Martin Molinero
357 lines
18 KiB
C#
357 lines
18 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 System.Linq;
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using QuantConnect.Data;
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using QuantConnect.Data.Custom;
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using QuantConnect.Data.Market;
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using QuantConnect.Indicators;
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using QuantConnect.Securities.Equity;
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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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/// This algorithm demonstrates the various ways you can call the History function,
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/// what it returns, and what you can do with the returned values.
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/// </summary>
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/// <meta name="tag" content="using data" />
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/// <meta name="tag" content="history and warm up" />
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/// <meta name="tag" content="history" />
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/// <meta name="tag" content="warm up" />
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public class HistoryAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private int _count;
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private SimpleMovingAverage _spyDailySma;
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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, 08); //Set Start Date
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SetEndDate(2013, 10, 11); //Set End Date
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SetCash(100000); //Set Strategy Cash
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// Find more symbols here: http://quantconnect.com/data
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var SPY = AddSecurity(SecurityType.Equity, "SPY", Resolution.Daily).Symbol;
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var CME_SP1 = AddData<QuandlFuture>("CHRIS/CME_SP1", Resolution.Daily).Symbol;
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// specifying the exchange will allow the history methods that accept a number of bars to return to work properly
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Securities["CHRIS/CME_SP1"].Exchange = new EquityExchange();
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// we can get history in initialize to set up indicators and such
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_spyDailySma = new SimpleMovingAverage(14);
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// get the last calendar year's worth of SPY data at the configured resolution (daily)
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var tradeBarHistory = History<TradeBar>("SPY", TimeSpan.FromDays(365));
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AssertHistoryCount("History<TradeBar>(\"SPY\", TimeSpan.FromDays(365))", tradeBarHistory, 250, SPY);
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// get the last calendar day's worth of SPY data at the specified resolution
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tradeBarHistory = History<TradeBar>("SPY", TimeSpan.FromDays(1), Resolution.Minute);
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AssertHistoryCount("History<TradeBar>(\"SPY\", TimeSpan.FromDays(1), Resolution.Minute)", tradeBarHistory, 390, SPY);
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// get the last 14 bars of SPY at the configured resolution (daily)
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tradeBarHistory = History<TradeBar>("SPY", 14).ToList();
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AssertHistoryCount("History<TradeBar>(\"SPY\", 14)", tradeBarHistory, 14, SPY);
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// get the last 14 minute bars of SPY
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tradeBarHistory = History<TradeBar>("SPY", 14, Resolution.Minute);
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AssertHistoryCount("History<TradeBar>(\"SPY\", 14, Resolution.Minute)", tradeBarHistory, 14, SPY);
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// we can loop over the return value from these functions and we get TradeBars
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// we can use these TradeBars to initialize indicators or perform other math
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foreach (TradeBar tradeBar in tradeBarHistory)
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{
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_spyDailySma.Update(tradeBar.EndTime, tradeBar.Close);
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}
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// get the last calendar year's worth of quandl data at the configured resolution (daily)
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var quandlHistory = History<QuandlFuture>("CHRIS/CME_SP1", TimeSpan.FromDays(365));
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AssertHistoryCount("History<Quandl>(\"CHRIS/CME_SP1\", TimeSpan.FromDays(365))", quandlHistory, 250, CME_SP1);
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// get the last 14 bars of SPY at the configured resolution (daily)
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quandlHistory = History<QuandlFuture>("CHRIS/CME_SP1", 14);
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AssertHistoryCount("History<Quandl>(\"CHRIS/CME_SP1\", 14)", quandlHistory, 14, CME_SP1);
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// get the last 14 minute bars of SPY
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// we can loop over the return values from these functions and we'll get Quandl data
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// this can be used in much the same way as the tradeBarHistory above
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_spyDailySma.Reset();
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foreach (QuandlFuture quandl in quandlHistory)
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{
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_spyDailySma.Update(quandl.EndTime, quandl.Value);
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}
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// get the last year's worth of all configured Quandl data at the configured resolution (daily)
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var allQuandlData = History<QuandlFuture>(TimeSpan.FromDays(365));
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AssertHistoryCount("History<QuandlFuture>(TimeSpan.FromDays(365))", allQuandlData, 250, CME_SP1);
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// get the last 14 bars worth of Quandl data for the specified symbols at the configured resolution (daily)
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allQuandlData = History<QuandlFuture>(Securities.Keys, 14);
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AssertHistoryCount("History<QuandlFuture>(Securities.Keys, 14)", allQuandlData, 14, CME_SP1);
