diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 50d750018fa..7f7dacfa7ce 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -238,6 +238,7 @@ Yahoo Finance:
    5. Parse known args from list of arguments and values provided by the user.
    6. Call the function contained in a `_view.py` file with the arguments parsed by argparse.
 ```
+@try_except
 def call_shorted(self, other_args: List[str]):
         """Process shorted command"""
         parser = argparse.ArgumentParser(
@@ -263,22 +264,19 @@ def call_shorted(self, other_args: List[str]):
             dest="export",
             help="Export dataframe data to csv,json,xlsx file",
         )
-        try:
-            if other_args:
-                if "-" not in other_args[0]:
-                    other_args.insert(0, "-n")
+        if other_args:
+            if "-" not in other_args[0]:
+                other_args.insert(0, "-n")
 
-            ns_parser = parse_known_args_and_warn(parser, other_args)
-            if not ns_parser:
-                return
+        ns_parser = parse_known_args_and_warn(parser, other_args)
+        if not ns_parser:
+            return
 
-            yahoofinance_view.display_most_shorted(
-                num_stocks=ns_parser.num,
-                export=ns_parser.export,
-            )
+        yahoofinance_view.display_most_shorted(
+            num_stocks=ns_parser.num,
+            export=ns_parser.export,
+        )
 
-        except Exception as e:
-            print(e, "\n")
 ```
 
 
diff --git a/README.md b/README.md
index 8a2cb6829c9..35752162faa 100644
--- a/README.md
+++ b/README.md
@@ -126,8 +126,6 @@ To read more on adding the environment keys and how to configure your X-server t
 
 If you'd like to see a video recording of the installation process, @JohnnyDankseed has made one available [here](https://www.youtube.com/watch?v=-DJJ-cfquDA).
 
-The user @mchow01 has made available a tutorial on [how to run the terminal on an Apple M1](https://mchow01.github.io/macos/system/gamestonk/2021/05/27/gamestonk-terminal-m1.html).
-
 This project supports Python 3.7, 3.8 and 3.9.
 
 Our current recommendation is to use this project with Anaconda's Python distribution - either full [__Anaconda3 Latest__](https://repo.anaconda.com/archive/) or [__Miniconda3 Latest__](https://repo.anaconda.com/archive/). Several features in this project utilize Machine Learning. Machine Learning Python dependencies are optional. If you decided to add Machine Learning features at a later point, you will likely have better user experience with Anaconda's Python distribution.
diff --git a/gamestonk_terminal/portfolio/portfolio_controller.py b/gamestonk_terminal/portfolio/portfolio_controller.py
index 0e7714651cb..41ba5161945 100644
--- a/gamestonk_terminal/portfolio/portfolio_controller.py
+++ b/gamestonk_terminal/portfolio/portfolio_controller.py
@@ -3,8 +3,13 @@ __docformat__ = "numpy"
 
 import argparse
 import os
+from os import listdir
+from os.path import isfile, join
+from typing import List
+from datetime import datetime
 
 from prompt_toolkit.completion import NestedCompleter
+import pandas as pd
 
 from gamestonk_terminal import feature_flags as gtff
 from gamestonk_terminal.helper_funcs import (
@@ -12,13 +17,23 @@ from gamestonk_terminal.helper_funcs import (
     MENU_GO_BACK,
     MENU_QUIT,
     MENU_RESET,
+    try_except,
+    valid_date,
+    check_positive_float,
 )
 from gamestonk_terminal.menu import session
 from gamestonk_terminal.portfolio.brokers import bro_controller
 from gamestonk_terminal.portfolio.portfolio_analysis import pa_controller
 from gamestonk_terminal.portfolio.portfolio_optimization import po_controller
+from gamestonk_terminal.portfolio import (
+    portfolio_view,
+    portfolio_model,
+    yfinance_model,
+    portfolio_helper,
+)
+from gamestonk_terminal.helper_funcs import parse_known_args_and_warn
 
-# pylint: disable=R1710
+# pylint: disable=R1710,E1101
 
 
 class PortfolioController:
@@ -37,6 +52,13 @@ class PortfolioController:
         "bro",
         "pa",
         "po",
+        "load",
+        "save",
+        "show",
+        "add",
+        "rmv",
+        "ar",
+        "rmr",
     ]
 
     CHOICES += CHOICES_MENUS
@@ -51,6 +73,18 @@ class PortfolioController:
         self.completer = NestedCompleter.from_nested_dict(
             {c: None for c in self.CHOICES}
         )
+        self.portfolio = pd.DataFrame(
+            columns=[
+                "Name",
+                "Type",
+                "Quantity",
+                "Date",
+                "Price",
+                "Fees",
+                "Premium",
+                "Side",
+            ]
+        )
 
     def print_help(self):
         """Print help"""
@@ -67,6 +101,19 @@ What do you want to do?
 >   bro         brokers holdings, \t\t supports: robinhood, alpaca, ally, degiro
 >   pa          portfolio analysis, \t\t analyses your custom portfolio
 >   po          portfolio optimization, \t optimal portfolio weights from pyportfolioopt
+
+Portfolio:
+    load        load data into the portfolio
+    save        save your portfolio for future use
+    show        show existing transactions
+    add         add a security to your portfolio
+    rmv         remove a security from your portfolio
+
+Reports:
+    ar          annual report for performance of a given portfolio
+
+Graphs:
+    rmr         graph your returns versus the market's returns
         """
         print(help_text)
 
@@ -143,6 +190,259 @@ What do you want to do?
         else:
             return True
 
