Battle Of The Portfolio Optimization Methods - YouTube

Hi Guys, my name is Denis and you are watching Close To algo trading.

today I'm gonna talk about portfolio optimization.

What do you think, does portfolio optimization really help us beat the market?

Stay with me and maybe we can answer this question.

Before we start, perhaps we need to know what Portfolio optimization means.

Portfolio optimization is the process of selecting proportions of various assets to include in

a portfolio, in such a way as to make the portfolio better than any other according

to specific constraints.

For my experiment I will try to maximize Sharpe Ratio.

Sharpe ratio is the measure of risk-adjusted return of a financial portfolio.

We will calculate it in the following way: Sharpe = (Portfolio Return) / (Portfolio Std)

because in our case risk free rate doesn't play any role and it can be 0.

Well, we need to allocate our money to different assets in the way that our portfolio has maximum

sharpe value.

The good news is that there are a lot of methods that already exist which can solve this task

for us.

Now that we know what we're going to optimize, let's move on to our participance.

Today, the following methods will take part in our battle:

- Classic Mean-variance optimization - Hierarchical Risk Parity created by Marcus

Lopez de Prado - The Critical Line Algorithm, which was specially

designed for portfolio optimization.

- Efficient Frontier with nonconvex optimizer

And we also have two more exotic methods LSTM Model for Sharpe Value optimization

Trained LSTM Model for prediction future allocation.

- The first one I found on the internet.

Guys adopt deep learning models to directly optimise the portfolio Sharpe ratio.

They claimed that this method showing the best performance over the testing period,

from 2011 to the end of April 2020, including the financial instabilities of the first quarter

of 2020 compared to other methods.

The implementation of this model you can find in the risk_model.py or in authors' github

Let's call this method LSTM, just because they use a simple LSTM network.

- The second method is different from all

others, I trained a simple network that predicted allocation based on the model from the first

method.

First what I want to know is which of these methods works better.

For that I chose to use a 125 days price period for all methods.

Set of assets was selected randomly, and it consisted of 10 stocks.

Then I randomly collected 10 periods of 125 prices from 2008 to 2020 and tried to optimize

the portfolio using the different methods.

The result was interesting, as you may see in most periods the best sharpe value have

MeanVariance and CLA method, and LSTM method shows very good results, in some cases it

outperforms others.

One very strange result shows other methods, they are very similar to random allocation.

For the first test i used only stocks, let do the same test but reduce our assets to

four etfs.

We can see that MeanVariance and CLA are the leaders and LSTM shows mixed results but still

looks pretty good.

What we can say, looking at these results.

Well, CLA and MeanVarians look very good, but will they perform so well if we will use

this allocation for our portfolio for future?

Let's check it.

For the tests, I decided to use backtrader and I created a very simple only long strategy.

I used 125 days of historical price to calculate allocation of assets and based on this

allocation i rebalance the portfolio.

Rebalancing was done every 22 days.

Once in a month.

There is one difference between the test of classic models and LSTM.

For each of classic model i did two tests, in first test i used normal calculation of

asset allocation based on 125 days prices, and in the second

test i collected historical price and the calculation always performed on the collected

data.

For LSTM, I didn't use collected data, and I always used a new model for calculation,

because if i tried to reuse the model, the allocation didn't change.

Also, we have a model that should predict future allocation.

For the stocks I collected data on 1000 training data elements from 2000 to 2006.

One training element consist of the 125 days and the allocation

for the next 22 days.

Next, a simple network was trained and used for the testing.

Same was done for ETFs test.

The test statistics for 2008-2010 period you can see on the table, and the sharp value

below in the graph.

Result is interesting, most of the methods show

results not far away from random, and some of them perform very poor.

However, our trained model outperforms all others methods.

Let's check another period that is not so close to our training data.

For 2011 - 2017 years the winner is HRP method with data collection,

but we can really see that all results are very close and not far away from the random

allocation.

Also I checked the period from 2018 to 2021, so the LSTM model does not work very well,

the trained model is a bit better.

CLA and MeanVarians are the winner.

In addition, I did a test for a specific period from 2012 to 2016, because if you check the

return graph, you can find that our trained model and also LSTM model perform very poorly.

From 2012 to 2013 our model loses money, but the spy is growing.

It is also visible here.

Maybe it is because I took the training for true allocation from the LSTM model.

But in this special period we can see that the random allocation outperforms other methods.

The final test was done on ETFs.

So, trained models a little outperforms other methods.

Based on these results, we can clearly see that the past allocation doesn't provide a

better allocation in the future and in some cases it is worse as random.

Model that try to predict future allocation look much more promising, but to get stable

results we have to add more data and more features.

However, at the moment it is quite difficult to rely on any of these methods.

Well.

that is all for today, I hope it was interesting and see ya in the next video.

Bye.