def sample_a21():
# 设置初始资金数
read_cash = 50000
###########################################################################################
# 回测生成买入时刻特征
abupy.env.g_enable_ml_feature = True
# 回测将symbols切割分为训练集数据和测试集数据
abupy.env.g_enable_train_test_split = True
# 下面设置回测时切割训练集，测试集使用的切割比例参数，默认为10，即切割为10份，9份做为训练，1份做为测试，
# 由于美股股票数量多，所以切割分为4份，3份做为训练集，1份做为测试集
abupy.env.g_split_tt_n_folds = 4
###########################################################################################
# 择时股票池
# choice_symbols = ['603993', '601998', '601992', '601991', '601989', '601988', '601985', '601939', '601933', '601919', '601901', '601899', '601898', '601881', '601877', '601857', '601828', '601818', '601808', '601800', '601788', '601766', '601727', '601688', '601669', '601668', '601633', '601628', '601618', '601607', '601601', '601600', '601398', '601390', '601360', '601328', '601288', '601238', '601229', '601225', '601211', '601186', '601169', '601166', '601155', '601111', '601108', '601088', '601018', '601012', '601009', '601006', '600999', '600958', '600919', '600900', '600893', '600887', '600837', '600816', '600795', '600703', '600690', '600688', '600663', '600660', '600606', '600600', '600588', '600585', '600518', '600487', '600406', '600398', '600383', '600362', '600346', '600340', '600309', '600297', '600221', '600196', '600188', '600176', '600115', '600111', '600104', '600061', '600050', '600048', '600036', '600031', '600030', '600029', '600028', '600025', '600023', '600019', '600018', '600016', '600015', '600011', '600010', '600000', '300498', '300433', '300124', '300072', '300070', '300059', '300015', '300003', '002739', '002736', '002714', '002600', '002558', '002493', '002475', '002456', '002450', '002415', '002310', '002252', '002241', '002236', '002202', '002142', '002120', '002044', '002027', '002024', '002010', '001979', '001965', '000895', '000776', '000725', '000617', '000166', '000069', '000063', '000039', '000002', '000001']
# choice_symbols = ['601398']
choice_symbols = [
'601398', '601988', '601939', '601328', '601288', '600887', '600029',
'000002'
]
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols,
n_folds=4,
commission_dict=commission_dict)
# 把运行的结果保存在本地，以便之后分析回测使用，保存回测结果数据代码如下所示
abu.store_abu_result_tuple(abu_result_tuple,
n_folds=4,
store_type=abupy.EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='tt_train_cn')
abu.store_abu_result_tuple(abu_result_tuple,
n_folds=4,
store_type=abupy.EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='tt_test_cn')
# AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=True)
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
# 筛出有交易结果的
orders_pd_atr = abu_result_tuple.orders_pd[
abu_result_tuple.orders_pd.result != 0]
orders_pd_atr.filter(
['buy_cnt', 'buy_pos', 'buy_price', 'profit', 'result'])
metrics.plot_returns_cmp(only_info=True)
# metrics.plot_buy_factors()
# metrics.plot_sell_factors()
metrics.plot_effect_mean_day()
# plt.show()
metrics.plot_keep_days()
# plt.show()
metrics.plot_max_draw_down()
def sample_94_2(from_cache=False):
"""
9.4_2 使用切割训练集测试集模式，且生成交易特征，回测训练集交易数据, mac pro顶配大概下面跑了4个小时
:return:
"""
# 关闭沙盒数据环境
abupy.env.disable_example_env_ipython()
from abupy import EMarketDataFetchMode
# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
# 回测生成买入时刻特征
abupy.env.g_enable_ml_feature = True
