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Add earnings estimates command from Business Insider

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This commit is contained in:
didier
2021-01-31 19:09:29 +00:00
parent a9561d83be
commit f073b50843
2 changed files with 120 additions and 1 deletions
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115
due_diligence/business_insider_api.py
View File

@@ -86,3 +86,118 @@ def price_target_from_analysts(l_args, df_stock, s_ticker, s_start, s_interval):
except:
print("")
return
# ----------------------------------------------- ESTIMATES -----------------------------------------------
def estimates(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='estimates',
description="""Yearly and quarter earnings estimates [Source: Business Insider API]""")
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
return
url_market_business_insider = f"https://markets.businessinsider.com/stocks/{s_ticker.lower()}-stock"
text_soup_market_business_insider = BeautifulSoup(requests.get(url_market_business_insider).text, "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)
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)
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)
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())
# 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
# 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_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.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'
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.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"])
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"])
print(df_year_estimates.to_string())
print("")
print("QUARTER ESTIMATES EARNINGS")
print(df_quarter_earnings.to_string())
print("")
print("QUARTER ESTIMATES REVENUES")
print(df_quarter_revenues.to_string())
print("")
print(text_soup_market_business_insider.find('div', {'class': "text_right instrument-description"}).text.strip())
print("")
except:
print("")
return

6
due_diligence/dd_menu.py
View File

@@ -39,6 +39,7 @@ def print_due_diligence(s_ticker, s_start, s_interval):
print(" analyst analyst prices and ratings of the company [Finviz]")
print(" rating rating of the company from strong sell to strong buy [FMP]")
print(" pt price targets over time [Business Insider]")
print(" est quarter and year analysts earnings estimates [Business Insider]")
print(" insider insider trading of the company [Finviz]")
print(" sec SEC filings [Market Watch]")
print(" short short interest [Quandl]")
@@ -56,7 +57,7 @@ def dd_menu(df_stock, s_ticker, s_start, s_interval):
'red', # Reddit
'short', # Quandl
'rating', # FMP
'pt', # BI
'pt', 'est', # BI
'insider', 'news', 'analyst', # Finviz
'warnings', 'sec']) # MW
@@ -104,6 +105,9 @@ def dd_menu(df_stock, s_ticker, s_start, s_interval):
elif ns_known_args.cmd == 'pt':
bi_api.price_target_from_analysts(l_args, df_stock, s_ticker, s_start, s_interval)
elif ns_known_args.cmd == 'est':
bi_api.estimates(l_args, s_ticker)
# FINANCIAL MODELING PREP API
elif ns_known_args.cmd == 'rating':
fmp_api.rating(l_args, s_ticker)