Overall Statistics |
Total Trades 153 Average Win 0.04% Average Loss -0.28% Compounding Annual Return -10.337% Drawdown 13.500% Expectancy -0.434 Net Profit -10.329% Sharpe Ratio -0.862 Probabilistic Sharpe Ratio 1.769% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 0.13 Alpha -0.073 Beta -0.07 Annual Standard Deviation 0.093 Annual Variance 0.009 Information Ratio -1.179 Tracking Error 0.156 Treynor Ratio 1.143 Total Fees $20.39 |
from QuantConnect import * from Selection.ManualUniverseSelectionModel import ManualUniverseSelectionModel class ManualCurrencySelectionModel(ManualUniverseSelectionModel): def __init__(self, equities): super().__init__([Symbol.Create(x, SecurityType.Forex, Market.Oanda) for x in equities])
# 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. from clr import AddReference AddReference("QuantConnect.Common") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Algorithm.Framework") AddReference("QuantConnect.Indicators") from QuantConnect import * from QuantConnect.Indicators import * from QuantConnect.Algorithm import * from QuantConnect.Algorithm.Framework import * from QuantConnect.Algorithm.Framework.Alphas import * class EMACrossAlphaModel_History(AlphaModel): '''Alpha model that uses an EMA cross to create insights''' def __init__(self, fastPeriod = 12, slowPeriod = 26, resolution = Resolution.Daily): '''Initializes a new instance of the EmaCrossAlphaModel class Args: fastPeriod: The fast EMA period slowPeriod: The slow EMA period''' self.fastPeriod = fastPeriod self.slowPeriod = slowPeriod self.resolution = resolution self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(resolution), fastPeriod) self.symbolDataBySymbol = {} resolutionString = Extensions.GetEnumString(resolution, Resolution) self.Name = '{}({},{},{})'.format(self.__class__.__name__, fastPeriod, slowPeriod, resolutionString) def Update(self, algorithm, data): '''Updates this alpha model with the latest data from the algorithm. This is called each time the algorithm receives data for subscribed securities Args: algorithm: The algorithm instance data: The new data available Returns: The new insights generated''' insights = [] for symbol, symbolData in self.symbolDataBySymbol.items(): if symbolData.Fast.IsReady and symbolData.Slow.IsReady: if symbolData.FastIsOverSlow: if symbolData.Slow > symbolData.Fast: insights.append(Insight.Price(symbolData.Symbol, self.predictionInterval, InsightDirection.Down)) elif symbolData.SlowIsOverFast: if symbolData.Fast > symbolData.Slow: insights.append(Insight.Price(symbolData.Symbol, self.predictionInterval, InsightDirection.Up)) symbolData.FastIsOverSlow = symbolData.Fast > symbolData.Slow return insights def OnSecuritiesChanged(self, algorithm, changes): '''Event fired each time the we add/remove securities from the data feed Args: algorithm: The algorithm instance that experienced the change in securities changes: The security additions and removals from the algorithm''' for added in changes.AddedSecurities: symbolData = self.symbolDataBySymbol.get(added.Symbol) if symbolData is None: # create fast/slow EMAs symbolData = SymbolData(added) symbolData.Fast = algorithm.EMA(added.Symbol, self.fastPeriod, self.resolution) symbolData.Slow = algorithm.EMA(added.Symbol, self.slowPeriod, self.resolution) self.symbolDataBySymbol[added.Symbol] = symbolData # Populate the EMA with past history history = algorithm.History(added.Symbol, max(self.slowPeriod, self.fastPeriod), self.resolution) for index, row in history.loc[added.Symbol].iterrows(): symbolData.Fast.Update(index, row["close"]) symbolData.Slow.Update(index, row["close"]) #algorithm.Log("Populating history {index} {close}".format(index=index, close=row["close"])) #algorithm.Log("Done populating history") else: # a security that was already initialized was re-added, reset the indicators symbolData.Fast.Reset() symbolData.Slow.Reset() class SymbolData: '''Contains data specific to a symbol required by this model''' def __init__(self, security): self.Security = security self.Symbol = security.Symbol self.Fast = None self.Slow = None # True if the fast is above the slow, otherwise false. # This is used to prevent emitting the same signal repeatedly self.FastIsOverSlow = False @property def SlowIsOverFast(self): return not self.FastIsOverSlow
from Execution.ImmediateExecutionModel import ImmediateExecutionModel from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel from ManualCurrencySelectionModel import ManualCurrencySelectionModel from EMACrossAlphaModel_History import EMACrossAlphaModel_History class UncoupledCalibratedRadiator(QCAlgorithm): def Initialize(self): # Set Start Date so that backtest has 5+ years of data self.SetStartDate(2016, 1, 1) self.SetEndDate(2017, 1, 1) # No need to set End Date as the final submission will be tested # up until the review date # Set $1m Strategy Cash to trade significant AUM self.SetCash(1000000) # Add a relevant benchmark, with the default being SPY #self.AddEquity('SPY') #self.SetBenchmark('SPY') # Use the Alpha Streams Brokerage Model, developed in conjunction with # funds to model their actual fees, costs, etc. # Please do not add any additional reality modelling, such as Slippage, Fees, Buying Power, etc. self.SetBrokerageModel(AlphaStreamsBrokerageModel()) self.AddAlpha(EMACrossAlphaModel_History(50, 200, Resolution.Daily)) self.SetExecution(ImmediateExecutionModel()) self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) self.AddUniverseSelection( ManualCurrencySelectionModel(["AUDCHF"])) def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. Arguments: data: Slice object keyed by symbol containing the stock data ''' # if not self.Portfolio.Invested: # self.SetHoldings("SPY", 1)