Merge branch 'master' into feature-2950-adds-csharp-alpha-stream-examples
This commit is contained in:
Jared
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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
|
||||
# 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.
|
||||
|
||||
'''
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Energy prices, especially Oil and Natural Gas, are in general fairly correlated,
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meaning they typically move in the same direction as an overall trend. This Alpha
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uses this idea and implements an Alpha Model that takes Natural Gas ETF price
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movements as a leading indicator for Crude Oil ETF price movements. We take the
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Natural Gas/Crude Oil ETF pair with the highest historical price correlation and
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then create insights for Crude Oil depending on whether or not the Natural Gas ETF price change
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is above/below a certain threshold that we set (arbitrarily).
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||||
|
||||
|
||||
|
||||
This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
|
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sourced so the community and client funds can see an example of an alpha.
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||||
'''
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||||
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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Indicators")
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AddReference("QuantConnect.Algorithm.Framework")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Indicators import *
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from QuantConnect.Algorithm.Framework import *
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from QuantConnect.Algorithm.Framework.Risk import *
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Algorithm.Framework.Execution import *
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from QuantConnect.Algorithm.Framework.Portfolio import *
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from QuantConnect.Algorithm.Framework.Selection import *
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import numpy as np
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from scipy import stats
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from scipy.stats import kendalltau
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from datetime import timedelta, datetime
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class EnergyETFPairsTradingAlgorithm(QCAlgorithmFramework):
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def Initialize(self):
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self.SetStartDate(2018, 1, 1) #Set Start Date
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self.SetCash(100000) #Set Strategy Cash
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natural_gas = ['UNG','BOIL','FCG']
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crude_oil = ['USO','UCO','DBO']
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symbols = [ Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in natural_gas + crude_oil ]
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## Set Universe Selection
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self.UniverseSettings.Resolution = Resolution.Minute
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self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
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self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
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## Custom Alpha Model
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self.SetAlpha(PairsAlphaModel(leading = natural_gas, following = crude_oil, history_days = 90, resolution = Resolution.Minute))
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## Equal-weight our positions, in this case 100% in USO
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(resolution = Resolution.Minute))
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## Immediate Execution Fill Model
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self.SetExecution(CustomExecutionModel())
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## Null Risk-Management Model
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self.SetRiskManagement(NullRiskManagementModel())
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def OnOrderEvent(self, orderEvent):
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if orderEvent.Status == OrderStatus.Filled:
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self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol))
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def OnEndOfAlgorithm(self):
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for kvp in self.Portfolio:
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if self.Portfolio[kvp.Key].Invested:
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self.Log('Invested in: ' + str(kvp.Key))
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class PairsAlphaModel:
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'''This Alpha model assumes that the ETF for natural gas is a good leading-indicator
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of the price of the crude oil ETF. The model will take in arguments for a threshold
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at which the model triggers an insight, the length of the look-back period for evaluating
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rate-of-change of UNG prices, and the duration of the insight'''
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def __init__(self, *args, **kwargs):
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self.difference_trigger = kwargs['difference_trigger'] if 'difference_trigger' in kwargs else 0.75
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self.lookback = kwargs['lookback'] if 'lookback' in kwargs else 5
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self.history_days = kwargs['history_days'] if 'history_days' in kwargs else 90 ## In days
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self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Hour
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self.prediction_interval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), 5) ## Arbitrary
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self.symbolDataBySymbol = {}
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self.next_update = None
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self.leading = kwargs['leading'] if 'leading' in kwargs else None
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self.following = kwargs['following'] if 'following' in kwargs else None
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self.tickers = self.leading + self.following
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self.ticker_list_of_lists = [[],[]]
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for i in range(len(self.leading)):
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for j in range(len(self.following)):
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self.ticker_list_of_lists[0].append(self.leading[i])
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self.ticker_list_of_lists[1] = self.following * 3
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def Update(self, algorithm, data):
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if (self.next_update is None) or (algorithm.Time > self.next_update):
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self.pairs = self.CorrelationPairsSelection(algorithm)
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self.next_update = algorithm.Time + (timedelta(days = 30))
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## Build a list to hold our insights
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insights = []
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## These lists hold the Symbol for the following ETF and data from the leading ETF that we need to pass into the Insight() constructor
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leading_data = []
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following_symbol = []
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for symbol, symbolData in self.symbolDataBySymbol.items():
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if symbol.Value == self.pairs[0]:
