def sample_933():
"""
9.3.3 GridSearch寻找最优参数
:return:
"""
from abupy import GridSearch
read_cash = 1000000
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usTSLA', 'usWUBA', 'usVIPS']
sell_factors_product, buy_factors_product = sample_932(show=False)
grid_search = GridSearch(read_cash, choice_symbols,
buy_factors_product=buy_factors_product,
sell_factors_product=sell_factors_product)
from abupy import ABuFileUtil
"""
注意下面的运行耗时大约1小时多，如果所有cpu都用上的话，也可以设置n_jobs为 < cpu进程数，一边做其它的一边跑
"""
# 运行GridSearch n_jobs=-1启动cpu个数的进程数
scores, score_tuple_array = grid_search.fit(n_jobs=-1)
"""
针对运行完成输出的score_tuple_array可以使用dump_pickle保存在本地，以方便修改其它验证效果。
"""
ABuFileUtil.dump_pickle(score_tuple_array, '../gen/score_tuple_array')
print('组合因子参数数量{}'.format(len(buy_factors_product) * len(sell_factors_product)))
print('最终评分结果数量{}'.format(len(scores)))
best_score_tuple_grid = grid_search.best_score_tuple_grid
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)
def sample_933():
"""
9.3.3 GridSearch寻找最优参数
:return:
"""
from abupy import GridSearch
read_cash = 1000000
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usTSLA', 'usWUBA', 'usVIPS']
sell_factors_product, buy_factors_product = sample_932(show=False)
grid_search = GridSearch(read_cash, choice_symbols,
buy_factors_product=buy_factors_product,
sell_factors_product=sell_factors_product)
from abupy import ABuFileUtil
"""
注意下面的运行耗时大约1小时多，如果所有cpu都用上的话，也可以设置n_jobs为 < cpu进程数，一边做其它的一边跑
"""
# 运行GridSearch n_jobs=-1启动cpu个数的进程数
scores, score_tuple_array = grid_search.fit(n_jobs=-1)
"""
针对运行完成输出的score_tuple_array可以使用dump_pickle保存在本地，以方便修改其它验证效果。
"""
ABuFileUtil.dump_pickle(score_tuple_array, '../gen/score_tuple_array')
print('组合因子参数数量{}'.format(len(buy_factors_product) * len(sell_factors_product)))
print('最终评分结果数量{}'.format(len(scores)))
best_score_tuple_grid = grid_search.best_score_tuple_grid
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)
def run_grid_search():
global scores, score_tuple_array
# 运行GridSearch n_jobs=-1启动cpu个数的进程数
scores, score_tuple_array = grid_search.fit(n_jobs=-1)
# 运行完成输出的score_tuple_array可以使用dump_pickle保存在本地，以方便之后使用
ABuFileUtil.dump_pickle(score_tuple_array, '../gen/score_tuple_array')