+    @try_except
+    def call_load(self, other_args: List[str]):
+        """Process load command"""
+        path = os.path.dirname(os.path.abspath(__file__))
+        path = os.path.abspath(os.path.join(path, "portfolios"))
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="load",
+            description="Load your portfolio",
+        )
+        parser.add_argument(
+            "-n",
+            "--name",
+            type=str,
+            choices=[f for f in listdir(path) if isfile(join(path, f))],
+            dest="name",
+            required="-h" not in other_args,
+            help="Name of file to be saved",
+        )
+        if other_args:
+            if "-n" not in other_args and "-h" not in other_args:
+                other_args.insert(0, "-n")
+
+        ns_parser = parse_known_args_and_warn(parser, other_args)
+        if not ns_parser:
+            return
+
+        self.portfolio = portfolio_model.load_df(ns_parser.name)
+        print("")
+
+    @try_except
+    def call_save(self, other_args: List[str]):
+        """Process save command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="save",
+            description="Save your portfolio",
+        )
+        parser.add_argument(
+            "-n",
+            "--name",
+            type=str,
+            dest="name",
+            required="-h" not in other_args,
+            help="Name of file to be saved",
+        )
+        if other_args and "-n" not in other_args and "-h" not in other_args:
+            other_args.insert(0, "-n")
+
+        ns_parser = parse_known_args_and_warn(parser, other_args)
+        if not ns_parser:
+            return
+
+        if (
+            ".csv" not in ns_parser.name
+            and ".xlsx" not in ns_parser.name
+            and ".json" not in ns_parser.name
+        ):
+            print(
+                "Please submit as 'filename.filetype' with filetype being csv, xlsx, or json\n"
+            )
+
+        portfolio_model.save_df(self.portfolio, ns_parser.name)
+
+    def call_show(self, _):
+        """Process show command"""
+        portfolio_view.show_df(self.portfolio, False)
+
+    def call_add(self, other_args: List[str]):
+        """Process add command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="add",
+            description="Adds an item to your portfolio",
+        )
+        parser.add_argument(
+            "-n",
+            "--name",
+            type=str,
+            dest="name",
+            required="-h" not in other_args,
+            help="Name of item to be added (for example a ticker for a stock)",
+        )
+        parser.add_argument(
+            "-t",
+            "--type",
+            dest="type",
+            type=str,
+            choices=["stock", "cash"],  # "bond", "option", "crypto",
+            default="stock",
+            help="Type of asset to add",
+        )
+        parser.add_argument(
+            "-q",
+            "--quantity",
+            dest="quantity",
+            type=check_positive_float,
+            default=1,
+            help="Amounts of the asset owned",
+        )
+        parser.add_argument(
+            "-d",
+            "--date",
+            dest="date",
+            type=valid_date,
+            default=datetime.now(),
+            help="Date: yyyy/mm/dd",
+        )
+        parser.add_argument(
+            "-p",
+            "--price",
+            dest="price",
+            type=check_positive_float,
+            required="-h" not in other_args,
+            help="Price purchased for asset",
+        )
+        parser.add_argument(
+            "-f",
+            "--fees",
+            dest="fees",
+            type=check_positive_float,
+            help="Fees paid for transaction",
+        )
+        # parser.add_argument(
+        #     "-r",
+        #     "--premium",
+        #     dest="premium",
+        #     type=check_positive_float,
+        #     help="Premium paid/received for the option",
+        # )
+        parser.add_argument(
+            "-a",
+            "--action",
+            type=str,
+            dest="action",
+            choices=["buy", "sell", "interest", "deposit", "withdrawal"],
+            default="buy",
+            help="Select what you did in the transaction",
+        )
+        if other_args:
+            if "-n" not in other_args and "-h" not in other_args:
+                other_args.insert(0, "-n")
+
+        ns_parser = parse_known_args_and_warn(parser, other_args)
+        if not ns_parser:
+            return
+        if ns_parser.type == "cash" and ns_parser.action not in [
+            "deposit",
+            "withdrawal",
+        ]:
+            print("Cash can only be deposited or withdrew\n")
+            return
+        if ns_parser.type != "cash" and ns_parser.action in ["deposit", "withdrawal"]:
+            print("Only cash can be deposited or withdrew\n")
+            return
+
+        if ns_parser.type == "stock":
+            if not portfolio_helper.is_ticker(ns_parser.name):
+                print("Invalid ticker\n")
+                return
+
+        data = {
+            "Name": ns_parser.name,
+            "Type": ns_parser.type,
+            "Quantity": ns_parser.quantity,
+            "Date": ns_parser.date,
+            "Price": ns_parser.price,
+            "Fees": ns_parser.fees,
+            "Premium": None,  # ns_parser.premium
+            "Side": ns_parser.action,
+        }
+        self.portfolio = self.portfolio.append([data])
+        self.portfolio.index = list(range(0, len(self.portfolio.values)))
+        print(f"{ns_parser.name.upper()} successfully added\n")
+
+    def call_rmv(self, _):
+        """Process rmv command"""
+        portfolio_view.show_df(self.portfolio, True)
+        to_rmv = int(input("\nType the index number you want to remove:\n"))
+        if 0 <= to_rmv < len(self.portfolio.index):
+            self.portfolio = self.portfolio.drop(self.portfolio.index[to_rmv])
+            self.portfolio.index = list(range(0, len(self.portfolio.values)))
+        else:
+            print(
+                f"Invalid index please use an integer between 0 and {len(self.portfolio.index)-1}\n"
+            )
+
+    @try_except
+    def call_ar(self, other_args: List[str]):
+        """Process ar command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="ar",
+            description="Create an annual report based on given portfolio",
+        )
+        parser.add_argument(
+            "-m",
+            "--market",
+            type=str,
+            dest="market",
+            default="SPY",
+            help="Choose a ticker to be the market asset",
+        )
+
+        ns_parser = parse_known_args_and_warn(parser, other_args)
+        if not ns_parser:
+            return
+
+        if self.portfolio.empty:
+            print("Please add items to the portfolio\n")
+            return
+
+        val, hist = portfolio_model.convert_df(self.portfolio)
+        if not val.empty:
+            portfolio_view.annual_report(val, hist, ns_parser.market)
+
+    @try_except
+    def call_rmr(self, other_args: List[str]):
+        """Process rmr command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="ret",
+            description="Graph of portfolio returns versus market returns",
+        )
+        parser.add_argument(
+            "-m",
+            "--market",
+            type=str,
+            dest="market",
+            default="SPY",
+            help="Choose a ticker to be the market asset",
+        )
+
+        ns_parser = parse_known_args_and_warn(parser, other_args)
+        if not ns_parser:
+            return
+
+        if self.portfolio.empty:
+            print("Please add items to the portfolio\n")
+            return
+
+        val, _ = portfolio_model.convert_df(self.portfolio)
+        if not val.empty:
+            df_m = yfinance_model.get_market(val.index[0], ns_parser.market)
+            portfolio_view.plot_overall_return(val, df_m, 365, ns_parser.market, True)
+        else:
+            print("Cannot generate a graph from an empty dataframe\n")
+
 