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leading_data.append(symbolData) ## Add symbol data if it's Natural Gas
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elif symbol.Value == self.pairs[1]:
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following_symbol.append(symbol) ## Stash the Symbol object if it's Crude Oil
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for i in range(len(following_symbol)):
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symbolData = leading_data[i]
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if symbolData.Return > self.difference_trigger: ## Check if Natural Gas returns are greater than the threshold we've set
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## If so, create and Insight with this information for Crude Oil
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insights.append(Insight(following_symbol[i], self.prediction_interval, InsightType.Price, InsightDirection.Up, symbolData.Return/100, None))
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elif symbolData.Return < -self.difference_trigger: ## Check if UNG returns are greater than the threshold we've set
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insights.append(Insight(following_symbol[i], self.prediction_interval, InsightType.Price, InsightDirection.Down, symbolData.Return/100, None))
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return insights
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def CorrelationPairsSelection(self, algorithm):
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tick_syl = self.ticker_list_of_lists
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tickers = self.tickers
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logreturn={}
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## Get log returns for each natural gas/oil ETF pair
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unique = list(set([item for sublist in self.ticker_list_of_lists for item in sublist]))
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df = algorithm.History(unique, self.history_days, Resolution.Daily)
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df = df['close'].unstack(level=0)
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df = (np.log(df) - np.log(df.shift(1))).dropna()
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for tick in tickers:
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logreturn[tick] = df[[tick]]
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## Estimate coefficients of different correlation measures
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tau_coef = []
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for i in range(len(tick_syl[0])):
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tik_x, tik_y= logreturn[tick_syl[0][i]], logreturn[tick_syl[1][i]]
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min_length = min(len(tik_x), len(tik_y))
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tau_coef.append(kendalltau(tik_x[:min_length], tik_y[:min_length])[0])
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index_max = tau_coef.index(max(tau_coef))
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pair = [tick_syl[0][index_max],tick_syl[1][index_max]]
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## Return the pair with highest historical correlation
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return pair
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def OnSecuritiesChanged(self, algorithm, changes):
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'''Event fired each time the we add/remove securities from the data feed
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Args:
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algorithm: The algorithm instance that experienced the change in securities
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changes: The security additions and removals from the algorithm'''
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for removed in changes.RemovedSecurities:
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algorithm.Log('Removed: ' + str(removed.Symbol))
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symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None)
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if symbolData is not None:
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symbolData.RemoveConsolidators(algorithm)
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# initialize data for added securities
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symbols = [ x.Symbol for x in changes.AddedSecurities ]
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history = algorithm.History(symbols, self.lookback, self.resolution)
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if history.empty: return
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tickers = history.index.levels[0]
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for ticker in tickers:
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symbol = SymbolCache.GetSymbol(ticker)
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if symbol not in self.symbolDataBySymbol:
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symbolData = SymbolData(symbol, self.lookback, self.resolution, algorithm)
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self.symbolDataBySymbol[symbol] = symbolData
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symbolData.WarmUpIndicators(history.loc[ticker])
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class SymbolData:
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'''Contains data specific to a symbol required by this model'''
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def __init__(self, symbol, lookback, resolution, algorithm):
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self.Symbol = symbol
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self.ROCP = RateOfChangePercent('{}.ROCP({})'.format(symbol, lookback), lookback)
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self.Consolidator = algorithm.ResolveConsolidator(self.Symbol, resolution)
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algorithm.RegisterIndicator(symbol, self.ROCP, self.Consolidator)
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self.previous = 0
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def RemoveConsolidators(self, algorithm):
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if self.Consolidator is not None:
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algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.Consolidator)
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def WarmUpIndicators(self, history):
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for tuple in history.itertuples():
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self.ROCP.Update(tuple.Index, tuple.close)
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@property
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def Return(self):
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return float(self.ROCP.Current.Value)
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@property
|
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def CanEmit(self):
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if self.previous == self.ROCP.Samples:
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return False
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self.previous = self.ROCP.Samples
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return self.ROCP.IsReady
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def __str__(self, **kwargs):
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return '{}: {:.2%}'.format(self.ROCP.Name, (1 + self.Return)**252 - 1)
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class CustomExecutionModel(ExecutionModel):
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'''Provides an implementation of IExecutionModel that immediately submits market orders to achieve the desired portfolio targets'''
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def __init__(self):
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'''Initializes a new instance of the ImmediateExecutionModel class'''
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self.targetsCollection = PortfolioTargetCollection()
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self.previous_symbol = None
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def Execute(self, algorithm, targets):
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'''Immediately submits orders for the specified portfolio targets.
