def adjusted_returns(self):
index_prices = PriceTable.head(self.lookback).loc[:, [self.index]]
index_returns = daily_returns(index_prices)
adj_returns = (1 + self.daily_returns[self.stock]) / (
1 + index_returns[self.index] * self.beta) - 1
adj_returns.name = self.adjusted_returns_df_column_name
return adj_returns.to_frame()
def base_price(self):
if self.base == self.stock:
return self.price_table.tail(1)[self.stock].iloc[0]
elif type(self.base) is str:
return PriceTable.head(self.lookback).tail(1)[self.base].iloc[0]
elif type(self.base) == float or type(self.base == int):
return self.base
else:
raise ValueError
def __init__(self, stock: 'str', lookback: 'int', base=None):
self.stock = stock
self.lookback = lookback
if base is None:
self.base = self.stock
else:
self.base = base
self.price_table = PriceTable.head(self.lookback)[[self.stock]]
self.daily_returns = daily_returns(self.price_table).head(
self.lookback)
def __init__(self, stock: 'str', index: 'str', lookback: 'int',
scrub_params: 'obj'):
"""The Beta object takes as parameters the stock, index, lookback, and scrubparams object"""
self.stock = stock
self.index = index
self.lookback = lookback
self.scrub_params = scrub_params
self.price_table = PriceTable.head(
self.lookback)[[self.stock, self.index]]
self.daily_returns = daily_returns(self.price_table)
self.num_data_points = self.daily_returns.shape[0]
def __init__(
self,
stock: 'str',
index: 'str',
lookback: 'int' = 252,
scrub_params: 'obj' = None,
# Optional Parameters as an alternative to entering scrub_params
stock_ceiling_params=DEFAULT_STOCK_CEILING_PARAMS,
index_floor_params=DEFAULT_INDEX_FLOOR_PARAMS,
best_fit_param=BEST_FIT_PERCENTILE):
"""The Beta object takes as parameters the stock, index, lookback, and scrub_params object.
The user can enter scrub_params OR a set of stock_ceiling_params, index_floor_params, and best_fit_param
, which map to scrub_params."""
"""Calculate the adjusted beta_value measurement for the stock and index over a lookback...
based on the three core adjustments:
- Stock Ceiling: Scrub data points where the stock moved more than the specified threshold.
- Index Floor: Scrub data points where the index moved less than the specified threshold.
- Best Fit Param: Keep only the n-percentile best fit points in the OLS regression
"""
self.stock = stock
self.index = index
self.lookback = lookback
if scrub_params is None:
self.scrub_params = get_scrub_params(
stock,
index,
lookback=252,
stock_ceiling_params=DEFAULT_STOCK_CEILING_PARAMS,
index_floor_params=DEFAULT_INDEX_FLOOR_PARAMS,
best_fit_param=BEST_FIT_PERCENTILE)
else:
self.scrub_params = scrub_params
self.price_table = PriceTable.head(
self.lookback)[[self.stock, self.index]]
self.daily_returns = daily_returns(self.price_table)
self.num_data_points = self.daily_returns.shape[0]