def adjusted_returns(self):
index_prices = PriceTable.head(self.lookback)[self.index]
index_returns = daily_returns(index_prices).to_frame()
df = (1 + self.daily_returns[self.stock]) / (
1 + index_returns[self.index] * self.beta) - 1
df.name = 'Adj. Returns'
return df
def __init__(self, stock: 'str', lookback: 'int'):
self.stock = stock
self.lookback = lookback
self.price_table = PriceTable.head(self.lookback)[[self.stock]]
self.daily_returns = daily_returns(self.price_table).head(
self.lookback)
def adjusted_returns(self):
index_prices = PriceTable.head(self.lookback)[self.index]
index_returns = daily_returns(index_prices).to_frame()
adj_returns_df = (1 + self.daily_returns[self.stock]) / (
1 + index_returns[self.index] * self.beta) - 1
adj_returns_df.name = self.adjusted_returns_df_column_name
adj_returns_df = adj_returns_df.to_frame()
return adj_returns_df
def __init__(self, stock: 'str', index: 'str', lookback: 'int', ScrubParams: 'obj'):
self.stock = stock
self.index = index
self.lookback = lookback
self.ScrubParams = ScrubParams
self.price_table = PriceTable.head(self.lookback)[[self.stock, self.index]]
self.daily_returns = daily_returns(self.price_table)
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',
ScrubParams: 'obj'):
self.stock = stock
self.index = index
self.lookback = lookback
self.ScrubParams = ScrubParams
#self.price_table = pickle.load(open('sp500_price_table.pkl', 'rb')).head(self.lookback)[[self.stock, self.index]]
self.price_table = PriceTable.head(
self.lookback)[[self.stock, self.index]]
#self.daily_returns = self.price_table / self.price_table.shift(-1) - 1
self.daily_returns = daily_returns(self.price_table)
def __init__(self, stock: 'str', index: 'str', beta: 'float',
to_graph: 'str', base: 'str', lookback: 'int'):
self.stock = stock
self.index = index
self.beta = beta
self.to_graph = to_graph
self.base = base
self.lookback = lookback
self.price_table = PriceTable.head(
self.lookback)[[self.stock, self.index]]
self.daily_returns = daily_returns(self.price_table).head(
self.lookback)
self.daily_returns['adj_returns'] = (1 + self.daily_returns[
self.stock]) / (1 + self.daily_returns[self.index] * self.beta) - 1
print(self.daily_returns.head(5))
import pprint
from pprint import pprint
import decimal
import statsmodels.formula.api as sm
from sklearn.linear_model import LinearRegression
from collections import namedtuple
import math
import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
import scipy.stats as ss
import statsmodels.api as sm
from ols import OLS
from ols2 import OLS as MainOLS
from paul_resources import PriceTable, daily_returns
from time_decorator import my_time_decorator
print(PriceTable.head(10))
class ScrubParams(object):
"""Three Parameters for Scrubbing Process; Sequentially Optional"""
def __init__(self,
stock_cutoff: 'float' = None,
index_cutoff: 'float' = None,
percentile_cutoff: 'float' = None):
self.stock_cutoff = stock_cutoff
self.index_cutoff = index_cutoff
self.percentile_cutoff = percentile_cutoff
def __repr__(self):
return "ScrubParams({}, {}, {})".format(self.stock_cutoff,
self.index_cutoff,