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How to use logReturn method in fMBT

Best Python code snippet using fMBT_python

Portfolio.py

Source:Portfolio.py

1"""2# https://www.investopedia.com/articles/basics/11/5-portfolio-protection-strategies.asp3# DIVERSIFICATION - Icludes 10-30 stocks (Eliminate Unsystematic Risks)4# NON CORRELATING ASSET (Eliminate Systematic Risks)5# IDENTIFICARE UN TREND6# ANALIZZARE LE COSE PER SETTORE7# VOLATILITA'8# ESPOSIZIONE AI FATTORI DI RISCHIO9"""10import os11import pandas as pd12import numpy as np13import random14import plotly.express as px15import matplotlib.pyplot as plt16from sklearn import preprocessing17from sklearn.model_selection import KFold18from sklearn.linear_model import LinearRegression19from scipy import stats20#from sklearn.linear_model import LinearRegression21#from statsmodels.graphics.tsaplots import plot_acf22dir = r"C:\Users\ebiadene\OneDrive - Deloitte (O365D)\00_Project_Proposal\01_Financial_Markets\Algoritmic_Trading\00_Data\SP500_Historical"23os.chdir(dir+ '\..\..')24SP500info = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')[0]25LargeCap = pd.read_html('https://www.tradingview.com/markets/stocks-usa/market-movers-large-cap/') 26# IMPORTAZIONE DATI E CREAZIONE DIZIONARIO27d = {}28prova = []29for entry in os.listdir(dir):30    if os.path.isfile(os.path.join(dir, entry)):31        d["{0}".format(entry)] = pd.read_csv(dir + '/' + entry)32        print(entry)33        prova.append(entry)34# CREAZIONE DATAFRAME RETURNS               35df_final = pd.DataFrame(columns=['ticker','slope','return_cum'])36df_close = pd.DataFrame(columns= ['ticker', 'date', 'close'])37df_return = pd.DataFrame(columns = ['ticker', 'date', 'return'])38df_return_cum = pd.DataFrame(columns = ['ticker', 'date', 'return_cum'])39df_logreturn = pd.DataFrame(columns = ['ticker', 'date', 'logreturn'])40df_logreturn_cum = pd.DataFrame(columns = ['ticker', 'date', 'logreturn_cum'])41for j in d.keys():     42    prova = d[j]43    prova = prova[prova['date'] >= '2019-01-01']44    prova['return'] = prova.close.pct_change()45    prova['return_cum'] = (prova['return'] + 1).cumprod()46    prova['logreturn'] = np.log(prova.close/prova.close.shift(1))47    prova['logreturn_cum'] = np.log(prova.close/prova.close.shift(1)).sum()48    prova['ticker'] = j.replace('.csv','') 49    Y = prova['date'].apply( lambda x : int( x.replace('-','') ))50    X = prova['close']51    slope, intercept, r_value, p_value, std_err = stats.linregress(Y, X)52    df = pd.DataFrame( {'ticker' : [j.replace('.csv','')], 'slope' : [slope], 'return_cum' : [prova['return_cum'].iloc[-1]]})53    df1 = prova[['ticker', 'date', 'close']]54    df2 = prova[['ticker', 'date', 'return']]55    df3 = prova[['ticker', 'date', 'return_cum']]56    df4 = prova[['ticker', 'date', 'logreturn']]57    df5 = prova[['ticker', 'date', 'logreturn_cum']]58    df_final = df_final.append(df)59    df_close = df_close.append(df1)60    df_return = df_return.append(df2)61    df_return_cum = df_return_cum.append(df3)62    df_logreturn = df_logreturn.append(df4)63    df_logreturn_cum = df_logreturn_cum.append(df5)64    d[j] = prova65    print(j)66# VISUALIZZAZIONE CURVE67fig = px.line(d['CARR.csv'], x='date', y='close')68fig.write_html('prova.html')69# CREAZIONE DB70stock_df = pd.merge(SP500info[['Symbol', 'Security', 'GICS Sector', 'GICS Sub-Industry']], df_final,71                    left_on = 'Symbol', right_on = 'ticker')72Sector = stock_df.groupby('GICS Sector').mean()73Industry = stock_df.groupby('GICS Sub-Industry').mean()74df_close1 = pd.pivot(df_close.dropna(), index = 'date', columns = 'ticker', values = 'close')75df_logreturn= df_logreturn.reset_index()76df_logreturn1 = pd.pivot(df_logreturn.dropna(), index = 'date', columns = 'ticker', values = 'logreturn')77cov_matrix = df_logreturn1.cov() * 25278# PORFOLIO OTIMIZATION 79# Simulating 5000 portfolios80num_port = 500081# Creating an empty array to store portfolio weights82all_wts = np.zeros((num_port, len(df_close1.columns)))83# Creating an empty array to store portfolio returns84port_returns = np.zeros((num_port))85# Creating an empty array to store portfolio risks86port_risk = np.zeros((num_port))87# Creating an empty array to store portfolio sharpe ratio88sharpe_ratio = np.zeros((num_port))89for i in range(num_port):90  #Setting random portfolio weights for each stock91  wts = np.random.uniform(size = 10)92  wts = wts/np.sum(wts)93  for j in wts:94      a = random.randint(0,493)95      all_wts[i,a] = j # saving weights in the array96  97  #Setting portolio returns based on logreturns and weights98  port_ret = np.sum(df_logreturn1.mean() * all_wts[i])99  port_ret = (port_ret + 1) ** 252 - 1100  port_returns[i] = port_ret # Saving Portfolio returns101  # Portfolio Risk102  port_sd = np.sqrt(np.dot(all_wts[i].T, np.dot(cov_matrix, all_wts[i])))103  port_risk[i] = port_sd104  105  # Portfolio Sharpe Ratio106  # Assuming 0% Risk Free Rate107  sr = port_ret / port_sd108  sharpe_ratio[i] = sr109  print(i)110names = df_close1.columns111min_var = all_wts[port_risk.argmin()]112print(min_var)113max_sr = all_wts[sharpe_ratio.argmax()]114print(max_sr)115import matplotlib.pyplot as plt116min_var = pd.Series(min_var, index=names)117min_var = min_var[(min_var.T != 0)]118min_var = min_var.sort_values()119fig = plt.figure()120ax1 = fig.add_axes([0.1,0.1,0.8,0.8])121ax1.set_xlabel('Asset')122ax1.set_ylabel("Weights")123ax1.set_title("Minimum Variance Portfolio weights")124min_var.plot(kind = 'bar')125plt.show();126max_sr = pd.Series(max_sr, index=names)127max_sr = max_sr[(max_sr.T != 0)]128max_sr = max_sr.sort_values()129fig = plt.figure()130ax1 = fig.add_axes([0.1,0.1,0.8,0.8])131ax1.set_xlabel('Asset')132ax1.set_ylabel("Weights")133ax1.set_title("Tangency Portfolio weights")134max_sr.plot(kind = 'bar')135plt.show();136fig = plt.figure()137ax1 = fig.add_axes([0.1,0.1,0.8,0.8])138ax1.set_xlabel('Risk')139ax1.set_ylabel("Returns")140ax1.set_title("Portfolio optimization and Efficient Frontier")141plt.scatter(port_risk, port_returns)...

