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risk_premium.py
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risk_premium.py
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import os
import time
import numpy as np
import pandas as pd
import datetime
from utils import *
from cftc import *
from chinamoney import *
start_time = '2000-1-1'
end_time = '2025-12-31'
def plot_risk_premium(index_t, index, index_rate, name1, bond_rate_t, bond_rate, name2, T):
t1, data1 = get_period_data(bond_rate_t, bond_rate, start_time, end_time, remove_nan=True)
t2, risk_premium = data_sub(index_t, 100/index_rate, t1, data1)
plot_mean_std([[t2, risk_premium, '风险溢价 = 1/'+name1+' - '+name2]], [[index_t, index, '指数']], T)
time.sleep(0.5)
def test1():
path = os.path.join(data_dir, '股指'+'.csv')
df = pd.read_csv(path)
t0 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
sz50 = np.array(df['上证50_x'], dtype=float)
cs300 = np.array(df['沪深300_x'], dtype=float)
zz500 = np.array(df['中证500_x'], dtype=float)
sz50_pe_ttm = np.array(df['上证50滚动市盈率'], dtype=float)
cs300_pe_ttm = np.array(df['沪深300滚动市盈率'], dtype=float)
zz500_pe_ttm = np.array(df['中证500滚动市盈率'], dtype=float)
sz50_pe_ttm_med = np.array(df['上证50滚动市盈率中位数'], dtype=float)
cs300_pe_ttm_med = np.array(df['沪深300滚动市盈率中位数'], dtype=float)
zz500_pe_ttm_med = np.array(df['中证500滚动市盈率中位数'], dtype=float)
sz50_pb = np.array(df['上证50市净率'], dtype=float)
cs300_pb = np.array(df['沪深300市净率'], dtype=float)
zz500_pb = np.array(df['中证500市净率'], dtype=float)
path = os.path.join(interest_rate_dir, '国债收益率'+'.csv')
df = pd.read_csv(path)
t1 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
cn10y = np.array(df['10Y'], dtype=float)
cn05y = np.array(df['5Y'], dtype=float)
rate = cn10y
rate_name = '中国国债收益率10年'
# t1, cn10y = get_period_data(t1, cn10y, start_time, end_time, remove_nan=True)
plot_mean_std([[t0, sz50_pe_ttm, '上证50滚动市盈率']], [], T=250*5, start_time='2012-01-01')
time.sleep(0.5)
plot_mean_std([[t0, cs300_pe_ttm, '沪深300滚动市盈率']], [], T=250*5, start_time='2012-01-01')
time.sleep(0.5)
plot_mean_std([[t0, zz500_pe_ttm, '中证500滚动市盈率']], [], T=250*5, start_time='2012-01-01')
time.sleep(0.5)
plot_risk_premium(t0, sz50, sz50_pe_ttm, '上证50滚动市盈率', t1, rate, rate_name, T=250*5)
plot_risk_premium(t0, cs300, cs300_pe_ttm, '沪深300滚动市盈率', t1, rate, rate_name, T=250*5)
plot_risk_premium(t0, zz500, zz500_pe_ttm, '中证500滚动市盈率', t1, rate, rate_name, T=250*5)
plot_risk_premium(t0, sz50, sz50_pe_ttm_med, '上证50滚动市盈率中位数', t1, rate, rate_name, T=250*5)
plot_risk_premium(t0, cs300, cs300_pe_ttm_med, '沪深300滚动市盈率中位数', t1, rate, rate_name, T=250*5)
plot_risk_premium(t0, zz500, zz500_pe_ttm_med, '中证500滚动市盈率中位数', t1, rate, rate_name, T=250*5)
plot_risk_premium(t0, sz50, sz50_pb, '上证50市净率', t1, rate, rate_name, T=250*5)
plot_risk_premium(t0, cs300, cs300_pb, '沪深300市净率', t1, rate, rate_name, T=250*5)
plot_risk_premium(t0, zz500, zz500_pb, '中证500市净率', t1, rate, rate_name, T=250*5)
# 持仓
def test3():
path = os.path.join(data_dir, '股指'+'.csv')
df = pd.read_csv(path).dropna()
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
cs300 = np.array(df['沪深300_x'], dtype=float)
t0, cs300 = get_period_data(t, cs300, start_time, end_time, remove_nan=True)
cftc_plot_financial(t0, cs300, '沪深300指数', code='244042', inst_name='ICE:MSCI新兴市场指数')
# 地产美元债
def test4():
path = os.path.join(data_dir, '股指'+'.csv')
df = pd.read_csv(path).dropna()
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
cs300 = np.array(df['沪深300_x'], dtype=float)
