def sample_91(show=True):
"""
9.1 度量的基本使用方法
:return:
"""
# 设置初始资金数
read_cash = 1000000
# 择时股票池
choice_symbols = [
'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA',
'usVIPS'
]
# 使用run_loop_back运行策略
abu_result_tuple, kl_pd_manager = abu.run_loop_back(
read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols,
n_folds=2)
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
if show:
metrics.plot_returns_cmp()
return metrics
def sample_814(show=True):
"""
8.1.4 对多支股票进行择时
:return:
"""
sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop}
sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
benchmark = AbuBenchmark()
buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
{'xd': 42, 'class': AbuFactorBuyBreak}]
choice_symbols = ['usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usWUBA', 'usVIPS']
capital = AbuCapital(1000000, benchmark)
orders_pd, action_pd, all_fit_symbols_cnt = ABuPickTimeExecute.do_symbols_with_same_factors(choice_symbols,
benchmark, buy_factors,
sell_factors, capital,
show=False)
metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
metrics.fit_metrics()
if show:
print('orders_pd[:10]:\n', orders_pd[:10].filter(
['symbol', 'buy_price', 'buy_cnt', 'buy_factor', 'buy_pos', 'sell_date', 'sell_type_extra', 'sell_type',
'profit']))
print('action_pd[:10]:\n', action_pd[:10])
metrics.plot_returns_cmp(only_show_returns=True)
return metrics
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_a21():
"""
A.2.1 数据模式的切换
:return:
"""
# 表A-1所示
print(ABuSymbolPd.make_kl_df('601398').tail())
# 局部使用enable_example_env_ipython，示例
abupy.env.enable_example_env_ipython()
# 如果本地有相应股票的缓存，可以使用如下代码强制使用本地缓存数据
# abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
# 设置初始资金数
read_cash = 1000000
# 择时股票池
choice_symbols = [
'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA',
'usVIPS'
]
# 使用run_loop_back运行策略
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols,
n_folds=2)
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp()
# *****************************************************************************************************************
# 切换数据源
abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
# 强制走网络数据源
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_NET
# 择时股票池
choice_symbols = [
'601398', '600028', '601857', '601318', '600036', '000002', '600050',
'600030'
]
# 使用run_loop_back运行策略
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols,
n_folds=2)
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp()
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 load_abu_result_tuple():
abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
abu_result_tuple_train = abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_cn')
abu_result_tuple_test = abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_cn')
metrics_train = AbuMetricsBase(*abu_result_tuple_train)
metrics_train.fit_metrics()
metrics_test = AbuMetricsBase(*abu_result_tuple_test)
metrics_test.fit_metrics()
return abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test
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_a22():
# 设置初始资金数
read_cash = 5000000
abupy.env.g_enable_ump_main_deg_block = True
abupy.env.g_enable_ump_main_jump_block = True
abupy.env.g_enable_ump_main_price_block = True
abupy.env.g_enable_ump_main_wave_block = True
# 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 = ['600036']
# choice_symbols = ['601398', '601988', '601939', '601328', '601288', '600887', '600029', '000002']
# choice_symbols = ['601398', '601988', '601939', '603993', '600999', '300059', '600900', '601328', '601288',
# '600887', '600029', '000002', '600196', '002024', '002241', '600050', '601989', '601992', '601901']
# choice_symbols = ['601398', '601988', '601939', '603993', '600196', '600660', '600703', '600887', '600999', '300059', '600900', '601328', '601288', '600887', '600029', '000002']
choice_symbols = load_today_stock_list()
