Hi Everyone,

First, thank you for taking the time :)

I want to implement a sector rotation ETF strategy using Arimas and Markowitz optimization. I however don't understand how to interacact with data in order to:

  1. Generate expected return signals from time series of close
  2. Mean-Variance Optimize 

I want to understand how data is structured in order to be able to pull it, play with it and make it go though several processes. I have basic understanding of Python and am able to create a backtest if given raw data but adjusting to this infrastucture is rough as you get started.  

Thanks again!

PS: This is where i'm at, so not very far:

import numpy as np

class BaseAlg(QCAlgorithm):

def Initialize(self):
self.SetCash(10000)

self.SetWarmUp(timedelta(250)) #very interesting feature
self.SetStartDate(2006,1,1)
self.SetEndDate(2020,11,31)

self.SetBenchmark("SPY")

Tickers=["VGT","VCR","VHT","VIS","VDC","VPU","VFH","VOX","VAW","VNQ","VDE"]
for i in Tickers:
self.AddEquity(i,Resolution.Hour)