def update_runtime_choices(self):
"""Update runtime choices"""
if session and gtff.USE_PROMPT_TOOLKIT:
self.choices["industry"] = {
i: None
for i in financedatabase_model.get_industries(
country=self.country, sector=self.sector)
}
self.choices["sector"] = {
s: None
for s in financedatabase_model.get_sectors(
industry=self.industry, country=self.country)
}
self.choices["country"] = {
c: None
for c in financedatabase_model.get_countries(
industry=self.industry, sector=self.sector)
}
self.completer = NestedCompleter.from_nested_dict(self.choices)
def call_industry(self, other_args: List[str]):
"""Process industry command"""
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="industry",
description=
"See existing industries, or set industry if arg specified",
)
parser.add_argument(
"-n",
"--name",
type=str,
dest="name",
nargs="+",
help="industry to select",
)
if other_args and "-" not in other_args[0][0]:
other_args.insert(0, "-n")
ns_parser = parse_known_args_and_warn(parser, other_args)
if ns_parser:
possible_industries = financedatabase_model.get_industries(
country=self.country,
sector=self.sector,
)
if ns_parser.name:
if " ".join(ns_parser.name) in possible_industries:
self.industry = " ".join(ns_parser.name)
# if we get the industry, then we also automatically know the sector
self.sector = financedatabase_model.get_sectors(
industry=self.industry)[0]
self.update_runtime_choices()
else:
console.print(
f"Industry '{' '.join(ns_parser.name)}' does not exist."
)
similar_cmd = difflib.get_close_matches(
" ".join(ns_parser.name),
possible_industries,
n=1,
cutoff=0.75,
)
if similar_cmd:
console.print(f"Replacing by '{similar_cmd[0]}'")
self.industry = similar_cmd[0]
# if we get the industry, then we also automatically know the sector
self.sector = financedatabase_model.get_sectors(
industry=self.industry)[0]
self.update_runtime_choices()
else:
similar_cmd = difflib.get_close_matches(
" ".join(ns_parser.name),
possible_industries,
n=1,
cutoff=0.5,
)
if similar_cmd:
console.print(f"Did you mean '{similar_cmd[0]}'?")
else:
for industry in possible_industries:
console.print(industry)
self.stocks_data = {}
console.print("")
def call_load(self, other_args: List[str]):
"""Process load command"""
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="load",
description=
"Load stock ticker and alter the industry, sector, country and market cap "
"accordingly for this ticker.",
)
parser.add_argument(
"-t",
"--ticker",
action="store",
dest="ticker",
required="-h" not in other_args,
help="Stock ticker",
)
if other_args and "-" not in other_args[0][0]:
other_args.insert(0, "-t")
ns_parser = parse_known_args_and_warn(parser, other_args)
if ns_parser:
df_stock_candidate = stocks_helper.load(ns_parser.ticker, )
if not df_stock_candidate.empty:
if "." in ns_parser.ticker:
self.ticker = ns_parser.ticker.upper().split(".")[0]
else:
self.ticker = ns_parser.ticker.upper()
data = yf.utils.get_json(
f"https://finance.yahoo.com/quote/{self.ticker}")
if "summaryProfile" in data:
self.country = data["summaryProfile"]["country"]
if self.country not in financedatabase_model.get_countries(
):
similar_cmd = difflib.get_close_matches(
self.country,
financedatabase_model.get_countries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.country = similar_cmd[0]
self.sector = data["summaryProfile"]["sector"]
if self.sector not in financedatabase_model.get_sectors():
similar_cmd = difflib.get_close_matches(
self.sector,
financedatabase_model.get_sectors(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.sector = similar_cmd[0]
self.industry = data["summaryProfile"]["industry"]
if self.industry not in financedatabase_model.get_industries(
):
similar_cmd = difflib.get_close_matches(
self.industry,
financedatabase_model.get_industries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.industry = similar_cmd[0]
if "price" in data:
mktcap = data["price"]["marketCap"]
if mktcap < 2_000_000_000:
self.mktcap = "Small"
elif mktcap > 10_000_000_000:
self.mktcap = "Large"
else:
self.mktcap = "Mid"
self.stocks_data = {}
self.update_runtime_choices()
def __init__(
self,
ticker: str,
queue: List[str] = None,
):
"""Constructor"""
super().__init__(queue)
self.country = "United States"
self.sector = "Financial Services"
self.industry = "Financial Data & Stock Exchanges"
self.mktcap = "Large"
self.exclude_exchanges = True
self.period = "Annual"
self.ticker = ticker
self.stocks_data: dict = {}
self.tickers: List = list()
