Overall Statistics |

Total Trades 5 Average Win 45.46% Average Loss 0% Compounding Annual Return 278.033% Drawdown 40.200% Expectancy 0 Net Profit 995.187% Sharpe Ratio 1.912 Loss Rate 0% Win Rate 100% Profit-Loss Ratio 0 Alpha 1.054 Beta 0.234 Annual Standard Deviation 0.562 Annual Variance 0.316 Information Ratio 1.739 Tracking Error 0.566 Treynor Ratio 4.604 Total Fees $1966.93 |

# 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. import clr clr.AddReference("System") clr.AddReference("QuantConnect.Algorithm") clr.AddReference("QuantConnect.Indicators") clr.AddReference("QuantConnect.Common") from System import * import numpy as np from QuantConnect import * from QuantConnect.Algorithm import * from QuantConnect.Indicators import * import decimal as d ### <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> ### <meta name="tag" content="indicators" /> ### <meta name="tag" content="indicator classes" /> ### <meta name="tag" content="moving average cross" /> ### <meta name="tag" content="strategy example" /> class MovingAverageCrossAlgorithm(QCAlgorithm): def Initialize(self): '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.''' self.SetStartDate(2016, 01, 01) #Set Start Date self.SetEndDate(2017, 10, 19) #Set End Date self.SetCash(10000) #Set Strategy Cash # Find more symbols here: http://quantconnect.com/data self.SetBrokerageModel(BrokerageName.GDAX) self.AddCrypto("BTCUSD", Resolution.Hour) # create a 15 day exponential moving average self.fast = self.EMA("BTCUSD", 15, Resolution.Daily); # create a 30 day exponential moving average self.slow = self.EMA("BTCUSD", 70, Resolution.Daily); self.previous = None def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.''' # 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 not self.slow.IsReady: return # only once per day if self.previous is not None and self.previous.date() == self.Time.date(): return # define a small tolerance on our checks to avoid bouncing tolerance = 0.00015; holdings = self.Portfolio["BTCUSD"].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 self.fast.Current.Value > self.slow.Current.Value * d.Decimal(1 + tolerance): self.Log("BUY >> {0}".format(self.Securities["BTCUSD"].Price)) self.SetHoldings("BTCUSD", 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 and self.fast.Current.Value < self.slow.Current.Value: self.Log("SELL >> {0}".format(self.Securities["BTCUSD"].Price)) self.Liquidate("BTCUSD") self.previous = self.Time