'''Function gets a 100 sample point frontier for given returns'''

# Special Case with fTarget=None, just returns average rets.

(na_avgrets, na_std, b_error) = tsu.OptPort(na_data, None)

# Declaring bounds on the optimized portfolio

na_lower = np.zeros(na_data.shape[1])

na_upper = np.ones(na_data.shape[1])

# Getting the range of possible returns with these bounds

(f_min, f_max) = tsu.getRetRange(na_data, na_lower, na_upper,

na_avgrets, s_type="long")

# Getting the step size and list of returns to optimize for.

f_step = (f_max - f_min) / 100.0

lf_returns = [f_min + x * f_step for x in range(101)]

# Declaring empty lists

lf_std = []

lna_portfolios = []

# Calling the optimization for all returns

for f_target in lf_returns:

(na_weights, f_std, b_error) = tsu.OptPort(na_data, f_target,

na_lower, na_upper, s_type="long")

lf_std.append(f_std)

lna_portfolios.append(na_weights)

'''Main Function'''

# S&P 100

ls_symbols = ['AAPL', 'ABT', 'ACN', 'AEP', 'ALL', 'AMGN', 'AMZN', 'APC', 'AXP', 'BA', 'BAC', 'BAX', 'BHI', 'BK', 'BMY', 'BRK.B', 'CAT', 'C', 'CL', 'CMCSA', 'COF', 'COP', 'COST', 'CPB', 'CSCO', 'CVS', 'CVX', 'DD', 'DELL', 'DIS', 'DOW', 'DVN', 'EBAY', 'EMC', 'EXC', 'F', 'FCX', 'FDX', 'GD', 'GE', 'GILD', 'GOOG', 'GS', 'HAL', 'HD', 'HNZ', 'HON', 'HPQ', 'IBM', 'INTC', 'JNJ', 'JPM', 'KFT', 'KO', 'LLY', 'LMT', 'LOW', 'MA', 'MCD', 'MDT', 'MET', 'MMM', 'MO', 'MON', 'MRK', 'MS', 'MSFT', 'NKE', 'NOV', 'NSC', 'NWSA', 'NYX', 'ORCL', 'OXY', 'PEP', 'PFE', 'PG', 'PM', 'QCOM', 'RF', 'RTN', 'SBUX', 'SLB', 'HSH', 'SO', 'SPG', 'T', 'TGT', 'TWX', 'TXN', 'UNH', 'UPS', 'USB', 'UTX', 'VZ', 'WAG', 'WFC', 'WMB', 'WMT', 'XOM']

# Creating an object of the dataaccess class with Yahoo as the source.

c_dataobj = da.DataAccess('Yahoo')

ls_all_syms = c_dataobj.get_all_symbols()

# Bad symbols are symbols present in portfolio but not in all syms

ls_bad_syms = list(set(ls_symbols) - set(ls_all_syms))

for s_sym in ls_bad_syms:

i_index = ls_symbols.index(s_sym)

ls_symbols.pop(i_index)

# Start and End date of the charts

dt_end = dt.datetime(2010, 1, 1)

dt_start = dt_end - dt.timedelta(days=365)

dt_test = dt_end + dt.timedelta(days=365)

# We need closing prices so the timestamp should be hours=16.

dt_timeofday = dt.timedelta(hours=16)

# Get a list of trading days between the start and the end.

ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)

ldt_timestamps_test = du.getNYSEdays(dt_end, dt_test, dt_timeofday)

# Reading just the close prices

df_close = c_dataobj.get_data(ldt_timestamps, ls_symbols, "close")

df_close_test = c_dataobj.get_data(ldt_timestamps_test, ls_symbols, "close")

# Filling the data for missing NAN values

df_close = df_close.fillna(method='ffill')

df_close = df_close.fillna(method='bfill')

df_close_test = df_close_test.fillna(method='ffill')

df_close_test = df_close_test.fillna(method='bfill')

# Copying the data values to a numpy array to get returns

na_data = df_close.values.copy()

na_data_test = df_close_test.values.copy()

# Getting the daily returns

tsu.returnize0(na_data)

tsu.returnize0(na_data_test)

# Calculating the frontier.

(lf_returns, lf_std, lna_portfolios, na_avgrets, na_std) = getFrontier(na_data)

(lf_returns_test, lf_std_test, unused, unused, unused) = getFrontier(na_data_test)

# Plotting the efficient frontier

plt.clf()

plt.plot(lf_std, lf_returns, 'b')

plt.plot(lf_std_test, lf_returns_test, 'r')

# Plot where the efficient frontier would be the following year

lf_ret_port_test = []

lf_std_port_test = []

for na_portfolio in lna_portfolios:

na_port_rets = np.dot(na_data_test, na_portfolio)

lf_std_port_test.append(np.std(na_port_rets))

lf_ret_port_test.append(np.average(na_port_rets))

plt.plot(lf_std_port_test, lf_ret_port_test, 'k')

# Plot indivisual stock risk/return as green +

for i, f_ret in enumerate(na_avgrets):

plt.plot(na_std[i], f_ret, 'g+')

# # Plot some arrows showing transistion of efficient frontier

# for i in range(0, 101, 10):

# plt.arrow(lf_std[i], lf_returns[i], lf_std_port_test[i] - lf_std[i],

# lf_ret_port_test[i] - lf_returns[i], color='k')

# Labels and Axis

plt.legend(['2009 Frontier', '2010 Frontier',

'Performance of \'09 Frontier in 2010'], loc='lower right')

plt.title('Efficient Frontier For S&P 100 ')

plt.ylabel('Expected Return')

plt.xlabel('StDev')