Portfolio optimization bug fixes (#3675)

* remove hcp

* add prams dict to statics

* little controller bug

* fix view bug

* avoid crash if one stock

* check params on setup

* create parameter class

* check and convert parameters using class

* change params name to avoid confusion

* create a parameter statics dict

* change some funcs names

* remove unused imports

* remove default dict

* optional type

* add multi choices

* cast only int and float

* fix completer

* fix bugs with parameter validation

* fix bugs with parameter validation

* add excel formatting

* sdk needs mapping as well, controller takes care of this in terminal

* small formating

* add some safe guard try except

* controller bugs

* oops

* change export path of parameters to portfolio folder

* add more commands to scripts

* catch optimization exception

* log errors

* black and exceptions

* add flag to test

* black

* flake8

* forgot this

* pylint

* change defaults

* fix ef default

* fix bl

* sync sdk defaults

* sync last defaults

* fix plot heat and add more choices to controller autocomplete

* patch weights

* fix wrong bool parsing

Co-authored-by: James Maslek <jmaslek11@gmail.com>

16 files changed, 1373 insertions, 699 deletions

diff --git a/openbb_terminal/miscellaneous/portfolio_examples/optimization/OpenBB_Parameters_Template_v1.0.0.xlsx b/openbb_terminal/miscellaneous/portfolio_examples/optimization/OpenBB_Parameters_Template_v1.0.0.xlsx
index 3b8a5afb170..56ffb4d5e05 100644
--- a/openbb_terminal/miscellaneous/portfolio_examples/optimization/OpenBB_Parameters_Template_v1.0.0.xlsx
+++ b/openbb_terminal/miscellaneous/portfolio_examples/optimization/OpenBB_Parameters_Template_v1.0.0.xlsx
diff --git a/openbb_terminal/miscellaneous/scripts/portfolio/test_po_show_plot_rpf.openbb b/openbb_terminal/miscellaneous/scripts/portfolio/test_po_show_plot_rpf.openbb
index 7146022d153..4277466898d 100644
--- a/openbb_terminal/miscellaneous/scripts/portfolio/test_po_show_plot_rpf.openbb
+++ b/openbb_terminal/miscellaneous/scripts/portfolio/test_po_show_plot_rpf.openbb
@@ -52,3 +52,4 @@ plot -pf name_0 -ct sector -he
 maxsharpe
 rpf -pf a,b,c
 rpf -pf name_0,maxsharpe_1
+exit
diff --git a/openbb_terminal/miscellaneous/scripts/portfolio/test_portfolio_boolean_args.openbb b/openbb_terminal/miscellaneous/scripts/portfolio/test_portfolio_boolean_args.openbb
index dec916dbccc..97abd141325 100644
--- a/openbb_terminal/miscellaneous/scripts/portfolio/test_portfolio_boolean_args.openbb
+++ b/openbb_terminal/miscellaneous/scripts/portfolio/test_portfolio_boolean_args.openbb
@@ -2,8 +2,9 @@ portfolio
 load --file Public_Equity_Orderbook.xlsx
 po
 params
-file --file OpenBB_Parameters_Template_v1.0.0.xlsx
+load OpenBB_Parameters_Template_v1.0.0.xlsx
 set ef
 arg tangency False
 arg log_returns True
-exit
\ No newline at end of file
+clear
+exit
diff --git a/openbb_terminal/miscellaneous/scripts/portfolio/test_portfolio_po.openbb b/openbb_terminal/miscellaneous/scripts/portfolio/test_portfolio_po.openbb
index 979549dab13..57a3e5d812d 100644
--- a/openbb_terminal/miscellaneous/scripts/portfolio/test_portfolio_po.openbb
+++ b/openbb_terminal/miscellaneous/scripts/portfolio/test_portfolio_po.openbb
@@ -1,13 +1,14 @@
 portfolio
 po
 load 60_40_Portfolio.xlsx
+file --file example.ini
 maxsharpe -s 2022-10-20
 minrisk -r 0.05
-maxutil -a 0.1
+maxutil -a 0.1 -ra 2 -tk 0.4 -tr 8
 maxret -e 2022-11-20
 maxdiv -mn 0.1
 maxdecorr -lr
-ef -t
+ef -t -se 69 -n 10
 equal -th 0.1
 riskparity -p 1y
 relriskparity -de 0.9
@@ -21,4 +22,21 @@ plot -pf MAXSHARPE_0 -dd
 plot -pf MAXSHARPE_0 -rc
 plot -pf MAXSHARPE_0 -he
 rpf NAME_6
+..
+load --file market.csv
+po
+equal
+maxsharpe -s 1995-05-07
+minrisk -r 0.08
+maxutil -a 0.1 -ra 2 -tk 0.4 -tr 8
+maxret -e 2022-11-20
+maxdiv -mn 0.1
+maxdecorr -lr -p max
+ef -t -se 420 -n 0
+equal -th 0.1
+riskparity -p 1y
+relriskparity -de 0.9
+hrp -cd pearson
+herc -at 0.1
+nco -rm EDaR -lr
 exit
diff --git a/openbb_terminal/portfolio/portfolio_optimization/optimizer_helper.py b/openbb_terminal/portfolio/portfolio_optimization/optimizer_helper.py
index c97832ce3fb..9284da4bf35 100644
--- a/openbb_terminal/portfolio/portfolio_optimization/optimizer_helper.py
+++ b/openbb_terminal/portfolio/portfolio_optimization/optimizer_helper.py
@@ -2,9 +2,14 @@
 __docformat__ = "numpy"
 
