Where do traders take their prices data from? How can it be different from their brokers'?
"This is a complicated subject, because professional traders don't rely on brokers for stock quotes.  They have access to market data using Level II terminals, which show them all of the prices (buy and sell) for a given stock.   Every publicly traded stock (at least in the U.S.) relies on firms called ""market makers"".  Market makers are the ones who ultimately actually buy and sell the shares of companies, making their money on the difference between what they bought the stock at and what they can sell it for.  Sometimes those margins can be in hundreds of a cent per share, but if you trade enough shares...well, it adds up. The most widely traded stocks (Apple, Microsoft, BP, etc) may have hundreds of market makers who are willing to handle share trades.  Each market maker sets their own price on what they'll pay (the ""bid"") to buy someone's stock who wants to sell and what they'll sell (the ""ask"") that share for to someone who wants to buy it.   When a market maker wants to be competitive, he may price his bid/ask pretty aggressively, because automated trading systems are designed to seek out the best bid/ask prices for their trade executions.  As such, you might get a huge chunk of market makers in a popular stock to all set their prices almost identically to one another.  Other market makers who aren't as enthusiastic will set less competitive prices, so they don't get much (maybe no) business.  In any case, what you see when you pull up a stock quote is called the ""best bid/ask"" price.  In other words, you're seeing the highest price a market maker will pay to buy that stock, and the lowest price that a market maker will sell that stock.  You may get a best bid from one market maker and a best ask from a different one.  In any case, consumers must be given best bid/ask prices. Market makers actually control the prices of shares.  They can see what's out there in terms of what people want to buy or sell, and they modify their prices accordingly.  If they see a bunch of sell orders coming into the system, they'll start dropping prices, and if people are in a buying mood then they'll raise prices. Market makers can actually ignore requests for trades (whether buy or sell) if they choose to, and sometimes they do, which is why a limit order (a request to buy/sell a stock at a specific price, regardless of its current actual price) that someone places may go unfilled and die at the end of the trading session.  No market maker is willing to fill the order. Nowadays, these systems are largely automated, so they operate according to complex rules defined by their owners.  Very few trades actually involve human intervention, because people can't digest the information at a fast enough pace to keep up with automated platforms. So that's the basics of how share prices work.  I hope this answered your question without being too confusing! Good luck!" To add a bit to Daniel Anderson's great answer, if you want to 'peek' at what a the set of bid and ask spreads looks like, the otc market page could be interesting (NOTE:  I'm NOT recommending that you trade Over The Counter.  Many of these stocks are amusingly scary):  http://www.otcmarkets.com/stock/ACBFF/quote You can see market makers essentially offering to buy or sell blocks of stock at a variety of prices.
"Bob should treat both positions as incomplete, and explore a viewpoint which does a better job of separating value from volatility. So we should start by recognizing that what Bob is really doing is trading pieces of paper (say Stocks from Fund #1 or Bonds from Fund #2, to pick historically volatile and non-volatile instruments.*) for pieces of paper (Greenbacks).  In the end, this is a trade, and should always be thought of as such.  Does Bob value his stocks more than his bonds?  Then he should probably draw from Fund #2.  If he values his bonds more, he should probably draw from Fund #1. However, both Bob and his financial adviser demonstrate an assumption: that an instrument, whether stock bond or dollar bill, has some intrinsic value (which may raise over time).  The issue is whether its perceived value is a good measure of its actual value or not. From this perspective, we can see the stock (Fund #1) as having an actual value that grows quickly (6.5% - 1.85% = 4.65%), and the bond (Fund #2) as having an actual value that grows slower (4.5% - 1.15$ = 3.35$).  Now the perceived value of the stocks is highly volatile.  The Chairman of the Fed sneezes and a high velocity trader drives a stock up or down at a rate that would give you whiplash. This perspective aligns with the broker's opinion.  If the stocks are low, it means their perceived value is artificially low, and selling it would be a mistake because the market is perceiving those pieces of paper as being worth less than they actually are.  In this case, Bob wins by keeping the stocks, and selling bonds, because the stocks are perceived as undervalued, and thus are worth keeping until perceptions change. On the other hand, consider the assumption we carefully slid into the argument without any fanfare: the assumption that the actual value of the stock aligns with its historical value.  ""Past performance does not predict future results.""  Its entirely possible that the actual value of the stocks is actually much lower than the historical value, and that it was the perceived value that was artificially higher.  It may be continuing to do so... who knows how overvalued the perceived value actually was!  