chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
@@ -0,0 +1,153 @@
+/*
+ * 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 QuantConnect.Data.Market;
+using System;
+
+namespace QuantConnect.Indicators
+{
+    /// <summary>
+    /// The Fisher transform is a mathematical process which is used to convert any data set to a modified
+    /// data set whose Probability Distribution Function is approximately Gaussian. Once the Fisher transform
+    /// is computed, the transformed data can then be analyzed in terms of it's deviation from the mean.
+    ///
+    /// The equation is y = .5 * ln [ 1 + x / 1 - x ] where
+    /// x is the input
+    /// y is the output
+    /// ln is the natural logarithm
+    ///
+    /// The Fisher transform has much sharper turning points than other indicators such as MACD
+    ///
+    /// For more info, read chapter 1 of Cybernetic Analysis for Stocks and Futures by John F. Ehlers
+    ///
+    /// We are implementing the latest version of this indicator found at Fig. 4 of
+    /// http://www.mesasoftware.com/papers/UsingTheFisherTransform.pdf
+    /// </summary>
+    public class FisherTransform : BarIndicator, IIndicatorWarmUpPeriodProvider
+    {
+        private const double _alpha = .33;
+        private double _previous;
+        private readonly Minimum _medianMin;
+        private readonly Maximum _medianMax;
+
+        /// <summary>
+        /// Initializes a new instance of the FisherTransform class with the default name and period
+        /// </summary>
+        /// <param name="period">The period of the WMA</param>
+        public FisherTransform(int period)
+            : this($"FISH({period})", period)
+        {
+        }
+
+        /// <summary>
+        /// A Fisher Transform of Prices
+        /// </summary>
+        /// <param name="name">string - the name of the indicator</param>
+        /// <param name="period">The number of periods for the indicator</param>
+        public FisherTransform(string name, int period)
+            : base(name)
+        {
+            // Initialize the local variables
+            _medianMax = new Maximum("MedianMax", period);
+            _medianMin = new Minimum("MedianMin", period);
+            WarmUpPeriod = period;
+        }
+
+        /// <summary>
+        /// Gets a flag indicating when this indicator is ready and fully initialized
+        /// </summary>
+        public override bool IsReady => _medianMax.IsReady && _medianMin.IsReady;
+
+        /// <summary>
+        /// Required period, in data points, for the indicator to be ready and fully initialized.
+        /// </summary>
+        public int WarmUpPeriod { get; }
+
+        /// <summary>
+        /// Computes the next value in the transform.
+        /// value1 is a function used to normalize price withing the last _period day range.
+        /// value1 is centered on its midpoint and then doubled so that value1 wil swing between -1 and +1.
+        /// value1 is also smoothed with an exponential moving average whose alpha is 0.33.
+        ///
+        /// Since the smoothing may allow value1 to exceed the _period day price range, limits are introduced to
+        /// preclude the transform from blowing up by having an input larger than unity.
+        /// </summary>
+        /// <param name="input">IndicatorDataPoint - the time and value of the next price</param>
+        protected override decimal ComputeNextValue(IBaseDataBar input)
+        {
+            var price = (input.Low + input.High) / 2m;
+            _medianMin.Update(input.EndTime, price);
+            _medianMax.Update(input.EndTime, price);
+
+            if (!IsReady) return 0;
+
+            var x = 0.0;
+            var y = 0.0;
+            var minL = _medianMin.Current.Value;
+            var maxH = _medianMax.Current.Value;
+
+            if (minL != maxH)
+            {
+                x = _alpha * 2 * ((double)((price - minL) / (maxH - minL)) - .5) + (1 - _alpha) * _previous;
+                y = FisherTransformFunction(x);
+            }
+            _previous = x;
+
+            return Convert.ToDecimal(y) + .5m * Current.Value;
+        }
+
+        /// <summary>
+        /// Resets this indicator to its initial state
+        /// </summary>
+        public override void Reset()
+        {
+            _previous = 0;
+            _medianMax.Reset();
+            _medianMin.Reset();
+            base.Reset();
+        }
+
+        /// <summary>
+        /// The Fisher transform is a mathematical process which is used to convert any data set to a modified
+        /// data set whose Probability Distribution Function is approximately Gaussian. Once the Fisher transform
+        /// is computed, the transformed data can then be analyzed in terms of it's deviation from the mean.
+        ///
+        /// The equation is y = .5 * ln [ 1 + x / 1 - x ] where
+        /// x is the input
+        /// y is the output
+        /// ln is the natural logarithm
+        ///
+        /// The Fisher transform has much sharper turning points than other indicators such as MACD
+        ///
+        /// For more info, read chapter 1 of Cybernetic Analysis for Stocks and Futures by John F. Ehlers
+        /// </summary>
+        /// <param name="x">Input</param>
+        /// <returns>Output</returns>
+        private double FisherTransformFunction(double x)
+        {
+            if (x > .999)
+            {
+                x = .999;
+            }
+            if (x < -.999)
+            {
+                x = -.999;
+            }
+
+            return .5 * Math.Log((1.0 + x) / (1.0 - x));
+        }
+    }
+}