def display_performance(
s_group: str,
sort_col: str = "Name",
ascending: bool = True,
export: str = "",
):
"""View group (sectors, industry or country) performance data. [Source: Finviz]
Parameters
----------
s_group : str
group between sectors, industry or country
sort_col : str
Column to sort by
ascending : bool
Flag to sort in ascending order
export : str
Export data to csv,json,xlsx or png,jpg,pdf,svg file
"""
df_group = finviz_model.get_valuation_performance_data(
s_group, "performance")
df_group = df_group.rename(
columns={
"Perf Week": "Week",
"Perf Month": "Month",
"Perf Quart": "3Month",
"Perf Half": "6Month",
"Perf Year": "1Year",
"Perf YTD": "YTD",
"Avg Volume": "AvgVolume",
"Rel Volume": "RelVolume",
})
df_group["Week"] = df_group["Week"].apply(
lambda x: float(x.strip("%")) / 100)
df_group = df_group.sort_values(by=sort_col, ascending=ascending)
df_group["Volume"] = df_group["Volume"] / 1_000_000
df_group["AvgVolume"] = df_group["AvgVolume"] / 1_000_000
df_group = df_group.rename(columns={
"Volume": "Volume (1M)",
"AvgVolume": "AvgVolume (1M)"
})
print_rich_table(
df_group.fillna(""),
show_index=False,
headers=df_group.columns,
title="Group Performance Data",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"performance",
df_group,
)
def view_group_data(s_group: str, data_type: str, export: str):
"""View group (sectors, industry or country) valuation/performance/spectrum data
Parameters
----------
s_group : str
group between sectors, industry or country
data_type : str
select data type to see data between valuation, performance and spectrum
export : str
Export data to csv,json,xlsx or png,jpg,pdf,svg file
"""
if data_type in ("valuation", "performance"):
df_group = finviz_model.get_valuation_performance_data(s_group, data_type)
if gtff.USE_TABULATE_DF:
print(
tabulate(
df_group.fillna(""),
showindex=False,
floatfmt=".2f",
headers=df_group.columns,
tablefmt="fancy_grid",
)
)
else:
print(df_group.fillna("").to_string(index=False))
print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
data_type,
df_group,
)
elif data_type == "spectrum":
finviz_model.get_spectrum_data(s_group)
print("")
img = Image.open(s_group + ".jpg")
plt.imshow(img)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"spectrum",
)
img.show()
else:
print(
"Invalid data type. Choose between valuation, performance and spectrum.\n"
)
def display_valuation(
s_group: str,
sort_col: str = "Name",
ascending: bool = True,
export: str = "",
):
"""View group (sectors, industry or country) valuation data. [Source: Finviz]
Parameters
----------
s_group : str
group between sectors, industry or country
sort_col : str
Column to sort by
ascending : bool
Flag to sort in ascending order
export : str
Export data to csv,json,xlsx or png,jpg,pdf,svg file
"""
df_group = finviz_model.get_valuation_performance_data(
s_group, "valuation")
df_group["Market Cap"] = df_group["Market Cap"].apply(
lambda x: float(x.strip("B")))
df_group.columns = [col.replace(" ", "") for col in df_group.columns]
df_group = df_group.sort_values(by=sort_col, ascending=ascending)
df_group["Volume"] = df_group["Volume"] / 1_000_000
df_group = df_group.rename(columns={"Volume": "Volume (1M)"})
if gtff.USE_TABULATE_DF:
print(
tabulate(
df_group.fillna(""),
showindex=False,
headers=df_group.columns,
tablefmt="fancy_grid",
floatfmt=".2f",
))
else:
print(df_group.fillna("").to_string(index=False))
print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"valuation",
df_group,
)
def display_valuation(
s_group: str,
sort_col: str = "Name",
ascending: bool = True,
export: str = "",
):
"""View group (sectors, industry or country) valuation data. [Source: Finviz]
Parameters
----------
s_group : str
group between sectors, industry or country
sort_col : str
Column to sort by
ascending : bool
Flag to sort in ascending order
