kmx | KMX Mixer - Hyacinth plugin | Plugin library
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KMX Mixer - Hyacinth plugin.
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QUESTION
I am currently attempting to run a for loop on about 500 stock tickers and attempting to create a chart for all of them. I have succeeded in doing this but my for loop breaks on this error Error in runSum(x, n) : n = 20 is outside valid range: [1, 5]
I got the stock tickers from BatchGetSymbols library.
Here is my script at the bottom is my for loop:
...ANSWER
Answered 2020-Apr-05 at 00:49After downloading the data, I get the following warning message:
QUESTION
I have a tsv file foo.tsv with colnames: "a", "b", "c", "d". I want to read this file and load its content to PDL matrix. File foo.tsv looks like this:
...ANSWER
Answered 2020-Jan-20 at 19:26It appears to be a bug that was fixed in 0.011.
QUESTION
My aim is to download stock prices using the quantmod library for a large number of ticker symbols (~700) and merge the results in a single dataframe which I will save as a csv file. I have a list of ticker symbols but not all of them are downloadable by quantmod.
So when I pass the list with the ticker symbols to the getSymbols() method, once it encounters a problem with a particular ticker symbol it stops and returns an exception. I am trying to circumvent this behavior with a for loop and the try method, but I fail.
Let's look at some code:
When I try to download two ticker symbols that are downloadable and then merge them into one xts object I succeed:
...ANSWER
Answered 2019-Apr-18 at 08:59Here is a code I tried. It seems working (although I tried only a subset of the vector):
QUESTION
I have the following two data frames:
...ANSWER
Answered 2019-Feb-20 at 18:11Good question , you may need melt
first then merge
then pivot
it back
QUESTION
I have a data frame containing data on population densities in cities dependent on the distance to the city center ('spatial distance profiles').
The dataframe looks like this (example):
...ANSWER
Answered 2018-Oct-06 at 15:57Assuming that the objective is to fill in the NA values using a linear regression of the non-NA values on the same line vs. the digit in the heading, first extract the numeric values in the column names forming x
. Then define a function which regresses the row values against x and predicts the NAs from that. Finally apply it to each row.
QUESTION
import pandas as pd
import requests
from bs4 import BeautifulSoup
from datetime import datetime
import pandas_datareader.data as web
tickers
['MMM', 'ABT', 'ABBV', 'ACN', 'ATVI', 'AYI', 'ADBE', 'AMD', 'AAP', 'AES', 'AET', 'AMG', 'AFL', 'A', 'APD', 'AKAM', 'ALK', 'ALB', 'ARE', 'ALXN', 'ALGN', 'ALLE', 'AGN', 'ADS', 'LNT', 'ALL', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AEE', 'AAL', 'AEP', 'AXP', 'AIG', 'AMT', 'AWK', 'AMP', 'ABC', 'AME', 'AMGN', 'APH', 'APC', 'ADI', 'ANDV', 'ANSS', 'ANTM', 'AON', 'AOS', 'APA', 'AIV', 'AAPL', 'AMAT', 'APTV', 'ADM', 'ARNC', 'AJG', 'AIZ', 'T', 'ADSK', 'ADP', 'AZO', 'AVB', 'AVY', 'BHGE', 'BLL', 'BAC', 'BK', 'BAX', 'BBT', 'BDX', 'BRK.B', 'BBY', 'BIIB', 'BLK', 'HRB', 'BA', 'BWA', 'BXP', 'BSX', 'BHF', 'BMY', 'AVGO', 'BF.B', 'CHRW', 'CA', 'COG', 'CDNS', 'CPB', 'COF', 'CAH', 'CBOE', 'KMX', 'CCL', 'CAT', 'CBG', 'CBS', 'CELG', 'CNC', 'CNP', 'CTL', 'CERN', 'CF', 'SCHW', 'CHTR', 'CHK', 'CVX', 'CMG', 'CB', 'CHD', 'CI', 'XEC', 'CINF', 'CTAS', 'CSCO', 'C', 'CFG', 'CTXS', 'CLX', 'CME', 'CMS', 'KO', 'CTSH', 'CL', 'CMCSA', 'CMA', 'CAG', 'CXO', 'COP', 'ED', 'STZ', 'COO', 'GLW', 'COST', 'COTY', 'CCI', 'CSRA', 'CSX', 'CMI', 'CVS', 'DHI', 'DHR', 'DRI', 'DVA', 'DE', 'DAL', 'XRAY', 'DVN', 'DLR', 'DFS', 'DISCA', 'DISCK', 'DISH', 'DG', 'DLTR', 'D', 'DOV', 'DWDP', 'DPS', 'DTE', 'DRE', 'DUK', 'DXC', 'ETFC', 'EMN', 'ETN', 'EBAY', 'ECL', 'EIX', 'EW', 'EA', 'EMR', 'ETR', 'EVHC', 'EOG', 'EQT', 'EFX', 'EQIX', 'EQR', 