mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-29 05:21:48 +00:00
Refactor commands because OCD
This commit is contained in:
didier
@@ -94,9 +94,11 @@ def equal_weight(stocks: List[str], other_args: List[str]):
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return
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if len(stocks) < 2:
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print("Please have at least 2 loaded tickers to calculate weights.\n")
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return
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values = {}
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for stock in stocks:
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values[stock] = ns_parser.value * round(1 / len(stocks), 5)
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@@ -208,38 +210,22 @@ def property_weighting(stocks: List[str], other_args: List[str]):
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return
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def ef_portfolio(stocks: List[str], port_type: str, other_args: List[str]):
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def max_sharpe(stocks: List[str], other_args: List[str]):
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"""
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Return a portfolio based on condition in port_type Defaults to 3m of historical data
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Return a portfolio that maximises the Sharpe Ratio
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Parameters
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----------
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stocks: List[str]
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List of the stocks to be included in the weights
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port_type: str
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Method to be used on ef object (example: max_sharpe, min_volatility, eff_risk, eff_ret)
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Returns
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-------
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weights: dict
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Dictionary of weights where keys are the tickers.
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"""
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d_descriptions = {
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"max_sharpe": """Maximise the Sharpe Ratio. The result is also referred to as the tangency portfolio,
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as it is the portfolio for which the capital market line is tangent to the efficient frontier.
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This is a convex optimization problem after making a certain variable substitution. See
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Cornuejols and Tutuncu (2006) <http://web.math.ku.dk/~rolf/CT_FinOpt.pdf> for more.""",
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"min_volatility": "optimizes for minimum volatility",
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"max_quadratic_utility": "maximises the quadratic utility, given some risk aversion",
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"efficient_risk": """Maximise return for a target risk. The resulting portfolio will have
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a volatility less than the target (but not guaranteed to be equal).""",
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"efficient_return": "Calculate the 'Markowitz portfolio', minimising volatility for a given target return.",
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}
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parser = argparse.ArgumentParser(
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add_help=False, prog=port_type, description=d_descriptions[port_type]
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add_help=False, prog="maxsharpe", description="Maximise the Sharpe Ratio"
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)
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parser.add_argument(
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"-p",
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@@ -265,90 +251,24 @@ def ef_portfolio(stocks: List[str], port_type: str, other_args: List[str]):
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help="Display a pie chart for weights",
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)
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if port_type == "max_sharpe":
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if other_args:
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if "-" not in other_args[0]:
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other_args.insert(0, "-r")
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parser.add_argument(
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"-r",
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"--risk-free-rate",
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type=float,
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dest="risk_free_rate",
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default=0.02,
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help="""Risk-free rate of borrowing/lending. The period of the risk-free rate
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should correspond to the frequency of expected returns.""",
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)
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elif port_type == "efficient_risk":
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if other_args:
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if "-" not in other_args[0]:
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other_args.insert(0, "-t")
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parser.add_argument(
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"-t",
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"--target-volatility",
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type=float,
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dest="target_volatility",
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default=0.1,
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help="The desired maximum volatility of the resulting portfolio",
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)
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parser.add_argument(
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"-n",
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"--market-neutral",
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action="store_true",
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default=False,
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dest="market_neutral",
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help="""whether the portfolio should be market neutral (weights sum to zero), defaults to False.
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Requires negative lower weight bound.""",
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)
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elif port_type == "efficient_return":
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if other_args:
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if "-" not in other_args[0]:
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other_args.insert(0, "-t")
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parser.add_argument(
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"-t",
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"--target-return",
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type=float,
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dest="target_return",
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default=0.1,
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help="the desired return of the resulting portfolio",
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)
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parser.add_argument(
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"-n",
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"--market-neutral",
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action="store_true",
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default=False,
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dest="market_neutral",
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help="""whether the portfolio should be market neutral (weights sum to zero), defaults to False.
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Requires negative lower weight bound.""",
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)
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elif port_type == "max_quadratic_utility":
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if other_args:
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if "-" not in other_args[0]:
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other_args.insert(0, "-r")
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parser.add_argument(
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"-r",
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"--risk-aversion",
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type=float,
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dest="risk_aversion",
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default=1,
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help="risk aversion parameter",
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)
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parser.add_argument(
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"-n",
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"--market-neutral",
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action="store_true",
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default=False,
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dest="market_neutral",
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help="""whether the portfolio should be market neutral (weights sum to zero), defaults to False.
