Trading and investing performance: year nine, part one

 A bit late this year, due to a confluence of holidays, book launches, university exam writing and various other things. Here lies within my performance for the UK tax year 2022-23. Previous years can be found here. 

TLDR: Not great, absolute or relative. It was indeed a complete anus - horrible!.

This will be a two parter this year. In this post I will look at my overall performance, with only a cursory look at my futures trading. In the second post I will focus on my futures trading.

Overview 

Investing:

• 1 UK stocks
• 2 Various stock ETFs
• 3 Various bond ETFs
• 4 A small amount of uninvested cash

Trading:

• 5 Futures contracts traded by my fully automated trading strategy
• 6 Cash as trading capital for the above, of which usually around a third is required for initial margin

Excluded from this analysis is:

• My house
• A cash buffer I keep to cover living expenses

I benchmark my investments in the following way:

• A: UK single stocks 
• Benchmarked against ISF, a cheap FTSE 100 ETF (FTSE 350 is probably a better benchmark but these ETFs tend to be more expensive).
• B: Long only stocks investments: All stock ETFs and UK stocks
• Benchmarked against a cheap global equity fund (VEVE)
• C: Long only bond investments: All bond ETFs
• Benchmarked against a cheap global bond fund (AGGG)
• D: Long only investments: All UK stocks, bond ETFs and stock ETFs
• Benchmarked against a cheap 80:20 fund. 
• E: Futures trading: Return from the futures contracts traded by my fully automated system. The denominator of performance here is the notional capital at risk in my account (usually less than the account value).
• Benchmarks are a similar fund run by my ex employers AHL, and the SG CTA index, adjusted for volatility.
• F: Everything: Long only investments, plus futures hedge, plus futures trading. I include the value of any cash included in my trading or investment accounts, since if I wasn't trading I could invest this. 
• For the benchmark I use a cheap 60:40 fund.

If you prefer maths, then the relationship to the first set of categories is:

A = 1

B = 1 + 2 

C = 3

D = B + C = 1 + 2  + 3

E = 5 + 6

F = D + 4 + E = 1 + 2 + 3 + 4  +5 + 6

Performance contribution

The figures shown are the contribution of each category to my total investment performance:

1) UK equities -1.7%

2) Stock ETFs -1.8%

3) Long only bonds 0.12%

5 & 6) Futures trading -1.3%

F) Total -4.7%

Here is another way of putting it:

Long only MTM (mark to market): -7.9%

Stock and ETF dividends, less fees: 4.6%

Futures: -1.3%

Now for the same figures as 'internal rates of return', which are effectively for the 'capital' employed in each area. I use the Excel function XIRR. You can't add these up, but they are comparable and account for flows between categories. Benchmarks are also shown.

A) UK equities -7.0% Benchmark +4.3%

B) Long only stocks -4.7%   Benchmark -2.4%

C) Long only bonds +1.34%  Benchmark -1.10%

D) Long only investments -4.1% Benchmark -2.1%

E) Systematic futures trading  -8.7% Benchmarks +5.0% -1.3% (vol matched)

F) Everything -4.7% Benchmark -1.9%

Well, that's a pretty dark picture. We know that 2022 wasn't great for 60:40 (my benchmark for 'everything') but things picked up in early 2023. Indeed my only outperformance came in long only bonds; maybe I should become a long only fixed income manager? (Wait, what?)

UK equities

This portfolio is traded using a system which I've explained before (value bias, with trailing stop of 30%), but which is not automated. Instead I have the stocks set up in a spreadsheet, with alerts reminding me when stop losses occur. If I sell then I download some data to pick the best value share.

