SnapTrader

MultiStage Exit Analysis… more…

2013-02-27T21:03:00.001-08:00

In a past post I discussed the various profit exits and how they impact overall results. We also use a 200 day simple moving average as an exit, which I wanted to look at this time. As with the 5% profit exit, there is no magic in the specific MA200 number. In fact, a good robust trading system should still work with slight variations to parameters like this. Below is a table showing some basic results for a 200 day SMA, as well as a 100 and 50 day stop.

*MA Exit*	200	100	50
ROI	265%	237%	205%
Max Drawdown	30.71%	33.32%	35.32%
Sharpe	1.92	1.90	1.97
UPI	45.36	40.09	41.65
Worst Month	-18.6%	-16.7%	-12.5%
2nd Worst Mo.	-15.7%	-15.0%	-7.6%
3rd Worst Mo.	-9.0%	-13.4%	-5.6%

As you can see, there are clear trade-offs with these different parameters. One is not necessarily better than another, as it depends on an individuals performance requirements or metrics. For example, if pure ROI is the most important thing then the MA200 has performed the best historically. But if the worst case losses in a given month are important (a way of measuring drawdown) then the MA50 has performed much better. These sorts of trade-offs are typical in trading systems.

Good Trading…

Correction to January Results!!!

2013-02-01T21:38:00.001-08:00

I posted the January results earlier today, and discovered that I made a mistake… oops! The modifications I made to the trading system weren’t put into the daily scan cycle until January 3, but January 2 was the first trading day of the year.

Put simply, trades from the 2nd were based on the previous version of the model, and I don’t think any were generated. This might all sound confusing, so let me just net it out for you. The results were less than what I provided in my earlier post. Here are the stats based on starting the updated system on the 3rd.

Net profit for the month	9.67%
Total number of trades	34
Average return per trade	1.71%
Win ratio	76.47%
Exposure	24.37%

I apologize for the earlier post, and should have caught this earlier. 9.67% is still a great return for the month, but a far cry from 14%.

Good Trading…

January Trading System Results

2013-02-01T10:53:00.001-08:00

I hope everyone had a great trading month! January was really a breakout month for the MultiStage Trading System. The two newest modifications have worked out quite well.

First, we are seeing a lot more trades. To a certain extent, trading is a numbers game. If we can identify trades with an “edge”, then more trades is better. And that’s what we are now seeing.

Second, using the 200SMA as a soft stop has also help control some runaway trades. Out of the 41 trades it was only triggered a very few times, but it contained a couple runaway losers, thereby reducing drawdowns.

It’s still very early with the modifications, but so far they seem to be working. As you can see, 41 trades is a nice amount of exposure, and therefore it really improved the overall model results. Not everyone will see 14% returns for the month, but I know of some that got very close. I’m interested in hearing from any of you that care to share your real results. Below are a few of the key statistics:

Net profit for the month	14.70%
Total number of trades	41
Average return per trade	2.11%
Win ratio	78.05%
Exposure	20.27%
Total time invested	*Less than 10 minutes a day!*

Now might be a good time to comment on why our real trading results don’t match the model exactly. I did an article a while back that is still accurate. The one category I didn’t mention was “execution”. Sometimes there are simply execution differences with brokers that cause us to get in and out of trades at different prices. Aside from that, as the article says, Commissions, Sequencing, Liquidity, and Mistakes are the other biggest culprits. That said, it’s been my experience that you can get pretty close.

Good Trading…

Exits for the MultiStage Trading System revisited

2013-01-09T06:41:00.001-08:00

For subscribers and followers of the MultiStage Trading System (currently version 9 on CalculatedReturns.com), you will remember that one of our three exit types is a profit stop at 5%. It’s worth noting that 5% is no magic number. Take a look at the following short study:

As you can see, there are trade offs to moving this exit, and “better” is a function of what you are trying to accomplish. If getting the highest percentage of winners is important a 3% stop has historically worked out “better”. If Sharpe ratios are important to you, then higher exits work out better.

