Fix portfolio optimization SDK bugs (#3582)

* Small improvement to text handling

* fix plot and engine

* change msg

* fix po engine

* fix rc

* fix error

* opt pytest

* bug fix

Co-authored-by: colin99d <colin99delahunty@gmail.com>
Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com>
Co-authored-by: James Maslek <jmaslek11@gmail.com>

5 files changed, 560 insertions, 318 deletions

diff --git a/openbb_terminal/portfolio/portfolio_optimization/po_engine.py b/openbb_terminal/portfolio/portfolio_optimization/po_engine.py
index 8a201bbe138..d46854f4c4c 100644
--- a/openbb_terminal/portfolio/portfolio_optimization/po_engine.py
+++ b/openbb_terminal/portfolio/portfolio_optimization/po_engine.py
@@ -1,4 +1,4 @@
-from typing import Dict, List
+from typing import Dict, List, Tuple
 import pandas as pd
 from openbb_terminal.portfolio.portfolio_optimization import (
     excel_model,
@@ -14,8 +14,7 @@ class PoEngine:
 
     def __init__(
         self,
-        symbols: List[str] = None,
-        categories: Dict[str, Dict[str, str]] = None,
+        symbols_categories: Dict[str, Dict[str, str]] = None,
         symbols_file_path: str = None,
         parameters_file_path: str = None,
     ):
@@ -23,9 +22,7 @@ class PoEngine:
 
         Parameters
         ----------
-        symbols : List[str]
-            List of symbols
-        categories : Dict[str, float], optional
+        symbols_categories : Dict[str, float], optional
             Categories, by default None
         symbols_file_path : str, optional
             Symbols file path, by default None
@@ -33,14 +30,18 @@ class PoEngine:
             Parameters file path, by default None
         """
 
-        self._categories: Dict[str, Dict[str, str]] = categories or {}
+        self._categories: Dict[str, Dict[str, str]] = {}
+        self._symbols: List[str] = []
         self._weights: Dict[str, float] = {}
         self._returns: pd.DataFrame = None
         self._params: Dict[str, float] = {}
         self._current_model: str
 
-        if symbols is not None:
-            self._symbols: List[str] = symbols
+        if symbols_categories is not None:
+            self._symbols, self._categories = PoEngine.__parse_dictionary(
+                symbols_categories
+            )
+
         elif symbols_file_path is not None:
             self._symbols, self._categories = excel_model.load_allocation(
                 symbols_file_path
@@ -53,6 +54,65 @@ class PoEngine:
                 parameters_file_path
             )
 
+    @staticmethod
+    def __parse_dictionary(
+        symbols_categories: Dict[str, Dict[str, str]]
+    ) -> Tuple[List[str], Dict[str, Dict[str, str]]]:
+        """Parse the categories dictionary
+
+        Parameters
+        ----------
+        symbols_categories : Dict[str, Dict[str, str]]
+            Categories
+
+        Returns
+        -------
+        Tuple[List[str], Dict[str, Dict[str, str]]]
+            Symbols and categories
+        """
+        if isinstance(symbols_categories, dict):
+            symbols = PoEngine.__get_symbols_from_categories(symbols_categories)
+        else:
+            raise TypeError("'symbols_categories' must be a dictionary.")
+
+        for symbol in symbols:
+            symbols_categories["CURRENCY"].setdefault(symbol, "USD")
+            symbols_categories["CURRENT_INVESTED_AMOUNT"].setdefault(symbol, "0")
+
+        return symbols, symbols_categories
+
+    @staticmethod
+    def __get_symbols_from_categories(
+        symbols_categories: Dict[str, Dict[str, str]]
+    ) -> List[str]:
+        """Get the symbols from the categories dictionary
+
+        Parameters
+        ----------
+        symbols_categories : Dict[str, Dict[str, str]], optional
+            Categories
+
+        Returns
+        -------
+        List[str]
+            List of symbols
+        """
+
+        try:
+            symbols = []
+            for item in symbols_categories.items():
+                _, values = item
+                for v in values.keys():
+                    symbols.append(v)
+
+            return list(set(symbols))
+
+        except Exception:
+            console.print(
+                "Unsupported dictionary format. See `portfolio.po.load` examples for correct format."
+            )
+            return []
+
     def get_symbols(self):
         return self._symbols
 