sz50 = np.array(df['上证50_x'], dtype=float)
zz500 = np.array(df['中证500_x'], dtype=float)
t0, cs300 = get_period_data(t, cs300, start_time, end_time, remove_nan=True)
t1, sz50 = get_period_data(t, sz50, start_time, end_time, remove_nan=True)
t2, zz500 = get_period_data(t, zz500, start_time, end_time, remove_nan=True)
path = os.path.join(data_dir, '中国房地产美元债 ETF'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
etf = np.array(df['close'], dtype=float)
c0 = correlation(t0, cs300, t, etf)
c1 = correlation(t1, sz50, t, etf)
c2 = correlation(t2, zz500, t, etf)
plot_two_axis([t0,t1,t2], [cs300,sz50,zz500], ['沪深300指数','上证50指数','中证500指数'], \
[t,t,t], [etf,etf,etf], ['中国房地产美元债 ETF','中国房地产美元债 ETF','中国房地产美元债 ETF'], \
['相关系数='+str(c0),'相关系数='+str(c1),'相关系数='+str(c2)], '2021-1-1', end_time)
# 申万行业指数
def test5():
path = os.path.join(data_dir, '风险溢价'+'.csv')
df = pd.read_csv(path).dropna()
t0 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
name = df.columns.tolist()
temp = np.array(name, dtype=str)
path = os.path.join(data_dir, '中国房地产美元债 ETF'+'.csv')
df1 = pd.read_csv(path)
t1 = pd.DatetimeIndex(pd.to_datetime(df1['time'], format='%Y-%m-%d'))
etf = np.array(df1['close'], dtype=float)
class1_dict = dict()
for i in range(len(temp)):
if ('申万一级指数(旧)' in temp[i]):
data = np.array(df[temp[i]], dtype=float)
c0 = correlation(t0, data, t1, etf)
class1_dict[temp[i]] = c0
# 排序
class1_dict_sorted = sorted(class1_dict.items(),key=lambda s:s[1])
print(class1_dict_sorted)
class2_dict = dict()
for i in range(len(temp)):
if ('申万二级指数(旧)' in temp[i]):
data = np.array(df[temp[i]], dtype=float)
c0 = correlation(t0, data, t1, etf)
class2_dict[temp[i]] = c0
# 排序
class2_dict_sorted = sorted(class2_dict.items(),key=lambda s:s[1])
print(class2_dict_sorted)
t0_list = list()
data0_list = list()
name0_list = list()
t1_list = list()
data1_list = list()
name1_list = list()
title_list = list()
for i in range(5):
t0_list.append(t0)
data0_list.append(np.array(df[class1_dict_sorted[i][0]], dtype=float))
name0_list.append(class1_dict_sorted[i][0])
t1_list.append(t1)
data1_list.append(etf)
name1_list.append('中国房地产美元债 ETF')
title_list.append('相关系数='+str(class1_dict_sorted[i][1]))
plot_two_axis(t0_list, data0_list, name0_list, t1_list, data1_list, name1_list, title_list, '2021-1-1', end_time)
t0_list = list()
data0_list = list()
name0_list = list()
t1_list = list()
data1_list = list()
name1_list = list()
title_list = list()
for i in range(5):
t0_list.append(t0)
data0_list.append(np.array(df[class1_dict_sorted[-i-1][0]], dtype=float))
name0_list.append(class1_dict_sorted[-i-1][0])
t1_list.append(t1)
data1_list.append(etf)
name1_list.append('中国房地产美元债 ETF')
title_list.append('相关系数='+str(class1_dict_sorted[-i-1][1]))
plot_two_axis(t0_list, data0_list, name0_list, t1_list, data1_list, name1_list, title_list, '2021-1-1', end_time)
t0_list = list()
data0_list = list()
name0_list = list()
t1_list = list()
data1_list = list()
name1_list = list()
title_list = list()
for i in range(5):
t0_list.append(t0)
data0_list.append(np.array(df[class2_dict_sorted[i][0]], dtype=float))
name0_list.append(class2_dict_sorted[i][0])
t1_list.append(t1)
data1_list.append(etf)
name1_list.append('中国房地产美元债 ETF')
title_list.append('相关系数='+str(class2_dict_sorted[i][1]))
plot_two_axis(t0_list, data0_list, name0_list, t1_list, data1_list, name1_list, title_list, '2021-1-1', end_time)
t0_list = list()
data0_list = list()
name0_list = list()
t1_list = list()
data1_list = list()
