This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

I’m beginning to see stop losses and R multiples in a different light lately, and especially since my last trade. I wanted to write about what a stop loss is and what it isn’t.

What A Stop Isn’t

Many people see their stop loss as the point that they admit that they were “wrong” about taking a position. “If it goes against you to the point of hitting your stop, then you were wrong about the trade” is the typical mentality. Your initial stop should not be the point at which you admit you were wrong about a position. That’s not what it’s for! Unless the trade spikes down to the stop instantaneously, then you were wrong about the trade long before that. For example, I was wrong about my PALM trade pretty much as soon as I entered. I stayed wrong for a couple of hours, and finally capitulated at my stop. Do you see the difference? It wasn’t the stop that proved me wrong, it was the price action immediately after my entry that clearly said that my position was wrong. By the time price got to my stop, my position had been cold and dead for a while. PALM may take off later, but my position was wrong at the time I took it. Waiting for a losing position to hit a stop to be declared wrong is like calling back a mugger who just ran off with your wallet to tell him he missed some extra cash in your pocket.

Think of it this way: Say you’re a mountain climber, and you like to scale cliffs to see the beautiful scenes at the top. But there’s a big downside–straight down! So what do you do? You use a safety line. You pick a place to put a bolt into the rock, and you tie off your line there. That is your stop loss. Why do you do this? It’s there to catch you should you happen to slip and be unable to recover. Do you wait until your safety line catches you to realize that you have begun to fall off the mountain? No! If you always did that, one of those times it may fail and you’ll take the big plunge. If you begin to slip, you catch yourself and reposition. In climbing, you might grab another outcropping or dig into the surface with your tools. In trading, that means you get out when you find that you’re going the wrong way.

So What Is A Stop Then, Wiseguy?

A stop loss serves two functions: To cap your maximum downside and help to size your position. When you choose a maximum amount you want to risk on a trade, that is capping your maximum downside. You shouldn’t think of it as your ante, or your pay-to-play. You want to lose $0 on every trade! Of course that’s not possible, but you should strive for it. When you pick an R value, that should be your worst-case-scenario loss: a managed loss that lets you keep trading long term should the worst come to pass. After picking that R size, you pair that with your actual stop price level to size your position to maintain your capped maximum downside. Usually, it makes sense to choose a stop based on the chart, support/resistance, candle highs/lows, etc. However, don’t just blindly approach the stop as the point at which the trade is proven wrong! The stop level may invalidate the setup if it is hit, but the trade may be above the stop and still not be proven correct as with my PALM trade.

So How Do I Know When My Position Is Wrong?

This is the easy part–your position is always wrong! Until proven correct, that is. If you enter and price moves in your favor, it is correct for the time being, and you have the luxury of waiting around to see if it bears fruit. But if you enter and you go underwater, have a plan to determine for yourself when to throw in the towel. And your stop loss point should not be that plan! On my PALM trade, I should have removed my position when the breakout did not occur for 2-3 candles after my entry, and I was still below my entry point.

Hopefully these thoughts will help all of us to lose smaller and faster, which is the only chance a trader has to survive the losing game of trading.

EDIT: Per Richard’s comment (#5), I think that some clarification is in order. I think that your timeframe has to come in to play in all of this. The trade management should always be done based on completed candles in your timeframe, with the only exception being your stop, which is your worst case loss at any time. When I say price moves in your favor, I am imagining that the action according to your timeframe completely clears your entry, like this:

That doesn’t mean that you are correct or not based on profitability tick by tick (unless that is your timeframe!). If the trade stalls, even after being proven correct, you have to determine if you should take the profit or stay. Every trade has a beginning and an end, even the “proven correct” ones. My point is, you have no business waiting around for a clear loss (the inverse of the picture above) to turn into a profit.

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

I took a trade in Palm, Inc. (NASDAQ: PALM) today. It was a gap up this morning, and I liked the action in the first four bars: Lots of white space, the second bar was a doji, followed by two green bars. It reminded me of the setup in my TTWO trade. I took an entry above the high of bar 4, knowing that it was a higher risk trade since it was below the OR high. I was ready to get out if it didn’t work. Or was I?

Background:

I traded through my TD Ameritrade account this time, which has a good deal of long-term investment money (about 10X my Zecco account; my Zecco money will soon be joining it). Because of that, I’ve taken the dollar amounts away from my results, since the newbie small-dollar trader journey is not really what I’m trying to portray anymore. Instead of trading a very tiny account that is 100% risk capital, I’m going to trade a much larger account that is about 15% risk capital. I see this as my next step in trading.

For example: Say I have $1000 to burn, and $10,000 that I “need” eventually. Instead of trading only the burn money, and grinding away a tiny bit at a time, I would trade the combined balance, but impose a drawdown limit on the account–absolute rock bottom would be if the $1000 goes away. Now a 1% of equity risk would be $100 per trade, instead of $10 as before. This would mean that 10 immediate losses in a row would hit my drawdown limit. A problem–now I have commissions, and if my trade risk size were only $100 then the commissions would be 20% of that. Yikes!

