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_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()
abu_result_tuple, kl_pd_manger = abu.run_loop_back(
read_cash,
buy_factors,
sell_factors,
stock_pickers,
choice_symbols=choice_symbols,
n_folds=2)
ABuProgress.clear_output()
help(abu.run_loop_back)
from abupy import AbuMetricsBase
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp()
metrics.plot_sharp_volatility_cmp()
metrics.plot_effect_mean_day()
metrics.plot_keep_days()
metrics.plot_sell_factors()
metrics.plot_max_draw_down()
from abupy import ABuScalerUtil
class MetricsDemo(AbuMetricsBase):
"""扩展自定义度量类示例"""
def _metrics_extend_stats(self):
"""
子类可扩展的metrics方法，子类在此方法中可定义自己需要度量的值:
本demo示例交易手续费和策略收益之间的度量对比
"""
commission_df = self.capital.commission.commission_df
commission_df['commission'] = commission_df.commission.astype(float)