 def menu():
     """Portfolio Menu"""
diff --git a/gamestonk_terminal/portfolio/portfolio_helper.py b/gamestonk_terminal/portfolio/portfolio_helper.py
new file mode 100644
index 00000000000..05c79ea8aff
--- /dev/null
+++ b/gamestonk_terminal/portfolio/portfolio_helper.py
@@ -0,0 +1,21 @@
+"""Portfolio Helper"""
+__docformat__ = "numpy"
+
+import yfinance as yf
+
+
+def is_ticker(ticker: str) -> bool:
+    """Determine whether a string is a valid ticker
+
+    Parameters
+    ----------
+    ticker : str
+        The string to be tested
+
+    Returns
+    ----------
+    answer : bool
+        Whether the string is a ticker
+    """
+    item = yf.Ticker(ticker)
+    return "previousClose" in item.info
diff --git a/gamestonk_terminal/portfolio/portfolio_model.py b/gamestonk_terminal/portfolio/portfolio_model.py
new file mode 100644
index 00000000000..775dd9583d5
--- /dev/null
+++ b/gamestonk_terminal/portfolio/portfolio_model.py
@@ -0,0 +1,351 @@
+"""Portfolio Model"""
+__docformat__ = "numpy"
+
+import os
+import math
+from datetime import date, timedelta, datetime
+from typing import List
+
+import numpy as np
+import pandas as pd
+
+from gamestonk_terminal.portfolio import portfolio_view, yfinance_model
+
+# pylint: disable=E1136
+# pylint: disable=unsupported-assignment-operation
+
+
+def save_df(df: pd.DataFrame, name: str) -> None:
+    """Saves the portfolio as a csv
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe to be saved
+    name : str
+        The name of the string
+    """
+    path = os.path.dirname(os.path.abspath(__file__))
+    path = os.path.abspath(os.path.join(path, "portfolios", name))
+    if ".csv" in name:
+        df.to_csv(path, index=False)
+    elif ".json" in name:
+        df.to_json(path, index=False)
+    elif ".xlsx" in name:
+        df = df.to_excel(path, index=False, engine="openpyxl")
+
+
+def load_df(name: str) -> pd.DataFrame:
+    """Load the portfolio from a csv
+
+    Parameters
+    ----------
+    name : str
+        The name of the string
+
+    Returns
+    ----------
+    data : pd.DataFrame
+        A DataFrame with historical trading information
+    """
+    if ".csv" not in name and ".xlsx" not in name and ".json" not in name:
+        print(
+            "Please submit as 'filename.filetype' with filetype being csv, xlsx, or json\n"
+        )
+        return pd.DataFrame()
+
+    try:
+        if ".csv" in name:
+            df = pd.read_csv(f"gamestonk_terminal/portfolio/portfolios/{name}")
+        elif ".json" in name:
+            df = pd.read_json(f"gamestonk_terminal/portfolio/portfolios/{name}")
+        elif ".xlsx" in name:
+            df = pd.read_excel(
+                f"gamestonk_terminal/portfolio/portfolios/{name}", engine="openpyxl"
+            )
+
+        df.index = list(range(0, len(df.values)))
+        df["Name"] = df["Name"].str.lower()
+        df["Type"] = df["Type"].str.lower()
+        df["Date"] = pd.to_datetime(df["Date"], format="%Y/%m/%d")
+        return df
+    except FileNotFoundError:
+        portfolio_view.load_info()
+        return pd.DataFrame()
+
+
+def add_values(
+    log: pd.DataFrame, changes: pd.DataFrame, cashes: pd.DataFrame
+) -> pd.DataFrame:
+    """Creates a new df with performance results
+
+    Parameters
+    ----------
+    log : pd.DataFrame
+        The dataframe that will have daily holdings
+    changes : pd.DataFrame
+        Transactions that changed holdings
+    cashes : pd.DataFrame
+        Cash changing transactions
+
+    Returns
+    ----------
+    log : pd.DataFrame
+        A dataframe with daily holdings
+    """
+    for index, _ in log.iterrows():
+        # Add stocks to dataframe
+        values = changes[changes["Date"] == index]
+        if len(values.index) > 0:
+            for _, sub_row in values.iterrows():
+                ticker = sub_row["Name"]
+                quantity = sub_row["Quantity"]
+                price = sub_row["Price"]
+                fees = sub_row["Fees"]
+                if math.isnan(fees):
+                    fees = 0
+                sign = -1 if sub_row["Side"].lower() == "sell" else 1
+                pos1 = log.cumsum().at[index, ("Quantity", ticker)] > 0
+                pos2 = (quantity * sign) > 0
+
+                if sub_row["Side"].lower() == "interest":
+                    log.at[index, ("Cost Basis", ticker)] = (
+                        log.at[index, ("Cost Basis", ticker)] + quantity * price
+                    )
+                    log.at[index, ("Cash", "Cash")] = log.at[
+                        index, ("Cash", "Cash")
+                    ] - (quantity * price)
+
+                elif (
+                    pos1 == pos2
+                    or log.cumsum().at[index, ("Quantity", ticker)] == 0
+                    or (quantity * sign) == 0
+                ):
+                    log.at[index, ("Quantity", ticker)] = (
+                        log.at[index, ("Quantity", ticker)] + quantity * sign
+                    )
+                    log.at[index, ("Cost Basis", ticker)] = (
+                        log.at[index, ("Cost Basis", ticker)]
+                        + fees
+                        + quantity * sign * price
+                    )
+                    log.at[index, ("Cash", "Cash")] = log.at[
+                        index, ("Cash", "Cash")
+                    ] - (fees + quantity * sign * price)
+                else:
+                    rev = (
+                        log.at[index, ("Profit", ticker)] + quantity * sign * price * -1
+                    )
+                    wa_cost = (
+                        quantity / log.cumsum().at[index, ("Quantity", ticker)]
+                    ) * log.cumsum().at[index, ("Cost Basis", ticker)]
+                    log.at[index, ("Profit", ticker)] = rev - wa_cost - fees
+                    log.at[index, ("Cash", "Cash")] = (
+                        log.at[index, ("Cash", "Cash")] + rev - fees
+                    )
+                    log.at[index, ("Quantity", ticker)] = (
+                        log.at[index, ("Quantity", ticker)] + quantity * sign
+                    )
+                    log.at[index, ("Cost Basis", ticker)] = (
+                        log.at[index, ("Cost Basis", ticker)] - wa_cost
+                    )
+        cash_vals = cashes[cashes["Date"] == index]
+        if len(cash_vals.index) > 0:
+            for _, sub_row in cash_vals.iterrows():
+                amount = sub_row["Price"]
+                if sub_row["Side"] == "deposit":
+                    d = 1
+                elif sub_row["Side"] == "withdrawal":
+                    d = -1
+                else:
+                    raise ValueError("Cash type must be deposit or withdrawal")
+                log.at[index, ("Cash", "Cash")] = (
+                    log.at[index, ("Cash", "Cash")] + d * amount
+                )
+                log.at[index, ("Cash", "User")] = (
+                    log.at[index, ("Cash", "User")] + d * amount
+                )
+    return log
+
+
+def merge_dataframes(
+    log: pd.DataFrame,
+    hist: pd.DataFrame,
+    changes: pd.DataFrame,
+    divs: pd.DataFrame,
+    uniques: List[str],
+) -> pd.DataFrame:
+    """Merge dataframes to create final dataframe
+
+    Parameters
+    ----------
+    log : pd.DataFrame
+        The new dataframe with daily holdings
+    hist : pd.DataFrame
+        Historical returns for stocks in portfolio
+    changes : pd.DataFrame
+        A log of past transactions
+    divs : pd.DataFrame
+        The dividend history for stocks in portfolio
+    unqiues: List[str]
+        A list of stocks in the portfolio
+
+    Returns
+    ----------
+    comb : pd.DataFrame
+        Thew new aggregated dataframe
+    """
+    comb = pd.merge(log, hist, how="left", left_index=True, right_index=True)
+    comb = comb.fillna(method="ffill")
+    comb = pd.merge(comb, divs, how="left", left_index=True, right_index=True)
+    comb = comb.fillna(0)
+
+    for uni in uniques:
+        comb[("Quantity", uni)] = comb[("Quantity", uni)].cumsum()
+        comb[("Cost Basis", uni)] = comb[("Cost Basis", uni)].cumsum()
+        comb[("Cash", "Cash")] = comb[("Cash", "Cash")] + (