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// NOTE: using different resolutions require that they are properly implemented in your data type, since
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// Quandl doesn't support minute data, this won't actually work, but if your custom data source has
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// different resolutions, it would need to be implemented in the GetSource and Reader methods properly
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//quandlHistory = History<QuandlFuture>("CHRIS/CME_SP1", TimeSpan.FromDays(7), Resolution.Minute);
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//quandlHistory = History<QuandlFuture>("CHRIS/CME_SP1", 14, Resolution.Minute);
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//allQuandlData = History<QuandlFuture>(TimeSpan.FromDays(365), Resolution.Minute);
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//allQuandlData = History<QuandlFuture>(Securities.Keys, 14, Resolution.Minute);
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//allQuandlData = History<QuandlFuture>(Securities.Keys, TimeSpan.FromDays(1), Resolution.Minute);
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//allQuandlData = History<QuandlFuture>(Securities.Keys, 14, Resolution.Minute);
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// get the last calendar year's worth of all quandl data
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allQuandlData = History<QuandlFuture>(Securities.Keys, TimeSpan.FromDays(365));
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AssertHistoryCount("History<QuandlFuture>(Securities.Keys, TimeSpan.FromDays(365))", allQuandlData, 250, CME_SP1);
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// the return is a series of dictionaries containing all quandl data at each time
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// we can loop over it to get the individual dictionaries
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foreach (DataDictionary<QuandlFuture> quandlsDataDictionary in allQuandlData)
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{
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// we can access the dictionary to get the quandl data we want
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var quandl = quandlsDataDictionary["CHRIS/CME_SP1"];
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}
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// we can also access the return value from the multiple symbol functions to request a single
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// symbol and then loop over it
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var singleSymbolQuandl = allQuandlData.Get("CHRIS/CME_SP1");
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AssertHistoryCount("allQuandlData.Get(\"CHRIS/CME_SP1\")", singleSymbolQuandl, 250, CME_SP1);
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foreach (QuandlFuture quandl in singleSymbolQuandl)
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{
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// do something with 'CHRIS/CME_SP1' quandl data
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}
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// we can also access individual properties on our data, this will
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// get the 'CHRIS/CME_SP1' quandls like above, but then only return the Low properties
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var quandlSpyLows = allQuandlData.Get("CHRIS/CME_SP1", "Low");
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AssertHistoryCount("allQuandlData.Get(\"CHRIS/CME_SP1\", \"Low\")", quandlSpyLows, 250);
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foreach (decimal low in quandlSpyLows)
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{
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// do something with each low value
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}
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// sometimes it's necessary to get the history for many configured symbols
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// request the last year's worth of history for all configured symbols at their configured resolutions
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var allHistory = History(TimeSpan.FromDays(365));
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AssertHistoryCount("History(TimeSpan.FromDays(365))", allHistory, 250, SPY, CME_SP1);
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// request the last days's worth of history at the minute resolution
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allHistory = History(TimeSpan.FromDays(1), Resolution.Minute);
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AssertHistoryCount("History(TimeSpan.FromDays(1), Resolution.Minute)", allHistory, 391, SPY, CME_SP1);
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// request the last 100 bars for the specified securities at the configured resolution
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allHistory = History(Securities.Keys, 100);
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AssertHistoryCount("History(Securities.Keys, 100)", allHistory, 100, SPY, CME_SP1);
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// request the last 100 minute bars for the specified securities
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allHistory = History(Securities.Keys, 100, Resolution.Minute);
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AssertHistoryCount("History(Securities.Keys, 100, Resolution.Minute)", allHistory, 101, SPY, CME_SP1);
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// request the last calendar years worth of history for the specified securities
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allHistory = History(Securities.Keys, TimeSpan.FromDays(365));
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AssertHistoryCount("History(Securities.Keys, TimeSpan.FromDays(365))", allHistory, 250, SPY, CME_SP1);
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// we can also specify the resolution
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allHistory = History(Securities.Keys, TimeSpan.FromDays(1), Resolution.Minute);
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AssertHistoryCount("History(Securities.Keys, TimeSpan.FromDays(1), Resolution.Minute)", allHistory, 391, SPY, CME_SP1);
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// if we loop over this allHistory, we get Slice objects
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foreach (Slice slice in allHistory)
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{
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// do something with each slice, these will come in time order
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// and will NOT have auxilliary data, just price data and your custom data
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// if those symbols were specified
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}
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// we can access the history for individual symbols from the all history by specifying the symbol
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// the type must be a trade bar!