Current portfolio:

Vistry Group                 9.67%

Centrica PLC                 7.93%

3i Group                 7.23%

Jupiter Fund Management         6.52%

Barratt Developments         5.77%

Bluefield Solar Income Fund 5.21%

Sequoia Economic Infrastructure 5.02%

NextEnergy Solar         4.98%

GCP Infrastructure Investment 4.82%

HICL Infrastructure         4.60%

OSB Group                 4.41%

Castings                 4.39%

Renewables Infrastructure 4.30%

Lloyds Banking Group         4.26%

Legal & General Group         4.23%

Phoenix Group                 3.46%

Johnson Matthey         3.46%

Aviva PLC                 3.45%

Persimmon PLC                 3.41%

Greencoat UK Wind         2.89%

Here is the performance of every share I held over the year. Percentages are taken from the start of year price, or from when I bought it during the year:

Name         MTM % Divi % TR %

3i Group Ord (L 54.1% 2.1% 56.3% HELD BOUGHT

Centrica PLC (L 39.5% 1.3% 40.7% HELD LEGACY

Castings PLC (L 16.9% 10.6% 27.5% HELD LEGACY

Central Asia Me 17.2% 9.1% 26.3% SOLD LEGACY

Imperial Brands 17.9% 5.5% 23.5% SOLD LEGACY

Centamin PLC (L 14.9% 6.7% 21.6% SOLD LEGACY

Barratt Develop 10.1% 2.1% 12.3% HELD LEGACY

Vistry Group PL 8.6% 3.3% 11.9% HELD BOUGHT

Jupiter Fund Ma 7.7% 4.1% 11.8% HELD BOUGHT

Lloyds Banking 6.0% 4.6% 10.6% HELD LEGACY

Greencoat UK Wi 3.7% 0.0% 3.7% HELD LEGACY

Johnson Matthey -0.4% 4.0% 3.6% HELD LEGACY

Rathbones Group 1.6% 0.0% 1.6% SOLD LEGACY

Aviva PLC (LSE: -27.9% 28.3% 0.3% HELD LEGACY

Renewables Infr -5.4% 5.0% -0.4% HELD LEGACY

HICL Infrastruc -3.3% 2.6% -0.7% HELD BOUGHT

NextEnergy Sola -4.0% 3.2% -0.8% HELD BOUGHT

Bluefield Solar -1.7% 0.0% -1.7% HELD BOUGHT

Legal & General -4.6% 2.1% -2.4% HELD BOUGHT

Phoenix Group H -11.3% 7.9% -3.4% HELD LEGACY

Tullow         -4.4% 0.0% -4.4% SOLD BOUGHT

Greencore Group -4.6% 0.0% -4.6% SOLD LEGACY

Sequoia Economi -5.2% 0.0% -5.2% HELD BOUGHT

Babcock Interna -5.8% 0.0% -5.8% SOLD LEGACY

GCP Infrastruct -9.1% 2.6% -6.5% HELD BOUGHT

Morgan Advanced -9.6% 1.9% -7.7% SOLD LEGACY

Hammerson PLC ( -12.2% 0.6% -11.6% SOLD LEGACY

Persimmon PLC ( -13.9% 0.0% -13.9% HELD BOUGHT

Currys         -18.2% 3.1% -15.1% SOLD BOUGHT

OSB Group PLC ( -17.2% 0.0% -17.2% HELD BOUGHT

Morgan Sindall -23.5% 2.6% -20.9% SOLD LEGACY

TP ICAP GROUP P -25.5% 3.7% -21.7% SOLD LEGACY

Investec PLC (L -26.7% 2.8% -23.9% SOLD LEGACY

Direct Line Ins -31.2% 5.5% -25.7% SOLD LEGACY

Aberdeen -31.9% 3.6% -28.3% SOLD BOUGHT

Redde Northgate -31.9% 3.5% -28.4% SOLD LEGACY

BT Group PLC (L -33.8% 2.9% -30.9% SOLD LEGACY

Taylor Woodrow -33.2% 0.0% -33.2% SOLD BOUGHT

Synthomer -54.2% 0.0% -54.2% SOLD BOUGHT

Note I generally use a 30% stop, but because the stops aren't placed I sometimes get gapped through (badly in the case of the final share). 

