Trading Strategy Design

In this post I want to share some thoughts on how to design great automated trading strategies – what to look for, and what to avoid.

For illustrative purposes I am going to use a strategy I designed for the ever-popular S&P500 e-mini futures contract.

The overall equity curve for the strategy is show below.

@ES Equity Curve

This is often the best place to start.  What you want to see, of course, is a smooth, upward-sloping curve, without too many sizable drawdowns, and one in which the strategy continues to make new highs.  This is especially important in the out-of-sample test period (Jan 2014- Jul 2015 in this case).  You will notice a flat period around 2013, which we will need to explore later.  Overall, however, this equity curve appears to fit the stereotypical pattern we hope to see when developing a new strategy.

Let’s move on look at the overall strategy performance numbers.

STRATEGY PERFORMANCE CHARACTERISTICS

@ES Perf Summary(click to enlarge)

 1. Net Profit
Clearly, the most important consideration.  Over the 17 year test period the strategy has produced a net profit  averaging around $23,000 per annum, per contract.  As a rough guide, you would want to see a net profit per contract around 10x the maintenance margin, or higher.

2. Profit Factor
The gross profit divided by the gross loss.  You want this to be as high as possible. Too low, as the strategy will be difficult to trade, because you will see sustained periods of substantial losses.  I would suggest a minimum acceptable PF in the region of 1.25.  Many strategy developers aim for a PF of 1.5, or higher.

3. Number of Trades
Generally, the more trades the better, at least from the point of view of building confidence in the robustness of strategy performance.  A strategy may show a great P&L, but if it only trades once a month it is going to take many many years of performance data to ensure statistical significance.  This strategy, on the other hand, is designed to trade 2-3 times a day.  Given that, and the length of the test period, there is little doubt that the results are statistically significant.

SSALGOTRADING AD

Profit Factor and number of trades are opposing design criteria – increasing the # trades tends to reduce the PF.  That consideration sets an upper bound on the # trades that can be accommodated, before the profit factor deteriorates to unacceptably low levels.  Typically, 4-5 trades a day is about the maximum trading frequency one can expect to achieve.

4. Win Rate
Novice system designers tend to assume that you want this to be as high as possible, but that isn’t typically the case.  It is perfectly feasible to design systems that have a 90% win rate, or higher, but which produce highly undesirable performance characteristics, such as frequent, large drawdowns.  For a typical trading system the optimal range for the win rate is in the region of 40% to 66%.  Below this range, it becomes difficult to tolerate the long sequences of losses that will result, without losing faith in the system.

5. Average Trade
This is the average net profit per trade.  A typical range would be $10 to $100.  Many designers will only consider strategies that have a higher average trade than this one, perhaps $50-$75, or more.  The issue with systems that have a very small average trade is that the profits can quickly be eaten up by commissions. Even though, in this case, the results are net of commissions, one can see a significant deterioration in profits if the average trade is low and trade frequency is high, because of the risk of low fill rates (i.e. the % of limit orders that get filled).  To assess this risk one looks at the number of fills assumed to take place at the high or low of the bar.  If this exceeds 10% of the total # trades, one can expect to see some slippage in the P&L when the strategy is put into production.

6. Average Bars
The number of bars required to complete a trade, on average.  There is no hard limit one can suggest here – it depends entirely on the size of the bars.  Here we are working in 60 minute bars, so a typical trade is held for around 4.5 hours, on average.   That’s a time-frame that I am comfortable with.  Others may be prepared to hold positions for much longer – days, or even weeks.

Perhaps more important is the average length of losing trades. What you don’t want to see is the strategy taking far longer to exit losing trades than winning trades. Again, this is a matter of trader psychology – it is hard to sit there hour after hour, or day after day, in a losing position – the temptation to cut the position becomes hard to ignore.  But, in doing that you are changing the strategy characteristics in a fundamental way, one that rarely produces a performance improvement.

What the strategy designer needs to do is to figure out in advance what the limits are of the investor’s tolerance for pain, in terms of maximum drawdown, average losing trade, etc, and design the strategy to meet those specifications, rather than trying to fix the strategy afterwards.

7. Required Account Size
It’s good to know exactly how large an account you need per contract, so you can figure out how to scale the strategy.  In this case one could hope to scale the strategy up to a 10-lot in a $100,000 account.  That may or may not fit the trader’s requirements and again, this needs to be considered at the outset.  For example, for a trader looking to utilize, say, $1,000,000 of capital, it is doubtful whether this strategy would fit his requirements without considerable work on the implementations issues that arise when trying to trade in anything approaching a 100 contract clip rate.

8. Commission
Always check to ensure that the strategy designer has made reasonable assumptions about slippage and commission.  Here we are assuming $5 per round turn.  There is no slippage, because the strategy executes using limit orders.

