Fixed incorrect data display using stocks/fa/est command (#4940)

* Removed empty row in OpenBB SDK table under Installation * Revamped `get_estimates` function * Linting * Fixed bugs, tests and modified args * Fixed test
2026-10-27 12:40:35 +00:00 · 2023-05-02 01:53:59 +05:30
parent c603a360ec
commit 3d7b934d16
8 changed files with 103 additions and 163 deletions
--- a/README.md
+++ b/README.md
@@ -80,7 +80,7 @@ If you wish to install the OpenBB Terminal or the OpenBB SDK, please use one of
 |:-|:-|
 |[PyPI](https://docs.openbb.co/terminal/installation/pypi)|If you wish to use the OpenBB SDK in Python or Jupyter Notebooks|
 |[Source](https://docs.openbb.co/terminal/installation/source)|If you wish to contribute to the development of the OpenBB Terminal|
-nbsp;|
+<!-- nbsp;| -->

 ## 2. Contributing

--- a/openbb_terminal/stocks/fundamental_analysis/business_insider_model.py
+++ b/openbb_terminal/stocks/fundamental_analysis/business_insider_model.py
@@ -3,7 +3,6 @@ __docformat__ = "numpy"

 import json
 import logging
-import re
 from typing import Tuple

 import pandas as pd
@@ -193,120 +192,61 @@ def get_estimates(symbol: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame
        "lxml",
    )

-    l_estimates_year_header = list()
-    l_estimates_quarter_header = list()
-    for estimates_header in text_soup_market_business_insider.findAll(
-        "th", {"class": "table__th text-right"}
-    ):
-        s_estimates_header = estimates_header.text.strip()
-        if s_estimates_header.isdigit():
-            l_estimates_year_header.append(s_estimates_header)
-        elif ("in %" not in s_estimates_header) and ("Job" not in s_estimates_header):
-            l_estimates_quarter_header.append(s_estimates_header)
+    # Get all tables and convert them to list of pandas dataframes
+    tables = text_soup_market_business_insider.find_all("table")
+    list_df = pd.read_html(str(tables))

-    l_estimates_year_metric = list()
-    for estimates_year_metric in text_soup_market_business_insider.findAll(
-        "td", {"class": "table__td black"}
-    ):
-        l_estimates_year_metric.append(estimates_year_metric.text)
+    # Get year estimates
+    df_year_estimates = list_df[3]
+    l_year_estimates_columns = df_year_estimates.columns.tolist()
+    l_year_estimates_columns[0] = "YEARLY ESTIMATES"
+    df_year_estimates.columns = l_year_estimates_columns
+    df_year_estimates.set_index("YEARLY ESTIMATES", inplace=True)

-    l_estimates_quarter_metric = list()
-    for estimates_quarter_metric in text_soup_market_business_insider.findAll(
-        "td", {"class": "table__td font-color-dim-gray"}
-    ):
-        l_estimates_quarter_metric.append(estimates_quarter_metric.text)
+    df_quarter = list_df[4]
+    date_row = dict()

-    d_metric_year = dict()
-    d_metric_quarter_earnings = dict()
-    d_metric_quarter_revenues = dict()
-    l_metrics = list()
-    n_metrics = 0
-    b_year = True
-    for idx, metric_value in enumerate(
-        text_soup_market_business_insider.findAll(
-            "td", {"class": "table__td text-right"}
-        )
-    ):
-        if b_year:
-            # YEAR metrics
-            l_metrics.append(metric_value.text.strip())
+    # Get quarter earnings estimates
+    df_quarter_earnings = df_quarter.iloc[0:5, :].reset_index(drop=True).copy()
+    df_quarter_earnings.drop(index=0, inplace=True)
+    l_quarter_earnings_columns = df_quarter_earnings.columns.tolist()
+    l_quarter_earnings_columns[0] = "QUARTER EARNINGS ESTIMATES"
+    date_row["QUARTER EARNINGS ESTIMATES"] = "Date"

