There are a few exclusions that I have commonly seen:
Excluding thinly traded stocks. The price that shows up in your data feed may not relate to actual tradable prices.
Filtering for ADR/Pink locals. You can find stocks listed in multiple places in ways that would lead you to think that they are great for pairs trades when actually they are the same ...
OptionMetrics has its flaws but it has been widely used in economics/finance research.
Regarding the Constantinides, Jackwerth and Perrakis (2008) paper I am unsure what their concern are. The Binsbergen et al. comment is easier to address. They basically have a confidential dataset that they use to estimate dividend strips. To do so, they need put call ...
You're looking at 1995 data. Back then, Edgar was just coming online. They did not have documents in electronic form. If you want data that old, you may have to pay a vendor, such as S&P.
If you look at the same Макдак for 2019 https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=0000063908 , everything can be downloaded.
You will have to build it yourself.
Reference to StackExchange question regarding data sources to build it.
What are the most comprehensive APIs for cryptocurrency market data?
Example Resources ( I have no affiliation, just first result on google)
I have used https://www.tickdata.com/ and https://www.quantgo.com/ I enjoy the simplistic nature of obtaining data that they use, so for someone new to quantitative finance like you, I recommend that you try them. https://www.quandl.com also have excellent quality data, easy to use APIs
I want to share my recent experience with QuantQuote. Long story short: just don't buy anything from them.
I put in my purchase and shared my credit card. I ordered 10 years of SPY minute data. A few days later I received last months data of random stocks. After realizing that I received the completely incorrect data, I emailed and called just about ...
I'm not sure I'd call it 'subjective' or 'pre-conditioned'. Traditionally, ensuring the absence of arbitrage is a guiding principle for pricing, while the risk-neutral measure is most frequently used for theoretical results. Assuming you'll be using your forecasting for trading activities, using AoA to determine price is the right way to go.
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.
There's a field of study called Statistics, which to a large extent tries to answer questions like that both in a financial setting and in experimental sciences. Try to read something about it. To your question, yes, people use the historical data this way, but usually, they perform a more rigorous statistical analysis, than just counting the number of times ...
If your model is only relating to historical price data of that single stock, then the model wouldn’t be useful. Historical price data is stochastic, and a lot of theory in financial mathematics is based on this idea, meaning the expected value of a stock at any point in the future has no memory of (and is completely independent of) past prices.
As you'd alluded to, selling the data underlying their indexes is the primary way index providers make money aside from ETF licensing. About the best you'll be able to do for free is current holdings with weights--most providers offer a top N list as well as a download of current holdings for most of their indexes. This will likely come with some ...
Use the Bloomberg Excel API and download the data using the Spreadsheet builder function in Excel. Firstly, select historical data in the spreadsheet builder, secondly type in your desired index using it's Bloomberg Ticker. Then you select PX_LAST, which gives you the last closing price. Then you specify your timeframe for which you'd like the data and data ...
This is the ticker information for Eurexchange Index Dividend Futures
This is the information for EURO STOXX 50® Index Dividend Futures (FEXD)
Enter those into BBG or Reuters to get historical data.
Contact Deutche Borse for historical data
404 on their historical data shop. Email them
I don't know of any single dataset that encompasses everything you're looking for, even including the standard heavyweight commercial sources (which cost $250k+ per year).
Equity data is typically distinct from ETF data, and fundamental data is typically separate still. High-frequency data is a different animal beyond those (eg, 1-min bars).
It kind ...
Many of the exchanges only have five minute minimums, so simply retrieving the one-minute candle would be impossible with a simple API call. However, you can set up a a program (using CCXT) that polls the exchanges every minute and collects all the relevant data that you may need. From there, you can recreate the one-minute candle that you desired initially.
https://www.quandl.com and https://www.tickdata.com/ have really comprehensive market data both intraday and end of day data. They have data for both America Exchanges and European Exchanges plus other exchanges around the world. You can try out their APIs and see if they will suit your needs. I am sure there are also other new vendors who could be offering ...
You can find historical currency options quotes in WRDS
. In WRDS, choose the option for Philadelphia Stock Exchange (PHLX) and then currency options. Make sure you have a WRDS account in order to access the data.
Yes you can build holiday calendars using Pandas, see here: Pandas Timeseries Docs
But you don't really need it here, just create a DateTimeIndex from your given column and lookup the specific date in the list:
import pandas as pd
df = pd.DataFrame(data=[['2018-08-03', 206.3064],
If you are looking into buying these data sets you should probably try https://bravenewcoin.com , https://www.coinapi.io/ and https://www.quandl.com . They are currently the ones you could try other than what has been mentioned above. It's a little hard to get such datasets for free, especially if they are clean. Most of the time you'll have to part with ...
You can fetch Indian stocks data from NSEpy.The data available on NESpy are:
1. Daily stock data
2. Stock futures data
3. Stock options data
4. Index futures data
5. Index options data
Example to get daily stock data from NSEpy in Python:
from nsepy import get_history
from datetime import datetime
start = datetime(2019, 1, 1)
end = datetime(2019, 30, 7)