def test_get_companies_per_sector_in_country(recorder):
result = financedatabase_model.get_companies_per_sector_in_country(
country="Georgia",
mktcap="Small Cap",
exclude_exchanges=True,
)
recorder.capture(result)
def display_companies_per_sector_in_country(
country: str,
mktcap: str = "",
exclude_exchanges: bool = True,
export: str = "",
raw: bool = False,
max_sectors_to_display: int = 15,
min_pct_to_display_sector: float = 0.015,
):
"""Display number of companies per sector in a specific country (and market cap). [Source: Finance Database]
Parameters
----------
country: str
Select country to get number of companies by each sector
mktcap: str
Select market cap of companies to consider from Small, Mid and Large
exclude_exchanges : bool
Exclude international exchanges
export: str
Format to export data as
raw: bool
Output all raw data
max_sectors_to_display: int
Maximum number of sectors to display
min_pct_to_display_sector: float
Minimum percentage to display sector
"""
companies_per_sector = financedatabase_model.get_companies_per_sector_in_country(
country, mktcap, exclude_exchanges)
companies_per_sector = dict(
OrderedDict(
sorted(companies_per_sector.items(),
key=lambda t: t[1],
reverse=True)))
for key, value in companies_per_sector.copy().items():
if value == 0:
del companies_per_sector[key]
if not companies_per_sector:
console.print("No companies found with these parameters!\n")
return
df = pd.DataFrame.from_dict(companies_per_sector, orient="index")
df.index.name = "Sector"
df.columns = ["Number of companies"]
df["Number of companies"] = df["Number of companies"].astype(int)
title = mktcap + " cap companies " if mktcap else "Companies "
title += f"per sector in {country}"
title += " excluding exchanges" if exclude_exchanges else " including exchanges"
if raw:
print_rich_table(df,
headers=list(df.columns),
show_index=True,
title=title)
else:
colors = [
"b",
"g",
"r",
"c",
"m",
"y",
"k",
"tab:blue",
"tab:orange",
"tab:gray",
"lightcoral",
"yellow",
"saddlebrown",
"lightblue",
"olive",
]
if len(companies_per_sector) > 1:
total_num_companies = sum(companies_per_sector.values())
min_companies_to_represent = round(min_pct_to_display_sector *
total_num_companies)
filter_sectors_to_display = (np.array(
list(companies_per_sector.values())) >
min_companies_to_represent)
if any(filter_sectors_to_display):
if not all(filter_sectors_to_display):
num_sectors_to_display = np.where(
~filter_sectors_to_display)[0][0]
if num_sectors_to_display < max_sectors_to_display:
max_sectors_to_display = num_sectors_to_display
else:
console.print(
"The minimum threshold percentage specified is too high, thus it will be ignored."
)
if len(companies_per_sector) > max_sectors_to_display:
companies_per_sector_sliced = dict(
list(
companies_per_sector.items())[:max_sectors_to_display -
1])
companies_per_sector_sliced["Others"] = sum(
dict(
list(companies_per_sector.items())
[max_sectors_to_display - 1:]).values())
legend, values = zip(*companies_per_sector_sliced.items())
else:
legend, values = zip(*companies_per_sector.items())
plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.pie(
values,
labels=legend,
colors=colors,
wedgeprops={
"linewidth": 0.5,
"edgecolor": "white"
},
labeldistance=1.05,
startangle=90,
)
plt.title(title)
plt.tight_layout()
if gtff.USE_ION:
plt.ion()
plt.show()
elif len(companies_per_sector) == 1:
console.print(
f"Only 1 sector found '{list(companies_per_sector.keys())[0]}'. No pie chart will be depicted."
)
else:
console.print("No sector found. No pie chart will be depicted.")
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"cps",
df,
)
def display_companies_per_sector_in_country(
country: str,
mktcap: str = "",
exclude_exchanges: bool = True,
export: str = "",
raw: bool = False,
max_sectors_to_display: int = 15,
min_pct_to_display_sector: float = 0.015,
external_axes: Optional[List[plt.Axes]] = None,
):
"""Display number of companies per sector in a specific country (and market cap). [Source: Finance Database]
Parameters
----------
country: str
Select country to get number of companies by each sector
mktcap: str
Select market cap of companies to consider from Small, Mid and Large
exclude_exchanges : bool
Exclude international exchanges
export: str
Format to export data as
raw: bool
Output all raw data
max_sectors_to_display: int
Maximum number of sectors to display
min_pct_to_display_sector: float
Minimum percentage to display sector
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
companies_per_sector = financedatabase_model.get_companies_per_sector_in_country(
country, mktcap, exclude_exchanges)
companies_per_sector = dict(
OrderedDict(
sorted(companies_per_sector.items(),
key=lambda t: t[1],
reverse=True)))
for key, value in companies_per_sector.copy().items():
if value == 0:
del companies_per_sector[key]
if not companies_per_sector:
console.print("No companies found with these parameters!\n")
return
df = pd.DataFrame.from_dict(companies_per_sector, orient="index")
df.index.name = "Sector"
df.columns = ["Number of companies"]
df["Number of companies"] = df["Number of companies"].astype(int)
title = mktcap + " cap companies " if mktcap else "Companies "
title += f"in {country}\n"
title += "excl. exchanges" if exclude_exchanges else " incl. exchanges"
if raw:
print_rich_table(df,
headers=list(df.columns),
show_index=True,
title=title)
else:
colors = theme.get_colors()
if len(companies_per_sector) > 1:
total_num_companies = sum(companies_per_sector.values())
min_companies_to_represent = round(min_pct_to_display_sector *
total_num_companies)
filter_sectors_to_display = (np.array(
list(companies_per_sector.values())) >
min_companies_to_represent)
if any(filter_sectors_to_display):
if not all(filter_sectors_to_display):
num_sectors_to_display = np.where(
~filter_sectors_to_display)[0][0]
if num_sectors_to_display < max_sectors_to_display:
max_sectors_to_display = num_sectors_to_display
else:
console.print(
"The minimum threshold percentage specified is too high, thus it will be ignored."