print(choice_symbols)
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols,
n_folds=1,
commission_dict=commission_dict)
# 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'])
# 保存交易结果
store_abu_result_out_put(abu_result_tuple)
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_817():
"""
8.1.7 使用并行来提升择时运行效率
:return:
"""
# 要关闭沙盒数据环境，因为沙盒里就那几个股票的历史数据, 下面要随机做50个股票
from abupy import EMarketSourceType
abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
abupy.env.disable_example_env_ipython()
# 关闭沙盒后，首先基准要从非沙盒环境换取，否则数据对不齐，无法正常运行
benchmark = AbuBenchmark()
# 当传入choice_symbols为None时代表对整个市场的所有股票进行回测
# noinspection PyUnusedLocal
choice_symbols = None
# 顺序获取市场后300支股票
# noinspection PyUnusedLocal
choice_symbols = ABuMarket.all_symbol()[-50:]
# 随机获取300支股票
choice_symbols = ABuMarket.choice_symbols(50)
capital = AbuCapital(1000000, benchmark)
sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
sell_factor2 = {
'stop_loss_n': 0.5,
'stop_win_n': 3.0,
'class': AbuFactorAtrNStop
}
sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
buy_factors = [{
'xd': 60,
'class': AbuFactorBuyBreak
}, {
'xd': 42,
'class': AbuFactorBuyBreak
}]
orders_pd, action_pd, _ = AbuPickTimeMaster.do_symbols_with_same_factors_process(
choice_symbols, benchmark, buy_factors, sell_factors, capital)
metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
abupy.env.enable_example_env_ipython()
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 load_abu_result_tuple():
abu_result_tuple_train = abu.load_abu_result_tuple(
n_folds=6,
store_type=abupy.EStoreAbu.E_STORE_CUSTOM_NAME,
# custom_name='tt_train_cn')
custom_name='18_6_top_train_cn')
abu_result_tuple_test = abu.load_abu_result_tuple(
n_folds=6,
store_type=abupy.EStoreAbu.E_STORE_CUSTOM_NAME,
# custom_name='tt_test_cn')
custom_name='18_6_top_test_cn')
metrics_train = AbuMetricsBase(*abu_result_tuple_train)
metrics_train.fit_metrics()
metrics_test = AbuMetricsBase(*abu_result_tuple_test)
metrics_test.fit_metrics()
return abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test
def load_abu_result_tuple():
abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
abu_result_tuple_train = abu.load_abu_result_tuple(
n_folds=5,
store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='train_cn')
abu_result_tuple_test = abu.load_abu_result_tuple(
n_folds=5,
store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='test_cn')
metrics_train = AbuMetricsBase(*abu_result_tuple_train)
metrics_train.fit_metrics()
metrics_test = AbuMetricsBase(*abu_result_tuple_test)
metrics_test.fit_metrics()
return abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test
def sample_a21():
# 设置初始资金数
read_cash = 500000000
# 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 = ['600999']
choice_symbols = ['601398', '601988', '601939', '603993', '600999', '300059', '600900', '601328', '601288', '600887', '600029', '000002']
# choice_symbols = ['002285', '600109']
choice_symbols = None
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors, sell_factors, stock_pickers, choice_symbols=choice_symbols,
n_folds=15, commission_dict=commission_dict)
print(abu_result_tuple.orders_pd)
# AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=False)
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
# plot_simple = abu_result_tuple.orders_pd[abu_result_tuple.orders_pd.profit_cg < 0]
# save=True保存在本地，耗时操作，需要运行几分钟
# ABuMarketDrawing.plot_candle_from_order(abu_result_tuple.orders_pd, save=False)
# 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()
# kl_pd = ABuSymbolPd.make_kl_df('601398', n_folds=15)
# plot_his_trade(abu_result_tuple.orders_pd, kl_pd)
# 保存交易结果
store_abu_result_out_put(abu_result_tuple)
metrics.plot_order_returns_cmp()
