import numpy as np
import pandas as pd

class DynamicMultidimensionalCoreWave(QCAlgorithm):

    def Initialize(self):

        #1. Required: Five years of backtest history
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2019,1,10)
        
        #2. Required: Alpha Streams Models:
        self.SetBrokerageModel(BrokerageName.AlphaStreams)

        #3. Required: Significant AUM Capacity
        self.SetCash(5000000)
        
        # Tech List
        self.tech_etf = ["XLK", "QQQ", "SOXX", "IGV", "VGT", "QTEC", "FDN", "FXL",
                         "TECL", "SOXL", "SKYY", "SMH", "KWEB", "FTEC", "SOXS", "TECS"]
        
        self.t_list = ["TECL", "TECS"]
        
        #5. Set Relevent Benchmark
        self.reference = "XLK"
        self.AddEquity(self.reference, Resolution.Minute)
        self.SetBenchmark(self.reference)
        
        # Add Equity ------------------------------------------------
        for i in range(len(self.tech_etf)):
            self.AddEquity(self.tech_etf[i],Resolution.Minute)
    
        # Schedue  ---------------------------------------------------
        self.Schedule.On(self.DateRules.EveryDay("XLK"), self.TimeRules.AfterMarketOpen(self.reference, 0), self.tech_trade)
        
        
    def OnData(self, data):
            pass
    
            
    def tech_trade(self):
        
        history = self.History(self.tech_etf, 5, Resolution.Daily)
        df_history = history['close'].unstack(level=0)
        
        tech_columns = df_history.columns
        self.Log('Tech_Symbols : Total ' + str(len(tech_columns)) + '\n' + str(tech_columns))
        self.Log('df_Tech_History : ' + '\n' + str(df_history) +'\n')
        
        history_tecs = self.History(self.t_list, 5, Resolution.Daily)
        df_tecs = history_tecs['close'].unstack(level=0)
        self.Log('df_TECL_TECS : ' + '\n' + str(df_tecs) +'\n')