 import argparse
+from typing import Any
 import pandas as pd
 
-from openbb_terminal.portfolio.portfolio_optimization import statics
+from openbb_terminal.portfolio.portfolio_optimization.statics import (
+    RISK_CHOICES,
+    OPTIMIZATION_PARAMETERS,
+    TERMINAL_TEMPLATE_MAP,
+)
 from openbb_terminal.rich_config import console
 
 # These are all the possible yfinance properties
@@ -146,8 +151,45 @@ def validate_risk_measure(risk_measure: str, warning: bool = True) -> str:
     str
         Validated risk measure
     """
-    if risk_measure.lower() in statics.RISK_CHOICES:
-        return statics.RISK_CHOICES[risk_measure.lower()]
+    if risk_measure.lower() in RISK_CHOICES:
+        return RISK_CHOICES[risk_measure.lower()]
     if warning:
         console.print("[yellow]Risk measure not found. Using 'MV'.[/yellow]")
     return "MV"
+
+
+def get_kwarg(key: str, kwargs: dict, default: Any = None) -> Any:
+    """Get a key from kwargs
+
+    If key is in kwargs, returns it.
+    Otherwise, if default provided, returns it.
+    Otherwise, if key is in OPTIMIZATION_PARAMETERS, returns it.
+
+    Parameters
+    ----------
+    key : str
+        The key to be searched
+    kwargs : dict
+        The kwargs to be searched
+    default : Any
+        The default value to be returned if the key is not found
+
+    Returns
+    -------
+    Any
+        The value of the key if it exists, else None
+    """
+
+    if key in kwargs:
+        return kwargs[key]
+
+    if default:
+        return default
+
+    # TODO: Remove this line when mapping between template and terminal is not needed
+    template_key = TERMINAL_TEMPLATE_MAP.get(key, key)
+
+    PARAMETER = OPTIMIZATION_PARAMETERS.get(template_key)
+    if PARAMETER is None:
+        return default
+    return PARAMETER.default
diff --git a/openbb_terminal/portfolio/portfolio_optimization/optimizer_model.py b/openbb_terminal/portfolio/portfolio_optimization/optimizer_model.py
index f4a1ec0675d..2a61e251633 100644
--- a/openbb_terminal/portfolio/portfolio_optimization/optimizer_model.py
+++ b/openbb_terminal/portfolio/portfolio_optimization/optimizer_model.py
@@ -21,7 +21,11 @@ from scipy.interpolate import interp1d
 from openbb_terminal.decorators import log_start_end
 from openbb_terminal.portfolio.portfolio_optimization import (
     yahoo_finance_model,
-    optimizer_helper,
+)
+from openbb_terminal.portfolio.portfolio_optimization.optimizer_helper import (
+    get_kwarg,
+    validate_risk_measure,
+    valid_property_infos,
 )
 from openbb_terminal.rich_config import console
 
