from data.finance import PriceTable
from utility.finance import get_total_return
print(PriceTable)
total_return = get_total_return('NBIX', lookback=252)
print('Total Return: ', total_return)
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
def get_betas_multiple_stocks(
stocks: 'iterable of stocks',
index: 'one 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):
scrub_params_all = [
get_scrub_params(stock, index, lookback, stock_ceiling_params,
index_floor_params, best_fit_param)
for stock in stocks
]
#betas = [create_beta_object_from_scrub_params(stocks[i], index, lookback, scrub_params_all[i]) for i in range(len(stocks))]
betas = [
Beta(stocks[i], index, lookback, scrub_params_all[i])
for i in range(len(stocks))
]
beta_values = [beta.beta_value for beta in betas]
# Returns Info
returns = [get_total_return(stock, lookback) for stock in stocks]
index_returns = [
get_total_return(index, lookback) for _ in range(len(stocks))
]
idio_returns = [
(1 + returns[i]) / (1 + index_returns[i] * beta_values[i]) - 1
for i in range(len(stocks))
]
# Beta to SPY Info
index_betas_to_SPY = [get_ETF_beta_to_SPY(stock) for stock in stocks]
betas_to_SPY = [
index_betas_to_SPY[i] * beta_values[i] for i in range(len(stocks))
]
# Prepare Information for the DataFrame in an Ordered Dictionary
info = OrderedDict([
# Index Symbol
('Index', [index for _ in range(len(stocks))]),
# OLS Info
('Beta', beta_values),
('Corr', [beta.corr for beta in betas]),
# 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',
[scrub_params.stock_cutoff for scrub_params in scrub_params_all]),
('Index_Cutoff',
[scrub_params.index_cutoff for scrub_params in scrub_params_all]),
('Percentile_Cutoff',
[scrub_params.percentile_cutoff
for scrub_params in scrub_params_all]),
('Percent_Days', [beta.percent_days_in_calculation for beta in betas])
])
# Create DataFrame
column_labels = info.keys()
table_info = list(zip(info.values()))
index_row = pd.Index(stocks, name='Stock')
#iterables_columns = [[index], column_labels]
iterables_columns = [['Index'], column_labels]
index_column = pd.MultiIndex.from_product(iterables_columns,
names=['Index', '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_stocks(stocks,
index,
lookback,
sd_cutoff_params=sd_cutoff_params,
percentile_cutoff=.85,
to_file=False,
file_name='default'):
# Log
#logger.info(stocks, index, lookback)
# Calculate ScrubParams
scrub_params_all = [
get_scrub_params_from_sd_cutoff_params(stock, index, lookback,
sd_cutoff_params,
percentile_cutoff)
for stock in stocks
]
# Establish Table Info
betas = [
Beta_StepTwo(stock, index, lookback, scrub_params)
for stock, scrub_params in zip(stocks, scrub_params_all)
]
beta_values = [beta.beta_value for beta in betas]
corrs = [beta.corr for beta in betas]
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 = [percentile_cutoff for stock in range(len(stocks))]
index_betas_to_SPY = [
get_ETF_beta_to_SPY(index) for stock in range(len(stocks))
]
betas_to_SPY = [
get_ETF_beta_to_SPY(index) * beta_values[i] for i in range(len(stocks))
]
returns = [get_total_return(stock, lookback) for stock in stocks]
index_returns = [get_total_return(index, lookback) for stock in stocks]
idio_returns = [
(1 + returns[i]) / (1 + index_returns[i] * beta_values[i]) - 1
for i in range(len(stocks))
]
percent_days_in_calc = [beta.percent_days_in_calculation for beta in betas]
# Create DataFrame
table_info = list(
zip(beta_values, corrs, stock_cutoffs, index_cutoffs,
percentile_cutoffs, index_betas_to_SPY, betas_to_SPY, returns,
index_returns, idio_returns, percent_days_in_calc))
InfoLabels = [
'Beta', 'Corr', 'Stock_Cutoff', 'Index_Cutoff', 'Percentile_Cutoff',
'Index_Beta_to_SPY', 'Beta_to_SPY', 'Return', 'Index_Return',
'Idio_Return', 'Percent_Days'
]
index_row = pd.Index(stocks, name='Stock')
iterables_columns = [[index], InfoLabels]
index_column = pd.MultiIndex.from_product(iterables_columns,
names=['Index', 'Beta_Info'])
df = pd.DataFrame(table_info, index=index_row, columns=index_column)
if to_file:
to_pickle_and_CSV(df, file_name)
return df