self.currency: str = ""
if ticker:
data = yf.utils.get_json(
f"https://finance.yahoo.com/quote/{ticker}")
if "summaryProfile" in data:
self.country = data["summaryProfile"]["country"]
if self.country not in financedatabase_model.get_countries():
similar_cmd = difflib.get_close_matches(
self.country,
financedatabase_model.get_countries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.country = similar_cmd[0]
self.sector = data["summaryProfile"]["sector"]
if self.sector not in financedatabase_model.get_sectors():
similar_cmd = difflib.get_close_matches(
self.sector,
financedatabase_model.get_sectors(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.sector = similar_cmd[0]
self.industry = data["summaryProfile"]["industry"]
if self.industry not in financedatabase_model.get_industries():
similar_cmd = difflib.get_close_matches(
self.industry,
financedatabase_model.get_industries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.industry = similar_cmd[0]
if "price" in data:
mktcap = data["price"]["marketCap"]
if mktcap < 2_000_000_000:
self.mktcap = "Small"
elif mktcap > 10_000_000_000:
self.mktcap = "Large"
else:
self.mktcap = "Mid"
if session and gtff.USE_PROMPT_TOOLKIT:
choices: dict = {c: {} for c in self.controller_choices}
choices["mktcap"] = {c: None for c in self.mktcap_choices}
choices["period"] = {c: None for c in self.period_choices}
choices["clear"] = {c: None for c in self.clear_choices}
choices["metric"] = {c: None for c in self.metric_choices}
# This menu contains dynamic choices that may change during runtime
self.choices = choices
self.completer = NestedCompleter.from_nested_dict(choices)
def test_get_industries(country, recorder, sector):
result = financedatabase_model.get_industries(country=country,
sector=sector)
recorder.capture(result)
def call_load(self, other_args: List[str]):
"""Process load command"""
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="load",
description=
"Load stock ticker to perform analysis on. When the data source" +
" is 'yf', an Indian ticker can be loaded by using '.NS' at the end,"
+ " e.g. 'SBIN.NS'. See available market in" +
" https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html.",
)
parser.add_argument(
"-t",
"--ticker",
action="store",
dest="ticker",
required="-h" not in other_args,
help="Stock ticker",
)
if other_args and "-" not in other_args[0][0]:
other_args.insert(0, "-t")
ns_parser = parse_known_args_and_warn(parser, other_args)
if ns_parser:
df_stock_candidate = stocks_helper.load(ns_parser.ticker, )
if not df_stock_candidate.empty:
if "." in ns_parser.ticker:
self.ticker = ns_parser.ticker.upper().split(".")[0]
else:
self.ticker = ns_parser.ticker.upper()
data = yf.utils.get_json(
f"https://finance.yahoo.com/quote/{self.ticker}")
if "summaryProfile" in data:
self.country = data["summaryProfile"]["country"]
if self.country not in financedatabase_model.get_countries(
):
similar_cmd = difflib.get_close_matches(
self.country,
financedatabase_model.get_countries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.country = similar_cmd[0]
self.sector = data["summaryProfile"]["sector"]
if self.sector not in financedatabase_model.get_sectors():
similar_cmd = difflib.get_close_matches(
self.sector,
financedatabase_model.get_sectors(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.sector = similar_cmd[0]
self.industry = data["summaryProfile"]["industry"]
if self.industry not in financedatabase_model.get_industries(
):
similar_cmd = difflib.get_close_matches(
self.industry,
financedatabase_model.get_industries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.industry = similar_cmd[0]
if "price" in data:
mktcap = data["price"]["marketCap"]
if mktcap < 2_000_000_000:
self.mktcap = "Small"
elif mktcap > 10_000_000_000:
self.mktcap = "Large"
else:
self.mktcap = "Mid"
self.stocks_data = {}
self.update_runtime_choices()
def __init__(
self,
ticker: str,
queue: List[str] = None,
):
"""Constructor"""
self.sia_parser = argparse.ArgumentParser(add_help=False, prog="sia")
self.sia_parser.add_argument(
"cmd",
choices=self.CHOICES,
)
self.completer: Union[None, NestedCompleter] = None
if session and gtff.USE_PROMPT_TOOLKIT:
self.choices: dict = {c: {} for c in self.CHOICES}
self.choices["mktcap"] = {c: None for c in self.mktcap_choices}
self.choices["clear"] = {c: None for c in self.clear_choices}
self.choices["metric"] = {c: None for c in self.metric_choices}
if queue:
self.queue = queue
else:
