pmwhite

I can see how you would draw that conclusion but that isn't correct. The point I'm trying to make is that averages are tools that need to be applied to the correct type of data. Much like a child who is handed a hammer who then sees nails everywhere, technical traders have averages and therefore attempt to use them on all types of price data and in all types of ways without evaluating the nature of the tool or the underlying data. Price data can be differenced and detrended. It is possible to apply averages to this type of data to get more useful results. However, as I stated before, this doesn't solve the complete problem but surely is better than 99% of the currently available technical indicators. In fact most of them would work better when applied to differenced and detrended data because most technical indicators are based on moving averages which are designed to work with time series with a stationary mean. By differencing and detrending the data it is possible to force the price data to have a stationary mean and thus price becomes a deterministic system.

The logical implication of this is that if human behavior ISN'T random (which it generally is not) then the market will be non-random. However, just because a trader who places orders does them for a particular economic reason that isn't random, does not mean that the combination of all those determined actions of all market participants are not random. I think it is useful to replace the term "random" with non-deterministic or unpredictable. Can you (or anyone) really predict what orders will come into the market next to drive price up or down? If the answer is no, then I would argue that the markets are non-deterministic and may be considered a random walk for analytical purposes. On a slightly different topic, Galton showed that when a large number of random events (coin tosses) are combined, they form a normal distribution. A reference may be found (among many sites) here: The Normal Curve and Galton's Board

I'm sure it's possible to create profitable systems out of moving averages in the short term or for a trader to profit from such in the short term. I have done this. But long term profitability is or should be the goal of any trader or system. The problem is that because the data that the average is applied to is of a higher order than the moving average, and because price data has stochastic trend, meaning the mean moves over time, and can have infinate variance, a moving average (which is designed to work on mean stationary data) does not adapt very well (nor can it). To create a profitable trading strategy using moving averages requires that the average adapt to current market conditions, which isn't possible just by applying a MA to price and optimizing it to past data. The reason this isn't possible is because the price data follows a stochastic trend, meaning the trend is not deterministic, follows no set pattern and because the mean moves and price doesn't have to return to the mean, the whole approach is flawed. As a result, the average will give false signals and will not do a very good job of describing the actual trading opportunities. Differencing and detrending the data will actually create data that is of the same order of integration as the MA. So a MA applied to differenced, detrended data should be useful. The kicker though, is that you don't buy or sell the diff/detrended data, you buy/sell the raw prices, so while a MA applied to differenced / detrended data my prove useful as an indicator, finding a fixed relationship that can be exploited is still a must if one wants to profit long-term from using a moving average strategy.

After getting 19 out of 20 correct on the 2nd try at Aurora, I'd have to say that the human eye can learn to identify the difference between a real data series and a computer generated series, and do it very quickly. But is this skill useful for profit? To the point regarding technical analysis: Most people who come into trading never question the roots of technical analysis or the nature of financial (price) data.That usually comes later after finding that the past may not do a very reliable job of predicting the future, despite how many lines or types of analysis are applied. Traders should be required to take a course in econometrics to learn about how to identify the true nature of price data and how to handle nonstationary time series. Financial data has stochastic trend. Because price is not deterministic, all those fancy TA indicators are useless when applied to raw price data, as they are all designed to work with stationary data. The real kicker is that in most cases those moving averages that are so often applied to price by traders to predict future price movement have a lower order of integration than the underlying price data! It's like trying to predict a complex system with a simple system. Exactly how is that going to work? If only traders could profit from buying or selling a moving average (rather than the underlying price). Then it would be relatively easy to profit.

I think NinjaTrader and MarketDelta software might meet your requirements with IB connectivity. I don't use either fwiw so each will have slightly different charting capabilities. MarketDelta should provide the charting you require.

Thanks to you and Blowfish for your responses. This brings me back to the reason I asked about the liquidity provider / taker definition. If I read your response correctly, then the order placement - namely limit orders determines providers and market orders determines takers. However, with what you've posted in this reply to gooni and to Blowfish, it sounds as if a liquidity provider, who starts off getting hit on a limit order, and who then becomes a liquidity taker when said provider attempts to exit from the position/inventory using a market order in an effort to balance inventory, should not be treated as a liquidity taker but perhaps as a liquidity provider in spite of a market order exit. Is that the correct reading from these posts? Is this where supply/demand for liquidity providers comes in?

I'm trying to refresh my memory of what "industry standard" bid/ask splitting might look like. It's my understanding that charting programs attribute ask transacted volume into the ask or buy column and bid transacted volume into the bid or sell column. Is this consistent with your understanding of how it's typically split between bid and ask?

Thanks for your reply. The definition of liquidity provider and taker is consistent with what I have read in the market microstructure literature. If the definition hinges on the type of order placed: limit for liquidity provider and market for liquidity taker, then at least one additional case needs to be resolved (at least in my mind). Let's say trader A places a marketable limit order to buy at the offer price. The order is partially filled and the remaining amount sits as the new bid price and size. Is trader A a liquidity taker (including the unfilled amount) because A's order consumed the liquidity at the offer, or both a taker and a liquidity provider because the remainder of A's order resides on the bid and thus new liquidity is created by A? Or is trader A simply a liquidity provider (even though liquidity was consumed) because a limit order was utilized? How would you analyze the hypothetical situation? TIA

Thank you for detailing your theory of market action. I have a few questions for clarification: I'm trying to understand if this particular tenant is crucial to the whole of the theory, or is more of an observation based on empirical evidence. Does b lead to the implication that liquidity providers are represented solely in the limit order book and market orders represent mostly liquidity takers with the exception being when excess inventory is being disposed of by liquidity providers via market order? Essentially I'm asking, does placing a limit order that is not immediately marketable make the order placer a liquidity provider under your definition? Is it possible that the profit motive by both categories of traders could unintentionally lead to a composite market quest for value? Does the profit motive of the operators necessarily disprove the value seeking hypothesis of markets? Could value/efficiency/utility result as an unintended result? What is the horizontal scale on the charts you posted? Transactions? Is the delta shown on the charts representative of the industry standard way of splitting bid and ask transactions? I have more questions but this will do for now. Thanks in advance for your responses!

First, thanks Jerry for providing such fantastic tools! I've been looking for a way to characterize the market in terms of statistics and this series of threads really cleared up a few things for me. I have a distribution related question - not HUP, but since you're active in this thread I thought I'd give it a shot here as it should be a relatively straightforward question. In a normal distribution you get approx 68% of the observances in the 1st standard deviation. In a developing distribution, if I want to compute the % of observances within the first standard deviation, do I measure it from the VWAP +/- Std Deviation, or from the PVP (POC) +/- Std Deviation? Thanks for your input!