Judge market efficiency from raw price action
The shortest-hand yet most reliable metric is daily volume / total shares outstanding.  A security with a high turnover rate will be more efficient than a lower one, ceteris paribus. The practical impacts are tighter spread and lower average percentage change between trades.  A security with a spread of 0% and an average change of 0% between trades is perfectly efficient.
"If markets are efficient they will be unpredictable.  If all past information is already priced into a stock then movement will only occur when new information is introduced.  Since no one can predict future events stocks will move in an unpredictable way.  If markets are inefficient in the short term, people should be able to figure out the long-term price movements.  This doesn't seem to be the case.  This is why the authoritative book on market efficiency is called ""A **Random** Walk Down Wall Street""." They could have different quotes as there are more than a few pieces here.  Are you talking a Real Time Level II quote or just a delayed quote?  Delayed quotes could vary as different companies would be using different time points in their data.  You aren't specifying exactly what kind of quote from which system are you using here. The key to this question is how much of a pinpoint answer do you want and how prepared are you to pay for that kind of access to the automated trades happening?  Remember that there could well be more than a few trades happening each millisecond and thus latency is something to be very careful here, regardless of the exchange as long as we are talking about first-world stock exchanges where there are various automated systems being used for trading. Different market makers is just a possible piece of the equation here.  One could have the same market maker but if the timings are different,e.g. if one quote is at 2:30:30 and the other is at 2:30:29 there could be a difference given all the trades processed within that second, thus the question is how well can you get that split second total view of bids and asks for a stock.  You want to get all the outstanding orders which could be a non-trivial task. Markets are definitely not efficient 100% of the time in thousands for ways. For example, why does implied volatility of options spike right before earnings when everyone already know the earnings date? Sentiment can also change in a blink of an eye based on analyst downgrades or a technical sell off. That said, most market inefficiencies are not salable. Its not possible to make a play off a illogical sell-off when your AUM can buy the company many times over. "I think that pattern is impossible, since the attempt to apply the second half would seem to prevent executing the first. Could you rewrite that as ""After the stock rises to $X, start watching for a drop of $Y from peak price; if/when that happens, sell."" Or does that not do what you want? (I'm not going to comment on whether the proposed programmed trading makes sense. Trying to manage things at this level of detail has always struck me as glorified guesswork.)" To answer your original question: There is proof out there. Here is a paper from the Federal Reserve Bank of St. Louis that might be worth a read.  It has a lot of references to other publications that might help answer your question(s) about TA.  You can probably read the whole article then research some of the other ones listed there to come up with a conclusion.  Below are some excerpts:  Abstract: This article introduces the subject of technical analysis in the foreign exchange market, with emphasis on its importance for questions of market efficiency.  “Technicians” view their craft, the study of price patterns, as exploiting traders’ psychological regularities.  The literature on technical analysis has established that simple technical trading rules on dollar exchange rates provided 15 years of positive, risk-adjusted returns during the 1970s and 80s before those returns were extinguished.  More recently, more complex and less studied rules have produced    more modest returns for a similar length of time.  Conventional explanations that rely on risk adjustment and/or central bank intervention do not plausibly justify the observed excess returns from following simple technical trading rules.  Psychological biases, however, could contribute to the profitability of these rules. We view the observed pattern of excess returns to technical trading rules as being consistent with an adaptive markets view of the world.    and  The widespread use of technical analysis in foreign exchange (and   other) markets is puzzling because it implies  that either traders are   irrationally making decisions on useless information or that past   prices contain useful  information for trading.  The latter   possibility would contradict the “efficient markets hypothesis,”   which holds that  no trading strategy should be able to generate   unusual profits on publicly available information—such as past    prices—except by bearing unusual risk. And the observed  level of   risk-adjusted profitability measures market  (in)efficiency. Therefore   much research effort has been directed toward determining whether   technical analysis is  indeed profitable or not. One of the earliest   studies, by Fama and Blume (1966), found no evidence that a particular   class of TTRs could earn abnormal profits in the stock market.   However, more recent research by Brock,  Lakonishok and LeBaron (1992)   and Sullivan, Timmermann an d White (1999) has provided contrary   evidence. And  many studies of the foreign exchange market have found   evidence that TTRs can generate persistent profits (Poole  6  (1967),   Dooley and Shafer (1984), Sweeney (1986), Levich and Thomas (1993),   Neely, Weller and Dittmar (1997),  Gençay (1999), Lee, Gleason and   Mathur (2001) and Martin (2001)). I would go even farther than Victor's answer.  There is little evidence that candlestick patterns and technical analysis in general have any predictive power.   Even if they did in the past, of which there is some evidence, in modern times they are so easy to do on computers that if they worked algorithmic traders would have scanned almost all traded stocks and bought/sold the stock before you even had a chance to look at the graph. While the best technical traders who are very good at quickly using pattern recognition across many indicators as Victor mentioned might be able to add some advantage.  The odds that a pattern so simple to code such as Bullish Engulfing would have predictive power is tiny. You can't tell for sure.  If there was such a technique then everyone would use it and the price would instantly change to reflect the future price value. However, trade volume does say something.  