export : str
Export data to csv,json,xlsx or png,jpg,pdf,svg file
"""
df_group = finviz_model.get_valuation_performance_data(
s_group, "valuation")
df_group["Market Cap"] = df_group["Market Cap"].apply(lambda x: float(
x.strip("B")) if x.endswith("B") else float(x.strip("M")) / 1000)
df_group.columns = [col.replace(" ", "") for col in df_group.columns]
df_group = df_group.sort_values(by=sort_col, ascending=ascending)
df_group["Volume"] = df_group["Volume"] / 1_000_000
df_group = df_group.rename(columns={"Volume": "Volume (1M)"})
print_rich_table(
df_group.fillna(""),
show_index=False,
headers=list(df_group.columns),
title="Group Valuation Data",
)
console.print()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"valuation",
df_group,
)
def test_get_valuation_performance_data(data_type, recorder):
result_df = finviz_model.get_valuation_performance_data(
group="Sector", data_type=data_type)
recorder.capture(result_df)
async def valuation_command(ctx, economy_group="sector"):
"""Valuation of sectors, industry, country [Finviz]"""
d_economy_group = {
"sector": "Sector",
"industry": "Industry",
"basic_materials": "Industry (Basic Materials)",
"communication_services": "Industry (Communication Services)",
"consumer_cyclical": "Industry (Consumer Cyclical)",
"consumer_defensive": "Industry (Consumer Defensive)",
"energy": "Industry (Energy)",
"financial": "Industry (Financial)",
"healthcare": "Industry (Healthcare)",
"industrials": "Industry (Industrials)",
"real_estate": "Industry (Real Estate)",
"technology": "Industry (Technology)",
"utilities": "Industry (Utilities)",
"country": "Country (U.S. listed stocks only)",
"capitalization": "Capitalization",
}
try:
# Debug user input
if cfg.DEBUG:
logger.debug("econ-valuation %s", economy_group)
# Select default group
if economy_group == "":
if cfg.DEBUG:
logger.debug("Use default economy_group: 'sector'")
economy_group = "sector"
# Check for argument
possible_groups = list(d_economy_group.keys())
if economy_group not in possible_groups:
raise Exception(
f"Select a valid group from {', '.join(possible_groups)}" # nosec
)
group = d_economy_group[economy_group]
# Retrieve data
df_group = finviz_model.get_valuation_performance_data(
group, "valuation")
# Check for argument
if df_group.empty:
raise Exception("No available data found")
# Output data
df = pd.DataFrame(df_group)
df = df.replace(np.nan, 0)
df = df.set_axis(
[
"Name",
"MarketCap",
"P/E",
"FwdP/E",
"PEG",
"P/S",
"P/B",
"P/C",
"P/FCF",
"EPSpast5Y",
"EPSnext5Y",
"Salespast5Y",
"Change",
"Volume",
],
axis="columns",
)
df["P/E"] = pd.to_numeric(df["P/E"].astype(float))
df["FwdP/E"] = pd.to_numeric(df["FwdP/E"].astype(float))
df["EPSpast5Y"] = pd.to_numeric(df["EPSpast5Y"].astype(float))
df["EPSnext5Y"] = pd.to_numeric(df["EPSnext5Y"].astype(float))
df["Salespast5Y"] = pd.to_numeric(df["Salespast5Y"].astype(float))
df["Volume"] = pd.to_numeric(df["Volume"].astype(float))
df["Volume"] = df["Volume"] / 1_000_000
formats = {
"P/E": "{:.2f}",
"FwdP/E": "{:.2f}",
"EPSpast5Y": "{:.2f}",
"EPSnext5Y": "{:.2f}",
"Salespast5Y": "{:.2f}",
"Change": "{:.2f}",
"Volume": "{:.0f}M",
}
for col, value in formats.items():
df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640
df = df.fillna("")
df.set_index("Name", inplace=True)
dindex = len(df.index)
fig = df2img.plot_dataframe(
df,
fig_size=(1600, (40 + (50 * dindex))),
col_width=[12, 5, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 4, 4],
tbl_cells=dict(
align=["left", "center"],
height=35,
),
template="plotly_dark",
font=dict(
family="Consolas",
size=20,
),
paper_bgcolor="rgba(0, 0, 0, 0)",
)
imagefile = "econ-valuation.png"
df2img.save_dataframe(fig=fig, filename=imagefile)
image = Image.open(imagefile)
image = autocrop_image(image, 0)
image.save(imagefile, "PNG", quality=100)
