How and Where can I easily pull data for the Dow 30?
The current Dow divisor is in Historical Divisor Changes. The OpenOffice GetQuote function offers fields for current dividend either in dollars or yield.
Trading data can be had cheaply from:  http://eoddata.com/products/historicaldata.aspx  The SEC will give you machine readable financial statements for American companies for free, but that only goes back 3 or 4 years.  Beyond that, you will have to pay for a rather expensive service like CapitalIQ or CRSP or whatever.  Note that you will need considerable programming knowledge to pull this off. I assume you're after a price time series and not a list of S&P 500 constituents?  Yahoo Finance is always a reasonable starting point. Code you're after is ^GSPC: https://finance.yahoo.com/quote/%5EGSPC/history?p=^GSPC  There's a download data button on the right side. I know of no free source for 10 years historical data on a large set of companies.  Now, if it's just a single company or small number that interest you, contact Investor Relations at the company(ies) in question; they may be willing to send you the data for free. You can pull up the VIX index on Google Finance by entering INDEXCBOE:VIX I've seen this tool.  I'm just having a hard time finding where I can just get a list of all the companies.    For example, you can get up to 100 results at a time, if I just search latest filings for 10-K.  This isn't really an efficient way to go about what I want. You're only counting one year.   Let's say 30 years of data, minutewise, for 2,000 stocks, and 50 characters per data line. That's 1.5TB of data.  Since the market has grown, that's an overestimate - 30 years ago there weren't 2,000 stocks in the NYSE - but it still gives us ballpark of roughly 1 TB.  Not an easy download. Wells Fargo has a good free product that you can sign up for on their website. Google Wells Fargo Economics. I think that will get you there. If you have a friend on a trading desk you can get the JPM morning note which is really good too. Google Finance and Yahoo Finance have been transitioning their API (data interface) over the last 3 months. They are currently unreliable. If you're just interested in historical price data, I would recommend either Quandl or Tiingo (I am not affiliated with either, but I use them as data sources). Both have the same historical data (open, close, high, low, dividends, etc.) on a daily closing for thousands of Ticker symbols. Each service requires you to register and get a unique token. For basic historical data, there is no charge. I've been using both for many months and the data quality has been excellent and API (at least for python) is very easy! If you have an inclination for python software development, you can read about the drama with Google and Yahoo finance at the pandas-datareader group at https://github.com/pydata/pandas-datareader. Robert Shiller published US Stock Market data from 1871.  Ken French also has historical data on his website.  Damodaran has a bunch of historical data, here is some historical S&P data. Check the answers to this Stackoverflow question https://stackoverflow.com/questions/754593/source-of-historical-stock-data a number of potential sources are listed I was going to comment above, but I must have 50 reputation to comment. This is a question that vexes me, and I've given it some thought in the past. Morningstar is a good choice for simple, well-organized financial histories.   It has more info available for free than some may realize.  Enter the ticker symbol, and then click either the Financials or the Key Ratios tab, and you will get 5-10 years of some key financial stats.  (A premium subscription is $185 per year, which is not too outrageous.) The American Association of Individual Investors (AAII) provides some good histories, and a screener, for a $29 annual fee. Zacks allows you to chart a metric like EPS going back a long ways, and so you can then click the chart in order to get the specific number.  That is certainly easier than sorting through financial reports from the SEC.  (A message just popped up to say that I'm not allowed to provide more than 2 links, so my contribution to this topic will end here.  You can do a search to find the Zacks website.  I love StackExchange and usually consult it for coding advice.  It just happens to be an odd coincidence that this is my first answer.  I might even have added that aside in a comment, but again, I can't comment as of yet.) It's problem, however, that the universe of free financial information is a graveyard of good resources that no longer exist.  