@@ -199,15 +203,15 @@ def get_equal_weights(
         Dictionary of weights where keys are the tickers, dataframe of stock returns
     """
 
-    interval = kwargs.get("interval", "3y")
-    start_date = kwargs.get("start_date", "")
-    end_date = kwargs.get("end_date", "")
-    log_returns = kwargs.get("log_returns", False)
-    freq = kwargs.get("freq", "D")
-    maxnan = kwargs.get("maxnan", 0.05)
-    threshold = kwargs.get("threshold", 0.0)
-    method = kwargs.get("method", "time")
-    value = kwargs.get("value", 1.0)
+    interval = get_kwarg("interval", kwargs)
+    start_date = get_kwarg("start_date", kwargs)
+    end_date = get_kwarg("end_date", kwargs)
+    log_returns = get_kwarg("log_returns", kwargs)
+    freq = get_kwarg("freq", kwargs)
+    maxnan = get_kwarg("maxnan", kwargs)
+    threshold = get_kwarg("threshold", kwargs)
+    method = get_kwarg("method", kwargs)
+    value = get_kwarg("value", kwargs)
 
     stock_prices = yahoo_finance_model.process_stocks(
         symbols, interval, start_date, end_date
@@ -271,17 +275,17 @@ def get_property_weights(
         Dictionary of portfolio weights or allocations
     """
 
-    interval = kwargs.get("interval", "3y")
-    start_date = kwargs.get("start_date", "")
-    end_date = kwargs.get("end_date", "")
-    log_returns = kwargs.get("log_returns", False)
-    freq = kwargs.get("freq", "D")
-    maxnan = kwargs.get("maxnan", 0.05)
-    threshold = kwargs.get("threshold", 0.0)
-    method = kwargs.get("method", "time")
-    value = kwargs.get("value", 1.0)
+    interval = get_kwarg("interval", kwargs)
+    start_date = get_kwarg("start_date", kwargs)
+    end_date = get_kwarg("end_date", kwargs)
+    log_returns = get_kwarg("log_returns", kwargs)
+    freq = get_kwarg("freq", kwargs)
+    maxnan = get_kwarg("maxnan", kwargs)
+    threshold = get_kwarg("threshold", kwargs)
+    method = get_kwarg("method", kwargs)
+    value = get_kwarg("value", kwargs)
 
-    s_property = kwargs.get("s_property", "marketCap")
+    s_property = get_kwarg("s_property", kwargs, default="marketCap")
 
     stock_prices = yahoo_finance_model.process_stocks(
         symbols, interval, start_date, end_date
@@ -427,29 +431,29 @@ def get_mean_risk_portfolio(
         DataFrame of stock returns.
     """
 