image = disnake.File(imagefile)
title = f"Economy: [Finviz] Valuation {group}"
embed = disnake.Embed(title=title, colour=cfg.COLOR)
embed.set_image(url=f"attachment://{imagefile}")
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
os.remove(imagefile)
await ctx.send(embed=embed, file=image)
except Exception as e:
embed = disnake.Embed(
title="ERROR Economy: [Finviz] Valuation",
colour=cfg.COLOR,
description=e,
)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
await ctx.send(embed=embed, delete_after=30.0)
def performance_command(economy_group="sector"):
"""Performance of sectors, industry, country [Finviz]"""
d_economy_group = {
"sector": "Sector",
"industry": "Industry",
"basic_materials": "Industry (Basic Materials)",
"communication_services": "Industry (Communication Services)",
"consumer_cyclical": "Industry (Consumer Cyclical)",
"consumer_defensive": "Industry (Consumer Defensive)",
"energy": "Industry (Energy)",
"financial": "Industry (Financial)",
"healthcare": "Industry (Healthcare)",
"industrials": "Industry (Industrials)",
"real_estate": "Industry (Real Estate)",
"technology": "Industry (Technology)",
"utilities": "Industry (Utilities)",
"country": "Country (U.S. listed stocks only)",
"capitalization": "Capitalization",
}
# Debug user input
if imps.DEBUG:
logger.debug("econ-performance %s", economy_group)
# Select default group
if not economy_group:
if imps.DEBUG:
logger.debug("Use default economy_group: 'sector'")
economy_group = "sector"
# Check for argument
possible_groups = list(d_economy_group.keys())
if economy_group not in possible_groups:
possible_group_list = ", ".join(possible_groups)
raise Exception(f"Select a valid group from {possible_group_list}") # nosec
group = d_economy_group[economy_group]
# Retrieve data
df_group = finviz_model.get_valuation_performance_data(group, "performance")
# Check for argument
if df_group.empty:
raise Exception("No available data found")
# Output data
df = pd.DataFrame(df_group)
df["Volume"] = df["Volume"] / 1_000_000
df["Avg Volume"] = df["Avg Volume"] / 1_000_000
formats = {
"Perf Month": "{:.2f}",
"Perf Quart": "{:.2f}%",
"Perf Half": "{:.2f}%",
"Perf Year": "{:.2f}%",
"Perf YTD": "{:.2f}%",
"Avg Volume": "{:.0f}M",
"Change": "{:.2f}%",
"Volume": "{:.0f}M",
}
for col, value in formats.items():
df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640
df = df.set_axis(
[
"Name",
"Week",
"Month",
"3 Mouth",
"6 Mouth",
"1 Year",
"YTD",
"Recom",
"Avg Volume",
"Rel Volume",
"Change",
"Volume",
],
axis="columns",
)
df.set_index("Name", inplace=True)
df = df.fillna("-")
df = df.applymap(lambda x: lambda_long_number_format(x, 2))
title = f"Economy: [WSJ] Performance {group}"
dindex = len(df.index)
if dindex > 2:
embeds: list = []
# Output
i, i2, end = 0, 0, 2
df_pg, embeds_img, images_list = pd.DataFrame(), [], []
while i < dindex:
df_pg = df.iloc[i:end]
df_pg.append(df_pg)
fig = imps.plot_df(
df_pg.transpose(),
fig_size=(800, 720),
col_width=[6, 10],
tbl_header=imps.PLT_TBL_HEADER,
tbl_cells=imps.PLT_TBL_CELLS,
font=imps.PLT_TBL_FONT,
row_fill_color=imps.PLT_TBL_ROW_COLORS,
paper_bgcolor="rgba(0, 0, 0, 0)",
)
fig.update_traces(cells=(dict(align=["center", "right"])))
imagefile = "econ_performance.png"
imagefile = imps.save_image(imagefile, fig)
if imps.IMAGES_URL or not imps.IMG_HOST_ACTIVE:
image_link = imps.multi_image(imagefile)
images_list.append(imagefile)
else:
image_link = imps.multi_image(imagefile)
embeds_img.append(
f"{image_link}",
)
embeds.append(
disnake.Embed(
title=title,
colour=imps.COLOR,
),
)
i2 += 1
i += 2
end += 2
# Author/Footer
for i in range(0, i2):
embeds[i].set_author(
name=imps.AUTHOR_NAME,
url=imps.AUTHOR_URL,
icon_url=imps.AUTHOR_ICON_URL,
)
embeds[i].set_footer(
text=imps.AUTHOR_NAME,
icon_url=imps.AUTHOR_ICON_URL,
)
i = 0
for i in range(0, i2):
embeds[i].set_image(url=embeds_img[i])