It seems that eventually everyone who provides this information wants to cash in on it.  littleadv, above, says that someone should be paid to organize all this information. However, think that some basic financial information, organized like normal data (and, hey, this is not rocket science, but Excel 101) should be readily available for free.  Maybe this is a project that needs to happen.  With a mission statement of not selling people out later on. The closest thing out there may be Quandl (can't link; do a search), which provides a lot of charts for free, and provides a beautiful and flexible API.  But its core US fundamental data, provided by Sharadar, costs $150 per quarter.  So, not even a basic EPS chart is available there for free. With all of the power that corporations have over our society, I think they could be tabulating this information for us, rather than providing it to us in a data-dumb format that is the equivalent of printing a SQL database as a PDF!  A company that is worth hundreds of billions on the stock market, and it can't be bothered to provide us with a basic Excel chart that summarizes its own historical earnings?  Or, with all that the government does to try to help us understand all of these investments, they cannot simply tabulate some basic financial information for us? This stuff matters a great deal to our lives, and I think that much of it could and should be available, for free, to all of us, rather than mainly to financial professionals and those creating glossy annual reports. So, I disagree that yet another entity needs to be making money off providing the BASIC transparency about something as simple as historical earnings. Thank you for indulging that tangent.  I know that SE prides itself on focused answers.  A wonderful resource that I greatly appreciate. "From the Vanguard page -  This seemed the easiest one as S&P data is simple to find. I use MoneyChimp to get -  which confirms that Vanguard's page is offering CAGR, not arithmetic Average. Note: Vanguard states ""For U.S. stock market returns, we use the Standard & Poor's 90 from 1926 through March 3, 1957,"" while the Chimp uses data from Nobel Prize winner, Robert Shiller's site." I had a question about this but it apparently wasn’t formed in the right way as I got no explanations and only downvotes, so let me try again.  Given the massive amount of info you gave, I tried to go through and find the data I was asking for- data behind the projections of such a loss.  Perhaps since I’m not a professional economist, It was not immediately apparent to me how to find the data behind the projections.   Would you mind demonstrating how any of these sources provide the data behind how such projections are made?  Or do you have any other advice as to how I could find an answer? There are several Excel spreadsheets for downloading stock quotes (from Yahoo Finance), and historical exchange rates at http://investexcel.net/financial-web-services-kb "Answering this question is weird, because it is not really precise in what you mean. Do you want all stocks in the US? Do you want a selection of stocks according to parameters?  Do you just want a cool looking graph? However, your possible misuse of the word derivative piqued my interest. Your reference to  gold and silver seems to indicate that you do not know what a derivative actually is. Or what it would do in a portfolio.  The straightforward way to ""see"" an efficient frontier is to do the following. For a set of stocks (in this case six ""randomly"" selected ones):      library(quantmod)  library(fPortfolio)  library(PerformanceAnalytics)     getSymbols(c(""STZ"", ""RAI"", ""AMZN"", ""MSFT"", ""TWX"", ""RHT""), from = ""2012-06-01"", to = ""2017-06-01"")     returns &lt;- NULL  tickerlist &lt;- c(""STZ"", ""RAI"", ""AMZN"", ""MSFT"", ""TWX"", ""RHT"")  for (ticker in tickerlist){   returns &lt;- cbind(returns, monthlyReturn(Ad(eval(as.symbol(ticker)))))  }  colnames(returns) &lt;- tickerlist  returns &lt;- as.timeSeries(returns)    frontier &lt;- portfolioFrontier(returns)  png(""frontier.png"", width = 800, height = 600)  plot(frontier, which = ""all"")  dev.off()    minvariancePortfolio(returns, constraints = ""LongOnly"")    Portfolio Weights:    STZ    RAI   AMZN   MSFT    TWX    RHT   0.1140 0.3912 0.0000 0.1421 0.1476 0.2051     Covariance Risk Budgets:    STZ    RAI   AMZN   MSFT    TWX    RHT   0.1140 0.3912 0.0000 0.1421 0.1476 0.2051     Target Returns and Risks:   mean    Cov   CVaR    VaR   0.0232 0.0354 0.0455 0.0360    https://imgur.com/QIxDdEI  The minimum variance portfolio of these six assets has a mean return is 0.0232 and variance is 0.0360. AMZN does not get any weight in the portfolio.  