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get rid of po -> ca/scr

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This commit is contained in:
Jamie
2021-04-12 17:08:15 -04:00
parent 52dab41d7e
commit dcf87e3017
2 changed files with 9 additions and 58 deletions
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2
gamestonk_terminal/comparison_analysis/ca_controller.py
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@@ -320,7 +320,7 @@ class ComparisonAnalysisController:
def call_po(self, _):
"""Open Portfolio Optimization menu with ticker and similar"""
return po_controller.menu_from_ca(self.ticker, self.similar)
return po_controller.menu([self.ticker] + self.similar)
def menu(stock: pd.DataFrame, ticker: str, start: datetime, interval: str):

65
gamestonk_terminal/portfolio_optimization/port_optimization_controller.py
View File

@@ -21,8 +21,6 @@ class PortfolioOptimization:
"help",
"q",
"quit",
"ca",
"scr",
"select",
"add",
"equal_weight",
@@ -55,8 +53,6 @@ class PortfolioOptimization:
print(" help show this menu again")
print(" q quit this menu, and shows back to main menu")
print(" quit quit to abandon program")
print(" > ca comparison analysis menu")
print(" > scr screener menu")
print(f"\nCurrent Tickers: {('None', ', '.join(tickers))[bool(tickers)]}")
print("")
print(" add add ticker to optimize")
@@ -64,16 +60,14 @@ class PortfolioOptimization:
print("")
print("Optimization:")
print("")
print(" Property weighted:")
print(" equal_weight equally weighted portfolio")
print(" mkt_cap marketcap weighted portfolio")
print(" div_yield dividend weighted portfolio\n")
print(" Mean Variance Optimization :")
print(" max_sharpe portfolio with maximum sharpe ratio")
print(" min_vol portfolio with minimum volatility")
print(" eff_risk portfolio that maximizes returns at given risk")
print(" eff_ret portfolio that minimizes risk at given return")
print(" show_ef show the efficient frontier")
print(" equal_weight equally weighted portfolio")
print(" mkt_cap marketcap weighted portfolio")
print(" div_yield dividend weighted portfolio")
print(" max_sharpe portfolio with maximum sharpe ratio")
print(" min_vol portfolio with minimum volatility")
print(" eff_risk portfolio that maximizes returns at given risk")
print(" eff_ret portfolio that minimizes risk at given return")
print(" show_ef show the efficient frontier")
print("")
plt.close("all")
@@ -105,13 +99,6 @@ class PortfolioOptimization:
"""Process Quit command - quit the program"""
return True
def call_ca(self, _):
return ca_controller.menu(pd.DataFrame(), self.ca_ticker, "", "1440min")
def call_scr(self, _):
return screener_controller.menu()
def call_add(self, other_args: List[str]):
self.add_stocks(self, other_args)
@@ -173,7 +160,6 @@ class PortfolioOptimization:
@staticmethod
def add_stocks(self, other_args: List[str]):
""" Add ticker to current list for optimization"""
parser = argparse.ArgumentParser(
add_help=False,
@@ -210,41 +196,6 @@ class PortfolioOptimization:
print("")
@classmethod
def from_ca_menu(cls, ticker: str, similar: List[str]):
return cls(set([ticker] + similar))
def menu_from_ca(ticker: str, similar: List[str]):
"""Portfolio Optimization Menu from ca menu that allows for jumping between"""
po_controller = PortfolioOptimization.from_ca_menu(ticker, similar)
po_controller.ca_ticker = ticker
po_controller.ca_similar = similar
po_controller.call_help([ticker] + similar)
while True:
# Get input command from user
if session and gtff.USE_PROMPT_TOOLKIT:
completer = NestedCompleter.from_nested_dict(
{c: None for c in po_controller.CHOICES}
)
an_input = session.prompt(
f"{get_flair()} (po)> ",
completer=completer,
)
else:
an_input = input(f"{get_flair()} (po)> ")
try:
process_input = po_controller.switch(an_input)
if process_input is not None:
return process_input
except SystemExit:
print("The command selected doesn't exist\n")
continue
def menu(tickers: List[str]):
"""Portfolio Optimization Menu"""