The Most Common Mistakes Traders Make

Here’s a simple guide to losing money in the financial markets. These are the most common mistakes traders make. Most traders make all three, but making even one is a recipe for losing.


Poorly Defined Exit Conditions

Before you enter a trade, you must have already defined your exit conditions. Every trade has at least three exit conditions:
  1. A Stop-Loss exit – this is the price that tells you, “my thesis is wrong, I need to close this trade at a loss.” The stop-loss order doesn’t have to be in the market, but it has to be a precise number, and the professional trader must make the commitment to close the trade once that price is hit.
  2. A Profit Target – this is the price you expect to reach to make a profit on your trade. Lacking a clearly defined profit target, you don’t have a clear idea of when to close the trade at a profit and will often let those profits melt away.
  3. A Trailing Stop Trigger – Once price begins moving in your direction, you want to have a strategy that will protect you so that a small profit doesn’t turn into a loss. You should pick a price somewhere between your entry and your profit target that – once hit – triggers your trailing stop strategy. (You do have a trailing stop strategy, right?


No Thesis

Successful traders can clearly state their “reason why” for the trade. They know how much profit they expect to make if their thesis is correct, how they will know if their thesis is incorrect, how much money they will lose if their thesis is incorrect, and how long they will leave the trade open before they know one way or another if their thesis is correct. Losing traders merely hope.

The five characteristics of a valid trade thesis are as follows:

  1. It has clearly defined entry conditions. These can be any combination of prices, indicators and/or external conditions that you wish, but they must be clear and unambiguous. You must know exactly why you are entering the trade, or – lacking all the conditions being met – know exactly why you are choosing not to enter the trade.  For example, “I will enter this trade long on the first day after the price closes up after having fallen below its 20 day moving average.”
  2. It has a clearly defined profit target. If you don’t know how much money you expect to make, you have no way of knowing how much money you can afford to risk.  For example: “I expect the price to reach the top of the 50 day price channel as of the day the order was opened.”
  3. It has clearly defined risk. You know how much money you will lose if your thesis is wrong. For example: “I will close this trade if the price drops more than eight percent from my entry price.”
  4. It has a defined expectancy. You know how much money you will make on average every time you execute this strategy. You will know roughly how many times you will win and how many times you will lose for a given number of trades. You will know your average expected loss per losing trade and your average expected win per winning trade.
  5. It has a time limit. Sometimes a trade hits none of your exits. In those cases, you also have a time limit set for your trade so that you can close it and move your money to something more profitable. For example: “If none of my exits are hit within 14 calendar days of my entry, I will close the trade.”


Failure to Test

Many traders will define their exits and their strategies, but never actually test those strategies before putting real money at risk. Any trade strategy that is clearly defined can also be tested against historical data. In fact, without such testing, it is impossible to know the expectancy of your trade strategy.

Maximum Value, Minimum Risk

When I trade, I am always looking for a combination of Maximum Value and Minimum Risk. That means not only making profitable trades, but also protecting my downside against loss and being in cash as much as possible.

This morning  I tested several DOW 30 stocks using my Touch SMA strategy. (See here, here, here and here for other studies I’ve already published.) In about half of the stocks, Buy & Hold outperformed Touch SMA. 

Here’s an example:

I tested Caterpillar (CAT) using my Touch SMA strategy.  The optimal strategy I found grew the portfolio about 40% since March 2009. In comparison, Buy & Hold would have grown it almost 60%. That’s a ton better.

But look deeper:

Touch SMA was in the market only 442 out of possible 1962 days. The P&L per day was a little over $31 for Touch SMA compared to just over $8 per day for Buy & Hold. The maximum drawdown on Buy & Hold was almost 39% compared to a max drawdown of 10% for Touch SMA.

There is no clear winner here. If you prefer making more money while taking bigger risks, then Buy & Hold would’ve been the right strategy in CAT the last 5 years. The upside is a 60% gain in value. The downside is that your money was tied up 100% of the time and you suffered a 39% drawdown.

But Touch SMA, while not generating the same profits, was in the market only about 20% as long as Buy & Hold, which means that money could have been put to use in other trades as well.

I don’t trust the market, I don’t trust my emotions, and I don’t trust the past. But I do trust systems that are built to protect my war-chest and slowly, consistently collect profits. Touch SMA is proving to both protect and profit.



