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_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 run_loo_back(choice_symbols,
ps=None,
n_folds=3,
start=None,
end=None,
only_info=False):
"""封装一个回测函数，返回回测结果，以及回测度量对象"""
if choice_symbols[0].startswith('us'):
abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_US
else:
abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN
abu_result_tuple, _ = abu.run_loop_back(cash,
buy_factors,
sell_factors,
ps,
start=start,
end=end,
n_folds=n_folds,
choice_symbols=choice_symbols)
ABuProgress.clear_output()
metrics = AbuMetricsBase.show_general(*abu_result_tuple,
returns_cmp=only_info,
only_info=only_info,
only_show_returns=True)
return abu_result_tuple, 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_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_loo_back(choice_symbols, start, end):
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
start=start,
end=end,
choice_symbols=choice_symbols,
n_process_pick=1)
ABuProgress.clear_output()
AbuMetricsBase.show_general(*abu_result_tuple,
returns_cmp=True,
only_info=True)
return abu_result_tuple
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_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_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_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_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 run_loo_back(choice_symbols,
ps=None,
n_folds=3,
start=None,
end=None,
only_info=False):
abu_result_tuple, _ = abu.run_loop_back(cash,
buy_factors,
sell_factors,
ps,
start=start,
end=end,
n_folds=n_folds,
choice_symbols=choice_symbols)
abu_result_tuple.orders_pd['buy_factor'] = abu_result_tuple.orders_pd[
'buy_factor'].apply(lambda bf: bf.split(':')[0])
ABuProgress.clear_output()
metrics = AbuMetricsBase.show_general(*abu_result_tuple,
returns_cmp=only_info,
only_info=only_info,
only_show_returns=True)
return abu_result_tuple, metrics
}, {
'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
}]
abupy.beta.atr.g_atr_pos_base = 0.5
abu_result_tuple, kl_pd_manger = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
start='2018-06-01',
end='2019-06-25',
choice_symbols=['btc'])
ABuProgress.clear_output()
print(abu_result_tuple)
AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=True)
print('over')
# ABuMarketDrawing.plot_simple_two_stock({'btc': btc, 'eth': eth})
# print(btc.tail(7))
# 卖出因子
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
}]
# 择时股票池
choice_symbols = [
'hk03333', 'hk00700', 'hk02333', 'hk01359', 'hk00656', 'hk03888', 'hk02318'
]
# 设置市场类型为港股
abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_HK
abu_result_tuple, kl_pd_manger = \
abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
n_folds=6,choice_symbols=choice_symbols)
ABuProgress.clear_output()
AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=True)
# {'class': AbuFactorBuyPutBreakSellOption, 'freq': '60m', 'xd': 8, 'xd1': 12, 'cp': 1, 'direction': -1,
# 'maturity': 5, 'option_max_num': 10, 'option_clear_proportion': 0.618, 'dynamic': True,
# 'premium': 5000, 'threshold': 0.3, 'notional': None, 'slippage': AbuSlippageBuyPutTick,
# 'calendar_type': 1, 'rate': 0.03, 'dividend': 0.03, 'position': {'class': AbuEuroOptionHedgeBuyPosition},
# 'sell_factors': [{'class': AbuFactorSellOptionHedge,
# 'position': AbuEuroOptionHedgeSellPosition, 'threshold': 0.3,
# 'slippage': AbuSlippageSellPutTick},
# {'class': AbuFactorSellClearPut,
# 'position': AbuEuroOptionHedgeSellPosition, 'slippage': AbuSlippageSellPutTick}
# ]}
]
abu_result_tuple, kl_pd_manager = abu.run_loop_back(
read_cash=20000000,
buy_factors=buy_factors,
sell_factors=sell_factors,
choice_symbols=us_choice_symbols,
start='2015-01-08',
end='2017-11-30')
store_abu_result_out_put_and_plot(abu_result_tuple,
show_log=True)
# import cProfile
#
# cProfile.run("""abu_result_tuple, kl_pd_manager = abu.run_loop_back(read_cash=30000000, buy_factors=buy_factors,
# sell_factors=sell_factors,
# choice_symbols=us_choice_symbols,
# start='2017-01-08',
# end='2018-11-30')""")
# 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
}]
# 注意这里把atr资金管理的仓位基数设置为0.5，即50%
abupy.beta.atr.g_atr_pos_base = 0.5
abu_result_tuple, kl_pd_manger = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
start='2013-09-01',
end='2017-07-26',
choice_symbols=None)
ABuProgress.clear_output()
AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=True)
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 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
#卖出因子
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
}]
#择时股票池
choice_symbols = [
'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA',
'usVIPS'
]
abu_result_tuple, kl_pd_manger = abu.run_loop_back(read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols,
n_folds=2)
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
# metrics.plot_returns_cmp()
metrics.plot_sharp_volatility_cmp()
metrics.plot_max_draw_down()
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
