descriptive_analysis.py

###############################################################################
#                             Importing Libraries                             #
# import the necessary packages by using command: pip install <package_name>  #
###############################################################################
# For data processing
import numpy as np
from numpy import percentile

# For handling system warnings
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

# For various graph features
from matplotlib import dates as mpl_dates
import matplotlib.pyplot as plt
from matplotlib.pylab import rcParams
from matplotlib import pylab
rcParams['figure.figsize'] = 15, 10

# For Statistical Analysis
import scipy.stats as ss
from scipy.stats import kurtosis
from scipy.stats import skew

# For candlestick
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mdates

# For handling dates
from datetime import datetime, timedelta, date
import dateutil

# For ignoring warnings
import warnings
warnings.filterwarnings("ignore")

# For plotting seaborn graph in  Risk Analysis
import seaborn as sns
sns.set_style('whitegrid')


###############################################################################
#                        Descriptive Analytics Menu                           #
###############################################################################
def descriptive_menu(stock_all_data,stock_closing_price):
    print ("-" * 100)
    descriptive_menu = "\nChoice of services offered in Descriptive Analysis:\n1. Statistical Information\n2. Graphical Visualisation\n3. Go back to the Main Menu\n"
    print(descriptive_menu)
    print ("-" * 100)
    descriptive_choice = input("Please enter your choice: ")
    while descriptive_choice != "3":
        try:
            if descriptive_choice == "1":
                descriptive_stats_menu(stock_all_data,stock_closing_price)
            elif descriptive_choice == "2":
                graph_menu(stock_all_data,stock_closing_price)
            else:
                print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
        except ValueError:
            print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
        except:
            print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")
        print ("-" * 100)
        print(descriptive_menu)
        print ("-" * 100)
        descriptive_choice = input("Please enter your choice: ")

###############################################################################
#               Descriptive Analytics- Statistics Analysis                    #
###############################################################################
def descriptive_stats_menu(stock_all_data,stock_closing_price):
    print ("-" * 100)
    descriptive_stats_menu = "\nSelect the key factor for statistical insights: \n1. Opening Price\n2. Closing Price\n3. Adjacent Closing Price\n4. High Price\n5. Low Price\n6. Volume\n7. Stock Information (First and Last 5 records)\n8. Go to Previous Menu\n"
    print(descriptive_stats_menu)
    print ("-" * 100)
    data_function_choice = input("Please enter your choice: ")
    while data_function_choice != "8":
        try:
            if data_function_choice == "1":
                selection = "Open Price"
                opening_Price = stock_all_data.Open
                Stats_Figures(opening_Price, selection)
            elif data_function_choice == "2":
                selection = "Close Price"
                closing_Price = stock_all_data.Close
                Stats_Figures(closing_Price, selection)
            elif data_function_choice == "3":
                selection = "Adj_Close Price"
                adjacent_closing = stock_all_data.Adj_Close
                Stats_Figures(adjacent_closing,selection)
            elif data_function_choice == "4":
                selection = "High Price"
                high_closing = stock_all_data.High
                Stats_Figures(high_closing, selection)
            elif data_function_choice == "5":
                selection = "Low Price"
                Low_closing = stock_all_data.Low
                Stats_Figures(Low_closing, selection)
            elif data_function_choice == "6":
                selection = "Volume"
                Volume = stock_all_data.Volume
                Stats_Figures(Volume, selection)
            elif data_function_choice == "7":
                print("-" * 100)
                print("The stock details for first 5 days  is :")
                print(stock_all_data.head())
                print("=" * 100)
                print("The stock details for last 5 days  is :")
                print(stock_all_data.tail())
                print("-" * 100)
            else:
                print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
        except ValueError as ex:
            print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
            print(ex.args[0])
        except Exception as ex:
            print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")
            print(ex.args[0])
        print ("-" * 100)
        print(descriptive_stats_menu)
        print ("-" * 100)
        data_function_choice = input("Please enter your choice: ")

