In this post I take a look at one of the greatest traders of all time, Jesse Livermore. Jesse was famously profiled in the classic investing tome Reminiscences of a Stock Operator, a book that has been called the best trading book every written.
This is a long piece, clocking in at around 9,000 words and it covers all of Jesse’s most important trading lessons. The quotes are taken directly from the original book by Edwin Lefèvre. Read more »
2015 has not been an ideal year for stock traders so far. As you know, we have seen plenty of volatility in China, Europe and Emerging Markets. The S&P 500 has been trading in a choppy range for much of the year and is currently showing a year-to-date return of only 1.57%.
Our trend following system is currently ahead of the benchmark index by a healthy margin and this is based on various metrics of performance and risk. Read more »
In the classic trading book, Reminiscences of a Stock Operator, Jesse Livermore spoke a little bit about a professional gambler called Pat Hearne. Pat would treat the markets like a casino game (such as roulette, faro or blackjack) and his strategy was to make a series of calculated bets, always looking for small, sure wins. Read more »
There is a lot of bad information in the trading world and the strategy of trend following is often misinterpreted. It can be especially hard for a beginner to get to the truth and that’s why I’ve put together this trend following PDF which you can download for free. Read more »
I am trialling a new trend following stock trading strategy. This strategy looks for stocks breaking out to new 52 week highs. The stock must also have decent sized volume, a PEG less than 1 and an RSI score less than 70. Trades are exited on a 20 week low. So far, results look promising but too early to say whether it will work longer term. I am paper trading this before taking it live.
Here are this week’s trend following stock picks. Both are in the auto parts industry suggesting that this is a good sector to be involved in as a whole. Normally I wouldn’t buy more than one stock in the same sector. Out of these two I’d go for MGA.
Magna International Inc ($MGA)
Magna International is an auto parts wholesaler based out of Canada with 128,000 employees in 29 countries. The stock has broken to a new 52 week high but the financials are in good condition. PEG is 0.99 and price to sales is 0.67. EPS has grown over 100% over the past 5 years and current ratio is 1.30. The stock also has 5% insider ownership.
Lear Corp ($LEA)
Lear was founded in Detroit, Michigan in 1917 and is a leading manufacturer of assemblies for the automotive and aircraft industries. PEG is 0.94 and forward PE is 10.30. EPS is expected to grow at over 18% over the next 5 years.
In my book, I detailed 20 original trading systems and tested those systems on historical stock data going back to the year 2000. Some of those systems produced reasonable results and indicated promise for future trading. Many were able to capture some nice trends like this one in CarMax Inc:
However, one back test is not enough and it’s necessary to scrutinize the system as much as possible before taking it live.
How to scrutinize and improve a trading system
In this post I look at how we can further scrutinize and improve a trading system. This information should be useful for analysing any system, whether you have created it from scratch or bought it from a third party.
The first step is to work on weaknesses in the system itself.
Start date bias
The biggest weakness with the systems described in my book is one of start date bias.
In other words, because each system buys a basket of 10 stocks on a particular date (1/8/2000), returns are extremely dependent on just which 10 stocks are bought at that time.
If the system started on the 1st September, instead of the 1st August for example, the basket of stocks bought would likely be very different. And, therefore, the type of return achieved could also be very different.
Unfortunately, the problem of start date bias cannot be completely overcome but there are some things we can do to make our results more reliable.
Move the start date
The most obvious choice is to step the system forward or backward and try different start dates. If the system performs in a similar fashion then it suggests it is robust.
If it performs poorly from different start dates then the good returns experienced previously are mostly likely a result of simply landing a lucky start date.
To take this further, let us take system 8 from the book, called ‘Breakout with EMA confirmation‘. This is a simple breakout strategy that uses an EMA crossover as a filter for trades. Actual statistics from my test of this system produced a CAR of 17.49% with a maximum drawdown of 27%.
If we move the start date forward by 6 months the system starts on the 1st February 2001 instead of the 1st August 2000. We get the following results:
As you can see, the start date doesn’t affect this system all that much.
Moving the test forward six months actually resulted in higher 10 year returns of 18.77% CAR, although maximum drawdown increased to -32%.
It therefore follows that the system is not significantly dependent on a lucky start date. That’s good news for this particular system.
Try different watch-lists
Another step to test the robustness of a system is to test it over different stock universes or watch-lists.
As I’ve said, the system’s returns are to a large extent reliant on the stocks that the system first holds.
Therefore, if we change the watch-list, we can see if the system performs as well on different numbers of stocks.
For example, instead of testing the system over the 500 stocks in the S&P 500, you could try testing it on 10 groups of 50 stocks or 50 groups of 10 stocks. Or you could run it over different stock markets such as the Nasdaq or London’s FTSE 100.
To test this, let us take System 8 again which was run over the whole universe of S&P 500 stocks and test it over 10 groups of 50 stocks instead.
As you can see from the table, the system did not perform as well over the individual watch-lists. The average CAR was lower and drawdown went up.
This suggests that the system does not perform so well on smaller watch-lists.
This could make sense since trend following systems work best when they are able to take advantage of lots of signals.
On the plus side, not one test lost money so this could be a good one for further optimisation.
We can further analyse the system by looking at the performance of individual tickers. Maybe some stocks just don’t trend and therefore could be left out of the watchlist? Maybe we could find a way to exit stocks that are not going in any direction?
As I stated previously, system traders also have the problem of incorporating delisted stocks into their back tests. This is a problem because delisted stock data can be expensive and hard to organise.
System traders have the difficulty of estimating how many stocks were taken off which exchange during the time period and incorporating them into the analysis.
Not all trading systems are affected when incorporating delisted data but to test this further, I took system 8 again and tested it on data that included delisted stocks.
For this test, I took a total watchlist of 4000 US stocks and included 1500 delisted stocks from the time period.
As you can see, incorporating the delisted tickers caused returns to fall significantly to just 7.90% CAR. Drawdown also went up to -45%.
I then wondered what would happen if I got rid of short positions altogether and the results were quite interesting.
You might expect short positions to help a system that trades delisted stocks but the opposite was the case.
As you can see from the table, trading the same system long only led to higher returns of 14.90% CAR. An encouraging sign.
These are just some of the steps to take when scrutinizing a trading system. The more pressure you put it under, the better it will perform when you take it live.
There is just one more prejudice that traders need to deal with which I will discuss now.
Is the data really out of sample?
In my book, I stress the importance of keeping some data out of sample and I believe I was correct in doing so. Keeping the data clean from contamination is the best way to test a system once it has been developed.
However, it is important to bring to light that no stock data is ever completely out of sample.
For example, I may not have back-tested any data between 2010-13 and I may not have knowledge of how each stock performed over that time. But, I do know what happened to the overall market during that time.
Since I take an interest in the markets I already know that the broader market was choppy in 2011, strong in 2012 and even stronger in 2013 and that gives me a bias when back-testing. Just like I know that in 2008 we had a big crash and in 2000 we had the tech bubble.
This is a bias that cannot be overcome no matter how scrupulous you are with data mining principles and keeping your data separate.
This is why it is so important to paper-trade live and why there will always be some risk involved with financial trading.
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People often want to know whether forex trading is profitable. They see a strategy online promising 1000% returns and millions of dollars in profits and wonder whether they too can become successful forex traders.
On the flip side, though, they will also have heard the stories of those traders who have lost money and in the back of their minds they will wonder if forex trading is no different to gambling.
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