9. Drawdown
Drawdowns are, of course, every investor’s bugbear.  No-one likes drawdowns that are either large, or lengthy in relation to the annual profitability of the strategy, or the average trade duration.  A $10,000 max drawdown on a strategy producing over $23,000 a year is actually quite decent – I have seen many e-mini strategies with drawdowns at 2x – 3x that level, or larger.  Again, this is one of the key criteria that needs to be baked into the strategy design at the outset, rather than trying to fix later.

 ANNUAL PROFITABILITY

Let’s now take a look at how the strategy performs year-by-year, and some of the considerations and concerns that often arise.

@ES Annual1. Performance During Downturns
One aspect I always pay attention to is how well the strategy performs during periods of high market stress, because I expect similar conditions to arise in the fairly near future, e.g. as the Fed begins to raise rates.

Here, as you can see, the strategy performed admirably during both the dot com bust of 1999/2000 and the financial crisis of 2008/09.

2. Consistency in the # Trades and % Win Rate
It is not uncommon with low frequency strategies to see periods of substantial variation in the # trades or win rate.  Regardless how good the overall performance statistics are, this makes me uncomfortable.  It could be, for instance, that the overall results are influenced by one or two exceptional years that are unlikely to be repeated.  Significant variation in the trading or win rate raise questions about the robustness of the strategy, going forward.  On the other hand, as here, it is a comfort to see the strategy maintaining a very steady trading rate and % win rate, year after year.

3. Down Years
Every strategy shows variation in year to year performance and one expects to see years in which the strategy performs less well, or even loses money. For me, it rather depends on when such losses arise, as much as the size of the loss.  If a loss occurs in the out-of-sample period it raises serious questions about strategy robustness and, as a result, I am very unlikely to want to put such a strategy into production. If, as here, the period of poor performance occurs during the in-sample period I am less concerned – the strategy has other, favorable characteristics that make it attractive and I am willing to tolerate the risk of one modestly down-year in over 17 years of testing.

INTRA-TRADE DRAWDOWNS

Many trades that end up being profitable go through a period of being under-water.  What matters here is how high those intra-trade losses may climb, before the trade is closed.  To take an extreme example, would you be willing to risk $10,000 to make an average profit of only $10 per trade?  How about $20,000? $50,000? Your entire equity?

The Maximum Average Excursion chart below shows the drawdowns on a trade by trade basis.  Here we can see that, over the 17 year test period, no trade has suffered a drawdown of much more than $5,000.  I am comfortable with that level. Others may prefer a lower limit, or be tolerant of a higher MAE.

MAE

Again, the point is that the problem of a too-high MAE is not something one can fix after the event.  Sure, a stop loss will prevent any losses above a specified size.  But a stop loss also has the unwanted effect of terminating trades that would have turned into money-makers. While psychologically comfortable, the effect of a stop loss is almost always negative  in terms of strategy profitability and other performance characteristics, including drawdown, the very thing that investors are looking to control.

 CONCLUSION
I have tried to give some general guidelines for factors that are of critical importance in strategy design.  There are, of course, no absolutes:  the “right” characteristics depend entirely on the risk preferences of the investor.

One point that strategy designers do need to take on board is the need to factor in all of the important design criteria at the outset, rather than trying (and usually failing) to repair the strategy shortcomings after the event.

 

 

 

Quant Strategies in 2018

Quant Strategies – Performance Summary Sept. 2018

The end of Q3 seems like an appropriate time for an across-the-piste review of how systematic strategies are performing in 2018.  I’m using the dozen or more strategies running on the Systematic Algotrading Platform as the basis for the performance review, although results will obviously vary according to the specifics of the strategy.  All of the strategies are traded live and performance results are net of subscription fees, as well as slippage and brokerage commissions.

Volatility Strategies

Those waiting for the hammer to fall on option premium collecting strategies will have been disappointed with the way things have turned out so far in 2018.  Yes, February saw a long-awaited and rather spectacular explosion in volatility which completely destroyed several major volatility funds, including the VelocityShares Daily Inverse VIX Short-Term ETN (XIV) as well as Chicago-based hedged fund LJM Partners (“our goal is to preserve as much capital as possible”), that got caught on the wrong side of the popular VIX carry trade.  But the lack of follow-through has given many volatility strategies time to recover. Indeed, some are positively thriving now that elevated levels in the VIX have finally lifted option premiums from the bargain basement levels they were languishing at prior to February’s carnage.  The Option Trader strategy is a stand-out in this regard:  not only did the strategy produce exceptional returns during the February melt-down (+27.1%), the strategy has continued to outperform as the year has progressed and YTD returns now total a little over 69%.  Nor is the strategy itself exceptionally volatility: the Sharpe ratio has remained consistently above 2 over several years.

