Daniel P Newman

Gold in 2020 - thoughts on Gold/AUD/USD exposure via the ASX

2020-08-29T00:00:00+00:00

A neat way to hold gold via the ASX and also be impartial AUD/USD exchange rate timing, is to hold a split of half QAU half PMGOLD. I lay out analysis and reasons.

QAU and PMGOLD are ETFs that track the price of gold. The key difference between them is that QAU is hedged back to AUD, so when AUD strengthens QAU does relatively better than PMGOLD. On the other hand, PMGOLD is USD based, so when AUD falls against USD, PMGOLD does relatively better than QAU.

The plot below shows that these gold price tracking ETFs, either hedged to AUD (QAU.AX) or non-hedged (PMGOLD.AX) can perform very differently over time due to AUD/USD movements.

Since it’s too hard to accurately predict how AUD/USD fx rates will move going forward, holding half of each QAU/PMGOLD means one would not have to worry about this - it eliminates some timing risks, related to AUD/USD fx.

Below are real examples on the timing/currency point.

First, if one happened to purchase a gold ETF via the ASX in Jan 2020 and then needed to sell for some during March 2020, the returns were great if they picked PMGOLD and poor if they picked QAU. This difference driven by the AUD/USD fx rate, as depicted below:

On the other hand, if one happened to purchase mid-March 2020 and needed to sell now then results are the opposite - returns would have been great if they’d picked PMGOLD and poor if they picked QAU. Again, the difference driven by the AUD/USD fx rate, as depicted below:

The final figure below shows the time period from the start of 2020 to now, but includes an extra line with the returns of holding half QAU/PMGOLD. This half AUD-hedged/non-hedged approach gives a way for Aussies to get gold exposure that does not have AUD/USD timing risk.

I’ll provide my code used to get/wrangle the data and make the figures here https://github.com/DanielPNewman/gold

Diversifying against equities while bonds are yielding ~zero and reserve banks are printing extreme amounts of money?

2020-07-12T00:00:00+00:00

My Sunday night thoughts on the topic.

The question

I was wondering if there isn’t an alternative to nominal bonds for diversifying against downturns in equities, that also gives protection against inflation¹. Since nominal bond yields across developed economies are now so close to zero, coupled with ongoing extreme amounts of money printing by central banks to combat the covid19-related economic slowdown, there is limited upside left in holding the large exposure to nominal bonds outlined in our previous portfolios².

So I asked the question, could I replace some (or all) of our nominal bonds with Inflation Linked (IL) bonds, while still holding a balanced asset mix diversified across currencies, geographies and asset classes?

The answer

My analysis found that the simplest way to deal with this was to keep my equities, commodities, gold and emerging markets target weights as they are, and simply switch nominal bonds to IL bonds instead. I thought of this idea after watching this video from Bridgewater. The whole video is excellent, but the last few mins (from around 27:45 onwards) lay out my motivations, succinctly.

I ran historic data through my volatility budgeting algorithm to figure out the new weights needed to do this and still keep the whole portfolio balanced in line with the all-weather principle. Interestingly the asset class weights come out neatly such that all that is required for this tactical change is to switch some (or all) nominal bonds to IL bonds. All other assets’ weights could stay basically as they were. The resulting asset mix could look very much like that in Table 1.

Table 1. A global ESG filtered all-weather portfolio that uses inflation linked (IL) bonds in place of nominal bonds³

Ticker	Portfolio Weight	Asset Class	Region	Exchange
VESG	18%	Equities	International (Developed Markets)	ASX
FAIR	6%	Equities	Australian	ASX
VGE	2.5%	Equities	International (Emerging Markets)	ASX
WIP	10%	Inflation linked Government Bonds	International (Developed Markets) Ex. US	NYSE
SCHP	20%	Inflation linked Government Bonds	US	NYSE
ILB	7%	Inflation linked Government Bonds	Australian	ASX
GSBE47	4%	Government Bonds (long term)	Australian	ASX
GGOV	10%	Government Bonds (long term)	International (Developed Markets)	ASX
IHEB	6.5%	Emerging Market Credit	International (Emerging Markets)	ASX
PMGOLD	8%	Gold	Universal	ASX
DBC	8%	Commodities	Universal	NYSE

The notes

¹ Previously, when equity markets have dropped, central banks have lowered interest rates to stimulate their economies, thus driving up bond prices which has counterbalanced the short-term downturn in equities to smooth out the ride in our volatility balanced portfolio. But now that interest rates are basically at 0%, this once trusty simulative lever of lowering interest rates is no longer going to be effective to offset drops in equity markets. So, what do we expect countries to do next time equity markets drop? For one thing, monetary policy is likely to get more extreme in terms of printing money. This extreme increase in money printing brings another risk for anybody holding large % of nominal bonds – inflation. Since bonds hold a promise to pay back their face value in cash, high inflation rates erode the purchasing power of cash and thus decrease the value of nominal bonds. IL Bonds correct for this, by increasing face value in line with inflation.

² Amy and I made a number of posts since our August 2019 blog outlining a defensive portfolio to weather economic storms. The follow-up posts in April 2020, after the market shocks caused by COVID 19, showed that both the original solution and our evolved solution of an Environmental, Social, and Governance (ESG) filtered version of this strategy performed exactly as designed and weathered the COVID 19 market storms well, for which we were very happy and grateful.

³ There is currently no way to get global IL bond exposure via the ASX, so we use TIP and WIP via the NYSE for our global IL bond exposure. Access to the NYSE also means we can use DBC for commodities exposure which we like since this ETF is far more liquid than the commodities ETF available on the ASX, ‘QCB’, which has very low trading volume leading to liquidity risk.

All-Weather Portfolio Update

2020-04-12T00:00:00+00:00

How has our all-weather strategy held up so far in the COVID-19 epidemic?
An Update

A number of people have asked how our investment strategy - outlined in our August 2019 blog post called Global “all-weather” portfolios - investment adventures of an Aussie couple - has fared in response to the Covid-19 epidemic.

The August 2019 blog shared our research into a strategy designed to perform relatively well in any economic environment. We are very glad that we did this research and took action based on our findings. It has served us well in these turbulent times!

Rather than replying to each person individually, Amy and I decided we’d do 2 things: 1) I’d write this update adding the latest data to the performance plots described in our original blog post, to show how that has fared during the time since we first published it; and 2) Amy would write a quick post showing our evolved solution executing an Environmental, Social, and Governance (ESG) filtered version of this strategy as a single ESG portfolio diversified across global geographies and risk balanced across asset classes.

But below are the updated versions of the plots from our original blog post - with new data added to cover the 8 months since we published it:

Domestic Australian assets - performance updates:

The original domestic Australian asset mix since we published it in Aug 2019

The domestic Australian “All-Seasons” and “Our Version” portfolios have had superior returns and smaller drawdowns compared to 100% equities via the ASX 300 accumulation index, since we published the asset mix in Aug 2019. The relevant securities and weights for each of these portfolios are shown in Table 2 of our original blog post.

Historical performance of the original domestic Australian asset mix

Historical performance simulations of domestic Australian “All-Seasons” and “Our Version” portfolios show these portfolios had considerably smaller and less frequent drawdowns compared to 100% equities via the ASX 300 accumulation index. Although the overall return of 100% equities (ASX 300) was the highest, one could have put money in/taken it out of All-Seasons and Our Version at any time since 2001 without suffering unpalatable losses. The same cannot be said for the ASX 300 which, on five occasions since 2001 has suffered drawdowns large enough to be unacceptable for our situation. The relevant securities and weights for each of these portfolios are shown in Table 2 of our original blog post. ETFs for the required asset classes were not available prior to 2012 so the performance shown is based on their underlying indexes. Historical bond performance was derived from futures price indices with coupon payments accumulated semi annually at historical 10 year yield rates. The commodities performance reflects underlying SPGCLEP commodities index only, excluding potential ETF distribution payments and currency hedging effects. We painstakingly sourced this historic index data from a variety of free sources and can’t guarantee its accuracy - if anybody reading this has access to a proprietary data source they’d like to share with us, please get in touch!
_{Note these simulated results do not include brokerage fees and assumes the desired portfolio balance is maintained throughout, whereas in practice the portfolio would be rebalanced periodically. Simulated historical performance results have inherent limitations since unlike an actual performance record, simulations do not reflect the cost of trading or the impact of actual trades on market factors such as volume and liquidity.}

Foreign Assets - performance updates:

The original foreign asset mix since we published it in Aug 2019

Our Versions of the unleveraged foreign asset mix have had superior returns and also smaller and less frequent drawdowns compared to 100% “Global Equities”. The relevant securities and weights for each of the portfolios depicted here are provided in Table 2 of our original blog post.

