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OpenBB/openbb_terminal/stocks/fundamental_analysis/fmp_view.py
Colin Delahunty 50c9d2c4ff Improve documentation (#5345) 
* added fixes

* Added better error handling

* Added fixes
2023-08-18 14:51:37 +00:00

808 lines
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""" Financial Modeling Prep View """
__docformat__ = "numpy"
import logging
import os
from datetime import datetime
from typing import Optional
import pandas as pd
from openbb_terminal import OpenBBFigure, theme
from openbb_terminal.decorators import check_api_key, log_start_end
from openbb_terminal.helper_funcs import (
export_data,
print_rich_table,
revert_lambda_long_number_format,
)
from openbb_terminal.rich_config import console
from openbb_terminal.stocks import stocks_helper
from openbb_terminal.stocks.fundamental_analysis import fmp_model
logger = logging.getLogger(__name__)
# pylint: disable=too-many-arguments, R1710
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def valinvest_score(
symbol: str, years: int, export: str = "", sheet_name: Optional[str] = None
):
"""
Value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts [Source: FMP]
The data is gathered from fmp and the scores are calculated using the valinvest library. The repository
For this library can be found here: https://github.com/astro30/valinvest
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
scores = pd.DataFrame.from_dict(
fmp_model.get_score(symbol, years), orient="index", columns=["Score"]
)
if not scores.empty:
updated_scores = []
for score in scores["Score"]:
updated_scores.append(f"{score:.2f}".rstrip("0").rstrip(".") + " %")
scores["Score"] = updated_scores
print_rich_table(
scores,
title=f"Value Investing Scores [{years} Years]",
show_index=True,
export=bool(export),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"scores",
scores,
sheet_name,
)
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_profile(symbol: str, export: str = "", sheet_name: Optional[str] = None):
"""Financial Modeling Prep ticker profile
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
profile = fmp_model.get_profile(symbol)
if not profile.empty:
console.print(f"\nImage: {profile.loc['image'][0]}")
console.print(f"\nDescription: {profile.loc['description'][0]}")
profile.drop(index=["description", "image"], inplace=True)
profile.columns = [" "]
print_rich_table(
profile,
title=f"{symbol.upper()} Profile",
show_index=True,
export=bool(export),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"scores",
profile,
sheet_name,
)
else:
logger.error("Could not get data")
console.print("[red]Unable to get data[/red]\n")
console.print()
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_enterprise(
symbol: str,
start_date: Optional[str] = None,
end_date: Optional[str] = None,
quarterly: bool = False,
method: str = "market_cap",
raw: bool = False,
export: str = "",
sheet_name: Optional[str] = None,
external_axes: bool = False,
):
"""Financial Modeling Prep ticker enterprise
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
start_date: str
Start date of the data
end_date: str
End date of the data
quarterly: bool
Flag to get quarterly data
plot: bool
Flag to plot the data
method: str
Type of data to plot, market_cap or enterprise_value
raw: bool
Flag to print raw data
export: str
Format to export data
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
df_fa = fmp_model.get_enterprise(symbol, start_date, end_date, quarterly)
# Re-order the returned columns so they are in a more logical ordering
df_fa = df_fa.reindex(
columns=[
"Symbol",
"Stock price",
"Number of shares",
"Market capitalization",
"Add total debt",
"Minus cash and cash equivalents",
"Enterprise value",
]
)
if df_fa.empty:
console.print("[red]No data available[/red]\n")
else:
df_fa_plot = df_fa.applymap(revert_lambda_long_number_format)
type_str = (
"Market capitalization" if method == "market_cap" else "Enterprise value"
)
fig = OpenBBFigure(yaxis_title=f"{type_str} in Billions")
fig.set_title(f"{type_str} of {symbol}")
fig.add_scatter(
x=df_fa_plot.index,
y=df_fa_plot[type_str].values / 1e9,
mode="lines",
name=type_str,
line_color=theme.up_color,
stackgroup="one",
)
if raw:
print_rich_table(
df_fa,