I personally look closely at the Ulcer Performance Index. This is a number which rewards bigger returns and penalizes drawdowns. For this number a higher exit, like 6% or 7%, is better. And for those of you bent on absolute optimization runs, the very best was 7% for short positions, and 6% for long positions. I suspect that’s a little curve-fit, however, as there really is no “magic number”.

Good Trading…

November Trading System Results

2012-12-02T13:26:00.001-08:00

I hope everyone had a great Thanksgiving holiday! The end of another year is rapidly approaching.

November started our by teasing us a little with some increasing volatility, but things rapidly settled down after the elections and the VIX fell back into the mid-teens. If you follow the MultiStage Trading System, you know that this makes for some boring trading and very little activity. Consquently, the majority of our trades (in fact, all but one) were initiated before mid-month.

As boring as the second half of the month was, we still were able to get exposure up to 19% given the action in the first two weeks. This enabled the system to turn in a respectable result. Below are a few of the key statistics:

Net profit for the month	4.83%
Total number of trades	23
Average return per trade	1.47%
Win ratio	65.22%
Exposure	19.44%
Total time invested	*Less than 10 minutes a day!*

Beginning immediately (first trading day of December… the 3rd) we have modified the system to a new version. The selection criteria has been improved a bit, but the most important part of the approach is a new exit based on the 200-day simple moving average. If you don’t follow this new exit your results will not match the back-tested results of the system, although they should be very close to the version 8 results. You can read more about that in the recent posts from last week.

Good Trading…

More MSv9 Trading System Data

2012-11-27T15:33:00.000-08:00

In my last post I shared the launch of a significant update to the MultiStage Trading System. The back-tested results from that post were actually my out-of-sample test, which was from 2012. Results from the prior 11 years were even more impressive.

	Version 8	Version 9
Max Drawdown%	36.82%	22.81%
Sharpe Ratio	2.95	3.30
Ulcer Perf. Index	66.13	103.97
Worst Month	-16.7%	-11.0%
Annual Return %	297.57%	315.51%

As mentioned last time, risk management is the primary benefit of the update, but overall reward is not penalized for the extra measure of caution.

Good Trading…

MultiStage Trading System Version 9 Coming Soon!

2012-11-26T07:42:00.001-08:00

It’s time for an upgrade! I’ve alluded a few times in this blog, and the SnapTrader blog, that I am working on improving the MultiStage Trading System. That’s an ongoing effort and will likely continue. The latest revision (version 9) is a substantial upgrade. It incorporates some new criteria for the development of signals, as well as a slight change to the exit rules.
Before I go into the changes, let me first share some of the results. There are a few reasons why I like this version better. First and foremost is risk management. Version 9 incorporates a simple dynamic stop that protects us from the extreme downside. I haven’t been shy about my concern with stops. For the most part stops have always hurt us, both in returns risk management… until now.
The results show the benefit of the new system by trimming drawdowns, and improving risk/reward measures immensely. For example, for the 2012 trading year compare the numbers below:

	Version 8	Version 9
Max Drawdown (trade)	35.14%	28.21%
Sharpe Ratio	2.19	2.62
Ulcer Perf. Index	27.67	32.97

It’s pretty clear that we’ve improved the risk management, but what about performance. I’ve added another row to the table with the total return for this year, through 11/26.

	Version 8	Version 9
Max Drawdown (trade)	35.14%	28.21%
Sharpe Ratio	2.19	2.62
Ulcer Perf. Index	27.67	32.97
Net Profit%	*59.51%*	*58.74%*

Now, please don’t get silly and expect you can duplicate these returns. There are many reasons why it’s virtually impossible to match them exactly, but I’m sure it’s easy to see why I much prefer this latest version to the trading system. There is very little difference in return, yet measurable risk improvements.
The signals for this new version will go live on 12/1/2012.

New Exit

All of these improvements come with a price. It’s not required, but without using this new exit back-tested results are much closer to the version 8 results. It’s a simple exit, but does require a daily look. Here is the rule:
If the stock’s closing price is below the 200 day simple moving average, enter an order to exit on the open for the next day.
That’s it. It’s simple, but it does require you to assess this each day. I find that the easiest way is to just click through my positions with the 200-day MA line on the chart. If you have been following the system you will know that there are rarely more than a few positions, so this isn’t really a big time sink. And again, it’s not required, but without it the improved results don’t test out.
Good Trading…

October Trading System Results

2012-11-01T08:51:00.001-07:00

Happy Halloween! It’s time for an October wrap-up the day after the biggest candy eating day of the year.

