def sean_seah_warnings(l_args, s_ticker):
parser = argparse.ArgumentParser(
prog="warnings",
description="""
Sean Seah warnings. Check: Consistent historical earnings per share;
Consistently high return on equity; Consistently high return on assets; 5x Net
Income > Long-Term Debt; and Interest coverage ratio more than 3. See
https://www.drwealth.com/gone-fishing-with-buffett-by-sean-seah/comment-page-1/
[Source: Market Watch]
""",
)
parser.add_argument(
"-i",
"--info",
action="store_true",
default=False,
dest="b_info",
help="provide more information about Sean Seah warning rules.",
)
parser.add_argument(
"-d",
"--debug",
action="store_true",
default=False,
dest="b_debug",
help="print insights into warnings calculation.",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if ns_parser.b_info:
filepath = "fundamental_analysis/info_sean_seah.txt"
with open(filepath) as fp:
line = fp.readline()
while line:
print(f"{line.strip()}")
line = fp.readline()
print("")
# From INCOME STATEMENT, get: 'EPS (Basic)', 'Net Income', 'Interest Expense', 'EBITDA'
url_financials = (
f"https://www.marketwatch.com/investing/stock/{s_ticker}/financials/income"
)
text_soup_financials = BeautifulSoup(
requests.get(url_financials,
headers={
"User-Agent": get_user_agent()
}).text,
"lxml",
)
# Define financials columns
a_financials_header = list()
for financials_header in text_soup_financials.findAll(
"th", {"class": "overflow__heading"}):
a_financials_header.append(
financials_header.text.strip("\n").split("\n")[0])
df_financials = pd.DataFrame(columns=a_financials_header[0:-1])
# Add financials values
soup_financials = text_soup_financials.findAll(
lambda tag: tag.name == "tr" and tag.get("class"
) == ["table__row"])
soup_financials += text_soup_financials.findAll(
"tr", {"class": "table__row is-highlighted"})
for financials_info in soup_financials:
financials_row = financials_info.text.split("\n")
if len(financials_row) > 5:
for item in financials_row:
if bool(re.search(r"\d", item)):
a_financials_info = financials_info.text.split("\n")
l_financials = [a_financials_info[2]]
l_financials.extend(a_financials_info[5:-2])
# Append data values to financials
df_financials.loc[len(
df_financials.index)] = l_financials
break
# Set item name as index
df_financials = df_financials.set_index("Item")
df_sean_seah = df_financials.loc[[
"EPS (Basic)", "Net Income", "Interest Expense", "EBITDA"
]]
# From BALANCE SHEET, get: 'Liabilities & Shareholders\' Equity', 'Long-Term Debt'
url_financials = f"https://www.marketwatch.com/investing/stock/{s_ticker}/financials/balance-sheet"
text_soup_financials = BeautifulSoup(
requests.get(url_financials,
headers={
"User-Agent": get_user_agent()
}).text,
"lxml",
)
# Define financials columns
a_financials_header = list()
for financials_header in text_soup_financials.findAll(
"th", {"class": "overflow__heading"}):
a_financials_header.append(
financials_header.text.strip("\n").split("\n")[0])
s_header_end_trend = "5-year trend"
df_financials = pd.DataFrame(
columns=a_financials_header[0:a_financials_header.
index(s_header_end_trend)])
# Add financials values
soup_financials = text_soup_financials.findAll(
lambda tag: tag.name == "tr" and tag.get("class"
) == ["table__row"])
soup_financials += text_soup_financials.findAll(
"tr", {"class": "table__row is-highlighted"})
for financials_info in soup_financials:
financials_row = financials_info.text.split("\n")
if len(financials_row) > 5:
for item in financials_row:
if bool(re.search(r"\d", item)):
a_financials_info = financials_info.text.split("\n")
l_financials = [a_financials_info[2]]
l_financials.extend(a_financials_info[5:-2])
# Append data values to financials
df_financials.loc[len(
df_financials.index)] = l_financials
break
# Set item name as index
df_financials = df_financials.set_index("Item")
# Create dataframe to compute meaningful metrics from sean seah book
df_sean_seah = df_sean_seah.append(df_financials.loc[[
"Total Shareholders' Equity",
"Liabilities & Shareholders' Equity",
"Long-Term Debt",
]])
# Clean these metrics by parsing their values to float
df_sean_seah = df_sean_seah.applymap(
lambda x: clean_data_values_to_float(x))
# Add additional necessary metrics
series = (df_sean_seah.loc["Net Income"] /
df_sean_seah.loc["Total Shareholders' Equity"])
series.name = "ROE"
df_sean_seah = df_sean_seah.append(series)
