Added Lab 8, 9, 10

Lab08/Lab08.py

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+import numpy as np
+from pandas import read_csv
+
+from scipy.stats import norm
+
+from mpl_toolkits.mplot3d import Axes3D
+import matplotlib.pyplot as plt
+
+def getS0(name='', market='BSE', index=False):
+    fields=['Close']
+    if index:
+        data = read_csv('./Data/'+market.lower()+'data1.csv', usecols=fields, index_col=False)
+    else:
+        data = read_csv('./Data/'+market+'Data/'+name+'.csv', usecols=fields, index_col=False)
+    return data.iloc[-1]['Close']
+
+def getHistoricalVolatility(data):
+    close = np.array(data['Close'])
+    change = np.zeros(len(close)-1)
+    for i in range(1, len(close)):
+        change[i-1] = (close[i]-close[i-1])/close[i-1]
+    historicalVolatility = np.std(change)*(252**0.5)
+    return historicalVolatility
+
+def getCall(S, K, r, t, sig):
+    d1 = (np.log(S/K)+t*(r+(sig**2)/2))/(sig*(t**0.5))
+    d2 = d1-sig*(t**0.5)
+    Nd1 = norm.cdf(d1)
+    Nd2 = norm.cdf(d2)
+    C = S*Nd1 - K*np.exp(-r*t)*Nd2
+    return C
+
+def getPut(S, K, r, t, sig):
+    d1 = (np.log(S/K)+t*(r+(sig**2)/2))/(sig*(t**0.5))
+    d2 = d1-sig*(t**0.5)
+    Nd1 = norm.cdf(-d1)
+    Nd2 = norm.cdf(-d2)
+    P = K*np.exp(-r*t)*Nd2 - S*Nd1
+    return P
+
+def model(name='', market='BSE', index=False, num_fig=0):
+    fields=['Close']
+    if index:
+        data = read_csv('./Data/'+market.lower()+'data1.csv', usecols=fields, index_col=False)
+    else:
+        data = read_csv('./Data/'+market+'Data/'+name+'.csv', usecols=fields, index_col=False)
+
+    if(index):
+        print('\t\t'+market+'Index')
+    else:
+        print('\t\tStock : '+stock+' Market : '+market)
+    ##################Q1####################
+    lastMonth = data.iloc[len(data)-21:]
+    sig = getHistoricalVolatility(lastMonth)
+    print("Historical Volatility = ", sig)
+
+    ##################Q2####################
+    S0 = data.iloc[-1]['Close']
+    A = np.arange(0.5, 1.6, 0.1)
+    K = S0
+    r = 0.05
+    t = 126/252
+
+    callPrice = np.zeros(len(A))
+    putPrice = np.zeros(len(A))
+
+    for i, a in enumerate(A):
+        callPrice[i] = getCall(S0, a*K, r, t, sig)
+        putPrice[i] = getPut(S0, a*K, r, t, sig)
+
+    print('Call Price = ', end='')
+    print(np.round(callPrice, 2))
+    print('Put Price = ', end='')
+    print(np.round(putPrice, 2))
+    print('\n')
+
+    ##################Q3###################
+    S0 = data.iloc[-1]['Close']
+    A = 1
+    K = A*S0
+    r = 0.05
+    t = 126/252
+    T = 21
+    start = len(data)-T
+    volatility = []
+    callPrices = []
+    putPrices = []
+
+    while(start >= 0):
+        monthlyData = data[start:-1]
+        sig = getHistoricalVolatility(monthlyData)
+        volatility.append(sig)
+        C = getCall(S0, K, r, t, sig)
+        P = getPut(S0, K, r, t, sig)
+        callPrices.append(C)
+        putPrices.append(P)
+        start = start-T
+
+    x = np.arange(1, len(volatility)+1)
+    y = np.array(volatility)
+    z_call = np.array(callPrices)
+    z_put = np.array(putPrices)
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111+num_fig, projection='3d')
+    ax.scatter(x, y, z_call, c='b', marker='o', label='Call Option')
+    ax.scatter(x, y, z_put, c='r', marker='o', label='Put Option')
+
+    ax.set_xlabel('Time (in number of months)')
+    ax.set_ylabel('Volatility')
+    ax.set_zlabel('Option Prices')
+    ax.legend()
+    if index:
+        plt.title(name+' Market = '+market)
+    else:
+        plt.title('Stock = '+name+' Market = '+market)
+    plt.show()
+
+stock_name=['ABB', 'AXISBANK', 'BALMLAWRIE', 'BHARTIARTL', 
+          'CUMMINSIND', 'EMAMILTD', 'GODREJIND', 
+          'HDFCBANK', 'HEROMOTOCO', 'HINDUNILVR', 
+          'INFY', 'IOC', 'ITC', 'LUPIN', 'M&M', 
+          'MAHABANK', 'NTPC', 'SBIN', 'SHREECEM', 'TCS']
+
+market_name = ['BSE', 'NSE']
+
+model('', 'BSE', index=True)
+model('', 'NSE', index=True)
+
+
+for i, market in enumerate(market_name):
+    for j, stock in enumerate(stock_name):
+        model(stock, market, num_fig=0)