i += 1
embeds[0].set_footer(text=f"Page 1 of {len(embeds)}")
choices = [
disnake.SelectOption(label="Home", value="0", emoji="🟢"),
]
output = {
"view": imps.Menu,
"title": title,
"embed": embeds,
"choices": choices,
"embeds_img": embeds_img,
"images_list": images_list,
}
else:
fig = imps.plot_df(
df.transpose(),
fig_size=(800, 720),
col_width=[6, 10],
tbl_header=imps.PLT_TBL_HEADER,
tbl_cells=imps.PLT_TBL_CELLS,
font=imps.PLT_TBL_FONT,
row_fill_color=imps.PLT_TBL_ROW_COLORS,
paper_bgcolor="rgba(0, 0, 0, 0)",
)
fig.update_traces(cells=(dict(align=["center", "right"])))
imagefile = imps.save_image("econ_performance.png", fig)
output = {
"title": title,
"imagefile": imagefile,
}
return output
async def performance_command(ctx, arg="sector"):
"""Gets the performance data of a entered sector from GST and sends it
Parameters
-----------
arg: str
sector (or any other input in the economy_group), -h or help
Returns
-------
discord message
Sends a message containing an embed of the performance data of the given arg
with pagination to the user
"""
economy_group = {
"sector": "Sector",
"industry": "Industry",
"basic_materials": "Industry (Basic Materials)",
"communication services": "Industry (Communication Services)",
"consumer_cyclical": "Industry (Consumer Cyclical)",
"consumer_defensive": "Industry (Consumer Defensive)",
"energy": "Industry (Energy)",
"financial": "Industry (Financial)",
"healthcare": "Industry (Healthcare)",
"industrials": "Industry (Industrials)",
"real_estate": "Industry (Real Estate)",
"technology": "Industry (Technology)",
"utilities": "Industry (Utilities)",
"country": "Country (U.S. listed stocks only)",
"capitalization": "Capitalization",
}
try:
# Debug
if cfg.DEBUG:
print(f"!stocks.economy.performance {arg}")
# Help
if arg == "-h" or arg == "help":
help_txt = "Group performance [Source: Finviz]\n"
possible_args = ""
for k, v in economy_group.items():
possible_args += f"\n{k}: {v}"
help_txt += "\nPossible arguments:\n"
help_txt += "<GROUP> Groups to get data from. Default: sector\n"
help_txt += f"The choices are:{possible_args}"
embed = discord.Embed(
title="Economy: [Finviz] Performance HELP",
description=help_txt,
colour=cfg.COLOR,
)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
await ctx.send(embed=embed)
else:
# Parse argument
try:
group = economy_group[arg]
except KeyError:
title = "ERROR Economy: [Finviz] Performance"
embed = discord.Embed(title=title, colour=cfg.COLOR)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
embed.set_description(
f"Entered group argument: {arg}"
"\nEnter a valid group argument, example: sector")
await ctx.send(embed=embed)
if cfg.DEBUG:
print("ERROR: Bad group argument entered")
return
df_group = finviz_model.get_valuation_performance_data(
group, "performance")
future_column_name = df_group["Name"]
df_group = df_group.transpose()
df_group.columns = future_column_name
df_group.drop("Name")
columns = []
initial_str = "Page 0: Overview"
i = 1
for col_name in df_group.columns.values:
initial_str += f"\nPage {i}: {col_name}"
i += 1
columns.append(
discord.Embed(
title=f"Economy: [Finviz] Performance {group}",
description=initial_str,
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
))
for column in df_group.columns.values:
columns.append(
discord.Embed(
description="```" +
df_group[column].fillna("").to_string() + "```",
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
))
await pagination(columns, ctx)
except Exception as e:
title = "INTERNAL ERROR"
embed = discord.Embed(title=title, colour=cfg.COLOR)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
embed.set_description(
"Try updating the bot, make sure DEBUG is True in the config "
"and restart it.\nIf the error still occurs open a issue at: "
"https://github.com/GamestonkTerminal/GamestonkTerminal/issues"