series = df_sean_seah.loc["EBITDA"] / df_sean_seah.loc[
"Interest Expense"]
series.name = "Interest Coverage Ratio"
df_sean_seah = df_sean_seah.append(series)
series = (df_sean_seah.loc["Net Income"] /
df_sean_seah.loc["Liabilities & Shareholders' Equity"])
series.name = "ROA"
df_sean_seah = df_sean_seah.append(series)
print(
df_sean_seah.applymap(lambda x: int_or_round_float(x)).to_string())
n_warnings = 0
print("\nWARNINGS:")
if np.any(df_sean_seah.loc["EPS (Basic)"].diff().dropna().values < 0):
print("NO consistent historical earnings per share")
n_warnings += 1
if ns_parser.b_debug:
sa_eps = np.array2string(
df_sean_seah.loc["EPS (Basic)"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" EPS: {sa_eps}")
sa_growth = np.array2string(
df_sean_seah.loc["EPS (Basic)"].diff().dropna().values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" Growth: {sa_growth} < 0")
if np.any(df_sean_seah.loc["ROE"].values < 0.15):
print("NOT consistently high return on equity")
n_warnings += 1
if ns_parser.b_debug:
sa_roe = np.array2string(
df_sean_seah.loc["ROE"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" ROE: {sa_roe} < 0.15")
if np.any(df_sean_seah.loc["ROA"].values < 0.07):
print("NOT consistently high return on assets")
n_warnings += 1
if ns_parser.b_debug:
sa_roa = np.array2string(
df_sean_seah.loc["ROA"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" ROA: {sa_roa} < 0.07")
if np.any(df_sean_seah.loc["Long-Term Debt"].values > 5 *
df_sean_seah.loc["Net Income"].values):
print("5x Net Income < Long-Term Debt")
n_warnings += 1
if ns_parser.b_debug:
sa_net_income = np.array2string(
df_sean_seah.loc["Net Income"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" NET Income: {sa_net_income}")
sa_5_long_term_debt = np.array2string(
5 * df_sean_seah.loc["Long-Term Debt"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(
f" lower than 5x Long-Term Debt: {sa_5_long_term_debt}")
if np.any(df_sean_seah.loc["Interest Coverage Ratio"].values < 3):
print("Interest coverage ratio less than 3")
n_warnings += 1
if ns_parser.b_debug:
sa_interest_coverage_ratio = np.array2string(
100 * df_sean_seah.loc["Interest Coverage Ratio"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(
f" Interest Coverage Ratio: {sa_interest_coverage_ratio} < 3"
)
if n_warnings == 0:
print("None. Good stonk")
print("")
except Exception as e:
print(e)
print("")
return
def get_sean_seah_warnings(
ticker: str, debug: bool = False
) -> Tuple[pd.DataFrame, List[str], List[str]]:
"""Get financial statements and prepare Sean Seah warnings
Parameters
----------
ticker : str
Ticker to look at
debug : bool, optional
[description], by default False
Returns
-------
pd.DataFrame
Dataframe of financials
List[str]
List of warnings
List[str]
List of debug messages
"""
# From INCOME STATEMENT, get: 'EPS (Basic)', 'Net Income', 'Interest Expense', 'EBITDA'
url_financials = (
f"https://www.marketwatch.com/investing/stock/{ticker}/financials/income"
)
text_soup_financials = BeautifulSoup(
requests.get(url_financials, headers={"User-Agent": get_user_agent()}).text,
"lxml",
)
# Define financials columns
a_financials_header = []
for financials_header in text_soup_financials.findAll(
"th", {"class": "overflow__heading"}
):
a_financials_header.append(financials_header.text.strip("\n").split("\n")[0])
df_financials = pd.DataFrame(columns=a_financials_header[0:-1])
# Add financials values
soup_financials = text_soup_financials.findAll(
lambda tag: tag.name == "tr" and tag.get("class") == ["table__row"]
)
soup_financials += text_soup_financials.findAll(
"tr", {"class": "table__row is-highlighted"}
)
for financials_info in soup_financials:
financials_row = financials_info.text.split("\n")
if len(financials_row) > 5:
for item in financials_row:
if bool(re.search(r"\d", item)):
a_financials_info = financials_info.text.split("\n")
l_financials = [a_financials_info[2]]
l_financials.extend(a_financials_info[5:-2])
# Append data values to financials
df_financials.loc[len(df_financials.index)] = l_financials
break
l_fin = ["EPS (Basic)", "Net Income", "Interest Expense", "EBITDA"]
if not all(elem in df_financials["Item"].values for elem in l_fin):
return pd.DataFrame(), [], []
# Set item name as index
df_financials = df_financials.set_index("Item")
df_sean_seah = df_financials.loc[l_fin]
# From BALANCE SHEET, get: 'Liabilities & Shareholders\' Equity', 'Long-Term Debt'
url_financials = (
