df_dict, symbol)
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"""
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eg: sum_rank
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usBIDU 10.0
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usFB 16.0
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usGOOG 12.0
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usNOAH 2.0
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usSFUN 14.0
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usTSLA 18.0
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usVIPS 6.0
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usWUBA 8.0
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"""
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if sum_rank is None:
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logging.info('{} not in corr df!!!'.format(symbol))
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return
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show_cnt = sum_rank.shape[0] - 1 if show_cnt > sum_rank.shape[0] else show_cnt
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# sort_values后即为rank排序结果按照参数show_cnt个数进行返回，第一个是自身掠过
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rank_head = sum_rank.sort_values(ascending=True)[1: show_cnt + 1]
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if show:
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kl_pd = ABuSymbolPd.make_kl_df(symbol, n_folds=1)
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# 获取top show_cnt个symbol金融时间序列
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mul_pd = ABuSymbolPd.make_kl_df(rank_head.index, n_folds=1)
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for cmp_symbol in rank_head.index:
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# 迭代所有的收盘价格序列进行数据缩放
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kl_pd_close, kl_pd_cmp_close = ABuScalerUtil.scaler_xy(kl_pd.close,
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mul_pd[cmp_symbol].close,
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type_look='look_max')
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with plt_show():
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# 缩放后的数据进行可视化对比
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kl_pd_close.plot()
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kl_pd_cmp_close.plot()
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plt.legend([symbol, cmp_symbol])
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plt.title('similar draw')
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"""
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eg：rank_head
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us_NYSE:.IXIC 4.0
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usFB 6.0
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usGOOG 8.0
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usWUBA 11.0
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usBIDU 11.0
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usSFUN 14.0
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usVIPS 16.0
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usNOAH 18.0
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"""
|
||
return rank_head
|
||
|
||
|
||
@from_local
|
||
def coint_similar(symbol, sum_rank=None, corr_jobs=(ECoreCorrType.E_CORE_TYPE_PEARS,
|
||
ECoreCorrType.E_CORE_TYPE_SPERM), show=True):
|
||
"""
|
||
首先找到的是最相关的top个，从top n个最相关的再找协整，只考虑pvalue，因为已经是从top n个最相关的再找协整
|
||
可视化整个过程
|
||
|
||
:param symbol: eg: 'usTSLA'
|
||
:param sum_rank: 已经缓存了的sum_rank数据，
|
||
eg: sum_rank
|
||
usBIDU 10.0
|
||
usFB 16.0
|
||
usGOOG 12.0
|
||
usNOAH 2.0
|
||
usSFUN 14.0
|
||
usTSLA 18.0
|
||
usVIPS 6.0
|
||
usWUBA 8.0