-            # Check if we have processed all year metrics
-            if n_metrics > len(l_estimates_year_metric) - 1:
-                b_year = False
-                n_metrics = 0
-                l_metrics = list()
-                idx_y = idx
+    # Adding Date info to add to dataframe
+    for col in l_quarter_earnings_columns[1:]:
+        key = col.split("ending")[0].strip()
+        value = col[col.find("ending") :].strip()
+        date_row[key] = value

-            # Add value to dictionary
-            if (idx + 1) % len(l_estimates_year_header) == 0:
-                d_metric_year[l_estimates_year_metric[n_metrics]] = l_metrics
-                l_metrics = list()
-                n_metrics += 1
-
-        if not b_year:
-            # QUARTER metrics
-            l_metrics.append(metric_value.text.strip())
-
-            # Check if we have processed all quarter metrics
-            if n_metrics > len(l_estimates_quarter_metric) - 1:
-                break
-
-            # Add value to dictionary
-            if (idx - idx_y + 1) % len(l_estimates_quarter_header) == 0:
-                if n_metrics < 4:
-                    d_metric_quarter_earnings[
-                        l_estimates_quarter_metric[n_metrics]
-                    ] = l_metrics
-                else:
-                    d_metric_quarter_revenues[
-                        l_estimates_quarter_metric[n_metrics - 4]
-                    ] = l_metrics
-                l_metrics = list()
-                n_metrics += 1
-
-    df_year_estimates = pd.DataFrame.from_dict(
-        d_metric_year, orient="index", columns=l_estimates_year_header
+    df_quarter_earnings.columns = date_row.keys()
+    date_row = pd.DataFrame(date_row, index=[0])
+    df_quarter_earnings = pd.concat([date_row, df_quarter_earnings]).reset_index(
+        drop=True
    )
-    df_year_estimates.index.name = "YEARLY ESTIMATES"
-    df_quarter_earnings = pd.DataFrame.from_dict(
-        d_metric_quarter_earnings,
-        orient="index",
-        columns=l_estimates_quarter_header,
+    df_quarter_earnings.set_index("QUARTER EARNINGS ESTIMATES", inplace=True)
+
+    # Setting date_row to empty dict object
+    date_row = dict()
+
+    # Get quarter revenues estimates
+    df_quarter_revenues = df_quarter.iloc[5:, :].reset_index(drop=True).copy()
+    df_quarter_revenues.drop(index=0, inplace=True)
+    l_quarter_revenues_columns = df_quarter_revenues.columns.tolist()
+    l_quarter_revenues_columns[0] = "QUARTER REVENUES ESTIMATES"
+    date_row["QUARTER REVENUES ESTIMATES"] = "Date"
+
+    # Adding Date info to add to dataframe
+    for col in l_quarter_revenues_columns[1:]:
+        key = col.split("ending")[0].strip()
+        value = col[col.find("ending") :].strip()
+        date_row[key] = value
+
+    df_quarter_revenues.columns = date_row.keys()
+    date_row = pd.DataFrame(date_row, index=[0])
+    df_quarter_revenues = pd.concat([date_row, df_quarter_revenues]).reset_index(
+        drop=True
    )
-    # df_quarter_earnings.index.name = 'Earnings'
-    df_quarter_revenues = pd.DataFrame.from_dict(
-        d_metric_quarter_revenues,
-        orient="index",
-        columns=l_estimates_quarter_header,
-    )
-    # df_quarter_revenues.index.name = 'Revenues'
-
-    if not df_quarter_earnings.empty:
-        l_quarter = list()
-        l_date = list()
-        for quarter_title in df_quarter_earnings.columns:
-            l_quarter.append(re.split("  ending", quarter_title)[0])
-            if len(re.split("  ending", quarter_title)) == 2:
-                l_date.append(
-                    "ending " + re.split("  ending", quarter_title)[1].strip()
-                )
-            else:
-                l_date.append("-")
-
-        df_quarter_earnings.index.name = "QUARTER EARNINGS ESTIMATES"
-        df_quarter_earnings.columns = l_quarter
-        df_quarter_earnings.loc["Date"] = l_date
-        df_quarter_earnings = df_quarter_earnings.reindex(
-            ["Date", "No. of Analysts", "Average Estimate", "Year Ago", "Publish Date"]
-        )
-
-    if not df_quarter_revenues.empty:
-        df_quarter_revenues.index.name = "QUARTER REVENUES ESTIMATES"
-        df_quarter_revenues.columns = l_quarter
-        df_quarter_revenues.loc["Date"] = l_date
-        df_quarter_revenues = df_quarter_revenues.reindex(
-            ["Date", "No. of Analysts", "Average Estimate", "Year Ago", "Publish Date"]
-        )
+    df_quarter_revenues.set_index("QUARTER REVENUES ESTIMATES", inplace=True)

