Refactor/asset stock market index (#107)

Refactoring the code:
Instead of having almost identical classes for Stock and Market, we now
introduce a parent class Asset for both.

README.md

@@ -7,7 +7,7 @@
     <img src="https://img.shields.io/github/stars/fmilthaler/FinQuant.svg?style=social&label=Star" alt='pypi'>
   </a>
   <a href="https://pypi.org/project/FinQuant">
-    <img src="https://img.shields.io/badge/pypi-v0.3.1-brightgreen.svg?style=popout" alt='pypi'>
+    <img src="https://img.shields.io/badge/pypi-v0.3.2-brightgreen.svg?style=popout" alt='pypi'>
   </a>
   <a href="https://github.com/fmilthaler/FinQuant">
     <img src="https://github.com/fmilthaler/finquant/actions/workflows/pytest.yml/badge.svg?branch=master" alt='GitHub Actions'>

README.tex.md

@@ -7,7 +7,7 @@
     <img src="https://img.shields.io/github/stars/fmilthaler/FinQuant.svg?style=social&label=Star" alt='pypi'>
   </a>
   <a href="https://pypi.org/project/FinQuant">
-    <img src="https://img.shields.io/badge/pypi-v0.3.1-brightgreen.svg?style=popout" alt='pypi'>
+    <img src="https://img.shields.io/badge/pypi-v0.3.2-brightgreen.svg?style=popout" alt='pypi'>
   </a>
   <a href="https://github.com/fmilthaler/FinQuant">
     <img src="https://github.com/fmilthaler/finquant/actions/workflows/pytest.yml/badge.svg?branch=master" alt='GitHub Actions'>

finquant/asset.py

@@ -0,0 +1,122 @@
+"""
+This module provides a public class ``Asset`` that represents a generic financial asset.
+It serves as the parent class for specialized asset classes like ``Stock`` and ``Market``
+in the finquant library.
+
+An asset is characterized by its historical price data, from which various quantities
+such as expected return, volatility, skewness, and kurtosis can be computed. The ``Asset``
+class provides common functionality and attributes that are shared among different types
+of assets.
+
+The specialized asset classes, ``Stock`` and ``Market``, inherit from the ``Asset`` class
+and add specific functionality tailored to their respective types.
+
+"""
+
+
+import numpy as np
+import pandas as pd
+
+from finquant.returns import daily_returns, historical_mean_return
+
+
+class Asset:
+    """
+    Parent class representing a generic financial asset.
+
+    :param ``data``: Historical price data of the asset as a ``pandas.Series``.
+    :param ``name``: Name of the asset.
+    :param ``asset_type``: Type of the asset (e.g., "Stock" or "Market index").
+
+    The ``Asset`` class provides common functionality and attributes for financial assets.
+    It represents a generic asset and serves as the parent class for specialized asset classes.
+
+    Attributes:
+        - ``data`` (``pandas.Series``): Historical price data of the asset.
+        - ``name`` (``str``): Name of the asset.
+        - ``asset_type`` (``str``): Type of the asset (e.g., "Stock" or "Market").
+        - ``expected_return`` (``float``): Expected return of the asset.
+        - ``volatility`` (``float``): Volatility of the asset.
+        - ``skew`` (``float``): Skewness of the asset.
+        - ``kurtosis`` (``float``): Kurtosis of the asset.
+
+    The ``Asset`` class provides methods to compute various quantities such as expected return,
+    volatility, skewness, and kurtosis based on the historical price data of the asset.
+
+    """
+
+    def __init__(self, data: pd.Series, name: str, asset_type: str = 'Market index') -> None:
+        """
+        :Input:
+         :data: ``pandas.Series``, of asset prices
+         :name: ``str``, Name of the asset
+         :asset_type: ``str`` (default: ``'Market index'``), Type of the asset (e.g., "Stock" or "Market index")
+        """
+        self.data = data
+        self.name = name
+        # compute expected return and volatility of asset
+        self.expected_return = self.comp_expected_return()
+        self.volatility = self.comp_volatility()
+        self.skew = self._comp_skew()
+        self.kurtosis = self._comp_kurtosis()
+        # type of asset
+        self.asset_type = asset_type
+
+    # functions to compute quantities
+    def comp_daily_returns(self) -> pd.Series:
+        """Computes the daily returns (percentage change) of the asset.
+        See ``finquant.returns.daily_returns``.
+        """
+        return daily_returns(self.data)
+
+    def comp_expected_return(self, freq=252) -> float:
+        """Computes the Expected Return of the asset.
+        See ``finquant.returns.historical_mean_return``.
+
+        :Input:
+         :freq: ``int`` (default: ``252``), number of trading days, default
+             value corresponds to trading days in a year
+
+        :Output:
+         :expected_return: ``float``, Expected Return of asset.
+        """
+        return historical_mean_return(self.data, freq=freq)
+
+    def comp_volatility(self, freq=252) -> float:
+        """Computes the Volatility of the asset.
+
+        :Input:
+         :freq: ``int`` (default: ``252``), number of trading days, default
+             value corresponds to trading days in a year
+
+        :Output:
+         :volatility: ``float``, Volatility of asset.
+        """
+        return self.comp_daily_returns().std() * np.sqrt(freq)
+
+    def _comp_skew(self) -> float:
+        """Computes and returns the skewness of the asset."""
+        return self.data.skew()
+
+    def _comp_kurtosis(self) -> float:
+        """Computes and returns the kurtosis of the asset."""
+        return self.data.kurt()
+
+    def properties(self):
+        """Nicely prints out the properties of the asset,
+        with customized messages based on the asset type.
+        """
+        # nicely printing out information and quantities of the asset
+        string = "-" * 50
+        string += f"\n{self.asset_type}: {self.name}"
+        string += f"\nExpected Return: {self.expected_return:0.3f}"
+        string += f"\nVolatility: {self.volatility:0.3f}"
+        string += f"\nSkewness: {self.skew:0.5f}"
+        string += f"\nKurtosis: {self.kurtosis:0.5f}"
+        string += "\n" + "-" * 50
+        print(string)
+
+    def __str__(self):
+        # print short description
+        string = f"Contains information about {self.asset_type}: {self.name}."
+        return string