)
if len(companies_per_sector) > max_sectors_to_display:
companies_per_sector_sliced = dict(
list(
companies_per_sector.items())[:max_sectors_to_display -
1])
companies_per_sector_sliced["Others"] = sum(
dict(
list(companies_per_sector.items())
[max_sectors_to_display - 1:]).values())
legend, values = zip(*companies_per_sector_sliced.items())
else:
legend, values = zip(*companies_per_sector.items())
# 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]")
return
(ax, ) = external_axes
plt.pie(
values,
labels=legend,
colors=colors,
wedgeprops={
"linewidth": 0.5,
"edgecolor": "white"
},
labeldistance=1.05,
startangle=45,
)
ax.set_title(title, fontsize=14)
if not external_axes:
theme.visualize_output()
elif len(companies_per_sector) == 1:
console.print(
f"Only 1 sector found '{list(companies_per_sector.keys())[0]}'. No pie chart will be depicted."
)
else:
console.print("No sector found. No pie chart will be depicted.")
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"cps",
df,
)
def cps_command(
country: str,
mktcap: str = "",
exclude_exchanges: bool = True,
raw: bool = False,
max_sectors_to_display: int = 15,
min_pct_to_display_sector: float = 0.015,
):
"""Display number of companies per sector in a specific country (and market cap). [Source: Finance Database]
Parameters
----------
country: str
Select country to get number of companies by each sector
mktcap: str
Select market cap of companies to consider from Small, Mid and Large
exclude_exchanges : bool
Exclude international exchanges
raw: bool
Output all raw data
max_sectors_to_display: int
Maximum number of sectors to display
min_pct_to_display_sector: float
Minimum percentage to display sector
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
companies_per_sector = financedatabase_model.get_companies_per_sector_in_country(
country, mktcap, exclude_exchanges
)
companies_per_sector = dict(
OrderedDict(
sorted(companies_per_sector.items(), key=lambda t: t[1], reverse=True)
)
)
for key, value in companies_per_sector.copy().items():
if value == 0:
del companies_per_sector[key]
if not companies_per_sector:
raise Exception("No companies found with these parameters!\n")
df = pd.DataFrame.from_dict(companies_per_sector, orient="index")
df.index.name = "Sector"
df.columns = ["Number of companies"]
df["Number of companies"] = df["Number of companies"].astype(int)
title = mktcap + " Cap Companies " if mktcap else "Companies "
title += f"in {country}"
title += " Excl. Exchanges" if exclude_exchanges else " incl. Exchanges"
title = textwrap.fill(title, 40)
title_plot = textwrap.indent(text=title, prefix="<br>")
if raw:
output = {
"title": "Stocks - Sector and Industry Analysis",
"description": f"{df}",
}
else:
if len(companies_per_sector) > 1:
total_num_companies = sum(companies_per_sector.values())
min_companies_to_represent = round(
min_pct_to_display_sector * total_num_companies
)
filter_sectors_to_display = (
np.array(list(companies_per_sector.values()))
> min_companies_to_represent
)
if any(filter_sectors_to_display):
if not all(filter_sectors_to_display):
num_sectors_to_display = np.where(~filter_sectors_to_display)[0][0]
if num_sectors_to_display < max_sectors_to_display:
max_sectors_to_display = num_sectors_to_display
else:
raise Exception(
"The minimum threshold percentage specified is too high, thus it will be ignored."
)
if len(companies_per_sector) > max_sectors_to_display:
companies_per_sector_sliced = dict(
list(companies_per_sector.items())[: max_sectors_to_display - 1]
)
companies_per_sector_sliced["Others"] = sum(
dict(
list(companies_per_sector.items())[max_sectors_to_display - 1 :]
).values()
)
legend, values = zip(*companies_per_sector_sliced.items())
else:
legend, values = zip(*companies_per_sector.items())
fig = go.Figure()
colors = [
"#ffed00",
"#ef7d00",
"#e4003a",
"#c13246",
"#822661",
"#48277c",
"#005ca9",
"#00aaff",
"#9b30d9",
"#af005f",
"#5f00af",
"#af87ff",
]
fig.add_trace(
go.Pie(
labels=legend,
values=values,
textinfo="label+percent",
showlegend=False,
)
)
fig.update_traces(
textposition="outside",
textfont_size=15,
marker=dict(
colors=colors,
line=dict(color="#F5EFF3", width=0.8),
),
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_layout(
margin=dict(l=40, r=0, t=80, b=40),
title=dict(
text=title_plot,
y=1,
x=0.5,
xanchor="center",
yanchor="top",
),
template=imps.PLT_CANDLE_STYLE_TEMPLATE,
colorway=colors,
font=imps.PLT_FONT,
)
elif len(companies_per_sector) == 1:
raise Exception(
f"Only 1 sector found '{list(companies_per_sector.keys())[0]}'. No pie chart will be depicted."
)
else:
raise Exception("No sector found. No pie chart will be depicted.")
imagefile = "sia_cps.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)
output = {
"title": "Stocks - Sector and Industry Analysis",
"description": plt_link,
"imagefile": imagefile,
}
return output