## Portfolio Diversification Strategies | Practical Implications

### How important is portfolio diversification?

When trading we will sometimes have to make sacrifices. There just aren’t enough hours in the day to do everything. We need to prioritize what we do effectively to gain the most benefit.

Depending on whether we are a discretionary trader or an algorithmic trader, will impact how you implement a portfolio diversification strategy.

#### Key considerations when implementing a Portfolio Diversification strategy

Time will always be a consideration. As discussed previously, diversification is an important component of risk management at the portfolio level, but it is not the ‘golden bullet’.

#### Portfolios need to be looked at holistically.

Where are our efforts best focused on at this time? It may be that your underlying trading strategy still needs work before you can benefit from implementing a diversification strategy.

Or it may be that your strategies are nice and robust, and you can now focus your time optimizing the portfolio risk side of things.

Ultimately, you’ll need to decide where your time is best served.

#### But how do I know what to do?

In the video, Martyn goes over some of his personal experiences with how he trades his portfolio of strategies. They don’t just provide you with great educational content, our presenters also trade the markets themselves. Where do they find the time!

Another consideration is how you trade.

If you’re a discretionary trader; your skill, and the number of monitors you have, may limit the number of assets you can manage at once. Again, your circumstances will dictate what works best for you.

A long-only, passive portfolio will need a different approach to diversification than that of a more active portfolio like the example in the video.

#### So, what can we take away from this?

We need time to implement what we know, and we need to know what to implement when we have the time.

Argh yes, the circle of life trading decisions.. 😌

Take a step back from your portfolio and look at it with a bird’s eye view. What is the most impactful thing you can do now that will help improve your trading?

#### Did you know we have a Slack group dedicated to trading?

Feel free to join here. We’d love to connect and talk shop.

If you’re new to portfolio diversification, consider reading this post to get some background on the topic.

Brought to you by Darwinex: UK FCA Regulated Broker, Asset Manager & Trader Exchange where Traders can legally attract Investor Capital and charge Performance Fees.

Risk disclosure:
https://www.darwinex.com/legal/risk-disclaimer

Content Disclaimer: The contents of this video (and all other videos by the presenter) are for educational purposes only, and are not to be construed as financial and/or investment advice.

## Is Portfolio Diversification alone, sufficient for managing risk?

Portfolio Diversification is a critical consideration when managing the risk of a portfolio of trading strategies.

If you’ve watched the previous introduction to trading diversification video you’ll know how important it is.

However, it isn’t the only consideration 💡

We’re going to discuss some things to be aware of when looking into risk management techniques.

Here are two considerations where diversification might not work as well as one would hope:

### 1) Black Swans

It’s okay, you don’t need protection from Natalie Portman. A black swan is an “unpredictable event that is beyond what is normally expected of a situation and has potentially severe consequences”.

#### Can you think of an example of a black swan?

Tag us on Twitter (@Darwinexchange) with your thoughts, and if you’ve been the victim of a black swan event?

During these events, previously uncorrelated assets tend to become correlated due to unforeseen circumstances. This can reduce the effectiveness of this method of risk management.

### 2) Market Randomness

Regardless of how correlated two assets are. There will be times when both move in the same direction.

This temporary correlation doesn’t mean the same forces are driving them.

Due to the sheer quantity of assets and the nature of the financial markets, there will be a level of randomness to the price action. Thus reducing the effectiveness of portfolio diversification.

Ultimately, knowledge is power. By understanding areas where diversification may not be as effective, we can take steps to mitigate these risks. Reducing the risk on your portfolio is a multi-stage process.

### Portfolio diversification is important, but it is only one aspect.

Hopefully, by the end of this series, you’ll feel comfortable implementing these valuable insights into your own portfolio.

#### Pop Quiz

If we diversify our portfolio across 4 uncorrelated assets, using the figures Martyn gives in the video, fill in the blank:

Diversification can only contribute to reducing the risk a max of __________% of the time.

The issues discussed here are just some of many that need to be considered when measuring risk.

Video Series: Why & How We Measure Risk differently at Darwinex

Watch the full playlist on YouTube here

Here’s video #1 where we describe the differences between Money Management and Risk.

Brought to you by Darwinex: UK FCA Regulated Broker, Asset Manager & Trader Exchange where Traders can legally attract Investor Capital and charge Performance Fees.