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Requires negative lower weight bound.""",
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)
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if other_args:
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if "-" not in other_args[0]:
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other_args.insert(0, "-r")
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parser.add_argument(
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"-r",
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"--risk-free-rate",
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type=float,
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dest="risk_free_rate",
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default=0.02,
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help="""Risk-free rate of borrowing/lending. The period of the risk-free rate
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should correspond to the frequency of expected returns.""",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return None
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if len(stocks) < 2:
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print("Please have at least 2 loaded tickers to calculate weights.\n")
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return
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@@ -359,36 +279,8 @@ def ef_portfolio(stocks: List[str], port_type: str, other_args: List[str]):
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sp = d_period[ns_parser.period]
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if port_type == "max_sharpe":
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ef_opt = dict(ef.max_sharpe(ns_parser.risk_free_rate))
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s_title = f"{sp} Weights that maximize Sharpe ratio with risk free level of {ns_parser.risk_free_rate}"
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elif port_type == "min_volatility":
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ef_opt = dict(ef.min_volatility())
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s_title = f"{sp} Weights that minimize volatility"
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elif port_type == "efficient_risk":
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ef_opt = dict(
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ef.efficient_risk(ns_parser.target_volatility, ns_parser.market_neutral)
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)
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s_title = f"{sp} Weights that maximize return with a maximum volatility of {ns_parser.target_volatility}"
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elif port_type == "efficient_return":
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ef_opt = dict(
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ef.efficient_return(ns_parser.target_return, ns_parser.market_neutral)
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)
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s_title = f"{sp} Weights that minimise volatility for a given target return of {ns_parser.target_return}"
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elif port_type == "max_quadratic_utility":
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ef_opt = dict(
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ef.max_quadratic_utility(
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ns_parser.risk_aversion, ns_parser.market_neutral
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)
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)
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s_title = f"{sp} Weights that maximise the quadratic utility with risk aversion of {ns_parser.risk_aversion}"
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else:
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raise ValueError("EF Method not found")
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ef_opt = dict(ef.max_sharpe(ns_parser.risk_free_rate))
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s_title = f"{sp} Weights that maximize Sharpe ratio with risk free level of {ns_parser.risk_free_rate}"
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weights = {
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key: ns_parser.value * round(value, 5) for key, value in ef_opt.items()
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@@ -410,6 +302,414 @@ def ef_portfolio(stocks: List[str], port_type: str, other_args: List[str]):
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return
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def min_volatility(stocks: List[str], other_args: List[str]):
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"""
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Return a portfolio that optimizes for minimum volatility
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Parameters
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----------
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stocks: List[str]
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List of the stocks to be included in the weights
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Returns
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-------
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weights: dict
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Dictionary of weights where keys are the tickers.
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"""
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parser = argparse.ArgumentParser(
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add_help=False,
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prog="min_volatility",
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description="Optimizes for minimum volatility",
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)
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parser.add_argument(
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"-p",
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"--period",
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default="3mo",
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dest="period",
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help="period to get yfinance data from",
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choices=period_choices,
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)
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parser.add_argument(
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"-v",
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"--value",
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dest="value",
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help="Amount to allocate to portfolio",
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type=float,
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default=1.0,
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)
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parser.add_argument(
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"--pie",
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action="store_true",
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dest="pie",
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default=False,
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help="Display a pie chart for weights",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return None
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if len(stocks) < 2:
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print("Please have at least 2 loaded tickers to calculate weights.\n")
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return
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period = ns_parser.period
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stock_prices = process_stocks(stocks, period)
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ef = prepare_efficient_frontier(stock_prices)
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sp = d_period[ns_parser.period]
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ef_opt = dict(ef.min_volatility())
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s_title = f"{sp} Weights that minimize volatility"
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weights = {
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key: ns_parser.value * round(value, 5) for key, value in ef_opt.items()
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}
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if ns_parser.pie:
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pie_chart_weights(weights, s_title)
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else:
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print(s_title)
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display_weights(weights)
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print("")
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ef.portfolio_performance(verbose=True)
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print("")
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except Exception as e:
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print(e)
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print("")
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return
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def max_quadratic_utility(stocks: List[str], other_args: List[str]):
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"""
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Return a portfolio that maximises the quadratic utility, given some risk aversion
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Parameters
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----------
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stocks: List[str]
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List of the stocks to be included in the weights
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Returns
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-------
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weights: dict
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Dictionary of weights where keys are the tickers.
