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.
123 lines
4.8 KiB
Python
123 lines
4.8 KiB
Python
# 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.Algorithm")
|
|
AddReference("QuantConnect.Common")
|
|
|
|
from System import *
|
|
from QuantConnect import *
|
|
from QuantConnect.Algorithm import QCAlgorithm
|
|
from QuantConnect.Data import SubscriptionDataSource
|
|
from QuantConnect.Python import PythonData
|
|
from datetime import date, timedelta, datetime
|
|
import decimal as d
|
|
|
|
### <summary>
|
|
### This algorithm shows how to grab symbols from an external api each day
|
|
### and load data using the universe selection feature. In this example we
|
|
### define a custom data type for the NYSE top gainers and then short the
|
|
### top 2 gainers each day
|
|
### </summary>
|
|
### <meta name="tag" content="using data" />
|
|
### <meta name="tag" content="universes" />
|
|
### <meta name="tag" content="custom universes" />
|
|
class CustomDataUniverseAlgorithm(QCAlgorithm):
|
|
|
|
def Initialize(self):
|
|
|
|
# Data ADDED via universe selection is added with Daily resolution.
|
|
self.UniverseSettings.Resolution = Resolution.Daily
|
|
|
|
self.SetStartDate(2015,1,5)
|
|
self.SetEndDate(2015,7,1)
|
|
self.SetCash(100000)
|
|
|
|
self.AddEquity("SPY", Resolution.Daily)
|
|
self.SetBenchmark("SPY")
|
|
|
|
# add a custom universe data source (defaults to usa-equity)
|
|
self.AddUniverse(NyseTopGainers, "universe-nyse-top-gainers", Resolution.Daily, self.nyseTopGainers)
|
|
|
|
def nyseTopGainers(self, data):
|
|
return [ x.Symbol for x in data if x["TopGainersRank"] <= 2 ]
|
|
|
|
|
|
def OnData(self, slice):
|
|
pass
|
|
|
|
def OnSecuritiesChanged(self, changes):
|
|
self._changes = changes
|
|
|
|
for security in changes.RemovedSecurities:
|
|
# liquidate securities that have been removed
|
|
if security.Invested:
|
|
self.Liquidate(security.Symbol)
|
|
self.Log("Exit {0} at {1}".format(security.Symbol, security.Close))
|
|
|
|
for security in changes.AddedSecurities:
|
|
# enter short positions on new securities
|
|
if not security.Invested and security.Close != 0:
|
|
qty = self.CalculateOrderQuantity(security.Symbol, -0.25)
|
|
self.MarketOnOpenOrder(security.Symbol, qty)
|
|
self.Log("Enter {0} at {1}".format(security.Symbol, security.Close))
|
|
|
|
|
|
class NyseTopGainers(PythonData):
|
|
def __init__(self):
|
|
self.count = 0
|
|
self.last_date = datetime.min
|
|
|
|
def GetSource(self, config, date, isLiveMode):
|
|
url = "http://www.wsj.com/mdc/public/page/2_3021-gainnyse-gainer.html" if isLiveMode else \
|
|
"https://www.dropbox.com/s/vrn3p38qberw3df/nyse-gainers.csv?dl=1"
|
|
|
|
return SubscriptionDataSource(url, SubscriptionTransportMedium.RemoteFile)
|
|
|
|
def Reader(self, config, line, date, isLiveMode):
|
|
|
|
if not isLiveMode:
|
|
# backtest gets data from csv file in dropbox
|
|
if not (line.strip() and line[0].isdigit()): return None
|
|
csv = line.split(',')
|
|
nyse = NyseTopGainers()
|
|
nyse.Time = datetime.strptime(csv[0], "%Y%m%d")
|
|
nyse.EndTime = nyse.Time + timedelta(1)
|
|
nyse.Symbol = Symbol.Create(csv[1], SecurityType.Equity, Market.USA)
|
|
nyse["TopGainersRank"] = int(csv[2])
|
|
return nyse
|
|
|
|
if self.last_date != date:
|
|
# reset our counter for the new day
|
|
self.last_date = date
|
|
self.count = 0
|
|
|
|
# parse the html into a symbol
|
|
if not line.startswith('<a href=\"/public/quotes/main.html?symbol='):
|
|
# we're only looking for lines that contain the symbols
|
|
return None
|
|
|
|
last_close_paren = line.rfind(')')
|
|
last_open_paren = line.rfind('(')
|
|
if last_open_paren == -1 or last_close_paren == -1:
|
|
return None
|
|
|
|
symbol_string = line[last_open_paren + 1:last_close_paren]
|
|
nyse = NyseTopGainers()
|
|
nyse.Time = date
|
|
nyse.EndTime = nyse.Time + timedelta(1)
|
|
nyse.Symbol = Symbol.Create(symbol_string, SecurityType.Equity, Market.USA)
|
|
nyse["TopGainersRank"] = self.count
|
|
self.count = self.count + 1
|
|
return nyse |