In this case, Bob wins by keeping the bonds.  In this case, the stocks may have ""underperformed"" to drive perceptions towards their actual value, and Bob has a great chance to get out from under this market. The reality is somewhere between them.  The actual values are moving, and the perceived values are moving, and the world mixes them up enough to make Scratchers lottery tickets look like a decent investment instrument.  So what can we do? Bob's broker has a smart idea, he's just not fully explaining it because it is unprofessional to do so.  Historically speaking, Bobs who lost a bunch of money in the stock market are poor judges of where the stock market is going next (arguably, you should be talking to the Joes who made a bunch of money. They might have more of a clue.).  Humans are emotional beings, and we have an emotional instinct to cut ties when things start to go south.  The market preys on emotional thinkers, happily giving them what they want in exchange for taking some of their money.  Bob's broker is quoting a well recognized phrase that is a polite way of saying ""you are being emotional in your judgement, and here is a phrasing to suggest you should temper that judgement."" Of course the broker may also not know what they're doing!  (I've seen arguments that they don't!)  Plenty of people listened to their brokers all the way to the great crash of 2008.  Brokers are human too, they just put their emotions in different places.  So now Bob has no clear voice to listen to.  Sounds like a trap! However, there is a solution.  Bob should think about more than just simple dollars.  Bob should think about the rest of his life, and where he would like the risk to appear.  If Bob draws from Fund #1 (liquidating stocks), then Bob has made a choice to realize any losses or gains early... specifically now.  He may win, he may lose.  However, no matter what, he will have a less volatile portfolio, and thus he can rely on it more in the long run. If Bob draws from Fund #2 (liquidating bonds) instead, then Bob has made a choice not to realize any losses or gains right away.  He may win, he may lose.  However, whether he wins or loses will not be clear, perhaps until retirement when he needs to draw on that money, and finds Fund #1 is still under-performing, so he has to work a few more years before retirement.  There is a magical assumption that the stock market will always continue rewarding risk takers, but no one has quite been able to prove it! Once Bob includes his life perspective in the mix, and doesn't look just at the cold hard dollars on the table, Bob can make a more educated decision.  Just to throw more options on the table, Bob might rationally choose to do any one of a number of other options which are not extremes, in order to find a happy medium that best fits Bob's life needs: * I intentionally chose to label Fund #1 as stocks and Fund #2 as bonds, even though this is a terribly crude assumption, because I feel those words have an emotional attachment associated to them which #1 and #2 simply do not.  Given that part of the argument is that emotions play a part, it seemed reasonable to dig into underlying emotional biases as part of my wording.  Feel free to replace words as you see fit to remove this bias if desired." "The formula for standard deviation is fairly simple in both the discrete and continuous cases. It's mostly safe to use the discrete case when working with adjusted closing prices. Once you've calculated the standard deviation for a given time period, the next task (in the simplest case) is to calculate the mean of that same period. This allows you to roughly approximate the distribution, which can give you all sorts of testable hypotheses. Two standard deviations (σ) away from the mean (μ) is given by:  It doesn't make any sense to talk about ""two standard deviations away from the price"" unless that price is the mean or some other statistic for a given time period. Normally you would look at how far the price is from the mean, e.g. does the price fall two or three standard deviations away from the mean or some other technical indicator like the Average True Range (an exponential moving average of the True Range), some support level, another security, etc.  For most of this answer, I'll assume we're using the mean for the chosen time period as a base. However, the answer is still more complicated than many people realize. As I said before, to calculate the standard deviation, you need to decide on a time period. For example, you could use S&P 500 data from Yahoo Finance and calculate the standard deviation for all adjusted closing prices since January 3, 1950. Downloading the data into Stata and applying the summarize command gives me: As you can probably see, however, these numbers don't make much sense. Looking at the data, we can see that the S&P 500 hasn't traded close to 424.4896 since November 1992. Clearly, we can't assume that this mean and standard deviation as representative of current market conditions. Furthermore, these numbers would imply that the S&P 500 is currently trading at almost three standard deviations away from its mean, which for many distribution is a highly improbable event. The Great Recession, quantitative easing, etc. may have changed the market significantly, but not to such a great extent.  