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rate.py
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rate.py
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import os
import time
import numpy as np
import akshare as ak
import pandas as pd
import datetime
from bokeh.io import output_file, show
from bokeh.layouts import column
from bokeh.plotting import figure
from bokeh.models import LinearAxis, Range1d, VBar, NumeralTickFormatter
from scipy.stats import linregress
from utils import *
from fredapi import Fred
from chinamoney import *
from fx import plot_cny
# 国内的一些利率
def plot_china_rate():
path = os.path.join(interest_rate_dir, '国债收益率'+'.csv')
df = pd.read_csv(path)
gov_t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
cn01y = np.array(df['1Y'], dtype=float)
cn02y = np.array(df['2Y'], dtype=float)
cn05y = np.array(df['5Y'], dtype=float)
cn10y = np.array(df['10Y'], dtype=float)
cn30y = np.array(df['30Y'], dtype=float)
path = os.path.join(interest_rate_dir, '同业存单'+'.csv')
df = pd.read_csv(path)
ib_t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
ib03m = np.array(df['AAA:3M'], dtype=float)
ib01y = np.array(df['AAA:1Y'], dtype=float)
path = os.path.join(interest_rate_dir, 'shibor'+'.csv')
df = pd.read_csv(path)
shibor_t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
shiboron = np.array(df['ON'], dtype=float)
shibor3m = np.array(df['3M'], dtype=float)
path = os.path.join(interest_rate_dir, '回购定盘利率'+'.csv')
df = pd.read_csv(path)
fr_t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
fr007 = np.array(df['FR007'], dtype=float)
fdr007 = np.array(df['FDR007'], dtype=float)
path = os.path.join(interest_rate_dir, '地方政府债'+'.csv')
df = pd.read_csv(path)
lgfv_t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
lgfv_1Y = np.array(df['AAA-:1Y'], dtype=float)
path = os.path.join(interest_rate_dir, 'LPR'+'.csv')
df = pd.read_csv(path)
lpr_t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
lpr1y = np.array(df['1Y'], dtype=float)
lpr5y = np.array(df['5Y'], dtype=float)
# rrp007 = np.array(df['公开市场操作:逆回购:7天:中标利率'], dtype=float)
# mlf = np.array(df['中期借贷便利(MLF):操作利率:1年'], dtype=float)
# rr = np.array(df['人民币存款准备金率:大型存款类金融机构(变动日期)'], dtype=float)
datas = [
[[[gov_t,cn02y,'国债收益率:2Y',''],
[gov_t,cn05y,'国债收益率:5Y',''],
[gov_t,cn10y,'国债收益率:10Y',''],
[gov_t,cn30y,'国债收益率:30Y','']],[],''],
[[[ib_t,ib03m,'同业存单AAA:3M',''],
[ib_t,ib01y,'同业存单AAA:1Y',''],
[gov_t,cn01y,'国债收益率:1Y',''],
[fr_t,fr007,'FR007',''],
[fr_t,fdr007,'FDR007',''],],[],''],
[[[ib_t,ib03m,'同业存单AAA:3M',''],
[ib_t,ib01y,'同业存单AAA:1Y',''],
[shibor_t,shiboron,'SHIBOR:ON',''],
[shibor_t,shibor3m,'SHIBOR:3M',''],],[],''],
[[[lpr_t,ib03m,'LPR:1Y',''],
[lpr_t,ib01y,'LPR:5Y',''],],[],''],
]
plot_many_figure(datas, start_time='2015-01-01')
t1, diff1 = data_sub(gov_t, cn10y, ib_t, ib01y)
datas = [
[[[gov_t,cn10y,'国债收益率:10Y',''],
[ib_t,ib01y,'同业存单AAA:1Y',''],],[],''],
[[[t1, diff1,'国债收益率:10Y - 同业存单AAA:1Y','style=vbar']],[],''],
]
plot_many_figure(datas, start_time='2015-01-01')
t1, diff = data_sub(lgfv_t, lgfv_1Y, gov_t, cn01y)
datas = [
[[[t1, diff,'地方政府债AAA-:1Y - CN1Y','color=black'],
],
[],''],
[[[lgfv_t,lgfv_1Y,'地方政府债AAA-:1Y',''],
[gov_t,cn01y,'国债收益率:1Y','']
],
[],''],
]
plot_many_figure(datas, start_time='2015-01-01')
