Jasper van Merle
192 lines
8.7 KiB
C#
192 lines
8.7 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.Data;
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using QuantConnect.Data.Auxiliary;
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using QuantConnect.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm to test volume adjusted behavior
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/// </summary>
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public class AdjustedVolumeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _aapl;
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private const string Ticker = "AAPL";
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private readonly FactorFile _factorFile = FactorFile.Read(Ticker, Market.USA);
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private readonly IEnumerator<decimal> _expectedAdjustedVolume = new List<decimal> { 1541213, 761013, 920088, 867077, 542487, 663132,
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374927, 379554, 413805, 377622 }.GetEnumerator();
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private readonly IEnumerator<decimal> _expectedAdjustedAskSize = new List<decimal> { 53900, 1400, 6300, 2100, 1400, 1400, 700,
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2100, 3500, 700 }.GetEnumerator();
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private readonly IEnumerator<decimal> _expectedAdjustedBidSize = new List<decimal> { 700, 2800, 700, 700, 700, 1400, 2800,
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2100, 7700, 700 }.GetEnumerator();
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public override void Initialize()
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{
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SetStartDate(2014, 6, 5); //Set Start Date
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SetEndDate(2014, 6, 5); //Set End Date
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UniverseSettings.DataNormalizationMode = DataNormalizationMode.SplitAdjusted;
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_aapl = AddEquity(Ticker, Resolution.Minute).Symbol;
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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">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice data)
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{
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if (!Portfolio.Invested)
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{
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SetHoldings(_aapl, 1);
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}
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if (data.Splits.ContainsKey(_aapl))
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{
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Log(data.Splits[_aapl].ToString());
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}
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if (data.Bars.ContainsKey(_aapl))
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{
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var aaplData = data.Bars[_aapl];
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// Assert our volume matches what we expect
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if (_expectedAdjustedVolume.MoveNext() && _expectedAdjustedVolume.Current != aaplData.Volume)
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{
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// Our values don't match lets try and give a reason why
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var dayFactor = _factorFile.GetSplitFactor(aaplData.Time);
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var probableAdjustedVolume = aaplData.Volume / dayFactor;
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if (_expectedAdjustedVolume.Current == probableAdjustedVolume)
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{
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throw new ArgumentException($"Volume was incorrect; but manually adjusted value is correct." +
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$" Adjustment by multiplying volume by {1 / dayFactor} is not occurring.");
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}
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else
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{
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throw new ArgumentException($"Volume was incorrect; even when adjusted manually by" +
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$" multiplying volume by {1 / dayFactor}. Data may have changed.");
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}
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}
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}
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if (data.QuoteBars.ContainsKey(_aapl))
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{
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var aaplQuoteData = data.QuoteBars[_aapl];
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// Assert our askSize matches what we expect
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if (_expectedAdjustedAskSize.MoveNext() && _expectedAdjustedAskSize.Current != aaplQuoteData.LastAskSize)
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{
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// Our values don't match lets try and give a reason why
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var dayFactor = _factorFile.GetSplitFactor(aaplQuoteData.Time);
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var probableAdjustedAskSize = aaplQuoteData.LastAskSize / dayFactor;
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if (_expectedAdjustedAskSize.Current == probableAdjustedAskSize)
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{
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throw new ArgumentException($"Ask size was incorrect; but manually adjusted value is correct." +
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$" Adjustment by multiplying size by {1 / dayFactor} is not occurring.");
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}
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else
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{
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throw new ArgumentException($"Ask size was incorrect; even when adjusted manually by" +
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$" multiplying size by {1 / dayFactor}. Data may have changed.");
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}
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}
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// Assert our bidSize matches what we expect
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if (_expectedAdjustedBidSize.MoveNext() && _expectedAdjustedBidSize.Current != aaplQuoteData.LastBidSize)
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{
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// Our values don't match lets try and give a reason why
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var dayFactor = _factorFile.GetSplitFactor(aaplQuoteData.Time);
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var probableAdjustedBidSize = aaplQuoteData.LastBidSize / dayFactor;
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if (_expectedAdjustedBidSize.Current == probableAdjustedBidSize)
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{
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throw new ArgumentException($"Bid size was incorrect; but manually adjusted value is correct." +
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$" Adjustment by multiplying size by {1 / dayFactor} is not occurring.");
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}
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else
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{
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throw new ArgumentException($"Bid size was incorrect; even when adjusted manually by" +
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$" multiplying size by {1 / dayFactor}. Data may have changed.");
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}
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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 Language[] Languages { get; } = { Language.CSharp };
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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", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "0%"},
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{"Drawdown", "0%"},
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{"Expectancy", "0"},
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{"Net Profit", "0%"},
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{"Sharpe Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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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"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0"},
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{"Annual Variance", "0"},
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{"Information Ratio", "0"},
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{"Tracking Error", "0"},
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{"Treynor Ratio", "0"},
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{"Total Fees", "$5.40"},
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{"Estimated Strategy Capacity", "$42000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Fitness Score", "0"},
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{"Kelly Criterion Estimate", "0"},
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{"Kelly Criterion Probability Value", "0"},
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{"Sortino Ratio", "0"},
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{"Return Over Maximum Drawdown", "0"},
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{"Portfolio Turnover", "0"},
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{"Total Insights Generated", "0"},
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{"Total Insights Closed", "0"},
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{"Total Insights Analysis Completed", "0"},
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{"Long Insight Count", "0"},
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{"Short Insight Count", "0"},
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{"Long/Short Ratio", "100%"},
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{"Estimated Monthly Alpha Value", "$0"},
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{"Total Accumulated Estimated Alpha Value", "$0"},
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{"Mean Population Estimated Insight Value", "$0"},
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{"Mean Population Direction", "0%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "0%"},
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{"Rolling Averaged Population Magnitude", "0%"},
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{"OrderListHash", "43a72d9759cdbd442d5b53a44370e579"}
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};
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}
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}
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