Could This Be Your ‘Perfect Fit’ for Troubled Times?

Business isn’t just about the money…

Some of my most rewarding experiences have come with no financial reward.

Some years ago, I took on a volunteer role at a local high school. It involved mentoring a group of Year Nine students through a week-long business simulation.

The aim of the program was to introduce the students to commerce. They had to manage a fictional company, competing online against other student groups.

Watching the students discover how business works was amazing. I saw them go from having a basic idea to developing a strong grasp in just a week.

And I loved it — I’ve gone back four times since. Helping people learn is special. It’s something I really enjoy.

I recently received an email from a member. It made my day. It’s why I get such satisfaction from passing on my experience. Have a read below…

Let me thank you for giving me what I’ve been looking for without ever knowing it existed. Algorithmic trading is the “perfect fit” for me, and I never would have found it without Quant Trader.

Ironically I stumbled across it while doing fundamental based research for my SMSF. I initially recommended it to a friend before I started thinking “Mmm, that sounds like something which might interest me…”

I’m now so absorbed with system trading that I’ve started to develop my own systems and would like to use Quant Trader as my performance benchmark.

Many thanks again for starting me on the path I wish I’d found many years ago! Your mentoring has been truly fantastic: you are a gifted teacher (speaking as an ex-teacher myself).

Member, Neil B

This sort of feedback means a lot. Neil has found a trading style that he can make his own. He’s also learning from my experience. That’s a satisfying outcome — I’m grateful to know I’m helping.

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It’s going to get technical

There’s a second part to Neil’s email which I’m about to show you. This section asks for more information about Quant Trader’s performance statistics. Here’s what he says…

I wonder if it would be possible to drill down to more detailed performance metrics.

If you could fill in the data for my table I would be eternally grateful:

Also, I would be very interested to see the results from a trade in Paladin Energy [ASX: PDN] (entering Dec 2013) to see how Quant Trader handled this one.

Below is the table Neil refers to. You’ll see I’ve included the performance figures he requests:

Metric Statistic
Compound annual growth rate 15.9%
Maximum drawdown 19.6%
Dollars won/loss ratio 3.72 to 1
Percent profit factor 3.72
Modified Sharpe 1.31
Number of trades 624
Expectancy $196
Win rate 58.3%
Average holding period (profits) 457
Largest Win 851%
Average win 46%
Average holding period (losses) 221
Average loss -17%
Largest Loss -65%

Now let me explain what this table is all about.

The data is for the seven years to 31 January 2016. It’s the result of backtesting, and shows the hypothetical performance of buy signals only.

I’m using the base of the GFC as the starting point. This includes four years in which the market rose, and three in which it fell. I’d describe these as typical trading conditions.

The figures also assume closing all open trades on the last day of the period.

You’ll be familiar with a few of the data fields — for example: ‘Number of Trades’ and ‘Win Rate’. But there are some you probably haven’t seen — such as ‘Modified Sharpe’ and ‘Expectancy’. I’ll explain these in a moment.

There’s an important point I need to make. I’m only measuring the performance of signal 1s. So this means the results are different to following signals 1 to 3.

Let me explain why I’m just using signal 1s.

When you or I trade, we have a set amount of capital. This determines how many stocks we can buy. So, if you have $50,000, and buy in $1,000 parcels, you can purchase 50 stocks.

Quant Trader doesn’t have this limitation. It won’t run out of capital and stop generating signals. The algorithms can access unlimited notional capital.

This approach works well for its intended purpose — stock identification and trade management.

But there are limitations. It means performance metrics like annual returns are difficult to calculate. You see, a key input is starting capital. And Quant Trader is not set up to function this way.

So here’s what I’ve done. For the purpose of helping Neil, I’ve set Quant Trader’s starting capital at $100,000. Please note, this is not an ongoing change, it’s just for this report.

I’ve also restricted Quant Trader from taking second and third signals. This means the capital base will be enough to cover every signal 1 up to the 100 company cap.

The other change I’ve made is to trade size. Rather than using the standard $1,000, the system will use 1% of capital. This allows the trade size to increase in line with profits.

Everything else is the same. Both the entry and exit triggers work exactly as before. So I believe the results are highly relevant to Quant Trader’s standard setup.

Let me show you what this looks like on a chart.

Source: Quant Trader

This shows the hypothetical performance of following every signal 1. As always, it doesn’t take account of costs or dividends.

Usually, I show you the hypothetical profit of following every signal. Today you’re seeing the growth in total equity. You’ll notice the starting capital of $100,000 rising to its present level of $280,000.

The key point of interest is the compound annual growth rate. This comes in at 15.9%.

We can now make a direct comparison to the All Ordinaries. Its corresponding return is around 5.7%. Quant Trader’s strategy is in front by a handy margin.

Now let me briefly explain some of the performance metrics. I won’t go into detail. If you’re interested, there are countless explanations on the internet.

Metric Explanation
Compound annual growth rate This is the annual percentage rate at which an account grows over the period.
Maximum drawdown This is reflects the largest fall in capital relative to a previous equity high.
Dollars won/loss ratio The ratio shows how many dollars the system hypothetically made for every dollar lost.
Percent profit factor Similar to the above. It divides total percentage profit by total percentage losses.
Modified Sharpe The Sharpe ratio is the classic measure of return verses risk. But it has limitations when used with trend following systems.
Number of trades The number of trades over the test period.
Expectancy This statistic shows the average payout for every $1,000 invested.
Win rate The percentage of profitable trades.
Average holding period (profits) The average numbers of days a winning trade was open.
Largest win The biggest percentage gain across all trades.
Average win The average of all profitable trades.
Average holding period (losses) The average numbers of days a losing trade was open.
Average loss The biggest percentage loss across all trades.
Largest loss The average of all losing trades.

This table has a lot more detail than most people want. These are the sort of statistics I look at when developing a system. So don’t worry if it’s all too technical — that’s my job.

Quant Trader is about simplifying the process. The details may be complex, but the strategy is simple: spread your bets, buy into strength, let your profits run, and cut your losses.

I know some people like deeper analysis. So if you’re this way inclined, I hope you’ve enjoyed this look at the more technical side to quant trading.

Quant Trader isn’t just about the signals — it’s also about learning. I want you to have the confidence and discipline to trade like a professional.

Until next week,


Editor’s note: Despite the recent market volatility, Quant Trader has recently cashed out several big winners. Last week, the system booked a 353% gain in Blackmores. The week before that, it was a 46% profit in Elders. And two weeks earlier, Quant Trader closed out Bellamy’s for a 74% gain. Imagine profits like these in your account. Get immediate access to Quant Trader’s signals – and claim a no-risk trial subscription – by clicking here.

Jason McIntosh

Jason McIntosh

Jason is a professional quantitative analyst. Before he graduated in 1991 he joined Bankers Trust — a Wall Street investment bank — to be a trader. After Bankers Trust was taken over in 1999, Jason, already financially independent, co-founded a stock market advisory and funds management business called Fat Prophets. At 37 he sold his part of that business and retired. These days, he’s a private trader and system developer. In 2014 he launched the wildly successful trading service: Quant Trader.

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