def get_best_betas():
best_indices = []
best_betas = []
best_corrs = []
for stock in stocks:
indices = ['SPY', 'XLV', 'IBB', 'XBI']
index_cutoffs = {'SPY': .015, 'XLV': .015, 'IBB': .015, 'XBI': .0225}
outcomes = []
for index in indices:
index_cutoff = index_cutoffs[index]
scrub_params = ScrubParams(stock_cutoff, index_cutoff,
percentile_cutoff)
beta = Beta(stock, index, lookback, scrub_params)
outcomes.append((index, beta.beta_value, beta.corr))
max_corr = max([i[2] for i in outcomes])
best_fit = [i for i in outcomes if i[2] == max_corr][0]
best_indices.append(best_fit[0])
best_betas.append(best_fit[1])
best_corrs.append(best_fit[2])
info = {
'Stock': stocks,
'Index': best_indices,
'Beta': best_betas,
'Corr': best_corrs
}
return pd.DataFrame(info).set_index('Stock').loc[:,
['Index', 'Beta', 'Corr']]
def get_scrub_params_from_cutoff_params(stock, index, lookback, cutoff_params,
percentile_cutoff):
stock_cutoff = get_stock_cutoff(stock, lookback,
cutoff_params.Percentile_Cutoff,
cutoff_params.Stock_STD_Cutoff)
index_cutoff = get_index_cutoff(index, lookback,
cutoff_params.Percentile_Cutoff,
cutoff_params.Index_STD_Cutoff)
return ScrubParams(stock_cutoff, index_cutoff, percentile_cutoff)
import copy
from beta_model.beta_class import Beta, ScrubParams
from beta_model.StockLine_Module import StockLineBetaAdjusted
if __name__ == '__Main__':
stock = 'ABBV'
index = 'XLV'
beta_lookback = 400
lookback = beta_lookback
scrub_params = ScrubParams(.075, .01, .8)
beta = Beta(stock, index, beta_lookback, scrub_params).beta_value
stock_line = StockLineBetaAdjusted(stock, lookback, beta, index)
adjusted_returns = stock_line.adjusted_returns
#print(adjusted_returns)
class Beta_StepTwo(Beta):
def __init__(self, stock: 'str', index: 'str', lookback: 'int',
scrub_params: 'obj'):
super().__init__(stock, index, lookback, scrub_params)
beta_value = Beta(self.stock, self.index, self.lookback,
self.scrub_params).beta_value
#print("{}, {}, Beta: {}".format(self.stock, self.index, beta_value))
self.stock_line = copy.deepcopy(
StockLineBetaAdjusted(self.stock, self.lookback, beta_value,
self.index))
self.adjusted_returns = self.stock_line.adjusted_returns
self.adjusted_returns_df_column_name = self.stock_line.adjusted_returns_df_column_name
def get_betas_over_iterable(stock,
index,
lookback=252,
stock_ceiling_params=DEFAULT_STOCK_CEILING_PARAMS,
index_floor_params=DEFAULT_INDEX_FLOOR_PARAMS,
best_fit_param=BEST_FIT_PERCENTILE,
save_to_file=False):
stock_ceiling_params = [stock_ceiling_params]
index_floor_params = [index_floor_params]
best_fit_param = [best_fit_param]
param_combos = list(
itertools.product(stock, index, lookback, stock_ceiling_params,
index_floor_params, best_fit_param))
counter = range(len(param_combos))
stocks = [i[0] for i in param_combos]
indices = [i[1] for i in param_combos]
lookbacks = [i[2] for i in param_combos]
#scrub_params_all = [get_scrub_params(stock, index, lookback, stock_ceiling_params, index_floor_params, best_fit_param) for params in param_combos]
scrub_params_all = [get_scrub_params(*params) for params in param_combos]
scrub_param_combos = list(zip(stocks, indices, lookbacks,
scrub_params_all))
betas = [
Beta(stocks[i], indices[i], lookbacks[i], scrub_params_all[i])
for i in counter
]
# OLS Info
beta_values = [beta.beta_value for beta in betas]
corrs = [beta.corr for beta in betas]
# Scrubbing Info
stock_cutoffs = [
scrub_params.stock_cutoff for scrub_params in scrub_params_all
]
index_cutoffs = [
scrub_params.index_cutoff for scrub_params in scrub_params_all
]
percentile_cutoffs = [
scrub_params.percentile_cutoff for scrub_params in scrub_params_all
]
percent_days_in_calc = [beta.percent_days_in_calculation for beta in betas]
# Beta to SPY Info
index_betas_to_SPY = [get_ETF_beta_to_SPY(index) for index in indices]
betas_to_SPY = [index_betas_to_SPY[i] * beta_values[i] for i in counter]
# Returns Info
stock_returns = [
get_total_return(stocks[i], lookbacks[i]) for i in counter
]
index_returns = [
get_total_return(indices[i], lookbacks[i]) for i in counter
]
idio_returns = [
(1 + stock_returns[i]) / (1 + index_returns[i] * beta_values[i]) - 1
for i in counter
]
# Unadjusted OLS Info
unadjusted_betas = [
Beta(stocks[i], indices[i], lookbacks[i],
ScrubParams(False, False, False)) for i in counter