Unisys: An Expected Trading Success

I changed my screening a little to find stocks with higher betas, but still heavily capitalized and very liquid. I also decided I would avoid stocks with ridiculously high or low P/Es. (Why? Just because.) One of the stocks that surfaced was Unisys, which is so old-school and boring I figured it would be a waste of time. But this analysis tool I built is so easy to use, I figured, “what the hey”, and I am glad I did. Running the Touch SMA analysis proved an unexpected success for this Unisys Trade. The details:

Unisys Trade Statistics

  • Company Name: Unisys Corp
  • Symbol: UIS
  • Market Cap: $1.3B
  • 3M Average Daily Volume: ~551000 shares
  • Time Period Analyzed:  November 2009  – 21 July 2014
  • Number of trading days: 1689
  • Number of trades from this signal: 73
  • Number of winning trades: 30
  • Number of losing trades: 43
  • Average Win per share: $3.56
  • Average Loss per share: -$0.75
  • Max Win per Share traded: $52.50
  • Max Loss per share traded: -$1.51
  • Max Drawdown: 34%

Trading Strategy for Unisys

Like the Noble Corp trade I posted earlier this week, this trade is a modification of my Touch SMA strategy I’ve been testing the last month or so. (See herehere here.) This trade has 3 setups. First, the price 2 days ago must be below the moving average. Second, the price yesterday has to be above the moving average and third, the close yesterday has to be above the open yesterday. I also added a trailing stop to this trade. (If it is not obvious, this is a Long-Only trade.

The optimal Moving Average length is 9 days, the Profit Target is 160% of the entry price, and the stop-loss trigger is 95% of the entry price. I normally test starting the first of January 2009, (for reasons why, see here), but UIS wasn’t trading above $10 till the end of November that year, so I didn’t trade it.

I’m not wild about the size of the drawdown, but even the famous Turtles accepted drawdowns as high as 40%.

Comparison of Trade to Buy & Hold

This trade is in the market a third as long as Buy & Hold would be. As it happens, no one in their right mind would have bought & held UIS over the time period tested, because it would be a negative trade. However, this strategy I used started with a $10,000 account and generated $37,000 in profits after subtracting fees, commissions and Cap Gains taxes.

If you’d be interested in video demonstrating the spreadsheet I use to do these analyses, drop me a note or leave a comment.

A Noble Trade in Noble Corp

This trade in Noble Corp, (symbol NE), is in the market only 219 days since January 2, 2009. By comparison, a Buy & Hold trade in NE was in the market for 2013 days. I love trades that beat B&H, and I love ’em even more when they keep my cash free for other uses 90% of the time.

Here’s the details:

Trade Statistics

  • Company Name: Noble Corp PLC
  • Symbol: NE
  • Market Cap: $8.4B
  • 3M Average Daily Volume: ~3.3M shares
  • Time Period Analyzed: 1 Jan 2009  – 21 July 2014
  • Number of trading days: 1396
  • Number of trades from this signal: 172
  • Number of winning trades: 76
  • Number of losing trades: 96
  • Average Win per share: $0.73
  • Average Loss per share: -$0.27
  • Max Win per Share traded: $2.02
  • Max Loss per share traded: -$0.46
  • Max Drawdown: 15%

Trading Strategy for Noble Corp PLC

This trade is a modification of my Touch SMA strategy I’ve been testing the last month or so. (See here, here & here.) This trade has 3 setups. First, the price 2 days ago must be below the moving average. Second, the price yesterday has to be above the moving average and third, the close yesterday has to be above the open yesterday. I also added a trailing stop to this trade. (If it is not obvious, this is a Long-Only trade.

Noble Corp has a higher volatility than the stocks I’ve been testing; the beta, (as of this writing), is 1.86. I don’t have any concerns about trading a higher beta stock, since I have a tight stop-loss and a trailing stop.

The optimal trading strategy here is a 4 Day Simple Moving Average, a stop-loss 1% below the entry price, a profit target of 105% of the entry price, and a trailing stop.