Historical performance of the original foreign asset mix

Historical performance simulations of “Our Version” of an unleveraged global All-Weather portfolio show it had lower overall returns but also considerably smaller and less frequent drawdowns compared to 100% “Global Equities”. Although 100% global equities gave a higher overall return over a longer period, our time-horizon and needs currently favour limiting drawdowns over maximising returns. The relevant securities and weights for each of the portfolios depicted here are provided in Table 2 of our original blog post.
_{Note these simulated results do not include brokerage fees and assumes the desired portfolio balance is maintained throughout, whereas in practice, the portfolio would be rebalanced periodically. Simulated historical performance results have inherent limitations since unlike an actual performance record, simulations do not reflect the cost of trading or the impact of actual trades on market factors such as volume and liquidity.}

An important point to reiterate is that the portfolios here are NOT the same as Bridgewater’s All-Weather. Bridgewater uses cheap leverage and sophisticated investment instruments to increase returns while still minimizing risk. These simplified portfolios we show here do not use leverage and thus have lower expected returns than Bridgewater’s All-Weather, but still reduces risk compared to 100% equities.

Our continued evolution

We wanted a single globally diversified portfolio filtered for ESG/ethical considerations and based upon the principles discussed here and in our original blog post…

So now Amy and I can share our evolved solution, executing an ESG filtered version of this strategy!

Looking forward, we’re still making good progress on figuring out how to cost-effectively leverage our lower volatility asset classes (e.g. the Treasury bonds) to have similar expected volatility/returns as equities, thus maintaining risk parity while increasing returns, like Bridgewater can. We are still ironing out more details around margin requirements, transaction costs, rebalancing, etc. and codifying our strategy into software, but it is looking very promising. So stay tuned for the future instalments!

This joke from our original blog post has proved to be telling since we published it in Aug 2019!

Trip Finished

2020-03-23T00:00:00+00:00

I have just finished a long holiday vagabonding around Asia and South America (Brazil) with my wonderful wife, Amy.

The trip was planned to last 11 months, but we cut it short to 9.5 months due to the Covid-19 epidemic. We have just arrived back in Melbourne and have started a compulsory 14-day self-isolation.

I felt like taking a few minutes to reflect and write a short blog looking back over the last 9 and half months.

And what an amazing 9.5 months it has been! We were lucky enough to visit diverse cultures across 11 countries (Singapore, Sri Lanka, Malaysia, Vietnam, Thailand, Philippines, Taiwan, New Zealand, Japan, Spain, Brazil) and get to know (and reconnect with) many wonderful people. We experienced so much amazing nature, sights, foods and people, too much to list here.

But on reflection, the trip wasn’t all about touring, eating and hanging out with new and old friends – I also made some large achievements and learned productive life skills along the way. I will try to take stock of them in the list below:

learned to surf
created a personal investment strategy
taught Amy some programming
learned to meditate as a habit
made a daily 12 min exercise routine a habit
learned to speak a little Mandarin Chinese
read excellent books
walked amazing hikes
worked on marriage foundations
skied powder snow
reconnected with old friends/colleagues living abroad
figured out my personal strengths and weaknesses
created a personal mission statement

It feels good to think back on some of these achievements and skills that I gained while we travelled around Asia and Brazil. I feel ready to be back home in Australia and enjoy the next chapter of life!

Teaching Lawyers to code? A Lawyer learns programming for data analysis/communication

2019-11-12T00:00:00+00:00

Amy works as a Lawyer and environmental sustainability consultant. She wanted to know some programming for data analysis/communication to help uncover and share useful environmental sustainability insights. I work as a Data Scientist so taught her some of my trade’s tools to help set up her own github account and then learn the R, Git and *nix commands necessary for her to:

pull environmental data from a google sheets URL,
process it,
visualise it,
knit the results to markdown format
and publish the results on her own github pages blog.

This was a satisfying and educational process for us both:

Amy learnt the data processing/visualisation skills needed to uncover and communicate environmental sustainability insights (more on those below).
I learnt about some fascinating ideas about how Project Drawdown’s solutions apply to the 17 Sustainable Development Goals (SDGs).

And it led to publishing an excellent and thought provoking post: Project Drawdown solutions meet many Sustainable Development Goals. Certainly worth a read!

On reflection, I encourage all data scientists to spend a little time teaching others (who want to learn) some of the basic tools of our trade. This can empower people (especially those from more “traditional” fields such as law) with new tools to explore and communicate data. It can only be a good thing if the ever increasing volumes of digital data are more often explored through multiple lenses of diverse areas of domain expertise.

Amy said that “for me, programming for data analysis and communication was such an unknown area that it was hard to know where to start. So to have somebody who can walk you through some of the basics to get you started is really empowering!”

Travel guide to clean water in Sri Lanka without single-use plastic

2019-10-28T00:00:00+00:00

Written by Amy Quinton and Daniel Newman

Have you ever assumed that if it’s not safe to drink the tap water while travelling, then you must rely on single-use bottled water?

We did…

But the great news is this is no longer the case. Better options exist and smart, responsible travellers around the world are seeking them out.

Here are some insights to help you join your fellow travellers in giving single-use plastic bottles the flick.

_{We are not finanically or otherwise affiliated with any of the products or services mentioned in this blog.}

Sri Lanka hydration guide

At the start of our Sri Lanka trip the number of plastic bottles we were consuming was growing at an alarming rate. We thought there must be a better way. And there was!

There are actually numerous clean water sources (some more obvious than others) that allowed us to significantly reduce our plastic bottle waste.

Personal purifier

Before getting into Sri Lanka’s network of water refill stations, a quick note on personal filter and purifier options. If you’re planning ahead for longer term or regular travel, having your own water purifier bottle or system can be great to cover your hydration needs. We now use “Travel Tap” water purifying systems which is very versitile and convenient, and there are plenty of other choices available.

But if you don’t have one of these magical water purifiers, never fear. Check out our tips below for staying hydrated in Sri Lanka without leaving a trail of plastic pollution behind you.

GetWater.Lk

GetWater.lk documents the network of water refill station options around Sri Lanka to help travellers avoid single-use bottles. You can check out Get Water’s website and use their nifty map to find water refill options near you. You can also contribute by adding additional clean water refill treasures that you discover during your travels.

Many of the places we found to re-fill our bottles weren’t yet recorded by Get Water. With this in mind, it’s definitely worth asking and looking around if Getwater.lk doesn’t show options near you. And if you find new options, add them!

Below we share with you just few of the many places and ways to refill, and things learnt along the way about water in Sri Lanka.

Arugambay

Waste Less ABay

On the main street in Arugambay you will find a free purified water refill station called WLAB. You can also drop off pastic water bottles for recycling there! It was actually WLAB who started the Getwater.lk initiative mentioned above!

Restuarants - Spice Trail & others

Find refreshing cold water served in a re-fillable glass bottle at restaurants like Spice Trail on the main road through Arugambay (picture of menu below).

Sigiriya

There are at least two free purified water re-fill stations in Sigiriya installed by Pina Organization, one in the Village (on Sigiriya Rd outside Central College) and one inside the main (western) entrance to Lion Rock.

How we discovered this

We first stumbled across a water station outside the school in Sigiriya Village. We took our bottles and refilled them before visiting Lion Rock. But we could have saved the extra walk because on arrival to Lion Rock we discovered another refill station specifically for tourists inside the entrance! Hopefully it is only a matter of time before these are more widely publicised.

Anuradhapura

You can actually refill at many of the local general stores in town for only 3 LKR per litre. Who knew?! A 1.5 litre plastic bottle costs between 70-150 rupees, so only 3 LKR per litre is quite a saving!

The photo below shows the kind of water tank to look for. This particular place was located on Niwanthaka Chethiya Road, Anuradhapura.

How we discovered this

We asked our excellent guest house, Heritage Lake View, if they had a verified clean water line like some other places. Our host said that wasn’t available at the property, and told us he re-fills his water drums in town for 3 LKR per litre. He took us to the general store to re-fill our water that afternoon (on our way to sightseeing) and even lent us one of his 7 litre drums to use during our stay.

So an easy option is to ask your accommodation where to refill, or if you can refill from their clean water supply (depending on the situation, you could offer a small amount to cover water costs, although this is usually not expected).

I’d tap that!