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Args:
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algorithm: The algorithm instance
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targets: The portfolio targets to be ordered'''
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self.targetsCollection.AddRange(targets)
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for target in self.targetsCollection.OrderByMarginImpact(algorithm):
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open_quantity = sum([x.Quantity for x in algorithm.Transactions.GetOpenOrders(target.Symbol)])
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existing = algorithm.Securities[target.Symbol].Holdings.Quantity + open_quantity
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quantity = target.Quantity - existing
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## Liquidate positions in Crude Oil ETF that is no longer part of the highest-correlation pair
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if (str(target.Symbol) != str(self.previous_symbol)) and (self.previous_symbol is not None):
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algorithm.Liquidate(self.previous_symbol)
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if quantity != 0:
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algorithm.MarketOrder(target.Symbol, quantity)
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self.previous_symbol = target.Symbol
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self.targetsCollection.ClearFulfilled(algorithm)
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@@ -0,0 +1,254 @@
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# 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("System")
|
||||
AddReference("QuantConnect.Common")
|
||||
AddReference("QuantConnect.Algorithm")
|
||||
AddReference("QuantConnect.Indicators")
|
||||
AddReference("QuantConnect.Algorithm.Framework")
|
||||
|
||||
from System import *
|
||||
from QuantConnect import *
|
||||
from QuantConnect.Orders.Fees import ConstantFeeModel
|
||||
from QuantConnect.Data.UniverseSelection import *
|
||||
from QuantConnect.Indicators import *
|
||||
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
|
||||
|
||||
from datetime import timedelta, datetime
|
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from math import ceil
|
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from itertools import chain
|
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|
||||
#
|
||||
# This alpha picks stocks according to Joel Greenblatt's Magic Formula.
|
||||
# First, each stock is ranked depending on the relative value of the ratio EV/EBITDA. For example, a stock
|
||||
# that has the lowest EV/EBITDA ratio in the security universe receives a score of one while a stock that has
|
||||
# the tenth lowest EV/EBITDA score would be assigned 10 points.
|
||||
#
|
||||
# Then, each stock is ranked and given a score for the second valuation ratio, Return on Capital (ROC).
|
||||
# Similarly, a stock that has the highest ROC value in the universe gets one score point.
|
||||
# The stocks that receive the lowest combined score are chosen for insights.
|
||||
#
|
||||
# Source: Greenblatt, J. (2010) The Little Book That Beats the Market
|
||||
#
|
||||
# This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
|
||||
# sourced so the community and client funds can see an example of an alpha.
|
||||
#
|
||||
|
||||
class GreenblattMagicFormulaAlgorithm(QCAlgorithmFramework):
|
||||
''' Alpha Streams: Benchmark Alpha: Pick stocks according to Joel Greenblatt's Magic Formula'''
|
||||
|
||||
def Initialize(self):
|
||||
|
||||
self.SetStartDate(2018, 1, 1)
|
||||
self.SetCash(100000)
|
||||
|
||||
#Set zero transaction fees
|
||||
self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
|
||||
|
||||
# select stocks using MagicFormulaUniverseSelectionModel
|
||||
self.SetUniverseSelection(GreenBlattMagicFormulaUniverseSelectionModel())
|
||||
|
||||
# Use MagicFormulaAlphaModel to establish insights
|
||||
self.SetAlpha(RateOfChangeAlphaModel())
|
||||
|
||||
# Equally weigh securities in portfolio, based on insights
|
||||
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
|
||||
|
||||
## Set Immediate Execution Model
|
||||
self.SetExecution(ImmediateExecutionModel())
|
||||
|
||||
## Set Null Risk Management Model
|
||||
self.SetRiskManagement(NullRiskManagementModel())
|
||||
|
||||
|
||||
class RateOfChangeAlphaModel(AlphaModel):
|
||||
'''Uses Rate of Change (ROC) to create magnitude prediction for insights.'''