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tradeBarHistory = allHistory.Get<TradeBar>("SPY");
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AssertHistoryCount("allHistory.Get(\"SPY\")", tradeBarHistory, 390, SPY);
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// we can access all the closing prices in chronological order using this get function
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var closeHistory = allHistory.Get("SPY", Field.Close);
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AssertHistoryCount("allHistory.Get(\"SPY\", Field.Close)", closeHistory, 390);
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foreach (decimal close in closeHistory)
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{
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// do something with each closing value in order
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}
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// we can convert the close history into your normal double array (double[]) using the ToDoubleArray method
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double[] doubleArray = closeHistory.ToDoubleArray();
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// for the purposes of regression testing, we're explicitly requesting history
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// using the universe symbols. Requests for universe symbols are filtered out
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// and never sent to the history provider.
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var universeSecurityHistory = History(UniverseManager.Keys, TimeSpan.FromDays(10)).ToList();
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if (universeSecurityHistory.Count != 0)
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{
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throw new Exception("History request for universe symbols incorrectly returned data. "
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+ "These requests are intended to be filtered out and never sent to the history provider.");
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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="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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_count++;
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if (_count > 5)
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{
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throw new Exception("Invalid number of bars arrived. Expected exactly 5");
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}
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if (!Portfolio.Invested)
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{
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SetHoldings("SPY", 1);
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Debug("Purchased Stock");
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}
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}
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private void AssertHistoryCount<T>(string methodCall, IEnumerable<T> history, int expected, params Symbol[] expectedSymbols)
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{
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history = history.ToList();
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var count = history.Count();
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if (count != expected)
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{
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throw new Exception(methodCall + " expected " + expected + ", but received " + count);
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}
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IEnumerable<Symbol> unexpectedSymbols = null;
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if (typeof(T) == typeof(Slice))
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{
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var slices = (IEnumerable<Slice>) history;
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unexpectedSymbols = slices.SelectMany(slice => slice.Keys)
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.Distinct()
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.Where(sym => !expectedSymbols.Contains(sym))
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.ToList();
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}
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else if (typeof(T).IsGenericType && typeof(T).GetGenericTypeDefinition() == typeof(DataDictionary<>))
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{
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if (typeof(T).GetGenericArguments()[0] == typeof(QuandlFuture))
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{
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var dictionaries = (IEnumerable<DataDictionary<QuandlFuture>>) history;
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unexpectedSymbols = dictionaries.SelectMany(dd => dd.Keys)
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.Distinct()
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.Where(sym => !expectedSymbols.Contains(sym))
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.ToList();
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}
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}
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else if (typeof(IBaseData).IsAssignableFrom(typeof(T)))
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{
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var slices = (IEnumerable<IBaseData>)history;
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unexpectedSymbols = slices.Select(data => data.Symbol)
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.Distinct()
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.Where(sym => !expectedSymbols.Contains(sym))
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.ToList();
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}
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else if (typeof(T) == typeof(decimal))
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{
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// if the enumerable doesn't contain symbols then we can't assert that certain symbols exist
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// this case is used when testing data dictionary extensions that select a property value,
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// such as dataDictionaries.Get("MySymbol", "MyProperty") => IEnumerable<decimal>
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return;
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}
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if (unexpectedSymbols == null)
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{
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throw new Exception("Unhandled case: " + typeof(T).GetBetterTypeName());
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}
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var unexpectedSymbolsString = string.Join(" | ", unexpectedSymbols);
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if (!string.IsNullOrWhiteSpace(unexpectedSymbolsString))
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{
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throw new Exception($"{methodCall} contains unexpected symbols: {unexpectedSymbolsString}");
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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, 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", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "364.889%"},
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{"Drawdown", "1.100%"},
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{"Expectancy", "0"},
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{"Net Profit", "1.698%"},
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{"Sharpe Ratio", "8.904"},
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{"Probabilistic Sharpe Ratio", "67.623%"},
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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", "1.575"},
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{"Beta", "0.072"},
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{"Annual Standard Deviation", "0.218"},
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{"Annual Variance", "0.047"},
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{"Information Ratio", "-11.876"},
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{"Tracking Error", "0.264"},
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{"Treynor Ratio", "26.924"},
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{"Total Fees", "$3.26"},
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{"Fitness Score", "0.251"},
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{"Kelly Criterion Estimate", "0"},
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{"Kelly Criterion Probability Value", "0"},
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{"Sortino Ratio", "79228162514264337593543950335"},
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{"Return Over Maximum Drawdown", "318.537"},
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{"Portfolio Turnover", "0.251"},
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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", "1268340653"}
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};
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/// <summary>
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/// Custom quandl data type for setting customized value column name. Value column is used for the primary trading calculations and charting.
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/// </summary>
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public class QuandlFuture : Quandl
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{
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/// <summary>
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/// Initializes a new instance of the <see cref="QuandlFuture"/> class.
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/// </summary>
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public QuandlFuture()
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: base(valueColumnName: "Settle")
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{
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}
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}
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}
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}
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