A note on “tap water”:

Not all tap water in Sri Lanka is created equal. While you can’t drink from just any tap, most accomodation and restaurants in certain areas actually have a special water line providing safe drinking water. It’s generally referred to as a “Government” or “Water Board” water line. Technically it comes out of a tap but it is not standard “tap water”. It is a potable water line verified by the Sri Lankan Water Board.

This was a big surprise to us. We started asking our accomodation if they had a Government water line. Often they did and were happy to refill our bottles for us. We understand some travellers may be wary. Ultimately you must assess the risk for yourself, but we drank a lot of Government line water without any issues, including in Trincomalee, Galle and Mirissa.

Below we share some information about water in Trincomalee and the South Coast, including some Government water line and filtered/mineral water options. (Some areas in Sri Lanka rely more heavily on ground water, which we understand varies in quality from place to place. We did not happen to drink ground/well water so do not comment further on this here.)

Trincomalee

Verified clean water lines are installed at most accomodation and restaurants in the area. Ask your accomodation if you can use their Government water line to re-fill your bottle.

The water tasted good and we had no issues. It is important to ask otherwise restaurants/accomodation tend to assume tourists whould rather single-use plastic bottles.

Sometimes we find it helps to explain we are trying to use less plastic to avoid leaving plastic pollution behind us when we travel. Our hosts have been very responsive to this and happy to accomodate this request! But if a host is reluctant or doesn’t understand our request, of course we respect that too.

How we discovered this

It was actaully a beachfront restaurant owner who first told us about this. He was concerned about plastic pollution, as were we, and he told us most properties in the area have at least one verified clean water line. A what in the who know?! How did we not know about this?! He went on to tell us that this was locally called a ‘sofia line’ (or something sounding like that!) after the french water company that installed the water infrastructure. We asked our accomodation (Sea Zone Hotel) and sure enough they had a line and showed us where to refill our bottles with clean, safe drinking water! There were separate water lines to the guest bathrooms (which are not safe for drinking) and there was only one drink safe ‘sofia line’ available outside and another one in the kitchen. The hotel owners told us it was mandatory for them to have that water line.

Galle Fort

Service water at resturants

Dinner at Pedlar’s Inn Cafe and Restaurant and coffee at Heritage Cafe and Bistro - both provided glasses of water free of charge. If you have your water bottle with you, you could ask your restaurant to re-fill it for you.

How we discovered this

At Pedlar’s Inn Cafe and Restaurant we noticed Sri Lankan people had glasses of water on their table so asked the waiter for the same. We’re not sure if it was from the Government water line or mineral water, but it tasted fine and quenched our thirst.

Our experience is that reputable establishments in Sri Lanka have water sanitation practices in place to prepare food, fresh juices etc. After enjoying drinks like Lime Juice that clearly had added water, we started to trust that water served at these venues would also be fine. And after 6 weeks and counting travelling in Sri Lanka, so far so good!

Unawatuna Beach, Galle

Hideout

The hideout cafe and restaurant offers free purified mineral water refills for patrons. We thoroughly enjoyed the tacos, burritos and bountry cake here too! If you just need water, we’re sure they would happily refill your bottle, maybe for a small fee.

Mirissa

We asked our friendly hosts at our Homestay (Sun Ray Rest) to use their Government / Water Board line for drinking water. They were very obliging and refilled our bottles for us. If you are open to trying this option, definitely ask your accomodation about it!

Dickwella / Hiriketiya Beach

Stay at Peak house for all your filtered water needs! We enjoyed the water filter here so didn’t need water from the Government line, however the host told us that most properties in the area do have a Government water line.

Verse Collective is trendy space in Dickwella that provides table water in re-fillable glass bottles rather than plastic. It has solid wifi too!

Winning the water game

Time to blow this single-use bottled water myth right out of the water!

Smart and experienced travellers around the world are seeking out alternatives to unnecessary and harmful single-use plastics. Forward thinking businesses and organisations are already offering solutions. Others are still developing the awareness and resources to do so.

So, now you know, join your fellow travellers in kicking the single-use plastic habit. Support organisations offering solutions and let locals know you want to help keep their country beautiful and ocean life healthy by not leaving a trail of plastic waste behind you.

Travel App feature?

Would you find it useful if patforms like Trip Advisor, Booking.com, Google Maps, AirBNB etc listed “water re-fill station” as a facility at the accomodation/restaurant/area? If so send them some direct feedback or let us know in the comments below to help get this added as a feature!

_{While using the tips shared here we have stayed healthy and never experienced any water related health issues in Sri Lanka. However, of course we cannot absolutely guarantee the quality of any drinking water supply.}

Global “all-weather” portfolios - investment adventures of an Aussie couple

2019-08-15T00:00:00+00:00

In this blog we share our path to applying US-centric All-Weather investment information to an Australian situation, with the result being a global All-Weather portfolio. This is by no means advice, rather we share our experience in case it is interesting or useful to others and to further develop our ideas. We embarked on this because our current situation necessitated reducing our investment timing risk and an “All-Weather” approach could smooth out the ride.

Our story - discovering “All-Weather” investment & why we wanted in

We (Amy and Dan) are a couple in our early 30s, just married, and enjoying the flexibility of renting without a house mortgage. Until 2018 we invested almost 100% of our (modest) savings in a portfolio of equities (aka. shares/stocks) - following advice given to people with long time-horizons to invest savings heavily in equities, preferably by dollar cost averaging, to maximize returns over the long run. We were happy to take on the risk of equities until we decided we might want to buy a house in the next 5 years and realized our equity investment time-horizon wasn’t so long-term after all.

We knew from the stock market’s history that our life-savings could quickly plummet in value and take years to recuperate. We didn’t want this to happen at the same time we planned to use our savings for a house deposit! So we started selling our equities and by the start of 2019 most of our savings were sitting in a cash savings account.

The problem was that it could be awhile before we actually purchased a house and we had a nagging sense that our savings should be doing more for us in the meantime. We no longer want the risk of being almost 100% in equities but we also aren’t happy holding mainly cash since any other asset class will provide more returns than cash over time. And we definitely weren’t interested in trying to time the equity market. So what to do?

When we heard about Bridgewater Associates’ “All-Weather” strategy to minimize downturns in any economic environment while outperforming cash this seemed like an elegant solution. Living in Melbourne with its reputation for four seasons in one day we’ve learnt to either be prepared for surprise changes in weather or face the consequences! [queue Four Seasons In One Day by Aussie band Crowded House]. The “All-Weather” analogy struck a chord and we thought having an investment portfolio designed to weather any economic storm would be awesome for our situation!

Image source.

The next question was how? There is no way we could afford the hedge fund giant’s minimum buy-in for their All-Weather fund, and the fund is closed to new investors anyway, so this was not an option. We looked around but couldn’t find anyone offering a suitable All-Weather style fund that we could access. So we decided to build one ourselves.

Fortunately, in 2014 the founder of Bridgewater Associates, Ray Dalio, shared his recommended asset types and weights for a very simplified version of All-Weather that an average person could implement themselves, in an interview with life/business strategist Tony Robbins. This helped us to get a sense of what our portfolio might look like. But because the advice was targeted at US citizens, we weren’t sure if it was directly applicable to us (as Australians) or how to practically implement it.

Our problem - how do we apply US-centric All-Weather advice to our Australian situation?

We found several blogs/articles about how a normal US citizen could implement their own All-Weather-like strategy but it was not clear how to do this in Australia. So we set out to find a solution that we could personally use, which we share in this blog.

But first, a brief background on what is an All-Weather portfolio?

What is an All-Weather portfolio?

_{“If you can’t predict the future with much certainty and you don’t know which particular economic conditions will unfold, then it seems reasonable to hold a mix of assets that can perform well across all different types of economic environments. Leverage helps make the impact of the asset classes similar.” (from Bridgewater’s The All-Weather Story)}

All-Weather is an approach to asset allocation designed to minimize downside and perform relatively well regardless of the prevailing economic environment, hence the name “All-Weather”. The concept was first implemented by Ray Dalio and his team at Bridgewater Associates, now the largest hedge fund in the world. In researching and developing All-Weather, Bridgewater recognized there are primarily two factors driving the value of any asset class - the levels of economic activity (growth) and inflation. Therefore, the economy can be broadly viewed as having four “environments”. These are:

(1) Rising growth; (2) Falling growth; (3) Rising inflation; and (4) Falling inflation.

Throughout history and across countries, distinct asset classes have consistently performed well during each of these four environments. So, there is a season for all assets, but unlike the real weather we never know which seasons are next or when the seasons will change, and worse, two seasons can occur at once! So surprises impact asset prices due to unexpected rises or falls in growth and inflation.