# 回测将symbols切割分为训练集数据和测试集数据
abupy.env.g_enable_train_test_split = True
# 下面设置回测时切割训练集，测试集使用的切割比例参数，默认为10，即切割为10份，9份做为训练，1份做为测试，
# 由于美股股票数量多，所以切割分为4份，3份做为训练集，1份做为测试集
abupy.env.g_split_tt_n_folds = 4
from abupy import EStoreAbu
if from_cache:
abu_result_tuple = \
abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_us')
else:
# 初始化资金200万，资金管理依然使用默认atr
read_cash = 5000000
# 每笔交易的买入基数资金设置为万分之15
abupy.beta.atr.g_atr_pos_base = 0.0015
# 使用run_loop_back运行策略，因子使用和之前一样，
# choice_symbols=None为全市场回测，5年历史数据回测
# 不同电脑运行速度差异大，mac pro顶配大概下面跑了4小时
# choice_symbols=None为全市场回测，5年历史数据回测
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=None,
start='2012-08-08',
end='2017-08-08')
# 把运行的结果保存在本地，以便之后分析回测使用，保存回测结果数据代码如下所示
abu.store_abu_result_tuple(abu_result_tuple,
n_folds=5,
store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_us')
print('abu_result_tuple.action_pd.deal.value_counts():\n',
abu_result_tuple.action_pd.deal.value_counts())
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
def run_loop_back():
global abu_result_tuple
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
choice_symbols=None,
start='2012-08-08',
end='2017-08-08')
# 把运行的结果保存在本地，以便之后分析回测使用，保存回测结果数据代码如下所示
abu.store_abu_result_tuple(abu_result_tuple,
n_folds=5,
store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_cn')
ABuProgress.clear_output()
def sample_94_4(from_cache=False):
"""
满仓乘数
9.4_4 《量化交易之路》中通过把初始资金扩大到非常大，但是每笔交易的买入基数却不增高，来使交易全部都成交，
再使用满仓乘数的示例，由于需要再次进行全市场回测，比较耗时。
下面直接示例通过AbuMetricsBase中的transform_to_full_rate_factor接口将之前的回测结果转换为使用大初始资金回测的结果
:return:
"""
metrics_test = sample_94_3(from_cache=True, show=False)
from abupy import EStoreAbu
if from_cache:
test_us_fr = abu.load_abu_result_tuple(
n_folds=5,
store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us_full_rate')
# 本地读取后使用AbuMetricsBase构造度量对象，参数enable_stocks_full_rate_factor=True, 即使用满仓乘数
test_frm = AbuMetricsBase(test_us_fr.orders_pd,
test_us_fr.action_pd,
test_us_fr.capital,
test_us_fr.benchmark,
enable_stocks_full_rate_factor=True)
test_frm.fit_metrics()
else:
test_frm = metrics_test.transform_to_full_rate_factor(n_process_kl=4,
show=False)
# 转换后保存起来，下次直接读取，不用再转换了
from abupy import AbuResultTuple
test_us_fr = AbuResultTuple(test_frm.orders_pd, test_frm.action_pd,
test_frm.capital, test_frm.benchmark)
abu.store_abu_result_tuple(test_us_fr,
n_folds=5,
store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us_full_rate')
"""
使用test_frm进行度量结果可以看到所有交易都顺利成交了，策略买入成交比例:100.0000%，但资金利用率显然过低，
它导致基准收益曲线和策略收益曲线不在一个量级上，无法有效的进行对比
"""
AbuMetricsBase.show_general(test_frm.orders_pd,
test_frm.action_pd,
test_frm.capital,
test_frm.benchmark,
only_show_returns=True)
"""转换出来的test_frm即是一个使用满仓乘数的度量对象，下面使用test_frm直接进行满仓度量即可"""
print(type(test_frm))
test_frm.plot_returns_cmp(only_show_returns=True)
# 如果不需要与基准进行对比，最简单的方式是使用plot_order_returns_cmp
metrics_test.plot_order_returns_cmp()
def sample_94_3(from_cache=False, show=True):
"""
9.4_3 使用切割好的测试数据集快，mac pro顶配大概下面跑了半个小时
:return:
"""
# 关闭沙盒数据环境
abupy.env.disable_example_env_ipython()
from abupy import EMarketDataFetchMode
# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
abupy.env.g_enable_train_test_split = False
# 使用切割好的测试数据
abupy.env.g_enable_last_split_test = True
# 回测生成买入时刻特征
abupy.env.g_enable_ml_feature = True
from abupy import EStoreAbu
if from_cache:
abu_result_tuple_test = \
abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us')
else:
read_cash = 5000000
abupy.beta.atr.g_atr_pos_base = 0.007
choice_symbols = None
abu_result_tuple_test, kl_pd_manager_test = abu.run_loop_back(
read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols,
start='2012-08-08',
end='2017-08-08')
abu.store_abu_result_tuple(abu_result_tuple_test,
n_folds=5,
store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us')
print('abu_result_tuple_test.action_pd.deal.value_counts():\n',
abu_result_tuple_test.action_pd.deal.value_counts())
metrics = AbuMetricsBase(*abu_result_tuple_test)
metrics.fit_metrics()
if show:
metrics.plot_returns_cmp(only_show_returns=True)
return metrics
def sample_94_2(from_cache=False):
"""
9.4_2 使用切割训练集测试集模式，且生成交易特征，回测训练集交易数据, mac pro顶配大概下面跑了4个小时
:return:
"""
# 关闭沙盒数据环境
abupy.env.disable_example_env_ipython()
from abupy import EMarketDataFetchMode
# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
# 回测生成买入时刻特征
abupy.env.g_enable_ml_feature = True
# 回测将symbols切割分为训练集数据和测试集数据
abupy.env.g_enable_train_test_split = True
# 下面设置回测时切割训练集，测试集使用的切割比例参数，默认为10，即切割为10份，9份做为训练，1份做为测试，
# 由于美股股票数量多，所以切割分为4份，3份做为训练集，1份做为测试集
abupy.env.g_split_tt_n_folds = 4
from abupy import EStoreAbu
if from_cache:
abu_result_tuple = \
abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_us')
else:
# 初始化资金200万，资金管理依然使用默认atr
read_cash = 5000000
# 每笔交易的买入基数资金设置为万分之15
abupy.beta.atr.g_atr_pos_base = 0.0015
# 使用run_loop_back运行策略，因子使用和之前一样，
# choice_symbols=None为全市场回测，5年历史数据回测
# 不同电脑运行速度差异大，mac pro顶配大概下面跑了4小时
# choice_symbols=None为全市场回测，5年历史数据回测
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors, sell_factors,
stock_pickers,
choice_symbols=None,
start='2012-08-08', end='2017-08-08')
# 把运行的结果保存在本地，以便之后分析回测使用，保存回测结果数据代码如下所示
abu.store_abu_result_tuple(abu_result_tuple, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_us')
print('abu_result_tuple.action_pd.deal.value_counts():\n', abu_result_tuple.action_pd.deal.value_counts())
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
def sample_94_3(from_cache=False, show=True):
"""
9.4_3 使用切割好的测试数据集快，mac pro顶配大概下面跑了半个小时
:return:
"""
# 关闭沙盒数据环境
abupy.env.disable_example_env_ipython()
from abupy import EMarketDataFetchMode
# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
abupy.env.g_enable_train_test_split = False
# 使用切割好的测试数据
abupy.env.g_enable_last_split_test = True
# 回测生成买入时刻特征
abupy.env.g_enable_ml_feature = True
from abupy import EStoreAbu
if from_cache:
abu_result_tuple_test = \
abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us')
else:
read_cash = 5000000
abupy.beta.atr.g_atr_pos_base = 0.007
choice_symbols = None
abu_result_tuple_test, kl_pd_manager_test = abu.run_loop_back(read_cash,
buy_factors, sell_factors, stock_pickers,
choice_symbols=choice_symbols, start='2012-08-08',
end='2017-08-08')
abu.store_abu_result_tuple(abu_result_tuple_test, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us')
print('abu_result_tuple_test.action_pd.deal.value_counts():\n', abu_result_tuple_test.action_pd.deal.value_counts())
metrics = AbuMetricsBase(*abu_result_tuple_test)
metrics.fit_metrics()
if show:
metrics.plot_returns_cmp(only_show_returns=True)
return metrics
def sample_94_4(from_cache=False):
"""
满仓乘数
9.4_4 《量化交易之路》中通过把初始资金扩大到非常大，但是每笔交易的买入基数却不增高，来使交易全部都成交，
再使用满仓乘数的示例，由于需要再次进行全市场回测，比较耗时。
下面直接示例通过AbuMetricsBase中的transform_to_full_rate_factor接口将之前的回测结果转换为使用大初始资金回测的结果
