wehub-resource-sync
227 lines
8.8 KiB
C#
227 lines
8.8 KiB
C#
/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*
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*/
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using System.Collections.Generic;
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using QuantConnect.Data;
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using QuantConnect.Interfaces;
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using QuantConnect.Indicators;
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using QuantConnect.Securities;
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using QuantConnect.Securities.Future;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Example algorithm for trading continuous future
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/// </summary>
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public class BasicTemplateFutureRolloverAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Dictionary<Symbol, SymbolData> _symbolDataBySymbol = new();
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2013, 10, 8);
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SetEndDate(2013, 12, 10);
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SetCash(1000000);
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var futures = new List<string> {
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Futures.Indices.SP500EMini
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};
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foreach (var future in futures)
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{
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// Requesting data
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var continuousContract = AddFuture(future,
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resolution: Resolution.Daily,
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extendedMarketHours: true,
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dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
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dataMappingMode: DataMappingMode.OpenInterest,
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contractDepthOffset: 0
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);
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var symbolData = new SymbolData(this, continuousContract);
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_symbolDataBySymbol.Add(continuousContract.Symbol, symbolData);
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}
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}
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/// <summary>
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/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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/// </summary>
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/// <param name="slice">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice slice)
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{
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foreach (var kvp in _symbolDataBySymbol)
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{
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var symbol = kvp.Key;
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var symbolData = kvp.Value;
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// Call SymbolData.Update() method to handle new data slice received
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symbolData.Update(slice);
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// Check if information in SymbolData class and new slice data are ready for trading
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if (!symbolData.IsReady || !slice.Bars.ContainsKey(symbol))
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{
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return;
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}
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var emaCurrentValue = symbolData.EMA.Current.Value;
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if (emaCurrentValue < symbolData.Price && !symbolData.IsLong)
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{
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MarketOrder(symbolData.Mapped, 1);
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}
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else if (emaCurrentValue > symbolData.Price && !symbolData.IsShort)
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{
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MarketOrder(symbolData.Mapped, -1);
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}
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}
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}
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/// <summary>
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/// Abstracted class object to hold information (state, indicators, methods, etc.) from a Symbol/Security in a multi-security algorithm
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/// </summary>
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public class SymbolData
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{
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private QCAlgorithm _algorithm;
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private Future _future;
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public ExponentialMovingAverage EMA { get; set; }
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public decimal Price { get; set; }
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public bool IsLong { get; set; }
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public bool IsShort { get; set; }
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public Symbol Symbol => _future.Symbol;
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public Symbol Mapped => _future.Mapped;
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/// <summary>
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/// Check if symbolData class object are ready for trading
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/// </summary>
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public bool IsReady => Mapped != null && EMA.IsReady;
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/// <summary>
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/// Constructor to instantiate the information needed to be hold
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/// </summary>
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public SymbolData(QCAlgorithm algorithm, Future future)
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{
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_algorithm = algorithm;
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_future = future;
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EMA = algorithm.EMA(future.Symbol, 20, Resolution.Daily);
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Reset();
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}
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/// <summary>
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/// Handler of new slice of data received
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/// </summary>
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public void Update(Slice slice)
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{
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if (slice.SymbolChangedEvents.TryGetValue(Symbol, out var changedEvent))
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{
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var oldSymbol = changedEvent.OldSymbol;
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var newSymbol = changedEvent.NewSymbol;
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var tag = $"Rollover - Symbol changed at {_algorithm.Time}: {oldSymbol} -> {newSymbol}";
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var quantity = _algorithm.Portfolio[oldSymbol].Quantity;
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// Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
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_algorithm.Liquidate(oldSymbol, tag: tag);
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_algorithm.MarketOrder(newSymbol, quantity, tag: tag);
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Reset();
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}
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Price = slice.Bars.ContainsKey(Symbol) ? slice.Bars[Symbol].Price : Price;
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IsLong = _algorithm.Portfolio[Mapped].IsLong;
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IsShort = _algorithm.Portfolio[Mapped].IsShort;
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}
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/// <summary>
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/// Reset RollingWindow/indicator to adapt to newly mapped contract, then warm up the RollingWindow/indicator
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/// </summary>
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private void Reset()
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{
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EMA.Reset();
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_algorithm.WarmUpIndicator(Symbol, EMA, Resolution.Daily);
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}
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/// <summary>
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/// Disposal method to remove consolidator/update method handler, and reset RollingWindow/indicator to free up memory and speed
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/// </summary>
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public void Dispose()
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{
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EMA.Reset();
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}
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}
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/// <summary>
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/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
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/// </summary>
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public bool CanRunLocally { get; } = true;
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/// <summary>
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/// This is used by the regression test system to indicate which languages this algorithm is written in.
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/// </summary>
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public List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
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/// <summary>
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/// Data Points count of all timeslices of algorithm
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/// </summary>
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public long DataPoints => 727;
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/// <summary>
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/// Data Points count of the algorithm history
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/// </summary>
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public int AlgorithmHistoryDataPoints => 2;
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/// <summary>
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/// Final status of the algorithm
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/// </summary>
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public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "3"},
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{"Average Win", "0.14%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "0.770%"},
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{"Drawdown", "0.100%"},
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{"Expectancy", "0"},
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{"Start Equity", "1000000"},
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{"End Equity", "1001341.4"},
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{"Net Profit", "0.134%"},
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{"Sharpe Ratio", "-0.494"},
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{"Sortino Ratio", "-0.544"},
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{"Probabilistic Sharpe Ratio", "23.043%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "100%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "-0.015"},
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{"Beta", "0.03"},
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{"Annual Standard Deviation", "0.004"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-5.235"},
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{"Tracking Error", "0.081"},
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{"Treynor Ratio", "-0.069"},
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{"Total Fees", "$6.45"},
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{"Estimated Strategy Capacity", "$780000000000.00"},
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{"Lowest Capacity Asset", "ES VMKLFZIH2MTD"},
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{"Portfolio Turnover", "0.42%"},
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{"Drawdown Recovery", "3"},
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{"OrderListHash", "d17bbe62c86730e5178528a3153df0e6"}
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};
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}
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}
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