Varying Position Size Still Doesn't Make Sense

Last year, I posted that increasing trading position size when volatility is lower didn't make sense for my trading system. When the volatility was low trades tended to lose more and win less. So, trading bigger because risk was supposedly lower didn't pay off.

Now I am using a more traditional trading system that wins by letting winners run and cutting losses short, without pretending to predict the direction of the market. Here, I have found that there is no correlation between the profit from trades and the maximum loss possible given the initial stop loss:

The chart shows all the trades in Bitcoin futures my system would have made in the last year The x-axis shows the maximum loss on the trade of one contract (assuming we can exit at the stop loss, i.e. no Black Swans). The y-axis shows the profit for the trade. There is a slightly positive correlation, though it is not statistically significant. On the other hand, you can see that realized losses do increase with increased initial risk. The system won 46% of the time with the average win 4.1 times bigger than the average loss. The average trade lasted 5 days.

If you adjust position size so that the initial risk of each trade is the same, returns do increase, but so does the maximum drawdown. If you scale back the average size of trades so that the maximum drawdown in percentage terms is the same as for trading with the same number of contracts each time, then returns turn out to be very similar for both strategies.

Bottom line is that varying position size increases returns but also drawdowns by a similar amount. If you care about drawdowns it doesn't help. So, I think I will focus on controlling drawdowns when choosing position size rather than equalizing initial risk.

Target Portfolio vs. the MSCI World Index

The graph shows monthly returns for the target portfolio vs. the MSCI World Index in Australian Dollar terms. The linear fit shows a beta of about 0.3 – if the market rises 1% more , the portfolio tends to rise 0.3%. Alpha is at around 8% per year. The orange line is a quadratic fit. This suggests that beta increases, the more the market rises, while for large down moves beta is zero. This is the kind of asymmetric relationship you want to get.

Margin Requirements for Bitcoin Futures Trading

I just discovered that while going long a Bitcoin futures contract requires margin of about USD 16843.75 per contract, going short requires initial margin of USD 200k at Interactive Brokers. Do they really think that Bitcoin could rise by a factor of 7-8x when the market is closed?* This makes it much harder for people to go short and contributes to the inefficiency of this market. Importantly there are no options on these futures, so you can't hedge against large adverse movements. I can't see anything about this asymmetry in margin on the CME site, so I assume that it is set by the broker.

* Stops will only work when the market is open. The Globex futures market is open 23/5 - closed one hour each day and over the weekend.

April 2019 Report

In April the Australian Dollar fell from USD 0.7096 to USD 0.7047. The MSCI World Index rose 3.18% and the S&P 500 3.72%. The ASX 200 rose 3.36%. All these are total returns including dividends. We gained 0.95% in Australian Dollar terms and 0.26% in US Dollar terms. Our currency neutral rate of return was 0.91%. The target portfolio gained 2.37% in Australian Dollar terms and the HFRI hedge fund index 1.57% in US Dollar terms. So, we under-performed our benchmarks.


Here again is a detailed report on the performance of all investments:

The table also shows the shares of these investments in net worth. At the bottom of the table I also include the Australian Dollars return from foreign currency movements, other net investment gains and losses - net interest and fees, and trading Bitcoin futures. Trading income was USD 733 for the month, which at an annualized rate was roughly a 7.3% rate of return on capital.  At the asset class level, only real estate lost money this month. Australian small cap stocks were the best performing asset class.

Things that worked very well this month:

• 3i, the UK private equity firm, and Generation Global shined. A few other funds beat the index. Tribeca bounced back from underperformance.

What really didn't work:

• Cadence and Bluesky sucked. Cadence went ex dividend and I couldn't be bothered to account for this properly in my accounts, so it will do better next month when I receive the dividend. However, it fell by more than the dividend and falling in an up-market is not good. I'm still willing to give them the benefit of the doubt that they will come back again. Bluesky was probably affected by troubles at the manager, also known as Bluesky, and lack of certainty about Wilson Asset Management taking over as the new manager. 
• I continue to be impressed by PSS(AP), where we are now in the balanced fund.

We moved away from our new long-run asset allocation * as we continued to accumulate bonds:

The main driver is continued movement of cash from my US bank account to Interactive Brokers where I am buying bonds before eventually transferring some of the money to our Australian bank accounts when the broker allows..... At the end of the month we bought 1/4 million Australian Dollars by transferring money from Falafeland. This means that we will buy new US bonds for a few months as the current ones mature rather than changing the proceeds into AUD immediately as the plan is to buy about AUD 50k per month. After the month end, I immediately made an AUD 90k non-concessional (after tax) contribution to superannuation. As I plan to roll over my retail super fund into a self-managed super fund after the start of the new financial year in July, I invested the money in the CFS Wholesale Conservative Fund.

On a regular basis, we also invest AUD 2k monthly in a set of managed funds, and there are also retirement contributions. Then there are distributions from funds and dividends. Other moves this month:

• USD 69k of corporate bonds matured (Royal Bank of Canada)  or were called (Goldman Sachs) and I bought USD 275k of USD bonds (Tokio Marine, Anglogold, General Motors, CNO, Scorpio Tankers, Woolworths, Safeway, and Hertz). There is still more than USD 100k to convert into bonds. I also bought 245 more shares (net) of CBAPH - Commonwealth Bank hybrid securities.
• I did some unsuccessful trading of gold futures and then bought 1000 more (net) shares of IAU - a gold ETF.
• I did some successful trading of Bitcoin futures.
• I sold my remaining shares in PIXX.AX and bought a small amount of OCP.AX at $1.98 a share.

* Total leverage includes borrowing inside leveraged (geared) mutual (managed) funds. The allocation is according to total assets including the true exposure in leveraged funds. We currently don't have any leveraged funds.

Bitcoin Trading Update

This morning Bitcoin plummeted several hundred dollars at the open of the US overnight session on Globex. It triggered both my sell stop to close my long April futures position at a profit of USD 194 and my sell stop to go short the May futures from 5375. This also triggered the set-up of a buy stop for the short position. Everything worked as it should. On the other hand, I was pretty lucky to come out with a profit from the long position, which at one point was much more in the money.

Updated Post on Labor's Tax Increase Proposals

I had forgotten about one of Labor's proposals to increase tax. Limiting tax free pensions to $75k per year. I've now added it to the list. It's number 13.

P.S.
Another one - limiting deductions ofr tax advice to $3,000 per year. Now 14 proposals on the list.

Update on Trading Research

I came to the conclusion that none of the trading tools I developed are reliable. They can match market behavior for a while and make money, but then the relationship breaks down. In the long run there is no relationship between any of these indicators and returns.

I wrote a back-testing program for Turtle type trend-following models. This allows me to optimize the time periods to use to maximize profits. There is the potential for over-fitting and unstable relationships here too. The answer I think is to regularly re-optimize as the market changes. This re-optimization is easy to do. Given that a wide range of values is profitable in the exercise I did, I don't think there would be a sudden failure. We will see.

In the backtest there were 78 trades with a 48% win rate. But wins were on average 3.45 times larger than losses. The annualized Sharpe ratio is 2.2. Here there is a negative correlation between the initial risk taken (amount of money lost if the stop is triggered) and profits. That means it makes sense to bet bigger when the risk is lower:

I am now trading Bitcoin futures with the optimized algorithm (Long Bitcoin since yesterday). Next step is to see if there are other futures I could trade. Stock index futures aren't more profitable than just going long the index.