Recognizing this, an All-Weather portfolio essentially comprises four sub-portfolios - one for each economic environment containing assets known to perform well in that environment. Asset weights are then balanced to achieve risk-parity between each of the four environments. Bridgewater’s diagram below shows the four economic environments containing examples of relevant asset types, with risk divided equally across environments.

Image taken from Bridgewater’s The All Weather Story

The All-Weather approach assumes we can’t forecast the future so we don’t know which particular growth/inflation conditions will unfold. Given this, it assigns weights such that there is an equal amount of risk (25%) in each of the 4 possible growth/inflation environments. Therefore the portfolio is designed to perform no matter which growth/inflation environment eventuates.

All-Seasons portfolio

As mentioned above, Tony Robbins popularised a simplified version of Dalio’s All-Weather, coined the “All-Seasons” portfolio in his 2014 book MONEY Master the Game: 7 Simple Steps to Financial Freedom. Robbin’s has also published a free version of the Ray Dalio All-Weather interview in this Yahoo Finance article which is worth a read.

In a nutshell, the Dalio/Robbins All-Seasons portfolio looks like this:

30% stocks
40% US long-term treasury bonds (20-25 years)
15% US intermediate-term treasury bonds (7-10 years)
7.5% gold
7.5% commodities

All-Seasons vs Bridewater’s All-Weather - what’s the difference?

An important point to clarify is that All-Seasons is NOT the same as Bridgewater’s All-Weather. Bridgewater uses cheap leverage and sophisticated investment instruments to increase returns while still minimizing risk. The simplified All-Seasons portfolio does not use leverage and thus has lower expected returns than Bridgewater’s All-Weather, but still reduces risk.

Throughout this article, we use the term “All-Weather” to convey the general concept of diversified, risk-balanced asset allocation developed by Bridgewater outlined above, acknowledging that All-Weather portfolios that utilise leverage appropriately, like Bridgewater do, will outperform non-leveraged versions. See the Q and A section below for more commentary on using leverage in a risk balanced investment strategy.

As an aside, we’re still working on figuring out how the average individual investor in Australia can cost-effectively leverage the lower volatility assets (e.g. Treasury bonds) to have similar expected volatility/returns as equities, thus maintaining risk parity while increasing returns, like Bridgewater can. One way might be through Treasury bond futures. But we need to iron out how to achieve this practically in a cost effect way. If you have the answer, let us know!

So, we set out to understand how to create our own All-Weather portfolio(s) without using leverage, for now.

Our solution - domestic and global portfolios

We wanted a globally diversified All-Weather portfolio. We achieved this by creating essentially two sub-portfolios - an All-Weather portfolio containing Australian domestic assets, and another All-Weather portfolio containing global assets for geographic diversification. First, we’ll share how we developed the domestic portfolio. Then we’ll delve into the global portfolios.

Click here to skip straight to our table of portfolios showing individual securities and weights.

Domestic

The All-Seasons portfolio outlined by Robbins’ has a domestic bent, comprising US stocks and government bonds (ie treasuries). We wanted exposure to both Australian and Global asset markets.

The relevant global ETFs available on the Australian Securities Exchange (ASX) exclude Australian assets (ie are “ex AU”). Therefore to have exposure to Australian and global markets it was necessary to include these securities separately (see the Q and A section below for more commentary on this).

We first located domestic Australian securities traded on the ASX matching each asset type suggested by Ray Dalio to Tony Robbins for the simplified All-Seasons portfolio. The Table 1 below includes the relevant Domestic securities we found.

Scroll down to Table 2 and for example portfolios that select one security from each asset type.

Table 1. Relevant securities for asset types in an Australian domestic “All-Seasons” portfolio.

Asset type	Relevant securities via ASX / unlisted fund
Equities	Vanguard Australian Shares Index ETF (VAS); BetaShares Australia 200 ETF (A200); BetaShares Australian Sustainability Leaders ETF (FAIR); Vanguard Index Australian Shares Fund (VAN0010AU)
Long-term Australian treasury bonds (~20-25 yrs)	Long-term exchange-traded treasury bonds (ETBs): - year of maturity - 2037 (GSBG37); - year of maturity - 2039 (GSBK39); - year of maturity - 2041 (GSBI41); - year of maturity - 2047 (GSBE47)
Intermediate Australian treasury bonds (5-10 yrs)	Vanguard Australian Government Bond Index ETF (VGB); BetaShares Australian Government Bond ETF (AGVT); iShares government bond index ETF (IGB)
Gold	BetaShares Gold Bullion ETF - Currency Hedged (QAU); ETFS Physical Gold (non-hedged) (GOLD); Perth Mint Gold (PMGOLD)
Commodities	BetaShares Commodities Basket ETF - Currency Hedged (synthetic) (QCB)

We understood that the underlying principles of the All-Weather approach are timeless and universal and therefore would apply in Australia (and globally). But we weren’t sure whether the asset class proportions recommended for the US All-Seasons would be the same in Australia. So we (particularly the data scientist among us) wanted to test it out on plenty of historical data before trusting it with our life savings. To sense test, we first looked at historical performance using the All-Seasons asset weights for both US and Australian assets.

Dan built an algorithm to perform risk-parity weighting in line with Bridgewater’s All-Weather principles. This was useful to:

confirm that Australian and global based asset classes have roughly the same relative risk-weightings as their US counterparts,
allow adjustment of the risk-parity weights to account for additional asset types, such as inflation linked (IL) bonds and emerging market (EM) credit, and
back-test the resulting portfolio weights to assess historical returns and more importantly, drawdowns, in terms of their size, duration and frequency.

Experimenting with different assets through this algorithm gave us comfort that the asset weightings recommended for the US are generally applicable in Australia (and globally). Since Bridgewater uses IL-bonds and EM credit in their All-Weather strategy, and since we can access these via the ASX and have an algorithm enabling us to risk-balance them, we also added IL bonds (and EM credit for global) to our versions of the All-Weather. Historical performance plots in Figure 1, using Australian domestic assets, shows that “our version” does a very good job of avoiding downturns compared to 100% equities/ASX300.

For sure, Figure 1 shows the overall return of 100% equities (ASX 300 accumulation) was the highest, but it also shows one could have put money in/taken it out of All-Seasons and Our Version at any time since 2001 without suffering unpalatable losses. This is what we are currently after! The same can not be said for 100% equities/ASX300 which, on four occasions between 2001 and 2019, suffered drawdowns large enough to be unacceptable for our current situation.

Domestic Australian All-Seasons and Our Versions of the asset mix protect capital, with less frequent and smaller drawdowns than the ASX 300

Figure 1. Historical performance simulations of domestic Australian “All-Seasons” and “Our Version” portfolios show these portfolios had considerably smaller and less frequent drawdowns compared to 100% equities via the ASX 300 acummulation index. The relevant securities and weights for each of these portfolios are shown in Table 2. ETFs for the required asset classes were not available prior to 2012 so the pre-2012 performance shown is based on their underlying indexes. Historical bond performance was derived from futures price indices with coupon payments accumulated semi annually at historical 10 year yield rates. The commodities performance reflects underlying SPGCLEP commodities index only, excluding potential ETF distribution payments. We painstakingly sourced this historic index data from a variety of free sources and can’t guarantee its accuracy - if anybody reading this has access to a proprietary data source they’d like to share with us, please get in touch!
_{Note these simulated results do not include brokerage fees and assumes the desired portfolio balance is maintained throughout, whereas in practice, the portfolio would be rebalanced periodically (e.g. every 6 months or so). Simulated historical performance results have inherent limitations since unlike an actual performance record, simulations do not reflect the cost of trading or the impact of actual trades on market factors such as volume and liquidity. See the Q and A section below for commentary on periodic rebalancing.}

Lower risk comes with lower expected returns
We’re not suggesting our simple All-Weather portfolio is optimal or will maximize returns. It almost certainly won’t. Rather, it is designed to protect assets by avoiding large drawdowns during economic upheavals and market downturns, while still providing reasonable returns above the cash interest rate. In our current circumstance the security of knowing we are less exposed to downturns is worth the decreased expected return potential.

Global

A limitation we saw of a domestic Aussie portfolio was a lack of geographical diversity. To get more exposure to global activities and therefore hopefully more resilience to economic surprises, we made a second All-Weather portfolio using global assets. (For more on the benefits of geographical diversification, including for All-Weather, see Bridgewater Associates’ April 2019 memo.)

We used Dan’s algorithm to create All-Weather risk parity weightings for global assets and back-test them. We used historic data from ETFs available on US securities exchanges for this task because they have existed for longer than their Australian ETF counterparts and therefore have more data.

