portfolio_creator.py

# ROLLING SHARPE RATIO
import datetime
import numpy as np 
import pandas as pd
import scipy.optimize as sco
import matplotlib.pyplot as plt
from pypfopt import risk_models
from pypfopt import expected_returns
from pandas_datareader import DataReader
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', 100)


n = 6 #number of stocks  
start = datetime.datetime.now() - datetime.timedelta(days=365)
end = datetime.date.today()

data = pd.read_csv('/Users/shashank/Downloads/Code/top_stocks_data(1).csv')

#data = data.set_index(['Company'])
data = data.sort_values('Sharpe Ratio', ascending = True)
data = data.tail(n)
data = data.reset_index()
data = data['Company']
data = data.values.tolist()

stocks = data
start = start
end = end


df = DataReader(stocks, 'yahoo', start=start, end=end)['Close']
#print (df.tail())

returns = df.pct_change()

plt.figure(figsize=(14, 7))
for c in returns.columns.values:
    plt.plot(returns.index, returns[c], lw=3, alpha=0.8,label=c)
plt.legend(loc='upper right', fontsize=12)
plt.ylabel('daily returns')

def portfolio_annualised_performance(weights, mean_returns, cov_matrix):
    returns = np.sum(mean_returns*weights ) *252
    std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252)
    return std, returns

def random_portfolios(num_portfolios, mean_returns, cov_matrix, risk_free_rate):
    results = np.zeros((3,num_portfolios))
    weights_record = []
    for i in range(num_portfolios):
        weights = np.random.random(n)
        weights /= np.sum(weights)
        weights_record.append(weights)
        portfolio_std_dev, portfolio_return = portfolio_annualised_performance(weights, mean_returns, cov_matrix)
        results[0,i] = portfolio_std_dev
        results[1,i] = portfolio_return
        results[2,i] = (portfolio_return - risk_free_rate) / portfolio_std_dev
    return results, weights_record

returns = df.pct_change()
mean_returns = returns.mean()
cov_matrix = returns.cov()
num_portfolios = 25000
risk_free_rate = 0.021

def neg_sharpe_ratio(weights, mean_returns, cov_matrix, risk_free_rate):
    p_var, p_ret = portfolio_annualised_performance(weights, mean_returns, cov_matrix)
    return -(p_ret - risk_free_rate) / p_var

def max_sharpe_ratio(mean_returns, cov_matrix, risk_free_rate):
    num_assets = len(mean_returns)
    args = (mean_returns, cov_matrix, risk_free_rate)
    constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
    bound = (0.0,1.0)
    bounds = tuple(bound for asset in range(num_assets))
    result = sco.minimize(neg_sharpe_ratio, num_assets*[1./num_assets,], args=args,
                        method='SLSQP', bounds=bounds, constraints=constraints)
    return result

def portfolio_volatility(weights, mean_returns, cov_matrix):
    return portfolio_annualised_performance(weights, mean_returns, cov_matrix)[0]

def min_variance(mean_returns, cov_matrix):
    num_assets = len(mean_returns)
    args = (mean_returns, cov_matrix)
    constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
    bound = (0.0,1.0)
    bounds = tuple(bound for asset in range(num_assets))

    result = sco.minimize(portfolio_volatility, num_assets*[1./num_assets,], args=args,
                        method='SLSQP', bounds=bounds, constraints=constraints)

    return result

def efficient_return(mean_returns, cov_matrix, target):
    num_assets = len(mean_returns)
    args = (mean_returns, cov_matrix)

    def portfolio_return(weights):
        return portfolio_annualised_performance(weights, mean_returns, cov_matrix)[1]

    constraints = ({'type': 'eq', 'fun': lambda x: portfolio_return(x) - target},
                   {'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
    bounds = tuple((0,1) for asset in range(num_assets))
    result = sco.minimize(portfolio_volatility, num_assets*[1./num_assets,], args=args, method='SLSQP', bounds=bounds, constraints=constraints)
    return result


def efficient_frontier(mean_returns, cov_matrix, returns_range):
    efficients = []
    for ret in returns_range:
        efficients.append(efficient_return(mean_returns, cov_matrix, ret))
    return efficients

def display_calculated_ef_with_random(mean_returns, cov_matrix, num_portfolios, risk_free_rate):
    results, _ = random_portfolios(num_portfolios,mean_returns, cov_matrix, risk_free_rate)
    
    max_sharpe = max_sharpe_ratio(mean_returns, cov_matrix, risk_free_rate)
    sdp, rp = portfolio_annualised_performance(max_sharpe['x'], mean_returns, cov_matrix)
    max_sharpe_allocation = pd.DataFrame(max_sharpe.x,index=df.columns,columns=['allocation'])
    max_sharpe_allocation.allocation = [round(i*100,2)for i in max_sharpe_allocation.allocation]
    max_sharpe_allocation = max_sharpe_allocation.T
    max_sharpe_allocation

