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_n_range = np.arange(1,2,1) # #设定买入后只持有天数，默认1
is_sell_today = True #设定买入n天后，当天还是隔天卖出。默认False。
# sell_atr_nstop_factor_grid = {'class':[AbuFactorSellNDay],'sell_n':sell_n_range}
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)
endDate = ['2020-02-26']
concept = symbols_dic['行业']
symbols = symbols_dic['数据']
windowSell = np.arange(7, 8, 1)
AbuBigNDown_grid2 = {
'startDate': startDate,
'endDate': endDate,
'windowBuy': windowBuy,
'windowSell': windowSell,
'poly': poly
}
buy_factors_product = ABuGridHelper.gen_factor_grid_4_jijin(
[AbuBigNDown_grid2])
print('买入因子参数共有{}种组合方式'.format(len(buy_factors_product)))
grid_search = GridSearch(100000,
choice_symbols=symbols,
buy_factors_product=buy_factors_product,
sell_factors_product=None,
start=startDate[0],
end=endDate[0],
concept=concept)
if __name__ == '__main__': #多线程必须内容，不可删除。
scores, score_tuple_array = grid_search.fit4JiJin(
n_jobs=1,
isJiJin=False,
showWindowBuy=showWindowBuy,
showWindowSell=showWindowSell,
concept=concept)
sell_atr_close_factor_grid
],
need_empty_sell=True)
buy_bk_factor_grid1 = {'class': [AbuFactorBuyBreak], 'xd': [42]}
buy_bk_factor_grid2 = {'class': [AbuFactorBuyBreak], 'xd': [60]}
buy_factors_product = ABuGridHelper.gen_factor_grid(
ABuGridHelper.K_GEN_FACTOR_PARAMS_BUY,
[buy_bk_factor_grid1, buy_bk_factor_grid2])
read_cash = 100000
choice_symbols = ['usAAPL', 'usTSLA', 'usTQQQ']
grid_search = GridSearch(read_cash,
choice_symbols,
buy_factors_product=buy_factors_product,
sell_factors_product=sell_factors_product)
scores = None
score_tuple_array = None
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')
def load_score_cache():
showWindowBuy = True
showWindowSell = False
showMutil = False
AbuBigNDown_grid2 = {
'jiJinCode': ['123456'],
'startDate': startDate,
'endDate': endDate,
'windowBuy': windowBuy,
'windowSell': windowSell,
'poly': poly
}
buy_factors_product = ABuGridHelper.gen_factor_grid_4_jijin(
[AbuBigNDown_grid2])
print('买入因子参数共有{}种组合方式'.format(len(buy_factors_product)))
grid_search = GridSearch(None,
jiJinCode,
buy_factors_product=buy_factors_product,
sell_factors_product=None,
n_folds=1,
start=startDate[0],
end=endDate[0])
if __name__ == '__main__': #多线程必须内容，不可删除。
keysObj = {'showMutil': showMutil}
scores, score_tuple_array = grid_search.fit4JiJin(
n_jobs=-1,
showWindowBuy=showWindowBuy,
showWindowSell=showWindowSell,
keysObj=keysObj)
'xd': [42]
}
buy_bk_factor_grid2 = {
'class': [AbuFactorBuyBreak],
'xd': [60]
}
buy_factors_product = ABuGridHelper.gen_factor_grid(
ABuGridHelper.K_GEN_FACTOR_PARAMS_BUY, [buy_bk_factor_grid1, buy_bk_factor_grid2])
# 有三种组合，分别为，只使用42日突破，只使用60日突破和同时使用42日和60日突破。
print('买入因子参数共有{}种组合方式'.format(len(buy_factors_product)))
print('买入因子组合形式为{}'.format(buy_factors_product))
# 3、进行gridsearch
read_cash = 100000
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU']
grid_search = GridSearch(read_cash, choice_symbols, buy_factors_product=buy_factors_product,
sell_factors_product=sell_factors_product)
scores,score_tuple_array=grid_search.fit(n_jobs=-1)
print('组合因子参数数量{}'.format(len(buy_factors_product) * len(sell_factors_product)))
# 如果从本地序列文件中读取则没有scores
print('最终评分结果数量{}'.format(len(scores)))
# 可视化策略
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)