This process meant we identified ETFs available on US securities exchanges that can be used to create a global All-Weather portfolio. We also included US only stocks and government bonds in the mix because the US-exchanged global shares and bond ETFs exclude US stocks and bonds. For completeness we’ve included these securities in Table 2 under columns “Global (NYSE/ NASDAQ)” and “Global with IL-bonds (NYSE/ NASDAQ)”.

Out of curiosity we added inflation-linked bonds to our Global (NYSE/ NASDAQ) portfolio. But given we could not find a global IL bond ETF on the ASX, we chose not to include IL bonds in our Global (ASX) portfolio. Of course it would be possible to access a Global IL bonds ETF via an international trading platform, but at this point we wanted to stick to ASX traded global securities to reduce complexity.

The historical performance plot in Figure 2 below shows that our Global version also does well at avoiding downturns compared to a 100% global equities allocation. Again, 100% equities gives a higher return, but our current time-horizon and needs favour limiting drawdowns over maximising returns.

Our Global asset mix protects capital during turbulence with less frequent and smaller drawdowns, compared to 100% Global Equities

Figure 2. Historical performance simulations of “Our Version” of an unleveraged global All-Weather portfolio show it had lower returns but also considerably smaller and less frequent drawdowns compared to 100% “Global Equities”. The relevant securities and weights for each of the portfolios depicted here are provided in Table 2.
_{Note these simulated results do not include brokerage fees and assumes the desired portfolio balance is maintained throughout, whereas in practice, the portfolio would be rebalanced periodically (e.g. every 6 months or so). Simulated historical performance results have inherent limitations since unlike an actual performance record, simulations do not reflect the cost of trading or the impact of actual trades on market factors such as volume and liquidity. See the Q and A section below for commentary on periodic rebalancing.}

Table 2. Relevant securities and their weights for each of the portfolios represented by the historical performance plots: All-Seasons (Domestic Australia), Our Version including IL Bonds (Domestic Australia), Our Version (Global-ASX), Our Version (Global-US NYSE/NASDAQ), Our Version including IL bonds (Global-US NYSE/NASDAQ). Historical performance simulations of these portfolios versus 100% equities are shown in Figure 1 and Figure 2. A short description for each ticker is provided in Table 3 below.

Asset type	All Seasons Domestic Aus (ASX)	Our Version Domestic Aus (ASX)	Our Version Global (ASX)	Our Version Global (NYSE/ NASDAQ)	Global with IL-bonds (NYSE/ NASDAQ)
Equities	30% VAS or FAIR	24% VAS or FAIR	20% VESG; 3% VGE	10% VEA; 11% VTI; 3% EEM	9% VEA; 11% VTI; 2% EEM
Emerging market credit	-	-	13% IHEB	12% EMB	10% EMB
Long-term treasury bonds (~20-25 yrs)	40% GSBK39 &/or GSBI41 &/or GSBE47*	25% GSBK39 &/or GSBI41 &/or GSBE47*	-	27% TLT	19% TLT
Intermediate treasury bonds (~7-10 yrs)	15% IGB	15% IGB	51% VIF	22% BWX	23% BWX
Inflation linked bonds	-	24% ILB	-	-	10% TIP; 5% WIP
Gold	7.5% PMGOLD	6% PMGOLD	7% GOLD	8% GLD	6% GLD
Commodities	7.5% QCB	6% QCB	6% QCB	7% DBC	5% DBC

Note on long-term treasuries in Australia:
Of note is that we could not find any ETFs on the ASX that tracked long term (~20-25 years) Australian treasury bonds. We found only individual exchange traded long-term treasury bonds (ETBs). These ETBs have lower liquidity than the ETFs which can make it difficult to buy and sell when desired. Because of this we considered not using long-term bonds at all. But since Dalio recommended including long-term bonds because they provide higher returns than intermediate (due to higher volatility), we decided to give the ETBs a go. Increased bond volatility and returns was particularly important to us since we have not yet figured out how to leverage up the volatility and returns of our bond holdings in a cost effective way.

Table 3. Description and Exchange for each security listed in the Table 2. It’s worth noting that BetaShares Australian Sustainability Leaders ETF (FAIR) provides an ethical/sustainable Aussie equities option, and Vanguard Ethically Conscious International Shares Index ETF (VESG) provides an ethical/sustainable Global equities option, we found these really interesting.

Ticker	Exchange	Description
BWX	NYSE	SPDR Barclays International Treasury Bond
DBC	NYSE	Invesco DB Commodity Index Tracking Fund
EEM	NYSE	iShares MSCI Emerging Markets ETF
EMB	NASDAQ	iShares JP Morgan USD Emerging Markets Bond ETF
FAIR	ASX	BetaShares Australian Sustainability Leaders ETF
GLD	NYSE	SPDR Gold Shares
GOLD	ASX	ETFS Physical Gold (non-hedged)
GSBE47	ASX	Exchange-traded Treasury Bond – maturity 2047
GSBG37	ASX	Exchange-traded Treasury Bond – maturity 2037
GSBI41	ASX	Exchange-traded Treasury Bond – maturity 2041
GSBK39	ASX	Exchange-traded Treasury Bond – maturity 2039
IGB	ASX	iShares government bond index ETF
ILB	ASX	iShares Government Inflation ETF
IHEB	ASX	iShares J.P. Morgan USD Emerging Markets Bond (AUD Hedged) ETF
QAU	ASX	BetaShares Gold Bullion ETF - Currency Hedged
QCB	ASX	BetaShares Commodities Basket ETF - Currency Hedged (synthetic)
PMGOLD	ASX	Perth Mint Gold ETF
TIP	NYSE	iShares TIPS Bond ETF
TLT	NASDAQ	iShares 20+ Year Treasury Bond ETF
VAS	ASX	Vanguard Australian Shares Index ETF
VESG	ASX	Vanguard Ethically Conscious International Shares Index ETF
VIF	ASX	Vanguard International Fixed Interest Index ETF (Hedged)
VGE	ASX	Vanguard FTSE Emerging Markets Shares ETF
VEA	NYSE	Vanguard FTSE Developed Markets ETF
VTI	NYSE	Vanguard Total Stock Market ETF
WIP	NYSE	SPDR FTSE International Government Inflation-Protected Bond ETF

Closing Remarks

In the hope it may be interesting or useful to others, we’ve shared our experiences of realising we had to switch our time-horizon from long- to medium-term when thinking about investing, and that an “All-Weather” approach could help us do this. We’ve also shared our path to overcoming the problem of how to apply US-centric All-Seasons/All-Weather advice to an Australian situation using both domestic and global assets - the result being a globally diversified All-Weather portfolio. We do not comment on what percentage should be allocated to domestic vs global assets, however we do discuss this further in the Q and A section.

It’s worth noting that if our personal situation was different, say already owning a house and not needing to draw on our savings for the next 20 years, we may just keep dollar cost averaging our savings into low cost passive equity index funds. But that’s not the case for us right now.

Also, we are not trying to convince anyone in any situation to take our approach. We know a lot of people are interested in All-Weather, and we are simply sharing our own process and experience in building these portfolios in case it is interesting/beneficial to others, and also to clarify and develop our own thinking. To be clear, we are not qualified in finance - our backgrounds are data science/neuroscience, and law/environmental sustainability.

But we’ve enjoyed finding a solution to protect our assets’ value during any economic environment in case we want to draw on it at any time. We hope our approach will tame the rollercoaster that comes with the usual bull-runs and drawdowns when entirely in equities (see Figure 3 below), and also decrease our discomfort if there is another big down-turn like 2008.

We plan to keep refining our All-Weather portfolios / approach as time permits, hopefully finding practical ways to a) leverage the low volatility assets up to the level of equities’ volatility thus maintaining our All-Weather risk balance while increasing our expected returns, b) include global IL bonds in our ASX based global portfolio, c) add more ESG compatible assets, d) improve the code in our all-weather-risk-parity repository to allow it be used for any country (pull-requests with improvements/additions welcome if any devs. are reading this!), and e) research whether or not there is a place for crypto currencies to be added into our All-Weather asset mix. So much to do, so little time! But we’ll see how we go.

Figure 3. For illustration purposes (read for the lulz) only may not reflect reality!

Questions & Answers

As we were trying to understand Bridgewater’s All-Weather principles, some obvious questions occurred (and were flagged) to us, which are addressed below.

Of course, we cannot comprehensively cover everything here. If you’re after a deeper understanding, we recommend reading Bridgewater Associates’ 2015 article ‘Our thoughts about Risk Parity and All Weather’, which addresses common questions and misconceptions about risk parity and All-Weather.