###############################################################################
#                Descriptive Analytics- Graphical Analysis                    #
###############################################################################
def graph_menu(stock_all_data,stock_closing_price):
    print ("-" * 100)
    graph_menu = "\nSelect the Visualisation Technique:\n1. Time Series\n2. Moving Averages & Rolling Standard Deviation\n3. Exponential Weighted Moving Average\n4. Weighted Moving Average\n5. Moving Average Convergence Divergence\n6. Risk Analysis\n7. Trendline\n8. Candlestick Chart\n9. Go to Previous Menu\n"
    print(graph_menu)
    print ("-" * 100)
    graph_choice = input("Please enter your choice: ")
    while graph_choice != "9":
        try:
            if graph_choice == "1":
                display_timeseries(stock_all_data,stock_closing_price)
            elif graph_choice == "2":
                display_MovingAverages_SD(stock_all_data,stock_closing_price)
            elif graph_choice == "3":
                display_exponentialWeightedAverage(stock_all_data,stock_closing_price)
            elif graph_choice == "4":
                weighted_moving_average(stock_all_data)                
            elif graph_choice == "5":
                MACD_Hist(stock_all_data,stock_closing_price)              
            elif graph_choice == "6":
                risk_analysis(stock_all_data,stock_closing_price)              
            elif graph_choice == "7":
                trend_line(stock_all_data,stock_closing_price)              
            elif graph_choice == "8":
                candlestick(stock_all_data) 
            else:
                print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
        except ValueError as v:
            print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
            print(v.args[0])
        except Exception as ex:
            print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input.." , "*" * 10, "\n")
            print(ex.args[0])
        print ("-" * 100)
        print(graph_menu)
        print ("-" * 100)
        graph_choice = input("Please enter your choice: ")


###############################################################################
#                      Display Statistics Information                         #
###############################################################################
def Stats_Figures(Price, selection):
    print ("-" * 100)
    print(f"Standard Deviation for {selection} is {np.std(Price)}")
    print(f"\nMean for {selection} is {np.mean(Price)}")
    print(f"\nQuartile range for {selection} is {percentile(Price,[25,50,75])}")
    max_value = max(Price)
    print(f"\nMaximum Value for {selection} is {max_value}")
    min_value = min(Price)
    print(f"\nMinimum Value for {selection} is {min_value}")
    print(f"\nRange between Maximum and Minimum Value for {selection} is : {max_value - min_value}")
    print(f"\nCo-efficient of Variation for {selection} is {ss.variation(Price)}")
    print(f"\nSkewness for {selection} is {skew(Price)}")
    print(f"\nKurtosis for {selection} is {kurtosis(Price)}")


###############################################################################
#             Display Time Series using Close Price or Volume                 #
###############################################################################
def display_timeseries(stock_all_data,stock_closing_price):
  print ("-" * 100)
  timeseries_menu = "\nSelect the key factor to display the time-series:\n1. Closing Price\n2. Opening Price\n3. Go to Previous Menu\n"
  print(timeseries_menu)
  print ("-" * 100)
  timeseries_choice = input("Please enter your choice: ")
  while timeseries_choice!= "3":
    try:
        if timeseries_choice == "1":
          stock_all_data['Close'].plot(linewidth=5,fontsize=20, legend = True)
          plt.xlabel("Date", fontsize = 20)
          plt.ylabel("Closing Price", fontsize = 20)
          plt.grid(True)
          plt.title('Raw Time Series - Closing Price', fontdict = {'fontsize' : 25})
          plt.show()
        elif timeseries_choice == "2":
          stock_all_data['Open'].plot(linewidth=5,fontsize=20, legend = True)
          plt.xlabel("Date", fontsize = 20)
          plt.ylabel("Opening Price", fontsize = 20)
          plt.grid(True)
          plt.title('Raw Time Series - Opening Price', fontdict = {'fontsize' : 25})
          plt.show()
        else:
          print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
    except ValueError:
          print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
    except Exception as ex:
          print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")
    print ("-" * 100)
    print(timeseries_menu)
    print ("-" * 100)
    timeseries_choice = input("Please enter your choice: ")


