Quant Trader: Winning Big from Unoriginal Ideas

Some say a sequel is never as good as the original.

The cynics will go a step further. They’ll tell you the sole purpose of a sequel is to milk every last dollar from the initial idea. In the process, they say, the creativity of the original is lost.

And they have a point.

Sequels to blockbusters like Jaws, Basic Instinct, and Ocean’s 11 were terrible. These films didn’t come close to the originals. They are some of the most forgettable movies of all time.

So that settles it. The original is always better — right?

Well, no. This is just a small handpicked sample of duds. Sure, there are plenty of sequels that flop. But there are also many that are as good as, if not better, than the original.

I’m a child of the 70s. I grew up watching movies like Star Wars, Rocky, and Indiana Jones. The sequels to these films were hugely successful — both critically, and financially.

You could say the same for more recent films like Harry Potter, The Lord of the Rings, and the latest Academy Award-winning Mad Max sequel.

So it pays to keep an open mind. The original isn’t necessarily the best. It may merely be the launching pad for a series of successful films.

Devil in the detail

All this draws parallels with trading.

You see, Quant Trader can signal a stock up to three times…and many people assume signal 1s are the best. But, just like movie sequels, you shouldn’t overlook signals 2 and 3.

Have a read of this email I received during the week…

I decided a while ago to only invest on signal 1s, as I thought this was the best strategy.

I’ve analysed a total of 176 signals from your email report Friday, 18 March 2016 (as shown below) and found my assumption was wrong, at least with this set of data. 

The results assume $1,000 on each trade.

Signal 1 Signal 2 Signal 3 Total
Signal Count 105 49 22 176
Profit $14,266 $7,402 $4,100 $25,768
Investment $105,000 $49,000 $22,000 $176,000
Return 13.6% 15.1% 18.6% 14.6%


What’s your back testing indicate?

I’m very impressed with your service and like very much how, as one of your subscribers said the other day, it takes all the emotion out of investing.


I like that Andrew is testing the data for himself. This is something I encourage you to do with any advisory service you try. It’s also an excellent way to learn how a strategy works.

But there’s a catch. You need to be careful how you do your analysis. It’s easy to make mistakes that weaken your results or, in the worst instances, invalidate them all together.

I’ve seen a few attempts to analyse Quant Trader’s performance in recent weeks. Some have been close to the mark, while others have missed the target altogether.

Andrew has done a good job of analysing the current portfolio. The overall profit of 14.6% comfortably beats the All Ordinaries loss of nearly 4%.

But there’s an important omission — he hasn’t included closed trades. This means Andrew is basing his conclusions on an incomplete data set.

Andrew isn’t alone. I see many people make similar mistakes when evaluating a strategy. This often leads to them make decisions that overlook key details.

I typically use open and closed trades when discussing performance. This gives you a complete picture. It also removes any bias from only focusing on one side of the ledger.

You see, Quant Trader’s strategy is to run winners, and cut losses. If you only consider open trades, then you miss many that were unsuccessful. This is why including closed trades is important.

Let me show you the performance of live signals so far. The start date is 17 November 2014, and I’m using all open and closed long trades.

Signal Average Profit Average Loss
1 26.7% -12.3%
2 29.6% -10.3%
3 30.4% -14.1%


The other piece of information you need is the success rate.

Signal Success Rate Number of Trades
1 57% 206
2 42% 125
3 44% 64


It’s interesting to see how the strategy is tracking. The fact that long signals are profitable during a bear market is a good start.

But, in my opinion, 16 months isn’t enough to draw conclusions. It’s merely a snapshot in time. I want to see how a strategy performs in varying conditions over the longer term.

I also look at the number of trades. The live data is only a relatively small sample. I want to see a strategy work many hundreds of times. But this depth of data takes time to compile.

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Stealing time

This is where back-testing comes in. It lets you test a strategy with historic data. Back-testing can give you a big advantage over regular traders. It can also save years of trial and error.

The previous tables gave us 16 months of data. Now let me show you two decades worth of trades.

Signal Average Profit Average Loss
1 39.7% -19.3%
2 35.4% -12.7%
3 36.5% -10.3%


To be clear, these numbers are from back-testing — they are not live signals. It covers the period between 1 January 1993 and 31 October 2014.

Now let’s see how the strike rates stack up.

Signal Success Rate Number of Trades
1 58% 2189
2 55% 1576
3 47% 1113


You can see there’s a clear link between the live signals and the back-testing. And that’s what I expect to see. The main difference is the results for live signals are lower.

There’s a simple explanation for this. The back-testing period contains multiple bull and bear cycles. Live signals, on the other hand, have mostly experienced bearish conditions.

So which are the best signals to follow?

Well, first, we need to standardise the results for signals 1, 2, and 3. I’ll do this by converting the data into a dollars won/dollars lost ratio. We can then make a comparison.

This is what I do. I multiply the average profit for signal 1s (39.7%) by the percentage of winners (58%). This gives us 0.2305.

I then do the same for losses (19.3% multiplied by 42%). This comes to 0.0808.

The last step is to divide 0.2305 by 0.0808. The result is 2.85.

In other words, for every $2.85 dollars won, $1 is lost. You can see all the ratios below.

Signal Dollars Won/

Dollars Lost Ratio

1 $2.85-to-$1
2 $3.40-to-$1
3 $3.12-to-$1


We can now look at performance from every angle.

Here’s a summary:

  • Signal 1s are the choice if you’re preference is win rate and percentage gain;
  • Signal 2s have the highest dollars won/lost ratio;
  • Signal 3s average loss is the lowest.

Personally, I wouldn’t get hung up on which signal type to follow. They each have a similar historical profile. I’d be comfortable buying any of them.

The key, as always, is to spread your risk widely. This increases the odds of getting on a few ‘blockbusters’. These are the trades that could make a big difference to your returns.

Until next week,


Editor’s note: Each day the markets are open, Quant Trader scans practically every ASX stock for opportunities. It recently identified several relatively low-risk trades with big upside…and another trade in a boom/bust sector that could be about to boom.

There’s a good chance you haven’t heard of some of these ‘hidden’ stocks. And that’s understandable — the ASX has over 2000 listings. But they’re out there. Why not let Quant Trader find them for you.

Learn more about a risk-free trial to Quant Trader 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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