Risk disclosure:
https://www.darwinex.com/legal/risk-disclaimer

Content Disclaimer: The contents of this video (and all other videos by the presenter) are for educational purposes only, and are not to be construed as financial and/or investment advice.

## How do good traders use leverage? – Part I

Good traders know that no more than between 5:1 and 10:1 D leverage is required to achieve 20% to 60% returns per annum, at 10% VaR.

## Background

In a recent Spanish podcast episode, Darwinex CEO Juan Colón shed light on behaviours of successful DARWIN providers (traders) at Darwinex.

Insights shared were as a result of analyzing over 3,000 DARWINs worth of data from the last 3 months.

Research objectives were to identify what leverage successful traders employ, success quantified as achieving a D-Score > 65.

Note: With the latest changes to the D-Score calculation, this threshold will change to 68 in future analyses.

### Why 65?

• It’s a challenging threshold for any DARWIN to achieve,
• Hence indicative of higher likelihood for success.

From a trader’s perspective, the aim was to to answer the question:

## Findings

DARWINs with a D-Score > 65 had the following D-leverage distribution:

1. Average: 5:1
2. Median: 3:1
3. Maximum: 25:1

Within these parameters, these traders achieved returns ranging from -15% to over 70% per annum.

This motivates another question:

### Why the dispersion in leverage?

Considering the DARWIN provider’s trading style (mode of operation) and risk appetite can explain the dispersion observed in these findings.

Different trading strategies employ different levels of leverage depending on these two modes.

For simplicity, let’s consider two main categories:

1. Continuous market exposure,
2. Scalping (quick entry/exit, 1 to 3 hours in the market on average)

### Continuous Market Exposure

DARWIN $LVS is an example of a highly successful trading strategy that maintains market exposure at all times. Analysis of the data reveals that David and Enrico ($LVS DARWIN Providers) maintain:

• A D-leverage between approximately 2:1 to 3:1,
• An underlying strategy VaR of 5 to 10%.

### Scalping

Such strategies typically enter and exit the market quickly.

They average trade durations between 1 and 3 hours on most occasions, and remain flat (out of the market) the rest of the time.

DARWINs $NTI and$ERQ are examples of scalping strategies that demonstrate these behaviours, employing D-leverage ranges of approximately between 5 to 8:1 and 7:1 respectively.

(Note: A DARWIN’s D-leverage distribution can be viewed on its main listing page, under “RETURN / RISK”.)

## Corollary

No more than between 5 and 10:1 D-leverage is required to achieve 20% to 60% returns per annum, at 10% VaR.

## Part II will discuss:

1) If the analysis above reveals that successful traders are able to achieve decent returns with less leverage, then why do a majority of traders still do otherwise?

2) At what VaR (%) is a trading strategy’s demise imminent? (and why).

Therein, we’ll describe how we arrive at the VaR, create a trading strategy to demonstrate the impact of various levels of VaR, and show Monte Carlo simulations to that effect (complete with sample code in Python/R shared via GitHub at a later stage).

## Hidden Markov Models & Regime Change: S&P500

In this post, we will employ a statistical time series approach using Hidden Markov Models (HMM), to firstly obtain visual evidence of regime change in the S&P500.

Detecting significant, unforeseen changes in underlying market conditions (termed “market regimes“) is one of the greatest challenges faced by algorithmic traders today. It is therefore critical that traders account for shifts in these market regimes during trading strategy development.

## Why use Hidden Markov Models?

Hidden Markov Models for Detecting Market Regime Change (Source: Wikipedia)

Hidden Markov Models infer “hidden states” in data by using observations (in our case, returns) correlated to these states (in our case, bullish, bearish, or unknown).

They are hence a suitable technique for detecting regime change, enabling algorithmic traders to optimize entries/exits and risk management accordingly.

We will make use of the depmixS4 package in R to analyse regime change in the S&P500 Index.

Hidden Markov Model – State Space Model (Source: StackExchange)

With any state-space modelling effort in quantitative finance, there are usually three main types of problems to address:

1. Prediction – forecasting future states of the market
2. Filtering – estimating the present state of the market
3. Smoothing – estimating the past states of the market

We will be using the Filtering approach.

Additionally, we will assume that since S&P500 returns are continuous, the probability of seeing a particular return R in time t, with market regime M being in state m, where the model used has parameter-set P, is described by a multivariate normal distribution with mean μ and standard deviation σ [1].

