230 lines
8.3 KiB
Python
230 lines
8.3 KiB
Python
# -*- encoding:utf-8 -*-
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from __future__ import print_function
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import seaborn as sns
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import warnings
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import numpy as np
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# noinspection PyUnresolvedReferences
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import abu_local_env
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from abupy import tl
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from abupy import abu
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import abupy
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warnings.filterwarnings('ignore')
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sns.set_context(rc={'figure.figsize': (14, 7)})
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"""
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量化相关性分析
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abu量化系统github地址：https://github.com/bbfamily/abu (您的star是我的动力！)
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abu量化文档教程ipython notebook：https://github.com/bbfamily/abu/tree/master/abupy_lecture
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本节建议对照阅读abu量化文档：第14节 量化相关性分析应用
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"""
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def sample_b0():
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"""
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相关分析默认强制使用local数据，所以本地无缓存，请先进行数据更新
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如果没有运行过abu量化文档－第十九节 数据源：中使用腾讯数据源进行数据更新，需要运行
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如果运行过就不要重复运行了：
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"""
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from abupy import EMarketTargetType, EMarketSourceType, EDataCacheType
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# 关闭沙盒数据环境
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abupy.env.disable_example_env_ipython()
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abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
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abupy.env.g_data_cache_type = EDataCacheType.E_DATA_CACHE_CSV
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# 首选这里预下载市场中所有股票的6年数据(做5年回测，需要预先下载6年数据)
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abu.run_kl_update(start='2011-08-08', end='2017-08-08', market=EMarketTargetType.E_MARKET_TARGET_US)
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def sample_b1():
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"""
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B1 皮尔逊相关系数
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:return:
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"""
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arr1 = np.random.rand(10000)
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arr2 = np.random.rand(10000)
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corr = np.cov(arr1, arr2) / np.std(arr1) * np.std(arr2)
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print('corr:\n', corr)
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print('corr[0, 1]:', corr[0, 1])
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print('np.corrcoef(arr1, arr2)[0, 1]:', np.corrcoef(arr1, arr2)[0, 1])
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# noinspection PyTypeChecker
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def sample_b2():
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"""
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B2 斯皮尔曼秩相关系数
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:return:
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"""
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arr1 = np.random.rand(10000)
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arr2 = arr1 + np.random.normal(0, .2, 10000)
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print('np.corrcoef(arr1, arr2)[0, 1]:', np.corrcoef(arr1, arr2)[0, 1])
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import scipy.stats as stats
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demo_list = [1, 2, 10, 100, 2, 1000]
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print('原始序列: ', demo_list)
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print('序列的秩: ', list(stats.rankdata(demo_list)))
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# 实现斯皮尔曼秩相关系数
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def spearmanr(a, b=None, axis=0):
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a, outaxis = _chk_asarray(a, axis)
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ar = np.apply_along_axis(stats.rankdata, outaxis, a)
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br = None
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if b is not None:
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b, axisout = _chk_asarray(b, axis)
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br = np.apply_along_axis(stats.rankdata, axisout, b)
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return np.corrcoef(ar, br, rowvar=outaxis)
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def _chk_asarray(a, axis):
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if axis is None:
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a = np.ravel(a)
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outaxis = 0
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else:
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a = np.asarray(a)
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outaxis = axis
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if a.ndim == 0:
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a = np.atleast_1d(a)
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return a, outaxis
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print('spearmanr(arr1, arr2)[0, 1]:', spearmanr(arr1, arr2)[0, 1])
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"""
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scipy.stats中直接封装斯皮尔曼秩相关系数函数stats.spearmanr()函数
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注意下面的方法速度没有上述自己实现计算spearmanr相关系数的方法快，因为附加计算了pvalue
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"""
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print('stats.spearmanr(arr1, arr2):', stats.spearmanr(arr1, arr2))
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"""
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B3 相关性使用示例
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"""
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"""
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【示例1】使用abu量化系统中的ABuSimilar.find_similar_with_xxx()函数找到与目标股票相关程度最高的股票可视化
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"""
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def sample_b3_1():
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"""
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【示例1】使用abu量化系统中的ABuSimilar.find_similar_with_xxx()函数找到与目标股票相关程度最高的股票可视化
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:return:
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"""
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# find_similar_with_cnt可视化与tsla相关top10，以及tsla相关性dict：cmp_cnt=252(252天)，加权相关，E_CORE_TYPE_PEARS(皮尔逊)
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from abupy import find_similar_with_cnt, ECoreCorrType
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_ = find_similar_with_cnt('usTSLA', cmp_cnt=252, show_cnt=10, rolling=True, show=True,