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"""
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parser = argparse.ArgumentParser(
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add_help=False,
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prog="max_quadratic_utility",
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description="Maximises the quadratic utility, given some risk aversion",
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)
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parser.add_argument(
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"-p",
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"--period",
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default="3mo",
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dest="period",
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help="period to get yfinance data from",
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choices=period_choices,
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)
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parser.add_argument(
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"-v",
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"--value",
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dest="value",
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help="Amount to allocate to portfolio",
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type=float,
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default=1.0,
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)
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parser.add_argument(
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"-n",
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"--market-neutral",
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action="store_true",
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default=False,
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dest="market_neutral",
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help="""whether the portfolio should be market neutral (weights sum to zero), defaults to False.
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Requires negative lower weight bound.""",
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)
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if "-n" not in other_args:
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parser.add_argument(
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"--pie",
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action="store_true",
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dest="pie",
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default=False,
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help="Display a pie chart for weights. Only if neutral flag is left False.",
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)
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if other_args:
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if "-" not in other_args[0]:
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other_args.insert(0, "-r")
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parser.add_argument(
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"-r",
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"--risk-aversion",
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type=float,
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dest="risk_aversion",
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default=1,
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help="risk aversion parameter",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return None
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if len(stocks) < 2:
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print("Please have at least 2 loaded tickers to calculate weights.\n")
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return
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period = ns_parser.period
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stock_prices = process_stocks(stocks, period)
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ef = prepare_efficient_frontier(stock_prices)
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sp = d_period[ns_parser.period]
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ef_opt = dict(
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ef.max_quadratic_utility(ns_parser.risk_aversion, ns_parser.market_neutral)
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)
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s_title = f"{sp} Weights that maximise the quadratic utility with risk aversion of {ns_parser.risk_aversion}"
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weights = {
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key: ns_parser.value * round(value, 5) for key, value in ef_opt.items()
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}
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if not ns_parser.market_neutral:
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if ns_parser.pie:
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pie_chart_weights(weights, s_title)
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ef.portfolio_performance(verbose=True)
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print("")
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return
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print(s_title)
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display_weights(weights)
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print("")
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ef.portfolio_performance(verbose=True)
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print("")
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except Exception as e:
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print(e)
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print("")
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return
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def efficient_risk(stocks: List[str], other_args: List[str]):
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"""
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Return a portfolio that maximises return for a target risk. The resulting portfolio will have
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a volatility less than the target (but not guaranteed to be equal)
|
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|
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Parameters
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----------
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stocks: List[str]
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List of the stocks to be included in the weights
|
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|
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Returns
|
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-------
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weights: dict
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Dictionary of weights where keys are the tickers.
|
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"""
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parser = argparse.ArgumentParser(
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add_help=False,
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prog="effrisk",
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description="""Maximise return for a target risk. The resulting portfolio will have
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a volatility less than the target (but not guaranteed to be equal)""",
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)
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parser.add_argument(
|
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"-p",
|
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"--period",
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default="3mo",
|
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dest="period",
|
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help="period to get yfinance data from",
|
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choices=period_choices,
|
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)
|
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parser.add_argument(
|
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"-v",
|
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"--value",
|
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dest="value",
|
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help="Amount to allocate to portfolio",
|
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type=float,
|
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default=1.0,
|
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)
|
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parser.add_argument(
|
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"-n",
|
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"--market-neutral",
|
||||
action="store_true",
|
||||
default=False,
|
||||
dest="market_neutral",
|
||||
help="""whether the portfolio should be market neutral (weights sum to zero), defaults to False.
