S&P 500 Earning Revisions Breadth¶

This calculates, for each GICS sector within the S&P 500, the number of companies with upward or downward EPS revisions over the past month. It can be used to track the breadth of change in analysts’ EPS estimates by sector.

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# Get counts of comps with positive and negative earnings revisions over the past month
from polars_bloomberg import BQuery

query = """
    let(
        #rev = net_chg(is_eps(fpt=BT, fpo=1, ae=e, dates=range(-1M, 0d)));
        #sector = classification_name(gics, 1);

        #up   = sum(group(if(#rev > 0, 1, 0), #sector));
        #down = sum(group(if(#rev < 0, 1, 0), #sector));
    )
    get(#up, #down)
    for(members("SPX Index"))
    preferences(dropCols=["ORIG_IDS", "#sector"])
    """

with BQuery() as bq:
    df = bq.bql(query).combine()
df.head()
# Get counts of comps with positive and negative earnings revisions over the past month
from polars_bloomberg import BQuery

query = """
    let(
        #rev = net_chg(is_eps(fpt=BT, fpo=1, ae=e, dates=range(-1M, 0d)));
        #sector = classification_name(gics, 1);

        #up   = sum(group(if(#rev > 0, 1, 0), #sector));
        #down = sum(group(if(#rev < 0, 1, 0), #sector));
    )
    get(#up, #down)
    for(members("SPX Index"))
    preferences(dropCols=["ORIG_IDS", "#sector"])
    """

with BQuery() as bq:
    df = bq.bql(query).combine()
df.head()

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shape: (5, 3)

ID	#up	#down
str	f64	f64
"Communication Services"	18.0	3.0
"Consumer Discretionary"	41.0	8.0
"Consumer Staples"	29.0	7.0
"Energy"	12.0	10.0
"Financials"	65.0	9.0