Trading Update

I've now been trading the ASX200 systematically, mostly on Plus500, for two weeks. I'm only ahead by about AUD 500, but that's not surprising. Actually, it's a good result given the small position size so far, and the potential for drawdowns. I've done more backtesting and improved the algorithm. About half of total returns would come from holding winning positions (during the day) overnight and I haven't done that yet. I also updated my soybean trading model with the last year or so of data and the algorithm I was using would continue to be profitable, so I am adding that. By contrast with the ASX200 model, this is a trend following model that doesn't trade very often. Finally, during the past week I did some trading of Treasury Wine around the high volatility in the stock with the earnings release and broker upgrades and downgrades. This involved far larger fluctuations in value than the systematic trading.

Tweaking the Target Portfolio

I've decided to slightly tweak the target portfolio weights to reflect a larger allocation to hedge funds. The top level allocation is 59% to equity and 41% to everything else. Then there is 10% to private equity (split between venture and buyout) and 49% to public equity. Within that 25% to long-only and 24% to hedge funds (was 21.5%) (also split 50/50 to Australian focused and foreign focused funds). And within the long-only 13% to Australia and 12% to the rest of the world. Within Australian 9% to large cap and 4% to small cap and within foreign 5% to the US and 7% to other countries. Within the "everything else category", 10% to gold, 10% to bonds, 10% to real estate, 5% to art, 5% to futures, and 1% to cash and everything else. So:

9% Australian large cap

4% Australian small cap

5% US stocks

7% ROW stocks

12% Australian focused hedge funds

12% Foreign focused hedge funds

5% Venture capital

5% Buyout PE

10% Gold

10% Real estate etc.

10% Bonds

5% Art

5% Futures

1% Cash etc.

Overall the whole portfolio is levered by about 20% (assets are 120% of equity). This 20% is roughly the value of our house, which isn't included in the above. We also try to maintain a 50:50 allocation to Australian Dollar exposure vs. foreign currency exposure. My job is mainly to choose funds and a couple of stocks and do a little trading in part of the futures allocation.

We are still overweight hedge funds after this change.

Plus500 Update

I got a statement from the new HSBC account and submitted it to Plus500 and it was approved! So, now I tried to withdraw $1,000. They at least allowed me to submit the request now. However, there was a statement that they will refund the original source of the money.... Which would mean they will send it back to the credit card we used to fund the account, even though that is a joint account. Let's see what happens. 

P.S. 8 February 

The money showed up in the new HSBC account! So we successfully withdrew money from Plus 500.

Hedge Funds Gained 0.92% in January

 HEDGE FUNDS GAIN IN JANUARY, NAVIGATING VOLATILITY

HFRI Event Driven, RVA & Crypto lead strategy performance;
Deep value Special Situations, Merger Arbitrage, Credit lead sub-strategies

CHICAGO, (February 5, 2021) – Hedge funds advanced in January to begin 2021, actively trading through a turbulent month dominated by a volatile surge in trading from retail investors concentrated in a handful of deep value equities with significant short interest. The HFRI Fund Weighted Composite Index® (FWC) gained +0.9 percent in January, while the investable HFRI 500 Fund Weighted Composite Index advanced +0.35 percent, according to data released today by HFR®, the established global leader in the indexation, analysis and research of the global hedge fund industry.

Reflecting the powerful trading trends, the HFRI FWC experienced a wide dispersion in constituent performance, as the top decile of the HFRI gained +11.6 percent, while the bottom decile declined -7.8 percent for the month. As reported previously by HFR, total hedge fund capital jumped to $3.6 trillion to begin 2021, a 4Q20 increase of $290 billion, representing the largest asset growth in industry history. Estimated 4Q20 net asset inflows totaled $3.0 billion, bringing total inflows for the second half of 2020 to $16.0 billion.

Event-Driven strategies, which often focus on out of favor, deep value equity strategies and situations, led strategy performance in January, with the investable HFRI 500 Event-Driven Index surging +3.0 percent for the month, while the HFRI Event-Driven (Total) Index gained +2.8 percent. ED sub-strategy gains were led by Merger Arbitrage, Special Situations, and Distressed exposures, strategies which categorically trade in deep value equity situations, including companies which are possible targets for restructuring, acquisitions or investor-driven strategy shifts. Following strong performance in 4Q20, the HFRI ED: Merger Arbitrage Index surged +4.0 percent in January, the HFRI ED: Special Situations Index advanced +3.8 percent, and the HFRI ED: Distressed Index added +2.6 percent. The investable HFRI 500 ED: Special Situations Index jumped +6.2 percent for the month, and the HFRI 500 ED: Merger Arbitrage Index advanced +5.1 percent.

The fixed income-based HFRI Relative Value (Total) Index gained +1.3 percent in January, while the HFRI 500 Relative Value Index advanced +1.2 percent for the month, led by the investable HFRI 500 RV: Fixed Income-Convertible Arbitrage Index, which jumped +3.5 percent, and the HFRI RV: Yield Alternatives Index, which added +4.0 percent.

Following the 2020 surge, Blockchain and Cryptocurrency exposures continued to deliver strong performance as cryptocurrencies hit record highs and as hedge funds increasingly incorporated related exposures into new and existing fund strategies. The HFR Blockchain Composite Index and HFR Cryptocurrency Index each surged over +48.0 percent in January.

Through intense stock volatility, the HFRI Equity Hedge (Total) Index advanced +0.8 percent for the month. Equity Hedge funds experienced a wide dispersion of sub-strategy performance led by the high beta, long-biased Energy and Fundamental Growth exposures. Following strong 4Q20 gains, the HFRI EH: Energy/Basic Materials Index surged +4.8 percent in January, while the HFRI EH: Fundamental Growth Index added +2.3 percent. Partially paring these gains, the HFRI EH: Sector-Technology Index declined -1.1 percent, and the HFRI EH: Multi-Strategy Index fell -0.8 percent for the month.

