wehub-resource-sync
193 lines
8.0 KiB
C#
193 lines
8.0 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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using QuantConnect.Data;
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using QuantConnect.Indicators;
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using QuantConnect.Interfaces;
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using QuantConnect.Storage;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// This algorithm showcases some features of the <see cref="IObjectStore"/> feature.
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/// One use case is to make consecutive backtests run faster by caching the results of
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/// potentially time consuming operations. In this example, we save the results of a
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/// history call. This pattern can be equally applied to a machine learning model being
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/// trained and then saving the model weights in the object store.
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/// </summary>
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public class ObjectStoreExampleAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private const string SPY_Close_ObjectStore_Key = "spy_close";
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private Symbol SPY;
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private Identity SPY_Close;
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private ExponentialMovingAverage SPY_Close_EMA10;
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private ExponentialMovingAverage SPY_Close_EMA50;
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// track last year of close and EMA10/EMA50
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private readonly RollingWindow<IndicatorDataPoint> SPY_Close_History = new RollingWindow<IndicatorDataPoint>(252);
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private readonly RollingWindow<IndicatorDataPoint> SPY_Close_EMA10_History = new RollingWindow<IndicatorDataPoint>(252);
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private readonly RollingWindow<IndicatorDataPoint> SPY_Close_EMA50_History = new RollingWindow<IndicatorDataPoint>(252);
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public override void Initialize()
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{
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SetStartDate(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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SPY = AddEquity("SPY", Resolution.Minute).Symbol;
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// define indicators on SPY daily closing prices
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SPY_Close = Identity(SPY, Resolution.Daily);
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SPY_Close_EMA10 = SPY_Close.EMA(10);
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SPY_Close_EMA50 = SPY_Close.EMA(50);
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// each time an indicator is updated, push the value into our history rolling windows
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SPY_Close.Updated += (sender, args) =>
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{
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// each time we receive new closing price data, push our window to the object store
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SPY_Close_History.Add(args);
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};
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SPY_Close_EMA10.Updated += (sender, args) => SPY_Close_EMA10_History.Add(args);
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SPY_Close_EMA50.Updated += (sender, args) => SPY_Close_EMA50_History.Add(args);
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if (ObjectStore.ContainsKey(SPY_Close_ObjectStore_Key))
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{
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// our object store has our historical data saved, read the data
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// and push it through the indicators to warm everything up
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var values = ObjectStore.ReadJson<IndicatorDataPoint[]>(SPY_Close_ObjectStore_Key);
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Debug($"{SPY_Close_ObjectStore_Key} key exists in object store. Count: {values.Length}");
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foreach (var value in values)
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{
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SPY_Close.Update(value);
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}
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}
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else
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{
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Debug($"{SPY_Close_ObjectStore_Key} key does not exist in object store. Fetching history...");
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// if our object store doesn't have our data, fetch the history to initialize
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// we're pulling the last year's worth of SPY daily trade bars to fee into our indicators
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var history = History(SPY, TimeSpan.FromDays(365), Resolution.Daily);
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foreach (var tradeBar in history)
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{
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SPY_Close.Update(tradeBar.EndTime, tradeBar.Close);
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}
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// save our warm up data so next time we don't need to issue the history request
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var array = SPY_Close_History.Reverse().ToArray();
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ObjectStore.SaveJson(SPY_Close_ObjectStore_Key, array);
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// Can also use ObjectStore.SaveBytes(key, byte[])
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// and to read ObjectStore.ReadBytes(key) => byte[]
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// we can also get a file path for our data. some ML libraries require model
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// weights to be loaded directly from a file path. The object store can provide
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// a file path for any key by: ObjectStore.GetFilePath(key) => string (file path)
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}
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}
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public override void OnData(Slice slice)
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{
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if (SPY_Close_EMA10 > SPY_Close && SPY_Close_EMA10 > SPY_Close_EMA50)
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{
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SetHoldings(SPY, 1m);
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}
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else if (SPY_Close_EMA10 < SPY_Close && SPY_Close_EMA10 < SPY_Close_EMA50)
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{
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SetHoldings(SPY, -1m);
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}
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else if (Portfolio[SPY].IsLong)
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{
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if (SPY_Close_EMA10 < SPY_Close_EMA50)
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{
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Liquidate(SPY);
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}
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}
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else if (Portfolio[SPY].IsShort)
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{
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if (SPY_Close_EMA10 > SPY_Close_EMA50)
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{
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Liquidate(SPY);
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}
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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 => 3943;
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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 => 249;
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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", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "271.453%"},
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{"Drawdown", "2.200%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "101691.92"},
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{"Net Profit", "1.692%"},
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{"Sharpe Ratio", "8.854"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "67.459%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "-0.005"},
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{"Beta", "0.996"},
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{"Annual Standard Deviation", "0.222"},
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{"Annual Variance", "0.049"},
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{"Information Ratio", "-14.565"},
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{"Tracking Error", "0.001"},
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{"Treynor Ratio", "1.97"},
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{"Total Fees", "$3.44"},
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{"Estimated Strategy Capacity", "$56000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "19.93%"},
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{"Drawdown Recovery", "3"},
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{"OrderListHash", "3da9fa60bf95b9ed148b95e02e0cfc9e"}
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};
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}
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}
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