Hi QC Community,

I'm getting frustrated & need some help. I have tried cloning many simple algorithms & changing the tickers to accept "BTCUSD" & I cant get a single one of them working without multiple errors one after another.

On another note, I am finding it very difficult to debug errors in QC, running the backtest just throws off huge error scripts but I cant even tell which line the issue might be in. You cant access cryptos in the notebook at the moment so cant use that for testing. Can anyone reccomend a good way for developing, testing & debugging these strategies in python? Should I stick to Jupyter notebooks or try get on Visual Studio?

Thanks

Strategy 1 - Simple Breakout

# 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 ChannelsAlgorithm(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); # Create Channels self.channel = self.DCH("BTCUSD",20,20,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.Securities["BTCUSD"].Price > self.channel.UpperBand: 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.Securities["BTCUSD"].Price < self.channel.LowerBand: self.Log("SELL >> {0}".format(self.Securities["BTCUSD"].Price)) self.Liquidate("BTCUSD") self.previous = self.Time

Strategy 2:

from datetime import datetime 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 from datetime import timedelta class DualThrustAlgorithm(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(2017,01,01) self.SetEndDate(2017,8,30) self.SetCash(100000) equityt = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour) # equity = self.AddSecurity(SecurityType.Crypto, "BTCUSD", Resolution.Hour) equity = self.AddCrypto("BTCUSD", Resolution.Daily) self.syls = equity.Symbol # schedule an event to fire every trading day for a security # the time rule here tells it to fire when market open # self.syl = equity.Symbol self.syl = "BTCUSD" # self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,5),Action(self.SetSignal)) self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.Every(timedelta(minutes=10)),Action(self.SetSignal)) self.selltrig = None self.buytrig = None self.currentopen = None def SetSignal(self): history = self.History("BTCUSD", 4, Resolution.Daily) k1 = 0.5 k2 = 0.5 self.high = [] self.low = [] self.close = [] for slice in history: bar = slice[self.syl] self.high.append(bar.High) self.low.append(bar.Low) self.close.append(bar.Close) # Pull the open price on each trading day self.currentopen = self.Portfolio[self.syl].Price HH, HC, LC, LL = max(self.high), max(self.close), min(self.close), min(self.low) if HH - LC >= HC - LL: signalrange = HH - LC else: signalrange = HC - LL self.selltrig = self.currentopen - decimal.Decimal(k1) * signalrange self.buytrig = self.currentopen + decimal.Decimal(k2) * signalrange def OnData(self,data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.''' holdings = self.Portfolio[self.syl].Quantity if self.Portfolio[self.syl].Price >= self.selltrig: if holdings >= 0: self.SetHoldings(self.syl, 0.8) else: self.Liquidate(self.syl) self.SetHoldings(self.syl, 0.8) elif self.Portfolio[self.syl].Price < self.selltrig: if holdings >= 0: self.Liquidate(self.syl) self.SetHoldings(self.syl, -0.8) else: self.SetHoldings(self.syl, -0.8) self.Log("open: "+ str(self.currentopen)+" buy: "+str(self.buytrig)+" sell: "+str(self.selltrig))