| Overall Statistics |
|
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset |
# 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 AlgorithmImports import *
### <summary>
### Demonstration of using an external custom datasource. LEAN Engine is incredibly flexible and allows you to define your own data source.
### This includes any data source which has a TIME and VALUE. These are the *only* requirements. To demonstrate this we're loading in "Bitcoin" data.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="custom data" />
### <meta name="tag" content="crypto" />
class CustomDataBitcoinAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2019, 9, 12)
self.SetEndDate(2019, 9, 15)
self.SetCash(100000)
# Define the symbol and "type" of our generic data:
self.btc = self.AddData(Bitcoin, "BTC").Symbol
def OnData(self, data):
if not data.ContainsKey(self.btc): return
strike = data[self.btc].GetProperty('strike')
# If we don't have any weather "SHARES" -- invest"
if not self.Portfolio.Invested:
# Weather used as a tradable asset, like stocks, futures etc.
# It's only OK to use SetHoldings with crypto when using custom data. When trading with built-in crypto data,
# use the cashbook. Reference https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateCryptoAlgorithm.py
self.SetHoldings(self.btc, 1)
self.Debug("Buying BTC 'Shares': BTC: {0}".format(strike))
self.Debug("Time: {0} {1}".format(datetime.now(), strike))
class Bitcoin(PythonData):
'''Custom Data Type: Bitcoin data from Quandl - http://www.quandl.com/help/api-for-bitcoin-data'''
def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("https://www.dropbox.com/s/jnu9klmuwhnssfg/orats%20reduced%20strikes.CSV?dl=1", SubscriptionTransportMedium.RemoteFile)
def Reader(self, config, line, date, isLiveMode):
coin = Bitcoin()
coin.Symbol = config.Symbol
# skip the header
if all([not x.isdigit() for x in line]): return None
data = line.split(',')
coin.Time = datetime.strptime(data[1], "%m/%d/%Y")
coin.EndTime = coin.Time + timedelta(days=1)
coin.Value = data[14]
# coin["Open"] = float(data[1])
# coin["High"] = float(data[2])
# coin["Low"] = float(data[3])
coin["strike"] = float(data[4])
# coin["VolumeBTC"] = float(data[5])
# coin["VolumeUSD"] = float(data[6])
# coin["WeightedPrice"] = float(data[7])
return coin