# (社融同比 - M2同比) 和 同业存单利率,国债利率
def z():
start_time = '2010-2-1'
end_time = '2029-10-10'
path = os.path.join(data_dir, '利率'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
ib_deposit_rate = np.array(df['同业存单(AAA)收盘到期收益率:1年'], dtype=float)
cn10y = np.array(df['中国国债收益率10年'], dtype=float)
t1, ib_deposit_rate = get_period_data(t, ib_deposit_rate, start_time, end_time, remove_nan=True)
t5, cn10y = get_period_data(t, cn10y, start_time, end_time, remove_nan=True)
path2 = os.path.join(data_dir, '货币供应量'+'.csv')
df2 = pd.read_csv(path2).fillna('0')
t2 = pd.DatetimeIndex(pd.to_datetime(df2['time'], format='%Y-%m'))
m2 = np.array(df2['M2'], dtype=float)
t2, m2 = get_period_data(t2, m2, start_time, end_time)
path3 = os.path.join(data_dir, '社会融资规模'+'.csv')
df3 = pd.read_csv(path3).fillna('0')
t3 = pd.DatetimeIndex(pd.to_datetime(df3['time'], format='%Y-%m'))
sf = np.array(df3['社会融资规模存量'], dtype=float)
t3, sf = get_period_data(t3, sf, start_time, end_time)
t2_yoy, m2_yoy = yoy(t2, m2)
t3_yoy, sf_yoy = yoy(t3, sf)
t_yoy, dff_yoy = data_sub(t3_yoy, sf_yoy, t2_yoy, m2_yoy)
fig1 = figure(frame_width=1800, frame_height=300, tools=TOOLS, x_axis_type = "datetime")
fig1.line(t_yoy, dff_yoy, line_width=2, line_color='black', legend_label='社融同比 - M2同比')
fig1.xaxis[0].ticker.desired_num_ticks = 20
fig1.legend.click_policy="hide"
fig2 = figure(frame_width=1800, frame_height=300, tools=TOOLS, x_range=fig1.x_range, x_axis_type = "datetime", y_axis_location="right")
fig2.line(t1, ib_deposit_rate/100, line_width=2, line_color='orange', legend_label='同业存单利率AAA 1年')
fig2.line(t5, cn10y/100, line_width=2, line_color='blue', legend_label='中国国债收益率:10年')
fig2.yaxis[0].formatter = NumeralTickFormatter(format='0.0%')
fig2.xaxis[0].ticker.desired_num_ticks = 20
fig2.legend.click_policy="hide"
show(column(fig1,fig2))
# HK
def plot_hk_rate():
start_time = '2015-1-1'
path = os.path.join(hkma_dir, 'hibor'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
hibor_on = np.array(df['ir_overnight'], dtype=float)
hibor_1m = np.array(df['ir_1m'], dtype=float)
hibor_3m = np.array(df['ir_3m'], dtype=float)
hibor_1y = np.array(df['ir_12m'], dtype=float)
path = os.path.join(data_dir, 'libor'+'.csv')
df = pd.read_csv(path)
t3 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
libor_3m = np.array(df['3M'], dtype=float)
path = os.path.join(interest_rate_dir, 'federal_fund_rate'+'.csv')
df = pd.read_csv(path)
t5 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
effr = np.array(df['Federal Funds Effective Rate'], dtype=float)
t4, diff_3m = data_sub(t3, libor_3m, t, hibor_3m)
t6, diff_on = data_sub(t5, effr, t, hibor_on)
path = os.path.join(hkma_dir, 'USDHKD'+'.csv')
df = pd.read_csv(path)
t1 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
usdhkd = np.array(df['hkd_fer_spot'], dtype=float)
path = os.path.join(hkma_dir, 'market_operation'+'.csv')
df = pd.read_csv(path)
t2 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
balance = np.array(df['closing_balance'], dtype=float)
path = os.path.join(data_dir, '恒生指数'+'.csv')
df = pd.read_csv(path)
t01 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
hsi = np.array(df['close'], dtype=float)
path = os.path.join(data_dir, '恒生科技指数'+'.csv')
df = pd.read_csv(path)