+            comb[("Quantity", uni)] * comb[("Dividend", uni)]
+        )
+        comb[("Holding", uni)] = (
+            np.where(
+                comb[("Quantity", uni)] > 0,
+                comb[("Close", uni)],
+                (2 * comb[("Close", uni)][0] - comb[("Close", uni)]),
+            )
+            * comb[("Quantity", uni)]
+        )
+        comb[("Profit", uni)] = comb[("Profit", uni)].cumsum()
+    comb[("Cash", "Cash")] = comb[("Cash", "Cash")].cumsum()
+    if len(changes["Date"]) > 0:
+        comb["holdings"] = comb.sum(level=0, axis=1)["Holding"]
+        comb["profits"] = comb.sum(level=0, axis=1)["Profit"]
+        comb["total_prof"] = comb["holdings"] + comb["profits"]
+        comb["total_cost"] = comb.sum(level=0, axis=1)["Cost Basis"]
+    return comb
+
+
+def convert_df(portfolio: pd.DataFrame) -> pd.DataFrame:
+    """Converts a df from activity to daily holdings
+
+    Parameters
+    ----------
+    portfolio : pd.DataFrame
+        The dataframe of transactions
+
+    Returns
+    ----------
+    data : pd.DataFrame
+        A dataframe with daily holdings of portfolio
+    hist : pd.DataFrame
+        The historical performance of tickers in portfolio
+    """
+    changes = portfolio.copy()
+    # Transactions sorted for only stocks
+    cashes = changes[changes["Type"] == "cash"]
+    if cashes.empty:
+        raise ValueError("Brokers require cash, input cash deposits")
+    changes = changes[changes["Type"] == "stock"]
+    uniques = list(set(changes["Name"].tolist()))
+    if uniques:
+        hist = yfinance_model.get_stocks(uniques, min(changes["Date"]))
+        divs = yfinance_model.get_dividends(uniques)
+    else:
+        hist, divs = pd.DataFrame(), pd.DataFrame()
+
+    divs = divs.fillna(0)
+    mini = min(cashes["Date"].to_list() + changes["Date"].to_list())
+    days = pd.date_range(mini, date.today() - timedelta(days=1), freq="d")
+    zeros = [0 for _ in uniques]
+    data = [zeros + zeros + zeros + [0] for _ in days]
+    vals = ["Quantity", "Cost Basis", "Profit"]
+    arrays = [[x for _ in uniques for x in vals] + ["Cash"], uniques * 3 + ["Cash"]]
+    tuples = list(zip(*arrays))
+    headers = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
+    log = pd.DataFrame(data, columns=headers, index=days)
+    log[("Cash", "User")] = 0
+    log = add_values(log, changes, cashes)
+    comb = merge_dataframes(log, hist, changes, divs, uniques)
+
+    return comb, hist
+
+
+def get_return(df: pd.DataFrame, df_m: pd.DataFrame, n: int) -> pd.DataFrame:
+    """Adds cumulative returns to a holdings df
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe of daily holdings
+    df_m : pd.DataFrame
+        The dataframe of market performance
+    n : int
+        The period to get returns for
+
+    Returns
+    ----------
+    comb : pd.DataFrame
+        Dataframe with holdings and returns
+    """
+    df = df.copy()
+    df = df[df.index >= datetime.now() - timedelta(days=n + 1)]
+    comb = pd.merge(df, df_m, how="left", left_index=True, right_index=True)
+    comb = comb.fillna(method="ffill")
+    comb = comb.dropna()
+    comb["return"] = (
+        comb[("Cash", "Cash")]
+        + (0 if "holdings" not in comb.columns else comb["holdings"])
+    ) / (
+        comb[("Cash", "Cash")].shift(1)
+        + comb[("Cash", "User")]
+        + (0 if "holdings" not in comb.columns else comb["holdings"].shift(1))
+    )
+    comb["return"] = comb["return"].fillna(1)
+    comb["return"] = comb["return"].cumprod() - 1
+    return comb
+
+
+def get_rolling_beta(
+    df: pd.DataFrame, hist: pd.DataFrame, mark: pd.DataFrame, n: pd.DataFrame
+) -> pd.DataFrame:
+    """Turns a holdings portfolio into a rolling beta dataframe
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe of daily holdings
+    hist : pd.DataFrame
+        A dataframe of historical returns
+    mark : pd.DataFrame
+        The dataframe of market performance
+    n : int
+        The period to get returns for
+
+    Returns
+    ----------
+    final : pd.DataFrame
+        Dataframe with rolling beta
+    """
+    df = df["Holding"]
+    uniques = df.columns.tolist()
+    res = df.div(df.sum(axis=1), axis=0)
+    res = res.fillna(0)
+    comb = pd.merge(
+        hist["Close"], mark["Market"], how="left", left_index=True, right_index=True
+    )
+    comb = comb.fillna(method="ffill")
+    df_var = comb.rolling(252).var().unstack()["Close"].to_frame(name="var")
+    for col in hist["Close"].columns:
+        df1 = (
+            comb.rolling(252).cov().unstack()[col]["Close"].to_frame(name=f"cov_{col}")
+        )
+        df_var = pd.merge(df_var, df1, how="left", left_index=True, right_index=True)
+        df_var[f"beta_{col}"] = df_var[f"cov_{col}"] / df_var["var"]
+    final = pd.merge(res, df_var, how="left", left_index=True, right_index=True)
+    final = final.fillna(method="ffill")
+    final = final.drop(columns=["var"] + [f"cov_{x}" for x in uniques])
+    for uni in uniques:
+        final[f"prod_{uni}"] = final[uni] * final[f"beta_{uni}"]
+    final = final.drop(columns=[f"beta_{x}" for x in uniques] + uniques)
+    final["total"] = final.sum(axis=1)
+    final = final[final.index >= datetime.now() - timedelta(days=n + 1)]
+    return final
diff --git a/gamestonk_terminal/portfolio/portfolio_view.py b/gamestonk_terminal/portfolio/portfolio_view.py
new file mode 100644
index 00000000000..1e45c4f8612
--- /dev/null
+++ b/gamestonk_terminal/portfolio/portfolio_view.py
@@ -0,0 +1,235 @@
+"""Portfolio View"""
+__docformat__ = "numpy"
+
+from datetime import datetime
+from io import BytesIO
+import os
+
+import pandas as pd
+from tabulate import tabulate
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+from reportlab.lib.utils import ImageReader
+from matplotlib import pyplot as plt
+import matplotlib.ticker as mtick
+
+from gamestonk_terminal import feature_flags as gtff
+from gamestonk_terminal.portfolio import portfolio_model, yfinance_model
+from gamestonk_terminal.portfolio import reportlab_helpers
+
+
+def load_info():
+    """Prints instructions to load a CSV
+
+    Returns
+    ----------
+    text : str
+        Information on how to load a csv
+    """
+    text = """
+In order to load a CSV do the following:
+
+1. Add headers to the first row, below is data for each column:\n
+\t1. Identifier for the asset (such as a stock ticker)
+\t2. Type of asset (stock, bond, option, crypto)
+\t3. The volume of the asset transacted
+\t4. The buy date in yyyy/mm/dd
+\t5. The Price paid for the asset
+\t6. Any fees paid during the transaction
+\t7. A premium paid or received if this was an option
+\t8. Whether the asset was bought (covered) or sold (shorted)\n
+2. Place this file in gamestonk_terminal/portfolio/portfolios\n
+        """
+    print(text)
+
+
+def show_df(df: pd.DataFrame, show: bool) -> None:
+    """Shows the given dataframe
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe to be shown
+    show : bool
+        Whether to show the dataframe index
+    """
+
+    df = df.dropna(how="all", axis=1).fillna("")
+
+    if gtff.USE_TABULATE_DF:
+        print(
+            tabulate(
+                df,
+                headers=df.columns,
+                floatfmt=".2f",
+                showindex=show,
+                tablefmt="fancy_grid",
+            ),
+            "\n",
+        )
+    else:
+        print(df.to_string, "\n")
+
+
+def plot_overall_return(
+    df: pd.DataFrame, df_m: pd.DataFrame, n: int, m_tick: str, plot: bool = False
+) -> ImageReader:
+    """Generates overall return graph
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe to be analyzed
+    df_m : pd.DataFrame
+        The dataframe for historical market performance
+    n : int
+        The number of days to include in chart
+    m_tick : str
+        The ticker for the market asset
+    plot : bool
+        Whether to plot the graph or return it for PDF
+
+    Returns
+    ----------
+    img : ImageReader
+        Overal return graph
+    """
+    plt.close("all")
+    comb = portfolio_model.get_return(df, df_m, n)
+    comb[("Market", "Return")] = (
+        comb[("Market", "Close")] - comb[("Market", "Close")][0]
