TimeSeriesIndicator.cs

/*
 * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
 * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
 *
 * Licensed 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.
*/
 
using System.Collections.Generic;
using System.Linq;
 
namespace QuantConnect.Indicators
{
    /// <summary>
    /// The base class for any Time Series-type indicator, containing methods common to most of such models.
    /// </summary>
    public abstract class TimeSeriesIndicator : IndicatorBase<IndicatorDataPoint>, IIndicatorWarmUpPeriodProvider
    {
        /// <summary>
        /// "Integration" constants
        /// </summary>
        protected double[] _diffHeads;
 
        /// <summary>
        /// Required period, in data points, for the indicator to be ready and fully initialized.
        /// </summary>
        public abstract int WarmUpPeriod { get; }
 
        /// <summary>
        /// A constructor for a basic Time Series indicator.
        /// </summary>
        /// <param name="name">The name of this indicator</param>
        protected TimeSeriesIndicator(string name)
            : base(name)
        {
        }
 
        /// <summary>
        /// Differences a time series d times.
        /// </summary>
        /// <param name="series">Series to difference</param>
        /// <param name="d">The differencing order</param>
        /// <param name="diffHeads">"Integration" constants</param>
        public static double[] DifferenceSeries(int d, double[] series, out double[] diffHeads)
        {
            diffHeads = new double[d];
            if (d == 0)
            {
                return null;
            }
 
            var localSeries = series;
            for (var j = 1; j <= d; j++)
            {
                var result = new double[localSeries.Length - 1];
                diffHeads[j - 1] = localSeries.Last();
 
                for (var i = 0; i <= localSeries.Length - 2; i++)
                {
                    result[i] = localSeries[i] - localSeries[i + 1];
                }
 
                localSeries = result;
            }
 
            return localSeries;
        }
 
        /// <summary>
        /// Undoes the differencing of a time series which has been differenced using <see cref="DifferenceSeries" />.
        /// https://github.com/statsmodels/statsmodels/blob/04f00006a7aeb1c93d6894caa420698400da6c33/statsmodels/tsa/tsatools.py#L758
        /// </summary>
        /// <param name="series">Series to un-difference</param>
        /// <param name="diffHeads">Series of "integration" constants for un-differencing</param>
        public static double[] InverseDifferencedSeries(double[] series, double[] diffHeads)
        {
            var localDiffs = new Stack<double>(diffHeads.Reverse());
            var localSeries = series.ToList();
            while (localDiffs.Count > 0)
            {
                var first = localDiffs.Pop();
                localSeries.Add(first);
                localSeries = CumulativeSum(localSeries, true);
            }
 
            return localSeries.ToArray();
        }
 
        /// <summary>
        /// Returns an array of lagged series for each of {1,...,p} lags.
        /// </summary>
        /// <param name="p">Max lag order</param>
        /// <param name="series">Series to calculate the lags of</param>
        /// <param name="includeT">Whether or not to include t with its lags in the output array</param>
        /// <returns>A list such that index i returns the series for i+1 lags</returns>
        public static double[][] LaggedSeries(int p, double[] series, bool includeT = false)
        {
            // P-defined lagging - for each X_t, return double[] of the relevant lagged terms
            var toArray = new List<double[]>();
            for (var t = p; t < series.Length; t++)
            {
                var localLag = new List<double>();
                for (var j = includeT ? 0 : 1; j <= p; j++)
                {
                    localLag.Add(series[t - j]);
                }
 
                toArray.Add(localLag.ToArray());
            }
 
            return toArray.ToArray();
        }
 
        /// <summary>
        /// Returns a series where each spot is taken by the cumulative sum of all points up to and including
        /// the value at that spot in the original series.
        /// </summary>
        /// <param name="series">Series to cumulatively sum over.</param>
        /// <param name="reverse">Whether to reverse the series before applying the cumulative sum.</param>
        /// <returns>Cumulatively summed series.</returns>
        public static List<double> CumulativeSum(List<double> series, bool reverse = false)
        {
            var localSeries = series;
            if (reverse)
            {
                localSeries.Reverse(); // For top-down
            }
 
            var sums = 0d;
            var outSeries = new List<double>();
            foreach (var val in localSeries)
            {
                sums += val;
                outSeries.Add(sums);
            }
 
            if (reverse)
            {
                outSeries.Reverse(); // Return to original order
            }
 
            return outSeries;
        }
    }
}