def execute(self, pool, start, end, data=None, data_config=None):
"""
计算选股结果
:param pool: list 待选股票池
data：进行选股操作所需要的数据
start:datetime 选股索引范围起始时间
end：datetime 选股索引范围结束时间
:return:selector_result:A MultiIndex Series indexed by date (level 0) and asset (level 1), containing
the values mean whether choose the asset or not.
-----------------------------------
date | asset |
-----------------------------------
| AAPL | 1
-----------------------
| BA | 1
-----------------------
2014-01-01 | CMG | 1
-----------------------
| DAL | 0
-----------------------
| LULU | -1
-----------------------
"""
if data is None:
data = DataAPI.get(symbols=tuple(pool),
start=start -
datetime.timedelta(days=self.max_window),
end=end,
**data_config)
factor = Factor_Volume003().get_factor(data, update=True)
quantiles = self.get_quantiles(factor)
selector_result = quantiles.copy()
selector_result[:] = 0
selector_result[quantiles == self.choose_quantile] = 1
return selector_result.loc[start:end]
def execute(self, pool, start, end, data=None, data_config=None):
"""
计算选股结果
:param pool: list 待选股票池
data：进行选股操作所需要的数据
start:datetime 选股索引范围起始时间
end：datetime 选股索引范围结束时间
:return:selector_result:A MultiIndex Series indexed by date (level 0) and asset (level 1), containing
the values mean whether choose the asset or not.
-----------------------------------
date | asset |
-----------------------------------
| AAPL | 1
-----------------------
| BA | 1
-----------------------
2014-01-01 | CMG | 1
-----------------------
| DAL | 0
-----------------------
| LULU | -1
-----------------------
"""
if data is None:
data = DataAPI.get(symbols=tuple(pool),
start=start -
datetime.timedelta(days=self.max_window),
end=end,
**data_config)
selector_result = list(
map(self.calculate_MACD_signal, data.iteritems()))
selector_result = pd.concat(selector_result, axis=1).stack()
selector_result.index.names = ["date", "asset"]
return selector_result.loc[start:end]
# 配置选股器所在包路径
Admin.PACKAGE_NAME = "fxdayu_alphaman.examples.factors"
# 初始选股范围设置
initial_codes = standard_code_style(
json.load(open('test_stock_pool.json'))["test_stock_pool"])
data_config = {"freq": "D", "api": "candle", "adjust": "after"}
# 测试参数设置
start = datetime.datetime(2017, 1, 1)
end = datetime.datetime(2017, 4, 18, 15)
periods = (1, 5, 10)
# 获取数据
data = DataAPI.get(symbols=tuple(initial_codes),
start=start - datetime.timedelta(days=100),
end=end,
**data_config)
prices = data.minor_xs("close")
def manage_factors_value_test(factor_name_list, data_config_dict):
# admin测试 -获得多个因子结果
factor_admin = Admin(*factor_name_list)
factors_dict = factor_admin.get_all_factors_value(
initial_codes,
start,
end,
all_factors_data_config_dict=data_config_dict)
return factor_admin, factors_dict
def instantiate_factor_and_get_factor_value(factor_name,
pool,
start,
end,
Factor=None,
data=None,
data_config={"freq": "D", "api": "candle", "adjust": "after"},
para_dict=None,
):
"""
计算某个因子指定时间段的因子值 (可支持直接用factor_name加载到对应因子的算法)
:param factor_name: 因子名称(str) 需确保传入的factor_name、因子的类名、对应的module文件名一致(不含.后缀),因子才能正确加载
:param pool: 股票池范围(list),如：["000001.XSHE","600300.XSHG",......]
:param start: 起始时间 (datetime)
:param end: 结束时间 (datetime)
:param Factor (optional): 因子(factor.factor.Factor object),可选.可以输入一个设计好的Factor类来执行计算.
:param data (optional): 计算因子需用到的数据,根据计算需求自行指定。(可选)
:param data_config (optional): 在data参数为None的情况下(不传入自定义数据),
可通过该参数调用fxdayu_data api 访问到数据 (dict),
与data参数二选一。
:param para_dict (optional): 外部指定因子里所用到的参数集(dict),为空则不修改原有参数。 形如:{"fast":5,"slow":10}
:return: factor_value:因子值 格式为一个MultiIndex Series，索引(index)为date(level 0)和asset(level 1),
包含一列factor值。形如：
-----------------------------------
date | asset |
-----------------------------------
| AAPL | 0.5
-----------------------
| BA | -1.1
-----------------------
2014-01-01 | CMG | 1.7
-----------------------
| DAL | -0.1
-----------------------
| LULU | 2.7
-----------------------
"""
from fxdayu_data import DataAPI
import datetime
# 实例化因子类
if Factor is None:
factor = _get_factor(factor_name, Admin.PACKAGE_NAME)()
else:
factor = Factor
# 接收外部传入的参数
if para_dict:
for para in list(para_dict.keys()):
setattr(factor, para, para_dict[para])
if data is None:
pn_data = DataAPI.get(symbols=tuple(pool),
start=start - datetime.timedelta(days=factor.max_window),
end=end,
**data_config)
else:
pn_data = data
# 因子计算结果获取
factor_value = factor.get_factor(pn_data, update=True)
return factor_value