def test_get_screener_data_no_limit(recorder):
result_df = finviz_model.get_screener_data(
preset_loaded="oversold",
data_type="overview",
limit=-1,
ascend=True,
)
recorder.capture(result_df)
def test_get_screener_data(data_type, recorder):
result_df = finviz_model.get_screener_data(
preset_loaded="top_gainers",
data_type=data_type,
limit=2,
ascend=True,
)
recorder.capture(result_df)
def test_get_screener_data_no_preset_loaded(mocker, recorder):
# MOCK D_SIGNALS
mocker.patch.object(
target=finviz_model,
attribute="d_signals",
new=[],
)
result_df = finviz_model.get_screener_data(
preset_loaded="oversold",
data_type="overview",
limit=2,
ascend=True,
)
recorder.capture(result_df)
def ownership_command(
preset: str = "template", sort: str = "", limit: int = 5, ascend: bool = False
):
"""Displays stocks based on own share float and ownership data [Finviz]"""
# Check for argument
if preset == "template" or preset not in so.all_presets:
raise Exception("Invalid preset selected!")
# Debug
if imps.DEBUG:
logger.debug("scr-ownership %s %s %s %s", preset, sort, limit, ascend)
# Check for argument
if limit < 0:
raise Exception("Number has to be above 0")
# Output Data
df_screen = get_screener_data(
preset,
"ownership",
limit,
ascend,
)
title = "Stocks: [Finviz] Ownership Screener"
if isinstance(df_screen, pd.DataFrame):
if df_screen.empty:
raise Exception("No data found.")
df_screen = df_screen.dropna(axis="columns", how="all")
if sort:
if sort in so.d_cols_to_sort["ownership"]:
df_screen = df_screen.sort_values(
by=sort,
ascending=ascend,
na_position="last",
)
else:
similar_cmd = difflib.get_close_matches(
" ".join(sort),
so.d_cols_to_sort["ownership"],
n=1,
cutoff=0.7,
)
if similar_cmd:
df_screen = df_screen.sort_values(
by=[similar_cmd[0]],
ascending=ascend,
na_position="last",
)
else:
raise ValueError(
f"Wrong sort column provided! Select from: {', '.join(so.d_cols_to_sort['ownership'])}"
)
df_screen.set_index("Ticker", inplace=True)
df_screen = df_screen.head(n=limit)
df_screen = df_screen.fillna("-")
dindex = len(df_screen.index)
df_screen = df_screen.applymap(lambda x: lambda_long_number_format(x, 2))
if dindex > 5:
embeds: list = []
# Output
i, i2, end = 0, 0, 5
df_pg, embeds_img, images_list = pd.DataFrame(), [], []
while i < dindex:
df_pg = df_screen.iloc[i:end]
df_pg.append(df_pg)
fig = imps.plot_df(
df_pg.transpose(),
fig_size=(800, 720),
col_width=[2, 1.5],
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 = "scr_ownership.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 += 5
end += 5
# 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_screen.transpose(),
fig_size=(800, 720),
col_width=[2, 1.5],
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("scr_ownership.png", fig)
output = {
"title": title,
"imagefile": imagefile,
}
return output
def overview_command(preset: str = "template",
sort: str = "",
limit: int = 5,
ascend: bool = False):
"""Displays stocks with overview data such as Sector and Industry [Finviz]"""
# Check for argument
if preset == "template" or preset not in so.all_presets:
raise Exception("Invalid preset selected!")
# Debug
if cfg.DEBUG:
logger.debug("scr-overview %s %s %s %s", preset, sort, limit, ascend)
# Check for argument
if limit < 0:
raise Exception("Number has to be above 0")
# Output Data
df_screen = get_screener_data(
preset,
"overview",
limit,
ascend,
)
description = ""
title = "Stocks: [Finviz] Overview Screener"
if isinstance(df_screen, pd.DataFrame):
if df_screen.empty:
raise Exception("No data found.")