t02 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
hstect = np.array(df['close'], dtype=float)
path = os.path.join(data_dir, '恒生中国企业指数'+'.csv')
df = pd.read_csv(path)
t03 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
hscei = np.array(df['close'], dtype=float)
datas = [
[[[t,hibor_3m,'hibor ON',''],
[t5,effr,'EFFR',''],],
[[t6,diff_on, 'libor ON - EFFR','style=vbar'],],''],
[[[t,hibor_3m,'hibor 3M',''],
[t3,libor_3m,'libor 3M',''],],
[[t4,diff_3m, 'libor - hibor','style=vbar'],],''],
[[[t1,usdhkd,'USDHKD','color=black'],], [],''],
[[[t02,hstect,'HSTECH','color=black'],], [],''],
[[[t03,hscei,'HSCEI','color=black'],], [],''],
[[[t2,balance,'HKMA BALANCE','color=red'],], [],''],
]
plot_many_figure(datas, max_height=1000, start_time=start_time)
# 汇率 和 利差
def plot_fx_vs_rate():
path = os.path.join(fx_dir, 'USDCNY'+'.csv')
usdcny_df = pd.read_csv(path)
usdcny_t = pd.DatetimeIndex(pd.to_datetime(usdcny_df['time'], format='%Y-%m-%d'))
usdcny = np.array(usdcny_df['close'], dtype=float)
path = os.path.join(fx_dir, 'USDJPY'+'.csv')
usdjpy_df = pd.read_csv(path)
usdjpy_t = pd.DatetimeIndex(pd.to_datetime(usdjpy_df['time'], format='%Y-%m-%d'))
usdjpy = np.array(usdjpy_df['close'], dtype=float)
path = os.path.join(interest_rate_dir, 'us_yield_curve'+'.csv')
us_df = pd.read_csv(path)
us_t = pd.DatetimeIndex(pd.to_datetime(us_df['time'], format='%Y-%m-%d'))
us10y = np.array(us_df['10Y'], dtype=float)
path = os.path.join(interest_rate_dir, '国债收益率'+'.csv')
cn_df = pd.read_csv(path)
cn_t = pd.DatetimeIndex(pd.to_datetime(cn_df['time'], format='%Y-%m-%d'))
cn10y = np.array(cn_df['10Y'], dtype=float)
path = os.path.join(interest_rate_dir, 'jgb'+'.csv')
jp_df = pd.read_csv(path)
jp_t = pd.DatetimeIndex(pd.to_datetime(jp_df['time'], format='%Y-%m-%d'))
jp10y = np.array(jp_df['10Y'], dtype=float)
us_cn_diff_t, us_cn_diff = data_sub(us_t, us10y, cn_t, cn10y)
us_jp_diff_t, us_jp_diff = data_sub(us_t, us10y, jp_t, jp10y)
datas = [
[[[usdcny_t,usdcny,'USDCNY',''],
],
[[us_cn_diff_t,us_cn_diff,'US10Y - CN10Y',''],],''],
[[[usdjpy_t,usdjpy,'USDJPY',''],
],
[[us_jp_diff_t,us_jp_diff,'US10Y - JP10Y',''],],''],
[[[us_t,us10y,'US10Y',''],
[cn_t,cn10y,'CN10Y',''],
[jp_t,jp10y,'JP10Y',''],
],
[],''],
]
plot_many_figure(datas, start_time='2012-11-01')
datas = [
[[[usdcny_t,usdcny,'USDCNY',''],
],
[[usdjpy_t,usdjpy,'USDJPY',''],],''],
]
plot_many_figure(datas, start_time='2012-11-01')
def test5():
path = os.path.join(data_dir, '利率'+'.csv')
df = pd.read_csv(path)
t0 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
us10y = np.array(df['美国:国债收益率:10年'], dtype=float)
eu10y = np.array(df['欧元区:公债收益率:10年'], dtype=float)
t01, us_eu10y = data_sub(t0, us10y, t0, eu10y)
path = os.path.join(data_dir, '汇率'+'.csv')
df = pd.read_csv(path)
t1 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
eurusd = 1/np.array(df['美元兑欧元'], dtype=float)
fig1 = figure(frame_width=1800, frame_height=300, tools=TOOLS, x_axis_type = "datetime", y_axis_location="right")
fig1.line(t1, eurusd, line_width=2, line_color='orange', legend_label='EURUSD')
fig1.xaxis[0].ticker.desired_num_ticks = 20
fig1.legend.click_policy="hide"
fig2 = figure(frame_width=1800, frame_height=300, tools=TOOLS, x_axis_type = "datetime", x_range=fig1.x_range, y_axis_location="right")