Mathematically, this can be expressed as:

$$p(R_t | M_t = m, P) = N(R_t | μ_m, σ_m)$$

As noted earlier, we will utilize the Dependent Mixture Models package in R (depmixS4) for the purposes of:

1. Fitting a Hidden Markov Model to S&P500 returns data.
2. Determining posterior probabilities of being in one of three market states (bullish, bearish or unknown), at any given time.

We will then use the plotly R graphing library to plot both the S&P500 returns, and the market states the index was estimated to have been in over time.

You may replicate the following R source code to conduct this analysis on the S&P500.

#### Step 1: Load required R libraries

library(quantmod) library(plotly) library(depmixS4)

#### Step 2: Get S&P500 data from June 2014 to March 2017

getSymbols("^GSPC", from="2014-06-01", to="2017-03-31")

#### Step 3: Calculate differenced logarithmic returns using S&P500 EOD Close prices.

sp500_temp = diff(log(Cl(GSPC))) sp500_returns = as.numeric(sp500_temp)

#### Step 4: Plot returns from (3) above on plot_ly scatter plot.

plot_ly(x = index(GSPC), y = sp500_returns, type="scatter", mode="lines") %>%

layout(xaxis = list(title="Date/Time (June 2014 to March 2017)"), yaxis = list(title="S&P500 Differenced Logarithmic Returns"))

## S&P500 Differenced Logarithmic Returns (June 2014 to March 2017)

S&P500 Differenced Logarithmic Returns (June 2014 to March 2017)

#### Step 5: Fit Hidden Markov Model to S&P500 returns, with three “states”

hidden_markov_model <- depmix(sp500_returns ~ 1, family = gaussian(), nstates = 3, data = data.frame(sp500_returns=sp500_returns))

model_fit <- fit(hidden_markov_model)

#### Step 6: Calculate posterior probabilities for each of the market states

posterior_probabilities <- posterior(model_fit)

#### Step 7: Overlay calculated probabilities on S&P500 cumulative returns

sp500_gret = 1 + sp500_returns
sp500_gret <- sp500_gret[-1]
sp500_cret = cumprod(sp500_gret)

plot_ly(name="Unknown", x = index(GSPC), y = posterior_probabilities$S1, type="scatter", mode="lines", line=list(color="grey")) %>% add_trace(name="Bullish", y = posterior_probabilities$S2, line=list(color="blue")) %>%

add_trace(name="Bearish", y = posterior_probabilities\$S3, line=list(color="red")) %>%

add_trace(name="S&P500", y = c(rep(NA,1), sp500_cret-1), line=list(color="black"))

## S&P500 Market Regime Probabilities (June 2014 to March 2017)

S&P500 Hidden Markov Model States (June 2014 to March 2017)

Interpretation: In any one “market regime”, the corresponding line/curve will “cluster” towards the top of the y-axis (i.e. near a probability of 100%).

For example, during a brief bullish run starting on 01 June 2014, the blue line/curve clustered near y-axis value 1.0. This correlates as you can see, with movement in the S&P500 (black line/curve). The same applies to bearish and “unknown” market states.

An interesting insight one can draw from this graphic, is how the Hidden Markov Model successfully reveals high volatility in the market between June 2014 and March 2015 (constantly changing states between bullish, bearish and unknown).

References:

[1] Murphy, K.P. (2012) Machine Learning – A Probabilistic Perspective, MIT Press.
https://www.cs.ubc.ca/~murphyk/MLbook/

Influences:

The honourable Mr. Michael Halls-Moore. QuantStart.com
http://www.quantstart.com/

* please activate CC mode to view subtitles.

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## Why Quantitative? – Serious Algorithmic Trading Series (Part 2)

In case you haven’t already, you may wish to read the first two posts of the Serious Algorithmic Trading Series, here and here.

Many individual traders will often hear the term “Quant” and may visualize either:

1. A sharply dressed City trader working in a hedge fund, sat in front of his Bloomberg or Thomson Reuters Eikon terminals, or
2. A stocky chap wearing a Batman t-shirt, with telescopes for specs (and possibly a doctorate in Nuclear Physics), sat in his basement trading room pouring over mathematical models, following a staple fast food diet.

For the avoidance of doubt: you do NOT need a university degree to become a Quantitative Trader (though having one does come in very handy, IF you were paying attention in class!)

Now, both these individuals above likely followed a similar academic (or independent learning) path to their current disposition.