|
||
:param corr_jobs: 默认：corr_jobs=(ECoreCorrType.E_CORE_TYPE_PEARS, ECoreCorrType.E_CORE_TYPE_SPERM)
|
||
可以再添加更多jobs
|
||
eg：
|
||
corr_jobs=(ECoreCorrType.E_CORE_TYPE_PEARS, ECoreCorrType.E_CORE_TYPE_SPERM,
|
||
ECoreCorrType.E_CORE_TYPE_SIGN, ECoreCorrType.E_CORE_TYPE_ROLLING)
|
||
注意每添加一种相关计算方法，耗时都会增加
|
||
:param show: 是否进行可视化
|
||
"""
|
||
cs = code_to_symbol(symbol)
|
||
symbol = cs.value
|
||
if sum_rank is None:
|
||
tmp_market = ABuEnv.g_market_target
|
||
# 强制把市场设置为一样的
|
||
ABuEnv.g_market_target = cs.market
|
||
corr_df_dict = ABuSimilar.multi_corr_df(corr_jobs)
|
||
# 恢复之前的市场
|
||
ABuEnv.g_market_target = tmp_market
|
||
"""
|
||
eg： corr_df_dict
|
||
{'pears':
|
||
usBIDU usFB usGOOG usNOAH usSFUN usTSLA usVIPS usWUBA
|
||
usBIDU 1.0000 0.3013 0.3690 0.4015 0.3680 0.3015 0.3706 0.4320
|
||
usFB 0.3013 1.0000 0.6609 0.2746 0.1978 0.4080 0.2856 0.2438
|
||
usGOOG 0.3690 0.6609 1.0000 0.3682 0.1821 0.3477 0.3040 0.2917
|
||
usNOAH 0.4015 0.2746 0.3682 1.0000 0.3628 0.2178 0.4645 0.4488
|
||
usSFUN 0.3680 0.1978 0.1821 0.3628 1.0000 0.2513 0.2843 0.4883
|
||
usTSLA 0.3015 0.4080 0.3477 0.2178 0.2513 1.0000 0.2327 0.3340
|
||
usVIPS 0.3706 0.2856 0.3040 0.4645 0.2843 0.2327 1.0000 0.4189
|
||
usWUBA 0.4320 0.2438 0.2917 0.4488 0.4883 0.3340 0.4189 1.0000
|
||
|
||
'sperm':
|
||
usBIDU usFB usGOOG usNOAH usSFUN usTSLA usVIPS usWUBA
|
||
usBIDU 1.0000 0.3888 0.4549 0.4184 0.3747 0.3623 0.4333 0.4396
|
||
usFB 0.3888 1.0000 0.7013 0.2927 0.2379 0.4200 0.3123 0.2216
|
||
usGOOG 0.4549 0.7013 1.0000 0.3797 0.2413 0.3871 0.3922 0.3035
|
||
usNOAH 0.4184 0.2927 0.3797 1.0000 0.3581 0.2066 0.4643 0.4382
|
||
usSFUN 0.3747 0.2379 0.2413 0.3581 1.0000 0.2645 0.3890 0.4693
|
||
usTSLA 0.3623 0.4200 0.3871 0.2066 0.2645 1.0000 0.2540 0.2801
|
||
usVIPS 0.4333 0.3123 0.3922 0.4643 0.3890 0.2540 1.0000 0.4080
|
||
usWUBA 0.4396 0.2216 0.3035 0.4382 0.4693 0.2801 0.4080 1.0000 }
|
||
"""
|
||
sum_rank = rank_corr_sum(corr_df_dict, symbol)
|
||
"""
|
||
eg: sum_rank
|
||
usBIDU 10.0
|
||
usFB 16.0
|
||
usGOOG 12.0
|
||
usNOAH 2.0
|
||
usSFUN 14.0
|
||
usTSLA 18.0
|
||
usVIPS 6.0
|
||
usWUBA 8.0
|
||
"""
|
||
if sum_rank is None:
|
||
logging.info('{} not in corr df!!!'.format(symbol))
|
||
return None, None
|
||
|
||
top_cnt = sum_rank.shape[0] if g_top_corr_cnt > sum_rank.shape[0] else g_top_corr_cnt
|
||
# 首先找到的是最相关的top个
|
||
rank_head = sum_rank.sort_values(ascending=True)[1:top_cnt]
|
||
|
||
# 使用symbol做标尺
|
||
benchmark = AbuBenchmark(symbol, n_folds=1)
|
||
# benchmark做为数据标尺获取最相关的top个金融时间数据
|
||
mul_pd = ABuSymbolPd.make_kl_df(rank_head.index, n_folds=1,
|
||
data_mode=EMarketDataSplitMode.E_DATA_SPLIT_UNDO,
|
||
benchmark=benchmark)
|
||
|
||
coint_dict = {}
|
||
for ind, cmp_symbol in enumerate(rank_head.index):
|
||
if cmp_symbol not in mul_pd:
|
||
continue
|
||
|
||
klpd_cmp = mul_pd[cmp_symbol]
|
||
if klpd_cmp is None:
|
||
continue
|
||
|
||
"""
|
||
coint返回值三个如下：
|
||
coint_t : float
|
||
t-statistic of unit-root test on residuals
|
||
pvalue : float
|
||
MacKinnon's approximate p-value based on MacKinnon (1994)
|
||
crit_value : dict
|
||
Critical values for the test statistic at the 1 %, 5 %, and 10 %
|
||
levels.
|
||
|
||
这里只考虑pvalue，因为已经是从top n个最相关的再找协整
|
||
"""
|
||
_, pvalue, _ = coint(benchmark.kl_pd.close, klpd_cmp.close)
|
||
if pvalue < g_coint_threshold:
|
||
# pvalue小于阀值即加入coint_dict字典
|
||
|
||
# 记录ind为了发现取多少个sort_values(ascending=True)[1:g_top_corr_cnt]能有良好的数据
|
||
# 即为了事后调整g_top_corr_cnt使用，并非实际需要
|
||
coint_dict[cmp_symbol] = (pvalue, ind)
|
||
p_value_sorted = sorted(zip(coint_dict.values(), coint_dict.keys()))
|
||
if len(p_value_sorted) == 0:
|
||
logging.info(
|
||
'len(p_value_sorted) == 0 please try change tl.similar.g_top_corr_cnt|tl.similar.g_coint_threshold!')
