What has changed
The API is gone. The new downloads need 1st to parse the Yahoo Finance page to find a hidden crumb and use it as the key for data retrieval over a 2nd URL.
The adjusted close is sometimes the adjusted, sometimes is the non-adjusted and sometimes is a different value
Lines with literally "null" as the value for the prices have been ...
From Yahoo! Finance Help
The Beta used is Beta of Equity. Beta is the monthly price change of a particular company relative to the monthly price change of the S&P500. The time period for Beta is 3 years (36 months) when available.
Source: https://help.yahoo.com/kb/finance/SLN2347.html?impressions=true (+Stock Price History)
For a stock market-listed security, volume typically represents the number of shares traded. The amount of money involved is called turnover or dollar volume.
A further complication to this relates to the rules of each exchange. Only certain types of trades/trade conditions affect the price of the security. Similarly, only certain types of trades/trade ...
When you sample stock market data, you really need to understand what source(s) and rules are being used, and any adjustments applied to the data. Different rules might also exist for different periodicities sampled too.
There are may different/methodologies applied to Consolidated tape price versus listed exchange price versus a specific exchange price.
Yahoo has changed their site structure. The new download URLs look like this:
These links originate from pages like this: https://finance.yahoo.com/quote/MSFT/history?period1=1463461200&period2=...
It gets its data from https://finance.yahoo.com/lookup/.
Please note: it is not possible to get all the symbols due to limitations set by Yahoo. About 75%-90% of all symbols are gathered using this script depending on type
For American stocks: if you are using Python 3, you can first, from a terminal, do
pip install get-all-tickers
from get_all_tickers import get_tickers as gt
list_of_tickers = gt.get_tickers()
# or if you want to save them to a CSV file
Alternatively, you can clone the file from https://github.com/shilewenuw/get_all_tickers/blob/...
Yahoo Finance calculates beta from monthly prices over a time of three years. The S&P500 is used as the benchmark
You need 37 monthly prices (so you can get 36 returns) on the first trading day of each month.
The final price should be on the first trading day of the previous month.
The first price should be on the first trading day of the month 36 ...
On the website interface, you can get a CEF's NAV by circumfixing X-...-X (prefix & suffix) to the ticker. For example, the NAV time series ticker corresponding to the PDI price series is XPDIX. Or, XBTZX (NAV), BTZ (price). That might work for you.
As can be seen from this example from Yahoo!Finance this should not happen (click on "+ The adjusted close"):
Another more complete example can be found here:
So my explanation is that this is a glitch in ...
You need returns for 36 months, in particular data from 37 months. Yahoo also uses unadjusted closing prices for the reference index as far as i know. The data from 8/1/2015 got to be an error, I checked multiply data sources and found no similarities. After interpolating that point i got a beta of 0.48.
It seems to be a bug on Yahoo Finance:
If we take a look at alternative sites such as Bloomberg and State Street they both report a NAV of 28.22 USD as of 1st of April, for the ETF with ticker SWRD.L (as seen on your second picture).
However, as seen on the State Street website, the same asset exist on the London Stock Exchange in GBP (ticker symbol SWLD.L) ...
I have found this tool: https://github.com/Benny-/Yahoo-ticker-symbol-downloader
It uses the search api https://finance.yahoo.com/_finance_doubledown/api/resource/searchassist;searchTerm=s6s?device=console&returnMeta=true
I've used various data sources (including Yahoo) and their data is sometimes wrong. You can write code for sniffing out the errors. For all providers I've had to add "cleaners" to look for errors and make adjustments. If you use Google Finance etc you'll have random errors too. If you're making a private database then the adjustments need to be tracked in a ...
Python version of Brian's R code:
Brian's code automatically grabs the first expense ratio it finds, which is the one you wanted. This version is a little more explicit.
import urllib2, re
stockSymbols = [ "VDIGX", "VFIAX" ]
expenses = [ [ "Fund", "Most Recent Expense Ratio" ] ]
for stockSymbol in stockSymbols:
Beta calculation varies quite a bit as you've already noted. Using monthly or weekly closing prices is fairly common though; I don't know of anyone who uses daily prices. Yahoo gets its data from CapitalIQ so you may want to look over there. Good luck!
Your best shot is really to get WRDS. If you get access you can replicate their research in few minutes (there is SAS code out there).
It's unlikely that you will be given the data for free. That data is actually expensive. That being said, any university with an econ of finance department has access to that data and gives it to students for free. So if you ...
5 year forward estimates comes from Refinitiv IBES (Institutional Broker Estimate System). It will be the mean estimate from all the analyst that cover the stock that report into IBES. It’s used industry wide.
Yes there is no data for 133. If you want to know which sectors have data try this
select * from yahoo.finance.industry where id in (select industry.id from yahoo.finance.sectors)
For example sector 111 has data
<industry id="111" name="Synthetics">
<company name="American Nano Silicon Technolo" symbol="ANNO"/>
Take a look at pandas-datareader.
you will need to use python and write something like this:
# import package and notebook setting
import pandas as pd
import pandas_datareader.data as web
# load data
start = datetime.datetime(2010,...
You can get a list of tickers for free using Finnhub's API.
You just need to request a free API key.
Check out the following documentation:
#pip install finnhub-python
import pandas as pd
#list of available exchanges
My actual area of research is equity securities, however, I was once called upon to evaluate an algorithm for fx trading as part of a due diligence. FX isn't centrally traded. There isn't a single feed. They cannot match because there isn't a unified price. It will gum up your regression. It is probably impossible to find out where they are getting ...
They are trying to make this more difficult. In matlab, you can bypass it by pretending to be a chrome user using weboptions, and pretending to access from Chrome:
options.UserAgent = 'Chrome';
Maybe something similar is possible in python?
You can download all the company names and values and a lot of other stuff from the Nasdaq or NYSE via http://www.nasdaq.com/screening/company-list.aspx in .csv , then you can process that with df = pd.read_csv(Filename.csv)
On the page you link:
TRADES data is adjusted for splits, but not dividends
ADJUSTED_LAST data is adjusted for splits and dividends. Requires TWS 967+.
So to get adjusted close, request ADJUSTED_LAST and use the Close field. To get the unadjusted close, request TRADES and use the Close field.
No. I’m watching the management of a penny stock manipulate its share price by announcing a reverse stock split and yahoo hasn’t adjusted its prices going on a week and a half. The algorithms thinks it’s a huge breakout and are buying. There’s plenty of opportunity to pickoff bad data.
Prices (and potentially volumes) have been adjusted for historical corporate actions. For example, if there was a 10:1 split in the past, then todays share is equivalent to 1/10th of a share before the split. This is the correct way to view price/volume time series.
Here it seems that corporate adjustments before a point were not applied. When you look at ...