name1_list = list()
title_list = list()
for i in range(5):
t0_list.append(t0)
data0_list.append(np.array(df[class2_dict_sorted[-i-1][0]], dtype=float))
name0_list.append(class2_dict_sorted[-i-1][0])
t1_list.append(t1)
data1_list.append(etf)
name1_list.append('中国房地产美元债 ETF')
title_list.append('相关系数='+str(class2_dict_sorted[-i-1][1]))
plot_two_axis(t0_list, data0_list, name0_list, t1_list, data1_list, name1_list, title_list, '2021-1-1', end_time)
# 大盘拥挤度 https://legulegu.com/stockdata/ashares-congestion
def test6():
path = os.path.join(data_dir, '股指'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
sz50 = np.array(df['上证50_x'], dtype=float)
cs300 = np.array(df['沪深300_x'], dtype=float)
zz500 = np.array(df['中证500_x'], dtype=float)
congestion = np.array(df['congestion'], dtype=float)
plot_two_axis([t,t,t],[sz50,cs300,zz500],['上证50','沪深300','中证500'],
[t,t,t],[congestion,congestion,congestion],['大盘拥挤度','大盘拥挤度','大盘拥挤度'],
['','',''],start_time,end_time)
# 股指 和 中美利差
def plot_cs300_vs_rate():
path = os.path.join(data_dir, '股指'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
cs300 = np.array(df['沪深300_x'], dtype=float)
sz50 = np.array(df['上证50_x'], dtype=float)
path = os.path.join(interest_rate_dir, '国债收益率'+'.csv')
df1 = pd.read_csv(path)
t1 = pd.DatetimeIndex(pd.to_datetime(df1['time'], format='%Y-%m-%d'))
path = os.path.join(interest_rate_dir, 'us_yield_curve'+'.csv')
df2 = pd.read_csv(path)
t2 = pd.DatetimeIndex(pd.to_datetime(df2['time'], format='%Y-%m-%d'))
us10y = np.array(df2['10Y'], dtype=float)
cn10y = np.array(df1['10Y'], dtype=float)
us02y = np.array(df2['2Y'], dtype=float)
cn02y = np.array(df1['2Y'], dtype=float)
t3, diff2 = data_sub(t1, cn10y, t2, us10y)
t4, diff3 = data_sub(t1, cn02y, t2, us02y)
datas = [
[[[t,cs300,'沪深300','color=black'],],
[[t3,diff2,'中美利差 10Y','color=orange'],
[t4,diff3,'中美利差 2Y','color=blue'],],''],]
plot_many_figure(datas, start_time='2017-01-01')
datas = [
[[[t,sz50,'上证50','color=black'],],
[[t3,diff2,'中美利差 10Y','color=orange'],
[t4,diff3,'中美利差 2Y','color=blue'],],''],]
plot_many_figure(datas, start_time='2017-01-01')
# 股指 和 房价
def plot_cs300_vs_house_price():
path = os.path.join(data_dir, '股指'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
cs300 = np.array(df['沪深300_x'], dtype=float)
sz50 = np.array(df['上证50_x'], dtype=float)
path = os.path.join(nbs_dir, '70个大中城市住宅销售价格指数'+'.csv')
df = pd.read_csv(path, header=[0,1])
t1 = pd.DatetimeIndex(pd.to_datetime(df['time']['time'], format='%Y-%m'))
# 上涨、持平、下跌城市个数
mom_new_hi_count, mom_new_eq_count, mom_new_lo_count = get_cs_price_change_count(df, '新建商品住宅销售价格指数(上月=100)')
yoy_new_hi_count, yoy_new_eq_count, yoy_new_lo_count = get_cs_price_change_count(df, '新建商品住宅销售价格指数(上年同月=100)')
mom_seh_hi_count, mom_seh_eq_count, mom_seh_lo_count = get_cs_price_change_count(df, '二手住宅销售价格指数(上月=100)')
yoy_seh_hi_count, yoy_seh_eq_count, yoy_seh_lo_count = get_cs_price_change_count(df, '二手住宅销售价格指数(上年同月=100)')
datas = [
[[[t,cs300,'沪深300','color=black'],],
[[t1,mom_new_lo_count,'新建商品住宅销售价格 下跌城市个数','color=darkgreen'],
[t1,mom_new_hi_count,'新建商品住宅销售价格 上涨城市个数','color=red'],
],''],]
plot_many_figure(datas, start_time='2012-01-01')
if __name__=="__main__":
# # 股指 和 房价
plot_cs300_vs_house_price()
# 股指 和 中美利差
plot_cs300_vs_rate()
# 风险溢价
test1()
# # 融资融券
# test2()
# cs300, MSCI EM 持仓
test3()
# 股指 和 地产美元债ETF
test4()
## 申万行业 和 地产美元债ETF
# test5()
# 大盘拥挤度 https://legulegu.com/stockdata/ashares-congestion
test6()
# # RMBS条件早偿率指数
# plot_rmbs()
# # CDS
plot_china_cds()