def sample_821_1():
"""
8.2.1_1 选股使用示例
:return:
"""
# 选股条件threshold_ang_min=0.0, 即要求股票走势为向上上升趋势
stock_pickers = [{'class': AbuPickRegressAngMinMax,
'threshold_ang_min': 0.0, 'reversed': False}]
# 从这几个股票里进行选股，只是为了演示方便
# 一般的选股都会是数量比较多的情况比如全市场股票
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usTSLA', 'usWUBA', 'usVIPS']
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager,
choice_symbols=choice_symbols,
stock_pickers=stock_pickers)
stock_pick.fit()
# 打印最后的选股结果
print('stock_pick.choice_symbols:', stock_pick.choice_symbols)
# 从kl_pd_manager缓存中获取选股走势数据，注意get_pick_stock_kl_pd为选股数据，get_pick_time_kl_pd为择时
kl_pd_noah = kl_pd_manager.get_pick_stock_kl_pd('usNOAH')
# 绘制并计算角度
deg = ABuRegUtil.calc_regress_deg(kl_pd_noah.close)
print('noah 选股周期内角度={}'.format(round(deg, 3)))
def sample_b3_4():
"""
【示例4】abu量化系统选股结合相关性，编写相关性选股策略
AbuPickSimilarNTop源代码请自行阅读，只简单示例使用。
:return:
"""
from abupy import AbuPickSimilarNTop
from abupy import AbuPickStockWorker
from abupy import AbuBenchmark, AbuCapital, AbuKLManager
benchmark = AbuBenchmark()
# 选股因子AbuPickSimilarNTop， 寻找与usTSLA相关性不低于0.95的股票
# 这里内部使用以整个市场作为观察者方式计算，即取值范围0-1
stock_pickers = [{
'class': AbuPickSimilarNTop,
'similar_stock': 'usTSLA',
'threshold_similar_min': 0.95
}]
# 从这几个股票里进行选股，只是为了演示方便，一般的选股都会是数量比较多的情况比如全市场股票
choice_symbols = [
'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA',
'usVIPS'
]
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pick = AbuPickStockWorker(capital,
benchmark,
kl_pd_manager,
choice_symbols=choice_symbols,
stock_pickers=stock_pickers)
stock_pick.fit()
print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols)
"""
通过选股因子first_choice属性执行批量优先选股操作，具体阅读源代码
"""
# 选股因子AbuPickSimilarNTop， 寻找与usTSLA相关性不低于0.95的股票
# 通过设置'first_choice':True，进行优先批量操作，默认从对应市场选股
stock_pickers = [{
'class': AbuPickSimilarNTop,
'first_choice': True,
'similar_stock': 'usTSLA',
'threshold_similar_min': 0.95
}]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pick = AbuPickStockWorker(capital,
benchmark,
kl_pd_manager,
choice_symbols=None,
stock_pickers=stock_pickers)
stock_pick.fit()
print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols)
def sample_b3_4():
"""
【示例4】abu量化系统选股结合相关性，编写相关性选股策略
AbuPickSimilarNTop源代码请自行阅读，只简单示例使用。
:return:
"""
from abupy import AbuPickSimilarNTop
from abupy import AbuPickStockWorker
from abupy import AbuBenchmark, AbuCapital, AbuKLManager
benchmark = AbuBenchmark()
# 选股因子AbuPickSimilarNTop， 寻找与usTSLA相关性不低于0.95的股票
# 这里内部使用以整个市场作为观察者方式计算，即取值范围0-1
stock_pickers = [{'class': AbuPickSimilarNTop,
'similar_stock': 'usTSLA', 'threshold_similar_min': 0.95}]
# 从这几个股票里进行选股，只是为了演示方便，一般的选股都会是数量比较多的情况比如全市场股票
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA', 'usVIPS']
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager, choice_symbols=choice_symbols,
stock_pickers=stock_pickers)
stock_pick.fit()
print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols)
"""
通过选股因子first_choice属性执行批量优先选股操作，具体阅读源代码
"""
# 选股因子AbuPickSimilarNTop， 寻找与usTSLA相关性不低于0.95的股票
# 通过设置'first_choice':True，进行优先批量操作，默认从对应市场选股
stock_pickers = [{'class': AbuPickSimilarNTop, 'first_choice': True,
'similar_stock': 'usTSLA', 'threshold_similar_min': 0.95}]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager, choice_symbols=None,
stock_pickers=stock_pickers)
stock_pick.fit()
print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols)
'received': False
}]
# 一般而言，我们是遍历整个股市来选股，这里我们就选择以下几个股票来做演示
choice_symbols = [
'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA',
'usVIPS'
]
# 开始执行
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pick = AbuPickStockWorker(capital,
benchmark,
kl_pd_manager,
choice_symbols=choice_symbols,
stock_pickers=stock_pickers)
stock_pick.fit()
print(stock_pick.choice_symbols)
# 绘图
kl_pd_SFUN = kl_pd_manager.get_pick_stock_kl_pd('usNOAH')
deg = ABuRegUtil.calc_regress_deg(kl_pd_SFUN.close)
print(deg)
# 上面使用worker的操作太麻烦，下面可以直接使用executer
stock_pickers = [{
'class': AbuPickRegressAngMinMax,
'threshold_ang_min': 0.0,