+    ) / comb[("Market", "Close")][0]
+    fig, ax = plt.subplots(figsize=(10, 5))
+    ax.plot(comb.index, comb["return"], color="tab:blue", label="Portfolio")
+    ax.plot(comb.index, comb[("Market", "Return")], color="orange", label=m_tick)
+
+    ax.set_ylabel("", fontweight="bold", fontsize=12, color="black")
+    ax.set_xlabel("")
+    ax.yaxis.set_label_coords(-0.1, 0.5)
+    ax.grid(True)
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    ax.spines["bottom"].set_visible(False)
+    ax.spines["left"].set_visible(False)
+    fig.suptitle(
+        "Cumulative Performance", y=0.99, fontweight="bold", fontsize=14, color="black"
+    )
+    ax.axhline(0, ls="-", lw=1, color="gray", zorder=1)
+    ax.axhline(0, ls="--", lw=1, color="black", zorder=2)
+    fig.set_facecolor("white")
+    ax.set_title(
+        "%s - %s"
+        % (df.index[:1][0].strftime("%Y/%m/%d"), df.index[-1:][0].strftime("%Y/%m/%d")),
+        fontsize=12,
+        color="gray",
+    )
+    ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
+    ax.set_facecolor("white")
+    ax.legend()
+    fig.autofmt_xdate()
+    if plot:
+        plt.show()
+        print("")
+        return None
+
+    imgdata = BytesIO()
+    fig.savefig(imgdata, format="png")
+    imgdata.seek(0)
+    return ImageReader(imgdata)
+
+
+def plot_rolling_beta(
+    df: pd.DataFrame, hist: pd.DataFrame, mark: pd.DataFrame, n: int
+) -> ImageReader:
+    """Returns a chart with the portfolio's rolling beta
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe to be analyzed
+    hist : pd.DataFrame
+        A dataframe of historical returns
+    market : pd.DataFrame
+        A dataframe of SPY returns
+    n : int
+        The number of days to include in chart
+
+    Returns
+    ----------
+    img : ImageReader
+        Rolling beta graph
+    """
+    plt.close("all")
+    final = portfolio_model.get_rolling_beta(df, hist, mark, n)
+
+    # final[final == 0] = np.nan
+    fig, ax = plt.subplots(figsize=(10, 5))
+    ax.plot(
+        final.index,
+        final["total"],
+        color="tab:blue",
+    )
+
+    ax.set_ylabel("", fontweight="bold", fontsize=12, color="black")
+    ax.set_xlabel("")
+    ax.yaxis.set_label_coords(-0.1, 0.5)
+    ax.grid(True)
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    ax.spines["bottom"].set_visible(False)
+    ax.spines["left"].set_visible(False)
+    fig.suptitle(
+        "Rolling Beta of Stocks", y=0.99, fontweight="bold", fontsize=14, color="black"
+    )
+    ax.axhline(0, ls="-", lw=1, color="gray", zorder=1)
+    ax.axhline(0, ls="--", lw=1, color="black", zorder=2)
+    fig.set_facecolor("white")
+    ax.set_title(
+        "%s - %s"
+        % (df.index[:1][0].strftime("%Y/%m/%d"), df.index[-1:][0].strftime("%Y/%m/%d")),
+        fontsize=12,
+        color="gray",
+    )
+    ax.set_facecolor("white")
+    fig.autofmt_xdate()
+    imgdata = BytesIO()
+    fig.savefig(imgdata, format="png")
+    imgdata.seek(0)
+    return ImageReader(imgdata)
+
+
+def annual_report(df: pd.DataFrame, hist: pd.DataFrame, m_tick: str) -> None:
+    """Generates an annual report
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe to be analyzed
+    m_tick : str
+        The ticker for the market asset
+    hist : pd.DataFrame
+        A dataframe of historical returns
+    """
+    df_m = yfinance_model.get_market(df.index[0], m_tick)
+    dire = os.path.dirname(os.path.abspath(__file__)).replace(
+        "gamestonk_terminal", "exports"
+    )
+
+    path = os.path.abspath(
+        os.path.join(
+            dire,
+            f"ar_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf",
+        )
+    )
+    report = canvas.Canvas(path, pagesize=letter)
+    reportlab_helpers.base_format(report, "Overview")
+    report.drawImage(
+        plot_overall_return(df, df_m, 365, m_tick, False), 15, 360, 600, 300
+    )
+    report.showPage()
+    reportlab_helpers.base_format(report, "Portfolio Analysis")
+    if "Holding" in df.columns:
+        report.drawImage(plot_rolling_beta(df, hist, df_m, 365), 15, 360, 600, 300)
+    report.save()
+
+    print("File save in:\n", path, "\n")
diff --git a/gamestonk_terminal/portfolio/reportlab_helpers.py b/gamestonk_terminal/portfolio/reportlab_helpers.py
new file mode 100644
index 00000000000..3dcb802a91e
--- /dev/null
+++ b/gamestonk_terminal/portfolio/reportlab_helpers.py
@@ -0,0 +1,30 @@
+"""Reportlab Helpers"""
+__docformat__ = "numpy"
+
+from datetime import datetime
+
+from reportlab.pdfgen import canvas
+
+
+def base_format(report: canvas.Canvas, header: str) -> None:
+    """Applies a base page format to each page
+
+    Parameters
+    ----------
+    report : canvas.Canvas
+        The report to be formatted
+    header : str
+        The header for the page
+    """
+    report.setLineWidth(0.3)
+    report.setFont("Helvetica", 12)
+    report.drawString(30, 750, "Gamestonk Terminal")
+    report.drawString(500, 750, datetime.now().strftime("%Y/%m/%d"))
+    report.drawString(275, 725, "Annual Report")
+    report.setFillColorRGB(255, 0, 0)
+    report.drawString(200, 710, "Warning: currently only analyzes stocks")
+    report.setFillColorRGB(0, 0, 0)
+    report.line(50, 700, 580, 700)
+    report.setFont("Helvetica", 20)
+    report.drawString(50, 670, header)
+    report.setFont("Helvetica", 12)
diff --git a/gamestonk_terminal/portfolio/yfinance_model.py b/gamestonk_terminal/portfolio/yfinance_model.py
new file mode 100644
index 00000000000..0f7026801b1
--- /dev/null
+++ b/gamestonk_terminal/portfolio/yfinance_model.py
@@ -0,0 +1,85 @@
+"""Yfinance model"""
+__docformat__ = "numpy"
+
+from typing import List
+from datetime import datetime, timedelta
+
+import yfinance as yf
+import pandas as pd
+
+
+def get_stocks(tickers: List[str], start: datetime) -> pd.DataFrame:
+    """Gets historic data for list of tickers
+
+    Parameters
+    ----------
+    tickers : List[str]
+        Tickers to get data for
+    start : datetime
+        First date in stock filtered dataframe
+
+    Returns
+    ----------
+    data : pd.DataFrame
+        Historic daily prices for stocks
+    """
+    df = yf.download(
+        tickers=tickers,
+        start=start - timedelta(days=255),
+        interval="1d",
+        progress=False,
+    )
+    df = df["Adj Close"]
+    if len(tickers) > 1:
+        df.columns = df.columns.str.lower()
+        arrays = [["Close" for _ in df.columns], [x.lower() for x in df.columns]]
+        tuples = list(zip(*arrays))
+        headers = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
+        df.columns = headers
+    if len(tickers) == 1:
+        df = df.to_frame(name=("Close", tickers[0]))
+    return df
+
+
+def get_dividends(tickers: List[str]) -> pd.DataFrame:
+    """Past dividends for list of tickers
+
+    Parameters
+    ----------
+    tickers : List[str]
+        Tickers to get data for
+
+    Returns
+    ----------
+    data : pd.DataFrame
+        Historic dividends for stocks
+    """
+    dfs = []
+    for ticker in tickers:
+        tick = yf.Ticker(ticker)
+        df = tick.dividends.to_frame(name=("Dividend", ticker))
+        dfs.append(df)
+    return pd.concat(dfs)
+
+
+def get_market(start: datetime, ticker: str = "SPY") -> pd.DataFrame:
+    """Get historical data for market asset
+
+    Parameters
+    ----------
+    ticker : str
+        Ticker to get data for
+    start : datetime
+        First date in stock filtered dataframe
+
+    Returns
+    ----------
+    data : pd.DataFrame
+        Historic prices for SPY
+    """
+    tick = yf.Ticker(ticker)
+    df = tick.history(
+        start=start - timedelta(days=255),
+        interval="1d",
+    )["Close"]
+    return df.to_frame(name=("Market", "Close"))
diff --git a/gamestonk_terminal/stocks/prediction_techniques/pred_controller.py b/gamestonk_terminal/stocks/prediction_techniques/pred_controller.py
index 2b0f9ac7395..459483f4bec 100644
--- a/gamestonk_terminal/stocks/prediction_techniques/pred_controller.py
+++ b/gamestonk_terminal/stocks/prediction_techniques/pred_controller.py
@@ -714,6 +714,7 @@ Models:
         finally:
             pred_helper.restore_env()
 