df_screen = df_screen.dropna(axis="columns", how="all")
if sort:
if " ".join(sort) in so.d_cols_to_sort["overview"]:
df_screen = df_screen.sort_values(
by=[" ".join(sort)],
ascending=ascend,
na_position="last",
)
else:
similar_cmd = difflib.get_close_matches(
" ".join(sort),
so.d_cols_to_sort["overview"],
n=1,
cutoff=0.7,
)
if similar_cmd:
description = f"Replacing '{' '.join(sort)}' by '{similar_cmd[0]}' so table can be sorted.\n\n"
df_screen = df_screen.sort_values(
by=[similar_cmd[0]],
ascending=ascend,
na_position="last",
)
else:
raise ValueError(
f"Wrong sort column provided! Select from: {', '.join(so.d_cols_to_sort['overview'])}"
)
df_screen = df_screen.fillna("")
future_column_name = df_screen["Ticker"]
df_screen = df_screen.head(n=limit).transpose()
df_screen.columns = future_column_name
df_screen.drop("Ticker")
embeds = []
choices = [
disnake.SelectOption(label="Overview", value="0", emoji="🟢"),
]
initial_str = description + "Overview"
i = 1
for column in df_screen.columns.values:
menu = f"\nPage {i}: {column}"
initial_str += f"\nPage {i}: {column}"
if i < 19:
choices.append(
disnake.SelectOption(label=menu,
value=f"{i}",
emoji="🟢"), )
if i == 20:
choices.append(
disnake.SelectOption(label="Max Reached",
value=f"{i}",
emoji="🟢"), )
i += 1
reports = [f"{initial_str}"]
embeds.append(
disnake.Embed(
title=title,
description=initial_str,
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
))
for column in df_screen.columns.values:
description = f"```{df_screen[column].fillna('')}```"
embeds.append(
disnake.Embed(
title=title,
description=description,
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
))
reports.append(f"{description}")
return {
"view": Menu,
"title": title,
"description": reports,
"embed": embeds,
"choices": choices,
}
def screener(
loaded_preset: str,
data_type: str,
limit: int = 10,
ascend: bool = False,
sort: str = "",
export: str = "",
) -> List[str]:
"""Screener one of the following: overview, valuation, financial, ownership, performance, technical.
Parameters
----------
loaded_preset: str
Preset loaded to filter for tickers
data_type : str
Data type string between: overview, valuation, financial, ownership, performance, technical
limit : int
Limit of stocks to display
ascend : bool
Order of table to ascend or descend
sort: str
Column to sort table by
export : str
Export dataframe data to csv,json,xlsx file
Returns
-------
List[str]
List of stocks that meet preset criteria
"""
df_screen = get_screener_data(
loaded_preset,
data_type,
0,
ascend,
)
if isinstance(df_screen, pd.DataFrame):
if df_screen.empty:
return []
df_screen = df_screen.dropna(axis="columns", how="all")
if sort:
if " ".join(sort) in d_cols_to_sort[data_type]:
df_screen = df_screen.sort_values(
by=[" ".join(sort)],
ascending=ascend,
na_position="last",
)
else:
similar_cmd = difflib.get_close_matches(
" ".join(sort),
d_cols_to_sort[data_type],
n=1,
cutoff=0.7,
)
if similar_cmd:
console.print(
f"Replacing '{' '.join(sort)}' by '{similar_cmd[0]}' so table can be sorted."
)
df_screen = df_screen.sort_values(
by=[similar_cmd[0]],
ascending=ascend,
na_position="last",
)
else:
console.print(
f"Wrong sort column provided! Provide one of these: {', '.join(d_cols_to_sort[data_type])}"
)
df_screen = df_screen.fillna("")
print_rich_table(
df_screen.head(n=limit),
headers=list(df_screen.columns),
show_index=False,
title="Finzin Screener",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
data_type,
df_screen,
)
return list(df_screen.head(n=limit)["Ticker"].values)
console.print("")
return []
async def valuation_command(ctx,
preset="template",
sort="",
limit="5",
ascend="False"):
"""Displays results from chosen preset focusing on valuation metrics [Finviz]"""
try:
# Check for argument
if preset == "template" or preset not in so.all_presets:
raise Exception("Invalid preset selected!")