@@ -66,16 +126,6 @@ class PoEngine:
         """
         return list(self._categories.keys())
 
-    def set_categories_dict(self, categories: Dict[str, Dict[str, str]]):
-        """Set the categories
-
-        Parameters
-        ----------
-        categories : Dict[str, Dict[str, str]]
-            Categories
-        """
-        self._categories = categories
-
     def get_category(self, category: str = None) -> Dict[str, str]:
         """Get the category
 
diff --git a/openbb_terminal/portfolio/portfolio_optimization/po_model.py b/openbb_terminal/portfolio/portfolio_optimization/po_model.py
index ce1ed827cb5..fbe77182ef6 100644
--- a/openbb_terminal/portfolio/portfolio_optimization/po_model.py
+++ b/openbb_terminal/portfolio/portfolio_optimization/po_model.py
@@ -59,7 +59,7 @@ def validate_parameters_type(parameters):
 
 @log_start_end(log=logger)
 def generate_portfolio(
-    symbols: List[str] = None,
+    symbols_categories: Dict[str, Dict[str, str]] = None,
     symbols_file_path: str = None,
     parameters_file_path: str = None,
 ) -> Union[PoEngine, None]:
@@ -67,8 +67,8 @@ def generate_portfolio(
 
     Parameters
     ----------
-    symbols : List[str], optional
-        List of symbols, by default None
+    symbols_categories: Dict[str, Dict[str, str]] = None
+        Categories, by default None
     symbols_file_path : str, optional
         Symbols file full path, by default None
     parameters_file_path : str, optional
@@ -82,25 +82,44 @@ def generate_portfolio(
     Examples
     --------
     >>> from openbb_terminal.sdk import openbb
-    >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
+    >>> d = {
+                "SECTOR": {
+                    "AAPL": "INFORMATION TECHNOLOGY",
+                    "MSFT": "INFORMATION TECHNOLOGY",
+                    "AMZN": "CONSUMER DISCRETIONARY",
+                },
+                "CURRENT_INVESTED_AMOUNT": {
+                    "AAPL": "100000.0",
+                    "MSFT": "200000.0",
+                    "AMZN": "300000.0",
+                },
+                "CURRENCY": {
+                    "AAPL": "USD",
+                    "MSFT": "USD",
+                    "AMZN": "USD",
+                },
+            }
+    >>> p = openbb.portfolio.po.load(symbols_categories=d)
     >>> weights, performance = openbb.portfolio.po.equal(portfolio_engine=p)
 
     >>> from openbb_terminal.sdk import openbb
-    >>> p = openbb.portfolio.po.load(symbols=["AAPL", "MSFT", "AMZN"])
+    >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
     >>> weights, performance = openbb.portfolio.po.equal(portfolio_engine=p)
     """
 
-    if symbols:
+    if symbols_file_path:
         return PoEngine(
-            symbols=symbols,
+            symbols_file_path=symbols_file_path,
             parameters_file_path=parameters_file_path,
         )
-    if symbols_file_path:
+
+    if symbols_categories:
         return PoEngine(
-            symbols_file_path=symbols_file_path,
+            symbols_categories=symbols_categories,
             parameters_file_path=parameters_file_path,
         )
-    console.print("No file or symbols provided")
+
+    console.print("No 'symbols_file_path' or 'symbols_categories' provided.")
     return None
 
 
@@ -163,14 +182,12 @@ def load_parameters_file(
 
 @log_start_end(log=logger)
 def validate_inputs(
-    symbols=None, portfolio_engine=None, kwargs=None
+    portfolio_engine=None, kwargs=None
 ) -> Tuple[List[str], PoEngine, dict]:
     """Check valid inputs
 
     Parameters
     ----------
-    symbols : List[str], optional
-        List of symbols, by default None
     portfolio_engine : PoEngine, optional
         Portfolio optimization engine, by default None
         Use `portfolio.po.load` to load a portfolio engine
@@ -183,15 +200,12 @@ def validate_inputs(
         List of symbols, Portfolio optimization engine, Keyword arguments
     """
 