Question: Is the All-Weather asset allocation based on past correlations between assets, which are unstable/unreliable going forward?

Answer: No, because:

The asset allocation is not based on the correlations between assets.
Rather, the portfolio is created based on the relationships of assets types to their environmental drivers. (There are four environmental drivers – economic growth, economic decline, inflation and deflation.)
While asset classes offer a risk premium that is similar once adjusting for risk, their inherent sensitivities to shifts in the economic environment are different.
Therefore, we can structure a portfolio of risk adjusted asset classes so that their environmental sensitivities reliably offset one another, leaving the risk premium as the driver of returns.

Question: Wouldn’t leveraging the bonds (or other less volatile assets) increase the risk of the portfolio?

Answer: No, because:

The leverage makes the volatility of your bonds’ equal to your equities and thus gives better diversification than possible without leverage. “For example, if I put 50% of my money in an unleveraged Treasury bond and 50% of my money in stocks, my portfolio would have been dominated by stocks during the 2008 financial crisis because stocks are more volatile; however, if I leveraged my bonds to have the same volatility as stocks, I would have had much better diversification and would have had much lower risks during the financial crisis.” (Bridgewater’s Engineering Targeted Returns and-Risks)
We wouldn’t need to use too much leverage, for example Bridgewater’s All-Weather strategy is only around 2 times leveraged.

Question: Is being Australian (earning and spending Australian dollars etcc) a good reason to run an Australian-only portfolio? Given Australia is such a small market compared to the rest of the world, wouldn’t it be better to come up with a currency hedging strategy to overlay across a global portfolio?

Answer: This is an important question in relation to portfolio diversification.

A desire to have geographical diversification was the reason we created a global All-Weather portfolio. The reasons for our approach to develop both the domestic and global portfolios separately are two-fold:
- First, it was simpler to apply the All-Weather principles to domestic and global separately because global All-Weather asset allocation is more complex than domestic only. Our limited resources and reliance on global index funds means our global risk weightings are less precise. This is in part because we cannot control the exact weightings the index funds use across countries. For example, the global stocks ETF is geographically weighted differently to the global bonds ETF.
- Second, the global ETFs traded on the ASX exclude Australian assets (ie are “ex AU”), therefore to have exposure to Australian markets it is necessary to include these securities separately.
Although we have noted that geographical diversification is important (see relevant Bridgewater Associates 2019 article and Vanguard Australia 2017 article on benefits of international diversification), we have not commented on how we think assets should be allocated across the Australian vs Global assets because it is a thorny issue. For example, if we were to allocate assets based on Australia’s market cap within global indices, we would only have about 3% Australian assets and the rest (97%) would be global. However, Vanguard Australia’s research shows that Australians tend to have a strong home bias when investing in shares. In 2014, Australians collectively held 66.5% Australian shares in their share portfolio, despite Australia representing only approximately 2.5% of the global market at that time. Robin Bowerman, Head of Corporate Affairs at Vanguard Australia comments that ‘home-bias investing ranks among the biggest forms of under-diversification’ (see Bowerman’s article). We have found that we are not completely immune to this ‘home bias’, even though we are aware of the problem!
According to Aidan Geysen, Senior Investment Strategist and Manager with Vanguard’s Investment Strategy Group, there are numerous reasons investors prefer home equities, some of which are behavioural and some that have sound investment rationale, including ‘a preference for the familiar, ease of access, aversion to currency risk, tax benefits and liability hedging to name a few’ (see Geysen’s article). Along with all information included in our article, we leave the reader to form their own view on what geographical asset allocation is most appropriate.

Question: How does your algorithm arrive at the target weights for the various asset classes?

Answer: The algorithm takes as input the users’ desired assets which the user has already pre-assigned into their relevant “environments” in this ‘portfolio-settings.yaml’ file (e.g. stocks go in the ‘rising growth’ and the ‘falling inflation’ environments because that’s when they tend to do well, IL-bonds assigned to the ‘rising inflation’ and ‘falling growth’ environments, etc). The algorithm then:

creates weights for risk-parity of the assets within each environment, essentially creating 4 “sub-portfolios”, a sub-portfolio for each environment. Then,
it looks at the overall volatility of each sub-portfolio and creates risk-parity weights between each of the 4 sub-portfolios so each takes on 25% of the risk. Then,
each asset’s final weight is determined by the sum of [its within environment weights]-multiplied by-[its overall environment weights], and the final ticker weight is output. The within- and between-environment risk-parity calculations are performed with the help of a python version of Ze Vinicius and Daniel Palomar’s ‘riskParityPortfolio’ package.

Question: How do you know when and by how much to rebalance the portfolio weights. Is this based on the prevailing growth and inflation environments? If so wouldn’t it require a measure and also a forecast for growth and inflation?

Answer:

This passive All-Weather approach assumes we can’t/won’t forecast the future so we don’t know which particular growth/inflation conditions will unfold. Given this, it assigns weights such that there is an equal amount of risk (25%) in each of the 4 possible growth/inflation environments. Therefore the portfolio is designed to perform no matter which growth/inflation environment dominates.
Regarding the question of when/how much to rebalance: how much is easy because we simply rebalance so our portfolio weights are back in line with the target weights already determined by the All-Weather risk-parity algorithm. These target weights shouldn’t change much year-to-year as they should be arrived at using many year’s historical volatility data. Each additional year of volatility data has small effect on overall volatility estimates.
But when to rebalance depends on how happy we are to let the weights drift considering brokerage costs. In this article on a simplified version of Ray Dalio’s All-Weather, Robbins recommends rebalancing the All-Seasons “at least annually” and notes that “if done properly, this can actually increase tax efficiency”. People’s tax situations vary so we don’t comment on a tax strategy here.
If a big financial upheaval, like in 2008, occurs in the middle of our usual rebalancing interim, it may be worth rebalancing prematurely. But assuming our portfolio weights are still within a few % of the targets weights (we’d assign some arbitrary threshold, say <10% abs. overall unbalance) there should be limited need to rebalance outside our chosen interval.

Question: What about including crypto currencies like BitCoin?

Answer: We could certainly consider adding crypto currencies like BitCoin into an asset mix, if we had good reasoning and evidence to decide in which of the four environments it should do well. But we have not yet done any research to confirm. We plan to research this in the future.

Want to know more about Bridgewater’s All-Weather investment strategy? See their articles:

Data Science Using Docker

2018-10-30T00:00:00+00:00

Wow, it’s been 2 years since I last wrote a blog post. Since then I finished work as a neuroscientist and started a data science job at SEEK. Loving the career change, I’m working in the AI platform services team where I use machine learning (ML) to help improve the efficiency of employment markets. I’m lucky to be surrounded by some excellent experienced data scientists and engineers and have learnt many useful skills. I plan to start blogging again to share some of the useful things I’ve picked up over the last 2 years.

One of the most useful and versatile tools I’ve picked up to help data science workflow is Docker.

What’s so good about using docker for interactive data science?

The thing I love about using docker is, it has eliminated the hassle of re-installing software and managing package/library/module versions every time I want to train ML models on a different machine – no more fighting module conflicts! You can make a docker image that has all your favourite data science tooling, and then use that image to easily build a container with your data science work environment that is identical every time, no matter what machine you build it on.

So no more “it worked when I ran it on my machine”!! ;-P

Some basic terminology

Dockerfile:

The ‘dockerfile’ is a text file with simple code that describes your docker image.

Docker image:

The docker ‘image’ describes the base operating system and all the other programs you want (for example, in my case: linux, R, dplyr python, fastText, xgboost, PyICU etc).

Docker container:

A running instance of an image is called a ‘container’. You can have one or many containers of the same image running on one or many physical host machines.

For want of an analogy… analogies are rarely perfect and this one is no exception, but in terms of baking a cake:

the dockerfile is your ingredients list,
the image is your recipe,
the container is the cake!
And the machine you are using is the oven.

You can bake as many cakes as you like with a given recipe. You can bake the exact same cake many times in different ovens. You can bake multiple cakes simultaneously in the same oven. As long as a machine has docker installed, your docker image is going to run and the container will work the same every time!