:return:
"""
metrics_test = sample_94_3(from_cache=True, show=False)
from abupy import EStoreAbu
if from_cache:
test_us_fr = abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us_full_rate')
# 本地读取后使用AbuMetricsBase构造度量对象，参数enable_stocks_full_rate_factor=True, 即使用满仓乘数
test_frm = AbuMetricsBase(test_us_fr.orders_pd, test_us_fr.action_pd, test_us_fr.capital, test_us_fr.benchmark,
enable_stocks_full_rate_factor=True)
test_frm.fit_metrics()
else:
test_frm = metrics_test.transform_to_full_rate_factor(n_process_kl=4, show=False)
# 转换后保存起来，下次直接读取，不用再转换了
from abupy import AbuResultTuple
test_us_fr = AbuResultTuple(test_frm.orders_pd, test_frm.action_pd, test_frm.capital, test_frm.benchmark)
abu.store_abu_result_tuple(test_us_fr, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us_full_rate')
"""
使用test_frm进行度量结果可以看到所有交易都顺利成交了，策略买入成交比例:100.0000%，但资金利用率显然过低，
它导致基准收益曲线和策略收益曲线不在一个量级上，无法有效的进行对比
"""
AbuMetricsBase.show_general(test_frm.orders_pd,
test_frm.action_pd, test_frm.capital, test_frm.benchmark, only_show_returns=True)
"""转换出来的test_frm即是一个使用满仓乘数的度量对象，下面使用test_frm直接进行满仓度量即可"""
print(type(test_frm))
test_frm.plot_returns_cmp(only_show_returns=True)
# 如果不需要与基准进行对比，最简单的方式是使用plot_order_returns_cmp
metrics_test.plot_order_returns_cmp()
def sample_1051_1(from_cache=False, show=True):
"""
10.5.1 回测中生成特征，切分训练测试集，成交买单快照: 数据准备
:return:
"""
from abupy import AbuMetricsBase
from abupy import AbuFactorBuyBreak
from abupy import AbuFactorAtrNStop
from abupy import AbuFactorPreAtrNStop
from abupy import AbuFactorCloseAtrNStop
# 关闭沙盒数据环境
abupy.env.disable_example_env_ipython()
from abupy import EMarketDataFetchMode
# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL，实际上run_kl_update最后会把设置set到FORCE_LOCAL
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
# 设置选股因子，None为不使用选股因子
stock_pickers = None
# 买入因子依然延用向上突破因子
buy_factors = [{
'xd': 60,
'class': AbuFactorBuyBreak
}, {
'xd': 42,
'class': AbuFactorBuyBreak
}]
# 卖出因子继续使用上一章使用的因子
sell_factors = [{
'stop_loss_n': 1.0,
'stop_win_n': 3.0,
'class': AbuFactorAtrNStop
}, {
'class': AbuFactorPreAtrNStop,
'pre_atr_n': 1.5
}, {
'class': AbuFactorCloseAtrNStop,
'close_atr_n': 1.5
}]
# 回测生成买入时刻特征
abupy.env.g_enable_ml_feature = True
# 回测将symbols切割分为训练集数据和测试集数据
abupy.env.g_enable_train_test_split = True
# 下面设置回测时切割训练集，测试集使用的切割比例参数，默认为10，即切割为10份，9份做为训练，1份做为测试，
# 由于美股股票数量多，所以切割分为4份，3份做为训练集，1份做为测试集
abupy.env.g_split_tt_n_folds = 4
from abupy import EStoreAbu
if from_cache:
abu_result_tuple = \
abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_us')
else:
# 初始化资金500万，资金管理依然使用默认atr
read_cash = 5000000
# 每笔交易的买入基数资金设置为万分之15
abupy.beta.atr.g_atr_pos_base = 0.0015
# 使用run_loop_back运行策略，因子使用和之前一样，
# choice_symbols=None为全市场回测，5年历史数据回测
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=None,
start='2012-08-08',
end='2017-08-08')
# 把运行的结果保存在本地，以便之后分析回测使用，保存回测结果数据代码如下所示
abu.store_abu_result_tuple(abu_result_tuple,
n_folds=5,
store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_us')
if show:
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
"＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊"
abupy.env.g_enable_train_test_split = False
# 使用切割好的测试数据
abupy.env.g_enable_last_split_test = True
from abupy import EStoreAbu
if from_cache:
abu_result_tuple_test = \
abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us')