headers=list(df_fa.columns),
title=f"{symbol} Enterprise Value",
show_index=True,
export=bool(export),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
method,
df_fa,
sheet_name,
)
return fig.show(external=raw or external_axes)
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_discounted_cash_flow(
symbol: str,
limit: int = 5,
quarterly: bool = False,
export: str = "",
sheet_name: Optional[str] = None,
):
"""Financial Modeling Prep ticker discounted cash flow
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
limit: int
Number to get
quarterly: bool
Flag to get quarterly data
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
dcf = fmp_model.get_dcf(symbol, limit, quarterly)
dcf = dcf[dcf.columns[::-1]]
dcf.columns = dcf.iloc[0].values
dcf = dcf.drop("Date")
if dcf.empty:
console.print("[red]No data available[/red]\n")
else:
print_rich_table(
dcf, title="Discounted Cash Flow", show_index=True, export=bool(export)
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"dcf",
dcf.transpose(),
sheet_name,
)
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_income_statement(
symbol: str,
limit: int = 5,
quarterly: bool = False,
ratios: bool = False,
plot: Optional[list] = None,
export: str = "",
sheet_name: Optional[str] = None,
):
"""Financial Modeling Prep ticker income statement
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
limit: int
Number to get
quarterly: bool
Flag to get quarterly data
ratios: bool
Shows percentage change, by default False
plot: list
List of row labels to plot
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
income = fmp_model.get_income(symbol, limit, quarterly, ratios, bool(plot))
if not income.empty:
fig = OpenBBFigure()
income.index = [
stocks_helper.INCOME_PLOT["FinancialModelingPrep"][i]
for i in [i.replace(" ", "_") for i in income.index.str.lower()]
]
if plot:
income_plot_data = income[income.columns[::-1]]
rows_plot = len(plot)
income_plot_data = income_plot_data.transpose()
if rows_plot == 1:
title = (
f"{plot[0].replace('_', ' ').title()} {'QoQ' if quarterly else 'YoY'} Growth of {symbol.upper()}"
if ratios
else f"{plot[0].replace('_', ' ').title()} of {symbol.upper()}"
)
fig.add_scatter(
x=income_plot_data.index,
y=income_plot_data[plot[0]],
mode="lines",
name=f'{symbol.upper()} {plot[0].replace("_", "")}',
)
fig.set_title(title)
else:
fig = OpenBBFigure.create_subplots(rows=rows_plot, cols=1)
for i in range(rows_plot):
fig.add_scatter(
x=income_plot_data.index,
y=income_plot_data[plot[i]],
mode="lines",
name=f'{symbol.upper()} {plot[i].replace("_", "")}',
row=i + 1,
col=1,
)
fig.set_title(f"{plot[i].replace('_', ' ')}", row=i + 1, col=1)
fig.show(external=fig.is_image_export(export))
else:
income = income[income.columns[::-1]]
# Snake case to english
income.index = income.index.to_series().apply(
lambda x: x.replace("_", " ").title()
)
print_rich_table(
income.drop(index=["Final Link", "Link"]),
headers=list(income.columns),
title=f"{symbol.upper()} Income Statement"
if not ratios
else f"{'QoQ' if quarterly else 'YoY'} Change of {symbol.upper()} Income Statement",
show_index=True,
export=bool(export),
)
console.print(income.loc["Final Link"].to_frame().to_string())
console.print()
console.print(income.loc["Link"].to_frame().to_string())
console.print()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"income",
income,
sheet_name,
fig,
)
else:
logger.error("Could not get data")
console.print("[red]Could not get data[/red]\n")
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_balance_sheet(
symbol: str,
limit: int = 5,
quarterly: bool = False,
ratios: bool = False,
plot: Optional[list] = None,
export: str = "",
sheet_name: Optional[str] = None,
):
"""Financial Modeling Prep ticker balance sheet
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
limit: int
Number to get
quarterly: bool
Flag to get quarterly data
ratios: bool
Shows percentage change, by default False
plot: list
List of row labels to plot
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
balance = fmp_model.get_balance(symbol, limit, quarterly, ratios, bool(plot))
if not balance.empty:
fig = OpenBBFigure()
balance.index = [
stocks_helper.BALANCE_PLOT["FinancialModelingPrep"][i]
for i in [i.replace(" ", "_") for i in balance.index.str.lower()]
