A method for generating a set of categorized pivot points from a time series of financial data for pattern recognition analysis for a user, the time series having time steps identified by a lag from an initial time step, the method comprising:
- determining, by a computer, a breakout direction for the initial time step in the time series comprising a series of time steps covering a time period, wherein each time step is associated with a time, and wherein the initial time step is a most recent time step in the time period of the time series and a final time step is an oldest time step in the time period of the time series;
- determining a maximum box size for the time series, wherein the maximum box size lacks intrinsic noise in the time series, wherein the intrinsic noise comprises a false pivot point that fails to represent a true change in direction in the time series;
- determining a minimum box size for the time series, wherein the minimum box size is based, in part, on intrinsic noise in the time series;
- determining a box size increment for the time series;
- after determining the breakout direction for the initial time step, the maximum box size, the minimum box size, and the box size increment, decreasing, by the computer, a box size by the box size increment from the maximum box size to the minimum box size to generate a series of decreasing box sizes, and for each box size in the series of decreasing box sizes, incrementing a time step from the initial time step to the final time step, and for each incremental time step, by the computer
- identifying a subsequent extreme value and subsequent end value for a subsequently considered time step based on the breakout direction and any previously identified reversals;
- determining a current extreme value for each incremental time step by comparing a previous extreme value with a subsequent extreme value and:
- replacing the previous extreme value with the subsequent extreme value if the subsequent extreme value corresponds to the breakout direction;
- maintaining the current extreme value if the subsequent extreme value does not correspond to the breakout direction;
- determining whether a reversal in the time series has occurred, wherein a reversal has occurred when the subsequent extreme value deviates from the breakout direction and the magnitude of the difference between the current extreme value and the subsequent end value is greater than or equal to a current box size of the series of decreasing box sizes, and:
- switching the breakout direction if a reversal in the time series is identified;
- and
- maintaining the breakout direction if a reversal in the time series is not identified;
- for each identified reversal, recording, by the computer, the current extreme value associated with the reversal, the associated lag from the initial time step, and the current box size associated with the reversal;
- determining that the largest box size at which the associated reversal is not associated with intrinsic noise and is identified within a desired resolution for pattern recognition analysis;
- categorizing, by the computer, the pivot points by the largest box size at which the associated reversal is identified and the associated lag from the initial time step;
- generating, by the computer using the pivot points by the largest box size at which the associated reversal is identified, the set of categorized pivot points for use as a filter in point and figure charting or as an input to a neural net;
- determining, by the computer, a trading pattern by analyzing the set of categorized pivot points starting from the initial time step and ending with the final time step;
- and
- presenting, by the computer, the trading pattern to the user.