+    @try_except
     def call_mc(self, other_args: List[str]):
         """Process mc command"""
         parser = argparse.ArgumentParser(
@@ -747,25 +748,22 @@ Models:
             help="Whether to model returns or log returns",
         )
 
-        try:
-            ns_parser = parse_known_args_and_warn(
-                parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED
-            )
-            if not ns_parser:
-                return
-            if self.target != "AdjClose":
-                print("MC Prediction designed for AdjClose prices")
-                return
+        ns_parser = parse_known_args_and_warn(
+            parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED
+        )
+        if not ns_parser:
+            return
+        if self.target != "AdjClose":
+            print("MC Prediction designed for AdjClose prices")
+            return
 
-            mc_view.display_mc_forecast(
-                data=self.stock[self.target],
-                n_future=ns_parser.n_days,
-                n_sims=ns_parser.n_sims,
-                use_log=ns_parser.dist == "lognormal",
-                export=ns_parser.export,
-            )
-        except Exception as e:
-            print(e, "\n")
+        mc_view.display_mc_forecast(
+            data=self.stock[self.target],
+            n_future=ns_parser.n_days,
+            n_sims=ns_parser.n_sims,
+            use_log=ns_parser.dist == "lognormal",
+            export=ns_parser.export,
+        )
 
