# 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.


import clr
from clr import AddReference
clr.AddReference("System")
clr.AddReference("QuantConnect.Algorithm")
clr.AddReference("QuantConnect.Indicators")
clr.AddReference("QuantConnect.Common")

from System import *
import numpy as np
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
import decimal as d

### <summary>
### In this example we look at the canonical 15/30 day moving average cross. This algorithm
### will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses
### back below the 30.
### </summary>
### <meta name="tag" content="warmup" />
### <meta name="tag" content="crypto" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="indicator classes" />
### <meta name="tag" content="moving average cross" />
### <meta name="tag" content="strategy example" />
class CryptoWarmupMovingAverageCross(QCAlgorithm):

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2016, 01, 01)  #Set Start Date
        self.SetEndDate(2017, 10, 19)    #Set End Date
        self.SetCash(10000)             #Set Strategy Cash

        # Set brokerage we are using: GDAX for cryptos
        self.SetBrokerageModel(BrokerageName.GDAX, AccountType.Cash)
        
        # Set crypto to BTC at 1 hour resolution
        self.AddCrypto("BTCUSD", Resolution.Daily)
        
        # Define windows in days for EMA
        fast_period = 15
        slow_period = 70

        # create a fast 12 day exponential moving average at daily resolution
        self.fast_btc = self.EMA("BTCUSD", fast_period, Resolution.Daily)

        # create a slow 27 day exponential moving average at daily resolution
        self.slow_btc = self.EMA("BTCUSD", slow_period, Resolution.Daily)

        # "slow_period + 1" because rolling window waits for one to fall off the back to be considered ready
        # History method returns a dict with a pandas.DataFrame
        history = self.History(["BTCUSD"], slow_period + 1)

        # Prints out the head & tail of the dataframe in Log; disabled
        #self.Log(str(history.loc["BTCUSD"].head()))
        #self.Log(str(history.loc["BTCUSD"].tail()))

        # Populate warmup data
        for index, row in history.loc["BTCUSD"].iterrows():
            self.fast_btc.Update(index, row["close"])
            self.slow_btc.Update(index, row["close"])

        # Log 
        
        # Log warmup status
        self.Log("FAST {0} READY. Samples: {1}".format("IS" if self.fast_btc.IsReady else "IS NOT", self.fast_btc.Samples))
        self.Log("SLOW {0} READY. Samples: {1}".format("IS" if self.slow_btc.IsReady else "IS NOT", self.slow_btc.Samples))

        self.previous = None


    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
        # a couple things to notice in this method:
        #  1. We never need to 'update' our indicators with the data, the engine takes care of this for us
        #  2. We can use indicators directly in math expressions
        #  3. We can easily plot many indicators at the same time

        # wait for our slow ema to fully initialize
        if not self.slow_btc.IsReady:
            return

        # only once per day
        if self.previous is not None and self.previous.date() == self.Time.date():
            return

        # define a small tolerance on our checks to avoid bouncing
        tolerance = 0.00015;

        # Determine number of BTC held
        holdings = self.Portfolio["BTCUSD"].Quantity
        
        # Log stats
        self.Log("Holding {} BTC".format(str(holdings)))
        self.Log("BTC held worth {}".format(str(holdings*self.Securities["BTCUSD"].Price)))
        
        # we only want to go long if we're currently short or flat
        if holdings <= 0:
            # if the fast is greater than the slow, we'll go long
            if self.fast_btc.Current.Value > self.slow_btc.Current.Value * d.Decimal(1 + tolerance):
                self.Log("BUY  >> {0}".format(self.Securities["BTCUSD"].Price))
                self.SetHoldings("BTCUSD", 1)

        # we only want to liquidate if we're currently long
        # if the fast is less than the slow we'll liquidate our long
        if holdings > 0 and self.fast_btc.Current.Value < self.slow_btc.Current.Value:
            self.Log("SELL >> {0}".format(self.Securities["BTCUSD"].Price))
            self.SetHoldings("BTCUSD", 0)
            
        self.previous = self.Time