def call_rnval(self, other_args: List[str]):
"""Process rnval command"""
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="rnval",
description="The risk neutral value of the options",
)
parser.add_argument(
"-p",
"--put",
action="store_true",
default=False,
help="Show puts instead of calls",
)
parser.add_argument(
"-m",
"--min",
type=float,
default=None,
dest="mini",
help="Minimum strike price shown",
)
parser.add_argument(
"-M",
"--max",
type=float,
default=None,
dest="maxi",
help="Maximum strike price shown",
)
parser.add_argument(
"-r",
"--risk",
type=float,
default=None,
dest="risk",
help="The risk-free rate to use",
)
ns_parser = parse_known_args_and_warn(parser, other_args)
if ns_parser:
if self.ticker:
if self.selected_date:
if sum(self.prices["Chance"]) == 1:
yfinance_view.risk_neutral_vals(
self.ticker,
self.selected_date,
ns_parser.put,
self.prices,
ns_parser.mini,
ns_parser.maxi,
ns_parser.risk,
)
else:
console.print("Total chances must equal one\n")
else:
console.print(
"No expiry loaded. First use `exp {expiry date}`\n")
else:
console.print("No ticker loaded. First use `load <ticker>`\n")
def test_risk_neutral_vals(mocker):
# MOCK CHARTS
mocker.patch(
target=
"gamestonk_terminal.stocks.options.yfinance_view.theme.visualize_output"
)
# MOCK EXPORT_DATA
mocker.patch(
target="gamestonk_terminal.stocks.options.yfinance_view.export_data")
yfinance_view.risk_neutral_vals(
ticker="PM",
exp="2022-01-07",
put=True,
df=pd.DataFrame(columns=["Price", "Chance"]),
mini=None,
maxi=None,
risk=None,
)
def test_risk_neutral_vals(mocker):
# MOCK CHARTS
mocker.patch.object(target=yfinance_view.gtff,
attribute="USE_ION",
new=True)
mocker.patch(
target="gamestonk_terminal.stocks.options.yfinance_view.plt.ion")
mocker.patch(
target="gamestonk_terminal.stocks.options.yfinance_view.plt.show")
# MOCK EXPORT_DATA
mocker.patch(
target="gamestonk_terminal.stocks.options.yfinance_view.export_data")
yfinance_view.risk_neutral_vals(
ticker="PM",
exp="2022-01-07",
put=True,
df=pd.DataFrame(columns=["Price", "Chance"]),
mini=None,
maxi=None,
risk=None,
)