]
if plot:
balance_plot_data = balance[balance.columns[::-1]]
rows_plot = len(plot)
balance_plot_data = balance_plot_data.transpose()
if rows_plot == 1:
fig.add_scatter(
x=balance_plot_data.index,
y=balance_plot_data[plot[0]],
mode="lines",
name=f'{symbol.upper()} {plot[0].replace("_", " ")}',
)
fig.set_title(
f"{plot[0].replace('_', ' ').lower()} {'QoQ' if quarterly else 'YoY'} Growth of {symbol.upper()}"
if ratios
else f"{plot[0].replace('_', ' ').title()} of {symbol.upper()}"
)
else:
fig = OpenBBFigure.create_subplots(rows=rows_plot, cols=1)
for i in range(rows_plot):
fig.add_scatter(
x=balance_plot_data.index,
y=balance_plot_data[plot[i]],
mode="lines",
name=f'{symbol.upper()} {plot[i].replace("_", " ")}',
row=i + 1,
col=1,
)
fig.set_title(f"{plot[i].replace('_', ' ')}", row=i + 1, col=1)
fig.show(external=fig.is_image_export(export))
else:
balance = balance[balance.columns[::-1]]
# Snake case to english
balance.index = balance.index.to_series().apply(
lambda x: x.replace("_", " ").title()
)
print_rich_table(
balance.drop(index=["Final Link", "Link"]),
headers=list(balance.columns),
title=f"{symbol.upper()} Balance Sheet",
show_index=True,
export=bool(export),
)
console.print(balance.loc["Final Link"].to_frame().to_string())
console.print()
console.print(balance.loc["Link"].to_frame().to_string())
console.print()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"balance",
balance,
sheet_name,
fig,
)
else:
logger.error("Could not get data")
console.print("[red]Could not get data[/red]\n")
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_cash_flow(
symbol: str,
limit: int = 5,
quarterly: bool = False,
ratios: bool = False,
plot: Optional[list] = None,
export: str = "",
sheet_name: Optional[str] = None,
):
"""Financial Modeling Prep ticker cash flow
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
limit: int
Number to get
quarterly: bool
Flag to get quarterly data
ratios: bool
Shows percentage change, by default False
plot: list
List of row labels to plot
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
cash = fmp_model.get_cash(symbol, limit, quarterly, ratios, bool(plot))
if not cash.empty:
fig = OpenBBFigure()
cash.index = [
stocks_helper.CASH_PLOT["FinancialModelingPrep"][i]
for i in [i.replace(" ", "_") for i in cash.index.str.lower()]
]
if plot:
cash_plot_data = cash[cash.columns[::-1]]
rows_plot = len(plot)
cash_plot_data = cash_plot_data.transpose()
if rows_plot == 1:
fig.add_scatter(
x=cash_plot_data.index,
y=cash_plot_data[plot[0]],
mode="lines",
name=f'{symbol.upper()} {plot[0].replace("_", " ")}',
)
fig.set_title(
f"{plot[0].replace('_', ' ').lower()} {'QoQ' if quarterly else 'YoY'} Growth of {symbol.upper()}"
if ratios
else f"{plot[0].replace('_', ' ').title()} of {symbol.upper()}"
)
else:
fig = OpenBBFigure.create_subplots(rows=rows_plot, cols=1)
for i in range(rows_plot):
fig.add_scatter(
x=cash_plot_data.index,
y=cash_plot_data[plot[i]],
mode="lines",
name=f'{symbol.upper()} {plot[i].replace("_", " ")}',
row=i + 1,
col=1,
)
fig.set_title(f"{plot[i].replace('_', ' ')}", row=i + 1, col=1)
fig.show(external=fig.is_image_export(export))
else:
cash = cash[cash.columns[::-1]]
# Snake case to english
cash.index = cash.index.to_series().apply(
lambda x: x.replace("_", " ").title()
)
print_rich_table(
cash.drop(index=["Final Link", "Link"]),
headers=list(cash.columns),
title=f"{symbol.upper()} Cash Flow",
show_index=True,
export=bool(export),
)
console.print(cash.loc["Final Link"].to_frame().to_string())
console.print()
console.print(cash.loc["Link"].to_frame().to_string())
console.print()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"cash",
cash,
sheet_name,
fig,
)
else:
logger.error("Could not get data")
console.print("[red]Could not get data[/red]\n")
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_key_metrics(
symbol: str,
limit: int = 5,
quarterly: bool = False,
export: str = "",
sheet_name: Optional[str] = None,
):
"""Financial Modeling Prep ticker key metrics
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
limit: int
Number to get
quarterly: bool
Flag to get quarterly data
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
key_metrics = fmp_model.get_key_metrics(symbol, limit, quarterly)
