sshariff01/optimization.py

## optimization.py
"""MC1-P2: Optimize a portfolio.

Copyright 2018, Georgia Institute of Technology (Georgia Tech)
Atlanta, Georgia 30332
All Rights Reserved

Template code for CS 4646/7646

Georgia Tech asserts copyright ownership of this template and all derivative
works, including solutions to the projects assigned in this course. Students
and other users of this template code are advised not to share it with others
or to make it available on publicly viewable websites including repositories
such as github and gitlab.  This copyright statement should not be removed
or edited.

We do grant permission to share solutions privately with non-students such
as potential employers. However, sharing with other current or future
students of CS 7646 is prohibited and subject to being investigated as a
GT honor code violation.

-----do not edit anything above this line---

Student Name: Shoabe Shariff
GT User ID: sshariff3
GT ID: 903272097
"""


import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
from util import get_data, plot_data
import scipy.optimize as spo
import math
import pdb

# This is the function that will be tested by the autograder
# The student must update this code to properly implement the functionality

def initial_allocations(syms):
    number_of_syms = len(syms)
    init_allocs = []
    while len(init_allocs) < number_of_syms:
        init_allocs.append(1.0 / number_of_syms)

    return np.asarray(init_allocs)


def neg_sharpe_ratio(allocs, prices):
    start_val = 1000000
    normalized_prices = prices.apply(lambda x: x/x[0], axis=0)
    alloced = normalized_prices * allocs
    pos_vals = alloced * start_val
    port_val = pos_vals.sum(axis=1)
    daily_rets = port_val.diff()[1:]

    return -1 * daily_rets.mean() / daily_rets.std()


def compute_daily_returns(df):
    daily_returns = df.copy()
    daily_returns[1:] = (df[1:] / df[:-1].values) - 1

    return daily_returns.ix[1:]


def optimize_portfolio(sd=dt.datetime(2008,1,1), ed=dt.datetime(2009,1,1), \
    syms=['GOOG','AAPL','GLD','XOM'], gen_plot=False):
    # Read in adjusted closing prices for given symbols, date range
    dates = pd.date_range(sd, ed)
    prices_all = get_data(syms, dates)  # automatically adds SPY
    prices = prices_all[syms]  # only portfolio symbols
    prices_SPY = prices_all['SPY']  # only SPY, for comparison later


    # find the allocations for the optimal portfolio
    # note that the values here ARE NOT meant to be correct for a test case
    init_allocs = initial_allocations(syms) # add code here to find the allocations
    bnds = []
    while len(bnds) < len(syms):
        bnds.append((0.0, 1.0))

    cnstrnts = ({ 'type': 'eq', 'fun': lambda inputs: 1.0 - np.sum(inputs) })
    optimizer_result = spo.minimize(neg_sharpe_ratio, init_allocs, args=(prices,), constraints=cnstrnts, bounds=bnds)
    allocs = optimizer_result.x

    start_val = 1000000
    normalized_prices = prices / prices.ix[0,:] # .apply(lambda x: x/x[0], axis=0)
    alloced = normalized_prices * allocs
    pos_vals = alloced * start_val
    port_val = pos_vals.sum(axis=1)
    daily_rets = compute_daily_returns(port_val)

    cr = (port_val[-1]/port_val[0]) - 1
    adr = daily_rets.mean()
    sddr = daily_rets.std()
    sr = math.sqrt(252) * daily_rets.mean() / daily_rets.std()

    # Get daily portfolio value
    # port_val = prices_SPY # add code here to compute daily portfolio values
    # Compare daily portfolio value with SPY using a normalized plot
    if gen_plot:
        # add code to plot here
        normalized_SPY = prices_SPY / prices_SPY.ix[0,:]
        daily_portfolio_value = alloced.sum(axis=1).to_frame()
        df_to_plot = daily_portfolio_value.join(normalized_SPY)

        df_temp = pd.concat([df_to_plot], axis=1)
        df_temp.columns=['Portfolio', 'SPY']
        df_temp.plot()

        plt.show()

    return allocs, cr, adr, sddr, sr

def test_code():
    # This function WILL NOT be called by the auto grader
    # Do not assume that any variables defined here are available to your function/code
    # It is only here to help you set up and test your code

    # Define input parameters
    # Note that ALL of these values will be set to different values by
    # the autograder!

    start_date = dt.datetime(2009,1,1)
    end_date = dt.datetime(2010,1,1)
    symbols = ['GOOG', 'AAPL', 'GLD', 'XOM', 'IBM']

    # Assess the portfolio
    allocations, cr, adr, sddr, sr = optimize_portfolio(sd = start_date, ed = end_date,\
        syms = symbols, \
        gen_plot = False)

    # Print statistics
    print "Start Date:", start_date
    print "End Date:", end_date
    print "Symbols:", symbols
    print "Allocations:", allocations
    print "Sharpe Ratio:", sr
    print "Volatility (stdev of daily returns):", sddr
    print "Average Daily Return:", adr
    print "Cumulative Return:", cr

if __name__ == "__main__":
    # This code WILL NOT be called by the auto grader
    # Do not assume that it will be called
    # test_code()
    # optimize_portfolio()
    optimize_portfolio(sd=dt.datetime(2008,6,1), ed=dt.datetime(2009,6,1), syms=['IBM','X','GLD','JPM'], gen_plot=True)
	"""MC1-P2: Optimize a portfolio.

