vishalbelsare

import numpy as np
import pandas as pd
from datetime import datetime
from fastparquet import write 



def compute_vwap(df):
    q = df['foreignNotional']
    p = df['price']

vishalbelsare

"""
Python code for fractional differencing of pandas time series
illustrating the concepts of the article "Preserving Memory in Stationary Time Series" 
by Simon Kuttruf

While this code is dedicated to the public domain for use without permission, the author disclaims any liability in connection with the use of this code.
"""

import numpy as np 
import pandas as pd

vishalbelsare

def dc(ohlcv, thresh=0.005):
    upturn_event = True
    p_h = p_l = ohlcv['close'][0]
    dc_ranges = defaultdict(list)
    tuples = tuple(ohlcv.itertuples())
    
    # here we find the timedelta between the timeseries index, 1 and 0, so 1 day for daily data
    step = tuples[1].Index - tuples[0].Index
    
    # loop over tuples of ohlcv and time as Index,

vishalbelsare

morningstar.com API

• Get key ratio, return csv format file

http://financials.morningstar.com/ajax/exportKR2CSV.html?t=<market>:<stock>

Market

• XHKG: Hong Kong Stock Exchange
• XASE: American Stock Exchange
• XNAS: Nasdaq Stock Exchange >* XNYS: New York Stock Exchange

vishalbelsare

vishalbelsare

import numba as nb
import numpy as np


def impact_perm(nu, gamma, beta):
    """Returns the permenant dollar price impact per unit time

    In paper as :math:`g(\nu)`

    Args:

vishalbelsare

vishalbelsare

;; See my explanation of this config here: https://github.com/jorgenschaefer/elpy/issues/1087
(provide 'my-python-config)

(require 'python)

;;; NOTE: PATH environment var must have your path to Anaconda
;;; environment, e.g., ~/anaconda/bin, as the last thing for
;;; everything else below to work.
;;; [[file:~/Encrypted/org-mode-repo/OrgModeFiles/howto-python.org]]
;;; explains how to work with Anaconda virtual envs and Emacs.

vishalbelsare

# basic factor analysis
# http://blog.alphaarchitect.com/2015/05/28/basic-factor-analysis-simple-tools-to-understand-what-drives-performance/

import pandas as pd
import pandas.io.data as web
import datetime, re, copy
import numpy as np
import statsmodels.formula.api as sm

start = datetime.date(2000,1,1)

vishalbelsare

# basic factor analysis
# http://blog.alphaarchitect.com/2015/05/28/basic-factor-analysis-simple-tools-to-understand-what-drives-performance/

import pandas as pd
import pandas.io.data as web
import datetime, re, copy
import numpy as np
import statsmodels.formula.api as sm

start = datetime.date(2000,1,1)