MTM is mark to market (the % price change since I bought it, or the start of the year), Divi(dend)% is self explanatory, TR is total return - the sum of the first two columns. The next column describes the current state of this position: do I still HOLD or have I SOLD. The final column shows when I obtained the position: at the start of the year (LEGACY) or it's something I subsequently BOUGHT.

Turnover was 167%, eg holding period just over 7 months. This is slightly down from last year. Average dividend was 6.2%. My XIRR was -7.0%; you might think that sounds okay given all the bad things you've heard about stocks, but actually the UK was a standour market in the general horror of the last 12 months and my benchmark (FTSE 100 ETF) earned 4.7%.

        XIRR bench

2016 – 2017 29.2% 25.1%

2017 – 2018 18.3% 2.2%

2018 – 2019 -2.3% 7.9%

2019 – 2020 -23.1% -24.2%

2020 – 2021 64.3% 29.0%

2021 – 2022 9.8% 16.5%

2022 – 2023 -7.0% 4.3%

Mean         12.74% 8.68%

Stdev         0.28 0.18

SR         0.45 0.49

Geo. Mean 9.8% 7.3%

Alpha 1.1%

Beta 1.34

Corr 0.84

So this has been my worst year in relative, and secondly only to COVID in absolute terms. After two straight years of underperformance, the benchmark is now a little ahead on SR, but I'm still winning on geometric mean.

Long only Stocks

As already noted this is consists of all my stock ETFs, plus my UK shares. With the recovery of value being one of the stories of last year, and the US/RoW spread narrowing with my permanent underweight to the overvalued US I had hoped for good things here. Some hope!

Here are the risk exposures by region within this sub-portfolio:

              Start of year      End      Long term   Target

Asia             14%             17%         15%       18%

EM               20%             26%         25%       23%

Europe           17%             18%         20%       14%

UK               29%             31%         25%       32%

US               20%             8%          15%       13%

Start of year and End are self explanatory, whilst Long term is the strategic allocation (see my spreadsheet). After I have snapshotted the year I rebalance the portfolio to the target level shown, taking account of tax and transfers between accounts. 

The rather wild differences between end of year and target are because I'm partially through switching m portfolio to ESG ETFs. This also meant that turnover was much higher than normal, but this will be a temporary situation.

The XIRR for my stocks was -4.7%, which isn't great the benchmark ETF I've chosen (IWRD) was only down -2.4% TR. Dividends were much healthier at 5.2%.

No stats as I only started breaking out performance like this last year, but you can see I'm underperforming for the second year in a row:

2021 - 2022 XIRR +6.1%,  benchmark +14.5%

2022 - 2023 XIRR -4.7%,  benchmark -2.4%

Benchmarking note: If you check back you may see slightly different numbers for the benchmarks. This is because I was using a mixture of random ETFs for stocks/bonds, and Vanguard preblended 60:40 and 80:20. This meant there was no consistency; eg the 60:40 fund actually did worse than a 60/40 blend of my stock and bond benchmarks. I was also unsure about some of the historic marks for my benchmarks, as some of them weren't done very precisely. So I repopulated my benchmark history with total return history in GBP terms, ex fees for:

• Bonds SUAG (US dollar only) until April 2018, then AGGG (all currencies, but not available until late 2017)
• Equities IWRD 
• 60:40 benchmark - a 60/40 blend of the above; so effectively annually rebalanced
• 80:20 benchmark - an 80/20 blend of the above

My FTSE 100 ISF benchmark for UK equities is unchanged.

As with what I did last year, if anything this makes my performance look a little worse on a relative basis so I think my hands are clean.

Long only Bonds

This is just ETFs. Here are the risk exposures:

              Start of year     End      Long term   Target

Asia             3%              5%         4%        2%

EM               40%             41%       24%        34%

Europe           35%             39%       24%        19%

UK               8%              2%        24%        25%

US               13%             13%       24%        22%

Last year I started treating my 'cash like' bond ETFs as cash, and I stick to that here. These are held in my IB trading account to make more efficient use of my margin; more below.

Again, there are some big deltas here due to my rebalancing into ESG which I couldn't complete in the tax year without incurring.... tax. Note that the tax allowance for capital gains in the UK is now just £2k, so this might take a while longer!

