from IPython.display import Markdown
import pandas as pd
from tulip.core.collection import TulipCollection
from tulip.data.bloomberg import BloombergClient as bb
from tulip.data.haver import HaverClient as hc
from tulip.plots import plot_area, plot_lines

from tulip.data.gs import GSClient as gs

import pycountry

from tulip.analysis.country_related.analytics import summarize_gs_eco_fct

ISO_2 = "CA"

pyctry = pycountry.countries.get(alpha_2=ISO_2)

Canada¶

Activity Indicators¶

Economic Forecasts (Brokers)¶

gs_eco_fct = gs.get_eco_forecast(geographyId=ISO_2)
gs_summary = summarize_gs_eco_fct(gs_eco_fct)
gs_summary = gs_summary[~gs_summary.index.str.contains("ngdp")].to_frame().T
gs_summary.style.set_caption(f"Goldman {pyctry.name} Economic Forecasts").format(
    precision=2
)

Current Activity Indicator (Goldman)¶

cai_series_soft_vs_hard = gs.get_CAI_series(
    geographyId=ISO_2,
    metricName=[
        "CAI_HEADLINE",
        "CAI_CONTRIBUTION_TYPE_HARD",
        "CAI_CONTRIBUTION_TYPE_SOFT",
    ],
    startDate="1980-01-01",
)
cai_series_soft_vs_hard = cai_series_soft_vs_hard.set_index("metricName", append=True)[
    "metricValue"
].unstack("metricName")
cai_series_soft_vs_hard.columns = ["Hard", "Soft", "Headline"]
cai_plot = plot_lines(
    cai_series_soft_vs_hard,
    show_0=True,
    title=f"<b>{pyctry.name} Current Activity Indicator</b> Updated: {pd.Timestamp.today().strftime('%Y-%m-%d')}",
    years_limit=4,
)
cai_plot

IFSD Nowcast¶

IFSD was founded in 2016 by Kevin Page, Canada’s first Parliamentary Budget Officer. IFSD is both a non-profit, Canadian think-tank and a global advisory practice

ifs_nowcast = bb.get_series("IFSDCAST Index")
ifs_nowcast.plot(years_limit=2, tick_suffix="%")

Retail Sales¶

retail_sales = bb.get_series("CARSCHNG Index")
retail_sales.info.title = "Retail Sales MoM"
retail_sales.plot(years_limit=2, tick_suffix="%")

PMIs¶

pmi = {
    "MPMICASA Index": "S&P Manufacturing PMI",
    "MPMICACA Index": "S&P Composite PMI",
    "IVEYSA   Index": "Ivey PMI",
}

pmi_collection = bb.create_collection(list(pmi.keys()))
for i, k in enumerate(pmi.values()):
    pmi_collection[i].info.title = k

pmi_collection.dashboard.table()

pmi_collection.dashboard.plots(hlines=50)

Growth Stats¶

Real GDP Growth¶

real_gdp_yoy = hc.get_series("V6E05783@CANADA")
real_gdp_yoy.ts = real_gdp_yoy.ts.pct_change(4)
real_gdp_yoy.plot(show_0=True, tick_format="0.0%")

GDP Detail¶

growth_stats = {
    "Real GDP": "yryr%(V6E05752@CANADA)",  # Canada: Real Gross Domestic Product at Market Prices
    "Fixed Investment": "yryr%(V6E05732@CANADA)",  # Canada: GDP: Gross Fixed Capital Formation
    "Inventories": "V6E05741@CANADA",  # Canada: GDP: Investment in Inventories
    "Consumption": "yryr%(V6E05723@CANADA)",  # Canada: GDP: Final Consumption Expenditure
    "Exports": "yryr%(V6E05745@CANADA)",  # Canada: GDP: Exports of Goods and Services
    "Imports": "yryr%(V6E05748@CANADA)",  # Canada: GDP: Imports of Goods and Services
}

growth_stats_collection = []
for k, v in growth_stats.items():
    growth_stats_collection.append(hc.get_series(v))

growth_stats_collection = TulipCollection(growth_stats_collection)
growth_stats_collection["yryr%(V6E05748@CANADA)"].good_is = -1
growth_stats_collection.dashboard.table()

growth_stats_collection.dashboard.plots(show_0=True)

Nominal GDP¶

#  Nominal GDP Growth
hc.get_series("yryr%(V6E05783@CANADA)").plot(show_0=True)  # Nominal GDP Growth

Prices¶

prices = {
    "CPI-Trim": "V1M85715@CANADA",
    "CPI-Median": "V1M85714@CANADA",
    "CPI-Common": "V1M85713@CANADA",
    "CPI YoY": "n156cy@g10",
    "Wages YoY": "canewcl@CANADA",
    "Inflation Expectations 1Y": "CANVXI1@CANADA",  # CSCE
    "Wage Growth Expectations 1Y": "CANVXW1@CANADA",
}

prices_collection = []
for k, v in prices.items():
    prices_collection.append(hc.get_series(v))

prices_collection = TulipCollection(prices_collection)
prices_collection.dashboard.table()

prices_df = prices_collection.df
prices_df.columns = list(prices.keys())
fig = plot_lines(
    prices_df.iloc[:, :3],
    years_limit=5,
    title="<b>Core Inflation Measures</b> (Preferred by the BoC)",
    tick_suffix="%",
)
fig.add_hline(2, line_width=1, annotation_text="Target")  # , annotation="Target")

