Have you ever seen anthing like this - Our approach is based on 2 very simple principles: 1. Processing raw data into Information = Equity All technical information about the condition of the market starts with the tick and those who best process those ticks will have the edge. The better the information that is the basis for the transaction, the more likely it is that the equity of the position will increase. 2. An imbalance in actual buying and selling volumes will precede most price moves. If we can determine and normalize the exact buy/sell pressure/imbalance then we should be able to predict the extent of the move. We also feel that in most cases basic trade decision support technologies haven't changed all that much in the last decade or two, while the technologies and techniques that are available to process this data have made tremendous advances. We feel that this opportunity is further enhanced by a gap in communication between those who understand the market domain at a very high level and those who understand these technologies at a very high level. Our multi-faceted approach includes, among other points, consideration of: Block Trades - In most markets the bulk of the volume is done by locals or other scalpers who close their position before the session's end. While the longer term, more committed market participant is a much smaller percentage of the day's trade, this is the trade that determines the extremes and drives price. We have built software that picks out these blocks (in the S&P less that 1% of trade is for 150 contracts or more) and shows us number of blocks, total block trade and block trade imbalance at every price during the trading day in real-time. We feel there is no better determinant of support and resistance during the session than those areas that show size trade by committed traders and size imbalance by those same traders. Money Flow - Using some of these algorithms and other work, we feel that we have developed formulae that accurately indicate very short term money flow in and out of the targeted market. Time/Price/Volume Continuum - We feel that time is much too important a component of the time/price/volume continuum to be treated as a constant so we use no time based charts. All of our charts are based on actual volume traded which helps us to better calculate and locate imbalances. It is constantly amazing to me how much more clear a properly adjusted volume chart is when compared to a time chart covering the same time period as the volume chart. While we are finding that the information described above is adequate decision support for profitable intra-session trading, we also use pre-processed and normalized data from these methods as inputs to: 1. Our own Time Series Analysis tools - Time Series Analysis is a branch of math sometimes referred to as spectrum analysis. Algorithms from this area of study are much used in missile and anti-missile tracking and interception. While missiles can move in more than one dimension, it is fortunate, for us, that price only moves in one dimension (up or down) and may be one of the most trackable/predictable of the components of the time/price/volume continuum. Speaking technically, what the software we have under development does is "transforms the one-dimensional time series to the trajectory matrix by means of a delay procedure, performs Singular Value Decomposition of the trajectory matrix so that it can reconstruct the original time series." Translated - we input a sequence of volume and price based inputs that make a "signal" that "looks" like price, our software breaks this signal down into several digital components, reproduces the signal without the delay and then, specifically, in our case, produces its best calculation of the mid point of the range for the next six bars. While most all of the mathematical components of such an approach are in the public domain, our approach, including input pre-processing, number of selected eigenvectors and target formulation are not. 2. Neural Networks - we use our own genetic routines to optimize the parameters and architecture of these networks, often use cascades of these networks and have the expertise with this technology to avoid overfitting. 3. Models developed in MARS (Multivariate Adaptive Regression Splines) from Salford Systems and other regression analysis tools are very easy for us to deploy as we have built tools to transform the finished functions into Trade Station's easy language so that a finished model is up and running online as quick as we can cut and paste. We determine whether to use linear (regression) or non-linear (neural networks) in any given situation by analyzing the results of runs made on test data that was not used in the model's development. We feel that it is a poor state of affairs that traders today still use charts that depict bars, candles and profiles when there is technology that can do a much better job of consolidating the information described above into a more precise trade decision support screen. We export tick data from Trade Station to a PostgreSQL db that drives the screen that is attached. This screen presents constantly upgraded: Projections of session High, Low & Close Total Volume, Block volume and Block and total volume imbalance at every price Percentage of normal volume for several time frames normalized as to time of day Volume Velocity in a gauge that shows precise contracts/share per minute Volume imbalance in at least 4 time frame via dynamic color pie charts Projections of the mid point of the range for the next six bars. We are a private company and offer no product or service to anybody, just trying to get this discussion off the ground.