else:
read_cash = 5000000
abupy.beta.atr.g_atr_pos_base = 0.007
choice_symbols = None
abu_result_tuple_test, kl_pd_manager_test = abu.run_loop_back(
read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols,
start='2012-08-08',
end='2017-08-08')
abu.store_abu_result_tuple(abu_result_tuple_test,
n_folds=5,
store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us')
if show:
metrics = AbuMetricsBase(*abu_result_tuple_test)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
print(abu_result_tuple.orders_pd[
abu_result_tuple.orders_pd.result != 0].head())
return abu_result_tuple, abu_result_tuple_test
def sample_1051_1(from_cache=False, show=True):
"""
10.5.1 回测中生成特征，切分训练测试集，成交买单快照: 数据准备
:return:
"""
from abupy import AbuMetricsBase
from abupy import AbuFactorBuyBreak
from abupy import AbuFactorAtrNStop
from abupy import AbuFactorPreAtrNStop
from abupy import AbuFactorCloseAtrNStop
# 关闭沙盒数据环境
abupy.env.disable_example_env_ipython()
from abupy import EMarketDataFetchMode
# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL，实际上run_kl_update最后会把设置set到FORCE_LOCAL
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
# 设置选股因子，None为不使用选股因子
stock_pickers = None
# 买入因子依然延用向上突破因子
buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
{'xd': 42, 'class': AbuFactorBuyBreak}]
# 卖出因子继续使用上一章使用的因子
sell_factors = [
{'stop_loss_n': 1.0, 'stop_win_n': 3.0,
'class': AbuFactorAtrNStop},
{'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},
{'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
]
# 回测生成买入时刻特征
abupy.env.g_enable_ml_feature = True
# 回测将symbols切割分为训练集数据和测试集数据
abupy.env.g_enable_train_test_split = True
# 下面设置回测时切割训练集，测试集使用的切割比例参数，默认为10，即切割为10份，9份做为训练，1份做为测试，
# 由于美股股票数量多，所以切割分为4份，3份做为训练集，1份做为测试集
abupy.env.g_split_tt_n_folds = 4
from abupy import EStoreAbu
if from_cache:
abu_result_tuple = \
abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_us')
else:
# 初始化资金500万，资金管理依然使用默认atr
read_cash = 5000000
# 每笔交易的买入基数资金设置为万分之15
abupy.beta.atr.g_atr_pos_base = 0.0015
# 使用run_loop_back运行策略，因子使用和之前一样，
# choice_symbols=None为全市场回测，5年历史数据回测
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors, sell_factors,
stock_pickers,
choice_symbols=None,
start='2012-08-08', end='2017-08-08')
# 把运行的结果保存在本地，以便之后分析回测使用，保存回测结果数据代码如下所示
abu.store_abu_result_tuple(abu_result_tuple, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_us')
if show:
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
"＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊"
abupy.env.g_enable_train_test_split = False
# 使用切割好的测试数据
abupy.env.g_enable_last_split_test = True
from abupy import EStoreAbu
if from_cache:
abu_result_tuple_test = \
abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us')
else:
read_cash = 5000000
abupy.beta.atr.g_atr_pos_base = 0.007
choice_symbols = None
abu_result_tuple_test, kl_pd_manager_test = abu.run_loop_back(read_cash,
buy_factors, sell_factors, stock_pickers,
choice_symbols=choice_symbols, start='2012-08-08',
end='2017-08-08')
abu.store_abu_result_tuple(abu_result_tuple_test, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_us')
if show:
metrics = AbuMetricsBase(*abu_result_tuple_test)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
print(abu_result_tuple.orders_pd[abu_result_tuple.orders_pd.result != 0].head())
return abu_result_tuple, abu_result_tuple_test