f"https://www.marketwatch.com/investing/stock/{ticker}/financials/balance-sheet"
)
text_soup_financials = BeautifulSoup(
requests.get(url_financials, headers={"User-Agent": get_user_agent()}).text,
"lxml",
)
# Define financials columns
a_financials_header = []
for financials_header in text_soup_financials.findAll(
"th", {"class": "overflow__heading"}
):
a_financials_header.append(financials_header.text.strip("\n").split("\n")[0])
s_header_end_trend = "5-year trend"
df_financials = pd.DataFrame(
columns=a_financials_header[0 : a_financials_header.index(s_header_end_trend)]
)
# Add financials values
soup_financials = text_soup_financials.findAll(
lambda tag: tag.name == "tr" and tag.get("class") == ["table__row"]
)
soup_financials += text_soup_financials.findAll(
"tr", {"class": "table__row is-highlighted"}
)
for financials_info in soup_financials:
financials_row = financials_info.text.split("\n")
if len(financials_row) > 5:
for item in financials_row:
if bool(re.search(r"\d", item)):
a_financials_info = financials_info.text.split("\n")
l_financials = [a_financials_info[2]]
l_financials.extend(a_financials_info[5:-2])
# Append data values to financials
df_financials.loc[len(df_financials.index)] = l_financials
break
# Set item name as index
df_financials = df_financials.set_index("Item")
# Create dataframe to compute meaningful metrics from sean seah book
df_sean_seah = df_sean_seah.append(
df_financials.loc[
[
"Total Shareholders' Equity",
"Liabilities & Shareholders' Equity",
"Long-Term Debt",
]
]
)
# Clean these metrics by parsing their values to float
df_sean_seah = df_sean_seah.applymap(lambda x: clean_data_values_to_float(x))
# Add additional necessary metrics
series = (
df_sean_seah.loc["Net Income"] / df_sean_seah.loc["Total Shareholders' Equity"]
)
series.name = "ROE"
df_sean_seah = df_sean_seah.append(series)
series = df_sean_seah.loc["EBITDA"] / df_sean_seah.loc["Interest Expense"]
series.name = "Interest Coverage Ratio"
df_sean_seah = df_sean_seah.append(series)
series = (
df_sean_seah.loc["Net Income"]
/ df_sean_seah.loc["Liabilities & Shareholders' Equity"]
)
series.name = "ROA"
df_sean_seah = df_sean_seah.append(series)
n_warnings = 0
warnings = []
debugged_warnings = []
if np.any(df_sean_seah.loc["EPS (Basic)"].diff().dropna().values < 0):
warnings.append("No consistent historical earnings per share")
n_warnings += 1
if debug:
sa_eps = np.array2string(
df_sean_seah.loc["EPS (Basic)"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
sa_growth = np.array2string(
df_sean_seah.loc["EPS (Basic)"].diff().dropna().values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
debugged_warnings.append(f"\tEPS: {sa_eps}\n\tGrowth: {sa_growth} < 0")
if np.any(df_sean_seah.loc["ROE"].values < 0.15):
warnings.append("NOT consistently high return on equity")
n_warnings += 1
if debug:
sa_roe = np.array2string(
df_sean_seah.loc["ROE"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
debugged_warnings.append(f"\tROE: {sa_roe} < 0.15")
if np.any(df_sean_seah.loc["ROA"].values < 0.07):
warnings.append("NOT consistently high return on assets")
n_warnings += 1
if debug:
sa_roa = np.array2string(
df_sean_seah.loc["ROA"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
debugged_warnings.append(f"\tROA: {sa_roa} < 0.07")
if np.any(
df_sean_seah.loc["Long-Term Debt"].values
> 5 * df_sean_seah.loc["Net Income"].values
):
warnings.append("5x Net Income < Long-Term Debt")
n_warnings += 1
if debug:
sa_5_net_income = np.array2string(
5 * df_sean_seah.loc["Net Income"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
sa_long_term_debt = np.array2string(
df_sean_seah.loc["Long-Term Debt"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
debugged_warnings.append(
f"\t5x NET Income: {sa_5_net_income}\n\tlower than Long-Term Debt: {sa_long_term_debt}"
)
if np.any(df_sean_seah.loc["Interest Coverage Ratio"].values < 3):
warnings.append("Interest coverage ratio less than 3")
n_warnings += 1
if debug:
sa_interest_coverage_ratio = np.array2string(
100 * df_sean_seah.loc["Interest Coverage Ratio"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
debugged_warnings.append(
f"\tInterest Coverage Ratio: {sa_interest_coverage_ratio} < 3"
)
console.print("")
return (
df_sean_seah.applymap(lambda x: int_or_round_float(x)),
warnings,
debugged_warnings,
)