The problem arises from the fact that security prices are usually non-stationary.. This means that the underlying distribution from which security prices are ""drawn"" shifts through time and space. For example, prices could be normally distributed in the 50's, then gamma distributed in the 60's because of a shock, then normally distributed again in the 70's. This implies that calculating summary statistics, e.g. mean, standard deviation, etc. are essentially meaningless for time periods in which prices could follow multiple distributions.  For this and other reasons, it's standard practice to look at the standard deviation of returns or differences instead of prices. I covered in detail the reasons for this and various procedures to use in another answer. In short, you can calculate the first difference for each period, which is merely the difference between the closing price of that period and the closing price of the previous period. This will usually give you a stationary process, from which you can obtain more meaningful values of the standard deviation, mean, etc.  Let's use the S&P500 as an example again. This time, however, I'm only using data from 1990 onwards, for the sake of simplicity (and to make the graphs a bit more manageable). The summary statistics look like this: and the graph looks like this; the mean is the central horizontal red line, and the top and bottom lines indicate one standard deviation above and below the mean, respectively.   As you can see, the graph seems to indicate that there were long periods in which the index was priced well outside this range. Although this could be the case, the graph definitely exhibits a trend, along with some seemingly exogenous shocks (see my linked answer).  Taking the first difference, however, yields these summary statistics: with a graph like this:  This looks a lot more reasonable. In periods of recession, the price appears much more volatile, and it breaches the +/- one standard deviation lines indicated on the graph.  This is only a simple summary, but using first differencing as part of the wider process of detrending/decomposing a time series is a good first step.  For some technical indicators, however, stationary isn't as relevant. This is the case for some types of moving averages and their associated indicators. Take Bollinger bands for instance. These are technical indicators that show a number of standard deviations above and below a moving average. Like any calculation of standard deviation, moving average, statistic, etc. they require data over a specified time period. The analyst chooses a certain number of historical periods, e.g. 20, and calculates the moving average for that many previous periods and the moving/rolling standard deviation for those same periods as well. The Bollinger bands represent the values a certain number of standard deviations away from the moving average at a given point in time. At this given point, you can calculate the value two standard deviations ""away from the value,"" but doing so still requires the historical stock price (or at least the historical moving average). If you're only given the price in isolation, you're out of luck.  Moving averages can indirectly sidestep some of the issues of stationarity I described above because it's straightforward to estimate a time series with a process built from a moving average (specifically, an auto-regressive moving average process) but the econometrics of time series is a topic for another day.  The Stata code I used to generate the graphs and summary statistics:" "&gt;  The Author clearly has no freaking idea what the hell he is talking about.  I have an idea of what's going on.  And my experience comes from earlier in my career, when I acted as an execution trader at several hedge funds.  Rebates are offered by the exchange so that the exchange can make money.  It isn't a public service or some great sacrifice.    Let me tell you how it works:  Somebody at XYZ exchange/bank takes you out for a nice steak dinner.  Then maybe they take you to the strip club.  There may be some blow involved.  If the broker is particularly nice, they'll pay for an experience in the *actual* ""champagne room.""    Then you go back to your desk on Monday, look at the flows you're disbursing to various brokers and exchanges, and make your ""adjustments"" based on how much you enjoy hanging out with the broker/salesperson.  Oh, and of course the ""rebates.""  Which to you barely make a difference, because you're just an execution trader.  You aren't in it to make money.  The analysts don't know what you're doing, and there's a good chance that the PM doesn't, either.   It's easy to do, because tracking ""best execution"" is beyond the comprehension of the SEC.  Oh, if they only knew...and could actually act on it.  But we all know they don't really want to, because almost to a man they're each waiting for their turn at the revolving door that will usher them on to a lucrative private sector career." "One wonders where the author thinks the monies for the generous Boomer pension payouts will come from?  And of course they utterly ignore the fact that things like 401k's and IRA's *require* that a person begin divesting (at a progressive rate) after one hits age 70-1/2.  Yes, post retirement fund sales (minus the taxes on the proceeds of course, which taxes are not to be ignored) these people COULD turn the entire remainder of those monies back around and then re-invest them back into the stock market as (non tax-deferred) retail stock purchasers ... but that is pretty unlikely to happen.  