Both statistical and implied volatility remained low, as it has been in the past few months. In fact, as I reported last month, volatility is about as low as it’s been in the past five years. This isn’t a bad thing, and in fact has been mostly good for that long buy-and-hold part of your portfolio, but it’s not the greatest for our trading system.

The MS8 system thrives on volatility, so when its slow, we don’t necessarily lose money, but it’s also not very active. October had a nice little bump in volatility starting around the 19th, and in fact about 2/3 of our trades took place just after this. Aside from that it was still pretty quiet.

Our overall exposure was about as low as I have seen, at just 9.35% (meaning that’s how much time we had cash in the market). Below are a few of the key statistics:

Net profit for the month	5.87%
Total number of trades	14
Average return per trade	2.54%
Win ratio	71.43%
Exposure	9.35%
Total time invested	*Less than 10 minutes a day!*

Low volatility means fewer trades. The average return per trade and win ration was just about typical for the system, so we just needed some more trips to the plate. As I said last month, we could really use some volatility! All in all, it’s hard to complain about a 5% month, however, and if I can do it with only 9% exposure it’s even better.

Good Trading…

September Trading System Results

2012-10-15T18:07:00.000-07:00

Volatility continues to be soft, as in recent months. In fact, volatility is about as low as it’s been in the past five years. As you can see by the picture below, there are only a couple of spots that have rivaled this one.

Implied volatility isn’t making the future look bright either. The VIX is also at it’s lowest point in the past 5 years or so:

Why all this talk about volatility? As you may remember from past articles, my own trading model, the MS8 Trading System, depends on, and even thrives on, volatility. All this “calmness” spelled a stinky month for the model.

Our overall exposure was lower than usual, at just 11.46% (meaning that’s how much time we had cash in the market). Below are a few of the key statistics:

Net profit for the month	-1.73%
Total number of trades	14
Average return per trade	-.74%
Win ratio	57.14%
Exposure	11.46%
Total time invested	*Less than 10 minutes a day!*

At the end of the day, the small number of trades and low winning percentage tells the story. With such a small amount of trades, the law of averages can’t work in our favor. It only takes a bad trade or two to mess up the month, and that’s exactly what we had. Hey guys, we could really use some volatility!

Good Trading…

August Trading System Results

2012-09-04T18:18:00.000-07:00

August volatility settled down quite a bit, so overall results were somewhat typical. The VIX high-point was the beginning of the month, and it just hovered in the teens from there.

An interesting side-note is that volume in August was down significantly. The trading volume on most major indices was down 10-15% from last month, and was the lowest we have seen in several years. Is it the calm before the storm?

Regarding my own trading model, August turned into a nice month for the MS8 Trading System. Our overall exposure was lower than usual, at just 7.55% (meaning that’s how much time we had cash in the market), and we ended the month with no open positions. Below are a few of the key statistics:

Net profit for the month	6.61
Total number of trades	13
Average return per trade	3.31
Win ratio	84.62
Exposure	7.55
Total time invested	*Less than 10 minutes a day!*

Most notable was the high win rate. Out of a total 13 trades, 11 (84.62%) of them were profitable. This is much better than our average win rate, so we’ll take it when we can get it.

Good Trading…

MNST exit...

2012-08-14T17:46:00.001-07:00

MNST closed up nicely today, and the CalculatedReturns.com strategy will take a Type 2 exit.

Monster Beverage Corp: NASDAQ:MNST quotes & news - Google Finance:

How and why investors struggle to follow their systems

2012-08-10T14:40:00.001-07:00

This is really a fantastic article on the psychological side of trading. I've mentioned many times that quantitative trading systems are easy to build, but hard to follow. We always want to second guess the data. Here's Greenblatt's take on that:

Joel Greenblatt on how and why investors struggle to follow the Magic Formula « Greenbackd:

Prepare for 2013 Global Meltdown

2012-08-10T10:00:00.001-07:00

There has been a lot of talk among the technical analysis world around the idea of market reversals and pull-backs. This article is a chilling assertions from economic fundamentals supporting that we may be in for some scary times.

