chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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+/*
+ * 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;
+using System.Collections.Concurrent;
+using System.Linq;
+using QuantConnect.Data;
+using QuantConnect.Data.Market;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Indicators;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// In this algorithm we demonstrate how to perform some technical analysis as
+    /// part of your coarse fundamental universe selection
+    /// </summary>
+    /// <meta name="tag" content="using data" />
+    /// <meta name="tag" content="indicators" />
+    /// <meta name="tag" content="universes" />
+    /// <meta name="tag" content="coarse universes" />
+    public class EmaCrossUniverseSelectionAlgorithm : QCAlgorithm
+    {
+        // tolerance to prevent bouncing
+        const decimal Tolerance = 0.01m;
+        private const int Count = 10;
+        // use Buffer+Count to leave a little in cash
+        private const decimal TargetPercent = 0.1m;
+        private SecurityChanges _changes = SecurityChanges.None;
+        // holds our coarse fundamental indicators by symbol
+        private readonly ConcurrentDictionary<Symbol, SelectionData> _averages = new ConcurrentDictionary<Symbol, SelectionData>();
+
+
+        // class used to improve readability of the coarse selection function
+        private class SelectionData
+        {
+            public readonly ExponentialMovingAverage Fast;
+            public readonly ExponentialMovingAverage Slow;
+
+            public SelectionData()
+            {
+                Fast = new ExponentialMovingAverage(100);
+                Slow = new ExponentialMovingAverage(300);
+            }
+
+            // computes an object score of how much large the fast is than the slow
+            public decimal ScaledDelta
+            {
+                get { return (Fast - Slow)/((Fast + Slow)/2m); }
+            }
+
+            // updates the EMA50 and EMA100 indicators, returning true when they're both ready
+            public bool Update(DateTime time, decimal value)
+            {
+                return Fast.Update(time, value) && Slow.Update(time, value);
+            }
+        }
+
+        /// <summary>
+        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+        /// </summary>
+        public override void Initialize()
+        {
+            UniverseSettings.Leverage = 2.0m;
+            UniverseSettings.Resolution = Resolution.Daily;
+
+            SetStartDate(2010, 01, 01);
+            SetEndDate(2015, 01, 01);
+            SetCash(100*1000);
+
+            AddUniverse(coarse =>
+            {
+                return (from cf in coarse
+                        // grab th SelectionData instance for this symbol
+                        let avg = _averages.GetOrAdd(cf.Symbol, sym => new SelectionData())
+                        // Update returns true when the indicators are ready, so don't accept until they are
+                        where avg.Update(cf.EndTime, cf.AdjustedPrice)
+                        // only pick symbols who have their 50 day ema over their 100 day ema
+                        where avg.Fast > avg.Slow*(1 + Tolerance)
+                        // prefer symbols with a larger delta by percentage between the two averages
+                        orderby avg.ScaledDelta descending
+                        // we only need to return the symbol and return 'Count' symbols
+                        select cf.Symbol).Take(Count);
+            });
+        }
+
+        /// <summary>
+        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+        /// </summary>
+        /// <param name="slice">Slice object keyed by symbol containing the stock data</param>
+        public override void OnData(Slice slice)
+        {
+            if (_changes == SecurityChanges.None) return;
+
+            // liquidate securities removed from our universe
+            foreach (var security in _changes.RemovedSecurities)
+            {
+                if (security.Invested)
+                {
+                    Liquidate(security.Symbol);
+                }
+            }
+
+            // we'll simply go long each security we added to the universe
+            foreach (var security in _changes.AddedSecurities)
+            {
+                SetHoldings(security.Symbol, TargetPercent);
+            }
+        }
+
+        /// <summary>
+        /// Event fired each time the we add/remove securities from the data feed
+        /// </summary>
+        /// <param name="changes">Object containing AddedSecurities and RemovedSecurities</param>
+        public override void OnSecuritiesChanged(SecurityChanges changes)
+        {
+            _changes = changes;
+        }
+    }
+}