-    interval = kwargs.get("interval", "3y")
-    start_date = kwargs.get("start_date", "")
-    end_date = kwargs.get("end_date", "")
-    log_returns = kwargs.get("log_returns", False)
-    freq = kwargs.get("freq", "D")
-    maxnan = kwargs.get("maxnan", 0.05)
-    threshold = kwargs.get("threshold", 0.0)
-    method = kwargs.get("method", "time")
-    value = kwargs.get("value", 1.0)
-    value_short = kwargs.get("value_short", 0.0)
-
-    risk_measure = kwargs.get("risk_measure", "MV")
-    objective = kwargs.get("objective", "Sharpe")
-    risk_free_rate = kwargs.get("risk_free_rate", 0.0)
-    risk_aversion = kwargs.get("risk_aversion", 1.0)
-    alpha = kwargs.get("alpha", 0.05)
-    target_return = kwargs.get("target_return", -1.0)
-    target_risk = kwargs.get("target_risk", -1.0)
-    mean = kwargs.get("mean", "hist")
-    covariance = kwargs.get("covariance", "hist")
-    d_ewma = kwargs.get("d_ewma", 0.94)
-
-    risk_measure = optimizer_helper.validate_risk_measure(risk_measure)
+    interval = get_kwarg("interval", kwargs)
+    start_date = get_kwarg("start_date", kwargs)
+    end_date = get_kwarg("end_date", kwargs)
+    log_returns = get_kwarg("log_returns", kwargs)
+    freq = get_kwarg("freq", kwargs)
+    maxnan = get_kwarg("maxnan", kwargs)
+    threshold = get_kwarg("threshold", kwargs)
+    method = get_kwarg("method", kwargs)
+    value = get_kwarg("value", kwargs)
+    value_short = get_kwarg("value_short", kwargs)
+
+    risk_measure = get_kwarg("risk_measure", kwargs)
+    objective = get_kwarg("objective", kwargs)
+    risk_free_rate = get_kwarg("risk_free_rate", kwargs)
+    risk_aversion = get_kwarg("risk_aversion", kwargs)
+    alpha = get_kwarg("alpha", kwargs)
+    target_return = get_kwarg("target_return", kwargs)
+    target_risk = get_kwarg("target_risk", kwargs)
+    mean = get_kwarg("mean", kwargs)
+    covariance = get_kwarg("covariance", kwargs)
+    d_ewma = get_kwarg("d_ewma", kwargs)
+
+    risk_measure = validate_risk_measure(risk_measure)
 
     stock_prices = yahoo_finance_model.process_stocks(
         symbols, interval, start_date, end_date
@@ -468,48 +472,64 @@ def get_mean_risk_portfolio(
         )
         return {}, pd.DataFrame()
 
-    risk_free_rate = risk_free_rate / time_factor[freq.upper()]
-
-    # Building the portfolio object
-    port = rp.Portfolio(returns=stock_returns, alpha=alpha)
+    if stock_returns.shape[1] < 2:
+        console.print(
+            f"[red]Given the parameters could only get data for '{stock_returns.columns[0]}'.[/red]\n"
+            "[red]Optimization needs at least two assets.[/red]\n",
+        )
+        return {}, pd.DataFrame()
 
-    # Estimate input parameters:
-    port.assets_stats(method_mu=mean, method_cov=covariance, d=d_ewma)
+    first_day = stock_returns.index[0].strftime("%Y-%m-%d")
+    console.print(
+        f"[yellow]First day of data respecting parameters: {first_day}[/yellow]\n"
+    )
 
-    # Budget constraints
-    port.upperlng = value
-    if value_short > 0:
-        port.sht = True
-        port.uppersht = value_short
-        port.budget = value - value_short
-    else:
-        port.budget = value
+    risk_free_rate = risk_free_rate / time_factor[freq.upper()]
 
-    # Estimate optimal portfolio:
-    model = "Classic"
-    hist = True
+    try:
+        # Building the portfolio object
+        port = rp.Portfolio(returns=stock_returns, alpha=alpha)
 
-    if target_return > -1:
-        port.lowerret = float(target_return) / time_factor[freq.upper()]
+        # Estimate input parameters:
+        port.assets_stats(method_mu=mean, method_cov=covariance, d=d_ewma)
 
-    if target_risk > -1:
-        if risk_measure not in ["ADD", "MDD", "CDaR", "EDaR", "UCI"]:
-            setattr(
-                port,
-                upper_risk[risk_measure],
-                float(target_risk) / time_factor[freq.upper()] ** 0.5,
-            )
+        # Budget constraints
+        port.upperlng = value
+        if value_short > 0:
+            port.sht = True
+            port.uppersht = value_short
+            port.budget = value - value_short
         else:
-            setattr(port, upper_risk[risk_measure], float(target_risk))
+            port.budget = value
 
