# 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 *
import numpy as np

### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="using quantconnect" />
### <meta name="tag" content="trading and orders" />
class BasicTemplateAlgorithm(QCAlgorithm):
    '''Basic template algorithm simply initializes the date range and cash'''

    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(2015,4, 28)  #Set Start Date
        self.SetEndDate(2021,4,8)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("SPY", Resolution.Daily)
        self.AddEquity("QQQ", Resolution.Daily)
        self.AddEquity("LRGF", Resolution.Daily)
        #self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
        
        self.SetBenchmark("SPY")
        #self.SetBenchmark("QQQ")
        mainChart = Chart("Equity Curve With Benchmark")
        mainChart.AddSeries(Series("Equity Curve", SeriesType.Candle, 0))
        mainChart.AddSeries(Series("Benchmark", SeriesType.Line, 0))
        self.AddChart(mainChart)
        self.scale = None

    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

        Arguments:
            data: Slice object keyed by symbol containing the stock data
        '''
        if self.scale == None:
            self.scale = 100000 / data["SPY"].Price
            
        if not self.Portfolio.Invested:
            #self.SetHoldings("SPY", 0.5)
            #self.SetHoldings("QQQ", 0.5)
            self.SetHoldings("LRGF", 1)
        self.Plot("Equity Curve With Benchmark", "Equity Curve", self.Portfolio.TotalPortfolioValue)
        self.Plot("Equity Curve With Benchmark", "Benchmark", self.Benchmark.Evaluate(self.Time) * self.scale)