Overall Statistics
Total Trades
1
Average Win
0%
Average Loss
0%
Compounding Annual Return
-100.000%
Drawdown
10.200%
Expectancy
0
Net Profit
-10.242%
Sharpe Ratio
-2.07
Probabilistic Sharpe Ratio
0%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
-5.297
Beta
-79.239
Annual Standard Deviation
0.483
Annual Variance
0.233
Information Ratio
-1.934
Tracking Error
0.489
Treynor Ratio
0.013
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
BTC.Bitcoin 2S
# 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/qt3a9np8yttsxma/orats%20reduced%20strikes%20with%20changed%20date%20format.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], "%Y-%m-%d")
        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