f"\n{e}")
await ctx.send(embed=embed)
if cfg.DEBUG:
print(e)
def valuation_command(economy_group="sector"):
"""Valuation of sectors, industry, country [Finviz]"""
d_economy_group = {
"sector": "Sector",
"industry": "Industry",
"basic_materials": "Industry (Basic Materials)",
"communication_services": "Industry (Communication Services)",
"consumer_cyclical": "Industry (Consumer Cyclical)",
"consumer_defensive": "Industry (Consumer Defensive)",
"energy": "Industry (Energy)",
"financial": "Industry (Financial)",
"healthcare": "Industry (Healthcare)",
"industrials": "Industry (Industrials)",
"real_estate": "Industry (Real Estate)",
"technology": "Industry (Technology)",
"utilities": "Industry (Utilities)",
"country": "Country (U.S. listed stocks only)",
"capitalization": "Capitalization",
}
# Debug user input
if cfg.DEBUG:
logger.debug("econ-valuation %s", economy_group)
# Select default group
if economy_group == "":
if cfg.DEBUG:
logger.debug("Use default economy_group: 'sector'")
economy_group = "sector"
# Check for argument
possible_groups = list(d_economy_group.keys())
if economy_group not in possible_groups:
raise Exception(
f"Select a valid group from {', '.join(possible_groups)}" # nosec
)
group = d_economy_group[economy_group]
# Retrieve data
df_group = finviz_model.get_valuation_performance_data(group, "valuation")
# Check for argument
if df_group.empty:
raise Exception("No available data found")
# Output data
df = pd.DataFrame(df_group)
df = df.replace(np.nan, 0)
df = df.set_axis(
[
"Name",
"MarketCap",
"P/E",
"FwdP/E",
"PEG",
"P/S",
"P/B",
"P/C",
"P/FCF",
"EPSpast5Y",
"EPSnext5Y",
"Salespast5Y",
"Change",
"Volume",
],
axis="columns",
)
df["P/E"] = pd.to_numeric(df["P/E"].astype(float))
df["FwdP/E"] = pd.to_numeric(df["FwdP/E"].astype(float))
df["EPSpast5Y"] = pd.to_numeric(df["EPSpast5Y"].astype(float))
df["EPSnext5Y"] = pd.to_numeric(df["EPSnext5Y"].astype(float))
df["Salespast5Y"] = pd.to_numeric(df["Salespast5Y"].astype(float))
df["Volume"] = pd.to_numeric(df["Volume"].astype(float))
df["Volume"] = df["Volume"] / 1_000_000
formats = {
"P/E": "{:.2f}",
"FwdP/E": "{:.2f}",
"EPSpast5Y": "{:.2f}",
"EPSnext5Y": "{:.2f}",
"Salespast5Y": "{:.2f}",
"Change": "{:.2f}",
"Volume": "{:.0f}M",
}
for col, value in formats.items():
df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640
df = df.fillna("")
df.set_index("Name", inplace=True)
dindex = len(df.index)
fig = df2img.plot_dataframe(
df,
fig_size=(1600, (40 + (50 * dindex))),
col_width=[12, 5, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 4, 4],
tbl_cells=dict(
align=["left", "center"],
height=35,
),
template="plotly_dark",
font=dict(
family="Consolas",
size=20,
),
paper_bgcolor="rgba(0, 0, 0, 0)",
)
imagefile = save_image("econ-valuation.png", fig)
return {
"title": "Economy: [Finviz] Valuation",
"imagefile": imagefile,
}
async def valuation_command(ctx, economy_group="sector"):
"""Valuation of sectors, industry, country [Finviz]"""
d_economy_group = {
"sector": "Sector",
"industry": "Industry",
"basic_materials": "Industry (Basic Materials)",
"communication_services": "Industry (Communication Services)",
"consumer_cyclical": "Industry (Consumer Cyclical)",
"consumer_defensive": "Industry (Consumer Defensive)",
"energy": "Industry (Energy)",
"financial": "Industry (Financial)",
"healthcare": "Industry (Healthcare)",
"industrials": "Industry (Industrials)",
"real_estate": "Industry (Real Estate)",
"technology": "Industry (Technology)",
"utilities": "Industry (Utilities)",
"country": "Country (U.S. listed stocks only)",
"capitalization": "Capitalization",
}
try:
# Debug user input
if cfg.DEBUG:
logger.debug("!economy.valuation %s", economy_group)
# Select default group
if economy_group == "":
if cfg.DEBUG:
logger.debug("Use default economy_group: 'sector'")
economy_group = "sector"
