Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
87 lines
3.7 KiB
Python
87 lines
3.7 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.Common")
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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.Securities import *
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from datetime import timedelta
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import decimal as d
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import numpy as np
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### <summary>
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### EMA cross with SP500 E-mini futures
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### In this example, we demostrate how to trade futures contracts using
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### a equity to generate the trading signals
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### It also shows how you can prefilter contracts easily based on expirations.
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### It also shows how you can inspect the futures chain to pick a specific contract to trade.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="futures" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="strategy example" />
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class FuturesMomentumAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2016, 1, 1)
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self.SetEndDate(2016, 8, 18)
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self.SetCash(100000)
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fastPeriod = 20
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slowPeriod = 60
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self._tolerance = d.Decimal(1 + 0.001)
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self.IsUpTrend = False
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self.IsDownTrend = False
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self.SetWarmUp(max(fastPeriod, slowPeriod))
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# Adds SPY to be used in our EMA indicators
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equity = self.AddEquity("SPY", Resolution.Daily)
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self._fast = self.EMA(equity.Symbol, fastPeriod, Resolution.Daily)
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self._slow = self.EMA(equity.Symbol, slowPeriod, Resolution.Daily)
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# Adds the future that will be traded and
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# set our expiry filter for this futures chain
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future = self.AddFuture(Futures.Indices.SP500EMini)
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future.SetFilter(timedelta(0), timedelta(182))
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def OnData(self, slice):
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if self._slow.IsReady and self._fast.IsReady:
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self.IsUpTrend = self._fast.Current.Value > self._slow.Current.Value * self._tolerance
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self.IsDownTrend = self._fast.Current.Value < self._slow.Current.Value * self._tolerance
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if (not self.Portfolio.Invested) and self.IsUpTrend:
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for chain in slice.FuturesChains:
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# find the front contract expiring no earlier than in 90 days
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contracts = filter(lambda x: x.Expiry > self.Time + timedelta(90), chain.Value)
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# if there is any contract, trade the front contract
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if len(contracts) == 0: continue
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contract = sorted(contracts, key = lambda x: x.Expiry, reverse=True)[0]
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self.MarketOrder(contract.Symbol , 1)
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if self.Portfolio.Invested and self.IsDownTrend:
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self.Liquidate()
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def OnEndOfDay(self):
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if self.IsUpTrend:
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self.Plot("Indicator Signal", "EOD",1)
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elif self.IsDownTrend:
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self.Plot("Indicator Signal", "EOD",-1)
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elif self._slow.IsReady and self._fast.IsReady:
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self.Plot("Indicator Signal", "EOD",0)
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def OnOrderEvent(self, orderEvent):
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self.Log(str(orderEvent)) |