self.queue = list()
self.country = "United States"
self.sector = "Financial Services"
self.industry = "Financial Data & Stock Exchanges"
self.mktcap = "Large"
self.exclude_exhanges = True
self.ticker = ticker
self.stocks_data: dict = {}
self.tickers: List = list()
if ticker:
data = yf.utils.get_json(
f"https://finance.yahoo.com/quote/{ticker}")
if "summaryProfile" in data:
self.country = data["summaryProfile"]["country"]
if self.country not in financedatabase_model.get_countries():
similar_cmd = difflib.get_close_matches(
self.country,
financedatabase_model.get_countries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.country = similar_cmd[0]
self.sector = data["summaryProfile"]["sector"]
if self.sector not in financedatabase_model.get_sectors():
similar_cmd = difflib.get_close_matches(
self.sector,
financedatabase_model.get_sectors(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.sector = similar_cmd[0]
self.industry = data["summaryProfile"]["industry"]
if self.industry not in financedatabase_model.get_industries():
similar_cmd = difflib.get_close_matches(
self.industry,
financedatabase_model.get_industries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
self.industry = similar_cmd[0]
if "price" in data:
mktcap = data["price"]["marketCap"]
if mktcap < 2_000_000_000:
self.mktcap = "Small"
elif mktcap > 10_000_000_000:
self.mktcap = "Large"
else:
self.mktcap = "Mid"
def menu(
ticker: str,
queue: List[str] = None,
):
"""Sector and Industry Analysis Menu"""
sia_controller = SectorIndustryAnalysisController(ticker, queue)
an_input = "HELP_ME"
while True:
# There is a command in the queue
if sia_controller.queue and len(sia_controller.queue) > 0:
# If the command is quitting the menu we want to return in here
if sia_controller.queue[0] in ("q", "..", "quit"):
print("")
if len(sia_controller.queue) > 1:
return sia_controller.queue[1:]
return []
# Consume 1 element from the queue
an_input = sia_controller.queue[0]
sia_controller.queue = sia_controller.queue[1:]
# Print the current location because this was an instruction and we want user to know what was the action
if an_input and an_input.split(
" ")[0] in sia_controller.CHOICES_COMMANDS:
print(f"{get_flair()} /stocks/sia/ $ {an_input}")
# Get input command from user
else:
# Display help menu when entering on this menu from a level above
if an_input == "HELP_ME":
sia_controller.print_help()
# Get input from user using auto-completion
if session and gtff.USE_PROMPT_TOOLKIT and sia_controller.choices:
sia_controller.choices["industry"] = {
i: None
for i in financedatabase_model.get_industries(
country=sia_controller.country,
sector=sia_controller.sector)
}
sia_controller.choices["sector"] = {
s: None
for s in financedatabase_model.get_sectors(
industry=sia_controller.industry,
country=sia_controller.country)
}
sia_controller.choices["country"] = {
c: None
for c in financedatabase_model.get_countries(
industry=sia_controller.industry,
sector=sia_controller.sector)
}
completer = NestedCompleter.from_nested_dict(
sia_controller.choices)
an_input = session.prompt(
f"{get_flair()} /stocks/sia/ $ ",
completer=completer,
search_ignore_case=True,
)
# Get input from user without auto-completion
else:
an_input = input(f"{get_flair()} /stocks/sia/ $ ")
try:
# Process the input command
sia_controller.queue = sia_controller.switch(an_input)
except SystemExit:
print(
f"\nThe command '{an_input}' doesn't exist on the /stocks/sia menu.",
end="",
)
similar_cmd = difflib.get_close_matches(
an_input.split(" ")[0] if " " in an_input else an_input,
sia_controller.CHOICES,
n=1,
cutoff=0.7,
)
if similar_cmd:
if " " in an_input:
candidate_input = (
f"{similar_cmd[0]} {' '.join(an_input.split(' ')[1:])}"
)
if candidate_input == an_input:
an_input = ""
sia_controller.queue = []
print("\n")
continue
an_input = candidate_input
else:
an_input = similar_cmd[0]
print(f" Replacing by '{an_input}'.")
sia_controller.queue.insert(0, an_input)
else:
print("\n")
def metric_command(
finance_key: str,
finance_metric: str,
ticker: str = "",
):
"""Display financials bars comparing sectors, industry, analysis, countries, market cap and excluding exchanges.
Parameters
----------
finance_key: str
Select finance key from Yahoo Finance(e.g. financialData, defaultKeyStatistics, summaryProfile)
finance_metric: str
Select finance metric from Yahoo Finance (e.g. operatingCashflow, revenueGrowth, ebitda, freeCashflow)
ticker: str
Company ticker to use as the basis.