Economist Warns: Prepare for 2013 Global Meltdown:

NUS trade results…

2012-08-09T17:28:00.000-07:00

For www.CalculatedReturns.com subscribers, NUS reached our limit price yesterday and was executed. I just noticed that today it reached the limit price and we exited according to plan.

Good Trading…

NUS trade executed…

2012-08-09T07:25:00.000-07:00

For www.CalculatedReturns.com subscribers, NuSkin (NUS) appeared yesterday on our candidate list, and triggered mid-day at our limit of $42.29. It’s up a little today, but hasn’t yet reached the profit target.

Good Trading…

July Trading System Results

2012-08-01T18:03:00.001-07:00

July was a semi-volatile month, to say the least. At least in real terms. The VIX, however, hovered in the teens, and closed at just under 19%. As with June, we saw several big declines, but by month end the price of the SP500 crept up ever so slightly.

The MS8 Trading System. ended up with another decent gain of 6.73%, but the real story was the trade activity. Below are a few of the key statistics:

Net profit for the month	6.73%
Total number of trades	38
Average return per trade	1.18%
Win ratio	57.89%
Exposure	26.81%
Total time invested	*Less than 10 minutes a day!*

The system gobbled up a whopping 38 trades during the month, keeping us invested nearly 27% of the time. Normally with that many trades you see much higher returns, but we didn’t have that this month. The average return on each trade, while still decidedly positive, was about half the historical average.

The win rate, at 57.89% was also quite a bit lower than usual. This month was profitable for the MS8, but a bit more of a grind than usual.

Good Trading…

The 10 Best, and Worst, Times in History to Invest | Calculated Returns

2012-07-24T12:17:00.001-07:00

Interesting Post by Mebane Faber...

The 10 Best, and Worst, Times in History to Invest | Calculated Returns:

'via Blog this'

Quantitative trading strategy based on Fuzzy control model regulation | Khamlichi | International Journal of Mathematics & Computation

2012-07-18T11:29:00.001-07:00

This is a nice article with a pretty good description of what Quant Trading is all about:

Quantitative trading strategy based on Fuzzy control model regulation | Khamlichi | International Journal of Mathematics & Computation:

Good Trading...

Out-of-sample Testing for the MultiStage Trading System

2012-07-12T01:42:00.000-07:00

In the last post I talked about out-of-sample back-testing during trading system development. As a follow-up I wanted to describe the different out-of-sample (OOS) tests I’ve used while developing the MultiStage Trading System.

I described basic OOS tests in my last post, which might consist of simply dividing test data into different date ranges. In order to think about applying this to MSv8 (and other versions) it’s important to remember that this trading system has evolved over several years. I’m currently on version 9, although v.8 is the current published version.

When I first started prototyping this idea I used a subset of the data I had, both by time, and by symbols. Specifically, I started testing on the NAS100. This symbol set seemed to reflect the volatility of the market better than the SP500, and it’s small size made it very quick to do optimizations. As far as time segmentation goes, much of the early development was done in 2007, and for a long time I used up to 2002 as my in-sample data.

The early tests were straightforward out-of-sample tests. I tested using data from 2003 to 2007, and used all of the symbols in the data base. At that time I had only about 6000, but it’s since expanded to about 9000. There was a little debugging to be done after this, so you have to be very careful not to change the way you are filtering data to remain bias-free.

Keeping in mind that OOS testing is really any testing that isn’t done on the original data set, that wasn’t the end of my OOS tests. I wanted to trade real money, of course, but real money is, well… real money. I’ve been developing trading systems for many years, and for many years prior to 2007, but I’m not about to part with my hard-earned money without some very cautious observation. Consequently, my first use of the the MultiStage Trading System (version 1) was simply paper trading. I traded in a sample account for a couple of months. Then I started with VERY small position sizes. In some cases just a few hundred dollars.

