def display_overview(country: str = "united states",
limit: int = 10,
export: str = ""):
"""Displays an overview of the main funds from a country.
Parameters
----------
country: str
Country to get overview for
limit: int
Number to show
export : str
Format to export data
"""
overview = investpy_model.get_overview(country=country, limit=limit)
overview["Assets (1B)"] = overview.total_assets / 1_000_000_000
overview = overview.drop(columns=["country", "total_assets"])
if gtff.USE_TABULATE_DF:
t_console.print(
rich_table_from_df(
overview,
title=f"[bold]Fund overview for {country.title()}[/bold]"))
else:
t_console.print(overview.to_string())
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
f"overview_{country.replace(' ','_')}",
overview,
)
t_console.print("\n")
def display_search(
by: str = "name",
value: str = "",
country: str = "united states",
limit: int = 10,
sortby: str = "",
ascending: bool = False,
):
"""Display results of searching for Mutual Funds
Parameters
----------
by : str
Field to match on. Can be name, issuer, isin or symbol
value : str
String that will be searched for
country: str
Country to filter on
limit: int
Number to show
sortby: str
Column to sort by
ascending: bool
Flag to sort in ascending order
"""
searches = investpy_model.search_funds(by, value)
if searches.empty:
t_console.print("No matches found.\n")
return
if country:
searches = searches[searches.country == country]
if searches.empty:
t_console.print(f"No matches found in {country}.\n")
return
searches = searches.drop(columns=["country", "underlying"])
if sortby:
searches = searches.sort_values(by=sortby, ascending=ascending)
# If we want to move forward with rich -- should rename this gtff
# Additionally, I recreated the tabulate functions with the rich.Table class.
if gtff.USE_TABULATE_DF:
t_console.print(
rich_table_from_df(
searches.head(limit),
show_index=False,
title=f"[bold]Mutual Funds with {by} matching {value}[/bold]",
))
else:
t_console.print(searches.head(limit).to_string())
t_console.print("\n")
def display_equity(fund: str):
"""Display equity holdings for fund
Parameters
----------
fund: str
Fund symbol
"""
title_map = {
"priceToCashflow": "Price To Cash Flow",
"priceToSales": "Price To Sales",
"priceToBookCat": "Price To Book Cat",
"priceToEarningsCat": "Price To Earnings Cat",
"medianMarketCapCat": "Median Market Cap Cat",
"threeYearEarningsGrowthCat": "3Yr Earnings Growth Cat",
"threeYearEarningsGrowth": "3Y Earnings Growth",
"medianMarketCap": "Median Market Cap",
"priceToEarnings": "Price To Earnings",
"priceToBook": "Price To Book",
"priceToSalesCat": "Price To Sales Cat",
"priceToCashflowCat": "Price To Cashflow Cat",
}
equity_hold = yfinance_model.get_information(fund)["equityHoldings"]
df_weight = pd.DataFrame.from_dict(equity_hold, orient="index")
df_weight = df_weight.apply(lambda x: round(100 * x, 3))
df_weight.index = df_weight.index.map(title_map)
if gtff.USE_TABULATE_DF:
console.print(
rich_table_from_df(
df_weight,
show_index=True,
index_name="Equity",
headers=["Holding"],
title=f"[bold]{fund.upper()} Equity Holdings[/bold] ",
))
else:
console.print(df_weight.to_string())
console.print("\n")
def display_fund_info(fund_name: str, country: str = "united states"):
"""Display fund infomration. Finds name from symbol first if name is false
Parameters
----------
fund: str
Fund name to get info for
country : str
Country of fund
"""
info = (investpy_model.get_fund_info(fund_name, country).reset_index(
drop=False).applymap(lambda x: np.nan if not x else x).dropna())
if gtff.USE_TABULATE_DF:
t_console.print(
rich_table_from_df(
info,
title=f"[bold]{fund_name.title()} Information[/bold]",
show_index=False,
headers=["Info", "Value"],
))
else:
t_console.print(info.to_string())
t_console.print("\n")
def display_sector(fund: str, min_pct_to_display: float = 5, export: str = ""):
"""Display sector weightings for fund
Parameters
----------
fund: str
Fund symbol
min_pct_to_display: float
Minimum percentage to display sector
export: str
Type of format to export data
"""
sector_weights = yfinance_model.get_information(fund)
if "sectorWeightings" not in sector_weights.keys():
console.print(
f"Sector Weights are not found f for {fund}. Either the symbol is incorrect or there is an issue "
"in pulling from yahoo.\n")
return
sector_weights = sector_weights["sectorWeightings"]
weights = {}
for weight in sector_weights:
weights.update(weight)
df_weight = pd.DataFrame.from_dict(weights, orient="index")
if df_weight.empty:
console.print("No sector data found.\n")
df_weight = df_weight.apply(lambda x: round(100 * x, 3))
df_weight.columns = ["Weight"]
df_weight.sort_values(by="Weight", inplace=True, ascending=False)
df_weight.index = [
"Real Estate" if x == "realestate" else x.replace("_", " ").title()
for x in df_weight.index
]
if gtff.USE_TABULATE_DF:
console.print(
rich_table_from_df(
df_weight,
show_index=True,
index_name="Sector",
headers=["Weight (%)"],
title=f"[bold]{fund.upper()} Sector Weightings[/bold] ",
))
else:
console.print(df_weight.to_string())
console.print("\n")
main_holdings = df_weight[df_weight.Weight > min_pct_to_display].to_dict()[
df_weight.columns[0]]
if len(main_holdings) < len(df_weight):
main_holdings["Others"] = 100 - sum(main_holdings.values())
legend, values = zip(*main_holdings.items())
leg = [f"{le}\n{round(v, 2)}%" for le, v in zip(legend, values)]
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
ax.pie(
values,
labels=leg,
wedgeprops={
"linewidth": 0.5,
"edgecolor": "white"
},
labeldistance=1.05,
startangle=90,
)
ax.set_title(f"Sector holdings of {fund.upper()}")
fig.tight_layout()
if gtff.USE_ION:
plt.ion()
plt.show()
export_data(export, os.path.dirname(os.path.abspath(__file__)), "sector",
df_weight)