合并两个图表（美国国债数据和美元 SPX）

Question

图表1

该程序创建了美国财政部发行的票据与（票据+债券）比率的图表。

import requests
import pandas as pd

from bokeh.plotting import figure, show
from bokeh.models   import NumeralTickFormatter, HoverTool
# ---------------------------------------------------------------------
page_size = 10000

url = 'https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/auctions_query'

params = {
    'fields' : 'record_date,issue_date,maturity_date,security_type,total_accepted',
    'filter' : 'record_date:gte:1900-01-01',
    'page[size]' : page_size
}

response = requests.get(url, params=params)

result_json = response.json()

df = pd.DataFrame(result_json['data'])
# ----------------------------------------------------------------------
df['record_date']   = pd.to_datetime(df['record_date'])
df['issue_date']    = pd.to_datetime(df['issue_date']) 
df['maturity_date'] = pd.to_datetime(df['maturity_date'])
df['auction_date']  = pd.to_datetime(df['auction_date'])

df['total_accepted'] = pd.to_numeric(df['total_accepted'], errors='coerce')

df['total_accepted_neg'] = df['total_accepted'] * -1
# ----------------------------------------------------------------------
bills = df[df['security_type'] == 'Bill']
notes = df[df['security_type'] == 'Note']
bonds = df[df['security_type'] == 'Bond']
# ----------------------------------------------------------------------
freq='Q'
# ----------------------------------------------------------------------
bills_issued   = bills.groupby(pd.Grouper(key='issue_date',    freq=freq))['total_accepted'].sum().to_frame()
notes_issued   = notes.groupby(pd.Grouper(key='issue_date',    freq=freq))['total_accepted'].sum().to_frame()
bonds_issued   = bonds.groupby(pd.Grouper(key='issue_date',    freq=freq))['total_accepted'].sum().to_frame()
# ----------------------------------------------------------------------
bills_notes_bonds_issued = bills_issued.merge(notes_issued, how='outer', on='issue_date').merge(bonds_issued, how='outer', on='issue_date')

bills_notes_bonds_issued.columns = ['bills', 'notes', 'bonds']

bills_notes_bonds_issued['bills_notes_ratio']  = bills_notes_bonds_issued['bills'] / bills_notes_bonds_issued['notes']

bills_notes_bonds_issued['bills_notes_bonds_ratio']  = bills_notes_bonds_issued['bills'] / (bills_notes_bonds_issued['notes'] + bills_notes_bonds_issued['bonds'])
# ----------------------------------------------------------------------
p = figure(title=f'Treasury Securities Auctions Data : {freq}', sizing_mode='stretch_both', x_axis_type='datetime', x_axis_label='date', y_axis_label='')

p.add_tools(HoverTool(
    tooltips=[ 
        ('issue_date',     '@issue_date{%F}'),
        ('total_accepted', '@total_accepted{$0.0a}') 
    ], 
    formatters={ '@issue_date': 'datetime' }))

p.yaxis.formatter = NumeralTickFormatter(format='0a')

p.line(x='issue_date', y='bills_notes_ratio', color='black', legend_label='Bills/Notes ratio', source=bills_notes_bonds_issued)

p.legend.click_policy = 'hide'

p.legend.location = 'top_left'

show(p)
# ----------------------------------------------------------------------

图表2

该程序绘制了标准普尔 500 指数图表：

import pandas as pd

from bokeh.plotting import figure, show
from bokeh.models   import NumeralTickFormatter, HoverTool

import yfinance as yf
# ---------------------------------------------------------------------
spx = yf.Ticker('^GSPC')

data = spx.history(start='1980-01-01', interval='1d')
# ----------------------------------------------------------------------
p = figure(title=f'SPX', sizing_mode='stretch_both', x_axis_type='datetime', x_axis_label='date', y_axis_label='price')

p.yaxis.formatter = NumeralTickFormatter(format='0a')

p.legend.click_policy = 'hide'

p.legend.location = 'top_left'

p.line(x='Date', y='Close', color='red', legend_label='S&P 500', source=data)

show(p)

问题

在同一张图表上绘制图表 1 和 2 的数据有什么好方法？

Answer 1

这是一种似乎有效的方法。

看来我需要明确指定框 y 轴的

y

范围。

# ----------------------------------------------------------------------
import requests
import pandas as pd

from bokeh.plotting import figure, show
from bokeh.models   import NumeralTickFormatter, HoverTool

from bokeh.models import LinearAxis, Range1d

import yfinance as yf
# ---------------------------------------------------------------------
page_size = 10000

url = 'https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/auctions_query'

params = {
    'fields' : 'record_date,issue_date,maturity_date,security_type,total_accepted',
    'filter' : 'record_date:gte:1900-01-01',
    'page[size]' : page_size
}

response = requests.get(url, params=params)

result_json = response.json()

df = pd.DataFrame(result_json['data'])
# ----------------------------------------------------------------------
df['record_date']   = pd.to_datetime(df['record_date'])
df['issue_date']    = pd.to_datetime(df['issue_date']) 
df['maturity_date'] = pd.to_datetime(df['maturity_date'])
df['auction_date']  = pd.to_datetime(df['auction_date'])

df['total_accepted'] = pd.to_numeric(df['total_accepted'], errors='coerce')

df['total_accepted_neg'] = df['total_accepted'] * -1
# ----------------------------------------------------------------------
bills = df[df['security_type'] == 'Bill']
notes = df[df['security_type'] == 'Note']
bonds = df[df['security_type'] == 'Bond']
# ----------------------------------------------------------------------
freq='Q'
# ----------------------------------------------------------------------
bills_issued   = bills.groupby(pd.Grouper(key='issue_date',    freq=freq))['total_accepted'].sum().to_frame()
notes_issued   = notes.groupby(pd.Grouper(key='issue_date',    freq=freq))['total_accepted'].sum().to_frame()
bonds_issued   = bonds.groupby(pd.Grouper(key='issue_date',    freq=freq))['total_accepted'].sum().to_frame()
# ----------------------------------------------------------------------
bills_notes_bonds_issued = bills_issued.merge(notes_issued, how='outer', on='issue_date').merge(bonds_issued, how='outer', on='issue_date')

bills_notes_bonds_issued.columns = ['bills', 'notes', 'bonds']

bills_notes_bonds_issued['bills_notes_ratio']  = bills_notes_bonds_issued['bills'] / bills_notes_bonds_issued['notes']

bills_notes_bonds_issued['bills_notes_bonds_ratio']  = bills_notes_bonds_issued['bills'] / (bills_notes_bonds_issued['notes'] + bills_notes_bonds_issued['bonds'])
# ----------------------------------------------------------------------
spx = yf.Ticker('^GSPC')

data = spx.history(start='1980-01-01', interval='1d')
# ----------------------------------------------------------------------
p = figure(title=f'Treasury Securities Auctions Data : {freq}', sizing_mode='stretch_both', x_axis_type='datetime', x_axis_label='date', y_axis_label='', y_range=(0, 10))

p.line(x='issue_date', y='bills_notes_ratio', color='black', legend_label='Bills/Notes ratio', source=bills_notes_bonds_issued)

p.extra_y_ranges = {"spx": Range1d(start=min(data['Close']), end=max(data['Close']))}

p.add_layout(LinearAxis(y_range_name="spx", axis_label='SPX'), 'right')

p.line(x='Date', y='Close', color='blue', legend_label='SPX', source=data, y_range_name="spx")

p.legend.click_policy = 'hide'

p.legend.location = 'top_left'

show(p)