8402b6f01e
It's important that we keep the factor files consistent with respect to the date that they were generated. This enables us to run the regression algorithms in the cloud and get the same results by using the factor files from the correct date.
149 lines
6.6 KiB
C#
149 lines
6.6 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 System;
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using System.Collections.Generic;
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using QuantConnect.Algorithm.Framework;
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using QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Data.Market;
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using QuantConnect.Indicators;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Demonstration algorithm showing how to easily convert an old algorithm into the framework.
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///
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/// 1. Make class derive from QCAlgorithmFrameworkBridge instead of QCAlgorithm.
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/// 2. When making orders, also create insights for the correct direction (up/down), can also set insight prediction period/magnitude/direction
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/// 3. Profit :)
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/// </summary>
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/// <meta name="tag" content="indicators" />
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/// <meta name="tag" content="indicator classes" />
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/// <meta name="tag" content="plotting indicators" />
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public class ConvertToFrameworkAlgorithm : QCAlgorithmFrameworkBridge, IRegressionAlgorithmDefinition // 1. Derive from QCAlgorithmFrameworkBridge
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{
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private MovingAverageConvergenceDivergence _macd;
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private readonly string _symbol = "SPY";
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public readonly int FastEmaPeriod = 12;
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public readonly int SlowEmaPeriod = 26;
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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(2004, 01, 01);
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SetEndDate(2015, 01, 01);
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AddSecurity(SecurityType.Equity, _symbol, Resolution.Daily);
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// define our daily macd(12,26) with a 9 day signal
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_macd = MACD(_symbol, FastEmaPeriod, SlowEmaPeriod, 9, MovingAverageType.Exponential, Resolution.Daily);
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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="data">TradeBars IDictionary object with your stock data</param>
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public void OnData(TradeBars data)
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{
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// wait for our indicator to be ready
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if (!_macd.IsReady) return;
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var holding = Portfolio[_symbol];
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var signalDeltaPercent = (_macd - _macd.Signal) / _macd.Fast;
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var tolerance = 0.0025m;
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// if our macd is greater than our signal, then let's go long
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if (holding.Quantity <= 0 && signalDeltaPercent > tolerance)
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{
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// 2. Call EmitInsights with insights created in correct direction, here we're going long
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// The EmitInsights method can accept multiple insights separated by commas
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EmitInsights(
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// Creates an insight for our symbol, predicting that it will move up within the fast ema period number of days
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Insight.Price(_symbol, TimeSpan.FromDays(FastEmaPeriod), InsightDirection.Up)
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);
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// longterm says buy as well
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SetHoldings(_symbol, 1.0);
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}
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// if our macd is less than our signal, then let's go short
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else if (holding.Quantity >= 0 && signalDeltaPercent < -tolerance)
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{
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// 2. Call EmitInsights with insights created in correct direction, here we're going short
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// The EmitInsights method can accept multiple insights separated by commas
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EmitInsights(
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// Creates an insight for our symbol, predicting that it will move down within the fast ema period number of days
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Insight.Price(_symbol, TimeSpan.FromDays(FastEmaPeriod), InsightDirection.Down)
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);
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// shortterm says sell as well
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SetHoldings(_symbol, -1.0);
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}
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// plot both lines
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Plot("MACD", _macd, _macd.Signal);
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Plot(_symbol, "Open", data[_symbol].Open);
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Plot(_symbol, _macd.Fast, _macd.Slow);
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}
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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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
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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 Trades", "85"},
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{"Average Win", "4.86%"},
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{"Average Loss", "-4.22%"},
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{"Compounding Annual Return", "-3.118%"},
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{"Drawdown", "53.000%"},
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{"Expectancy", "-0.053"},
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{"Net Profit", "-29.437%"},
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{"Sharpe Ratio", "-0.085"},
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{"Loss Rate", "56%"},
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{"Win Rate", "44%"},
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{"Profit-Loss Ratio", "1.15"},
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{"Alpha", "0.051"},
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{"Beta", "-3.3"},
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{"Annual Standard Deviation", "0.18"},
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{"Annual Variance", "0.033"},
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{"Information Ratio", "-0.196"},
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{"Tracking Error", "0.18"},
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{"Treynor Ratio", "0.005"},
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{"Total Fees", "$756.69"},
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{"Total Insights Generated", "85"},
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{"Total Insights Closed", "85"},
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{"Total Insights Analysis Completed", "84"},
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{"Long Insight Count", "42"},
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{"Short Insight Count", "43"},
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{"Long/Short Ratio", "97.67%"},
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{"Estimated Monthly Alpha Value", "$-572966.7"},
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{"Total Accumulated Estimated Alpha Value", "$-76743320"},
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{"Mean Population Estimated Insight Value", "$-902862.6"},
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{"Mean Population Direction", "53.5714%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "43.5549%"},
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{"Rolling Averaged Population Magnitude", "0%"}
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};
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}
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}
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