How to get Started

Install Docker
Test installation worked by running the simple hello-world Docker image:
- in your terminal/command prompt docker run hello-world
To start with, you may like to use an pre-made public image created for data science tooling, pulled directly from dockerhub. There are many of these available for free for on dockerhub which as a nice search function.
- for example, you could download this Jupyter Notebook Data Science Stack using docker pull jupyter/datascience-notebook
Alternatively, create your own custom docker image, with the exact tooling and versions you want. For example, below I discuss the files needed to edit and build my own custom docker image:

My docker image for training ml models

I made this docker image to help with my data science work flow. Specifically it allows me to quickly and easily set up the required versions of my tooling/packages (python3.6, R, dplyr xgboost, fastText, pyICU etc.) in a container on other machines.

The image can be pulled as is from directly from my public docker repo using terminal command:

docker pull danielpnewman/training-tools

Alternatively you can update my docker files and rebuild your own custom image using the steps below. :-)

Making your own docker image, ideal for model training using Python3.6, xgboost, fastText, PyICU, R, dplyr, and any other data science tools you like:

Clone the files from my docker image
- git@github.com:DanielPNewman/training-docker-files.git
If needed update the Dockerfile with required software.
If needed update requirements with required python packages.
Build local docker image from Dockerfile in ~/training-docker-files directory, this code tags the image name as “danielpnewman/training-tools”, which can be changed to whatever name you like:
- cd training-docker-files
- docker build -t danielpnewman/training-tools .
Put training data, scripts etc. into local /to-mount directory and then mount it into the docker container when you build it using this command:
- docker run --interactive --tty --volume $(pwd)/to-mount:/training/to-mount danielpnewman/training-tools
- Note you can mount multiple directories:
  - docker run --interactive --tty --volume $(pwd)/to-mount:/training/to-mount --volume $(pwd)/scripts:/training/scrips danielpnewman/training-tools
You can close the terminal of an active docker session and then log back into it later using its CONTAINER ID, e.g:

- exec -it d40b2796e7ca /bin/bash

Very basic docker cheat sheet

List Docker CLI commands

docker docker container --help

Display Docker version and info

docker --version docker version docker info

Execute Docker image

docker run hello-world

List Docker images

docker image ls

List Docker containers (running, all, all in quiet mode)

docker container ls docker container ls --all docker container ls -aq

Using R to create a word cloud from my PhD thesis text

2016-11-27T00:00:00+00:00

Median Property Prices 2005-16 - PART 2!

2016-10-22T00:00:00+00:00

A few weeks back I made a blog post with this nice little .gif below, of change over time in Median Melbourne Property Prices ($) from 2005-2016 - see my previous blog on 29 Sep 2016 :

Well I’ve just come back to looking at that data set and this time I’ve plotted the % change per annum and overall, and also absolute $ change from 2005-2016 on some interactive plots. These plots allow you to zoom in, hover over a suburb to see more info, or click on a suburb to open a new window and explore that suburb in more detail.

The R code I used to make the plots below is here.

Explore below, it’s interesting to see that SYNDAL has the greatest per annum and overall % growth, however it’s TOORAK that has by far has the highest absolute $ growth over the same period of time.

The ggiraph package gives ggplot2 nice reactivity to user input

2016-10-20T00:00:00+00:00

Using the plotly package to give your ggplot2 plots simple reactivity to user input

2016-09-30T00:00:00+00:00

First make up some fake revenue data for a company with a number of shops operating in each State from 2012 to 2015:

### Install/load required packages
#List of R packages required for this analysis:
required_packages <- c("ggplot2", "stringr", "plotly", "dplyr")
#Install required_packages:
new.packages <- required_packages[!(required_packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
#Load required_packages:
lapply(required_packages, require, character.only = TRUE)

#Set decimal points and disable scientific notation
options(digits=3, scipen=999) 

#Make up some fake data
df<-data_frame(state=rep(c("New South Wales", 
                 "Victoria", 
                 "Queensland",
                 "Western Australia",
                 "South Australia",
                 "Tasmania"), 36)) %>%
    group_by(state) %>%
    mutate(year=c(rep(2012, 9), rep(2013,9),rep(2014, 9),rep(2015, 9))) %>%
    group_by(state, year) %>%
    mutate(`store ID` = str_c("shop_#",as.character(seq_along(state)))) %>%
    group_by(state, year, `store ID`) %>%
    mutate(`Revenue ($)` =  ifelse(state=="New South Wales", sample(x=c(1000000:9000000), 1),
                            ifelse(state=="Victoria", sample(x=c(1000000:7000000), 1),
                            ifelse(state=="Queensland", sample(x=c(1000000:5000000), 1),
                            ifelse(state=="Western Australia",sample(x=c(100000:2000000), 1),
                            ifelse(state=="South Australia",sample(x=c(100000:900000), 1),       
                            ifelse(state=="Tasmania", sample(x=c(100000:2000000), 1), NA)))))))

Now visualise this data using ggplot:

ggplot(df, aes(state, `Revenue ($)`, colour=state, label = `store ID`)) +
    geom_boxplot() + 
    geom_point() +
    theme(axis.title.x =  element_blank(),
          axis.text.x  =  element_blank(), 
          axis.title.y = element_text(face="bold", size=12),
          axis.text.y  = element_text(angle=0, vjust=0.5, size=11),
          legend.title = element_text(size=12, face="bold"),
          legend.text = element_text(size = 12, face = "bold"),
          plot.title = element_text(face="bold", size=14)) + 
    ggtitle("Store Revenue per State from 2012 to 2015") +
    facet_wrap(~year)

Now make the plot reactive to the user’s mouse by wrapping plotly’s ggplotly() function around it:

p<-ggplotly(ggplot(df, aes(state, `Revenue ($)`, colour=state, label = `store ID`)) +
    geom_boxplot() + 
    geom_point() +
    theme(axis.title.x =  element_blank(),
          axis.text.x  =  element_blank(), 
          axis.title.y = element_text(face="bold", size=12),
          axis.text.y  = element_text(angle=0, vjust=0.5, size=10),
          legend.title = element_text(size=12, face="bold"),
          legend.text = element_text(size = 12, face = "bold"))+
    facet_wrap(~year))


##Publish to plotly
# plotly_POST(p, filename = "dans_plotly_example")

This type of simple plot made using plotly and ggplot2 in R are great because they have some basic “reactivity” to user input, (e.g. hover mouse over data point and lable appears with info. about data point like “store ID”” for example), but they do not need to be hosted on a server - they are simple enough to be knitted into a stand-alone HTML document.

Median Melbourne Property Prices ($) from 2005-2016

2016-09-29T00:00:00+00:00

I stumbled across an interesting raw dataset from Victorian Government, Australia today, It had house and apartment prices in Melbourne, Australia from 2005-March 2016 http://www.dtpli.vic.gov.au/property-and-land-titles/property-information/property-prices

Thought it worth a look…

So I wrote some R code to import the data from excel spreadsheet, tidy it, and then make this animated plot which I think is cool because you can get some insight from it in a much faster than just looking through the raw data in an excel spreadsheet. I thought it was interesting how much faster Houses are going up in value compared to Apartments. Also interesting how most of the price increases are in on the SouthEast side of Melbourne. I will drill into this dataset further when I get time, zooming in to certain suburbs, plotting vacant land over time (also included in the raw data), etc etc.

The R code I used to make the plot below is here

Click the plot to enlarge

Python for an R and matlab user

2016-09-24T00:00:00+00:00

I managed to find a few spare hours this weekend so I’m trying out Python for the first time. I usually use Matlab and R for data processing, visualisation and statistics, but I wanted to give Python a try, since some of my friends at Vokke seem to really love it.

It’s early days so I haven’t actually managed to produce anything useful with Python yet, but I thought I’d start to document the steps I’m taking to learn Python for data science, from the point of view of a Matlab and R user.

First off, I downloaded and installed Anaconda which includes a distribution of Python, plus all the popular python packages you might need for data science.
Then I searched for an IDE that I like the feel of. Anaconda comes with a couple of IDE’s including one called “Spyder” which I thought seemed very good. However, I ended up deciding on using the Rodeo IDE for starters. The reason I decided on Rodeo is it is set out very similarly to the Rstudio and matlab IDEs, so I’m a little more comfortable with it to start with.
Third I started searching for the “python equivalents” to my favourite R packages for data science. I’m a major fan of most of Hadley Wickham’s’s R packages including ggplot2, dplyr, tidyr, lubridate, readr and readxl. So far for python I’ve found:
- Pandas seems to be the popular package for manipulating data in python, but another package that seems closer to dplyr in R, is dplython which maintains the functional programing ideas of dplyr, including my favourite feature from magrittr and dplyr: the pipe-operator!
- The python plotting packages seaborn, bokeh and matplotlib all seem really nice. Matplotlib in particular seems very familiar to the plotting system in matlab. But since I’ve recently become very comfortable using Hadley’s ggplot2 ‘grammar of graphics’ type plotting system, I think ggplot for python will suit me perfectly for starters!