fig2.line(t01, us_eu10y, line_width=2, line_color='orange', legend_label='US10Y-EU10Y')
fig2.xaxis[0].ticker.desired_num_ticks = 20
fig2.legend.click_policy="hide"
show(column(fig1,fig2))
def get_central_bank_interest_rate():
# 美联储
df = ak.macro_bank_usa_interest_rate()
prefix = df['商品'][0]
df.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df.drop(['商品'], axis=1, inplace=True)
# 欧洲央行
df1 = ak.macro_bank_euro_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
# 新西兰联储
df1 = ak.macro_bank_newzealand_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
# 中国人民银行
df1 = ak.macro_bank_china_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
# 瑞士央行
df1 = ak.macro_bank_switzerland_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
# 英国央行
df1 = ak.macro_bank_english_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
# 澳洲联储
df1 = ak.macro_bank_australia_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
# 俄罗斯
df1 = ak.macro_bank_russia_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
# 印度
df1 = ak.macro_bank_india_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
# 巴西
df1 = ak.macro_bank_brazil_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
# 日本
df1 = ak.macro_bank_japan_interest_rate()
prefix = df1['商品'][0]
df1.rename(columns={'日期':'time', '今值':prefix+'_今值', '预测值':prefix+'_预测值', '前值':prefix+'_前值'}, inplace=True)
df1.drop(['商品'], axis=1, inplace=True)
df = pd.merge(df, df1, on='time', how='outer')
df['time'] = df['time'].apply(lambda x:pd.to_datetime(x, format='%Y%m%d'))
df.sort_values(by = 'time', inplace=True)
df['time'] = df['time'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d'))
path = os.path.join(data_dir, '各国央行利率决议'+'.csv')
df.to_csv(path, encoding='utf-8', index=False)
def plot_central_bank_interest_rate():
path = os.path.join(data_dir, '各国央行利率决议'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
data1 = np.array(df['美联储利率决议_今值'], dtype=float)
data2 = np.array(df['欧元区利率决议_今值'], dtype=float)
data3 = np.array(df['中国人民银行利率报告_今值'], dtype=float)
data4 = np.array(df['澳大利亚利率决议报告_今值'], dtype=float)
data5 = np.array(df['印度利率决议报告_今值'], dtype=float)
data6 = np.array(df['英国利率决议报告_今值'], dtype=float)
data7 = np.array(df['新西兰利率决议报告_今值'], dtype=float)
data8 = np.array(df['巴西利率决议报告_今值'], dtype=float)
data9 = np.array(df['日本利率决议报告_今值'], dtype=float)
datas = [[t, data1, '美联储利率决议_今值'],
[t, data2, '欧元区利率决议_今值'],
[t, data3, '中国人民银行利率报告_今值'],
[t, data4, '澳大利亚利率决议报告_今值'],
[t, data5, '印度利率决议报告_今值'],
[t, data6, '英国利率决议报告_今值'],
[t, data7, '新西兰利率决议报告_今值'],
[t, data8, '巴西利率决议报告_今值'],
[t, data9, '日本利率决议报告_今值'],]
plot_one_figure(datas)
def plot_us_inflation_expectattion_vs_rate():
path = os.path.join(interest_rate_dir, 'us_yield_curve'+'.csv')
df = pd.read_csv(path)
t0 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
us10y = np.array(df['10Y'], dtype=float)
path = os.path.join(interest_rate_dir, 'us_inflation_expectation'+'.csv')
df = pd.read_csv(path)
t1 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
ie10y = np.array(df['10Y'], dtype=float)
if __name__=="__main__":
plot_china_rate()
# plot_cny()
# plot_fx_vs_rate()
# plot_hk_rate()
# test2()
# test5()
# plot_china_cds()
# get_central_bank_interest_rate()
# plot_central_bank_interest_rate()
pass