-    if symbols:
-        portfolio_engine = PoEngine(symbols=symbols)
-        parameters = kwargs
-    elif portfolio_engine:
+    if portfolio_engine:
         symbols = portfolio_engine.get_symbols()
         parameters = portfolio_engine.get_params().copy()
         parameters.update(kwargs)
     else:
-        console.print("No 'symbols' or 'portfolio_engine' provided.")
+        console.print("No 'portfolio_engine' provided.")
 
     validate_parameters_type(parameters)
 
@@ -263,7 +277,7 @@ def get_portfolio_performance(weights: Dict, data: pd.DataFrame, **kwargs) -> Di
 
 @log_start_end(log=logger)
 def get_maxsharpe(
-    portfolio_engine: PoEngine = None, symbols: List[str] = None, **kwargs
+    portfolio_engine: PoEngine = None, **kwargs
 ) -> Tuple[pd.DataFrame, Dict]:
     """Optimize Sharpe ratio weights
 
@@ -272,8 +286,6 @@ def get_maxsharpe(
     portfolio_engine : PoEngine, optional
         Portfolio optimization engine, by default None
         Use `portfolio.po.load` to load a portfolio engine
-    symbols : List[str], optional
-        List of symbols, by default None
     interval : str, optional
         Interval to get data, by default '3y'
     start_date : str, optional
@@ -367,14 +379,25 @@ def get_maxsharpe(
     Examples
     --------
     >>> from openbb_terminal.sdk import openbb
-    >>> openbb.portfolio.po.maxsharpe(symbols=["AAPL", "MSFT", "AMZN"])
-    (      value
-     AAPL    1.0
-     MSFT    0.0
-     AMZN    0.0,
-     {'Return': 0.3448948339574538,
-      'Volatility': 0.36513261935342495,
-      'Sharpe ratio': 0.9445741510802071})
+    >>> d = {
+                "SECTOR": {
+                    "AAPL": "INFORMATION TECHNOLOGY",
+                    "MSFT": "INFORMATION TECHNOLOGY",
+                    "AMZN": "CONSUMER DISCRETIONARY",
+                },
+                "CURRENT_INVESTED_AMOUNT": {
+                    "AAPL": "100000.0",
+                    "MSFT": "200000.0",
+                    "AMZN": "300000.0",
+                },
+                "CURRENCY": {
+                    "AAPL": "USD",
+                    "MSFT": "USD",
+                    "AMZN": "USD",
+                },
+            }
+    >>> p = openbb.portfolio.po.load(symbols_categories=d)
+    >>> weights, performance = openbb.portfolio.po.maxsharpe(portfolio_engine=p)
 
     >>> from openbb_terminal.sdk import openbb
     >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
@@ -382,7 +405,7 @@ def get_maxsharpe(
     """
 
     valid_symbols, valid_portfolio_engine, valid_kwargs = validate_inputs(
-        symbols, portfolio_engine, kwargs
+        portfolio_engine, kwargs
     )
     if not valid_symbols:
         return pd.DataFrame()
@@ -400,7 +423,7 @@ def get_maxsharpe(
 
 @log_start_end(log=logger)
 def get_minrisk(
-    portfolio_engine: PoEngine = None, symbols: List[str] = None, **kwargs
+    portfolio_engine: PoEngine = None, **kwargs
 ) -> Tuple[pd.DataFrame, Dict]:
     """Optimize minimum risk weights
 