fig = plot_lines(
    prices_df.iloc[:, -3:].ffill(),
    years_limit=5,
    title="<b>Inflation Surveys</b>",
    tick_suffix="%",
)
fig.add_hline(2, line_width=1, annotation_text="Target")  # , annotation="Target")

business_outlook_survey_prices_dict = {
    "Below 1 percent": "B156IE1@CANADA",
    "1 to 2 percent": "B156IE2@CANADA",
    "2 to 3 percent": "B156IE3@CANADA",
    "Above 3 percent": "B156IE4@CANADA",
    "No response": "B156IE5@CANADA",
}

business_outlook_survey_prices = []
for k, v in business_outlook_survey_prices_dict.items():
    business_outlook_survey_prices.append(hc.get_series(v))

business_outlook_survey_prices = TulipCollection(business_outlook_survey_prices)
business_outlook_survey_prices.dashboard.table()

bos_prices = business_outlook_survey_prices.df
bos_prices.columns = list(business_outlook_survey_prices_dict.keys())

# Define custom colors for each category
colors = {
    "Above 3 percent": "red",
    "2 to 3 percent": "coral",
    "1 to 2 percent": "lightgreen",
    "Below 1 percent": "green",
    "No response": "gray",
}

fig = plot_area(
    bos_prices,
    title="<b>Business Outlook Survey - Inflation Expectations</b>",
    tick_suffix="%",
    logo=False,
)

for trace in fig.data:
    if trace.name in colors:
        trace.line.color = colors[trace.name]

fig.show()

Other faster, idiosyncratic price data points available in Canada

weekly_price = {
    "Commodity Price Index": "P156CP@INTWKLY",
    "Commodity Price Index Ex Energy": "P156CPX@INTWKLY",
    "Gasoline Price": "P156GRT@INTWKLY",
    "Pork Prices": "P156HGV@INTWKLY",
}

weekly_price_collection = []
for k, v in weekly_price.items():
    weekly_price_collection.append(hc.get_series(v))

weekly_price_collection = TulipCollection(weekly_price_collection)
weekly_price_collection.dashboard.table()

Employment¶

# Employment metrics
employment = {
    "EI Claims": "E156CEA@INTWKLY",  # Canada: Employment Insurance: Claims Established
    "EI Recipients": "E156ATA@INTWKLY",  # Canada: Employment Insurance: Active Recipients
    "Job Loss Probability": "CANVXJL@CANADA",  # Canada: Consumer Expectations Survey: Prob of Losing Job
}

employment_collection = []
for k, v in employment.items():
    employment_collection.append(hc.get_series(v))

employment_collection = TulipCollection(employment_collection)
employment_collection.good_is = {
    "E156CEA@INTWKLY": -1,  # Lower claims is good
    "E156ATA@INTWKLY": -1,  # Lower recipients is good
    "CANVXJL@CANADA": -1,  # Lower probability of job loss is good
}
employment_collection.dashboard.table()

employment_collection.dashboard.plots()

Output Gap¶

output_gap = hc.get_series("B156IGAP@CANADA")
output_gap.plot(show_0=True, tick_suffix="%")

weekly_price_collection.dashboard.plots(years_limit=3)

Trade¶

trade_stats = {
    "Current Account Balance % GDP": "s156bcpg@g10",
}

trade_collection = []
for k, v in trade_stats.items():
    trade_collection.append(hc.get_series(v))

trade_collection = TulipCollection(trade_collection)
trade_collection.dashboard.table()

trade_collection.dashboard.plots(show_0=True)

Surveys¶

surveys = {
    "Business Barometer 3M": "CFO3@CANADA",  # Canada: Business Barometer Index: 3 Month Outlook
    "Business Barometer 1Y": "CFAA@CANADA",  # Business Barometer Index: Canada
    "Policy Uncertainty": "CEPUIN@CANADA",  # Canada: News-Based Policy Uncertainty Index
}

surveys_collection = []
for k, v in surveys.items():
    surveys_collection.append(hc.get_series(v))

surveys_collection = TulipCollection(surveys_collection)
surveys_collection.good_is = {
    "CFO3@CANADA": 1,  # Higher business confidence is good
    "CFAA@CANADA": 1,  # Higher business confidence is good
    "CEPUIN@CANADA": -1,  # Lower uncertainty is good
}
surveys_collection.dashboard.table()

surveys_collection.dashboard.plots(years_limit=3, hlines=[50, 50])

Credit Creation¶

debt_stats = {
    "Non-Financial Debt % GDP": "S156zdnp@g10",
    "Household Debt to Disposable Income": "S156zdhi@g10",
    "Household Debt % GDP": "S156zdhp@g10",
}

debt_collection = []
for k, v in debt_stats.items():
    debt_collection.append(hc.get_series(v))

debt_collection = TulipCollection(debt_collection)
debt_collection.dashboard.table()

debt_collection.dashboard.plots()

Budget¶

government_finance = {
    "Public Debt": "N156FDP@G10",  # Canada: Business Barometer Index: 3 Month Outlook
    "Govt Budget Surplus/Deficit": "H156FGBP@G10",  # Business Barometer Index: Canada
}

government_finance_collection = []
for k, v in government_finance.items():
    government_finance_collection.append(hc.get_series(v))

government_finance_collection = TulipCollection(government_finance_collection)

government_finance_collection.dashboard.table()

government_finance_collection.dashboard.plots()

Asset Pricing¶

# Asset Piricing

# Yields and Stock

from tulip_mania.notebook_related import notebook_updated

notebook_updated()