import numpy as np
import pandas as pd


class MyAlgo(QCAlgorithm):
    
        
    def Initialize(self):

        self.SetCash(1000000)
        # Start and end dates for the backtest.
        self.SetStartDate(2017,1,1)
        self.SetEndDate(2017,1,30)
        
        self.AddEquity("SPY", Resolution.Minute)
        
        # My list
        self.stock_list = ["QQQ", "IVV", "XLF", "IWM"]
        
        for i in range(len(self.stock_list)):
            self.AddEquity(self.stock_list[i],Resolution.Minute)
        
        
        
        # Schedule the rebalance function (once everyday 30 min. after market open)
        self.Schedule.On(self.DateRules.EveryDay("SPY"), 
        self.TimeRules.AfterMarketOpen("SPY", 30), 
        Action(self.rebalance))
        
        
    
    def OnData(self, data):
        pass


    def rebalance(self):

        # Close History --------------------------------------------
        
        # Call History (Daily)
        history = self.History(self.stock_list, 60, Resolution.Daily)
        # Unstack QC dataframe
        price_history = history['close'].unstack(level=0)
        # Call Current History
        current = self.History(self.stock_list, 1, Resolution.Minute)
        if current.empty: return   # if data is missing (empty), return
        curr_history = current['close'].unstack(level=0)
        # Combine all in one
        price_history = price_history.append(curr_history.iloc[0])
        
        # Re-create a base DF to work on 
        df = pd.DataFrame(price_history.values, columns = price_history.columns)
        df = df.dropna()
        
        # Volume   History   ----------------------------------------------
        
        vol_history = history['volume'].unstack(level=0)
        curr_vol_history = current['volume'].unstack(level=0)
        vol_history = vol_history.append(curr_vol_history.iloc[0])
        
        # Re-create a volume DF to work on 
        df2 = pd.DataFrame(vol_history.values, columns = vol_history.columns)
        df2 = df2.dropna()
        
        # ------------------------------------------------------------
        
        # Get sma(20)
        sma = df[-20:].mean(0).to_frame().T
        sma.index = ['SMA20']
        
        # Get current close
        curr_close = df.iloc[-1].to_frame().T
        curr_close.index = ['Curr_Close']
        
        # Get Volume 
        curr_vol = df2.iloc[-1].to_frame().T
        curr_vol.index = ['Volume']
        
        # Concat all the DFs
        table = pd.concat([sma, curr_close, curr_vol])
        
        self.Log('Table :' +'\n'+ str(table))