Macro Examples¶
© 2025 Jim Domeij & Marek Ozana
These examples show how to use polars-bloomberg for macro analysis, inspired by ideas from my friend Jim Domeij.
Thank you, Jim, for contributing your perspective and making it possible for others to learn from your macro thinking.
In [1]:
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import altair as alt
import polars as pl
from polars_bloomberg import BQuery
import altair as alt
import polars as pl
from polars_bloomberg import BQuery
Visualizing S&P 500 Valuation Scenarios¶
This chart shows potential S&P 500 price levels and returns for the next 12 months. It uses a scenario matrix driven by two factors: Forward P/E ratios (vertical axis, from historical averages) and Forward EPS growth (horizontal axis, centered on consensus). Each cell displays the resulting index price and percentage return from the current price. This helps investors visualize risk and potential upside under different market conditions.
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query = """
let(
#last_price = px_last();
#12m_trail_eps = headline_eps_market(fpt=LTM);
#12m_fwd_eps = headline_eps_market(fpt=BT, fpo=1);
#epsg_consensus = #12m_fwd_eps/#12m_trail_eps - 1;
#current_pe = headline_pe_ratio(fpt=BT, fpo=1);
#5y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-5Y, 0D, frq=W)));
#10y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-10Y, 0D, frq=W)));
#20y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-20Y, 0D, frq=W)));
#35y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-35Y, 0D, frq=W)));
#max_pe = max(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-35Y, 0D, frq=W)));
)
get(
#last_price,
#12m_trail_eps,
#12m_fwd_eps,
#epsg_consensus,
#current_pe,
#5y_avg_pe,
#10y_avg_pe,
#20y_avg_pe,
#35y_avg_pe,
#max_pe
)
preferences(dropCols=['CURRENCY', 'DATE', 'AS_OF_DATE', 'REVISION_DATE', 'PERIOD_END_DATE'])
for('SPX Index')
""" # noqa: E501
with BQuery() as bq:
res = bq.bql(query)
df = res.combine()
df
query = """
let(
#last_price = px_last();
#12m_trail_eps = headline_eps_market(fpt=LTM);
#12m_fwd_eps = headline_eps_market(fpt=BT, fpo=1);
#epsg_consensus = #12m_fwd_eps/#12m_trail_eps - 1;
#current_pe = headline_pe_ratio(fpt=BT, fpo=1);
#5y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-5Y, 0D, frq=W)));
#10y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-10Y, 0D, frq=W)));
#20y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-20Y, 0D, frq=W)));
#35y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-35Y, 0D, frq=W)));
#max_pe = max(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-35Y, 0D, frq=W)));
)
get(
#last_price,
#12m_trail_eps,
#12m_fwd_eps,
#epsg_consensus,
#current_pe,
#5y_avg_pe,
#10y_avg_pe,
#20y_avg_pe,
#35y_avg_pe,
#max_pe
)
preferences(dropCols=['CURRENCY', 'DATE', 'AS_OF_DATE', 'REVISION_DATE', 'PERIOD_END_DATE'])
for('SPX Index')
""" # noqa: E501
with BQuery() as bq:
res = bq.bql(query)
df = res.combine()
df
Out[ ]:
shape: (1, 11)
| ID | #last_price | #12m_trail_eps | #12m_fwd_eps | #epsg_consensus | #current_pe | #5y_avg_pe | #10y_avg_pe | #20y_avg_pe | #35y_avg_pe | #max_pe |
|---|---|---|---|---|---|---|---|---|---|---|
| str | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 |
| "SPX Index" | 6850.44 | 269.074714 | 306.176329 | 0.137886 | 22.36133 | 20.048068 | 18.824081 | 16.315795 | 16.525469 | 24.071401 |