@@ -409,8 +432,6 @@ def get_minrisk(
     portfolio_engine : PoEngine, optional
         Portfolio optimization engine, by default None
         Use `portfolio.po.load` to load a portfolio engine
-    symbols : List[str], optional
-        List of symbols, by default None
     interval : str, optional
         Interval to get data, by default '3y'
     start_date : str, optional
@@ -504,14 +525,25 @@ def get_minrisk(
     Examples
     --------
     >>> from openbb_terminal.sdk import openbb
-    >>> openbb.portfolio.po.minrisk(symbols=["AAPL", "MSFT", "AMZN"])
-        (        value
-         AAPL  0.25044
-         MSFT  0.49509
-         AMZN  0.25447,
-         {'Return': 0.2248615963428331,
-          'Volatility': 0.32736590080425004,
-          'Sharpe ratio': 0.6868815468880802})
+    >>> d = {
+                "SECTOR": {
+                    "AAPL": "INFORMATION TECHNOLOGY",
+                    "MSFT": "INFORMATION TECHNOLOGY",
+                    "AMZN": "CONSUMER DISCRETIONARY",
+                },
+                "CURRENT_INVESTED_AMOUNT": {
+                    "AAPL": "100000.0",
+                    "MSFT": "200000.0",
+                    "AMZN": "300000.0",
+                },
+                "CURRENCY": {
+                    "AAPL": "USD",
+                    "MSFT": "USD",
+                    "AMZN": "USD",
+                },
+            }
+    >>> p = openbb.portfolio.po.load(symbols_categories=d)
+    >>> weights, performance = openbb.portfolio.po.minrisk(portfolio_engine=p)
 
     >>> from openbb_terminal.sdk import openbb
     >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
@@ -519,7 +551,7 @@ def get_minrisk(
     """
 
     valid_symbols, valid_portfolio_engine, valid_kwargs = validate_inputs(
-        symbols, portfolio_engine, kwargs
+        portfolio_engine, kwargs
     )
     if not valid_symbols:
         return pd.DataFrame()
@@ -537,7 +569,7 @@ def get_minrisk(
 
 @log_start_end(log=logger)
 def get_maxutil(
-    portfolio_engine: PoEngine = None, symbols: List[str] = None, **kwargs
+    portfolio_engine: PoEngine = None, **kwargs
 ) -> Tuple[pd.DataFrame, Dict]:
     """Optimize maximum utility weights
 
@@ -546,8 +578,6 @@ def get_maxutil(
     portfolio_engine : PoEngine, optional
         Portfolio optimization engine, by default None
         Use `portfolio.po.load` to load a portfolio engine
-    symbols : List[str], optional
-        List of symbols, by default None
     interval : str, optional
         Interval to get data, by default '3y'
     start_date : str, optional
@@ -641,14 +671,25 @@ def get_maxutil(
     Examples
     --------
     >>> from openbb_terminal.sdk import openbb
-    >>> openbb.portfolio.po.maxutil(symbols=["AAPL", "MSFT", "AMZN"])
-        (      value
-         AAPL    1.0
-         MSFT    0.0
-         AMZN    0.0,
-         {'Return': 0.3448948339574538,
-          'Volatility': 0.36513261935342495,
-          'Sharpe ratio': 0.9445741510802071})
+    >>> d = {
+                "SECTOR": {
+                    "AAPL": "INFORMATION TECHNOLOGY",
+                    "MSFT": "INFORMATION TECHNOLOGY",
+                    "AMZN": "CONSUMER DISCRETIONARY",
+                },
+                "CURRENT_INVESTED_AMOUNT": {
+                    "AAPL": "100000.0",
+                    "MSFT": "200000.0",
+                    "AMZN": "300000.0",
+                },
+                "CURRENCY": {
+                    "AAPL": "USD",
+                    "MSFT": "USD",
+                    "AMZN": "USD",
+                },
+            }
+    >>> p = openbb.portfolio.po.load(symbols_categories=d)
+    >>> weights, performance = openbb.portfolio.po.maxutil(portfolio_engine=p)
 
     >>> from openbb_terminal.sdk import openbb
     >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
@@ -656,7 +697,7 @@ def get_maxutil(
     """
 
     valid_symbols, valid_portfolio_engine, valid_kwargs = validate_inputs(
-        symbols, portfolio_engine, kwargs
+        portfolio_engine, kwargs
     )
     if not valid_symbols:
         return pd.DataFrame()
@@ -674,7 +715,7 @@ def get_maxutil(
 
 @log_start_end(log=logger)
 def get_maxret(
-    portfolio_engine: PoEngine = None, symbols: List[str] = None, **kwargs
+    portfolio_engine: PoEngine = None, **kwargs
 ) -> Tuple[pd.DataFrame, Dict]:
     """Optimize maximum return weights
 