|
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Requires negative lower weight bound.""",
|
||||
)
|
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if "-n" not in other_args:
|
||||
parser.add_argument(
|
||||
"--pie",
|
||||
action="store_true",
|
||||
dest="pie",
|
||||
default=False,
|
||||
help="Display a pie chart for weights. Only if neutral flag is left False.",
|
||||
)
|
||||
|
||||
if other_args:
|
||||
if "-" not in other_args[0]:
|
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other_args.insert(0, "-t")
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--target-volatility",
|
||||
type=float,
|
||||
dest="target_volatility",
|
||||
default=0.1,
|
||||
help="The desired maximum volatility of the resulting portfolio",
|
||||
)
|
||||
|
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
return None
|
||||
|
||||
if len(stocks) < 2:
|
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print("Please have at least 2 loaded tickers to calculate weights.\n")
|
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return
|
||||
|
||||
period = ns_parser.period
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||||
stock_prices = process_stocks(stocks, period)
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ef = prepare_efficient_frontier(stock_prices)
|
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|
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sp = d_period[ns_parser.period]
|
||||
|
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ef_opt = dict(
|
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ef.efficient_risk(ns_parser.target_volatility, ns_parser.market_neutral)
|
||||
)
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s_title = f"{sp} Weights that maximize return with a maximum volatility of {ns_parser.target_volatility}"
|
||||
|
||||
weights = {
|
||||
key: ns_parser.value * round(value, 5) for key, value in ef_opt.items()
|
||||
}
|
||||
|
||||
if not ns_parser.market_neutral:
|
||||
if ns_parser.pie:
|
||||
pie_chart_weights(weights, s_title)
|
||||
ef.portfolio_performance(verbose=True)
|
||||
print("")
|
||||
return
|
||||
|
||||
print(s_title)
|
||||
display_weights(weights)
|
||||
print("")
|
||||
ef.portfolio_performance(verbose=True)
|
||||
print("")
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
print("")
|
||||
return
|
||||
|
||||
|
||||
def efficient_return(stocks: List[str], other_args: List[str]):
|
||||
"""
|
||||
Return a portfolio that minimises volatility for a given target return ('Markowitz portfolio')
|
||||
|
||||
Parameters
|
||||
----------
|
||||
stocks: List[str]
|
||||
List of the stocks to be included in the weights
|
||||
|
||||
Returns
|
||||
-------
|
||||
weights: dict
|
||||
Dictionary of weights where keys are the tickers.
|
||||
"""
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
prog="effret",
|
||||
description="Calculate the 'Markowitz portfolio', minimising volatility for a given target return",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-p",
|
||||
"--period",
|
||||
default="3mo",
|
||||
dest="period",
|
||||
help="period to get yfinance data from",
|
||||
choices=period_choices,
|
||||
)
|
||||
parser.add_argument(
|
||||
"-v",
|
||||
"--value",
|
||||
dest="value",
|
||||
help="Amount to allocate to portfolio",
|
||||
type=float,
|
||||
default=1.0,
|
||||
)
|
||||
parser.add_argument(
|
||||
"-n",
|
||||
"--market-neutral",
|
||||
action="store_true",
|
||||
default=False,
|
||||
dest="market_neutral",
|
||||
help="""whether the portfolio should be market neutral (weights sum to zero), defaults to False.