if not key_metrics.empty:
key_metrics = key_metrics[key_metrics.columns[::-1]]
print_rich_table(
key_metrics,
headers=list(key_metrics.columns),
title=f"{symbol.upper()} Key Metrics",
show_index=True,
export=bool(export),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"metrics",
key_metrics,
sheet_name,
)
else:
logger.error("Could not get data")
console.print("[red]Could not get data[/red]\n")
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_financial_ratios(
symbol: str,
limit: int = 5,
quarterly: bool = False,
export: str = "",
sheet_name: Optional[str] = None,
):
"""Financial Modeling Prep ticker ratios
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
limit: int
Number to get
quarterly: bool
Flag to get quarterly data
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
ratios = fmp_model.get_key_ratios(symbol, limit, quarterly)
if not ratios.empty:
ratios = ratios[ratios.columns[::-1]]
print_rich_table(
ratios,
headers=list(ratios.columns),
title=f"{symbol.upper()} Ratios",
show_index=True,
export=bool(export),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"grratiosowth",
ratios,
sheet_name,
)
else:
logger.error("Could not get data")
console.print("[red]Could not get data[/red]\n")
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_financial_statement_growth(
symbol: str,
limit: int = 5,
quarterly: bool = False,
export: str = "",
sheet_name: Optional[str] = None,
):
"""Financial Modeling Prep ticker growth
Parameters
----------
symbol : str
Fundamental analysis ticker symbol
limit: int
Number to get
quarterly: bool
Flag to get quarterly data
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
"""
growth = fmp_model.get_financial_growth(symbol, limit, quarterly)
if not growth.empty:
print_rich_table(
growth,
headers=list(growth.columns),
title=f"{symbol.upper()} Growth",
show_index=True,
export=bool(export),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"growth",
growth,
sheet_name,
)
else:
logger.error("Could not get data")
console.print("[red]Could not get data[/red]\n")
def add_color(value: str) -> str:
if "buy" in value.lower():
value = f"[green]{value}[/green]"
elif "sell" in value.lower():
value = f"[red]{value}[/red]"
return value
@log_start_end(log=logger)
def rating(
symbol: str, limit: int = 10, export: str = "", sheet_name: Optional[str] = None
):
"""Display ratings for a given ticker. [Source: Financial Modeling Prep]
Parameters
----------
symbol: str
Stock ticker symbol
limit: int
Number of last days ratings to display
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Export dataframe data to csv,json,xlsx file
"""
df = fmp_model.get_rating(symbol)
if (isinstance(df, pd.DataFrame) and df.empty) or (
not isinstance(df, pd.DataFrame) and not df
):
return
# TODO: This could be displayed in a nice rating plot over time
df = df.astype(str).applymap(lambda x: add_color(x))
print_rich_table(
df,
headers=df.columns,
show_index=True,
title="Rating",
export=bool(export),
limit=limit,
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"rot",
df,
sheet_name,
)
@log_start_end(log=logger)
@check_api_key(["API_KEY_FINANCIALMODELINGPREP"])
def display_price_targets(
symbol: str, limit: int = 10, export: str = "", sheet_name: Optional[str] = None
):
"""Display price targets for a given ticker. [Source: Financial Modeling Prep]
Parameters
----------
symbol : str
Symbol
limit: int
Number of last days ratings to display
export: str
Export dataframe data to csv,json,xlsx file
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
"""
columns_to_show = [
"publishedDate",
"analystCompany",
"adjPriceTarget",
"priceWhenPosted",
]
price_targets = fmp_model.get_price_targets(symbol)
if price_targets.empty:
console.print(f"[red]No price targets found for {symbol}[/red]\n")
return
price_targets["publishedDate"] = price_targets["publishedDate"].apply(
lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%fZ").strftime(
"%Y-%m-%d %H:%M"
)
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"pt",
price_targets,
sheet_name,
)
print_rich_table(
price_targets[columns_to_show].head(limit),
headers=["Date", "Company", "Target", "Posted Price"],
show_index=False,
title=f"{symbol.upper()} Price Targets",
)
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