HW1 NEW.py

Source:HW1 NEW.py

1import pandas as pd2import numpy as np3import matplotlib.pyplot as plt4import scipy.stats as stats5from scipy.stats import ttest_ind6import statsmodels.stats.api as sms7GE=pd.read_csv('C:/Users/anivia/Desktop/geDJ.txt',sep="\s+",header=None,names=['date','open','high','low','close','vol'])8SP=pd.read_csv('https://www.math.ust.hk/~macwyu/MAFS5110_2018-2019/MAFS5110_2018-2019/Chapter_1/sp500.txt',sep = "\s+")9logreturn_GE=np.diff(np.log(np.array(GE["close"])))10logreturn_sp500 = np.diff(np.log(np.array(SP["close"])))11da2 = pd.concat([pd.DataFrame(logreturn_GE), pd.DataFrame(logreturn_sp500)], axis = 1)12#da2.columns=['date','open','high','low','close','vol','logreturn_sp500']13#da2.index=da.index[1:]14da2.columns = ["logreturn_GE","logreturn_sp500"]15da2.boxplot(column=['logreturn_GE','logreturn_sp500'])16plt.show()17print(stats.mood(logreturn_sp500,logreturn_GE))18print('H0 can be rejected, the variances are significantly different')19print(ttest_ind(logreturn_sp500,logreturn_GE,equal_var=True))20print('Means are insignificantly different')21#cm=sms.CompareMeans(sms.DescrStatsW(logreturn_sp500),sms.DescrStatsW(logreturn_GE))22#print('C.I. is ',cm.tconfint_diff())23print('so they are not equal.')24from scipy.stats import ranksums25print(ranksums(logreturn_sp500, logreturn_GE))...

anaHW1.py

Source:anaHW1.py

1# -*- coding: utf-8 -*-2"""3Created on Mon Oct  1 17:35:09 20184@author: anivia5"""6import pandas as pd7import numpy as np8import matplotlib.pyplot as plt9import scipy.stats as stats10from scipy.stats import ttest_ind11import statsmodels.stats.api as sms12GE=pd.read_csv('C:/Users/anivia/Desktop/geDJ.txt',sep="\s+",header=None,names=['date','open','high','low','close','vol'])13SP=pd.read_csv('https://www.math.ust.hk/~macwyu/MAFS5110_2018-2019/MAFS5110_2018-2019/Chapter_1/sp500.txt',sep = "\s+")14logreturn_GE=np.diff(np.log(np.array(GE["close"])))15logreturn_sp500 = np.diff(np.log(np.array(SP["close"])))16da2 = pd.concat([pd.DataFrame(logreturn_GE), pd.DataFrame(logreturn_sp500)], axis = 1)17#da2.columns=['date','open','high','low','close','vol','logreturn_sp500']18#da2.index=da.index[1:]19da2.columns = ["logreturn_GE","logreturn_sp500"]20da2.boxplot(column=['logreturn_GE','logreturn_sp500'])21plt.show()22print(stats.mood(logreturn_sp500,logreturn_GE))23print('H0 can be rejected, the variances are significantly different')24print(ttest_ind(logreturn_sp500,logreturn_GE,equal_var=True))25print('')26cm=sms.CompareMeans(sms.DescrStatsW(logreturn_sp500),sms.DescrStatsW(logreturn_GE))...

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