]
unadjusted_beta_values = [beta.beta_value for beta in unadjusted_betas]
unadjusted_corrs = [beta.corr for beta in unadjusted_betas]
# Prepare Information for the DataFrame in an Ordered Dictionary
table_info_dict = OrderedDict([
#Unadjusted OLS Info
('Unadj. Beta', unadjusted_beta_values),
('Unadj. Corr', unadjusted_corrs),
# OLS Info
('Beta', beta_values),
('Corr', corrs),
# Beta to SPY Info
('Index_Beta_to_SPY', index_betas_to_SPY),
('Beta_to_SPY', betas_to_SPY),
# Returns Info
('Stock_Return', stock_returns),
('Index_Return', index_returns),
('Idio_return', idio_returns),
# Scrubbing Info
('Stock_Cutoff', stock_cutoffs),
('Index_Cutoff', index_cutoffs),
('Percentile_Cutoff', percentile_cutoffs),
('Percent_Days', percent_days_in_calc)
])
parameters = [stock, index, lookback]
for parameter in parameters:
if len(parameter) > 1:
iterable = parameter
else:
pass
if iterable == stock:
not_iterable = index[0]
elif iterable == index:
not_iterable = stock[0]
elif iterable == lookback:
not_iterable = 'Stock: {}, Index {}'.format(stock[0], index[0])
else:
raise ValueError
# Create DataFrame
column_labels = table_info_dict.keys()
table_info = list(zip(*table_info_dict.values()))
#index_row = pd.Index(indices, name = 'Index')
index_row = pd.Index(iterable, name='Index')
iterables_columns = [[not_iterable], column_labels]
#iterables_columns = [[stock], column_labels]
index_column = pd.MultiIndex.from_product(iterables_columns,
names=['Stock', 'Beta_Info'])
df = pd.DataFrame(table_info, index=index_row, columns=index_column)
if save_to_file:
to_pickle_and_CSV(df, file_name)
return df
def get_betas_multiple_indices(
stock,
indices: 'iterable of indices',
lookback=252,
stock_ceiling_params=DEFAULT_STOCK_CEILING_PARAMS,
index_floor_params=DEFAULT_INDEX_FLOOR_PARAMS,
best_fit_param=BEST_FIT_PERCENTILE,
save_to_file=False):
scrub_params_all = [
get_scrub_params(stock, index, lookback, stock_ceiling_params,
index_floor_params, best_fit_param)
for index in indices
]
betas = [
Beta(stock, indices[i], lookback, scrub_params_all[i])
for i in range(len(indices))
]
# OLS Info
beta_values = [beta.beta_value for beta in betas]
corrs = [beta.corr for beta in betas]
# Scrubbing Info
stock_cutoffs = [
scrub_params.stock_cutoff for scrub_params in scrub_params_all
]
index_cutoffs = [
scrub_params.index_cutoff for scrub_params in scrub_params_all
]
percentile_cutoffs = [
scrub_params.percentile_cutoff for scrub_params in scrub_params_all
]
percent_days_in_calc = [beta.percent_days_in_calculation for beta in betas]
# Beta to SPY Info
index_betas_to_SPY = [get_ETF_beta_to_SPY(index) for index in indices]
betas_to_SPY = [
index_betas_to_SPY[i] * beta_values[i] for i in range(len(indices))
]
# Returns Info
returns = [get_total_return(stock, lookback) for _ in indices]
index_returns = [get_total_return(index, lookback) for index in indices]
idio_returns = [
(1 + returns[i]) / (1 + index_returns[i] * beta_values[i]) - 1
for i in range(len(indices))
]
# Unadjusted OLS Info
unadjusted_betas = [
Beta(stock, indices[i], lookback, ScrubParams(False, False, False))
for i in range(len(indices))
]
unadjusted_beta_values = [beta.beta_value for beta in unadjusted_betas]
unadjusted_corrs = [beta.corr for beta in unadjusted_betas]
# Prepare Information for the DataFrame in an Ordered Dictionary
table_info_dict = OrderedDict([
#Unadjusted OLS Info
('Unadj. Beta', unadjusted_beta_values),
('Unadj. Corr', unadjusted_corrs),
# OLS Info
('Beta', beta_values),
('Corr', corrs),
# Beta to SPY Info
('Index_Beta_to_SPY', index_betas_to_SPY),
('Beta_to_SPY', betas_to_SPY),
# Returns Info
('Stock_Return', returns),
('Index_Return', index_returns),
('Idio_return', idio_returns),
# Scrubbing Info
('Stock_Cutoff', stock_cutoffs),
('Index_Cutoff', index_cutoffs),
('Percentile_Cutoff', percentile_cutoffs),
('Percent_Days', percent_days_in_calc)
])
# Create DataFrame
column_labels = table_info_dict.keys()
table_info = list(zip(*table_info_dict.values()))
index_row = pd.Index(indices, name='Index')
iterables_columns = [[stock], column_labels]
index_column = pd.MultiIndex.from_product(iterables_columns,
names=['Stock', 'Beta_Info'])
df = pd.DataFrame(table_info, index=index_row, columns=index_column)
if save_to_file:
to_pickle_and_CSV(df, file_name)
return df