import pandas as pd
import numpy as np
from datetime import timedelta
from collections import deque



class DynamicTransdimensionalEngine(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 1, 1)
        self.SetEndDate(2020, 1, 15)
        self.SetCash(100000)
        # self.UniverseSettings.Resolution = Resolution.Daily
        # self.AddUniverse(self.CoarseSelectionFunction)
        # self.sadfs = {}
    
    # def CoarseSelectionFunction(self, universe):  
    #     selected = []
    #     universe = sorted(universe, key=lambda c: c.DollarVolume, reverse=True)
    #     universe = [c for c in universe if c.Price > 10][:20]
        
    #     for coarse in universe:
    #         symbol = coarse.Symbol

    #         if symbol not in self.sadfs:
    #             self.sadfs[symbol] = SelectionData() 
    #         self.sadfs[symbol].update(coarse)  # OVAJ DIO MI JE SUMNJIV
            
    #         if  self.sadfs[symbol].is_ready() and self.sadfs[symbol].sadf < 1:
    #             selected.append(symbol)

    #     return selected[:10]

    # def OnSecuritiesChanged(self, changes):
    #     for security in changes.RemovedSecurities:
    #         self.Liquidate(security.Symbol)
       
    #     for security in changes.AddedSecurities:
    #         self.SetHoldings(security.Symbol, 0.10)

# class SelectionData(object):
#     def __init__(self):
#         self.sadf = SadfIndicator('sadf', 150)

#     def is_ready(self):
#         return self.sadf.IsReady
    
#     def update(self, input):
#         self.sadf.Update(input)

# class SadfIndicator():
#     def __init__(self, name, period):
#         self.Name = name
#         self.Time = datetime.min
#         self.Value = 0
#         self.IsReady = False
#         self.queue = deque(maxlen=period)
#         self.queueTime = deque(maxlen=period)
#         self.CurrentReturn = 0

#     def sadf_last(self, close):
#         sadf_linear = get_sadf(
#             close,
#             min_length=50,
#             add_const=True,
#             model='linear',
#             # phi=0.5,
#             lags=4)
#         if len(sadf_linear) > 0:
#             last_value = sadf_linear.values[-1].item()
#         else:
#             last_value = 0
#         return last_value
        
#     def Update(self, input):
#         self.queue.appendleft(input.Close)
#         self.queueTime.appendleft(input.EndTime)
#         self.Time = input.EndTime
#         if len(self.queue) > 100:
#             close_ = pd.Series(self.queue, index=self.queueTime).rename('close').sort_index()
#             self.CurrentReturn = close_.pct_change(periods=1)[-1]
#             self.Value = self.sadf_last(close=close_)
#         count = len(self.queue)
#         self.IsReady = count == self.queue.maxlen
#         return self.IsReady