# Debug
if cfg.DEBUG:
print(f"!stocks.scr.valuation {preset} {sort} {limit} {ascend}")
# Check for argument
if not limit.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
limit = int(limit)
if limit < 0:
raise Exception("Number has to be above 0")
if ascend.lower() == "false":
ascend = False
elif ascend.lower() == "true":
ascend = True
else:
raise Exception("ascend argument has to be true or false")
# Output Data
df_screen = get_screener_data(
preset,
"valuation",
limit,
ascend,
)
description = ""
if isinstance(df_screen, pd.DataFrame):
if df_screen.empty:
return []
df_screen = df_screen.dropna(axis="columns", how="all")
if sort:
if " ".join(sort) in so.d_cols_to_sort["valuation"]:
df_screen = df_screen.sort_values(
by=[" ".join(sort)],
ascending=ascend,
na_position="last",
)
else:
similar_cmd = difflib.get_close_matches(
" ".join(sort),
so.d_cols_to_sort["valuation"],
n=1,
cutoff=0.7,
)
if similar_cmd:
description = f"Replacing '{' '.join(sort)}' by '{similar_cmd[0]}' so table can be sorted.\n\n"
df_screen = df_screen.sort_values(
by=[similar_cmd[0]],
ascending=ascend,
na_position="last",
)
else:
raise ValueError(
f"Wrong sort column provided! Select from: {', '.join(so.d_cols_to_sort['valuation'])}"
)
df_screen = df_screen.fillna("")
future_column_name = df_screen["Ticker"]
df_screen = df_screen.head(n=limit).transpose()
df_screen.columns = future_column_name
df_screen.drop("Ticker")
columns = []
initial_str = description + "Page 0: Overview"
i = 1
for column in df_screen.columns.values:
initial_str = initial_str + "\nPage " + str(i) + ": " + column
i += 1
columns.append(
discord.Embed(
title="Stocks: [Finviz] Valuation Screener",
description=initial_str,
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
))
for column in df_screen.columns.values:
columns.append(
discord.Embed(
title="Stocks: [Finviz] Valuation Screener",
description="```" +
df_screen[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 Stocks: [Finviz] Valuation Screener",
colour=cfg.COLOR,
description=e,
)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
await ctx.send(embed=embed)
async def ownership_command(ctx,
preset: str = "template",
sort: str = "",
limit: int = 5,
ascend: bool = False):
"""Displays stocks based on own share float and ownership data [Finviz]"""
try:
# Check for argument
if preset == "template" or preset not in so.all_presets:
raise Exception("Invalid preset selected!")
# Debug
if cfg.DEBUG:
logger.debug("!stocks.scr.ownership %s %s %s %s", preset, sort,
limit, ascend)
# Check for argument
if limit < 0:
raise Exception("Number has to be above 0")
# Output Data
df_screen = get_screener_data(
preset,
"ownership",
limit,
ascend,
)
description = ""
if isinstance(df_screen, pd.DataFrame):
if df_screen.empty:
return []
df_screen = df_screen.dropna(axis="columns", how="all")
if sort:
if " ".join(sort) in so.d_cols_to_sort["ownership"]:
df_screen = df_screen.sort_values(
by=[" ".join(sort)],
ascending=ascend,
na_position="last",
)
else:
similar_cmd = difflib.get_close_matches(
" ".join(sort),
so.d_cols_to_sort["ownership"],
n=1,
cutoff=0.7,
)
if similar_cmd:
description = f"Replacing '{' '.join(sort)}' by '{similar_cmd[0]}' so table can be sorted.\n\n"
df_screen = df_screen.sort_values(
by=[similar_cmd[0]],
ascending=ascend,
na_position="last",
)
else:
raise ValueError(
f"Wrong sort column provided! Select from: {', '.join(so.d_cols_to_sort['ownership'])}"
)
df_screen = df_screen.fillna("")
future_column_name = df_screen["Ticker"]
df_screen = df_screen.head(n=limit).transpose()
df_screen.columns = future_column_name
df_screen.drop("Ticker")
columns = []
choices = [
disnake.SelectOption(label="Overview", value="0",
emoji="🟢"),
]
initial_str = description + "Overview"
i = 1
for column in df_screen.columns.values:
menu = f"\nPage {i}: {column}"
initial_str += f"\nPage {i}: {column}"
if i < 19:
choices.append(
disnake.SelectOption(label=menu,
value=f"{i}",
emoji="🟢"), )
if i == 20:
choices.append(
disnake.SelectOption(label="Max Reached",
value=f"{i}",
emoji="🟢"), )
i += 1
columns.append(
disnake.Embed(
title="Stocks: [Finviz] Ownership Screener",
description=initial_str,
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
))
for column in df_screen.columns.values:
columns.append(
disnake.Embed(
title="Stocks: [Finviz] Ownership Screener",
description="```" +
df_screen[column].fillna("").to_string() + "```",
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
))
await ctx.send(embed=columns[0], view=Menu(columns, choices))
except Exception as e:
embed = disnake.Embed(
title="ERROR Stocks: [Finviz] Ownership Screener",
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)
async def performance_command(ctx,
preset="template",
sort="",
limit="5",
ascend="False"):
"""Displays stocks and sort by performance categories [Finviz]"""
try:
# Check for argument
if preset == "template" or preset not in so.all_presets:
raise Exception("Invalid preset selected!")