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
self.lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
|
||||
self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Daily
|
||||
self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), self.lookback)
|
||||
self.symbolDataBySymbol = {}
|
||||
|
||||
def Update(self, algorithm, data):
|
||||
insights = []
|
||||
for symbol, symbolData in self.symbolDataBySymbol.items():
|
||||
if symbolData.CanEmit:
|
||||
insights.append(Insight.Price(symbol, self.predictionInterval, InsightDirection.Up, symbolData.Return, None))
|
||||
return insights
|
||||
|
||||
def OnSecuritiesChanged(self, algorithm, changes):
|
||||
|
||||
# clean up data for removed securities
|
||||
for removed in changes.RemovedSecurities:
|
||||
symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None)
|
||||
if symbolData is not None:
|
||||
symbolData.RemoveConsolidators(algorithm)
|
||||
|
||||
# initialize data for added securities
|
||||
symbols = [ x.Symbol for x in changes.AddedSecurities ]
|
||||
history = algorithm.History(symbols, self.lookback, self.resolution)
|
||||
if history.empty: return
|
||||
|
||||
tickers = history.index.levels[0]
|
||||
for ticker in tickers:
|
||||
symbol = SymbolCache.GetSymbol(ticker)
|
||||
|
||||
if symbol not in self.symbolDataBySymbol:
|
||||
symbolData = SymbolData(symbol, self.lookback)
|
||||
self.symbolDataBySymbol[symbol] = symbolData
|
||||
symbolData.RegisterIndicators(algorithm, self.resolution)
|
||||
symbolData.WarmUpIndicators(history.loc[ticker])
|
||||
|
||||
|
||||
class SymbolData:
|
||||
'''Contains data specific to a symbol required by this model'''
|
||||
def __init__(self, symbol, lookback):
|
||||
self.Symbol = symbol
|
||||
self.ROC = RateOfChange('{}.ROC({})'.format(symbol, lookback), lookback)
|
||||
self.Consolidator = None
|
||||
self.previous = 0
|
||||
|
||||
def RegisterIndicators(self, algorithm, resolution):
|
||||
self.Consolidator = algorithm.ResolveConsolidator(self.Symbol, resolution)
|
||||
algorithm.RegisterIndicator(self.Symbol, self.ROC, self.Consolidator)
|
||||
|
||||
def RemoveConsolidators(self, algorithm):
|
||||
if self.Consolidator is not None:
|
||||
algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.Consolidator)
|
||||
|
||||
def WarmUpIndicators(self, history):
|
||||
for tuple in history.itertuples():
|
||||
self.ROC.Update(tuple.Index, tuple.close)
|
||||
|
||||
@property
|
||||
def Return(self):
|
||||
return float(self.ROC.Current.Value)
|
||||
|
||||
@property
|
||||
def CanEmit(self):
|
||||
if self.previous == self.ROC.Samples:
|
||||
return False
|
||||
|
||||
self.previous = self.ROC.Samples
|
||||
return self.ROC.IsReady
|
||||
|
||||
def __str__(self, **kwargs):
|
||||
return '{}: {:.2%}'.format(self.ROC.Name, (1 + self.Return)**252 - 1)
|
||||
|
||||
|
||||
class GreenBlattMagicFormulaUniverseSelectionModel(FundamentalUniverseSelectionModel):
|
||||
'''Defines a universe according to Joel Greenblatt's Magic Formula, as a universe selection model for the framework algorithm.
|
||||
From the universe QC500, stocks are ranked using the valuation ratios, Enterprise Value to EBITDA (EV/EBITDA) and Return on Assets (ROA).
|
||||
'''
|
||||
|
||||
def __init__(self,
|
||||
filterFineData = True,
|
||||
universeSettings = None,
|
||||
securityInitializer = None):
|
||||
'''Initializes a new default instance of the MagicFormulaUniverseSelectionModel'''
|
||||
super().__init__(filterFineData, universeSettings, securityInitializer)
|
||||
|
||||
# Number of stocks in Coarse Universe
|
||||
self.NumberOfSymbolsCoarse = 500
|
||||
# Number of sorted stocks in the fine selection subset using the valuation ratio, EV to EBITDA (EV/EBITDA)
|
||||
self.NumberOfSymbolsFine = 20
|
||||
# Final number of stocks in security list, after sorted by the valuation ratio, Return on Assets (ROA)
|
||||
self.NumberOfSymbolsInPortfolio = 10
|
||||
|
||||
self.lastMonth = -1
|
||||
self.dollarVolumeBySymbol = {}
|
||||
self.symbols = []
|
||||
|
||||
def SelectCoarse(self, algorithm, coarse):
|
||||
'''Performs coarse selection for constituents.
|
||||
The stocks must have fundamental data
|
||||
The stock must have positive previous-day close price
|
||||
The stock must have positive volume on the previous trading day'''
|
||||
|
||||
month = algorithm.Time.month
|
||||
if month == self.lastMonth:
|
||||
return self.symbols
|
||||
|
||||
self.lastMonth = month
|
||||
|
||||
# The stocks must have fundamental data
|
||||
# The stock must have positive previous-day close price
|
||||
# The stock must have positive volume on the previous trading day
|
||||
filtered = [x for x in coarse if x.HasFundamentalData
|
||||
and x.Volume > 0
|
||||
and x.Price > 0]
|
||||
# sort the stocks by dollar volume and take the top 1000
|
||||
top = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)[:self.NumberOfSymbolsCoarse]
|
||||
|
||||
self.dollarVolumeBySymbol = { i.Symbol: i.DollarVolume for i in top }
|
||||
|
||||
self.symbols = list(self.dollarVolumeBySymbol.keys())
|
||||
|
||||
return self.symbols
|
||||
|
||||
|
||||
def SelectFine(self, algorithm, fine):
|
||||
'''QC500: Performs fine selection for the coarse selection constituents
|
||||
The company's headquarter must in the U.S.