-    weights = port.optimization(
-        model=model,
-        rm=risk_measure,
-        obj=objective,
-        rf=risk_free_rate,
-        l=risk_aversion,
-        hist=hist,
-    )
+        # Estimate optimal portfolio:
+        model = "Classic"
+        hist = True
+
+        if target_return > -1:
+            port.lowerret = float(target_return) / time_factor[freq.upper()]
+
+        if target_risk > -1:
+            if risk_measure not in ["ADD", "MDD", "CDaR", "EDaR", "UCI"]:
+                setattr(
+                    port,
+                    upper_risk[risk_measure],
+                    float(target_risk) / time_factor[freq.upper()] ** 0.5,
+                )
+            else:
+                setattr(port, upper_risk[risk_measure], float(target_risk))
+
+        weights = port.optimization(
+            model=model,
+            rm=risk_measure,
+            obj=objective,
+            rf=risk_free_rate,
+            l=risk_aversion,
+            hist=hist,
+        )
+
+    except Exception as _:
+        weights = None
 
     if weights is not None:
         weights = weights.round(5)
@@ -1032,19 +1052,19 @@ def get_max_diversification_portfolio(
         DataFrame of stock returns.
     """
 
-    interval = kwargs.get("interval", "3y")
-    start_date = kwargs.get("start_date", "")
-    end_date = kwargs.get("end_date", "")
-    log_returns = kwargs.get("log_returns", False)
-    freq = kwargs.get("freq", "D")
-    maxnan = kwargs.get("maxnan", 0.05)
-    threshold = kwargs.get("threshold", 0.0)
-    method = kwargs.get("method", "time")
-    value = kwargs.get("value", 1.0)
-    value_short = kwargs.get("value_short", 0.0)
+    interval = get_kwarg("interval", kwargs)
+    start_date = get_kwarg("start_date", kwargs)
+    end_date = get_kwarg("end_date", kwargs)
+    log_returns = get_kwarg("log_returns", kwargs)
+    freq = get_kwarg("freq", kwargs)
+    maxnan = get_kwarg("maxnan", kwargs)
+    threshold = get_kwarg("threshold", kwargs)
+    method = get_kwarg("method", kwargs)
+    value = get_kwarg("value", kwargs)
+    value_short = get_kwarg("value_short", kwargs)
 
-    covariance = kwargs.get("covariance", "hist")
-    d_ewma = kwargs.get("d_ewma", 0.94)
+    covariance = get_kwarg("covariance", kwargs)
+    d_ewma = get_kwarg("d_ewma", kwargs)
 
     stock_prices = yahoo_finance_model.process_stocks(
         symbols, interval, start_date, end_date
@@ -1058,24 +1078,29 @@ def get_max_diversification_portfolio(
         method=method,
     )
 
-    # Building the portfolio object
-    port = rp.Portfolio(returns=stock_returns)
+    try:
+        # Building the portfolio object
+        port = rp.Portfolio(returns=stock_returns)
 
-    # Estimate input parameters:
-    port.assets_stats(method_mu="hist", method_cov=covariance, d=d_ewma)
-    port.mu = stock_returns.std().to_frame().T
+        # Estimate input parameters:
+        port.assets_stats(method_mu="hist", method_cov=covariance, d=d_ewma)
+        port.mu = stock_returns.std().to_frame().T
 
-    # Budget constraints
-    port.upperlng = value
-    if value_short > 0:
-        port.sht = True
-        port.uppersht = value_short
-        port.budget = value - value_short
-    else:
-        port.budget = value
+        # Budget constraints
+        port.upperlng = value
+        if value_short > 0:
+            port.sht = True
+            port.uppersht = value_short
+            port.budget = value - value_short
+        else:
+            port.budget = value
 
-    # Estimate optimal portfolio:
-    weights = port.optimization(model="Classic", rm="MV", obj="Sharpe", rf=0, hist=True)
+        # Estimate optimal portfolio:
+        weights = port.optimization(
+            model="Classic", rm="MV", obj="Sharpe", rf=0, hist=True
+        )
+    except Exception as _:
+        weights = None
 
     if weights is not None:
         weights = weights.round(5)
@@ -1151,19 +1176,19 @@ def get_max_decorrelation_portfolio(
         DataFrame of stock returns.
     """
 