"""
logger.info("metrics")
exclude_exchanges: bool = True
limit: int = 10
ticker = ticker.lower()
if ticker:
data = yfinance.utils.get_json(f"https://finance.yahoo.com/quote/{ticker}")
if "summaryProfile" in data:
country = data["summaryProfile"]["country"]
if country not in financedatabase_model.get_countries():
similar_cmd = difflib.get_close_matches(
country,
financedatabase_model.get_countries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
country = similar_cmd[0]
sector = data["summaryProfile"]["sector"]
if sector not in financedatabase_model.get_sectors():
similar_cmd = difflib.get_close_matches(
sector,
financedatabase_model.get_sectors(),
n=1,
cutoff=0.7,
)
if similar_cmd:
sector = similar_cmd[0]
industry = data["summaryProfile"]["industry"]
if industry not in financedatabase_model.get_industries():
similar_cmd = difflib.get_close_matches(
industry,
financedatabase_model.get_industries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
industry = similar_cmd[0]
if "price" in data:
mktcap = data["price"]["marketCap"]
if mktcap < 2_000_000_000:
mktcap = "Small"
elif mktcap > 10_000_000_000:
mktcap = "Large"
else:
mktcap = "Mid"
stocks_data = financedatabase_model.get_stocks_data(
country, sector, industry, mktcap, exclude_exchanges
)
metric_data = {}
for symbol in list(stocks_data.keys()):
if finance_key in stocks_data[symbol] and "quoteType" in stocks_data[symbol]:
stock_name = stocks_data[symbol]["quoteType"]["longName"]
metric = (
stocks_data[symbol][finance_key][finance_metric]
if stocks_data[symbol][finance_key] is not None
and finance_metric in stocks_data[symbol][finance_key]
else None
)
if metric and stock_name:
metric_data[stock_name] = (metric, symbol)
if len(metric_data) > 1:
metric_data = dict(
OrderedDict(
sorted(metric_data.items(), key=lambda t: t[1][0], reverse=True)
)
)
company_names = list()
company_metrics = list()
company_tickers = list()
for name, metric in metric_data.items():
company_names.append(name)
company_metrics.append(metric[0])
company_tickers.append(metric[1])
company_name = np.array(company_names)[:limit]
company_metric = np.array(company_metrics)[:limit]
company_ticker = np.array(company_tickers)[:limit]
magnitude = 0
while max(company_metric) > 1_000 or abs(min(company_metric)) > 1_000:
company_metric = np.divide(company_metric, 1_000)
magnitude += 1
# check if the value is a percentage
if (magnitude == 0) and all(company_metric >= 0) and all(company_metric <= 1):
unit = "%"
company_metric = company_metric * 100
else:
unit = " KMBTP"[magnitude]
colors = [
"#ffed00",
"#ef7d00",
"#e4003a",
"#c13246",
"#822661",
"#48277c",
"#005ca9",
"#00aaff",
"#9b30d9",
"#af005f",
"#5f00af",
"#af87ff",
]
fig = go.Figure()
i = 0
for name, metric, tickers in zip(
company_name[::-1], company_metric[::-1], company_ticker[::-1]
):
if len(name.split(" ")) > 6 and len(name) > 40:
name = (
f'{" ".join(name.split(" ")[:4])}\n{" ".join(name.split(" ")[4:])}'
)
df_name = []
df_metric = []
df_tickers = []
df_name.append(name)
df_metric.append(metric)
df_tickers.append(tickers)
fig.add_trace(
go.Bar(
name=tickers,
y=[name],
x=[metric],
orientation="h",
marker=dict(
color=colors[i],
line=dict(color="rgb(248, 248, 249)", width=1),
),
),
)
i += 1
metric_title = (
"".join(
" " + char if char.isupper() else char.strip()
for char in finance_metric
)
.strip()
.capitalize()
)
benchmark = np.median(company_metric)
if unit != " ":
units = f" [{unit}] "
else:
units = " "
title = f"{metric_title.capitalize()}{units}with benchmark of {benchmark:.2f} {unit}<br>"
title += mktcap + " cap companies " if mktcap else "Companies "
if industry:
title += f"in {industry} industry<br>"
elif sector:
title += f"in {sector} sector<br>"
if country:
title += f"in {country}"
title += " " if (industry or sector) else "<br>"
title += (
"(excl. data from international exchanges)"
if exclude_exchanges
else "(incl. data from international exchanges)"
)
fig.add_vline(
x=benchmark,
fillcolor="grey",
opacity=1,
layer="below",
line_width=3,
line=dict(color="grey", dash="dash"),
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_layout(
margin=dict(l=40, r=0, t=100, b=20),
template=imps.PLT_CANDLE_STYLE_TEMPLATE,
title=title,
colorway=colors,
font=imps.PLT_FONT,
legend={"traceorder": "reversed"},
)
imagefile = "sia_metrics.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": "Stocks - Sector and Industry Analysis",
"description": plt_link,
"imagefile": imagefile,
}