I was a big buyer of bonds during the year, more than doubling my allocation. This is because the strongly negative relative momentum in bonds abated, and I tilted back to something closer to my strategic allocation.

This also means the XIRR figures might be a bit weird, but what the hell, I made 1.34% versus the index (AGGG) losing 1.1%. My only green figure relative and absolute for the year, and I will take it. 

2021 - 2022 XIRR +1.53%,  benchmark -2.97%

2022 - 2023 XIRR +1.34%,  benchmark -1.10%

Based on two years of performance, I am the bond king now - sorry Bill

Long only investments

All of the above, in one easy to find place. You already know what is currently in this portfolio, and the regional risk exposures, so that just leaves the macro asset class level exposure. Here it is in cash terms:

              Start of year      End      Long term   Target

Bonds            5%             10%          22%        12%

Stock            95%            90%          78%        88%

And here's risk terms:

              Start of year      End      Long term   Target

Bonds            3%              6%          10%        7%

Stock            97%             94%         90%        93%

Given I did badly in stocks, and I mostly own stocks at a higher level than the 80:20 benchmark I use as of last year, it's no surprise that my XIRR here of -4.1% was below par compared to the benchmark of -2.1%.

        XIRR bench

2015 – 2016 6.1% 0.4%

2016 – 2017 22.3% 28.4%

2017 – 2018 1.3% -0.5%

2018 – 2019 4.0% 11.7%

2019 – 2020 -17.5% -6.4%

2020 – 2021 34.8% 33.4%

2021 – 2022 5.9% 11.0%

2022 – 2023 -4.11% -2.1%

Mean         6.60% 9.48%

Stdev         15.9% 14.7%

SR         0.41 0.65

Geo. Mean       5.6% 8.7%

Alpha        -3.0%

Beta         1.02

Corr         0.93

Futures trading (brief)

As I noted at the start of the post, I'll just put a very cursory look at my futures trading in here, with a subsequent follow up post to look at more details. All figures are as a % of my notional capital, which will usually be more than I have in my account. 

MTM: -9.7%

Interest: 1.3%

Fees: -0.06%

Commissions: -0.21%

Net futures trading: -8.7%

'Interest' includes dividends on 'cash like' short bond ETFs I hold to make a slightly more efficient use of my cash; I've recently (in this financial year) added a bit more to this sub portfolio. It's quite interesting how interest has gone from being irrelevant to actually adding something to performance.

As I've done in previous years I compare this to two benchmarks, 'Bench2' the SGA CTA index, and a 'Bench1' a fund run by AHL, my ex employers. My loss was worse than both; on a vol corrected basis bench1 made 5% (admittedly on the back of a loss last year), and bench2 only dropped 1.3%

        Me Bench1 Bench2

2014 – 2015 58.2% 70.2% 50.7%

2015 – 2016 23.2% -8.7% -1.6%

2016 – 2017 -14.0% -6.2% -25.5%

2017 – 2018 -3.7% 7.5% -4.4%

2018 – 2019 5.2% 8.1% 0.8%

2019 – 2020 39.7% 22.6% 9.3%

2020 – 2021 0.4% 0.8% 12.7%

2021 – 2022 27.0% -5.2% 38.3%

2022 - 2023 -8.71% 5.0% -1.30%

Mean         14.1% 10.4% 8.8%

Std dev         24.3% 24.3% 23.1%

SR         0.58 0.43 0.38

Geom mean 11.9% 8.5% 6.7%

Correlation 0.71 0.80

alpha         6.7% 6.8%

beta         0.71 0.84

So on a longer term basis, I'm still doing well here. The relatively low correlation with AHL is kind of interesting as well. 

Everything

Let's see what the final score is. The denominator here will be the total value of all the securities and investable cash I have, including both cash for futures margin and any temporarily univested cash in other accounts. I do exclude some cash I hold as a buffer, since that can't be invested. 