@@ -683,8 +724,6 @@ def get_maxret(
     portfolio_engine : PoEngine, optional
         Portfolio optimization engine, by default None
         Use `portfolio.po.load` to load a portfolio engine
-    symbols : List[str], optional
-        List of symbols, by default None
     interval : str, optional
         Interval to get data, by default '3y'
     start_date : str, optional
@@ -778,14 +817,25 @@ def get_maxret(
     Examples
     --------
     >>> from openbb_terminal.sdk import openbb
-    >>> openbb.portfolio.po.maxret(symbols=["AAPL", "MSFT", "AMZN"])
-    (      value
-     AAPL    1.0
-     MSFT    0.0
-     AMZN    0.0,
-     {'Return': 0.3448948339574538,
-      'Volatility': 0.36513261935342495,
-      'Sharpe ratio': 0.9445741510802071})
+    >>> d = {
+                "SECTOR": {
+                    "AAPL": "INFORMATION TECHNOLOGY",
+                    "MSFT": "INFORMATION TECHNOLOGY",
+                    "AMZN": "CONSUMER DISCRETIONARY",
+                },
+                "CURRENT_INVESTED_AMOUNT": {
+                    "AAPL": "100000.0",
+                    "MSFT": "200000.0",
+                    "AMZN": "300000.0",
+                },
+                "CURRENCY": {
+                    "AAPL": "USD",
+                    "MSFT": "USD",
+                    "AMZN": "USD",
+                },
+            }
+    >>> p = openbb.portfolio.po.load(symbols_categories=d)
+    >>> weights, performance = openbb.portfolio.po.maxret(portfolio_engine=p)
 
     >>> from openbb_terminal.sdk import openbb
     >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
@@ -793,7 +843,7 @@ def get_maxret(
     """
 
     valid_symbols, valid_portfolio_engine, valid_kwargs = validate_inputs(
-        symbols, portfolio_engine, kwargs
+        portfolio_engine, kwargs
     )
     if not valid_symbols:
         return pd.DataFrame()
@@ -811,7 +861,7 @@ def get_maxret(
 
 @log_start_end(log=logger)
 def get_maxdiv(
-    portfolio_engine: PoEngine = None, symbols: List[str] = None, **kwargs
+    portfolio_engine: PoEngine = None, **kwargs
 ) -> Tuple[pd.DataFrame, Dict]:
     """Optimize diversification weights
 
@@ -820,8 +870,6 @@ def get_maxdiv(
     portfolio_engine : PoEngine, optional
         Portfolio optimization engine, by default None
         Use `portfolio.po.load` to load a portfolio engine
-    symbols : List[str], optional
-        List of symbols, by default None
     interval : str, optional
         Interval to get data, by default '3y'
     start_date : str, optional
@@ -892,14 +940,25 @@ def get_maxdiv(
     Examples
     --------
     >>> from openbb_terminal.sdk import openbb
-    >>> openbb.portfolio.po.maxdiv(symbols=["AAPL", "MSFT", "AMZN"])
-    (        value
-     AAPL  0.33696
-     MSFT  0.26766
-     AMZN  0.39538,
-     {'Return': 0.21717206203731806,
-      'Volatility': 0.3310292858117002,
-      'Sharpe ratio': 0.6560509034866852})
+    >>> d = {
+                "SECTOR": {
+                    "AAPL": "INFORMATION TECHNOLOGY",
+                    "MSFT": "INFORMATION TECHNOLOGY",
+                    "AMZN": "CONSUMER DISCRETIONARY",
+                },
+                "CURRENT_INVESTED_AMOUNT": {
+                    "AAPL": "100000.0",
+                    "MSFT": "200000.0",
+                    "AMZN": "300000.0",
+                },
+                "CURRENCY": {
+                    "AAPL": "USD",
+                    "MSFT": "USD",
+                    "AMZN": "USD",
+                },
+            }
+    >>> p = openbb.portfolio.po.load(symbols_categories=d)
+    >>> weights, performance = openbb.portfolio.po.maxdiv(portfolio_engine=p)
 