…annnd that’s all I’ve got time for today, BUT I plan to keep updating this post with more info, as I come across it, that I think could be useful for somebody learning python for data science who is coming from a background of R and Matlab….so stay tuned!!

Is open science the way forward?

2016-01-07T00:00:00+00:00

I’ll be finishing my PhD over the next two months, exciting times! Since I’ve got a thesis to write, I’ll try to keep this post short (or at least written in a short amount of time!). I have to give another shout out to The Peer Reviewers’ Openness Initiative (PRO) which is one of several excellent new initiatives in support of open science, and which has already received over 200 signatories. Basically, PRO outlines a mechanism whereby peer reviewers require access to data/analysis code/materials (or at least a reason from the authors why these things are not provided) before conducting a comprehensive review. This is designed to shift incentives and achieve the goal of creating the expectation of open science practices. The advantages that will come with mass uptake of open science practices, particularly in relationship to the PRO initiative, have recently been outlined in excellent blogs by researchers who are more accomplished and qualified than me (e.g. see here , here , here , here , here and here).

So this post is not about rehashing their excellent points. Rather, I wish to add another perspective to this discussion, from the viewpoint of a very early career researcher.

Since I am currently considering post-PhD career paths (e.g. post-doc positions, industry positions), initiatives like PRO are important to me, because they give me a sense of hope that over time the incentives in academic science will change to encourage open science. I’ve noticed over the last few years that the scientific publication process (at least in psychology, cognitive science, and neuroscience where I’ve been interested) is very slowly moving more in line with the ideals of open, transparent and reproducible research. I’m excited to get on board with this open science movement as much as possible early in my career - I’ve just submitted a final research paper to contribute towards my PhD, and I’ve chosen to submit it to a fully open access journal and make all of the related raw data, analysis scripts and paradigm code open source, so my results are reproducible. Hurrah!!! (I’m yet to submit a pre-registered report, but that’s next on my list of publication goals). So personally I am enthusiastic about open science. And I wonder if this attitude is shared amongst my peers?

Are early career researchers enthusiastic about open science?

I would love to see some valid data addressing this question.
Anecdotal evidence from my conversations with friends/colleagues who are at similar career stages, suggests that many early career researchers agree that open science is the way forward. When I’ve chatted about getting on board with the open science movement (e.g. by signing PRO , sharing data/analysis scripts, pre-registering studies etc.), my colleagues have unanimously agreed that it is a good idea and the best way forward for science, for these reasons .

So what’s stopping early career researchers practicing open science?

There may be many perceived barriers to implementing open science practises, which are worth addressing (see links in first paragraph). But here I just wanted to comment on one of these barriers which seems to be at the forefront of early career researchers’ minds- people have pointed out to me that currently the pressures and incentives set up in academic science, particularly for early career researchers, do not always encourage or reward the extra time taken to learn and implement open science practices. One main point of concern that I’ve heard is a potential loss in the number of papers you can produce given the extra time taken to learn/implement open science practices, since criteria for awards, post-doc positions, promotions, etc. are often heavily weighted on the of number of publications, and not necessarily on how ‘open’ the science is.

Is this a valid concern? I cannot comment on changes (or lack of changes) in the weighting of open science practices as a criteria for awards, post-doc positions, promotions, etc. across research institutes. Though Felix Schonbrodt has an excellent piece about changing hiring practices towards research transparency here. I can however comment on the perceived potential loss in the number of papers you can produce given the extra time taken to learn/implement open science practices:

It definitely doesn’t take much extra time! HURRAY! It can feel like a burden at first, but there are many online tools to help with open science (e.g. the OFS, or see this excellent post, Full Stack Science; a guide to open science infrastructure, about using GitHub, Docker Hub, FigShare,Travis CI and Zenodo), and once you get started it’s faster, easier and more enjoyable than you may imagine. It took me 4 weeks part-time (20 hours per week, so a total of 80 hours) back at the end of 2014/start of 2015 to learn how to use R markdown and github properly to share analysis and paradigm code. I took my time to learn it thoroughly, and this was coming from a point of complete ignorance about R markdown and github. There are many free online options to help learn such skills, for example I learned free via these two courses The Data Scientist’s Toolbox and Reproducible Research .
And it took me about half an hour to work out that my university has an account with figshare and then to upload my 24GB of raw data, and use figshare’s neat “Generate private link” function for reviewers to access my data which can then be manually switched to public when you need it to be (i.e. once your paper is accepted for publication).

So a total of ~80 hours of my time to gain the skills to make my analysis reproducible and share raw data. And now because I have these skills and enjoy implementing them as a usual part of my analysis pipeline, it will take me no extra time to make my data and analysis code open in the future. In fact, this 80 hours of work actually saves me time in the long run, because of the advantages of reproducible code; it is easier to check for errors, if you comment it well you spend less time figuring out what you did later, etc. So you get back all of that 80 hours pretty quickly. In the long run, the initial time-investment pays recurring dividends. So regarding applying for awards, post-doc positions, etc. I’m banking on the hope that the reputational gain from doing fully open science from now on, not to mention the time it saves me in the long run, will be worth more than a small one-off expenditure of ~80 hours that it took me to learn the necessary skills.
Furthermore, if I ever need to leave academia and pursue work in industry then having programing, or at least scripting, skills for data analysis along with git/github for version control and reproducible research makes you more valuable for data science/analytics jobs in industry, than only knowing how to analyse data with the old point-and-click style methods with software like SPSS etc.

So from my viewpoint, as a very early career researcher, it definitely appears that open science is the way forward.

Open science FTW!

Methods for Open Science/Reproducible Research

2014-11-26T00:00:00+00:00

I’m excited about a new initiative to promote data and analysis/paradigm code sharing, called the The Peer Reviewers Openness Initiative (PRO) https://opennessinitiative.org//. Openness and transparency are core values of science. PRO outlines practical steps to improve open science, and I would like to see improved openness of code and data particularly in my areas of the behavioural sciences and cognitive neuroscience.

Technology (the internet) has advanced to a point now where open access data and code for scientific publications is possible, however the uptake of open science practices has been slow for a number of reasons. For one thing, better incentives are needed for the transition to open science practices. But I think another key thing holding our area of science back is that most researchers in our area don’t even know what things like GitHub, Mozilla Science Lab, and FigShare, etc are (I didn’t until very recently). Also at the outset it seems like a bit hassle to learn how to use these tools. Even though I’m now finding it is not too bad – I’m a beginner with these kinds of open science methods but there are some really good free short courses from Johns Hopkins University to help learn how to use some of the tools for open science, I’ve provided links to 3 of these below:

(1) https://www.coursera.org/course/datascitoolbox – a short course on tools for open science including how to use GitHub to make all of your code available (and also use other’s code!)

(2) https://www.coursera.org/course/rprog – a short course to use** R** to script your whole analysis for a publication from start to finish to show others exactly what was done from raw data to results (any stats software that allows scripting will do, but R is better than SPSS, for example, since R can be downloaded for free). I’ve just started making the switch from SPSS to R for my inferential statistics since R makes sharing analysis code easier. I will still likely do my signal processing in MATLAB though.

(3) https://www.coursera.org/course/repdata – a short course on tools for** Reproducible Research** – e.g. combine GitHub, R pubs, FigShare to make both the data and code easily available and citable with a DOI

I’m going through these courses in my spare time at the moment, and hope to make my next scientific publication fully open access, in line with the ideal of reproducible research, so that other scientists can verify and build upon my findings.

Open science FTW!

My visit to the Cognitive Neuroscience Unit (CNU) at Deakin University

2014-11-24T00:00:00+00:00

This is my first blog post, and it is actually going to be a shout out to somebody else’s blog! A couple of weeks ago I went over to Deakin University and gave a short presentation to the Cognitive Neuroscience Unit (CNU) at Deakin. I presented some of the work we have been doing in our lab at Monash. The CNU blogged about my talk here:

http://cogexneuro.blogspot.com.au/2014/11/spatial-attention-asymmetries-in.html

It was great to meet the members of the newly formed Deakin CNU, they have attracted an excellent team of researchers there lead by Peter Enticott. I was impressed by the enthusiasm for cognitive neuroscience shown by the members of the CNU. After the talk I was shown a tour around the labs at the Deakin CNU, and I was very impressed with their facilities for transcranial magnetic stimulation (TMS). I believe the CNU was only established this year (2014), but have already been productive and I’m expecting to see more exciting research from them in the years to come!