Create simple_returns.py

Author: randerson112358

simple_returns.py

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+# -*- coding: utf-8 -*-
+"""Simple_Returns.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1boqmTDSjvGNyxahoW4FmIBy0YQsIqE4I
+"""
+
+#Description: This is a python program for finance. 
+#             This program will show you how to compute portfolio simple returns, 
+#             get daily returns and volatility etc.
+
+#Resources:
+# Efficient Frontier => https://towardsdatascience.com/efficient-frontier-portfolio-optimisation-in-python-e7844051e7f
+# How To Be A Successful Investor => https://towardsdatascience.com/how-to-be-a-successful-investor-simple-portfolio-analysis-with-python-7b66fc90fa68
+# Financial Python => https://www.quantconnect.com/tutorials/introduction-to-financial-python/rate-of-return,-mean-and-variance
+# How To Calculate stock returns in Python => https://www.codingfinance.com/post/2018-04-03-calc-returns-py/
+# Log Returns VS Simple Returns => https://fintechprofessor.com/tag/log-returns-vs-simple-returns/
+# A Tale Of Two Returns => https://www.portfolioprobe.com/2010/10/04/a-tale-of-two-returns/ 
+# Stack Overflow => https://stackoverflow.com/questions/20000726/calculate-daily-returns-with-pandas-dataframe
+# Get business days: https://stackoverflow.com/questions/2224742/most-recent-previous-business-day-in-python
+# Log Returns for beginners => https://www.coursera.org/lecture/risk-governance-engage-the-board/video-3-2c-log-returns-for-beginners-Jsnqa 
+# Why we use log returns in finance => https://www.youtube.com/watch?v=PtoUlt3V0CI 
+# Market Lessons: Why we use log returns => http://mktlssns.blogspot.com/2010/04/why-we-use-log-returns.html 
+# Magic of log returns => https://investmentcache.com/magic-of-log-returns-concept-part-1/
+# An easy mistake with returns => https://www.r-bloggers.com/an-easy-mistake-with-returns/
+# Sharp Ratio => https://www.investopedia.com/terms/s/sharperatio.asp
+# Simple Calculations to Determine Return on Investments => https://www.thebalance.com/determine-return-on-investment-3140687 
+# How Covariance used portfolio theory => https://www.investopedia.com/ask/answers/041315/how-covariance-used-portfolio-theory.asp
+
+# Import the libraries
+from datetime import datetime
+import numpy as np
+import pandas as pd
+import pandas_datareader as web
+import matplotlib.pyplot as plt
+plt.style.use('fivethirtyeight')
+
+# Get the stock symbols in your portfolio, 
+# I am using FAANG for the portfolio stock
+# FAANG is an acronym for the market's five most popular and best-performing tech stocks 
+#   (Facebook, Amazon, Apple, Netflix, & Google)
+stockSymbols = ["FB", "AMZN", "AAPL", "NFLX", "GOOG"]
+
+#Get the stock starting date
+stockStartDate = '2013-01-01'
+
+# Get todays date and format it in the form YYYY-MM-DD
+today = datetime.today().strftime('%Y-%m-%d')
+
+print(today)
+
+# Get the number of assests in the portfolio
+numAssets = len(stockSymbols)
+# Print the number of assests in your portfolio
+print('You have '+ str(numAssets)+ ' assets in your portfolio')
+
+# Create a function to get the stock price(s) of the portfolio
+def getMyPortfolio(stocks= stockSymbols, start = stockStartDate, end = today, col='Adj Close'):
+    #data = web.get_data_yahoo(stocks, start = start, end = end)[col]
+    data =  web.DataReader(stocks, data_source='yahoo', start=start, end=end)[col]
+    return data
+
+# Get the stock portfolio Adj. Close price and store it into my_stocks variable
+my_stocks = getMyPortfolio(stockSymbols)
+# Show my stocks
+my_stocks
+
+# Create a function to visualize the portfolio
+def showGraph(stocks= stockSymbols,start=stockStartDate, end=today, col='Adj Close'):
+  
+  # Create the title 'Portfolio (High,	Low,	Open,	Close,	Volume,	Adj Close) Price History
+  title = 'Portfolio ' + col + ' Price History'
+  #Get the stocks
+  my_stocks = getMyPortfolio(stocks= stocks, start=start, end=end, col = col)
+  
+  # Visualize the price history
+  plt.figure(figsize=(12.2,4.5)) #width = 14in, height = 6in shows better
+  # Loop through each stock and plot the Adj Close for each day
+  for c in my_stocks.columns.values:
+    plt.plot( my_stocks[c],  label=c)#plt.plot( X-Axis , Y-Axis, line_width, alpha_for_blending,  label)
+  
+  plt.title(title)
+  plt.xlabel('Date',fontsize=18)
+  plt.ylabel(col +' Price USD ($)',fontsize=18)
+  plt.legend(my_stocks.columns.values, loc='upper left')
+  plt.show()
+
+# Show the adjusted close price of FAANG and see how the stocks compare with each other
+showGraph(stockSymbols)
+
+# Calculate Simple Returns
+daily_simple_returns = my_stocks.pct_change(1) # 1 for ONE DAY lookback for each individual return  NOTE:simple return = new/old - 1
+# monthly_simple_returns = my_stocks.pct_change(21) # 21 for ONE MONTH lookback for each individual return
+# annual_simple_returns = my_stocks.pct_change(253) # 253 for ONE YEAR lookback for each individual return
+# Show the daily simple returns
+daily_simple_returns
+
+# Show the stock correlation
+# Covariance and correlation are two mathematical concepts which are commonly used in statistics. 
+# When comparing data samples from different populations, 
+# covariance is used to determine how much two random variables vary together 
+# (the directional relationship between two asset prices),
+# whereas correlation is used to determine when a change in one variable can result in a change in another.
+
+# A correlation value of 1 means two stocks have a perfect positive correlation. If one stock moves up while the other goes down,