The bottom line is a loss of 4.7% vs the 60:40 benchmark down just 1.9%. Dividends were 4.6%; without those there would obviously have been a near double digit loss. Here's the history:

        XIRR bench

2014 – 2015 14.40% 2.0%

2016 – 2017 18.20% 24.8%

2017 – 2018 0.60% -3.0%

2018 – 2019 4.40% 10.4%

2019 – 2020 -6.60% -2.3%

2020 – 2021 27.90% 23.4%

2021 – 2022 8.25% 7.5%

2022 – 2023 -4.66% -1.9%

Mean         7.81% 7.61%

Stdev         11.8% 11.2%

SR         0.66 0.68

Geo. Mean 6.8% 7.1%

Alpha 1.1%

Beta 0.89

Corr 0.93

Summary and future plans

With writing a book and all, last year didn't see me do very much to my portfolio. I did some tidying up; now all my UK stocks are in a single account which makes attribution easier, and I started the move to ESG. 

I hoped to introduce some new futures strategies but didn't; my first act of the new year once I've written up my futures performance is going to be to add a bunch of new markets to my strategy, and then subsequently think about other strategies some more (principally RV and faster MR, although not quite in the way described in my new book - watch this space). I'm also painfully aware that I have a heap of work to do on pysystemtrade.

One other thing is that it seems to take longer and longer to write this post every year. It's a huge hacked together spreadsheet, full of checks and balances in case I miss something (I acidentally put a decimal point in the wrong place, and 'lost' 150k!). I really ought to automate the process somehow; which would involve keeping better records and writing some code. Hopefully I will have time this year.

Advanced Futures Trading Strategies

 Tommorrow marks the official release of my 4th book:

Henceforth to be known as AFTS

(I've had authors copies since the 24th February, but modern supply chains being what they are it takes considerably longer for the book to arrive in the hands of my readers; although plenty of people on twitter have been bragging about receiving their copies earlier).

You can buy it here (why haven't you already!), or read about the detail a bit more here. It's my longest book so far, and in my personal opinion the best written as well (I feel like I'm getting better with practice!). 

What I thought I would do in this post is explain in some more detail why I decided to write it, what it's about, and how it relates to my other books.

Why I decided to write it

The motivation for AFTS came from a number of different sources. Firstly, I don't think I'm bragging when I say I have a fair amount of experience in trading futures. But I've never really written about specifically trading them in my books, except to discuss how to adapt my workhorse forecasting/vol adjusting portfolio management equations to that particular asset class, as well as the odd reference to the fact you have to roll them every now and then. An obvious exception is 'Leveraged Trading', where they appear as one of five classes of leveraged instruments that I specifically discuss.

The second motivation was that I'm painfully aware that my books are rather short on specific trading strategies. All of them include simple trading rules, mostly of the moving average crossover and carry type, but most of the bad reviews on amazon have come from people who were expecting rather more than that. Of course you guys know that really isn't the point of my books. Systematic Trading was about designing your own strategies, whilst the point of Leveraged Trading was mostly to get people to trade in a safe and cost efficient way; the underlying trading rule is secondary to this purpose.

Of course there are plenty of books on trading futures (I normally recommend Jack Schwagers work here), and there are also compendiums full of trading strategies (Perry Kaufman seems to have written the longest), but I still felt I could offer something unique with my purely systematic approach.

So my third source of motivation was a book I wrote a few years ago, but which very few people will ever read. When I took over the fixed income team at AHL the team consisted of a disparate group of people, not all of whom had deep familarity with the asset class. So I wrote a 'book', which mostly consisted of a lot of graphs showing how various instruments had behaved in the past, their volatility patterns, how the yield curve had behaved, and how various types of trading strategies performed across different instruments. I can't remember the exact title, but if I wrote such a book today I would call it the 'Fixed Income Almanac'. 

It once occured to me that a 'Futures Almanac' would be an interesting project. There used to be a book (probably out of print, and certainly out of date now we have the internet) that listed all the futures markets in the world (perhaps someone old like me can put the title in the comments). I imagined a book with a couple of pages per market, with a graph showing the price and performance of various simple trading strategies, plus some interesting market statistics.