'ESS', 'EL', 'ES', 'RE', 'EXC', 'EXPE', 'EXPD', 'ESRX', 'EXR', 'XOM', 'FFIV', 'FB', 'FAST', 'FRT', 'FDX', 'FIS', 'FITB', 'FE', 'FISV', 'FLIR', 'FLS', 'FLR', 'FMC', 'FL', 'F', 'FTV', 'FBHS', 'BEN', 'FCX', 'GPS', 'GRMN', 'IT', 'GD', 'GE', 'GGP', 'GIS', 'GM', 'GPC', 'GILD', 'GPN', 'GS', 'GT', 'GWW', 'HAL', 'HBI', 'HOG', 'HRS', 'HIG', 'HAS', 'HCA', 'HCP', 'HP', 'HSIC', 'HSY', 'HES', 'HPE', 'HLT', 'HOLX', 'HD', 'HON', 'HRL', 'HST', 'HPQ', 'HUM', 'HBAN', 'HII', 'IDXX', 'INFO', 'ITW', 'ILMN', 'IR', 'INTC', 'ICE', 'IBM', 'INCY', 'IP', 'IPG', 'IFF', 'INTU', 'ISRG', 'IVZ', 'IQV', 'IRM', 'JEC', 'JBHT', 'SJM', 'JNJ', 'JCI', 'JPM', 'JNPR', 'KSU', 'K', 'KEY', 'KMB', 'KIM', 'KMI', 'KLAC', 'KSS', 'KHC', 'KR', 'LB', 'LLL', 'LH', 'LRCX', 'LEG', 'LEN', 'LUK', 'LLY', 'LNC', 'LKQ', 'LMT', 'L', 'LOW', 'LYB', 'MTB', 'MAC', 'M', 'MRO', 'MPC', 'MAR', 'MMC', 'MLM', 'MAS', 'MA', 'MAT', 'MKC', 'MCD', 'MCK', 'MDT', 'MRK', 'MET', 'MTD', 'MGM', 'KORS', 'MCHP', 'MU', 'MSFT', 'MAA', 'MHK', 'TAP', 'MDLZ', 'MON', 'MNST', 'MCO', 'MS', 'MOS', 'MSI', 'MYL', 'NDAQ', 'NOV', 'NAVI', 'NTAP', 'NFLX', 'NWL', 'NFX', 'NEM', 'NWSA', 'NWS', 'NEE', 'NLSN', 'NKE', 'NI', 'NBL', 'JWN', 'NSC', 'NTRS', 'NOC', 'NCLH', 'NRG', 'NUE', 'NVDA', 'ORLY', 'OXY', 'OMC', 'OKE', 'ORCL', 'PCAR', 'PKG', 'PH', 'PDCO', 'PAYX', 'PYPL', 'PNR', 'PBCT', 'PEP', 'PKI', 'PRGO', 'PFE', 'PCG', 'PM', 'PSX', 'PNW', 'PXD', 'PNC', 'RL', 'PPG', 'PPL', 'PX', 'PCLN', 'PFG', 'PG', 'PGR', 'PLD', 'PRU', 'PEG', 'PSA', 'PHM', 'PVH', 'QRVO', 'PWR', 'QCOM', 'DGX', 'RRC', 'RJF', 'RTN', 'O', 'RHT', 'REG', 'REGN', 'RF', 'RSG', 'RMD', 'RHI', 'ROK', 'COL', 'ROP', 'ROST', 'RCL', 'CRM', 'SBAC', 'SCG', 'SLB', 'SNI', 'STX', 'SEE', 'SRE', 'SHW', 'SIG', 'SPG', 'SWKS', 'SLG', 'SNA', 'SO', 'LUV', 'SPGI', 'SWK', 'SBUX', 'STT', 'SRCL', 'SYK', 'STI', 'SYMC', 'SYF', 'SNPS', 'SYY', 'TROW', 'TPR', 'TGT', 'TEL', 'FTI', 'TXN', 'TXT', 'TMO', 'TIF', 'TWX', 'TJX', 'TMK', 'TSS', 'TSCO', 'TDG', 'TRV', 'TRIP', 'FOXA', 'FOX', 'TSN', 'UDR', 'ULTA', 'USB', 'UAA', 'UA', 'UNP', 'UAL', 'UNH', 'UPS', 'URI', 'UTX', 'UHS', 'UNM', 'VFC', 'VLO', 'VAR', 'VTR', 'VRSN', 'VRSK', 'VZ', 'VRTX', 'VIAB', 'V', 'VNO', 'VMC', 'WMT', 'WBA', 'DIS', 'WM', 'WAT', 'WEC', 'WFC', 'HCN', 'WDC', 'WU', 'WRK', 'WY', 'WHR', 'WMB', 'WLTW', 'WYN', 'WYNN', 'XEL', 'XRX', 'XLNX', 'XL', 'XYL', 'YUM', 'ZBH', 'ZION', 'ZTS']
data_source = 'google'
start='2017-01-01'
end = '2018-01-01'
for i in tickers:
web.DataReader(i, data_source, start, end).to_csv(i+'.csv')
...ANSWER
Answered 2018-Feb-22 at 11:17Well, for some reason, Google don't seem to have the day-series price data for Lockheed Martin for 2017 (the ticker symbol your query failed on.)
Also, Google don't like automated queries on financial data: if I run your code I regularly get a response including the text "We're sorry... but your computer or network may be sending automated queries. To protect our users, we can't process your request right now."
If you just want the price data, one simple answer is to use Yahoo instead. Here's a test program (based on your code) that I ran on Yahoo, to check for how many of your symbols it could supply price data:
QUESTION
I am attempting to make a Python program that does the following: Given a list of tickers (stock symbols), form a list of links associated to each one at the website www.thestreet.com, to find their Analyst Rating. Then, (this is using the Mechanize import) search for the rating (buy, hold, or sell) on the webpage. Return a dictionary with key (ticker) and entry (buy, sell, or hold).
The code works fine when I enter one ticker at a time, Streetdict['AAP'] for instance returns ['AAP', 'Buy!'] as expected. However when grouped with other tickers, it provides "Error!" as I have defined it.
Here's the code:
...ANSWER
Answered 2017-Mar-16 at 16:04The problem is in the if statements in TheStreetchecker
function here:
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