# metrics.plot_buy_factors()
metrics.plot_sell_factors()
# metrics.show_general(*abu_result_tuple, only_show_returns=True)
# metrics.plot_effect_mean_day()
# plt.show()
# metrics.plot_keep_days()
# plt.show()
metrics.plot_max_draw_down()
def sample_91(show=True):
"""
9.1 度量的基本使用方法
:return:
"""
# 设置初始资金数
read_cash = 1000000
# 择时股票池
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usTSLA', 'usWUBA', 'usVIPS']
# 使用run_loop_back运行策略
abu_result_tuple, kl_pd_manager = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols, n_folds=2)
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
if show:
metrics.plot_returns_cmp()
return metrics
def sample_817():
"""
8.1.7 使用并行来提升择时运行效率
:return:
"""
# 要关闭沙盒数据环境，因为沙盒里就那几个股票的历史数据, 下面要随机做50个股票
from abupy import EMarketSourceType
abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
abupy.env.disable_example_env_ipython()
# 关闭沙盒后，首先基准要从非沙盒环境换取，否则数据对不齐，无法正常运行
benchmark = AbuBenchmark()
# 当传入choice_symbols为None时代表对整个市场的所有股票进行回测
# noinspection PyUnusedLocal
choice_symbols = None
# 顺序获取市场后300支股票
# noinspection PyUnusedLocal
choice_symbols = ABuMarket.all_symbol()[-50:]
# 随机获取300支股票
choice_symbols = ABuMarket.choice_symbols(50)
capital = AbuCapital(1000000, benchmark)
sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop}
sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
{'xd': 42, 'class': AbuFactorBuyBreak}]
orders_pd, action_pd, _ = AbuPickTimeMaster.do_symbols_with_same_factors_process(
choice_symbols, benchmark, buy_factors, sell_factors,
capital)
metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
abupy.env.enable_example_env_ipython()
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()
from abupy import ABuPickTimeExecute
orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors(
[
'usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usWUBA', 'usVIPS'
],
benchmark,
buy_factors,
sell_factors,
capital,
show=False)
print(orders_pd[:10])
# 3) metric the profit
from abupy import AbuMetricsBase
metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
# 4) evaluate win_rate(为了计算胜率)
win_rate = metrics.win_rate
gains_mean = metrics.gains_mean
losses_mean = -metrics.losses_mean
# 5) add position control
# n天向上突破买入策略(加入买入价格策略,加入仓位控制策略)
buy_factors = [{
'xd': 60,
'class': AbuFactorBuyBreak,
'slippage': AbuSlippageBuyMean,
'win_rate': win_rate,
'gains_mean': gains_mean,
'losses_mean': losses_mean,
'position': AbuKellyPosition
def main():
## 读股票
symbols = read_symbols()
print("一共有多少个股票? %d" % len(symbols))
# 设置初始资金数
read_cash = 1000000
# 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
}]
buy_factors = [{
'xd': 42,
'class': AbuFactorBuyBreak,
'position': {
'class': AbuABinPosition
}
}]
# 卖出因子继续使用上一节使用的因子
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
}]
sell_factors = [{
'stop_loss_n': 1.0,
'stop_win_n': 20.0,
'class': AbuFactorAtrNStop
}]
#sell_factors = [ ]
# 择时股票池
# 使用run_loop_back运行策略
# 买入因子依然延用向上突破因子
buy_factors = [{
'xd': 60,
'class': AbuSDBreak
}, {
'xd': 42,
'class': AbuSDBreak
}]
buy_factors = [{
'xd': 60,
'class': AbuFactorBuyBreak
}, {
'xd': 42,
'class': AbuFactorBuyBreak
}]
# 卖出因子继续使用上一节使用的因子
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
}]
choice_symbols = [
'hk03333', 'hk00700', 'hk02333', 'hk01359', 'hk00656', 'hk03888',
'hk02318'
]
choice_symbols = [
'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA',
'usVIPS'
]
abupy.env.g_market_target = abupy.EMarketTargetType.E_MARKET_TARGET_HK
abupy.env.g_market_target = abupy.EMarketTargetType.E_MARKET_TARGET_US
abu_result_tuple, kl_pd_manger = abu.run_loop_back(
read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols,
n_folds=6)
ABuProgress.clear_output()
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp()
pd.set_option('display.expand_frame_repr', False)
pd.options.display.max_rows = 999