-    interval = kwargs.get("interval", "3y")
-    start_date = kwargs.get("start_date", "")
-    end_date = kwargs.get("end_date", "")
-    log_returns = kwargs.get("log_returns", False)
-    freq = kwargs.get("freq", "D")
-    maxnan = kwargs.get("maxnan", 0.05)
-    threshold = kwargs.get("threshold", 0.0)
-    method = kwargs.get("method", "time")
-    value = kwargs.get("value", 1.0)
-    value_short = kwargs.get("value_short", 0.0)
+    interval = get_kwarg("interval", kwargs)
+    start_date = get_kwarg("start_date", kwargs)
+    end_date = get_kwarg("end_date", kwargs)
+    log_returns = get_kwarg("log_returns", kwargs)
+    freq = get_kwarg("freq", kwargs)
+    maxnan = get_kwarg("maxnan", kwargs)
+    threshold = get_kwarg("threshold", kwargs)
+    method = get_kwarg("method", kwargs)
+    value = get_kwarg("value", kwargs)
+    value_short = get_kwarg("value_short", kwargs)
 
-    covariance = kwargs.get("covariance", "hist")
-    d_ewma = kwargs.get("d_ewma", 0.94)
+    covariance = get_kwarg("covariance", kwargs)
+    d_ewma = get_kwarg("d_ewma", kwargs)
 
     stock_prices = yahoo_finance_model.process_stocks(
         symbols, interval, start_date, end_date
@@ -1177,26 +1202,30 @@ def get_max_decorrelation_portfolio(
         method=method,
     )
 
-    # Building the portfolio object
-    port = rp.Portfolio(returns=stock_returns)
+    try:
 
-    # Estimate input parameters:
-    port.assets_stats(method_mu="hist", method_cov=covariance, d=d_ewma)
-    port.cov = rp.cov2corr(port.cov)
+        # Building the portfolio object
+        port = rp.Portfolio(returns=stock_returns)
 
-    # Budget constraints
-    port.upperlng = value
-    if value_short > 0:
-        port.sht = True
-        port.uppersht = value_short
-        port.budget = value - value_short
-    else:
-        port.budget = value
+        # Estimate input parameters:
+        port.assets_stats(method_mu="hist", method_cov=covariance, d=d_ewma)
+        port.cov = rp.cov2corr(port.cov)
 
-    # Estimate optimal portfolio:
-    weights = port.optimization(
-        model="Classic", rm="MV", obj="MinRisk", rf=0, hist=True
-    )
+        # Budget constraints
+        port.upperlng = value
+        if value_short > 0:
+            port.sht = True
+            port.uppersht = value_short
+            port.budget = value - value_short
+        else:
+            port.budget = value
+
+        # Estimate optimal portfolio:
+        weights = port.optimization(
+            model="Classic", rm="MV", obj="MinRisk", rf=0, hist=True
+        )
+    except Exception as _:
+        weights = None
 
     if weights is not None:
         weights = weights.round(5)
@@ -1284,26 +1313,26 @@ def get_black_litterman_portfolio(
         DataFrame of stock returns.
     """
 
-    interval = kwargs.get("interval", "3y")
-    start_date = kwargs.get("start_date", "")
-    end_date = kwargs.get("end_date", "")
-    log_returns = kwargs.get("log_returns", False)
-    freq = kwargs.get("freq", "D")
-    maxnan = kwargs.get("maxnan", 0.05)
-    threshold = kwargs.get("threshold", 0.0)
-    method = kwargs.get("method", "time")
-    value = kwargs.get("value", 1.0)
-    value_short = kwargs.get("value_short", 0.0)
-
-    benchmark = kwargs.get("benchmark", None)
-    p_views = kwargs.get("p_views", None)
-    q_views = kwargs.get("q_views", None)
-    objective = kwargs.get("objective", "Sharpe")
-    risk_free_rate = kwargs.get("risk_free_rate", 0)
-    risk_aversion = kwargs.get("risk_aversion", 1)
-    delta = kwargs.get("delta", None)
-    equilibrium = kwargs.get("equilibrium", True)
-    optimize = kwargs.get("optimize", True)
+    interval = get_kwarg("interval", kwargs)
+    start_date = get_kwarg("start_date", kwargs)
+    end_date = get_kwarg("end_date", kwargs)
+    log_returns = get_kwarg("log_returns", kwargs)
+    freq = get_kwarg("freq", kwargs)
+    maxnan = get_kwarg("maxnan", kwargs)
+    threshold = get_kwarg("threshold", kwargs)
+    method = get_kwarg("method", kwargs)
+    value = get_kwarg("value", kwargs)
+    value_short = get_kwarg("value_short", kwargs)
+
+    benchmark = get_kwarg("benchmark", kwargs)
+    p_views = get_kwarg("p_views", kwargs)
+    q_views = get_kwarg("q_views", kwargs)
+    objective = get_kwarg("objective", kwargs)
+    risk_free_rate = get_kwarg("risk_free_rate", kwargs)
+    risk_aversion = get_kwarg("risk_aversion", kwargs)
+    delta = get_kwarg("delta", kwargs)
+    equilibrium = get_kwarg("equilibrium", kwargs)
+    optimize = get_kwarg("optimize", kwargs)
 
