import pandas as pd
import numpy as np
import datetime
import statsmodels.api as sm

import plotly.plotly as py
import plotly.graph_objs as go
import plotly.tools as tls
from plotly.graph_objs import *
import plotly.graph_objs as go
import cufflinks as cf

py.sign_in('DemoAccount','2qdyfjyr7o')

第一节：Fama-French三因子模型

# 拿到基准收益率
index_change_rate = get_price_change_rate('000001.XSHG',start_date='20160514', end_date='20170515')
# 拿到无风险利率
risk_free_rate = get_yield_curve(start_date='20160514', end_date='20170515', tenor=None, country='cn')['0S']
# 反年化处理
risk_free_rate = (risk_free_rate+1)**(1/244)-1
# 取与基准相同的交易日
risk_free_rate = risk_free_rate[list(index_change_rate.index)]
price_change_rate = pd.DataFrame()
price_change_rate['risk_free_rate'] = risk_free_rate
price_change_rate['000001.XSHG'] = index_change_rate
price_change_rate

# 取2016-05-15的估值数据作为SMB和HML的计算标准
all_stocks_value = get_fundamentals(
    query(
        fundamentals.eod_derivative_indicator.pb_ratio,
        fundamentals.eod_derivative_indicator.market_cap)
    , '2016-05-15', interval = '1d' ,report_quarter = False)
all_stocks_value = all_stocks_value.major_xs(all_stocks_value.major_axis[0])
all_stocks_value['bp_ratio'] = 1/all_stocks_value['pb_ratio']
del all_stocks_value['pb_ratio']
all_stocks_value = all_stocks_value.dropna(how = 'any')
all_stocks_value

# 拿到所有股票的日收益率数据
all_price_change_rate = get_price_change_rate(list(all_stocks_value.index),start_date='20160514', end_date='20170515')
all_price_change_rate