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I've read a couple books on candlesticks, but they are all from at least 8 years ago. It seems like it has grown in popularity since then, and a lot of platforms offer the algorithms for people to use. Has this accessibility diminished the effect of these algorithms?

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    What do algorithmic orders have to do with Candlesticks? – Bob Baerker Oct 16 at 20:32
  • @BobBaerker There are algorithms made just for candlesticks. Most online brokers now make them. By algorithm I mean scan. – Mardymar Oct 16 at 21:59
  • OK, got it. Candlestick scans are above my pay grade. – Bob Baerker Oct 16 at 22:16
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    Some do suggest that the reliability of technical patterns breaks down when too many people use them. So, if too many are using candlesticks, it may be the case that its methods begin to break-down. Of course, this assumes that any success originally attributed to a particular technical pattern was not just random coincidence and luck, which, personally, I think is most likely to be the case. – Nick Oct 17 at 0:43
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    @Mardymar - Good job! You have definitively proved that I don't work 'at any place'. – Bob Baerker Oct 17 at 2:50
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The difficulty with candlestick analysis is that the underlying data is inaccurate, particularly for thinly traded securities, with occasional blocks.

Large orders happen "off the tape," and are inserted into the record later. What happens is that if you put in an order for 10,000 shares, it may be made up of dozens of smaller orders. Those small trades never make it onto the tape. Instead, they are averaged together and reported after the final closing trade is completed at a point where it would not impact the market. If the high or low for the day happen to be in the block order then that information is lost as the average is the only thing reported.

A blip on the ticker you see moments before may have happened slowly over the last few hours. The only potential warning sign is the volume.

It also makes it look like trades are less frequent than they are. If you had a trade of 100, 100 and 400 shares over three minutes, evenly spaced, then it could look like 100,400 or 100,100, or 100, or 400 depending on which pieces ended up in the block order. It could also be 100, 100, 400 if none were used for the block order.

If you could filter out large orders from the ticker and restricted yourself to small trades, then you would have an actual time-series, though censored. Censoring is its own branch of statistics.

There are highly technical methods that could be used to estimate the impact of the block order but they go so far beyond the skills required for candlestick analysis that no one who could use them would likely use candlestick analysis.

For candlestick analysis to be useful, you would need an unusually high level of technical skill. A statistician could do it, some mathematicians could do it, and a handful of economists could do it. Because of the techniques required to perform it in real-time, you would probably have to code it in Rust, C or C++. That really shrinks the pool of people down.

While I personally suspect that some technical methods work in specific cases, I think most don't. I think of technical methods like surfing. You cannot surf on most beaches or rivers. Under certain virtuous circumstances, you can ride a surfboard. The rest of the time, you will fail.

Having read at least some of the academic literature on the topic, I don't think anyone really knows. The obvious suspects can be ruled out, but it isn't a literature that is broad or deep.

One last side note, if candlestick analysis worked it would have had to work "in the old days," when trades were thin, far and few between. Even two decades ago the NYSE had a post for stocks that only trade once every two weeks. When volumes were small, the record would have been more accurate. With things like program trading and other such developments, the candlesticks are not accurate.

  • "For candlestick analysis to be useful, you would need an unusually high level of technical skill". I'm a programmer for a living and fairly good at math. Do you know any literature that could be relevent? – Mardymar Oct 17 at 17:16
  • @Mardymar I would start in two areas, the Bayesian analysis of missing data and missing variables, as well as traditional censoring and traditional decision theory. There is a clever article at statweb.stanford.edu/~owen/courses/306a/… that gives an example based on data that was never collected. There is also quite a bit on market microstructure, but it is specific enough to exchanges and types of markets and it is vast that I would suggest choosing a target and then searching the literature. – Dave Harris Oct 17 at 23:51
  • @Mardymar what I would ignore is traditional time series analysis. Most market data is not a time series. I can think of strategies that I would use, but I have other fish to fry at the moment. I have a devil of a software problem. It is making me think I may need to find a way to build a language and I am certainly not the person to attempt that. I code, I am not a computer scientist. – Dave Harris Oct 17 at 23:55
  • @Mardymar do assume that the distribution of returns lacks a first moment. Look at youtu.be/R3fcVUBgIZw. However, it is safe to assume that prices are statically normal. – Dave Harris Oct 17 at 23:58

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