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corr_type=ECoreCorrType.E_CORE_TYPE_PEARS)
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# find_similar_with_se可视化与tsla相关top10，以及tsla相关性dict：从'2012-01-01'直到'2017-01-01'5年数据，非加权相关，皮尔逊
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from abupy import find_similar_with_se
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_ = find_similar_with_se('usTSLA', start='2012-01-01', end='2017-01-01', show_cnt=10, rolling=False,
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show=True, corr_type=ECoreCorrType.E_CORE_TYPE_PEARS)
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# find_similar_with_folds可视化与tsla相关top10，以及tsla相关性dict：n_folds=3(3年数据)，
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# 非加权相关，E_CORE_TYPE_SPERM斯皮尔曼
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from abupy import find_similar_with_folds
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_ = find_similar_with_folds('usTSLA', n_folds=3, show_cnt=10, rolling=False, show=True,
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corr_type=ECoreCorrType.E_CORE_TYPE_SPERM)
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"""
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【示例2】使用abu量化系统中的ABuTLSimilar.calc_similar()函数计算两支股票相对整个市场的相关性评级rank
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"""
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def sample_b3_2():
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"""
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【示例2】使用abu量化系统中的ABuTLSimilar.calc_similar()函数计算两支股票相对整个市场的相关性评级rank
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:return:
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"""
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# 以整个市场作为观察者，usTSLA与usNOAH的相关性
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rank_score, sum_rank = tl.similar.calc_similar('usNOAH', 'usTSLA')
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print('rank_score', rank_score)
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from abupy import find_similar_with_cnt
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net_cg_ret = find_similar_with_cnt('usTSLA', cmp_cnt=252, show=False)
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# 以usTSLA作为观察者，它与usNOAH的相关性数值
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for ncr in net_cg_ret:
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if ncr[0] == 'usNOAH':
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print(ncr[1])
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break
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"""
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以整个市场作为观察者，与usTSLA相关性TOP 10可视化
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直接将calc_similar返回的sum_rank传入calc_similar_top直接用，不用再计算了
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"""
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tl.similar.calc_similar_top('usTSLA', sum_rank)
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"""
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【示例3】相关与协整组成的一个简单量化选股策略, 使用封装好的函数coint_similar()
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"""
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def sample_b3_3():
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"""
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【示例3】相关与协整组成的一个简单量化选股策略, 使用封装好的函数coint_similar()
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:return:
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"""
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tl.similar.coint_similar('usTSLA')
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"""
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【示例4】abu量化系统选股结合相关性，编写相关性选股策略
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"""
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def sample_b3_4():
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"""
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【示例4】abu量化系统选股结合相关性，编写相关性选股策略
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AbuPickSimilarNTop源代码请自行阅读，只简单示例使用。
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:return:
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"""
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from abupy import AbuPickSimilarNTop
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from abupy import AbuPickStockWorker
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from abupy import AbuBenchmark, AbuCapital, AbuKLManager
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benchmark = AbuBenchmark()
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# 选股因子AbuPickSimilarNTop， 寻找与usTSLA相关性不低于0.95的股票
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# 这里内部使用以整个市场作为观察者方式计算，即取值范围0-1
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stock_pickers = [{'class': AbuPickSimilarNTop,
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'similar_stock': 'usTSLA', 'threshold_similar_min': 0.95}]
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# 从这几个股票里进行选股，只是为了演示方便，一般的选股都会是数量比较多的情况比如全市场股票
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choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA', 'usVIPS']
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capital = AbuCapital(1000000, benchmark)
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kl_pd_manager = AbuKLManager(benchmark, capital)
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stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager, choice_symbols=choice_symbols,
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stock_pickers=stock_pickers)
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stock_pick.fit()
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print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols)
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"""
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通过选股因子first_choice属性执行批量优先选股操作，具体阅读源代码
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"""
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# 选股因子AbuPickSimilarNTop， 寻找与usTSLA相关性不低于0.95的股票
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# 通过设置'first_choice':True，进行优先批量操作，默认从对应市场选股
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stock_pickers = [{'class': AbuPickSimilarNTop, 'first_choice': True,
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'similar_stock': 'usTSLA', 'threshold_similar_min': 0.95}]
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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kl_pd_manager = AbuKLManager(benchmark, capital)
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stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager, choice_symbols=None,
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stock_pickers=stock_pickers)
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stock_pick.fit()
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print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols)
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if __name__ == "__main__":
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# sample_b0()
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sample_b1()
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# sample_b2()
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# sample_b3_1()
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# sample_b3_2()
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# sample_b3_3()
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# sample_b3_4()
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