It kind of means that the other assets span it and it does not provide any additional diversification benefit.   Let us add two ETFs that track gold and silver to the mix, and see how little difference it makes:    getSymbols(c(""GLD"", ""SLV""), from = ""2012-06-01"", to = ""2017-06-01"")    returns &lt;- NULL  tickerlist &lt;- c(""STZ"", ""RAI"", ""AMZN"", ""MSFT"", ""TWX"", ""RHT"", ""GLD"", ""SLV"")  for (ticker in tickerlist){   returns &lt;- cbind(returns, monthlyReturn(Ad(eval(as.symbol(ticker)))))  }  colnames(returns) &lt;- tickerlist  returns &lt;- as.timeSeries(returns)   frontier &lt;- portfolioFrontier(returns)    png(""weights.png"", width = 800, height = 600)  weightsPlot(frontier)  dev.off()    # Optimal weights  out &lt;- minvariancePortfolio(returns, constraints = ""LongOnly"")  wghts &lt;- getWeights(out)  portret1 &lt;- returns%*%wghts  portret1 &lt;- cbind(monthprc, portret1)[,3]  colnames(portret1) &lt;- ""Optimal portfolio""    # Equal weights  wghts &lt;- rep(1/8, 8)  portret2 &lt;- returns%*%wghts  portret2 &lt;- cbind(monthprc, portret2)[,3]  colnames(portret2) &lt;- ""Equal weights portfolio""   png(""performance_both.png"", width = 800, height = 600)  par(mfrow=c(2,2))  chart.CumReturns(portret1, ylim = c(0, 2))  chart.CumReturns(portret2, ylim = c(0, 2))  chart.Drawdown(portret1, main = ""Drawdown"", ylim = c(-0.06, 0))  chart.Drawdown(portret2, main = ""Drawdown"", ylim = c(-0.06, 0))  dev.off()   https://imgur.com/sBHGz7s   Adding gold changes the minimum variance mean return to 0.0116 and the variance stays about the same 0.0332. You can see how the weights change at different return and variance profiles in the picture.  The takeaway is that adding gold decreases the return but does not do a lot for the risk of the portfolio.  You also notice that silver does not get included in the minimum variance efficient portfolio (and neither does AMZN).  https://imgur.com/rXPbXau  We can also compare the optimal weights to an equally weighted portfolio and see that the latter would have performed better but had much  larger drawdowns. Which is because it has a higher volatility, which might be undesirable.  ---  Everything below here is false, but illustrative.  So what about the derivative part? Let us assume you bought an out of the money call option with a strike of 50 on MSFT at the beginning of the  time series and held it to the end.  We need to decide on the the annualized cost-of-carry rate, the annualized rate of interest, the time to maturity is measured in years, the annualized  volatility of the underlying security is proxied by the historical volatility.   library(fOptions)   monthprc &lt;- Ad(MSFT)[endpoints(MSFT, ""months"")]  T &lt;- length(monthprc) # 60 months, 5 years  vol &lt;- sd(returns$MSFT)*sqrt(12) # annualized volatility    optprc &lt;- matrix(NA, 60, 1)  for (t in 1:60) {   s &lt;- as.numeric(monthprc[t])   optval &lt;- GBSOption(TypeFlag = ""c"", S = s, X = 50, Time = (T - t) / 12, r = 0.001, b = 0.001, sigma = vol)   optprc[t] &lt;- optval@price  }    monthprc &lt;- cbind(monthprc, optprc)  colnames(monthprc) &lt;- c(""MSFT"", ""MSFTCall50"")    MSFTCall50rets &lt;- monthlyReturn(monthprc[,2])  colnames(MSFTCall50rets) &lt;- ""MSFTCall50rets""    returns &lt;- merge(returns, MSFTCall50rets)    wghts &lt;- rep(1/9, 9)    portret3 &lt;- returns%*%wghts  portret3 &lt;- cbind(monthprc, portret3)[,3]  colnames(portret3) &lt;- ""Equal weights derivative portfolio""    png(""performance_deriv.png"", width = 800, height = 600)  par(mfrow=c(2,2))  chart.CumReturns(portret2, ylim = c(0, 4.5))  chart.CumReturns(portret3, ylim = c(0, 4.5))  chart.Drawdown(portret2, main = ""Drawdown"", ylim = c(-0.09, 0))  chart.Drawdown(portret3, main = ""Drawdown"", ylim = c(-0.09, 0))  dev.off()  https://imgur.com/SZ1xrYx   Even though we have a massively profitable instrument in the derivative. The portfolio analysis does not include it  because of the high volatility. However, if we just use equal weighting and essentially take a massive position in  the out of the money call (which would not be possible in real life), we get huge drawdowns and volatility, but  the returns are almost two fold. But nobody will sell you a five year call.  Others can correct any mistakes or misunderstandings in the above. It hopefully gives a starting point.  Read more at:   https://en.wikipedia.org/wiki/Modern_portfolio_theory https://en.wikipedia.org/wiki/Option_(finance)  The imgur album:  https://imgur.com/a/LoBEY"