|
||
return None, None
|
||
|
||
if show:
|
||
cmp_cnt = np.minimum(len(p_value_sorted), g_coint_show_max)
|
||
# 只取item[1]，[0]是ind
|
||
symbols = [item[1] for item in p_value_sorted[:cmp_cnt]]
|
||
mul_pd_swap = mul_pd.swapaxes('items', 'minor')
|
||
close_panel_pd = mul_pd_swap['close'][symbols]
|
||
"""
|
||
转轴后只取收盘价格序列
|
||
eg： close_panel_pd
|
||
usFB usGOOG usNOAH usVIPS usWUBA us_NYSE:.IXIC
|
||
2015-07-24 96.95 623.56 23.40 20.250 65.25 5088.629
|
||
2015-07-27 94.17 627.26 22.16 19.990 62.89 5039.776
|
||
2015-07-28 95.29 628.00 22.94 20.200 60.32 5089.207
|
||
2015-07-29 96.99 631.93 23.35 20.260 59.89 5111.730
|
||
2015-07-30 95.21 632.59 22.87 19.700 60.24 5128.785
|
||
... ... ... ... ... ... ...
|
||
2016-07-20 121.92 741.19 25.11 13.630 48.17 5089.930
|
||
2016-07-21 120.61 738.63 25.51 13.690 49.25 5073.900
|
||
2016-07-22 121.00 742.74 25.50 13.510 49.21 5100.160
|
||
2016-07-25 121.63 739.77 25.57 13.390 49.84 5097.628
|
||
2016-07-26 121.64 740.92 24.75 13.655 50.36 5084.629
|
||
"""
|
||
# 将数据scale到一个级别上，注意使用mean_how=True，避免极值的干扰
|
||
close_panel_pd = ABuScalerUtil.scaler_matrix(close_panel_pd, mean_how=True)
|
||
"""
|
||
ABuScalerUtil.scaler_matrix缩放后的数据矩阵如下所示
|
||
eg： close_panel_pd
|
||
usFB usGOOG usNOAH usVIPS usWUBA
|
||
2015-07-24 4451.7674 4311.1198 4477.3494 6601.2284 5980.4246
|
||
2015-07-27 4324.1148 4336.7006 4240.0882 6516.4719 5764.1211
|
||
2015-07-28 4375.5432 4341.8168 4389.3332 6584.9290 5528.5703
|
||
2015-07-29 4453.6041 4368.9877 4467.7825 6604.4882 5489.1591
|
||
... ... ... ... ... ...
|
||
2016-07-20 5598.3443 5124.3808 4804.5404 4443.1972 4414.9740
|
||
2016-07-21 5538.1915 5106.6817 4881.0762 4462.7564 4513.9603
|
||
2016-07-22 5556.0995 5135.0971 4879.1628 4404.0788 4510.2942
|
||
2016-07-25 5585.0280 5114.5633 4892.5566 4364.9604 4568.0362
|
||
2016-07-26 5585.4872 5122.5141 4735.6581 4451.3468 4615.6963
|
||
"""
|
||
# 可视化scaler_matrix操作后的close
|
||
close_panel_pd.plot(figsize=ABuEnv.g_plt_figsize)
|
||
plt.title('close panel pd scaler_matrix')
|
||
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
|
||
plt.show()
|
||
|
||
close_panel_pd_cp = copy.deepcopy(close_panel_pd)
|
||
|
||
for col in close_panel_pd_cp.columns:
|
||
"""
|
||
做一个一摸一样的pd就是为了得到投票权重表便于运算：
|
||
|
||
close_panel_pd_cp[col] = benchmark.kl_pd.close
|
||
|
||
将所有数据列都使用标尺的数据进行替换，结果是每一列的数据都相同，
|
||
比如这样，列数据都和标尺一样
|
||
usFB usGOOG usNOAH usVIPS usWUBA us_NYSE:.IXIC
|
||
2015-07-24 265.41 265.41 265.41 265.41 265.41 265.41
|
||
2015-07-27 253.01 253.01 253.01 253.01 253.01 253.01
|
||
2015-07-28 264.82 264.82 264.82 264.82 264.82 264.82
|
||
2015-07-29 263.82 263.82 263.82 263.82 263.82 263.82
|
||
2015-07-30 266.79 266.79 266.79 266.79 266.79 266.79
|
||
... ... ... ... ... ... ...
