from jqlib import alpha191
from jqlib.alpha191 import *
from jqfactor import get_factor_values
from jqfactor import Factor
from jqfactor import analyze_factor
warnings.filterwarnings('ignore') 

# 导入函数库
from jqfactor import Factor, calc_factors

# 定义因子，聚宽的因子定义标准形式
class ALPHA013(Factor):
    name = 'alpha013_name'
    max_window = 1
    dependencies = ['high','low','volume','money']
    def calc(self, data):
        high = data['high']
        low = data['low']
        vwap = data['money']/data['volume']
        return (np.power(high*low,0.5) - vwap).mean()

# 定义因子
class GROSSPROFITABILITY(Factor):
    name = 'gross_profitability'
    max_window = 1
    dependencies = ['total_operating_revenue','total_operating_cost','total_assets']
    def calc(self, data):
        total_operating_revenue = data['total_operating_revenue']
        total_operating_cost = data['total_operating_cost']
        total_assets = data['total_assets']
        gross_profitability = (total_operating_revenue - total_operating_cost)/total_assets
        return gross_profitability.mean()
# 定义股票池
securities = ['600000.XSHG','600016.XSHG']
# 计算因子值
factors = calc_factors(securities, [ALPHA013(),GROSSPROFITABILITY()], start_date='2017-01-01', end_date='2017-02-01',  use_real_price=False, skip_paused=False)

# 查看因子值
factors['gross_profitability'].head()

#也可以不按聚宽的方式定义因子，直接用函数来自定义定义，只是接下来处理数据麻烦一些，
def factor_cal(pool,date):
    df = get_price(pool,end_date=date,count=21,fields=['close'])['close'] 
    far = df.iloc[-1,:]/df.iloc[0,:] - 1
    return far
#测试函数，看下面的数据结构，和上面的数据结构和上面的是有所不同的，需要用标准因子模板处理，因此自定义因子还是建议用标准模板，
#这样可以直接调用'analyze_factor'方法进行分析。
factor_cal(['000001.XSHE','600000.XSHG'],'2019-07-12')

far = analyze_factor(factor=ALPHA013, start_date='2017-01-01', end_date='2017-02-01',weight_method='mktcap',universe='000300.XSHG', industry='jq_l1', quantiles=8, periods=(1,5,22))
#分析结束后通过不同属性获取数据
far.ic_monthly #月度信息比率

#分析结束后通过不同属性获取数据
far.mean_return_std_by_quantile #获取按分位数分组加权平均因子收益

#
far.create_full_tear_sheet(demeaned=False, group_adjust=False, by_group=False, turnover_periods=None, avgretplot=(5, 15), std_bar=False)

# 计算指定调仓周期的各分位数每日累积收益
df = far.calc_cumulative_return_by_quantile(period=5)

#进行数据展示
df.plot(figsize=(15,6))