# Debug
if cfg.DEBUG:
logger.debug("!stocks.scr.performance %s %s %s %s", preset, sort,
limit, ascend)
# Check for argument
if not limit.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
limit = int(limit)
if limit < 0:
raise Exception("Number has to be above 0")
if ascend.lower() == "false":
ascend = False
elif ascend.lower() == "true":
ascend = True
else:
raise Exception("ascend argument has to be true or false")
# Output Data
df_screen = get_screener_data(
preset,
"performance",
limit,
ascend,
)
d_cols_to_sort = {
"performance": [
"Ticker",
"Perf Week",
"Perf Month",
"Perf Quart",
"Perf Half",
"Perf Year",
"Perf YTD",
"Volatility W",
"Volatility M",
"Recom",
"Avg Volume",
"Rel Volume",
"Price",
"Change",
"Volume",
],
}
description = ""
if isinstance(df_screen, pd.DataFrame):
if df_screen.empty:
return []
df_screen = df_screen.dropna(axis="columns", how="all")
if sort:
if " ".join(sort) in d_cols_to_sort["performance"]:
df_screen = df_screen.sort_values(
by=[" ".join(sort)],
ascending=ascend,
na_position="last",
)
else:
similar_cmd = difflib.get_close_matches(
" ".join(sort),
d_cols_to_sort["performance"],
n=1,
cutoff=0.7,
)
if similar_cmd:
description = f"Replacing '{' '.join(sort)}' by '{similar_cmd[0]}' so table can be sorted.\n\n"
df_screen = df_screen.sort_values(
by=[similar_cmd[0]],
ascending=ascend,
na_position="last",
)
else:
raise ValueError(
"Wrong sort column provided! Provide one of these:"
f"{', '.join(d_cols_to_sort['performance'])}")
df_screen = df_screen.fillna("")
future_column_name = df_screen["Ticker"]
df_screen = df_screen.head(n=limit).transpose()
df_screen.columns = future_column_name
df_screen.drop("Ticker")
columns = []
initial_str = description + "Page 0: Overview"
i = 1
for column in df_screen.columns.values:
initial_str = initial_str + "\nPage " + str(i) + ": " + column
i += 1
columns.append(
discord.Embed(
title="Stocks: [Finviz] Performance Screener",
description=initial_str,
colour=cfg.COLOR,
).set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
))
for column in df_screen.columns.values:
columns.append(
discord.Embed(
title="Stocks: [Finviz] Performance Screener",
description="```" +
df_screen[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 Stocks: [Finviz] Performance Screener",
colour=cfg.COLOR,
description=e,
)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
await ctx.send(embed=embed)
def screener(other_args: List[str], loaded_preset: str, data_type: str) -> List[str]:
"""Screener
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
ticker : str
Loaded preset filter
data_type : str
Data type string between: overview, valuation, financial, ownership, performance, technical
Returns
-------
List[str]
List of stocks that meet preset criteria
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="screener",
description="""
Prints screener data of the companies that meet the pre-set filtering.
The following information fields are expected: overview, valuation, financial,
ownership, performance, technical. Note that when the signal parameter (-s)
is specified, the preset is disregarded. [Source: Finviz]
""",
)
parser.add_argument(
"-p",
"--preset",
action="store",
dest="preset",
type=str,
default=loaded_preset,
help="Filter presets",
choices=[
preset.split(".")[0]
for preset in os.listdir(presets_path)
if preset[-4:] == ".ini"
],
)
parser.add_argument(
"-s",
"--signal",
action="store",
dest="signal",
type=str,
default=None,
help="Signal",
choices=list(d_signals.keys()),
)
parser.add_argument(
"-l",
"--limit",
action="store",
dest="limit",
type=check_positive,
default=0,
help="Limit of stocks to print",
)
parser.add_argument(
"-a",
"--ascend",
action="store_true",
default=False,
dest="ascend",
help="Set order to Ascend, the default is Descend",
)
parser.add_argument(
"-e",
"--export",
action="store_true",
dest="exportFile",
help="Save list as a text file",
)
parser.add_argument(
"-m",
"--mill",
action="store_true",
dest="mill",
help="Run papermill on list",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return []
df_screen = get_screener_data(
ns_parser.preset,
data_type,
ns_parser.signal,
ns_parser.limit,
ns_parser.ascend,
)
if isinstance(df_screen, pd.DataFrame):
if df_screen.empty:
return []
print(df_screen.to_string())
print("")
if ns_parser.exportFile:
now = datetime.now()
if not os.path.exists("reports/screener"):
os.makedirs("reports/screener")
with open(
f"reports/screener/{ns_parser.signal}-{now.strftime('%Y-%m-%d_%H:%M:%S')}",
"w",
) as file:
file.write(df_screen.to_string(index=False) + "\n")
if ns_parser.mill:
for i in range(len(df_screen)):
ticker = [df_screen.iat[i, 0]]
due_diligence_view.due_diligence_report(ticker)
return list(df_screen["Ticker"].values)
print("")
return []
except Exception as e:
print(e, "\n")
return []
