Overall Statistics |

Total Trades 36 Average Win 0.22% Average Loss -0.12% Compounding Annual Return 13.890% Drawdown 1.300% Expectancy 0.077 Net Profit 0.166% Sharpe Ratio 1.242 Loss Rate 61% Win Rate 39% Profit-Loss Ratio 1.77 Alpha -0.218 Beta 0.358 Annual Standard Deviation 0.069 Annual Variance 0.005 Information Ratio -6.164 Tracking Error 0.124 Treynor Ratio 0.24 Total Fees $112.67 |

/* * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ using System; using System.Linq; using QuantConnect.Data.Market; using QuantConnect.Indicators; namespace QuantConnect.Algorithm.Examples { /// <summary> /// In this example we look at the canonical 15/30 day moving average cross. This algorithm /// will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses /// back below the 30. /// </summary> public class MovingAverageCrossAlgorithm : QCAlgorithm { private const string Symbol = "SPY"; private DateTime previous; private ExponentialMovingAverage fast; private ExponentialMovingAverage slow; private SimpleMovingAverage[] ribbon; /// <summary> /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized. /// </summary> public override void Initialize() { // set up our analysis span SetStartDate(2013, 10, 07); SetEndDate(2013, 10, 11); // request SPY data with minute resolution AddSecurity(SecurityType.Equity, Symbol, Resolution.Minute); // create a 15 day exponential moving average fast = EMA(Symbol, 15, Resolution.Minute); // create a 30 day exponential moving average slow = EMA(Symbol, 30, Resolution.Minute); int ribbonCount = 8; int ribbonInterval = 15; ribbon = Enumerable.Range(0, ribbonCount).Select(x => SMA(Symbol, (x + 1)*ribbonInterval, Resolution.Minute)).ToArray(); } /// <summary> /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. /// </summary> /// <param name="data">TradeBars IDictionary object with your stock data</param> public void OnData(TradeBars data) { // a couple things to notice in this method: // 1. We never need to 'update' our indicators with the data, the engine takes care of this for us // 2. We can use indicators directly in math expressions // 3. We can easily plot many indicators at the same time // wait for our slow ema to fully initialize if (!slow.IsReady) return; // define a small tolerance on our checks to avoid bouncing const decimal tolerance = 0.00015m; var holdings = Portfolio[Symbol].Quantity; // we only want to go long if we're currently short or flat if (holdings <= 0) { // if the fast is greater than the slow, we'll go long if (fast > slow * (1 + tolerance)) { Log("BUY >> " + Securities[Symbol].Price); SetHoldings(Symbol, 1.0); } } // we only want to liquidate if we're currently long // if the fast is less than the slow we'll liquidate our long if (holdings > 0 && fast < slow) { Log("SELL >> " + Securities[Symbol].Price); Liquidate(Symbol); } Plot(Symbol, "Price", data[Symbol].Price); // easily plot indicators, the series name will be the name of the indicator Plot(Symbol, fast, slow); Plot("Ribbon", ribbon); previous = Time; } } }