|
||
2016-07-20 228.36 228.36 228.36 228.36 228.36 228.36
|
||
2016-07-21 220.50 220.50 220.50 220.50 220.50 220.50
|
||
2016-07-22 222.27 222.27 222.27 222.27 222.27 222.27
|
||
2016-07-25 230.01 230.01 230.01 230.01 230.01 230.01
|
||
2016-07-26 225.93 225.93 225.93 225.93 225.93 225.93
|
||
"""
|
||
close_panel_pd_cp[col] = benchmark.kl_pd.close
|
||
"""
|
||
将复刻后的close_panel_pd_cp与原始close_panel_pd求差后，再进行scaler_std
|
||
ABuScalerUtil.scaler_std(close_panel_pd_cp - close_panel_pd):
|
||
|
||
usFB usGOOG usNOAH usVIPS usWUBA us_NYSE:.IXIC
|
||
2015-07-24 0.9705 1.7793 0.7405 -1.6987 -1.9294 -1.0803
|
||
2015-07-27 1.2277 1.6619 1.1473 -1.6270 -1.5697 -0.8853
|
||
2015-07-28 1.1393 1.6826 0.8987 -1.6831 -1.1334 -1.0866
|
||
2015-07-29 0.9629 1.5955 0.7550 -1.7035 -1.0656 -1.2124
|
||
2015-07-30 1.1519 1.5906 0.9265 -1.5197 -1.1169 -1.2878
|
||
... ... ... ... ... ... ...
|
||
2016-07-21 -1.5539 -0.8188 -0.0710 0.3755 0.5784 -1.2418
|
||
2016-07-22 -1.5899 -0.9012 -0.0644 0.4354 0.5879 -1.3728
|
||
2016-07-25 -1.6371 -0.8138 -0.0746 0.4819 0.4997 -1.3179
|
||
2016-07-26 -1.6473 -0.8509 0.2018 0.3922 0.4085 -1.2702
|
||
"""
|
||
regular_diff = ABuScalerUtil.scaler_std(close_panel_pd_cp - close_panel_pd)
|
||
regular_diff.plot(figsize=ABuEnv.g_plt_figsize)
|
||
plt.title('regular diff')
|
||
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
|
||
plt.show()
|
||
"""
|
||
distance_votes = regular_diff.sum(axis=1):
|
||
|
||
投票机制，获取投票coint的差值
|
||
distance_votes
|
||
distance_votes
|
||
2015-07-24 -1.2181
|
||
2015-07-27 -0.0451
|
||
2015-07-28 -0.1825
|
||
2015-07-29 -0.6682
|
||
2015-07-30 -0.2555
|
||
...
|
||
2016-07-20 -2.5541
|
||
2016-07-21 -2.7316
|
||
2016-07-22 -2.9049
|
||
2016-07-25 -2.8618
|
||
2016-07-26 -2.7658
|
||
......................
|
||
"""
|
||
distance_votes = regular_diff.sum(axis=1)
|
||
|
||
votes_std = distance_votes.std()
|
||
votes_mean = distance_votes.mean()
|
||
above = votes_mean + votes_std
|
||
below = votes_mean - votes_std
|
||
close_regular = ABuScalerUtil.scaler_std(benchmark.kl_pd.close)
|
||
close_regular = (close_regular * distance_votes.max() / 2)
|
||
|
||
with plt_show():
|
||
# noinspection PyUnresolvedReferences
|
||
close_regular.plot()
|
||
distance_votes.plot()
|
||
|
||
plt.axhline(votes_mean, color='r')
|
||
plt.axhline(above, color='c')
|
||
plt.axhline(below, color='g')
|
||
|
||
plt.title('coint distance votes')
|
||
plt.legend(['close regular', 'distance votes', 'votes mean', 'dvotes above', 'dvotes below'],
|
||
bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
|
||
return p_value_sorted, sum_rank
|