    return df_year_estimates, df_quarter_earnings, df_quarter_revenues
--- a/openbb_terminal/stocks/fundamental_analysis/business_insider_view.py
+++ b/openbb_terminal/stocks/fundamental_analysis/business_insider_view.py
@@ -215,7 +215,7 @@ def display_estimates(
        df_quarter_revenues,
    ) = business_insider_model.get_estimates(symbol)

-    if estimate == "annualearnings":
+    if estimate == "annual_earnings":
        print_rich_table(
            df_year_estimates,
            headers=list(df_year_estimates.columns),
@@ -231,7 +231,7 @@ def display_estimates(
            sheet_name,
        )

-    elif estimate == "quarterearnings":
+    elif estimate == "quarter_earnings":
        print_rich_table(
            df_quarter_earnings,
            headers=list(df_quarter_earnings.columns),
@@ -247,7 +247,7 @@ def display_estimates(
            sheet_name,
        )

-    elif estimate == "annualrevenue":
+    elif estimate == "quarter_revenues":
        print_rich_table(
            df_quarter_revenues,
            headers=list(df_quarter_revenues.columns),
--- a/openbb_terminal/stocks/fundamental_analysis/fa_controller.py
+++ b/openbb_terminal/stocks/fundamental_analysis/fa_controller.py
@@ -92,7 +92,7 @@ class FundamentalAnalysisController(StockBaseController):
    PATH = "/stocks/fa/"

    SHRS_CHOICES = ["major", "institutional", "mutualfund"]
-    ESTIMATE_CHOICES = ["annualrevenue", "annualearnings", "quarterearnings"]
+    ESTIMATE_CHOICES = ["annual_earnings", "quarter_earnings", "quarter_revenues"]
    CHOICES_GENERATION = True