|
||||
Requires negative lower weight bound.""",
|
||||
)
|
||||
if "-n" not in other_args:
|
||||
parser.add_argument(
|
||||
"--pie",
|
||||
action="store_true",
|
||||
dest="pie",
|
||||
default=False,
|
||||
help="Display a pie chart for weights. Only if neutral flag is left False.",
|
||||
)
|
||||
|
||||
if other_args:
|
||||
if "-" not in other_args[0]:
|
||||
other_args.insert(0, "-t")
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--target-return",
|
||||
type=float,
|
||||
dest="target_return",
|
||||
default=0.1,
|
||||
help="the desired return of the resulting portfolio",
|
||||
)
|
||||
|
||||
try:
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
return None
|
||||
|
||||
if len(stocks) < 2:
|
||||
print("Please have at least 2 loaded tickers to calculate weights.\n")
|
||||
return
|
||||
|
||||
period = ns_parser.period
|
||||
stock_prices = process_stocks(stocks, period)
|
||||
ef = prepare_efficient_frontier(stock_prices)
|
||||
|
||||
sp = d_period[ns_parser.period]
|
||||
|
||||
ef_opt = dict(
|
||||
ef.efficient_return(ns_parser.target_return, ns_parser.market_neutral)
|
||||
)
|
||||
s_title = f"{sp} Weights that minimise volatility for a given target return of {ns_parser.target_return}"
|
||||
|
||||
weights = {
|
||||
key: ns_parser.value * round(value, 5) for key, value in ef_opt.items()
|
||||
}
|
||||
|
||||
if not ns_parser.market_neutral:
|
||||
if ns_parser.pie:
|
||||
pie_chart_weights(weights, s_title)
|
||||
ef.portfolio_performance(verbose=True)
|
||||
print("")
|
||||
return
|
||||
|
||||
print(s_title)
|
||||
display_weights(weights)
|
||||
print("")
|
||||
ef.portfolio_performance(verbose=True)
|
||||
print("")
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
print("")
|
||||
return
|
||||
|
||||
|
||||
def show_ef(stocks: List[str], other_args: List[str]):
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
|
||||
@@ -5,11 +5,13 @@ __docformat__ = "numpy"
|
||||
import argparse
|
||||
from typing import List
|
||||
import matplotlib.pyplot as plt
|
||||
from datetime import datetime
|
||||
from prompt_toolkit.completion import NestedCompleter
|
||||
from gamestonk_terminal import feature_flags as gtff
|
||||
from gamestonk_terminal.helper_funcs import get_flair, parse_known_args_and_warn
|
||||
from gamestonk_terminal.menu import session
|
||||
from gamestonk_terminal.portfolio_optimization import optimizer_view
|
||||
from gamestonk_terminal.comparison_analysis import ca_controller
|
||||
|
||||
|
||||
class PortfolioOptimization:
|
||||
@@ -28,6 +30,7 @@ class PortfolioOptimization:
|
||||
"effrisk",
|
||||
"maxquadutil",
|
||||
"ef",
|
||||
"ca",
|
||||
"yolo",
|
||||
]
|
||||
|
||||
@@ -74,6 +77,9 @@ class PortfolioOptimization:
|
||||
print("")
|
||||
print(" ef show the efficient frontier")
|
||||
print("")
|
||||
if tickers:
|
||||
print(" > ca comparison analysis for selected tickers")
|
||||
print("")
|
||||
|
||||
def switch(self, an_input: str):
|
||||
"""Process and dispatch input
|
||||
@@ -122,23 +128,23 @@ class PortfolioOptimization:
|
||||
|
||||
def call_maxsharpe(self, other_args: List[str]):
|
||||
"""Process maxsharpe command"""
|
||||
optimizer_view.ef_portfolio(self.tickers, "max_sharpe", other_args)
|
||||
optimizer_view.max_sharpe(self.tickers, other_args)
|
||||
|
||||
def call_minvol(self, other_args: List[str]):
|
||||
"""Process minvol command"""
|
||||
optimizer_view.ef_portfolio(self.tickers, "min_volatility", other_args)
|
||||
optimizer_view.min_volatility(self.tickers, other_args)
|
||||
|
||||
def call_maxquadutil(self, other_args: List[str]):
|
||||
"""Process maxquadutil command"""
|
||||
optimizer_view.ef_portfolio(self.tickers, "max_quadratic_utility", other_args)
|
||||
optimizer_view.max_quadratic_utility(self.tickers, other_args)
|
||||
|
||||
def call_effrisk(self, other_args: List[str]):
|
||||
"""Process effrisk command"""
|
||||
optimizer_view.ef_portfolio(self.tickers, "efficient_risk", other_args)
|
||||
optimizer_view.efficient_risk(self.tickers, other_args)
|
||||
|
||||
def call_effret(self, other_args: List[str]):
|
||||
"""Process effret command"""
|
||||
optimizer_view.ef_portfolio(self.tickers, "efficient_return", other_args)
|
||||
optimizer_view.efficient_return(self.tickers, other_args)
|
||||
|
||||
def call_ef(self, other_args):
|
||||
"""Process ef command"""
|
||||
|
||||
Reference in New Issue
Block a user