def sample_935_2():
"""
9.3.5_2 不同权重的评分: 只考虑胜率
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print(
'../gen/score_tuple_array not exist! please execute sample_933 first!'
)
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 只有第一项为1，其他都是0代表只考虑胜率来评分
scorer = WrsmScorer(score_tuple_array, weights=[1, 0, 0, 0])
# 返回按照评分排序后的队列
scorer_returns_max = scorer.fit_score()
# index[-1]为最优参数序号
best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital,
best_score_tuple_grid.benchmark,
only_info=False)
# 最后打印出只考虑胜率下最优结果使用的买入策略和卖出策略
print(
'best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n',
best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors)
def sample_934():
"""
9.3.4 度量结果的评分
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print(
'../gen/score_tuple_array not exist! please execute sample_933 first!'
)
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 实例化一个评分类WrsmScorer，它的参数为之前GridSearch返回的score_tuple_array对象
scorer = WrsmScorer(score_tuple_array)
print('scorer.score_pd.tail():\n', scorer.score_pd.tail())
# score_tuple_array[658]与grid_search.best_score_tuple_grid是一致的
sfs = scorer.fit_score()
# 打印前15个高分组合
print('sfs[::-1][:15]:\n', sfs[::-1][:15])
def sample_935_2():
"""
9.3.5_2 不同权重的评分: 只考虑胜率
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print('../gen/score_tuple_array not exist! please execute sample_933 first!')
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 只有第一项为1，其他都是0代表只考虑胜率来评分
scorer = WrsmScorer(score_tuple_array, weights=[1, 0, 0, 0])
# 返回按照评分排序后的队列
scorer_returns_max = scorer.fit_score()
# index[-1]为最优参数序号
best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital,
best_score_tuple_grid.benchmark,
only_info=False)
# 最后打印出只考虑胜率下最优结果使用的买入策略和卖出策略
print('best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n', best_score_tuple_grid.buy_factors,
best_score_tuple_grid.sell_factors)
def sample_935_1():
"""
9.3.5_1 不同权重的评分: 只考虑投资回报来评分
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print('../gen/score_tuple_array not exist! please execute sample_933 first!')
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 实例化WrsmScorer，参数weights，只有第二项为1，其他都是0，
# 代表只考虑投资回报来评分
scorer = WrsmScorer(score_tuple_array, weights=[0, 1, 0, 0])
# 返回排序后的队列
scorer_returns_max = scorer.fit_score()
# 因为是倒序排序，所以index最后一个为最优参数
best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
# 由于篇幅，最优结果只打印文字信息
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital,
best_score_tuple_grid.benchmark,
only_info=True)
# 最后打印出只考虑投资回报下最优结果使用的买入策略和卖出策略
print('best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n', best_score_tuple_grid.buy_factors,
best_score_tuple_grid.sell_factors)
def sample_935_1():
"""
9.3.5_1 不同权重的评分: 只考虑投资回报来评分
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print('../gen/score_tuple_array not exist! please execute sample_933 first!')
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 实例化WrsmScorer，参数weights，只有第二项为1，其他都是0，
# 代表只考虑投资回报来评分
scorer = WrsmScorer(score_tuple_array, weights=[0, 1, 0, 0])
# 返回排序后的队列
scorer_returns_max = scorer.fit_score()
# 因为是倒序排序，所以index最后一个为最优参数
best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
# 由于篇幅，最优结果只打印文字信息
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital,
best_score_tuple_grid.benchmark,
only_info=True)
# 最后打印出只考虑投资回报下最优结果使用的买入策略和卖出策略
print('best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n', best_score_tuple_grid.buy_factors,
best_score_tuple_grid.sell_factors)
def sample_934():
"""
9.3.4 度量结果的评分
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print('../gen/score_tuple_array not exist! please execute sample_933 first!')
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 实例化一个评分类WrsmScorer，它的参数为之前GridSearch返回的score_tuple_array对象
scorer = WrsmScorer(score_tuple_array)
print('scorer.score_pd.tail():\n', scorer.score_pd.tail())
# score_tuple_array[658]与grid_search.best_score_tuple_grid是一致的
sfs = scorer.fit_score()
# 打印前15个高分组合
print('sfs[::-1][:15]:\n', sfs[::-1][:15])
for sell_factors_product_item in sell_factors_product:
# sell_factors_product_item[0]['slippage'] = AbuSlippageSellOpen
sell_factors_product_item[0]['slippage'] = AbuSlippageSellClose
# sell_factors_product_item[0]['slippage'] = AbuSlippageSellMinMax
print('卖出因子参数共有{}种组合方式'.format(len(sell_factors_product)))
ABuEnv.date_str = datetime.datetime.now().strftime("_%Y_%m_%d")
grid_search = GridSearch(read_cash,choice_symbols,buy_factors_product=buy_factors_product,
sell_factors_product=sell_factors_product,n_folds=1,start=startDate, end=endDate)
if __name__ == '__main__': #多线程必须内容，不可删除。
scores,score_tuple_array = grid_search.fit(n_jobs=-1)
store_python_obj(score_tuple_array, 'score_tuple_array'+ABuEnv.date_str, show_log=False)
#-------权重方式开始------
if score_tuple_array != None:
scorer = WrsmScorer(score_tuple_array, weights=[1, 0, 0, 0])
scorer_returns_max = scorer.fit_score()
best_result_tuple = score_tuple_array[scorer_returns_max.index[-1]]
print(best_result_tuple.buy_factors) #打印最优因子
print(best_result_tuple.sell_factors) #打印最优因子
store_abu_result_tuple(best_result_tuple, n_folds=None, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
custom_name='best_1_0_0_0')
AbuMetricsBase.show_general(best_result_tuple.orders_pd, best_result_tuple.action_pd,
best_result_tuple.capital, best_result_tuple.benchmark, only_info=False)
scorer = WrsmScorer(score_tuple_array, weights=[0, 1, 0, 0])
scorer_returns_max = scorer.fit_score()
best_result_tuple = score_tuple_array[scorer_returns_max.index[-1]]
print(best_result_tuple.buy_factors) # 打印最优因子
print(best_result_tuple.sell_factors) # 打印最优因子
store_abu_result_tuple(best_result_tuple, n_folds=None, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,