###############################################################################
#             Display Moving Averages and SD using Close Price                #
###############################################################################
def display_MovingAverages_SD(stock_all_data_ma,stock_closing_price):
    stock_all_data = stock_all_data_ma.copy()
    try:
        #Rolling Statistics
        window_value = int(input("\nEnter Window Size: "))
        stock_all_data['rollmean'] = stock_all_data.Close.rolling(window_value, center = True, min_periods = 1).mean()
        stock_all_data['rollSTD'] = stock_all_data.Close.rolling(window_value, center = True, min_periods = 1).std()
        # plt.figure(figsize = (15,10))
        plt.xlabel("Date", fontsize = 15)
        plt.ylabel("Closing Price", fontsize = 15)
        plt.plot(stock_all_data['Close'], label = 'Original')
        plt.plot(stock_all_data['rollmean'], label = 'Rolling Mean')
        plt.plot(stock_all_data['rollSTD'], label = 'Rolling STD')
        plt.grid(True)
        plt.legend(loc = 'best')
        plt.title('Moving Averages & Standard Deviation', fontdict = {'fontsize' : 20})
        plt.show()
    except:
        print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")


###############################################################################
#      Display Weighted Moving Averages using Close Price                     #
###############################################################################

def weighted_moving_average(stock_all_data_wma):
    try:
        #Weighted Moving Average
         stock_all_data = stock_all_data_wma.copy()
         window_value = int(input("\nEnter Window Size: "))
         weight_no = np.arange(1,window_value+1)
         stock_all_data['WMA'] = stock_all_data['Close'].rolling(window_value).apply(lambda Close: np.dot(Close,weight_no/weight_no.sum()),raw= True)
         plt.xlabel("Date", fontsize = 15)
         plt.ylabel("Closing Price", fontsize = 15)
         plt.plot(stock_all_data['WMA'], color = 'blue')
         plt.title('Weighted Moving Averages', fontdict = {'fontsize' : 20})
         plt.show()
    except:
        print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")

###############################################################################
#      Display Exponential Weighted Moving Averages using Close Price         #
###############################################################################

def display_exponentialWeightedAverage(stock_all_data_ewa,stock_closing_price):
    try:
        #Exponential Weighted Average
         stock_all_data = stock_all_data_ewa.copy()
         window_value = int(input("\nEnter Window Size: "))
         stock_all_data['EWM'] = stock_all_data['Close'].ewm(span = window_value, adjust = True).mean()
         plt.xlabel("Date", fontsize = 15)
         plt.ylabel("Closing Price", fontsize = 15)
         plt.plot(stock_all_data['EWM'], color = 'red')
         plt.title('Exponential Weighted Moving Averages', fontdict = {'fontsize' : 20})
         plt.show()
    except:
        print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")


###############################################################################
#               Display MACD and Histogram using Close Price                  #
###############################################################################
def MACD_Hist(stock_all_data,stock_closing_price):
        try:
            fig, ax = plt.subplots(figsize = (15,10))
            plt.xlabel("Date", fontsize = 15)
            plt.ylabel("Closing Price", fontsize = 15)
            stock_closing_price['EXP1'] = stock_closing_price['Close'].ewm(span = 12, adjust = False).mean()
            stock_closing_price['EXP2'] = stock_closing_price['Close'].ewm(span = 26, adjust = False).mean()
            stock_closing_price['MACD'] = stock_closing_price['EXP1'] - stock_closing_price['EXP2']
            stock_closing_price['EXP3'] = stock_closing_price['MACD'].ewm(span = 9, adjust = False).mean()
            stock_closing_price['HIST'] = stock_closing_price['MACD'] + stock_closing_price['EXP3']
            plt.plot(stock_closing_price['Date'],stock_closing_price['Close'], label = 'Close', color = 'black')
            plt.plot(stock_closing_price['Date'],stock_closing_price['EXP1'], label = 'EXP 1-12', color = 'blue')
            plt.plot(stock_closing_price['Date'],stock_closing_price['EXP2'], label = 'EXP 2-26', color = 'red')
            plt.legend(loc = 'best')
            plt.title('Moving Average Convergence Divergence', fontdict = {'fontsize' : 20})
            plt.grid(True)
            plt.show()

            fig, ax = plt.subplots(figsize = (15,10))
            plt.xlabel("Date", fontsize = 15)
            plt.ylabel("Closing Price", fontsize = 15)
            ax.bar(stock_closing_price['Date'], stock_closing_price['HIST'], width = 1, label = 'Hist')
            ax.xaxis_date
            plt.plot(stock_closing_price['Date'],stock_closing_price['MACD'], label = 'MACD', color = 'blue')
            plt.plot(stock_closing_price['Date'],stock_closing_price['EXP3'], label = 'MACD-9', color = 'red')
            plt.axhline(0,color='grey',linewidth=3,linestyle='-.')
            plt.title('MACD Histogram', fontdict = {'fontsize' : 20})
            plt.grid(True)
            plt.show()

        except Exception as ex:
            print(ex.args[0])
            print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")


