# 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.
####
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#







####       ALEX U HERE ????? 6 AM
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#          
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import clr
clr.AddReference("System")
clr.AddReference("QuantConnect.Algorithm")
clr.AddReference("QuantConnect.Indicators")
clr.AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
#from datetime import datetime
### <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="indicators" />
### <meta name="tag" content="indicator classes" />
### <meta name="tag" content="moving average cross" />
### <meta name="tag" content="strategy example" />
class MyAlgo(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(2019, 11, 20)    #Set Start Date
       # self.SetEndDate(2019, 7, )      #Set End Date
        self.SetCash(200)             #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("TCMD", Resolution.Minute)
        self.forex = self.AddEquity("TCMD", Resolution.Minute)
        self.psar = self.PSAR(self.forex.Symbol, .005, .005, .05, Resolution.Minute)
        self.Securities["TCMD"].FeeModel = ConstantFeeModel(.02)
       
       
        #self.previous = None
        
        # 1 daily
     #   if self.previous is not None and self.previous.date() == self.Time.date():
      #      return


   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.fast.IsReady:
       #     return

        

        # define a small tolerance on our checks to avoid bouncing
        tolerance = 0.000000000015

        
        # we only want to go long if we're currently short or flat
        if self.Portfolio["TCMD"].Quantity <= 0:
            # if the fast is greater than the slow, we'll go long
            if self.psar.Current.Value < self.Securities["TCMD"].Price:
                self.Log("BUY  >> {0}".format(self.Securities["TCMD"].Close))
                #self.SetHoldings("UGAZ", 1.0)
                self.MarketOrder("TCMD", 1)

        if self.Portfolio["TCMD"].Quantity > 0:
            # if the fast is greater than the slow, we'll go long
            if self.psar.Current.Value > self.Securities["TCMD"].Price:
                self.Log("BUY  >> {0}".format(self.Securities["TCMD"].Close))
                #self.SetHoldings("UGAZ", 1.0)
                self.MarketOrder("TCMD", -1)

      
         
         ###
    
    
    
    
    
    
    
    
    #####