BlackArbsCEO

import os
import sys
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import time
import asyncio
from fake_useragent import UserAgent
'''set path variables'''
project_dir = "YOUR/PROJECT/DIR"

BlackArbsCEO

import pandas as pd
import numpy as np
import re

# ================================================
class option_parser:
    def __init__(self, symbol, response):
        self.symbol = symbol
        self.response = response
    # ------------------------------------------------

BlackArbsCEO

import asyncio
import aiohttp

# ================================================
# for first run only
class first_async_scraper:
    def __init__(self):
        pass

    async def _fetch(self, symbol, url, session, headers):

BlackArbsCEO

# -*- coding: utf-8 -*-
"""
Created on Fri Sep  2 13:26:48 2016

@author: Brian Christopher, CFA [Blackarbs LLC]
"""

import time
import pandas as pd
import numpy as np

BlackArbsCEO

# -*- coding: utf-8 -*-
"""
Created on Fri Sep  2 13:23:34 2016

@author: Brian Christopher, CFA [Blackarbs LLC]
"""

import pandas as pd
from  more_itertools import unique_everseen
import requests

BlackArbsCEO

# -*- coding: utf-8 -*-

import time
t0 = time.clock()

import pandas as pd
from pandas.tseries.offsets import BDay
import numpy as np
import datetime as dt
from copy import copy

BlackArbsCEO

{"metadata": {"language_info": {"mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "name": "python", "pygments_lexer": "ipython3", "version": "3.4.3", "nbconvert_exporter": "python"}, "kernelspec": {"name": "python3", "language": "python", "display_name": "Python 3"}}, "cells": [{"source": "###**BlackArbs LLC Independent Research**", "cell_type": "markdown", "metadata": {}}, {"source": "\n####SPDR ETF Composite Sector Valuation [Saturday 4.11.2015]\n------------\n\nThe following Ipython Notebook examines the **Implied Cost of Capital (ICC)** method of valuation for purposes of trade/portfolio positioning. The goal is to identify asymmetric investing opportunities due to incongruence between *'recent'* historical valuations and forward looking expectations of earnings growth. \n\nI will attempt to accomplish the goal by first examining composite returns based on ETF category groupings. Then I will compare the historical data vs the forward looking **ICC*

BlackArbsCEO

BlackArbsCEO

'''
Netfonds import 5 days of intraday data
'''

import numpy as np
import pandas as p
from pandas.tseries.offsets import *
import datetime as dt
import matplotlib.pyplot as plt
size = (14,10)