def get_comparison_data(data_type: str, similar: List[str]):
"""Screener Overview
Parameters
----------
data_type : str
Data type between: overview, valuation, financial, ownership, performance, technical
Returns
----------
pd.DataFrame
Dataframe with overview, valuation, financial, ownership, performance or technical
"""
if data_type == "overview":
screen = overview.Overview()
elif data_type == "valuation":
screen = valuation.Valuation()
elif data_type == "financial":
screen = financial.Financial()
elif data_type == "ownership":
screen = ownership.Ownership()
elif data_type == "performance":
screen = performance.Performance()
elif data_type == "technical":
screen = technical.Technical()
else:
print("Invalid selected screener type")
return pd.DataFrame()
screen.set_filter(ticker=",".join(similar))
df_screen = screen.ScreenerView(verbose=0)
return df_screen
def get_screener_data(preset_loaded: str, data_type: str, signal: str,
limit: int, ascend: bool):
"""Screener Overview
Parameters
----------
preset_loaded : str
Loaded preset filter
data_type : str
Data type between: overview, valuation, financial, ownership, performance, technical
signal : str
Signal to use to filter data
limit : int
Limit of stocks filtered with presets to print
ascend : bool
Ascended order of stocks filtered to print
Returns
----------
pd.DataFrame
Dataframe with loaded filtered stocks
"""
preset_filter = configparser.RawConfigParser()
preset_filter.optionxform = str # type: ignore
preset_filter.read(presets_path + preset_loaded + ".ini")
d_general = preset_filter["General"]
d_filters = {
**preset_filter["Descriptive"],
**preset_filter["Fundamental"],
**preset_filter["Technical"],
}
d_filters = {k: v for k, v in d_filters.items() if v}
if data_type == "overview":
screen = overview.Overview()
elif data_type == "valuation":
screen = valuation.Valuation()
elif data_type == "financial":
screen = financial.Financial()
elif data_type == "ownership":
screen = ownership.Ownership()
elif data_type == "performance":
screen = performance.Performance()
elif data_type == "technical":
screen = technical.Technical()
else:
print("Invalid selected screener type")
return pd.DataFrame()
if signal:
screen.set_filter(signal=d_signals[signal])
else:
if "Signal" in d_general:
screen.set_filter(filters_dict=d_filters,
signal=d_general["Signal"])
else:
screen.set_filter(filters_dict=d_filters)
if "Order" in d_general:
if limit > 0:
df_screen = screen.ScreenerView(
order=d_general["Order"],
limit=limit,
ascend=ascend,
)
else:
df_screen = screen.ScreenerView(order=d_general["Order"],
ascend=ascend)
else:
if limit > 0:
df_screen = screen.ScreenerView(limit=limit, ascend=ascend)
else:
df_screen = screen.ScreenerView(ascend=ascend)
return df_screen
def get_screener_data(preset_loaded: str, data_type: str, limit: int,
ascend: bool):
"""Screener Overview
Parameters
----------
preset_loaded : str
Loaded preset filter
data_type : str
Data type between: overview, valuation, financial, ownership, performance, technical
limit : int
Limit of stocks filtered with presets to print
ascend : bool
Ascended order of stocks filtered to print
Returns
----------
pd.DataFrame
Dataframe with loaded filtered stocks
"""
if data_type == "overview":
screen = overview.Overview()
elif data_type == "valuation":
screen = valuation.Valuation()
elif data_type == "financial":
screen = financial.Financial()
elif data_type == "ownership":
screen = ownership.Ownership()
elif data_type == "performance":
screen = performance.Performance()
elif data_type == "technical":
screen = technical.Technical()
else:
console.print("Invalid selected screener type")
return pd.DataFrame()
if preset_loaded in d_signals:
screen.set_filter(signal=d_signals[preset_loaded])
try:
if limit > 0:
df_screen = screen.screener_view(limit=limit, ascend=ascend)
else:
df_screen = screen.screener_view(ascend=ascend)
except IndexError:
console.print("[red]Invalid data provided by the website[/red]\n")
return pd.DataFrame()
else:
preset_filter = configparser.RawConfigParser()
preset_filter.optionxform = str # type: ignore
preset_filter.read(presets_path + preset_loaded + ".ini")
d_general = preset_filter["General"]
d_filters = {
**preset_filter["Descriptive"],
**preset_filter["Fundamental"],
**preset_filter["Technical"],
}
for section in ["General", "Descriptive", "Fundamental", "Technical"]:
for key, val in {**preset_filter[section]}.items():
if key not in d_check_screener:
console.print(
f"The screener variable {section}.{key} shouldn't exist!\n"
)
return pd.DataFrame()
if val not in d_check_screener[key]:
console.print(
f"Invalid [{section}] {key}={val}. "
f"Choose one of the following options:\n{', '.join(d_check_screener[key])}.\n"
)
return pd.DataFrame()
d_filters = {k: v for k, v in d_filters.items() if v}
screen.set_filter(filters_dict=d_filters)
if "Order" in d_general:
if limit > 0:
df_screen = screen.screener_view(
order=d_general["Order"],
limit=limit,
ascend=ascend,
)
else:
df_screen = screen.screener_view(order=d_general["Order"],
ascend=ascend)
else:
if limit > 0:
df_screen = screen.screener_view(limit=limit, ascend=ascend)
else:
df_screen = screen.screener_view(ascend=ascend)
return df_screen