def load_data(settings):
data = {}
# general stock parameters
expense_ratios, leverage_factors, underlying_index, dividend_yield = define_stock_parameters(
)
data['expense_ratios'] = expense_ratios
data['leverage_factors'] = leverage_factors
data['underlying_index'] = underlying_index
data['dividend_yield'] = dividend_yield
# define the dates axis that will be mutual for all the portfolio ingredients
date_start = settings['date_start']
date_end = settings['date_end']
dates, _ = load_stock_data(
'SP500', date_start,
date_end) # SP500 index has the "oldest" trading dates data
inds_years, label_years = get_year_labels(dates)
data['dates'] = dates
data['inds_years'] = inds_years
data['label_years'] = label_years
# load libor_rate (relevant to leveraged etf sim)
libor_rate = get_libor_rate(dates)
data['libor_rate'] = libor_rate
# check that portfolio fractions are all positive (and remove zeros if exist)
stock_names = settings['ideal_portfolio_fractions'].keys()
zero_stocks = []
for stock_name in stock_names:
if settings['ideal_portfolio_fractions'][stock_name] < 0:
raise ValueError('negative portfolio fraction for ' +
str(stock_name))
elif settings['ideal_portfolio_fractions'][stock_name] == 0:
zero_stocks += [stock_name]
for stock_name in zero_stocks:
settings['ideal_portfolio_fractions'].pop(stock_name)
# stocks to be a part of the portfolio
for stock_name in stock_names:
index_name = underlying_index[stock_name]
_, stock_values = load_stock_data(index_name,
date_start,
date_end,
dividend_yield=dividend_yield,
settings=settings)
data[stock_name] = stock_values
return settings, data
# matplotlib.use('TkAgg')
plt.rcParams.update({'font.size': 12})
matplotlib.rcParams.update({'axes.prop_cycle': cycler(color='bgrcmyk')})
plt.close('all')
# date_start = '1980-01-01'
date_start = '1986-01-01'
date_end = '2020-09-30'
index_name_list = []
index_name_list += ['SP500']
index_name_list += ['NDX100']
for index_name in index_name_list:
dates, _ = load_stock_data(index_name, date_start, date_end)
inds_years, label_years = get_year_labels(dates)
dividends = get_dividend_yield(dates, index_name=index_name)
# plots
plt.figure(1)
plt.plot(dividends, label=index_name, linewidth=2)
plt.xticks(inds_years, label_years, rotation='vertical')
plt.ylabel('dividend yield %')
plt.title('Dividends Data')
plt.legend()
plt.grid(True)
plt.tight_layout()
# plot_data = False
plot_data = True
plot_close_adjusted = False
# plot_close_adjusted = True
settings = define_default_settings()
expense_ratios, leverage_factors, underlying_index, dividend_yield = define_stock_parameters(
)
if plot_data:
for stock_name in stock_name_list:
dates, index_values = load_stock_data(stock_name,
date_start,
date_end,
dividend_yield=dividend_yield,
settings=settings)
if plot_close_adjusted:
_, index_adjusted_values = load_stock_data(stock_name,
date_start,
date_end,
close_type='Adj Close')
inds_years, label_years = get_year_labels(dates)
# plots
plt.figure(1)
plt.plot(index_values, label=stock_name, linewidth=1)
if plot_close_adjusted and stock_name not in [
'SP500', 'SP500TR', 'NDX100', 'NDX100TR'
]:
import matplotlib
from data_functions import load_stock_data, get_libor_rate
from aux_functions import get_year_labels
from cycler import cycler
import matplotlib.pyplot as plt
# matplotlib.use('TkAgg')
plt.rcParams.update({'font.size': 12})
matplotlib.rcParams.update({'axes.prop_cycle': cycler(color='bgrcmyk')})
plt.close('all')
# date_start = '1980-01-01'
date_start = '1986-01-01'
date_end = '2020-09-30'
dates, _ = load_stock_data('SP500', date_start, date_end)
inds_years, label_years = get_year_labels(dates)
libor_rate = get_libor_rate(dates)
# plots
plt.figure(1)
plt.plot(libor_rate, linewidth=2)
plt.xticks(inds_years, label_years, rotation='vertical')
plt.ylabel('libor rate %')
plt.title('LIBOR 1-month loan rate')
plt.grid(True)
plt.tight_layout()