###############################################################################
#         Risk Analysis using Daily Returns & Avg. Daily Returns              #
###############################################################################
def risk_analysis(stock_all_data_ra,stock_closing_price):
    stock_all_data = stock_all_data_ra.copy()
    print("-" * 100)
    riskanalysis_menu = "\nSelect any one parameter for Risk Analysis:\n1. Daily Returns of Stock\n2. Average Daily Return using Histogram\n3. Go back to Previous Menu\n"
    print(riskanalysis_menu)
    print("-" * 100)
    riskanalysis_choice = input("Please enter your choice: ")
    stock_all_data['Daily Return'] = stock_all_data['Close'].pct_change()
    while riskanalysis_choice != "3":
      try:
          if riskanalysis_choice == "1":
            #pct_change to find the percent change for each day
            #plotting the daily return percentage
            stock_all_data['Daily Return'].plot(figsize=(15,10),fontsize=20, legend = True, marker='o', linestyle='--')
            plt.xlabel("Date", fontsize = 20)
            plt.grid(True)
            plt.legend(loc='best')
            plt.title('Risk Analysis- Daily Returns', fontdict = {'fontsize' : 22})
            plt.show()
          elif riskanalysis_choice == "2":
            #average daily return using a histogram
            sns.distplot(stock_all_data['Daily Return'].dropna(), bins=100, color='magenta')
            plt.xlabel("Daily Return", fontsize = 20)
            plt.grid(True)
            plt.title('Risk Analysis- Average Daily Returns', fontdict = {'fontsize' : 22})
            plt.show()
          else:
            print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
      except ValueError:
            print("\n" , "*" * 10 , "Input Error: Please enter a valid option." , "*" * 10, "\n")
      except Exception as ex:
            print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")
            print(ex)
      print ("-" * 100)
      print(riskanalysis_menu)
      print ("-" * 100)
      riskanalysis_choice = input("Please enter your choice: ")

###############################################################################
#           Display Trend Line on Scatter Plot using Close Price              #
###############################################################################
def trend_line(stock_all_data,stock_closing_price):
    try:
        fig, ax = plt.subplots()
        dates = stock_all_data['Close'].keys().date
        closing_val = stock_closing_price['Close'].values

        plt.title('Trend Line Graph', fontdict = {'fontsize' : 20})
        plt.xlabel('Date', fontsize = 15)
        plt.ylabel('Closing Price', fontsize = 15)

        x = mdates.date2num(dates)
        y = closing_val
        plt.scatter(x, y)
        z = np.polyfit(x, y, 1)
        p = np.poly1d(z)
        plt.plot(x,p(x),"r--")

        loc = mdates.AutoDateLocator()
        plt.gca().xaxis.set_major_locator(loc)
        plt.gca().xaxis.set_major_formatter(mdates.AutoDateFormatter(loc))
        plt.gcf().autofmt_xdate()
        plt.show()

    except Exception as e:
        print(e.args[0])
        print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")


###############################################################################
#         Display Candlestick Chart using Open, High, Low, Close values       #
###############################################################################
def candlestick(stock_all_data):
    try:
        stock_all_data = stock_all_data[['Open', 'High', 'Low', 'Close']]
        stock_all_data.reset_index(inplace=True)
        stock_all_data['Date'] = stock_all_data['Date'].map(mdates.date2num)

        ax = plt.subplot()
        ax.grid(True)
        ax.set_xlabel('xlabel', fontsize=12)
        ax.set_ylabel('ylabel', fontsize=12)
        ax.set_axisbelow(True)
        ax.set_title('Candlestick Chart', fontdict = {'fontsize' : 20})
        ax.xaxis_date()

        candlestick_ohlc(ax, stock_all_data.values, width=0.75, colorup='g', colordown='r')
        plt.xlabel('Date', fontsize = 15)
        plt.ylabel('Price', fontsize = 15)
        plt.show()

    except Exception as e:
        print("\n" , "*" * 10 , "Sorry an error occurred. Please try again with valid input." , "*" * 10, "\n")
        print(e.args[0])