Comparison of Trade to Buy & Hold

Buy & Hold obviously has only one trade during the same time period. The first trade in this strategy occurred on Jan 9, 2009 when the price was $24.01. The last trade occurred on July 15 when the price was 32.51. Starting with a $10,000 account, I was able to buy 416 shares. During that time period, B&H generated a net profit of $13,540.19 whereas my strategy generated $16,701.35. Both amounts are net of commissions, fees and taxes. I assumed a 28% Cap Gains tax rate on the short-term trading, and a 7.56% Long Term Cap Gains tax-rate on Buy & Hold.

Given that this strategy strategy is in cash 90% of the time, and still beats B&H, I would consider it wildly successful. It’s certainly not profitably enough to live on, but – if you consider that this generated $6700 on a $10,000 investment over a period of only 219 days, that’s a pretty terrific return. Obviously, I would need to combine this strategy with 10 or 20 others to keep my money working all the time, but this kind of strategy is just what is needed in a successful fund.

If there are any details about this strategy I didn’t cover, or you have any questions about it, drop me a note or leave a comment.

Bubble Markets and Back Testing


I saw a question on the Reddit /r/algotrading forum about back-testing and how much data one “should” use. As far as I am concerned, the only rules are “whatever works”, but within that framework, I do have some rules I follow. Bubble markets and back-testing affect one another profoundly.

When running back-tests on the strategies I build, I don’t test anything prior to January 2, 2009. I don’t go back any farther than that because of my beliefs about the market. (Remember, you don’t trade the market, you trade your beliefs about the market.) What I believe is that we’ve had 4 entirely different markets in the last 20 years, and the only one in which I can perform valid testing is the most recent.

The Internet Bubble Market: 1994-2001

The first market we had was the internet boom period – more accurately called the Internet Bubble – that started around 1994 and ended with the Dot Com Crash of early 2001. In fairness, this was merely the last 7 years of the Great Bull Market that started with Reagan and ended with the reign of Bush II. This was the time when millions of peoples thought they were genius traders because all you had to do to make money was buy some stock. This was also the heyday of the day-trading craze where everyone who could install highspeed data lines in big room could open their “day trading” company and sell seats. Any back-testing that is performed during this period is highly suspect, because the conditions that prevailed in that market simply don’t exist today.

The Real Estate Bubble Market: 2001-2008

The Dot Com Crash led to a wide-spread despair that Happy Days were never gonna Be Here Again. This was clearly unacceptable, so the Fed does what the Fed does best: goose the market. The Fed decided to slash interest rates in order to spur borrowing. That led to the Great Real Estate Bubble that topped out in October 2007 and ended with the collapse of Bear-Stearns in March 2008.

This was the period when any stock that had anything related to “Real Estate” in its name or prospectus exploded off the charts. This was also the period where “financial engineering” gained ascendancy, which also led to the gross over-valuation of financial stocks. Tech stocks were as shunned during this period as they were embraced during the Dot Com Boom. Any back-testing that covers this period is suspect, especially if  it includes real estate, finance or tech stocks. Like the Dot Com Boom period that preceded it, the conditions that existed then do not exist now.

The End of the World As We Know It Market: March 2007-March 2008

The period from March 2007-March 2008 was dominated by the collapse or  near-collapse of all the Too-Big-To-Be-Allowed-To-Fail financial institutions, the bankruptcy of General Motors, TARP and assorted bailout games, and Bernie Madoff’s $50 billion fraud. It was a time of overwhelming panic, when fundamentals were irrelevant and technicals no longer worked. I don’t consider anything that happened during that market to have any bearing on the current market.

The QE/HFT Bubble Market: March 2009-Present

When the S&P500 bottomed out at 666 in March 2009, the Fed resorted to the the only tool they had left: create money out of thin air. That was the advent of “Quantitative Easing” which utterly changed the nature of the market. Vast quantities of brand new cash came pouring into the market by way of the Fed’s largesse. Not only did fundamentals no longer matter, (most of the big financial institutions were technically insolvent during the time leading up to the QE era), not only dud technicals no longer work, but now the high-frequency traders were starting to take over the market.

Due to the advent of QE and the takeover of the markets by HFT, this market is completely unlike any that has come before it. We are now trading in a market where the dominant realities are not earnings or the promise of future earnings, but by the quantity of newly created money flooding in, and the success of competing trading algorithms. The only period that matters is this one. Don’t bother back-testing data prior to March 2009 because it is no longer relevant.