Both paper trading, and these small “test” positions could both be viewed as further OOS testing. But there’s more: This system has evolved over time. Somewhere around mid-2008 v5 and then v6 were born. In each case, I took the new versions and compared them with the old data. Then I tested them on the OOS data, which was new since the publishing of the prior system. Also, in each case I went back to the paper-trading model, and then the small position sizes before jumping in with both feet. In 2009 v7 was developed, and in 2011 I began using v8. In each case I followed this pattern.

At this point, there has been several forward-looking years of real-time, real-money trading activity, which pretty much puts to rest any concern of data-snooping or curve fitting. The main point to see is that there are several different safeguards you can take during system development to be sure you are being objective with the data.

Good Trading…

Out-of-sample Testing for Trading System Development

2012-07-10T01:41:00.001-07:00

In the past couple of weeks I’ve received several emails asking about my back-testing approach, especially as it relates to out-of-sample testing. If you’re not familiar with out-of-sample testing, let me first give a quick overview of what it’s all about.

When a person is developing a trading system we all put on our best pair of rose-colored goggles and go to work. After all, who wants to be a pessimist when our goal is to find a trading system that will make us lots of money. As a result, it’s possible for a developer to over-optimize a system to the point where it’s really designed around the test data. Optimizing is the process of refining your original system to provide the very best results. And with our rosy outlook, it’s easy to get carried away with this.

For example, if my biggest losing trade happened to take place on a Wednesday, I could simply come up with a new rule (the fancy name is filter) that says “no trading on Wednesdays”. The problem is that some rules may not make sense. In fact, maybe (probably) that bad trade on a Wednesday was just a coincidence. Some filters have real value, while some simply filter out coincidental outcomes. The ones with real value are likely to be effective in the future, while the coincidences aren’t.

How then, does a developer avoid falling into the trap of over-optimization, or as Ernie Chan calls it, “data snooping”? One answer is something called out-of-sample testing. The way this works is that data is segmented into two or more parts, perhaps by date, or some other criteria. The system is then developed and optimized on the first part (the in-sample). Once the system is pretty much complete, the final strategy is then tested on the remaining data, which is the out-of-sample data. If results are similar then you’ve probably been relatively honest with the data. If they aren’t, you may have a curve-fit, “data-snooped” condition.

In short, out-of-sample data is any part of the data that isn’t part of the original testing, system development, and optimization. That might include an extra data set, as described above, or it could include paper trading that happens in the future. It can also include real time, real money trading that happens long after system development.

The truth is we NEVER know for sure if a system will work in the future, but by doing adequate out-of-sample testing we can reduce the risk of a system that was fit to some historical data set. In the next segment I’ll talk about several types of out-of-sample testing that I’ve done on the MultiStage Trading System.

Good Trading….

Evaluating Trading System Performance – Part 3

2012-06-28T23:27:00.000-07:00

In the last couple of posts I’ve beat up on the idea of “sum of the trades” approach to system development, so today is the day we engage in a little self-deprecation. That’s right, no system is perfect, and I think it’s important to understand even your own warts.

The truth is, “sum of the trades” can be an effective way of demonstrating an edge on an individual trade, but a trading system is much more than that. It should include position sizing and portfolio management.

At CalculatedReturns we take this more holistic approach to evaluating performance. The results posted on the website are determined by simulating the correct position sizes within an overall portfolio. If the system generates a trade for a day, but the system is already fully invested, we don’t take that trade. This way we can a more “real world’ view of the system performance during back-testing.

That being said, there are still many reasons that the results of a trading system can’t be duplicated. If you are a subscriber to CalculatedReturns, or have been following along, you might wonder why your results don’t match the back-tested results. I've mentioned - several times - that actual results may vary significantly from the historical model. So, why is that?

I’ve traded this system for several years, and there is no question that my results don’t match exactly. One of the ways I monitor my execution is to run the model at the end of each month. Then I take the model trades (the ones produced by the back-tester for the past month) and compare them with my actual trades. Why wouldn't they match perfectly? Well, they don't always and there are a host of reasons... here are a few:

Commissions: I normally include commissions when I run my back-tests, particularly when I have a system at the final stages of development. My commission rate through Interactive Brokers is $0.005 per share (half penny per share). That's the rate I use, but you may have a completely different rate.