finquant/market.py

@@ -1,86 +1,46 @@
-"""This module provides a public class ``Market`` that holds and calculates quantities of a market index"""
+"""
+This module provides a public class ``Market`` that represents a market index.
+It serves as a specialized asset class within the finquant library.
+
+A market index represents the performance of a specific market or a segment of the market,
+such as the S&P 500 or NASDAQ. The ``Market`` class is designed to hold and calculate quantities
+related to a market index, such as expected return, volatility, skewness, and kurtosis.
+
+The ``Market`` class extends the ``Asset`` class from ``finquant.asset`` and inherits its
+common functionality and attributes for financial assets. It provides additional methods
+and attributes specific to market indices.
+
+"""
 
 import numpy as np
 import pandas as pd
-
+from finquant.asset import Asset
 from finquant.returns import daily_returns, historical_mean_return
 
 
-class Market:
-    """Object that contains information about a market index.
-    To initialise the object, it requires a name and information about
-    the index given as ``pandas.Series`` data structure.
+class Market(Asset):
+    """
+    Class representing a market index.
+
+    :param data: Historical price data of the market index as a ``pandas.Series``.
+
+    The ``Market`` class extends the ``Asset`` class and represents a specific type of asset,
+    specifically a market index.
+    It requires historical price data for the market index to initialize an instance.
+
     """
 
     def __init__(self, data: pd.Series) -> None:
         """
         :Input:
          :data: ``pandas.Series`` of market index prices
         """
-        self.name = data.name
-        self.data = data
-        # compute expected return and volatility of market index
-        self.expected_return = self.comp_expected_return()
-        self.volatility = self.comp_volatility()
-        self.skew = self._comp_skew()
-        self.kurtosis = self._comp_kurtosis()
+        super().__init__(data, name=data.name, asset_type="Market index")
         self.daily_returns = self.comp_daily_returns()
 
     # functions to compute quantities
     def comp_daily_returns(self) -> pd.Series:
         """Computes the daily returns (percentage change) of the market index.
-        See ``finance_portfolio.returns.daily_returns``.
+        See ``finquant.returns.daily_returns``.
         """
-        return daily_returns(self.data)
-
-    def comp_expected_return(self, freq=252) -> float:
-        """Computes the Expected Return of the market index.
-        See ``finance_portfolio.returns.historical_mean_return``.
-
-        :Input:
-         :freq: ``int`` (default: ``252``), number of trading days, default
-             value corresponds to trading days in a year
-
-        :Output:
-         :expected_return: Expected Return of market index.
-        """
-        return historical_mean_return(self.data, freq=freq)
-
-    def comp_volatility(self, freq=252) -> float:
-        """Computes the Volatility of the market index.
-
-        :Input:
-         :freq: ``int`` (default: ``252``), number of trading days, default
-             value corresponds to trading days in a year
-
-        :Output:
-         :volatility: volatility of market index.
-        """
-        return self.comp_daily_returns().std() * np.sqrt(freq)
-
-    def _comp_skew(self) -> float:
-        """Computes and returns the skewness of the market index."""
-        return self.data.skew()
-
-    def _comp_kurtosis(self) -> float:
-        """Computes and returns the Kurtosis of the market index."""
-        return self.data.kurt()
-
-    def properties(self):
-        """Nicely prints out the properties of the market index:
-        Expected Return, Volatility, Skewness, and Kurtosis.
-        """
-        # nicely printing out information and quantities of market index
-        string = "-" * 50
-        string += f"\nMarket index: {self.name}"
-        string += f"\nExpected Return:{self.expected_return:0.3f}"
-        string += f"\nVolatility: {self.volatility:0.3f}"
-        string += f"\nSkewness: {self.skew:0.5f}"
-        string += f"\nKurtosis: {self.kurtosis:0.5f}"
-        string += "-" * 50
-        print(string)
-
-    def __str__(self):
-        # print short description
-        string = "Contains information about market index " + str(self.name) + "."
-        return string
+        return daily_returns(self.data)