# Check for argument
possible_groups = list(d_economy_group.keys())
if economy_group not in possible_groups:
raise Exception(
f"Select a valid group from {', '.join(possible_groups)}" # nosec
)
group = d_economy_group[economy_group]
# Retrieve data
df_group = finviz_model.get_valuation_performance_data(group, "valuation")
# Debug user output
if cfg.DEBUG:
logger.debug(df_group.to_string())
# Output data
future_column_name = df_group["Name"]
df_group = df_group.transpose()
df_group.columns = future_column_name
df_group.drop("Name")
columns = []
initial_str = "Page 0: Overview"
i = 1
for col_name in df_group.columns.values:
initial_str += f"\nPage {i}: {col_name}"
i += 1
columns.append(
discord.Embed(
title=f"Economy: [Finviz] Valuation {group}",
description=initial_str,
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
)
for column in df_group.columns.values:
columns.append(
discord.Embed(
description="```" + df_group[column].fillna("").to_string() + "```",
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
)
await pagination(columns, ctx)
except Exception as e:
embed = discord.Embed(
title="ERROR Economy: [Finviz] Valuation",
colour=cfg.COLOR,
description=e,
)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
await ctx.send(embed=embed)
def performance_command(economy_group="sector"):
"""Performance of sectors, industry, country [Finviz]"""
d_economy_group = {
"sector": "Sector",
"industry": "Industry",
"basic_materials": "Industry (Basic Materials)",
"communication_services": "Industry (Communication Services)",
"consumer_cyclical": "Industry (Consumer Cyclical)",
"consumer_defensive": "Industry (Consumer Defensive)",
"energy": "Industry (Energy)",
"financial": "Industry (Financial)",
"healthcare": "Industry (Healthcare)",
"industrials": "Industry (Industrials)",
"real_estate": "Industry (Real Estate)",
"technology": "Industry (Technology)",
"utilities": "Industry (Utilities)",
"country": "Country (U.S. listed stocks only)",
"capitalization": "Capitalization",
}
# Debug user input
if cfg.DEBUG:
logger.debug("econ-performance %s", economy_group)
# Select default group
if not economy_group:
if cfg.DEBUG:
logger.debug("Use default economy_group: 'sector'")
economy_group = "sector"
# Check for argument
possible_groups = list(d_economy_group.keys())
if economy_group not in possible_groups:
possible_group_list = ", ".join(possible_groups)
raise Exception(
f"Select a valid group from {possible_group_list}") # nosec
group = d_economy_group[economy_group]
# Retrieve data
df_group = finviz_model.get_valuation_performance_data(
group, "performance")
# Check for argument
if df_group.empty:
raise Exception("No available data found")
# Output data
df = pd.DataFrame(df_group)
df = df.replace(np.nan, 0)
df["Volume"] = df["Volume"] / 1_000_000
df["Avg Volume"] = df["Avg Volume"] / 1_000_000
formats = {
"Perf Month": "{:.2f}",
"Perf Quart": "{:.2f}",
"Perf Half": "{:.2f}",
"Perf Year": "{:.2f}",
"Perf YTD": "{:.2f}",
"Avg Volume": "{:.0f}M",
"Change": "{:.2f}",
"Volume": "{:.0f}M",
}
for col, value in formats.items():
df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640
df = df.set_axis(
[
"Name",
"Week",
"Month",
"3Month",
"6Month",
"1Year",
"YTD",
"Recom",
"Avg Vol.",
"RelVolume",
"Change",
"Volume",
],
axis="columns",
)
df = df.fillna("")
df.set_index("Name", inplace=True)
dindex = len(df.index)
fig = df2img.plot_dataframe(
df,
fig_size=(1500, (40 + (50 * dindex))),
col_width=[10, 3, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3.5],
tbl_cells=dict(
align=["left", "center"],
height=35,
),
template="plotly_dark",
font=dict(
family="Consolas",
size=20,
),
paper_bgcolor="rgba(0, 0, 0, 0)",
)
imagefile = save_image("econ-performance.png", fig)
return {
"title": f"Economy: [WSJ] Performance {group}",
"imagefile": imagefile,
}
