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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

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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,

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"""
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

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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']

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{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

vishalbelsare

# lazy data frame
#
# like idata.frame, but exploits lazy evaluation + macro code generation
# instead of active bindings
library(vadr)

lazy.frame <- function(df, enclos=parent.frame(), ...) UseMethod("lazy.frame")

lazy.frame.lazy.frame <- function(df, ...) df

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"""
A script to automatically export bookmarks from Firefox's SQLite database.

There does not seem to be a programmatic way to get Firefox to export its bookmarks in
the conventional HTML format. However, you can access the bookmark information directly
in Firefox's internal database, which is what this script does.

Always be careful when working with the internal database! If you delete data, you will
likely not be able to recover it.

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"""
This function takes observable macro factors (surprises) as inputs and creates macro factor mimicking portfolios (MFMPs) as outputs. It uses
a novel machine-learning approach, the Principal Components Instrumental Variables FMP Estimator,
described by Jurczenko and Teiletche (2020) in Macro Factor-Micking Portfolios:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3363598
The methodology addresses many of the common problems associated with macro factors and multifactor risk modeling and, as they show,
is superior to other common FMP approaches.
We describe the steps of the PCIV algorithm below, as well as in our Medium post:
For a more detailed explanation, see the link to the paper above.
Note that access to macroeconomic and base assets is required to estimate macro factor-mimicking portfolios. See the macro factors

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# high-low spread estimator (hlse)
def hlse(ohlc_df, frequency='daily'):
    """
    Computes the high-low spread estimator, an estimate of bid-offer spreads, a measure of liquidity risk.
    See Corwin & Schultz (2011) for details: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1106193

    Parameters
    ----------
    ohlc_df: DataFrame
        DataFrame with DatetimeIndex and Open, High, Low and Close (OHLC) prices from which to compute the high-low spread estimates.