    def __init__(
@@ -1858,7 +1858,7 @@ class FundamentalAnalysisController(StockBaseController):
            help="Estimates to get",
            dest="estimate",
            choices=self.ESTIMATE_CHOICES,
-            default="annualearnings",
+            default="annual_earnings",
        )
        ns_parser = self.parse_known_args_and_warn(
            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
--- a/tests/openbb_terminal/stocks/fundamental_analysis/csv/test_business_insider_model/test_get_estimates_year_estimates.csv
+++ b/tests/openbb_terminal/stocks/fundamental_analysis/csv/test_business_insider_model/test_get_estimates_year_estimates.csv
@@ -1,28 +1,28 @@
 YEARLY ESTIMATES,2023,2024,2025,2026,2027
-Revenue,"103,351","135,186","168,507",-,-
+Revenue,103351,135186,168507,-,-
 Dividend,0.00,0.00,0.00,-,-
 Dividend Yield (in %),-,-,-,-,-
 EPS,4.08,5.65,6.91,5.83,6.98
 P/E Ratio,45.95,33.21,27.13,32.17,26.86
-EBIT,"14,600","21,973","27,740","30,032","44,029"
-EBITDA,"19,429","27,963","38,063","37,754","38,937"
-Net Profit,"13,681","19,370","23,763","21,385","26,126"
-Net Profit Adjusted,"13,713","19,366","23,746","21,385","26,126"
-Pre-Tax Profit,"14,437","22,015","28,932","29,404","31,787"
-Net Profit (Adjusted),"14,363","22,392","29,878","29,404","31,787"
+EBIT,14600,21973,27740,30032,44029
+EBITDA,19429,27963,38063,37754,38937
+Net Profit,13681,19370,23763,21385,26126
+Net Profit Adjusted,13713,19366,23746,21385,26126
+Pre-Tax Profit,14437,22015,28932,29404,31787
+Net Profit (Adjusted),14363,22392,29878,29404,31787
 EPS (Non-GAAP) ex. SOE,4.08,5.65,6.91,5.83,6.98
 EPS (GAAP),3.49,5.25,6.18,5.73,6.65
-Gross Income,"23,184","32,468","40,980","37,949","42,464"
-Cash Flow from Investing,"-7,935","-9,482","-10,201","-12,681",-
-Cash Flow from Operations,"19,523","24,129","27,935","29,376","35,875"
+Gross Income,23184,32468,40980,37949,42464
+Cash Flow from Investing,-7935,-9482,-10201,-12681,-
+Cash Flow from Operations,19523,24129,27935,29376,35875
 Cash Flow from Financing,-478,-810,100,-,-
 Cash Flow per Share,4.92,7.39,6.94,-,-
-Free Cash Flow,"10,236","14,300","15,203","18,405","22,501"
+Free Cash Flow,10236,14300,15203,18405,22501
 Free Cash Flow per Share,2.84,4.55,4.61,6.10,-
 Book Value per Share,17.00,22.50,28.90,31.25,-
-Net Debt,"-24,814","-36,525","-52,052",-,-
-Research & Development Exp.,"3,565","4,327","4,852","5,192","5,250"
-Capital Expenditure,"7,692","8,522","8,950","9,832",-
-"Selling, General & Admin. Exp.","4,614","5,569","6,495","6,145","5,725"
-Shareholder’s Equity,"57,825","76,028","97,431","99,807",-
-Total Assets,"99,781","126,117","153,798","155,216",-
+Net Debt,-24814,-36525,-52052,-,-
+Research & Development Exp.,3565,4327,4852,5192,5250
+Capital Expenditure,7692,8522,8950,9832,-
+"Selling, General & Admin. Exp.",4614,5569,6495,6145,5725
+Shareholder’s Equity,57825,76028,97431,99807,-
+Total Assets,99781,126117,153798,155216,-
--- a/tests/openbb_terminal/stocks/fundamental_analysis/test_business_insider_view.py
+++ b/tests/openbb_terminal/stocks/fundamental_analysis/test_business_insider_view.py
@@ -83,5 +83,5 @@ def test_price_target_from_analysts_plt():
@pytest.mark.record_stdout
 def test_estimates():
    business_insider_view.display_estimates(
-        symbol="TSLA", estimate="annualearnings", export=None
+        symbol="TSLA", estimate="annual_earnings", export=None
    )
--- a/tests/openbb_terminal/stocks/fundamental_analysis/test_fa_controller.py
+++ b/tests/openbb_terminal/stocks/fundamental_analysis/test_fa_controller.py
@@ -575,7 +575,7 @@ def test_call_func_expect_queue(expected_queue, queue, func):
            [],
            {
                "symbol": "TSLA",
-                "estimate": "annualearnings",
+                "estimate": "annual_earnings",
                "export": "",
                "sheet_name": None,
            },
--- a/tests/openbb_terminal/stocks/fundamental_analysis/txt/test_business_insider_view/test_estimates.txt
+++ b/tests/openbb_terminal/stocks/fundamental_analysis/txt/test_business_insider_view/test_estimates.txt
@@ -1,29 +1,29 @@
-                                   2023     2024     2025     2026     2027