     stock_prices = yahoo_finance_model.process_stocks(
         symbols, interval, start_date, end_date
@@ -1349,32 +1378,35 @@ def get_black_litterman_portfolio(
     weights = pd.DataFrame(weights)
 
     if optimize:
-        # Building the portfolio object
-        port = rp.Portfolio(returns=stock_returns)
-
-        # Estimate input parameters:
-        port.assets_stats(method_mu="hist", method_cov="hist")
-        port.mu_bl = pd.DataFrame(mu).T
-        port.cov_bl = pd.DataFrame(cov)
-
-        # Budget constraints
-        port.upperlng = value
-        if value_short > 0:
-            port.sht = True
-            port.uppersht = value_short
-            port.budget = value - value_short
-        else:
-            port.budget = value
-
-        # Estimate optimal portfolio:
-        weights = port.optimization(
-            model="BL",
-            rm="MV",
-            obj=objective,
-            rf=risk_free_rate,
-            l=risk_aversion,
-            hist=True,
-        )
+        try:
+            # Building the portfolio object
+            port = rp.Portfolio(returns=stock_returns)
+
+            # Estimate input parameters:
+            port.assets_stats(method_mu="hist", method_cov="hist")
+            port.mu_bl = pd.DataFrame(mu).T
+            port.cov_bl = pd.DataFrame(cov)
+
+            # Budget constraints
+            port.upperlng = value
+            if value_short > 0:
+                port.sht = True
+                port.uppersht = value_short
+                port.budget = value - value_short
+            else:
+                port.budget = value
+
+            # Estimate optimal portfolio:
+            weights = port.optimization(
+                model="BL",
+                rm="MV",
+                obj=objective,
+                rf=risk_free_rate,
+                l=risk_aversion,
+                hist=True,
+            )
+        except Exception as _:
+            weights = None
 
     if weights is not None:
         weights = weights.round(5)
@@ -1478,25 +1510,25 @@ def get_ef(
         frontier, mu, cov, stock_returns, weights, X1, Y1, port
     """
 
-    interval = kwargs.get("interval", "3y")
-    start_date = kwargs.get("start_date", "")
-    end_date = kwargs.get("end_date", "")
-    log_returns = kwargs.get("log_returns", False)
-    freq = kwargs.get("freq", "D")
-    maxnan = kwargs.get("maxnan", 0.05)
-    threshold = kwargs.get("threshold", 0.05)
-    method = kwargs.get("method", "time")
-    value = kwargs.get("value", 1.0)
-    value_short = kwargs.get("value_short", 0.0)
-
-    risk_measure = kwargs.get("risk_measure", "MV")
-    risk_free_rate = kwargs.get("risk_free_rate", 0.0)
-    alpha = kwargs.get("alpha", 0.05)
-    n_portfolios = kwargs.get("n_portfolios", 100)
-    seed = kwargs.get("seed", 123)
+    interval = get_kwarg("interval", kwargs)
+    start_date = get_kwarg("start_date", kwargs)
+    end_date = get_kwarg("end_date", k