+# they would have a perfect negative correlation, noted by a value of -1
+
+# The correlation will always have a measurement value between -1 and 1, and it adds a strength value on how the stocks move together. ... In short, covariance tells you that two variables change 
+# the same way while correlation reveals how a change in one variable affects a change in the other.
+daily_simple_returns.corr()
+
+# Show the covariance matrix for simple returns
+# Covariance is an important measurement used in modern portfolio theory.
+# Modern Portfolio Theory attempts to determine an efficient frontier 
+# (The efficient frontier is the set of optimal portfolios that offer the highest expected return for a defined level of risk or the lowest risk for a given level of expected return.) for a mix of assets in a portfolio. 
+# The efficient frontier seeks to optimize the maximum return versus the degree of risk for the overall combined assets 
+# in the portfolio.
+
+# Covariance can tell how the stocks move together
+# The diagonal entries of the covariance matrix are the variances and the other entries are the covariances
+# The covariance of two stocks tells you how likely they are to increase or decrease simultaneously.
+
+# Variance (σ^2) in statistics is a measurement of the spread between numbers in a data set. 
+# It measures how far each number in the set is from the mean and therefore from every other number in the set. 
+# In finance variance is a measure of dispersion and, most of the time variance is a synonym for risk. 
+# The higher the variance of an asset price, the higher risk the asset bears along with a higher return and a higher volatility
+# The lower the variance of an asset price, the lower risk the asset bears along with a lower return and a lower volatility
+
+# Variance measures the stocks volatility if you take the square root, e.g. sqrt(variance) = σ = volatility = standard deviation
+# NOTE: Volatility is the degree of variation of a trading price series over time as measured by the standard deviation of logarithmic returns.
+# Show the covariance matrix for simple returns
+daily_simple_returns.cov()
+
+# Show the variance
+daily_simple_returns.var()
+
+# Print the standard deviation σ (or volatility or sqrt(variance)) for daily simple returns 
+print("The Stock Volatility:")
+daily_simple_returns.std()
+
+# Visualize the stocks daily simple returns / volatility and see how the stocks compare to each other
+plt.figure(figsize=(12,4.5)) #Set the figure size (width, height)
+
+# Loop through each stock and plot the simple returns for each day
+for c in daily_simple_returns.columns.values:
+  plt.plot(daily_simple_returns.index, daily_simple_returns[c], lw=2, label=c)#plt.plot( X-Axis , Y-Axis, line_width, alpha_for_blending,  label)
+
+# Place the legend in the upper left corner with font size of 10
+plt.legend(loc='upper right', fontsize=10) 
+
+plt.title('Volatility')
+plt.ylabel('Daily Simple Returns') #Label the Y-axis simple returns 
+plt.xlabel('Date')
+plt.show()
+
+# Show the mean / average of the daily simple return
+dailyMeanSimpleReturns = daily_simple_returns.mean() 
+
+# Print the daily mean simple return
+print("The daily mean simple return: ")
+print(dailyMeanSimpleReturns)
+
+# Calculate the expected portfolio daily performance with random weights 
+# [0.4, 0.1, 0.3,0.1,0.1] => 40% FB, 10% AMZN, 30% AAPL, 10% NFLX, 10% GOOG
+randomWeights = np.array([0.4, 0.1, 0.3,0.1,0.1])
+portfolioSimpleReturn = np.sum(dailyMeanSimpleReturns*randomWeights) #NOTE: Be sure to account for rounding of decimal
+
+# Print the daily expected portfolio return
+print("The daily expected portfolio return: " +str(portfolioSimpleReturn))
+
+# Get the yearly simple return, we multiply by 253 instead of 365 because their are approximately 253 trading days in a year
+# The NYSE and NASDAQ average about 252 or 253 trading days a year. In 2018 there were 252 trading days in 2020 there will be 253.
+# This is from 365.25 (days on average per year) * 5/7 (proportion work days per week) - 6 (weekday holidays) - 3*5/7 (fixed date holidays) = 252.75 ≈ 253. 
+# Print the expected annual portfolio simple return
+print("Expected annualised portfolio simple return : "+ str(portfolioSimpleReturn * 253))
+
+# Calculate the growth of our investment or in other words,
+# calculate the total returns from our investment,to do this we need to calculate the cumulative returns 
+# from that investment. 
+# The Daily cumulative simple return for n-periods:
+#    The simple return from period_1 + 1 times the simple return from period_2 + 1 times ... the simple return from period_n
+#    (1+simple_return_1) * (1+simple_return_2) * ... *(1+simple_return_n)
+#    Example: (daily_simple_returns["GOOG"][1] + 1) * (daily_simple_returns["GOOG"][2] + 1) = 1.020353
+#                    0.000581 * 0.019760 =  1.020353
+dailyCumulSimplReturn = (daily_simple_returns+1).cumprod()
+# Show the cumulative simple return
+dailyCumulSimplReturn
+
+# Visualize the daily cumulative simple returns
+fig = plt.figure(figsize=(12.2,4.5))
+for c in dailyCumulSimplReturn.columns.values:
+  plt.plot(dailyCumulSimplReturn.index, dailyCumulSimplReturn[c], lw=2, label=c)#plt.plot( X-Axis , Y-Axis, line_width, alpha_for_blending,  label)
+
+# Place the legend in the upper left corner with font size of 10
+plt.legend(loc='upper left', fontsize=10) 
+plt.xlabel("Date")
+plt.ylabel("Growth of $1 investment")
+plt.title("Daily Cumulative Simple Returns")
+plt.show()