     >>> from openbb_terminal.sdk import openbb
     >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
@@ -907,7 +966,7 @@ def get_maxdiv(
     """
 
     valid_symbols, valid_portfolio_engine, valid_kwargs = validate_inputs(
-        symbols, portfolio_engine, kwargs
+        portfolio_engine, kwargs
     )
     if not valid_symbols:
         return pd.DataFrame()
@@ -925,7 +984,7 @@ def get_maxdiv(
 
 @log_start_end(log=logger)
 def get_maxdecorr(
-    portfolio_engine: PoEngine = None, symbols: List[str] = None, **kwargs
+    portfolio_engine: PoEngine = None, **kwargs
 ) -> Tuple[pd.DataFrame, Dict]:
     """Optimize decorrelation weights
 
@@ -934,8 +993,6 @@ def get_maxdecorr(
     portfolio_engine : PoEngine, optional
         Portfolio optimization engine, by default None
         Use `portfolio.po.load` to load a portfolio engine
-    symbols : List[str], optional
-        List of symbols, by default None
     interval : str, optional
         Interval to get data, by default '3y'
     start_date : str, optional
@@ -984,14 +1041,25 @@ def get_maxdecorr(
     Examples
     --------
     >>> from openbb_terminal.sdk import openbb
-    >>> openbb.portfolio.po.maxdecorr(symbols=["AAPL", "MSFT", "AMZN"])
-    (        value
-     AAPL  0.33444
-     MSFT  0.24963
-     AMZN  0.41593,
-     {'Return': 0.2142767096699773,
-      'Volatility': 0.33184082287769623,
-      'Sharpe ratio': 0.6457213666835423})
+    >>> d = {
+                "SECTOR": {
+                    "AAPL": "INFORMATION TECHNOLOGY",
+                    "MSFT": "INFORMATION TECHNOLOGY",
+                    "AMZN": "CONSUMER DISCRETIONARY",
+                },
+                "CURRENT_INVESTED_AMOUNT": {
+                    "AAPL": "100000.0",
+                    "MSFT": "200000.0",
+                    "AMZN": "300000.0",
+                },
+                "CURRENCY": {
+                    "AAPL": "USD",
+                    "MSFT": "USD",
+                    "AMZN": "USD",
+                },
+            }
+    >>> p = openbb.portfolio.po.load(symbols_categories=d)
+    >>> weights, performance = openbb.portfolio.po.maxdecorr(portfolio_engine=p)
 
     >>> from openbb_terminal.sdk import openbb
     >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
@@ -999,7 +1067,7 @@ def get_maxdecorr(
     """
 
     valid_symbols, valid_portfolio_engine, valid_kwargs = validate_inputs(
-        symbols, portfolio_engine, kwargs
+        portfolio_engine, kwargs
     )
     if not valid_symbols:
         return pd.DataFrame()
@@ -1017,7 +1085,7 @@ def get_maxdecorr(
 
 @log_start_end(log=logger)
 def get_blacklitterman(
-    portfolio_engine: PoEngine = None, symbols: List[str] = None, **kwargs
+    portfolio_engine: PoEngine = None, **kwargs
 ) -> Tuple[pd.DataFrame, Dict]:
     """Optimize decorrelation weights
 
@@ -1026,8 +1094,6 @@ def get_blacklitterman(
     portfolio_engine : PoEngine, optional
         Portfolio optimization engine, by default None
         Use `portfolio.po.load` to load a portfolio engine
-    symbols : List[str], optional
-        List of symbols, by default None
     interval : str, optional
         Interval to get data, by default '3y'
     start_date : str, optional
@@ -1085,14 +1151,25 @@ def get_blacklitterman(
     Examples
     --------
     >>> from openbb_terminal.sdk import openbb
-    >>> openbb.portfolio.po.blacklitterman(symbols=["AAPL", "MSFT", "AMZN"])
-    (        value
-     AAPL  0.48920
-     MSFT  0.28391
-     AMZN  0.22689,
-     {'Return': 0.2563301105112327,
-      'Volatility': 0.33132073874339424,
-      'Sharpe ratio': 0.7736615325784322})
+    >>> d = {
+                "SECTOR": {
+                    "AAPL": "INFORMATION TECHNOLOGY",
+                    "MSFT": "INFORMATION TECHNOLOGY",
+                    "AMZN": "CONSUMER DISCRETIONARY",
+                },
+                "CURRENT_INVESTED_AMOUNT": {
+                    "AAPL": "100000.0",
+                    "MSFT": "200000.0",
+                    "AMZN": "300000.0",
+                },
+                "CURRENCY": {
+                    "AAPL": "USD",
+                    "MSFT": "USD",
+                    "AMZN": "USD",
+                },
+            }
+    >>> p = openbb.portfolio.po.load(symbols_categories=d)
+    >>> weights, performance = openbb.portfolio.po.blacklitterman(portfolio_engine=p)
 
