Portfolio selection

AI & Machine learning
1

sir, 

I have taken hierarchical risk parity course for portfolio management. my question is can i use two types of  machine  learning techniques for portfolio construction and for optimization.



I mean can we use deep neural network for stock selection and construction and then we can apply hierarchical Risk parity for optimization. Because my topic is Hybridization of ML techniques to optimize portfolio means  here i have to  combine at least two techniques of ML to get result. any ML techniques .

how can you help me 

2

Hi Keerti,



You are right when you say you can combine different ML techniques to arrive at a robust trading strategy, The example which you have given seems like a good start. You can screen stocks first and then use hierarchical risk parity on these stocks to optimise the portfolio allocation. 



Hope this helps.



 

3

ya i know i can use two techniques together. but i want to use deeplearning teechnique for asset selection . and then hierarchical risk parity for optimization.



here i have collectednifty-500 data close and volume data . but i want ohlcv data of all these nifty-500 data together so that i can fetch ohlcv candelstick images.   



i want to use these candlestick images as input to deeplearning autoencoder to select assets sectorwise.  once i get finaldata will implement hierarchical risk parity.



can  anybody help me to select assets ohlcv nifty 500 data altogethe and   cluster them sectorwise.



please help me  with code

4

Hi Keerti,



If you are using Yahoo finance for retrieving data, then it is possible to get information on the sector the asset belongs to. For example, running the following code will help you get the data of the sector to which Reliance and Infosys belong:



import yfinance as yf



reliance = yf.Ticker("RELIANCE.NS")

infy = yf.Ticker("INFY.NS")



print(reliance.info['sector'])

print(infy.info['sector'])



You can use this information to create clusters sectorwise.



Hope this helps.