Michael Handschuh
18a559943e
- Adds log to display the python version the algorithm is using. - Fixes python algorithms that were failing because of small subtleties like leading zeroes. - Updates pythonnet with a version compiled with python 3.6 flags - Changes in DockerfileFoundation: we now use miniconda to manage the python environment. - Took the opportunity to add NTLK (#1349), Tensorforce (#1369) and PyTorch/Pyro (#1385). - Changes readme in Algorithm.Python to show steps to install miniconda
108 lines
4.3 KiB
Python
108 lines
4.3 KiB
Python
# 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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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Indicators")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Data import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Indicators import *
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from System.Collections.Generic import List
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import decimal as d
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### <summary>
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### In this algorithm we demonstrate how to perform some technical analysis as
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### part of your coarse fundamental universe selection
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="universes" />
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### <meta name="tag" content="coarse universes" />
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class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):
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def Initialize(self):
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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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self.SetStartDate(2010,1,1) #Set Start Date
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self.SetEndDate(2015,1,1) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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self.UniverseSettings.Resolution = Resolution.Daily
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self.UniverseSettings.Leverage = 2
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self.coarse_count = 10
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self.averages = { };
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# this add universe method accepts two parameters:
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# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
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self.AddUniverse(self.CoarseSelectionFunction)
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# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
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def CoarseSelectionFunction(self, coarse):
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# We are going to use a dictionary to refer the object that will keep the moving averages
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for cf in coarse:
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if cf.Symbol not in self.averages:
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self.averages[cf.Symbol] = SymbolData(cf.Symbol)
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# Updates the SymbolData object with current EOD price
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avg = self.averages[cf.Symbol]
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avg.update(cf.EndTime, cf.Price)
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# Filter the values of the dict: we only want up-trending securities
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values = list(filter(lambda x: x.is_uptrend, self.averages.values()))
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# Sorts the values of the dict: we want those with greater difference between the moving averages
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values.sort(key=lambda x: x.scale, reverse=True)
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for x in values[:self.coarse_count]:
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self.Log('symbol: ' + str(x.symbol.Value) + ' scale: ' + str(x.scale))
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# we need to return only the symbol objects
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return [ x.symbol for x in values[:self.coarse_count] ]
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# this event fires whenever we have changes to our universe
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def OnSecuritiesChanged(self, changes):
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# liquidate removed securities
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for security in changes.RemovedSecurities:
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if security.Invested:
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self.Liquidate(security.Symbol)
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# we want 20% allocation in each security in our universe
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for security in changes.AddedSecurities:
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self.SetHoldings(security.Symbol, 0.1)
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class SymbolData(object):
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def __init__(self, symbol):
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self.symbol = symbol
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self.tolerance = d.Decimal(1.01)
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self.fast = ExponentialMovingAverage(100)
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self.slow = ExponentialMovingAverage(300)
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self.is_uptrend = False
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self.scale = 0
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def update(self, time, value):
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if self.fast.Update(time, value) and self.slow.Update(time, value):
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fast = self.fast.Current.Value
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slow = self.slow.Current.Value
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self.is_uptrend = fast > slow * self.tolerance
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if self.is_uptrend:
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self.scale = (fast - slow) / ((fast + slow) / d.Decimal(2.0)) |