|
||||
The stock must be traded on either the NYSE or NASDAQ
|
||||
At least half a year since its initial public offering
|
||||
The stock's market cap must be greater than 500 million
|
||||
|
||||
|
||||
Magic Formula: Rank stocks by Enterprise Value to EBITDA (EV/EBITDA)
|
||||
Rank subset of previously ranked stocks (EV/EBITDA), using the valuation ratio Return on Assets (ROA)'''
|
||||
|
||||
# QC500:
|
||||
## The company's headquarter must in the U.S.
|
||||
## The stock must be traded on either the NYSE or NASDAQ
|
||||
## At least half a year since its initial public offering
|
||||
## The stock's market cap must be greater than 500 million
|
||||
filteredFine = [x for x in fine if x.CompanyReference.CountryId == "USA"
|
||||
and (x.CompanyReference.PrimaryExchangeID == "NYS" or x.CompanyReference.PrimaryExchangeID == "NAS")
|
||||
and (algorithm.Time - x.SecurityReference.IPODate).days > 180
|
||||
and x.EarningReports.BasicAverageShares.ThreeMonths * x.EarningReports.BasicEPS.TwelveMonths * x.ValuationRatios.PERatio > 5e8]
|
||||
count = len(filteredFine)
|
||||
if count == 0: return []
|
||||
|
||||
myDict = dict()
|
||||
percent = float(self.NumberOfSymbolsFine / count)
|
||||
|
||||
# select stocks with top dollar volume in every single sector
|
||||
for key in ["N", "M", "U", "T", "B", "I"]:
|
||||
value = [x for x in filteredFine if x.CompanyReference.IndustryTemplateCode == key]
|
||||
value = sorted(value, key=lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse = True)
|
||||
myDict[key] = value[:ceil(len(value) * percent)]
|
||||
|
||||
|
||||
# stocks in QC500 universe
|
||||
topFine = list(chain.from_iterable(myDict.values()))[:self.NumberOfSymbolsCoarse]
|
||||
|
||||
|
||||
# Magic Formula:
|
||||
## Rank stocks by Enterprise Value to EBITDA (EV/EBITDA)
|
||||
## Rank subset of previously ranked stocks (EV/EBITDA), using the valuation ratio Return on Assets (ROA)
|
||||
|
||||
|
||||
# sort stocks in the security universe of QC500 based on Enterprise Value to EBITDA valuation ratio
|
||||
sortedByEVToEBITDA = sorted(topFine, key=lambda x: x.ValuationRatios.EVToEBITDA , reverse=True)
|
||||
|
||||
# sort subset of stocks that have been sorted by Enterprise Value to EBITDA, based on the valuation ratio Return on Assets (ROA)
|
||||
sortedByROA = sorted(sortedByEVToEBITDA[:self.NumberOfSymbolsFine], key=lambda x: x.ValuationRatios.ForwardROA, reverse=False)
|
||||
|
||||
# retrieve list of securites in portfolio
|
||||
top = sortedByROA[:self.NumberOfSymbolsInPortfolio]
|
||||
self.symbols = [f.Symbol for f in top]
|
||||
|
||||
return self.symbols
|
||||
@@ -1,4 +1,4 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<Project ToolsVersion="12.0" DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
|
||||
<Import Project="$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props" Condition="Exists('$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props')" />
|
||||
<PropertyGroup>
|
||||
@@ -37,8 +37,10 @@
|
||||
</PropertyGroup>
|
||||
<ItemGroup>
|
||||
<Content Include="Alphas\ForexCalendarAlpha.py" />
|
||||
<Content Include="Alphas\GasAndCrudeOilEnergyCorrelationAlpha.py" />
|
||||
<Content Include="Alphas\GlobalEquityMeanReversionIBSAlpha.py" />
|
||||
<Content Include="Alphas\IntradayReversalCurrencyMarketsAlpha.py" />
|
||||
<Content Include="Alphas\GreenblattMagicFormulaAlgorithm.py" />
|
||||
<Content Include="Alphas\MeanReversionLunchBreakAlpha.py" />
|
||||
<Content Include="Alphas\SykesShortMicroCapAlpha.py" />
|
||||
<Content Include="Alphas\RebalancingLeveragedETFAlpha.py" />
|
||||
|
||||
Reference in New Issue
Block a user