     >>> from openbb_terminal.sdk import openbb
     >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
@@ -1100,7 +1177,7 @@ def get_blacklitterman(
     """
 
     valid_symbols, valid_portfolio_engine, valid_kwargs = validate_inputs(
-        symbols, portfolio_engine, kwargs
+        portfolio_engine, kwargs
     )
     if not valid_symbols:
         return pd.DataFrame()
@@ -1118,7 +1195,7 @@ def get_blacklitterman(
 
 @log_start_end(log=logger)
 def get_ef(
-    portfolio_engine: PoEngine = None, symbols: List[str] = None, **kwargs
+    portfolio_engine: PoEngine = None, **kwargs
 ) -> Tuple[
     pd.DataFrame,
     pd.DataFrame,
@@ -1136,8 +1213,6 @@ def get_ef(
     portfolio_engine : PoEngine, optional
         Portfolio optimization engine, by default None
         Use `portfolio.po.load` to load a portfolio engine
-    symbols : List[str], optional
-        List of symbols, by default None
     interval : str, optional
         Interval to get data, by default '3y'
     start_date : str, optional
@@ -1202,7 +1277,25 @@ def get_ef(
     Examples
     --------
     >>> from openbb_terminal.sdk import openbb
-    >>> frontier = openbb.portfolio.po.ef(symbols=["AAPL", "MSFT", "AMZN"])
+    >>> d = {
+                "SECTOR": {
+                    "AAPL": "INFORMATION TECHNOLOGY",
+                    "MSFT": "INFORMATION TECHNOLOGY",
+                    "AMZN": "CONSUMER DISCRETIONARY",
+                },
+                "CURRENT_INVESTED_AMOUNT": {
+                    "AAPL": "100000.0",
+                    "MSFT": "200000.0",
+                    "AMZN": "300000.0",
+                },
+                "CURRENCY": {
+                    "AAPL": "USD",
+                    "MSFT": "USD",
+                    "AMZN": "USD",
+                },
+            }
+    >>> p = openbb.portfolio.po.load(symbols_categories=d)
+    >>> weights, performance = openbb.portfolio.po.ef(portfolio_engine=p)
 
     >>> from openbb_terminal.sdk import openbb
     >>> p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
@@ -1210,7 +1303,7 @@ def get_ef(
     """
 
     valid_symbols, valid_portfolio_engine, valid_kwargs = validate_inputs(
-        symbols, portfolio_engine, kwargs
+        portfolio_engine, kwargs
     )
     if not valid_symbols:
         return pd.DataFrame()
@@ -1218,7 +1311,7 @@ def get_ef(
     frontier, mu, cov, returns, weights, X1, Y1, port = optimizer_model.get_ef(
         symbols=valid_symbols, **valid_kwargs
     )
-    valid_portfolio_engine.set_weights(weights=weights)
+    valid_portfolio_engine.set_weights(weights=weights.to_dict()["weights"])
     valid_portfolio_engine.set_returns(returns=returns)
 
     return frontier, mu, cov, returns, weights, X1, Y1, port
@@ -1226,7 +1319,7 @@ def get_ef(
 
 @log_start_end(log=logger)
 def get_riskparity(
-    portfolio_engine: PoEngine = None, symbols: List[str] = None, **kwargs
+    portfolio_engine: PoEngine = None, **kwargs
 ) -> Tuple[pd.DataFrame, Dict]:
     """Optimize with Risk Parity using the risk budgeting approach
 
@@ -1235,8 +1