 
 def menu(ticker: str, start: datetime, interval: str, stock: pd.DataFrame):
diff --git a/gamestonk_terminal/stocks/quantitative_analysis/factors_model.py b/gamestonk_terminal/stocks/quantitative_analysis/factors_model.py
index ac664711101..8f8b014f7d0 100644
--- a/gamestonk_terminal/stocks/quantitative_analysis/factors_model.py
+++ b/gamestonk_terminal/stocks/quantitative_analysis/factors_model.py
@@ -4,14 +4,21 @@ __docformat__ = "numpy"
 from urllib.request import urlopen
 from zipfile import ZipFile
 from io import BytesIO
+from typing import Tuple
 
 import statsmodels.api as sm
 import yfinance as yf
 import pandas as pd
 
 
-def get_fama_raw():
-    """Gets base Fama French data to calculate risk"""
+def get_fama_raw() -> pd.DataFrame:
+    """Gets base Fama French data to calculate risk
+
+    Returns
+    ----------
+    fama : pd.DataFrame
+        A data with fama french model information
+    """
     with urlopen(
         "http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip"
     ) as url:
@@ -42,7 +49,18 @@ def get_fama_raw():
 
 
 def get_historical_5(ticker: str):
-    """Get 5 year monthly historical performance for a ticker with dividends filtered"""
+    """Get 5 year monthly historical performance for a ticker with dividends filtered
+
+    Parameters
+    ----------
+    ticker : str
+        A ticker in string form
+
+    Returns
+    ----------
+    data : pd.DataFrame
+        A dataframe with historical information
+    """
     tick = yf.Ticker(ticker)
     df = tick.history(period="5y", interval="1mo")
     df = df[df.index.to_series().apply(lambda x: x.day == 1)]
@@ -51,8 +69,21 @@ def get_historical_5(ticker: str):
     return df
 
 
-def capm_information(ticker):
-    """Provides information that relates to the CAPM model"""
+def capm_information(ticker) -> Tuple[float, float]:
+    """Provides information that relates to the CAPM model
+
+    Parameters
+    ----------
+    ticker : str
+        A ticker in string form
+
+    Returns
+    ----------
+    beta : float
+        The beta for a stock
+    sys : float
+        The systematic risk for a stock
+    """
     df_f = get_fama_raw()
     df_h = get_historical_5(ticker)
     df = df_h.join(df_f)
diff --git a/poetry.lock b/poetry.lock
index c83cf520311..e7ea617c2e4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2447,6 +2447,20 @@ category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "reportlab"
+version = "3.6.2"
+description = "The Reportlab Toolkit"
+category = "main"
+optional = false
+python-versions = ">=3.6, <4"
+
+[package.dependencies]
+pillow = ">=4.0.0"
+
+[package.extras]
+rlpycairo = ["rlPyCairo (>=0.0.5)"]
+
 [[package]]
 name = "requests"
 version = "2.26.0"
@@ -3334,7 +3348,7 @@ prediction = ["tensorflow"]
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.7"
-content-hash = "647fe08199b9ba50dca3198d36f21ceba3b8f1cb98374beae93c9d00bfc05915"
+content-hash = "6c4d80ae2a7bdf631b6fda05880e4bcbb347a95f44b7f697853b0de8aa0f05d6"
 