Of course such a book wouldn't be commercially viable, would go out of date, and more worryingly for me would become a license for overfitting ("Look, from page 925, Iron has clearly the best momentum ewmac4,16 performance"). 

However it struck me that if I did write a book about trading strategies, I could make it 'alamanac-like' by testing my strategies over a huge set of futures markets, and showing the results.  

What it's about

In a sense AFTS is several books in one. 

Ostensibly, it's a book containing 30 trading strategies specifically for trading futures. All strategies are completely described, and I show full backtest results for each of them, in many cases breaking them down by asset class. I explain their strengths and weaknesses, and I even include some strategies I wouldn't personally trade myself. 

The strategies sprawl over the first five parts of the book. Part one is about long only, moving average crossovers, and carry. In part two I extend and poke around with these two basic strategies. By the end of it you will have a much fuller understanding of carry and trend, or have died trying. In part three I move on to other directional strategies. In here I also include a chapter with a detailed and coherent description of my dynamic optimisation strategy. 

Parts four and five cover ground you don't normally see me occuping. Part four is about fast mean reversion strategies, where we have to use novel execution techniques to cope with the resulting high turnover. Part five is about relative value; something I used to do plenty of back in the day, but which I no longer trade. 

Then in part six I discuss some of the 'plumbing' of futures trading: when to roll, optimal execution, risk management and cash management. These things are important - they won't make you a fortune but they can lose you one - but are rarely discussed in trading books. I've touched on many of these topics in my blog, but hopefully even my hard core fans will find it useful to have them systematically written up in one place.

However there is more to AFTS than meets the eye. The first few chapters move from trading a single contract, to trading with fixed vol estimates, to varying vol estimates, to adding a trend filter, then a filter with forecasts and buffering, and finally multiple filters. This allows the reader to understand the value and meaning behind each of these steps. 

In a sense these first few chapters are an empirical and logical  justification for 'my' style of trading. They also allow people to understand how a trading strategy will transform the return properties of the underyling distribution of each asset class; for example turning the VIX/V2X complex from heavily positive skew but crap returns, into still positive skew but also positive returns. Finally this approach also allows me to introduce my position sizing framework in a gradual fashion, making it easy to digest. 

Although this isn't a book about backtesting or system design - that would be Systematic Trading and perhaps an as yet unwritten book that goes into backtesting in more detail - I also use AFTS to gently remind or introduce concepts around over fitting and robust optimisation.

How it relates to my other books

If you're reading this blog post, there's a good chance you already own at least one of my other books, so you might be wondering if it's worth giving me some more of your money (TLDR: Yes! Give me your money. Look at all this great free content I'm giving you. I deserve it!). If you don't own any of my books - freeloader! - you may be wondering which you should buy first. OK, you should buy all of them, right now; but you might be wondering what order to read them in.

If you are a futures trader, but relatively inexperienced, I would start with my first book Leveraged Trading. Once you have a good understanding of the asset class, then it would make sense to move on to Advanced Futures Trading Strategies. My first book, Systematic Trading, is probably one to read next if you want to move on to designing your own trading strategies.

If you are not a futures trader, there is still a chance that the strategies in AFTS will be relevant to you, especially if you are trading 'futures-like' instruments like CFDs or spread-bets. Leveraged trading will help you understand how to pretend these things are really futures.

Smart Portfolios of course is a book about investing and not trading, but naturally still worth buying.

Credits

That's all I have to say, except I hope you all enjoy reading it. Roll the credits!

Commissioning editor: Craig Pearce

Cover: Chris Parker

Content reviewers: Thomas Smith, Helder Palaro, Tansu Demirbilek and Riccardo Ronco

Regular nudging on his podcast to make sure I was still writing: Niels Kaastrup-Larsen

Special thanks to: Doug Hohner (for dynamic optimisation)

Assistant proof-reader: Darcey the Cat.

Darcey the cat, looking for spelling mistakes