Gerardo Salazar
4102c83e88
* OnEndOfDayRegressionAlgorithm - Since the EndTime of the hourly benchmark is during the day, the OnEndOfDay method gets called one less time than usual. Updates statistics * CustomUniverseWithBenchmarkRegressionAlgorithm.cs - modified algorithm so that it works with hourly benchmark. Previously only tested for Daily benchmark * BasicTemplateAlgorithm.py - Modified resolution to be Resolution.Minute, just like it is in C# * CustomDataRegressionAlgorithm.py - Remove warmup call from Initialize * IndicatorSuiteAlgorithm.py - Adds PythonQuandl import to fix import error
114 lines
4.7 KiB
Python
114 lines
4.7 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.Core")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import QCAlgorithm
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from QuantConnect.Data import SubscriptionDataSource
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from QuantConnect.Python import PythonData
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from datetime import datetime
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import json
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### <summary>
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### Regression test to demonstrate importing and trading on custom data.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="importing data" />
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### <meta name="tag" content="custom data" />
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### <meta name="tag" content="crypto" />
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### <meta name="tag" content="regression test" />
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class CustomDataRegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2011,9,13) # Set Start Date
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self.SetEndDate(2015,12,1) # Set End Date
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self.SetCash(100000) # Set Strategy Cash
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resolution = Resolution.Second if self.LiveMode else Resolution.Daily
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self.AddData(Bitcoin, "BTC", resolution)
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def OnData(self, data):
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if not self.Portfolio.Invested:
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if data['BTC'].Close != 0 :
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self.Order('BTC', self.Portfolio.MarginRemaining/abs(data['BTC'].Close + 1))
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class Bitcoin(PythonData):
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'''Custom Data Type: Bitcoin data from Quandl - http://www.quandl.com/help/api-for-bitcoin-data'''
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def GetSource(self, config, date, isLiveMode):
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if isLiveMode:
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return SubscriptionDataSource("https://www.bitstamp.net/api/ticker/", SubscriptionTransportMedium.Rest)
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#return "http://my-ftp-server.com/futures-data-" + date.ToString("Ymd") + ".zip"
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# OR simply return a fixed small data file. Large files will slow down your backtest
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return SubscriptionDataSource("https://www.quantconnect.com/api/v2/proxy/quandl/api/v3/datasets/BCHARTS/BITSTAMPUSD.csv?order=asc&api_key=WyAazVXnq7ATy_fefTqm", SubscriptionTransportMedium.RemoteFile)
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def Reader(self, config, line, date, isLiveMode):
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coin = Bitcoin()
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coin.Symbol = config.Symbol
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if isLiveMode:
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# Example Line Format:
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# {"high": "441.00", "last": "421.86", "timestamp": "1411606877", "bid": "421.96", "vwap": "428.58", "volume": "14120.40683975", "low": "418.83", "ask": "421.99"}
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try:
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liveBTC = json.loads(line)
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# If value is zero, return None
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value = liveBTC["last"]
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if value == 0: return None
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coin.Time = datetime.now()
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coin.Value = value
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coin["Open"] = float(liveBTC["open"])
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coin["High"] = float(liveBTC["high"])
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coin["Low"] = float(liveBTC["low"])
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coin["Close"] = float(liveBTC["last"])
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coin["Ask"] = float(liveBTC["ask"])
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coin["Bid"] = float(liveBTC["bid"])
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coin["VolumeBTC"] = float(liveBTC["volume"])
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coin["WeightedPrice"] = float(liveBTC["vwap"])
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return coin
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except ValueError:
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# Do nothing, possible error in json decoding
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return None
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# Example Line Format:
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# Date Open High Low Close Volume (BTC) Volume (Currency) Weighted Price
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# 2011-09-13 5.8 6.0 5.65 5.97 58.37138238, 346.0973893944 5.929230648356
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if not (line.strip() and line[0].isdigit()): return None
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try:
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data = line.split(',')
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coin.Time = datetime.strptime(data[0], "%Y-%m-%d")
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coin.Value = float(data[4])
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coin["Open"] = float(data[1])
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coin["High"] = float(data[2])
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coin["Low"] = float(data[3])
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coin["Close"] = float(data[4])
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coin["VolumeBTC"] = float(data[5])
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coin["VolumeUSD"] = float(data[6])
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coin["WeightedPrice"] = float(data[7])
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return coin
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except ValueError:
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# Do nothing, possible error in json decoding
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return None |