Sequencing: A few times of year something happens that almost assures variance from the model. Each day I scan for signals and enter orders accordingly. Normally, it's just a few orders and even if they all triggered - a rare occurrence - I would have enough equity in my account to cover the orders. On rare occasion, MANY signals are returned... sometimes 40 or 50 or more. When this happens it is likely that I would not have enough equity to cover all the trades, so I will buy the first few that trigger in the order that they trigger. The back-tested results may vary from this. The back-tester I use (AmiBroker) is looking at end-of-day data. So it doesn’t know the order in which trades trigger throughout the day. Thus, there is some variation.

Liquidity: Every once in a while there is a very fine line between reality and modeling (aka. backtesting). Trades that look like they should work in a backtest, don't quite work in the real world. For example, I might have an order to buy at a price... call it $20.00. The low of the day was indeed $20; right at the price of my limit order. The problem is that it was the extreme low for the day, didn't stay there long, and didn't trade much volume at that level. I had a similar circumstance about two weeks ago. My order triggered and partially filled, but that was it. The price never went back and I never got the complete fill. In backtesting it looks just fine, but in the real world it didn't work out that way.

Liquidity – Part 2: There is another aspect of the system which will also cause massive variance from the system results, but only over the long term. If you examine the chart in our results table with the month-by-month returns, or even the general performance data, you will see some amazing results. The MSv9 Trading System has averaged in excess of 200% returns over a 10 year period. Once you do that for a while your account gets so big, and position sizes are so big that they are no longer realistic. For example, recently I ran a test where the last trade of the test was a trade on IDIX. After 10 years of these amazing returns, the simulated account size was so huge that the 15% position size was $88 Billion. Oops! IDIX total market cap is only $1B. Simulation has it’s limitations and it’s good to know what they are. The flip-side is that this was a profitable trade. And if you were just starting out, that trade, and even that year’s results, could have been replicated. It just isn’t realistic over the long run once your account gets that large. I figure once mine is that large I’ll figure out another approach <grin>

Mistakes: While I make every effort to follow the trading parameters precisely, occasionally I just mess it up. Mistakes are tracked and measured to determine their impact, and how to avoid them in the future. Trading automation is employed for several steps of the trading process, which reduces many errors, but mistakes still happen. Each month I compare my actual trades to the trades of the model. Rarely are there variances, but in the event of a mistake it shows me exactly where I didn’t follow the model precisely. There are also mental mistakes that happen. I’ve steeled myself, to a degree, against these biases, but early on I second-guessed the system quite often. A few losing trades in a row will really test your resolve and stress the psychological aspects of the trader. Those mistakes usually cost money.

Generally, these are the four types of variances, that I see, and they do happen. Real world results don't match exactly, but they should always be directionally correct. Over time I’ve been able to minimize the variation and get results very close to the back-tested system.

One final episode coming next time, with a video of what we look for in trading system results.

Good Trading…

Evaluating Trading System Performance – Part 2

2012-06-24T16:59:00.000-07:00

Following up on my last post, I want to start sharing some information from websites around the web and show you the fallacy that is “sum of the trades”. There is a website… uh, let’s call it MrDayTrade… that publishes incredible returns. Just look at these results and I think you’ll agree they’re remarkable, even rivaling CalculatedReturns… or do they.

And now for the fine print. The first clue that there is something amiss can be found in the FAQ document. There is a question that addresses the precise issue we’re after:

I won’t restate this, you can read it for yourself, but I will translate. In order to get a return equal to the published results you would have committed 100% of your portfolio value to each trade. It’s not really possible, logical, or safe to do this, yet that’s what you’re signing up for here.
Now let’s drill down a little. If I pick the 2011 year (118.51%) I can see the actual trades… follow along in the video and I’ll explain a bit more:

Sum of the trades video

I’m not picking on this particular site (well, okay I am a little). There are MANY like this. I’m simply stressing that it’s important to do your homework on any of these subscription services when evaluating system performance. In Part 3 I will share some thoughts about the CalculatedReturns systems some of the pitfalls there.