	Copyright 2018, Georgia Institute of Technology (Georgia Tech)
	Atlanta, Georgia 30332
	All Rights Reserved

	Template code for CS 4646/7646

	Georgia Tech asserts copyright ownership of this template and all derivative
	works, including solutions to the projects assigned in this course. Students
	and other users of this template code are advised not to share it with others
	or to make it available on publicly viewable websites including repositories
	such as github and gitlab. This copyright statement should not be removed
	or edited.

	We do grant permission to share solutions privately with non-students such
	as potential employers. However, sharing with other current or future
	students of CS 7646 is prohibited and subject to being investigated as a
	GT honor code violation.

	-----do not edit anything above this line---

	Student Name: Shoabe Shariff
	GT User ID: sshariff3
	GT ID: 903272097
	"""


	import pandas as pd
	import matplotlib.pyplot as plt
	import numpy as np
	import datetime as dt
	from util import get_data, plot_data
	import scipy.optimize as spo
	import math
	import pdb

	# This is the function that will be tested by the autograder
	# The student must update this code to properly implement the functionality

	def initial_allocations(syms):
	number_of_syms = len(syms)
	init_allocs = []
	while len(init_allocs) < number_of_syms:
	init_allocs.append(1.0 / number_of_syms)

	return np.asarray(init_allocs)


	def neg_sharpe_ratio(allocs, prices):
	start_val = 1000000
	normalized_prices = prices.apply(lambda x: x/x[0], axis=0)
	alloced = normalized_prices * allocs
	pos_vals = alloced * start_val
	port_val = pos_vals.sum(axis=1)
	daily_rets = port_val.diff()[1:]

	return -1 * daily_rets.mean() / daily_rets.std()


	def compute_daily_returns(df):
	daily_returns = df.copy()
	daily_returns[1:] = (df[1:] / df[:-1].values) - 1

	return daily_returns.ix[1:]


	def optimize_portfolio(sd=dt.datetime(2008,1,1), ed=dt.datetime(2009,1,1), \
	syms=['GOOG','AAPL','GLD','XOM'], gen_plot=False):
	# Read in adjusted closing prices for given symbols, date range
	dates = pd.date_range(sd, ed)
	prices_all = get_data(syms, dates) # automatically adds SPY
	prices = prices_all[syms] # only portfolio symbols
	prices_SPY = prices_all['SPY'] # only SPY, for comparison later


	# find the allocations for the optimal portfolio
	# note that the values here ARE NOT meant to be correct for a test case
	init_allocs = initial_allocations(syms) # add code here to find the allocations
	bnds = []
	while len(bnds) < len(syms):
	bnds.append((0.0, 1.0))

	cnstrnts = ({ 'type': 'eq', 'fun': lambda inputs: 1.0 - np.sum(inputs) })
	optimizer_result = spo.minimize(neg_sharpe_ratio, init_allocs, args=(prices,), constraints=cnstrnts, bounds=bnds)
	allocs = optimizer_result.x

	start_val = 1000000
	normalized_prices = prices / prices.ix[0,:] # .apply(lambda x: x/x[0], axis=0)
	alloced = normalized_prices * allocs
	pos_vals = alloced * start_val
	port_val = pos_vals.sum(axis=1)
	daily_rets = compute_daily_returns(port_val)

	cr = (port_val[-1]/port_val[0]) - 1
	adr = daily_rets.mean()
	sddr = daily_rets.std()
	sr = math.sqrt(252) * daily_rets.mean() / daily_rets.std()

	# Get daily portfolio value
	# port_val = prices_SPY # add code here to compute daily portfolio values
	# Compare daily portfolio value with SPY using a normalized plot
	if gen_plot:
	# add code to plot here
	normalized_SPY = prices_SPY / prices_SPY.ix[0,:]
	daily_portfolio_value = alloced.sum(axis=1).to_frame()
	df_to_plot = daily_portfolio_value.join(normalized_SPY)

	df_temp = pd.concat([df_to_plot], axis=1)
	df_temp.columns=['Portfolio', 'SPY']
	df_temp.plot()

	plt.show()

	return allocs, cr, adr, sddr, sr

	def test_code():
	# This function WILL NOT be called by the auto grader
	# Do not assume that any variables defined here are available to your function/code
	# It is only here to help you set up and test your code

	# Define input parameters
	# Note that ALL of these values will be set to different values by
	# the autograder!

	start_date = dt.datetime(2009,1,1)
	end_date = dt.datetime(2010,1,1)
	symbols = ['GOOG', 'AAPL', 'GLD', 'XOM', 'IBM']

	# Assess the portfolio
	allocations, cr, adr, sddr, sr = optimize_portfolio(sd = start_date, ed = end_date,\
	syms = symbols, \
	gen_plot = False)

	# Print statistics
	print "Start Date:", start_date
	print "End Date:", end_date
	print "Symbols:", symbols
	print "Allocations:", allocations
	print "Sharpe Ratio:", sr
	print "Volatility (stdev of daily returns):", sddr
	print "Average Daily Return:", adr
	print "Cumulative Return:", cr

	if __name__ == "__main__":
	# This code WILL NOT be called by the auto grader
	# Do not assume that it will be called
	# test_code()
	# optimize_portfolio()
	optimize_portfolio(sd=dt.datetime(2008,6,1), ed=dt.datetime(2009,6,1), syms=['IBM','X','GLD','JPM'], gen_plot=True)