net.ajaskey.market.ta.methods.RegressionMethods.java Source code

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Here is the source code for net.ajaskey.market.ta.methods.RegressionMethods.java

Source

package net.ajaskey.market.ta.methods;

import java.util.ArrayList;
import java.util.Calendar;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;

import org.apache.commons.math3.stat.regression.SimpleRegression;

import net.ajaskey.market.ta.Utils;

/**
 * This class provide linear regression data for a set of time/price values. The
 * class is extended from
 * org.apache.commons.math3.stat.regression.SimpleRegression and all of the
 * methods of the parent class are available for use. Methods provide by this
 * class are for convenience.
 *
 * @author Andy Askey
 *         <p>
 *         PTV-Parser Copyright (c) 2015, Andy Askey. All rights reserved.
 *         </p>
 *         <p>
 *         Permission is hereby granted, free of charge, to any person obtaining
 *         a copy of this software and associated documentation files (the
 *         "Software"), to deal in the Software without restriction, including
 *         without limitation the rights to use, copy, modify, merge, publish,
 *         distribute, sublicense, and/or sell copies of the Software, and to
 *         permit persons to whom the Software is furnished to do so, subject to
 *         the following conditions:
 *
 *         The above copyright notice and this permission notice shall be
 *         included in all copies or substantial portions of the Software.
 *         </p>
 *
 *         <p>
 *         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
 *         EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
 *         MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
 *         NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
 *         BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
 *         ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 *         CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 *         SOFTWARE.
 *         </p>
 *
 */
public class RegressionMethods extends SimpleRegression {

    /**
     *
     * This class contains a data point that has been added to the regression data
     * set.
     *
     *
     */
    public class DataPoint {

        public Calendar daDate;
        public long daDays;
        public double daPrice;

        /**
         * This method serves as a constructor for the class.
         *
         */
        public DataPoint(Calendar cal, double price) {
            this.daDate = cal;
            this.daDays = Utils.getTimeSpan(cal, RegressionMethods.this.baseCal);
            this.daPrice = price;
        }
    }

    /**
     *
     * This class is used to sort a list of DataPoint objects.
     *
     *
     */
    public class SortPoints implements Comparator<DataPoint> {

        @Override
        public int compare(DataPoint d1, DataPoint d2) {
            int retval = 0;
            if (d1.daDays > d2.daDays) {
                retval = 1;
            } else if (d1.daDays > d2.daDays) {
                retval = -1;
            }
            return retval;
        }
    }

    /**
     *
     */
    private static final long serialVersionUID = 1L;

    List<DataPoint> points = new ArrayList<>();
    private Calendar baseCal = null;
    private int dataCount;

    private Calendar minDate = null;
    private Calendar maxDate = null;

    /**
     * This method serves as a constructor for the class.
     *
     */
    public RegressionMethods() {
        this.baseCal = Calendar.getInstance();
        this.baseCal.set(1900, Calendar.JANUARY, 1, 12, 0, 1);
        this.dataCount = 0;
        this.minDate = Calendar.getInstance();
        this.maxDate = this.baseCal;
    }

    /**
     *
     * net.ajaskey.market.ta.methods.addData
     *
     * @param price
     * @param dates
     * @param days
     */
    public void addData(double[] price, Calendar[] dates, long days) {

        final long knt = Math.min(days, price.length);
        for (int i = 0; i < knt; i++) {
            final long dayNumber = Utils.getTimeSpan(dates[i], this.baseCal);
            this.addData(dayNumber, price[i]);

            if (dates[i].before(this.minDate)) {
                this.minDate = dates[i];
            }

            if (dates[i].after(this.maxDate)) {
                this.maxDate = dates[i];
            }

            this.points.add(new DataPoint(dates[i], price[i]));

            this.dataCount++;
        }
        Collections.sort(this.points, new SortPoints());
        System.out.println(Utils.calendarToString(this.minDate));
        System.out.println(Utils.calendarToString(this.maxDate));
    }

    /**
     *
     * net.ajaskey.market.ta.methods.findX
     *
     * @param cal
     * @return
     */
    public double findX(Calendar cal) {
        double ret = 0;
        try {
            ret = Utils.getTimeSpan(cal, this.baseCal);
        } catch (final Exception e) {
            ret = 0;
        }
        return ret;
    }

    /**
     *
     * net.ajaskey.market.ta.methods.getOutput
     *
     * @param cal
     * @return
     */
    public RegressionOutput getOutput(Calendar cal) {
        try {
            if (this.dataCount > 0) {
                final RegressionOutput output = new RegressionOutput(this, Utils.getTimeSpan(cal, this.baseCal));

                return output;
            }
        } catch (final Exception e) {
            e.printStackTrace();
        }
        return null;
    }

}

/**
 * double sumDelta = 0; int knt=0; for (int i = 0; i < spx.getDataCount(); i++)
 * { dd = TickerData.getDataOfDate(spx, spx.getDate(i)); if (dd != null) { yy =
 * dd.getClose();
 * 
 * x = rm.findX(spx.getDate(i)); y = rm.sreg.predict(x);
 * 
 * delta = yy - y; sumDelta += delta/err; knt++; } }
 * 
 * x = rm.findX(cal); y = rm.sreg.predict(x); double avgDelta =
 * sumDelta/(double)knt; double expectedPrice = y * (1.0+avgDelta);
 * System.out.printf("%.3f %.2f",avgDelta, expectedPrice);
 * 
 */