-YEARLY ESTIMATES                                                           
-Revenue                         103,418  135,351  168,507  194,661  295,169
-Dividend                           0.00     0.00     0.00        -        -
-Dividend Yield (in %)                 -        -        -        -        -
-EPS                                4.07     5.65     6.91     5.83     6.89
-P/E Ratio                         50.51    36.42    29.74    35.29    29.83
-EBIT                             14,555   21,972   27,741   30,025   43,704
-EBITDA                           19,519   27,962   38,066   37,741   38,504
-Net Profit                       13,506   19,372   23,768   21,372   25,801
-Net Profit Adjusted              13,533   19,368   23,752   21,372   25,801
-Pre-Tax Profit                   14,454   22,034   28,932   29,404   31,787
-Net Profit (Adjusted)            14,363   22,392   29,878   29,404   31,787
-EPS (Non-GAAP) ex. SOE             4.07     5.65     6.91     5.83     6.89
-EPS (GAAP)                         3.47     5.25     6.18     5.72     6.54
-Gross Income                     23,115   32,466   40,982   37,942   41,972
-Cash Flow from Investing         -7,935   -9,482  -10,201  -12,681        -
-Cash Flow from Operations        19,523   24,129   27,935   29,376   35,875
-Cash Flow from Financing           -478     -810      100        -        -
-Cash Flow per Share                4.92     7.39     6.94        -        -
-Free Cash Flow                   10,541   14,300   15,203   18,405   22,501
-Free Cash Flow per Share           2.84     4.55     4.61     6.10        -
-Book Value per Share              17.00    22.50    28.90    31.25        -
-Net Debt                        -24,814  -36,525  -52,052        -        -
-Research & Development Exp.       3,555    4,325    4,846    5,179    5,162
-Capital Expenditure               7,672    8,522    8,950    9,832        -
-Selling, General & Admin. Exp.    4,618    5,572    6,500    6,160    5,755
-Shareholder’s Equity             57,825   76,028   97,431   99,807        -
-Total Assets                     99,781  126,117  153,798  155,216        -
+                                  2023    2024    2025    2026    2027
+YEARLY ESTIMATES                                                      
+Revenue                         103418  135351  168507  194661  295169
+Dividend                          0.00    0.00    0.00       -       -
+Dividend Yield (in %)                -       -       -       -       -
+EPS                               4.07    5.65    6.91    5.83    6.89
+P/E Ratio                        50.51   36.42   29.74   35.29   29.83
+EBIT                             14555   21972   27741   30025   43704
+EBITDA                           19519   27962   38066   37741   38504
+Net Profit                       13506   19372   23768   21372   25801
+Net Profit Adjusted              13533   19368   23752   21372   25801
+Pre-Tax Profit                   14454   22034   28932   29404   31787
+Net Profit (Adjusted)            14363   22392   29878   29404   31787
+EPS (Non-GAAP) ex. SOE            4.07    5.65    6.91    5.83    6.89
+EPS (GAAP)                        3.47    5.25    6.18    5.72    6.54
+Gross Income                     23115   32466   40982   37942   41972
+Cash Flow from Investing         -7935   -9482  -10201  -12681       -
+Cash Flow from Operations        19523   24129   27935   29376   35875
+Cash Flow from Financing          -478    -810     100       -       -
+Cash Flow per Share               4.92    7.39    6.94       -       -
+Free Cash Flow                   10541   14300   15203   18405   22501
+Free Cash Flow per Share          2.84    4.55    4.61    6.10       -
+Book Value per Share             17.00   22.50   28.90   31.25       -
+Net Debt                        -24814  -36525  -52052       -       -
+Research & Development Exp.       3555    4325    4846    5179    5162
+Capital Expenditure               7672    8522    8950    9832       -
+Selling, General & Admin. Exp.    4618    5572    6500    6160    5755
+Shareholder’s Equity             57825   76028   97431   99807       -
+Total Assets                     99781  126117  153798  155216       -