org.apache.poi.ss.formula.functions.Irr Maven / Gradle / Ivy

Show more of this group Show more artifacts with this name
Show all versions of poi Show documentation
Apache POI - Java API To Access Microsoft Format Files
There is a newer version: 5.2.5
/* ====================================================================
   Licensed to the Apache Software Foundation (ASF) under one or more
   contributor license agreements.  See the NOTICE file distributed with
   this work for additional information regarding copyright ownership.
   The ASF licenses this file to You under the Apache License, Version 2.0
   (the "License"); you may not use this file except in compliance with
   the License.  You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
==================================================================== */

package org.apache.poi.ss.formula.functions;

import org.apache.poi.ss.formula.eval.*;

/**
 * Calculates the internal rate of return.
 *
 * Syntax is IRR(values) or IRR(values,guess)
 *
 * @see Wikipedia on IRR
 * @see Excel IRR
 */
public final class Irr implements Function {


    public ValueEval evaluate(final ValueEval[] args, final int srcRowIndex, final int srcColumnIndex) {
        if(args.length == 0 || args.length > 2) {
            // Wrong number of arguments
            return ErrorEval.VALUE_INVALID;
        }

        try {
            double[] values = AggregateFunction.ValueCollector.collectValues(args[0]);
            double guess;
            if(args.length == 2) {
                guess = NumericFunction.singleOperandEvaluate(args[1], srcRowIndex, srcColumnIndex);
            } else {
                guess = 0.1d;
            }
            double result = irr(values, guess);
            NumericFunction.checkValue(result);
            return new NumberEval(result);
        } catch (EvaluationException e){
            return e.getErrorEval();
        }
    }

    /**
     * Computes the internal rate of return using an estimated irr of 10 percent.
     *
     * @param income the income values.
     * @return the irr.
     */
    public static double irr(double[] income) {
        return irr(income, 0.1d);
    }


    /**
     * Calculates IRR using the Newton-Raphson Method.
     * 
     * Starting with the guess, the method cycles through the calculation until the result
     * is accurate within 0.00001 percent. If IRR can't find a result that works
     * after 20 tries, the Double.NaN<> is returned.
     * 
     * 
     *   The implementation is inspired by the NewtonSolver from the Apache Commons-Math library,
     *   @see http://commons.apache.org
     * 
     *
     * @param values        the income values.
     * @param guess         the initial guess of irr.
     * @return the irr value. The method returns Double.NaN
     *  if the maximum iteration count is exceeded
     *
     * @see 
     *     http://en.wikipedia.org/wiki/Internal_rate_of_return#Numerical_solution
     * @see 
     *     http://en.wikipedia.org/wiki/Newton%27s_method
     */
    public static double irr(double[] values, double guess) {
        final int maxIterationCount = 20;
        final double absoluteAccuracy = 1E-7;

        double x0 = guess;
        double x1;

        int i = 0;
        while (i < maxIterationCount) {

            // the value of the function (NPV) and its derivate can be calculated in the same loop
            final double factor = 1.0 + x0;
            int k = 0;
            double fValue = values[k];
            double fDerivative = 0;
            for (double denominator = factor; ++k < values.length; ) {
                final double value = values[k];
                fValue += value / denominator;
                denominator *= factor;
                fDerivative -= k * value / denominator;
            }

            // the essense of the Newton-Raphson Method
            x1 = x0 - fValue/fDerivative;

            if (Math.abs(x1 - x0) <= absoluteAccuracy) {
                return x1;
            }

            x0 = x1;
            ++i;
        }
        // maximum number of iterations is exceeded
        return Double.NaN;
    }
}