Risk Premia, Risk Parity and Liquid Alternatives produced mixed performance in January, led by equity and commodity exposures. The HFR Bank Systematic Risk Premia Equity Index advanced +2.2 percent for the month, while the HFR BSRP Commodity Index gained +1.6 percent. The HFR Risk Parity Vol 12 Institutional Index fell -0.2 percent in January, while the HFRI-I Liquid Alternative UCITS Index posted a narrow loss of -0.14 percent for the month, driven by the -0.3 percent decline in the HFRI-I UCITS Macro Index.

Uncorrelated Macro strategies posted a narrow gain in January, with the HFRI Macro (Total) Index advancing +0.2 percent, while the HFRI 500 Macro Index added +0.1 percent. Macro sub-strategy performance was led by the HFRI Macro: Discretionary Thematic Index, which gained +1.8 percent for the month, and the HFRI Macro: Multi-Strategy Index, which added +1.1 percent.

"Hedge funds effectively navigated the idiosyncratic stock trading volatility which focused on deep value equities with high short interest, with this trend driving gains across Event Driven strategies which categorically focus on inexpensive, out of favor equities that are experiencing fundamental, structural transition in the underlying businesses. While certain sub-strategies declined in January, as is evidenced by the wide dispersion in performance, as a direct result of the size, breadth and diverse nature of hedge fund strategies, overall industry performance was positive for the month," stated Kenneth J. Heinz, President of HFR. "While significant financial market attention has been focused on a handful of funds and small number of equities impacted by these recent trading trends, the overall hedge fund industry is comprised of over 9,100 funds managing nearly $3.6 trillion across a highly diverse range of strategies, which include significant capital exposure to out of favor, deep value equities. With an emphasis also on opportunistic positioning and sustained capital appreciation achieved through specialized long-short portfolio management, leading institutions are likely to continue expanding allocations to hedge funds as a tool for achieving their long-term portfolio objectives."

Finished First Week of Trading

I traded each day according to the rules and had 3 wins and 2 losses and ended up AUD 219 for the week. If that is a typical week then I would need to trade ten times bigger to make a decent "wage" from this. That would be 4 full futures contracts (AUD 670k notional value). But that would expose the account to a possible daily loss of AUD 50k during a market crisis like the March crash last year. I would feel that is just too much for even a AUD 1 million SMSF, though for our full portfolio it is reasonable theoretically. So, we would need to use a constant risk strategy to reduce the risk to AUD 10k every day. That also feels like a big risk in trading if it is a daily thing. So, I doubt we get up to that size of trading any time soon. 

If this is a successful strategy I will likely pursue trying to build an automated system later this year. I previously explored Capitalise, but found they couldn't implement a system like this with variable position size based on risk. So, unless they have improved it'd probably mean learning Python, but that isn't a bad thing...

Why a Constant Risk Trading Strategy Works

A constant risk trading strategy in the day-trading context is where your maximum loss possible is a constant. There are other definitions in trend-following etc contexts where you choose a position size so that the typical or expected daily movement up or down is a constant over time and markets.

Using the first definition, once you choose a stop loss, you compute the loss per contract or share if the stop was triggered and divide the fixed maximum loss figure by that to find out how many contracts or shares to buy. For example, if you are willing to risk $2000 on a trade and a contract will lose $1000 in the worst case, you buy (or sell) two contracts. 

The reason this works is the maximum downside loss is capped but by trading more contracts when possible the upside is increased. This helps create asymmetry. 

This graph shows possible loss on the x-axis and trading results (in backtest) on the y-axis, when trading one SPI contract over the last 13 months:

And this is what happens to the returns when the maximum loss is set at the average $2,300 (yes the stop loss can occasionally be exceeded when the opening price of the day is above the entry stop):

 

This has a lower information ratio (similar to Sharpe ratio but without deducting the risk free rate) than the one contract strategy though. It misses out on really big gains when volatility is high. But if you combine the two and set a minimum maximum loss at $2,300 you get better results than either strategy alone. Below $2,300 potential loss you trade a variable number of contracts and above it one contract only:

This has a higher information ratio than either simple strategy but it does take on a lot more risk at times than the constant risk strategy, which for a small account trading full size futures contracts could mean just not trading when volatility is high.

2nd Day of Trading

Today's trade was a winner, recovering a bit more than half of yesterday's loss. This is going to need a lot more research. Especially around the relationship between volatility and returns. I suspect that it's not worth trading when volatility is low and maybe there is an upside limit to how much volatility to tolerate. Now I have 13 months of data (from Barchart) nicely organized. Just need to merge all the spreadsheets together and test hypotheses.

Trading Once Again

Just over a year ago, I decided to stop systematic trading. This wasn't the first time. The problem with my trading systems was that they were overfitted to the data. They worked well for a while but then didn't. I did try one similar approach that is not tuned to the data. Yes, I said that this wasn't for me. But then over this weekend, I thought: "Maybe it is?" :) So, I downloaded a bunch more data and did backtests. It would have done especially well during the COVID-19 crash and OK in other months in the last year. So, I decided to try it today again. I used the Plus500 account, which I had decided to shutdown but hadn't managed to do yet. At least this allowed me to trade a smaller position - only AUD 10 per SPI point. The full size futures contract is AUD 25 per point. Well, I was stopped out for a AUD 960 loss... In the backtesting, getting stopped out is fairly unusual. Initially, the market fell and the short trade was profitable. Then it reversed and had a steep rally. 

 

Most losing trades have to be manually closed at the end of the day. So bad luck on my first trade. I'll try a few more and see how I go...