In addition, the Boomers as a generation have become accustomed to a far higher lifestyle (newer cars, larger &amp; fancier homes, more vacations, etc) than the previous generation(s), and so -- just as with many other things where Boomers ""broke the mold"" -- relying the the data from those generations as an economic model of how much wealth retirees tend to ""cash in"" and how they spend (versus what they ""sit on"" and hoard for their heirs) may be rather foolish. (Somewhat akin to building an economic model on 20 years of data that proclaimed ""Americans don't default on their mortgages""... yeah that turned out to NOT be very wise.)  And then finally, I wonder if this author really comprehends the meaning of the phrase ""at the margin""; after all *everyone* in a neighborhood doesn't have to default on their mortgage to drive house prices down, in fact all it takes is a lack of BUYERS and prices cannot be sustained.  So, the problem with the Boomer ""en masse"" retirement is not JUST the possibility that they will ""all sell everything at the same time""; but rather that the ""demographic bulge"" will at a certain tipping point, change from buyer-hoarders, to divester-sellers, and that inevitably WILL have a major affect on the float and thus the prices of stocks... and just as inevitably, at some stage of that change, herd behavior (even *panicked* herd behavior) will take over.  It really doesn't take much to drive a stock down... nor even an entire stock market. (If the authors' wisdom were true, would we EVER have had any stock market crashes in the first place?)" Graphs are nothing but a representation of data. Every time a trade is made, a point is plotted on the graph. After points are plotted, they are joined in order to represent the data in a graphical format.  Think about it this way.  1.) Walmart shuts at 12 AM. 2.)Walmart is selling almonds at $10 a pound.  3.) Walmart says that the price is going to reduce to $9 effective tomorrow. 4.) You are inside the store buying almonds at 11:59 PM.  5.) Till you make your way up to the counter, it is already 12:01 AM, so the store is technically shut. 6.) However, they allow you to purchase the almonds since you were already in there.  7.) You purchase the almonds at $9 since the day has changed. 8.) So you have made a trade and it will reflect as a point on the graph. 9.) When those points are joined, the curves on the graph will be created. 10.) The data source is Walmart's system as it reflects the sale to you. ( In your case the NYSE exchange records this trade made). Buying a stock is just like buying almonds. There has to be a buyer. There has to be a seller. There has to be a price to which both agree. As soon as all these conditions are met, and the trade is made, it is reflected on the graph. The only difference between the graphs from 9 AM-4 PM, and 4 PM-9 AM is the time. The trade has happened regardless and NYSE(Or any other stock exchange) has recorded it! The graph is just made from that data. Cheers. "Google's RSI is using a 10 period on 2 minute bars - i.e. it is based upon the last 20 minutes of data. Yahoo's RSI is using a 14 period lookback on an undetermined timeframe (you could maybe mouse-over and see what incremental part of the chart is giving) and given the ""choppier"" price chart, probably 30 second or 1 minute bars.   Given the difference in both the period specified and the periodicity of the charts - you should expect different results." I used to trade power for a closed end hedge fund.   Yes, weather derivatives are very important.  They help power traders / utilities hedge for unaccountable variables, IE weather.  For example, lets say it costs a utility $50 an hour to produce power for the load when it is 80 degrees outside.  Lets say I trade the contract with them to guarantee the weather will be under 80 degrees.  If the weather is higher than 80 degrees, more people turn in their AC, the load on the grid goes up, and the utility has to start generating power at $70 an hour.    Under this contract, I would be liable to pay the utility the net difference in their cost (the additional $20 per hour they generate per mw).  In that case I am a loser.  If the power comes in under 80 degrees, I make money as I priced (sold) the contract at a premium according to the risk I calulated for offereing the contract.  This has many many applications, but yes, its not a weird thing to trade.  Hope this helps. "This was an article meant for mass consumption, written by a Yale law professor and an individual who has a PhD in economics (in addition to his practical, on the job experience managing the Yale endowment).  I'm having a hard time believing that it was ""poorly argued.""  As for proof, that's the sort of thing you find in financial and economic journals (for example, [The Effect of Maker-Taker Fees on Investor Order Choice and Execution Quality in U.S. Stock Markets](http://people.stern.nyu.edu/jhasbrou/SternMicroMtg/SternMicroMtg2015/Papers/MakerTakerODonoghue.pdf)).    One of the direct takeaways from the above paper states:  *""I find that total trading cost to investors increases, when the taker fee and maker rebate increase, even if the net fee is held fixed. The total trading cost represents the net-of-fees bid-ask spread and the brokerage commission to an investor wanting to buy and then sell the same stock.""*  I'm not here to argue for the paper.  I'm really here to tell you that these guys have far more of a clue than you realize.  ~~A dash of humility on your part may be in order, given the fact that you've already admitted to the reality that you aren't sure of any of this yourself.~~  *Edit*:  Thought I was responding to a different thread."