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)
sell_atr_nstop_factor_grid = {'class':[AbuFactorSellJiJinWave]}
# sell_atr_nstop_factor_grid = {'class':[AbuFactorSellMinMax]}
sell_factors_product = ABuGridHelper.gen_factor_grid(ABuGridHelper.K_GEN_FACTOR_PARAMS_SELL,[sell_atr_nstop_factor_grid])
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()