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:
- Generate expected return signals from time series of close
- 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)
Shile Wen
Hi Quentin,
This Strategy Library Tutorial explains how to process and manipulate time-series data, and also provides a code example that is well commented. Furthermore, this algorithm demonstrates how to do Sharpe optimization, which is very similar to Mean-Variance optimization.
Best,
Shile Wen
Quentin
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