def test_get_stocks_data(mocker, recorder):
target = "gamestonk_terminal.stocks.sector_industry_analysis.financedatabase_model.yf.utils.get_json"
mocker.patch(target=target, return_value=YF_UTILS_GET_DICT)
result = financedatabase_model.get_stocks_data(
country="Georgia",
sector="Industrials",
industry="Conglomerates",
marketcap="",
exclude_exchanges=True,
)
recorder.capture(result)
def display_bars_financials(
finance_key: str,
finance_metric: str,
country: str,
sector: str,
industry: str,
marketcap: str = "",
exclude_exchanges: bool = True,
limit: int = 10,
export: str = "",
raw: bool = False,
already_loaded_stocks_data: Dict = None,
):
"""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)
country: str
Search by country to find stocks matching the criteria.
sector : str
Search by sector to find stocks matching the criteria.
industry : str
Search by industry to find stocks matching the criteria.
marketcap : str
Select stocks based on the market cap.
exclude_exchanges: bool
When you wish to include different exchanges use this boolean.
limit: int
Limit amount of companies displayed
export: str
Format to export data as
raw: bool
Output all raw data
already_loaded_stocks_data: Dict
Dictionary of filtered stocks data that has been loaded before
Returns
-------
dict
Dictionary of filtered stocks data
list
List of tickers filtered
"""
if already_loaded_stocks_data:
stocks_data = already_loaded_stocks_data
else:
stocks_data = financedatabase_model.get_stocks_data(
country, sector, industry, marketcap, 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])
metric_finance_col = ("".join(
" " + char if char.isupper() else char.strip()
for char in finance_metric).strip().capitalize())
df_all = pd.DataFrame({
"Company": company_names,
metric_finance_col: company_metrics
})
if len(df_all) > limit:
console.print(
f"Limiting the amount of companies displayed to {limit}.")
company_name = np.array(company_names)[:limit]
company_metric = np.array(company_metrics)[:limit]
company_ticker = np.array(company_tickers)[:limit]
df = df_all.head(limit)
if raw:
print_rich_table(df,
headers=list(df.columns),
show_index=False,
title="Bars Financials")
else:
plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
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]
for name, metric, ticker 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:])}'
plt.barh(name, metric, label=ticker)
handles, _ = plt.gca().get_legend_handles_labels()
plt.legend(reversed(handles),
reversed(company_ticker[::-1]),
loc="lower right")
metric_title = ("".join(
" " + char if char.isupper() else char.strip()
for char in finance_metric).strip().capitalize())
benchmark = np.median(company_metric)
plt.axvline(x=benchmark, lw=3, ls="--", c="k")
if unit != " ":
units = f" [{unit}] "
else:
units = " "
title = f"{metric_title.capitalize()}{units}with benchmark of {benchmark:.2f} {unit}\n"
title += marketcap + " cap companies " if marketcap else "Companies "
if industry:
title += f"in {industry} industry\n"
elif sector:
title += f"in {sector} sector\n"
if country:
title += f"in {country}"
title += " " if (industry or sector) else "\n"
title += ("(excluding data from international exchanges)"
if exclude_exchanges else
"(including data from international exchanges)")
plt.title(title)
if gtff.USE_ION:
plt.ion()
plt.tight_layout()
plt.show()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
finance_metric,
df_all,
)
if not export:
console.print("")
return stocks_data, company_tickers
def display_bars_financials(
finance_key: str,
finance_metric: str,
country: str,
sector: str,
industry: str,
marketcap: str = "",
exclude_exchanges: bool = True,
limit: int = 10,
export: str = "",
raw: bool = False,
already_loaded_stocks_data: Dict = None,
external_axes: Optional[List[plt.Axes]] = None,
):
"""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)
country: str
Search by country to find stocks matching the criteria.
sector : str
Search by sector to find stocks matching the criteria.
industry : str
Search by industry to find stocks matching the criteria.
marketcap : str
Select stocks based on the market cap.
exclude_exchanges: bool
When you wish to include different exchanges use this boolean.
limit: int
Limit amount of companies displayed
export: str
Format to export data as
raw: bool
Output all raw data
already_loaded_stocks_data: Dict
Dictionary of filtered stocks data that has been loaded before
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
Returns
-------
dict
Dictionary of filtered stocks data
list
List of tickers filtered
"""
if already_loaded_stocks_data:
stocks_data = already_loaded_stocks_data
else:
stocks_data = financedatabase_model.get_stocks_data(
country, sector, industry, marketcap, 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])
metric_finance_col = ("".join(
" " + char if char.isupper() else char.strip()
for char in finance_metric).strip().capitalize())
df_all = pd.DataFrame({
"Company": company_names,
metric_finance_col: company_metrics
})
if len(df_all) > limit:
console.print(
f"Limiting the amount of companies displayed to {limit}.")
company_name = np.array(company_names)[:limit]
company_metric = np.array(company_metrics)[:limit]
company_ticker = np.array(company_tickers)[:limit]
df = df_all.head(limit)
if raw:
print_rich_table(df,
headers=list(df.columns),
show_index=False,
title="Bars Financials")
else:
# This plot has 1 axis
if not external_axes:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
if len(external_axes) != 1:
logger.error("Expected list of one axis item.")
console.print(
"[red]Expected list of one axis item./n[/red]")
# set returns statement to be compatible with others
return dict(), list()
(ax, ) = external_axes
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] if magnitude != 0 else ""
colors = iter(theme.get_colors())
for name, metric, ticker 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:])}'
ax.barh(f"{name} ({ticker})",
metric,
label=ticker,
color=next(colors))
metric_title = ("".join(
" " + char if char.isupper() else char.strip()
for char in finance_metric).strip().capitalize())
benchmark = np.median(company_metric)
ax.axvline(x=benchmark, lw=3, ls="--", c="grey")
title = f"The {metric_title.title()} (benchmark: {benchmark:.2f}{unit}) of "
title += marketcap + " cap companies " if marketcap else "Companies "
if industry:
title += f"in {industry} industry "
elif sector:
title += f"in {sector} sector "
if country:
title += f"in {country}"
title += " " if (industry or sector) else ""
title += ("(excl. data from international exchanges)"
if exclude_exchanges else
"(incl. data from international exchanges)")
ax.set_title(title, wrap=True, fontsize=11)
labels = ax.get_xticks().tolist()
ax.set_xticks(labels)
ax.set_xticklabels([f"{label}{unit}" for label in labels])
theme.style_primary_axis(ax)
if not external_axes:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
finance_metric,
df_all,
)
return stocks_data, company_tickers
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,
}