Hedged Volatility Trading

Investors’ chief concern with strategies that rely on collecting option premiums is that eventually they may blow up.  For those looking for a more nuanced approach to managing tail risk the Hedged Volatility strategy may be the way to go.  Like many strategies in the volatility space the strategy looks to generate alpha by trading VIX ETF products;  but unlike the great majority of competitor offerings, this strategy also uses ETF options to hedge tail risk exposure.  While hedging costs certainly acts as a performance drag, the results over the last few years have been compelling:  a CAGR of 52% with a Sharpe Ratio close to 2.

F/X Strategies

One of the common concerns for investors is how to diversify their investment portfolios, especially since the great majority of assets (and strategies) tend to exhibit significant positive correlation to equity indices these days. One of the characteristics we most appreciate about F/X strategies in general and the F/X Momentum strategy in particular is that its correlation to the equity markets over the last several years has been negligible.    Other attractive features of the strategy include the exceptionally high win rate – over 90% – and the profit factor of 5.4, which makes life very comfortable for investors.  After a moderate performance in 2017, the strategy has rebounded this year and is up 56% YTD, with a CAGR of 64.5% and Sharpe Ratio of 1.89.

Equity Long/Short

Thanks to the Fed’s accommodative stance, equity markets have been generally benign over the last decade to the benefit of most equity long-only and long-short strategies, including our equity long/short Turtle Trader strategy , which is up 31% YTD.  This follows a spectacular 2017 (+66%) , and is in line with the 5-year CAGR of 39%.   Notably, the correlation with the benchmark S&P500 Index is relatively low (0.16), while the Sharpe Ratio is a respectable 1.47.

Equity ETFs – Market Timing/Swing Trading

One alternative to the traditional equity long/short products is the Tech Momentum strategy.  This is a swing trading strategy that exploits short term momentum signals to trade the ProShares UltraPro QQQ (TQQQ) and ProShares UltraPro Short QQQ (SQQQ) leveraged ETFs.  The strategy is enjoying a banner year, up 57% YTD, with a four-year CAGR of 47.7% and Sharpe Ratio of 1.77.  A standout feature of this equity strategy is its almost zero correlation with the S&P 500 Index.  It is worth noting that this strategy also performed very well during the market decline in Feb, recording a gain of over 11% for the month.

Futures Strategies

It’s a little early to assess the performance of the various futures strategies in the Systematic Strategies portfolio, which were launched on the platform only a few months ago (despite being traded live for far longer).    For what it is worth, both of the S&P 500 E-Mini strategies, the Daytrader and the Swing Trader, are now firmly in positive territory for 2018.   Obviously we are keeping a watchful eye to see if the performance going forward remains in line with past results, but our experience of trading these strategies gives us cause for optimism.

Conclusion:  Quant Strategies in 2018

There appear to be ample opportunities for investors in the quant sector across a wide range of asset classes.  For investors with equity market exposure, we particularly like strategies with low market correlation that offer significant diversification benefits, such as the F/X Momentum and F/X Momentum strategies.  For those investors seeking the highest risk adjusted return, option selling strategies like the Option Trader strategy are the best choice, while for more cautious investors concerned about tail risk the Hedged Volatility strategy offers the security of downside protection.  Finally, there are several new strategies in equities and futures coming down the pike, several of which are already showing considerable promise.  We will review the performance of these newer strategies at the end of the year.

Go here for more information about the Systematic Algotrading Platform.

Day Trading System in VIX Futures – JonathanKinlay.com

This is a follow up to my earlier post on a Calendar Spread Strategy in VIX Futures (more information on calendar spreads ).

The strategy trades the front two months in the CFE VIX futures contract, generating an annual profit of around $25,000 per spread.

DAY TRADING SYSTEM
I built an equivalent day trading system in VIX futures in Trading Technologies visual ADL language, using 1-min bar data for 2010, and tested the system out-of-sample in 2011-2014. (for more information on X-Trader/ ADL go here).

The annual net PL is around $20,000 per spread, with a win rate of 67%.   On the downside, the profit factor is rather low and the average trade is barely 1/10 of a tick). Note that this is net of Bid-Ask spread of 0.05 ($50) and commission/transaction costs of $20 per round turn.  These cost assumptions are reasonable for online trading at many brokerage firms.

SSALGOTRADING AD

However, the strategy requires you to work the spread to enter passively (thereby reducing the cost of entry).  This is usually only feasible on a  platform suitable for a high frequency trading, where you can assume that your orders have acceptable priority in the limit order queue.  This will result in a reasonable proportion of your passive bids and offers will be executed.  Typically the spread trade is held throughout the session, exiting on close (since this is a day trading system).

Overall, while the trading system characteristics are reasonable, the spread strategy is better suited to longer (i.e. overnight) holding periods, since the VIX futures market is not the most liquid and the tick value is large.  We’ll take a look at other day trading strategies in more liquid products, like the S&P 500 e-mini futures, for example, in another post.

High Freq Strategy Equity Curve(click to enlarge)

 

High Frequency Perf Results

(click to enlarge)