Good Trading…

Evaluating Trading System Performance – Part 1

2012-06-21T18:48:00.001-07:00

If you Google “Trading System”, or “Trading Signals” you will find an enormous number of websites with services allowing you to subscribe to the signals produced by their trading system. I’m not opposed to that, of course, as it’s exactly what we do at CalculatedReturns. And in fact, there are some of these services that are actually reputable and worthwhile. A few (ahem…), even produce excellent real world returns. I have a pet peeve, however, around how some “less scrupulous” sites choose to demonstrate their performance.

Over the next several posts, I intend to expose the “sum of the trades” approach to performance results. It just ain’t right! I promise not to be too harsh, as it’s not my objective to be defensive, or sling mud. My real goal is just to educate those who might not be looking, about how to evaluate system performance for themselves. Consequently, the names have been changed to protect the innocent.

Stay tuned for Part 2, and the “Sum of the trades SMACKDOWN”!

Good Trading…

Keeping a Trade Journal

2012-06-21T01:31:00.000-07:00

One of the habits I’ve developed over time is keeping a precise record of every trade. I have logged every trade I’ve taken for many years now, and that record has proven invaluable. There are lots of reasons to create and keep a trade log. To name just a few:

It creates discipline: Great traders have great discipline. Trading is a mechanic and the trader most often gets in the way when they have poor discipline and fail the execution.
It creates routine: Part of my daily routine is updating the trade log. It keeps my head in the game and ensures that I execute all of the details of my trading plan.
It reminds me of the big picture: Sometimes I have a few losing trades in a row and I think, “Why am I doing this again?” If I survey my trade log over the past year and see the overall gains it reminds me that a few days of losers is just part of the big picture. Overall my trading rules win, and my trade log shows it.

And now for the Grand-daddy of reasons to keep a trade log:

It helps me evaluate my plan AND my execution:

If I start losing, there are only two reason. Either I failed the plan, or the plan failed me. The only way to tell which is the case is to evaluate your trade log against the plan. At the end of each month I run a report showing all of the monthly trades (the same ones we publish on CalculatedReturns.com). Then I compare those trades with my trade log. Did I take every trade? Did I enter at the right prices? Did I exit at the right prices? Did I correctly set my position sizes. If I did all of those things correctly I should have replicated the results of my trading system. Over the long haul the trading system works, but it’s up to me to execute.

I’ve made a sample of my trade log available to Members and posted it here. You can take it and use it as is, or you can modify it in anyway you’ve seen fit. By the way, even though I've posted this in Excel I don't use it that way or with any other offline spreadsheet, which you could certainly do. Instead, I keep it on Google Docs (I’m a big fan) so it’s available to me anywhere. You can also use Docs offline, so you can get to it when you’re not connected. Finally, here's a short video that will explain how I use it.

Good Trading…

May 2012 Trading Results Wrap-up

2012-06-02T18:42:00.000-07:00

As I write this, the market has just finished it’s biggest downturn in recent weeks. Many investors are wondering where things will go, and the global economy, eclipsed by recent events in Greece and the broader Eurozone, is throwing up question marks right and left. Investors don’t like question marks.

Regarding my own model, May was another average-ish month for the MS8 Trading System… in fact slightly below average. Our overall exposure was just over 15% (meaning that’s how much time we were invested), and we ended the month mainly in cash. That being said, a profit is a profit and this is still an excellent one. Below are a few of the key statistics:

Net profit for the month	4.72%
Total number of trades	22
Average return per trade	1.45%
Win ratio	72.73%
Exposure	15.70%
Total time invested	*Less than 10 minutes a day!*

There was one massively stinky trade in BVSN, which lost nearly 35% of it’s value. Those are painful, but a trading system is a strategy that relies on the averages working out over the long haul. Even with that ugly trade we still pulled out a nice profit.

Below is a picture of the profit distribution of the individual trades. This is a pretty typical distribution… well… except for that ugly one on the left.

Good Trading…