 [metadata.files]
 absl-py = [
@@ -4150,8 +4164,6 @@ lxml = [
     {file = "lxml-4.6.3-cp27-cp27m-win_amd64.whl", hash = "sha256:8157dadbb09a34a6bd95a50690595e1fa0af1a99445e2744110e3dca7831c4ee"},
     {file = "lxml-4.6.3-cp27-cp27mu-manylinux1_i686.whl", hash = "sha256:7728e05c35412ba36d3e9795ae8995e3c86958179c9770e65558ec3fdfd3724f"},
     {file = "lxml-4.6.3-cp27-cp27mu-manylinux1_x86_64.whl", hash = "sha256:4bff24dfeea62f2e56f5bab929b4428ae6caba2d1eea0c2d6eb618e30a71e6d4"},
-    {file = "lxml-4.6.3-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_24_i686.whl", hash = "sha256:64812391546a18896adaa86c77c59a4998f33c24788cadc35789e55b727a37f4"},
-    {file = "lxml-4.6.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl", hash = "sha256:c1a40c06fd5ba37ad39caa0b3144eb3772e813b5fb5b084198a985431c2f1e8d"},
     {file = "lxml-4.6.3-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:74f7d8d439b18fa4c385f3f5dfd11144bb87c1da034a466c5b5577d23a1d9b51"},
     {file = "lxml-4.6.3-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:f90ba11136bfdd25cae3951af8da2e95121c9b9b93727b1b896e3fa105b2f586"},
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@@ -4201,28 +4213,12 @@ markdown = [
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+    {file = "reportlab-3.6.2.tar.gz", hash = "sha256:f0c4b47b012d893b0b9f5703cf6f01b5593714a3fc1e7dc73efbbfe26bb7e16a"},
+]
 requests = [
     {file = "requests-2.26.0-py2.py3-none-any.whl", hash = "sha256:6c1246513ecd5ecd4528a0906f910e8f0f9c6b8ec72030dc9fd154dc1a6efd24"},
     {file = "requests-2.26.0.tar.gz", hash = "sha256:b8aa58f8cf793ffd8782d3d8cb19e66ef36f7aba4353eec859e74678b01b07a7"},
diff --git a/pyproject.toml b/pyproject.toml
index a4cb517f571..9b67c02ae77 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -82,6 +82,7 @@ coverage = "^5.5"
 tensorflow = {version = "2.6.0", optional = true}
 jsonschema = "3.2.0"
 financedatabase = "1.0.0"
+reportlab = "^3.6.2"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.2"
diff --git a/requirements-full.txt b/requirements-full.txt
index 90bdeb6ef9f..4269b88a9b7 100644
--- a/requirements-full.txt
+++ b/requirements-full.txt
@@ -180,6 +180,7 @@ qtpy==1.11.2; python_version >= "3.6" and python_full_version < "3.0.0" or pytho
 quandl==3.6.1; python_version >= "3.5"
 rapidfuzz==1.8.2; python_version >= "2.7"
 regex==2020.11.13
+reportlab==3.6.2; python_version >= "3.6" and python_version < "4"
 requests-oauthlib==1.3.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
 requests-unixsocket==0.2.0; python_version >= "3.6"
 requests==2.26.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0")
diff --git a/requirements.txt b/requirements.txt
index ddf38c6f9f8..004d61c14c6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -163,6 +163,7 @@ qtpy==1.11.2; python_version >= "3.6" and python_full_version < "3.0.0" or pytho
 quandl==3.6.1; python_version >= "3.5"
 rapidfuzz==1.8.2; python_version >= "2.7"
 regex==2020.11.13
+reportlab==3.6.2; python_version >= "3.6" and python_version < "4"
 requests-oauthlib==1.3.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0"
 requests-unixsocket==0.2.0; python_version >= "3.6"
 requests==2.26.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0")
diff --git a/website/content/portfolio/add/_index.md b/website/content/portfolio/add/_index.md
new file mode 100644
index 00000000000..83a02063f02
--- /dev/null
+++ b/website/content/portfolio/add/_index.md
@@ -0,0 +1,19 @@
+```
+usage: add [-n NAME] [-t {stock,bond,option,crypto,cash}] [-q QUANTITY] [-d DATE] [-p PRICE] [-f FEES]
+            [-r PREMIUM] [-a {buy,sell,interest,deposit,withdrawal}] [-h]
+```
+
+Add an asset to the current portfolio
+
+```
+optional arguments:
+  -n, NAME --name NAME  the name of the asset
+  -t, --type            the type of asset
+  -q, --quantity        the quantity of the asset
+  -d, --date            the date the transaction occurred
+  -p, --price           the price of the asset (per unit)
+  -f, --fees            the fees paid for the transaction (total)
+  -r, --premium         the premium paid/received for the asset
+  -a, --action          select what you did in the transaction
+  -h, --help            show this help message
+```
diff --git a/website/content/portfolio/ar/_index.md b/website/content/portfolio/ar/_index.md
new file mode 100644
index 00000000000..1accc74d646
--- /dev/null
+++ b/website/content/portfolio/ar/_index.md
@@ -0,0 +1,12 @@
+```
+usage: ar [-m MARKET] [-h]
+```
+
+Generate an a PDF annual report for a portfolio
+
+```
+optional arguments:
+  -m MARKET, --market MARKEt
+                        the asset that represents the market
+  -h, --help            show this help message
+```
diff --git a/website/content/portfolio/load/_index.md b/website/content/portfolio/load/_index.md
new file mode 100644
index 00000000000..8f073f53981
--- /dev/null
+++ b/website/content/portfolio/load/_index.md
@@ -0,0 +1,11 @@
+```
+usage: load [-n NAME] [-h]
+```
+
+Load a file from gamestonk_terminal/portfolio/portfolios
+
+```
+optional arguments:
+  -n, NAME --name NAME  the name of the csv file to load
+  -h, --help            show this help message (default: False)
+```
diff --git a/website/content/portfolio/ret/_index.md b/website/content/portfolio/ret/_index.md
new file mode 100644
index 00000000000..192241f23b6
--- /dev/null
+++ b/website/content/portfolio/ret/_index.md
@@ -0,0 +1,12 @@
+```
+usage: ret [-m MARKET] [-h]
+```
+
+Show portfolio returns versus market returns
+
+```
+optional arguments:
+  -m MARKET, --market MARKEt
+                        the asset that represents the market
+  -h, --help            show this help message
+```
diff --git a/website/content/portfolio/rmv/_index.md b/website/content/portfolio/rmv/_index.md
new file mode 100644
index 00000000000..a96f9602fc9
--- /dev/null
+++ b/website/content/portfolio/rmv/_index.md
@@ -0,0 +1,10 @@
+```
+usage: rmv [-h]
+```
+
+Removes an item from the portfolio
+
+```
+optional arguments:
+  -h, --help            show this help message (default: False)
+```
diff --git a/website/content/portfolio/save/_index.md b/website/content/portfolio/save/_index.md
new file mode 100644
index 00000000000..cbfdce38b11
--- /dev/null
+++ b/website/content/portfolio/save/_index.md
@@ -0,0 +1,11 @@
+```
+usage: save [-n NAME] [-h]
+```
+
+Save a file from gamestonk_terminal/portfolio/portfolios
+
+```
+optional arguments:
+  -n, NAME --name NAME  the name of the csv file to save
+  -h, --help            show this help message (default: False)
+```
diff --git a/website/content/portfolio/show/_index.md b/website/content/portfolio/show/_index.md
new file mode 100644
index 00000000000..ade3fad0c61
--- /dev/null
+++ b/website/content/portfolio/show/_index.md
@@ -0,0 +1,10 @@
+```
+usage: show [-h]
+```
+
+Shows the active portfolio
+
+```
+optional arguments:
+  -h, --help            show this help message (default: False)
+```