It’s even possible that the City trader was once a frequent visitor to his own basement trading room, before being spotted by a financial recruiter and beamed up to the 39th floor.

This prompts a few questions:

## Why do Quants become Quants in the first place?

There are numerous advantages to quantitative trading, advantages that not only reduce the financial impact of discretionary decision-making going wrong, but also help measure performance in a manner that permits efficient future learning.

For the purpose of distinction, it must be noted that unlike algorithmic trading that employs technical analysis, the quantitative approach to algorithmic trading involves primarily Bayesian Statistics and Time Series Analysis.

These are more effective methods of generating uncorrelated risk-adjusted returns, all too often ignored by the retail fundamental or technical analyst. Those who recognize their advantages, naturally progress down a path to becoming Quants.

## Is Quantitative Trading reserved for just institutions?

In short, no it isn’t. Find out why in part 1 of this series here.

## Why should this interest retail traders?

The world of quantitative trading extends far beyond that of technical moving average crossovers, candlestick patterns, martingales, grids, and off-the-shelf trading systems celebrating their latest and greatest “trading secrets” that resulted in their most sensationally overfitted backtests to date.

It does so because the markets are a much more complex phenomenon than we think, and undergo several micro-structure and regime changes frequently.

Darwinex – The Open Trader Exchange

Note: To protect investors from overfitted systems, all strategies listed as DARWINS on the Darwin Exchange are analyzed for proficiency in 12 investment attributes. Investors therefore have the ability to immediately assess whether a strategy’s current equity curve is likely to persist in future or not, something standard backtest results cannot reveal.

Indeed one could go as far as to say that the market is a stochastic process in and of itself.

Generating uncorrelated risk-adjusted returns in as seemingly stochastic an environment as this, is therefore a mammoth task.

Why more retail traders haven’t then embraced quantitative methods (that enable the discovery and subsequent exploitation of inefficiencies in stochastic processes) can only be attributed to a lack of awareness and “approachable” learning resources.

We will aim to address this in Part 3 of this series, “The Quantitative Approach to Algorithmic Trading”, and all subsequent posts that fall in the same category.

1. ### Alpha is an elusive beast.

Alpha is difficult to discover, gradually loses its profitability as more capital is allocated to its exploitation, and inevitably transitions from a source of risk-adjusted profitability to a source of risk.

Quantitative methods allow traders to not only discover and exploit alpha, but to do so in a manner that maximises its profitability horizon and minimizes risk of decay.

Quantitative analysis also facilitates the creation of statistical testing frameworks that can forecast risk of alpha decay in advance.

Note: DARWIN investors benefit from a Scalability/Capacity investment attribute  displayed on each asset listed on the Darwin Exchange. It is a score ranging from 1 to 10 that indicates how much capital the underlying strategy can support without decay. The higher the score, the more assets under management (AuM) it can attract.

2. ### Statistically Robust vs. Regular Backtesting

Quantitative traders can make use of statistical tools that add another layer of validity to backtests.

In simple terms, if you’re currently examining a backtest and taking its results with a “grain of salt”, substitute the latter with some rigorous quantitative testing and you have a much more credible (or not) backtest on your hands.

3. ### Quantitative Trading encourages thinking in “Portfolios”

Common behaviours among a large segment of individual traders include deploying a single trading strategy on a single asset, or deploying the same strategy on a mix of assets, without careful consideration given to weighted allocation as would be the case in an institutional setting.

In the former’s case, the trader has put all his/her eggs in one basket.

In the latter’s case, the trader has employed logic that goes something like “if it’s a robust strategy, it will perform well in all markets, under all market conditions.”

Unfortunately, this logic is prone to leading traders into intractable situations where the lack of an appropriate risk-adjusted allocation matrix may lead to unexplained underperformance.

Construct DARWIN Portfolios

Quantitative analysis allows traders to construct portfolios of assets or strategies (or both), such as to maximize their aggregate profitability while minimizing their aggregate risk. This is simply not possible to achieve without quantitative methods being employed.

Note: Investors on the Darwin Exchange can construct uncorrelated portfolios of DARWINS using our proprietary diagnostics toolkit. Twelve investment attributes (as of Darwinex Reloaded) allow investors to not only select DARWINS that match their criteria for investment, but also reduce overall portfolio risk through adequate diversification. As of Darwinex Reloaded, investors are even rewarded for diversifying their DARWIN portfolios, through the introduction of Diversification Rebates.