    min_vol = min_variance(mean_returns, cov_matrix)
    sdp_min, rp_min = portfolio_annualised_performance(min_vol['x'], mean_returns, cov_matrix)
    min_vol_allocation = pd.DataFrame(min_vol.x,index=df.columns,columns=['allocation'])
    min_vol_allocation.allocation = [round(i*100,2)for i in min_vol_allocation.allocation]
    min_vol_allocation = min_vol_allocation.T
    '''
    print ("-"*80)
    print ("Maximum Sharpe Ratio Portfolio Allocation\n")
    print ("Annualised Return:", round(rp,2))
    print ("Annualised Volatility:", round(sdp,2))
    print ("\n")
    print (max_sharpe_allocation)
    print ("-"*80)
    print ("Minimum Volatility Portfolio Allocation\n")
    print ("Annualised Return:", round(rp_min,2))
    print ("Annualised Volatility:", round(sdp_min,2))
    print ("\n")
    print (min_vol_allocation)
    '''
    plt.figure(figsize=(10, 7))
    plt.scatter(results[0,:],results[1,:],c=results[2,:],cmap='YlGnBu', marker='o', s=10, alpha=0.3)
    plt.colorbar()
    plt.scatter(sdp,rp,marker='*',color='r',s=500, label='Maximum Sharpe ratio')
    plt.scatter(sdp_min,rp_min,marker='*',color='g',s=500, label='Minimum volatility')

    target = np.linspace(rp_min, 0.32, 50)
    efficient_portfolios = efficient_frontier(mean_returns, cov_matrix, target)
    plt.plot([p['fun'] for p in efficient_portfolios], target, linestyle='-.', color='black', label='efficient frontier')
    plt.title('Calculated Portfolio Optimization based on Efficient Frontier')
    plt.xlabel('annualised volatility')
    plt.ylabel('annualised returns')
    plt.legend(labelspacing=0.8)

display_calculated_ef_with_random(mean_returns, cov_matrix, num_portfolios, risk_free_rate)

def display_ef_with_selected(mean_returns, cov_matrix, risk_free_rate):
    max_sharpe = max_sharpe_ratio(mean_returns, cov_matrix, risk_free_rate)
    sdp, rp = portfolio_annualised_performance(max_sharpe['x'], mean_returns, cov_matrix)
    max_sharpe_allocation = pd.DataFrame(max_sharpe.x,index=df.columns,columns=['allocation'])
    max_sharpe_allocation.allocation = [round(i*100,2)for i in max_sharpe_allocation.allocation]
    max_sharpe_allocation = max_sharpe_allocation.T
    max_sharpe_allocation

    min_vol = min_variance(mean_returns, cov_matrix)
    sdp_min, rp_min = portfolio_annualised_performance(min_vol['x'], mean_returns, cov_matrix)
    min_vol_allocation = pd.DataFrame(min_vol.x,index=df.columns,columns=['allocation'])
    min_vol_allocation.allocation = [round(i*100,2)for i in min_vol_allocation.allocation]
    min_vol_allocation = min_vol_allocation.T
    
    an_vol = np.std(returns) * np.sqrt(252)
    an_rt = mean_returns * 252
    
    print ("-"*80)
    print ("Maximum Sharpe Ratio Portfolio Allocation\n")
    print ("Annualised Return:", round(rp,2))
    print ("Annualised Volatility:", round(sdp,2))
    print ("\n")
    print (max_sharpe_allocation)
    print ("-"*80)
    print ("Minimum Volatility Portfolio Allocation\n")
    print ("Annualised Return:", round(rp_min,2))
    print ("Annualised Volatility:", round(sdp_min,2))
    print ("\n")
    print (min_vol_allocation)
    print ("-"*80)
    print ("Individual Stock Returns and Volatility\n")
    for i, txt in enumerate(df.columns):
        print (txt,":","annualised return",round(an_rt[i],2),", annualised volatility:",round(an_vol[i],2))
    print ("-"*80)
    
    fig, ax = plt.subplots(figsize=(10, 7))
    ax.scatter(an_vol,an_rt,marker='o',s=200)

    for i, txt in enumerate(df.columns):
        ax.annotate(txt, (an_vol[i],an_rt[i]), xytext=(10,0), textcoords='offset points')
    ax.scatter(sdp,rp,marker='*',color='r',s=500, label='Maximum Sharpe ratio')
    ax.scatter(sdp_min,rp_min,marker='*',color='g',s=500, label='Minimum volatility')

    target = np.linspace(rp_min, 0.34, 50)
    efficient_portfolios = efficient_frontier(mean_returns, cov_matrix, target)
    ax.plot([p['fun'] for p in efficient_portfolios], target, linestyle='-.', color='black', label='efficient frontier')
    ax.set_title('Portfolio Optimization with Individual Stocks')
    ax.set_xlabel('annualised volatility')
    ax.set_ylabel('annualised returns')
    ax.legend(labelspacing=0.8)

display_ef_with_selected(mean_returns, cov_matrix, risk_free_rate)

stocks = data
v = 100000 # total port. value

df = DataReader(stocks, 'yahoo', start, end)['Close']

# Calculate expected returns and sample covariance
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)

# Optimise for maximal Sharpe ratio
ef = EfficientFrontier(mu, S, gamma=0)
raw_weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
ef.portfolio_performance(verbose=True)

latest_prices = get_latest_prices(df)

da = DiscreteAllocation(cleaned_weights, latest_prices, total_portfolio_value=v)
allocation, leftover = da.lp_portfolio()
print ('-'*80)
print ('To maximize sharpe and diversification:')
print("Discrete allocation:", allocation)
print("Funds remaining: ${:.2f}".format(leftover))