If you have a lemonade stand and offer a large glass of ice cold lemonade for 1c on a hot summer day I'm pretty sure you'll have high trading volume.  If you offer it for $5000 the trading volume is going to be around zero. Since the supply of lemonade is presumably limited at some point dropping the price further isn't going to increase the number of transactions.   Trade volumes reflect to some degree the difference of valuations between buyers and sellers and the supply and demand.  It's another piece of information that you can try looking at and interpreting.  If you can be more successful at this than the majority of others on the market (not very likely) you may get a small edge.  I'm willing to bet that high frequency trading algorithms factor volume into their trading decisions among multiple other factors. There are about 250 trading days in a year. There are also about 1,900 stocks listed on the NYSE.  What you're asking for would require about 6.2M rows of data. Depending on the number of attributes you're likely looking at a couple GB of data.  You're only getting that much information through an API or an FTP. "First, to mention one thing - better analysis calls for analyzing a range of outcomes, not just one; assigning a probability on each, and comparing the expected values. Then moderating the choice based on risk tolerance. But now, just look at the outcome or scenario of 3% and time frame of 2 days. Let's assume your investable capital is exactly $1000 (multiply everything by 5 for $5,000, etc.). A. Buy stock: the value goes to 103; your investment goes to $1030; net return is $30, minus let's say $20 commission (you should compare these between brokers; I use one that charges 9.99 plus a trivial government fee). B. Buy an call option at 100 for $0.40 per share, with an expiration 30 days away (December 23). This is a more complicated. To evaluate this, you need to estimate the movement of the value of a 100 call, $0 in and out of the money, 30 days remaining, to the value of a 100 call, $3 in the money, 28 days remaining. That movement will vary based on the volatility of the underlying stock, an advanced topic; but there are techniques to estimate that, which become simple to use after you get the hang of it. At any rate, let's say that the expected movement of the option price in this scenario is from $0.40 to $3.20. Since you bought 2500 share options for $1000, the gain would be 2500 times 2.8 = 7000. C. Buy an call option at 102 for $0.125 per share, with an expiration 30 days away (December 23). To evaluate this, you need to estimate the movement of the value of a 102 call, $2 out of the money, 30 days remaining, to the value of a 102 call, $1 in the money, 28 days remaining. That movement will vary based on the volatility of the underlying stock, an advanced topic; but there are techniques to estimate that, which become simple to use after you get the hang of it. At any rate, let's say that the expected movement of the option price in this scenario is from $0.125 to $ 1.50. Since you bought 8000 share options for $1000, the gain would be 8000 times 1.375 = 11000. D. Same thing but starting with a 98 call. E. Same thing but starting with a 101 call expiring 60 days out. F., ... Etc. - other option choices. Again, getting the numbers right for the above is an advanced topic, one reason why brokerages warn you that options are risky (if you do your math wrong, you can lose. Even doing that math right, with a bad outcome, loses). Anyway you need to ""score"" as many options as needed to find the optimal point. But back to the first paragraph, you should then run the whole analysis on a 2% gain. Or 5%. Or 5% in 4 days instead of 2 days. Do as many as are fruitful. Assess likelihoods. Then pull the trigger and buy it. Try these techniques in simulation before diving in! Please! One last point, you don't HAVE to understand how to evaluate projected option price movements if you have software that  does that for you. I'll punt on that process, except to mention it. Get the general idea? Edit P.S. I forgot to mention that brokers need love for handling Options too. Check those commission rates in your analysis as well." "Of course, which is why you need to have a scoring function / utility function for the ""filters"", i.e. Are you going to value it by rate of accuracy hor by a metric where wins = +2, losses = -1, such that it uses a criteria like that to decide whether or not a filter adds value, (some even use a compound effect i.e. wins = 2+e^(1+w) where w is the consecutive wins).  A metric like the above would capture the trade off between predictive power and profit. Also some traders watch their Max DD very carefully so they may be very risk averse." "Sure, and I made it clear in my post that I'm willing to accept that I may be wrong.  But the author does a poor job of arguing his point, his argument in the article is based on an unsubstantiated claim that a certain activity is bad. He says it ""impairs price performance"" but offers no proof that this occurs.  Now granted, I haven't had a chance to look at the data either, but as myself and other posters have mentioned, this claim is quite debatable. I need a bit more material to be convinced." You can statistically estimate the maximal loss/gain over a period of time T by the highest loss/gain during any of the same length time intervals in during the life of the stock. Using logarithmic prices to be more accurate. The Sharpe ratio is, perhaps, the method you are looking for.  That said, not really sure beta is a meaningful metric, as there are plenty of safe bets to be made on volatile stocks (and, conversely, unsafe bets to be made on non-volatile ones). Another one I have seen mentioned used is Equity Feed. It had varies levels of the software depending on the markets you want and can provide level 2 quotes if select that option. http://stockcharts.com/ is also a great tool I see mentioned with lots of free stuff. The only way for a mutual fund to default is if it inflated the NAV. I.e.: it reports that its investments worth more than they really are. Then, in case of a run on the fund, it may end up defaulting since it won't have the money to redeem shares at the NAV it published. When does it happen? When the fund is mismanaged or is a scam. This happened, for example, to the fund Madoff was managing. This is generally a sign of a Ponzi scheme or embezzlement. How can you ensure the funds you invest in are not affected by this? You'll have to read the fund reports, check the independent auditors' reports and check for clues. Generally, this is the job of the SEC - that's what they do as regulators. But for smaller funds, and private (i.e.: not public) investment companies, SEC may not be posing too much regulations.