One of the most commonly voiced disadvantages of Quantitative Trading is that it’s a complex discipline to master.

So is learning to drive a car for the very first time. The right instructor can be the difference between a confident future driver and a jittery one.

Quantitative trading is no different. Over the course of these posts (that aim to make the complex simple), the best most approachable educational material will always be referenced for the reader’s benefit.

The fruits of a Quant’s labour are far sweeter than one can imagine. You will be well served by visiting the Darwin Exchange Leaderboard for inspiration.

The next post will begin our journey into the Quantitative Approach to Algorithmic Trading.

The Darwinex Team

Do you have what it takes? – Join the Darwinex Trader Movement!

Darwinex – The Open Trader Exchange

## Can individual traders really compete with large institutions?

This post is the first of a four-part “Serious Algorithmic Trading” series, introduced here.

A trader will at one point or another, question how practical it is to compete with large institutions in the same markets.

After all, it is “big money” at the end of the day that moves these markets.

The reality is that institutions face several regulatory, technological, structural and capital constraints that individual traders don’t.

It’s these very constraints that also lead to institutional funds often exhibiting a degree of predictability that is visible to – and can potentially be exploited by – individual traders. This phenomenon will be discussed in more detail in post #3, “The Quantitative Approach to Algorithmic R&D”.

The answer to the question is therefore YES.

Let’s examine why in the contexts of Scalability & Market Impact, Risk Management, and Trading Technology.

## Scalability & Market Impact

Individual traders can design and execute trading strategies that target small inefficiencies in the market.

Such inefficiencies often have capacities up to a fairly limited amount of capital before they lose their profitability.

This limited capital support usually ranges from a few hundred thousand to a few million dollars, and is therefore of little to no interest to institutional funds trading with a substantially larger capital base.

Note: For the benefit of the Darwinex community, a Scalability/Capacity investment attribute is displayed on each DARWIN asset listed on the Darwin Exchange. It is a score ranging from 1 to 10 that indicates how invest-able the underlying strategy is. The higher the score, the more assets under management (AuM) the strategy can support.

Furthermore, individual trader capitalization is much lower than that of institutions. This is an advantage as retail trading activity in highly liquid markets cannot therefore create any substantial market impact.

## Risk Management

Risk Management

Without middle or compliance offices enforcing industry standards and regulatory oversight, individual traders have the option to model their own risk management techniques as they deem fit, promoting flexibility that can indeed contribute to generating excess returns.

However,

While this may certainly be an advantage for some experienced traders, it is a double-edged sword.

At no risk of being “overruled”, individual traders are at greater risk of exercising “nonoptimal” risk management decisions, often leading to negative outcomes, e.g. accounts blowing up due to excessive use of leverage or aggressive risk management that wouldn’t otherwise have been permitted in an institutional setting.

Note: The Darwin Exchange solves this problem for investors in DARWINs. Traders listing strategies on the Darwin Exchange never manage investor capital themselves.

Darwinex manages all DARWIN assets, and enforces its own risk management to deliver a fixed Value-at-Risk to investors, thereby insulating them from the underlying strategy’s specific risk profile. Traders simply license their intellectual property to Darwinex in exchange for 20% performance fees on any profits generated for investors.

In addition, a Risk Management investment attribute is displayed on all listed DARWIN assets. It is a score ranging from 1 to 10, indicating the ability of the underlying strategy to yield stable risk with consistent use of leverage. The higher the score, the more invest-able the strategy is.

Furthermore, with no enforcement of industry best practices and risk management oversight, individual traders often find themselves modeling risk at the execution level (e.g. stop losses and take profits), without much consideration given to risk at the portfolio level (e.g. mixture of assets or strategies deployed on the same account).

Note: Darwinex addresses this problem for investors in DARWINs. Our Investor Platform enables investors to see the correlation and diversification benefit of selecting any mix of DARWIN assets, before making any investment decisions.

Institutional traders do not enjoy the same flexibility as retail traders, in terms of their choice of technology for trading and strategy development.

Retail traders can choose from a large selection of servers, hardware, trading platforms, programming languages and toolkits, without corporate IT policy or a predetermined list of “permitted systems” affecting their technology preferences.

The only disadvantage that accompanies this flexibility though, is that it can get quite expensive (relative to a retail trader’s available funding) to purchase hardware, software and server subscriptions. These costs must therefore be paid for by traders themselves, whereas in institutions they would most likely be mitigated by management fees charged.