Analysis:

I entered as stated above. As I’ve been re-reading Phantom of the Pits, I read some things last night that I should have obeyed today (paraphrased below):

Don’t wait for your stop to prove the trade wrong! Assume it’s wrong until proven otherwise. Trading is a losing game, and if your position is not proven correct, then reduce or remove it! Don’t let the market tell you that you were wrong. It’s your job to know when you are wrong. You have to learn to be wrong, fast!

I kept watching PALM go sideways, waiting for the breakout above $19.00 that never came. I had opportunities to get out at -0.4R, but stayed with it. In retrospect, it was not proven correct within 3-4 bars, and I should have bailed on it instead of stay with it, especially since it was in the red rather than the other way around. I didn’t want to take the $50 loss, so I stayed with it until the stop was hit for the full 1R loss. Not good. I should have been wrong, fast! That’s the only way to survive in trading.

Takeaway:

Listen to the Phantom! Get out if your wrong trade (that’s all of them) is not proven correct (within 2-3 candles)!

Trade Summary:

PALM Long
Entry: $19.03, Stop: $18.86, Target: $19.63
R: $0.17/share, Exit: $18.86
P/L: -1.00R

Stocks Mentioned In This Article
Stock	Links
PALM	| |

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

Here’s something you will probably not see very often (with a few notable exceptions from the blogosphere)–A public admission of a humiliatingly bad trade. This morning, I traded Fremont General Corporation (NYSE: FMT). It was up strong in pre-market, and they had positive news of new credit being extended to them. I had no setup, I just wanted to buy the open and ride it up. I entered as FMT broke the pre-market high:

I sold by reflex based on it looking like it would go on to hit the pre-market low. I realized by then that I had made a mistake, but I wasn’t going to wait around, hoping for the loss to turn around. Ironically, that would have been the thing to do in this case :(

I made so many mistakes it’s almost laughable. Here they are, in no particular order:

1. I’ve been sick all week and not sleeping well—not a good time to be making decisions.
2. I ignored my 2% risk rule, and just put my whole buying power down for my position size—over 10x my normal R size!!
3. I didn’t have a clear stop point other than the pre-market low—I just bailed when it plunged down.
4. I wanted to hit a “home run” and get a good 10% on the one trade—I wanted something for nothing!
5. I wanted to trade today. I went looking for something to trade, rather than waiting for a setup to appear.
6. Zecco recently instituted a $2000 minimum rule for margin accounts. They hadn’t had any problem with a small balance before, but now they lock up my buying power every day, and I have to ask them to manually release it. Don’t ever trade with some kind of restriction in place like that! It locks down your options and messes with your mental state.
7. I told myself that I wouldn’t trade while I get a swing-trading system going, but I broke that promise and traded anyway.
8. I traded on options exp. Friday, breaking another of my rules.

So what now? I need to regroup. I’m taking the cash out of my Zecco account. I’m not exactly sure of where to go from here, but one thing’s for sure: I’m really disgusted at myself for doing this. At least it’s a big loss out of a small account, rather than a proportional chunk out of a more substantial account. I didn’t blow out my account, but a loss this large (about -15%) is completely unacceptable and entirely avoidable. It’s not so much the dollars involved as it is the percentages. I trade a small account on purpose, until I can be consistently profitable and weed out mistakes like this one.

I hope that by posting this trade, it reinforces to me not to do this again!, and that it can serve as a warning to others. Like Phantom of the Pits says, the most important part of trading is behavior modification–without that, you will lose, just like I did today.

Trade Summary:

FMT Long 280 Shares
Entry: $9.35, Stop: $8.37, Target: $12.00
R: $274.40, Exit: $8.64
P/L: -0.72R, or ($198.80)

Stocks Mentioned In This Article
Stock	Links
FMT	| |

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

Daytrading is risky, huh? Well, today my daytrading capital was safely in cash. The markets went bananas today, and I stayed out to be conservative. Mark-to-Market Net loss: 0%.

My long term investments in my 401k, on the other hand, happily vomited out all the gains I had made so far this year. Yesterday I was up 4%, and today I’m about breakeven. I would have sold on the open today with the futures down so much, but thanks to the stifling of liquidity in 401k plans and mutual funds, I have to take the full loss of today’s close, or hold on hoping for a bounce-back (which is what I’m doing). Mark-to-Market Net loss: 4%.

I would always rather be in a liquid position than an illiquid position. The entire reason that our capitalistic system works is that I can trade work or assets now for work or assets in the future, through money–Liquidity. To me, liquidity (or the lack thereof) is the real source of risk in trading, investing or even business. Timeframe is NOT the source of risk. However, the liquidity of the particular market is not the only thing. People who are highly leveraged in a position so that they can’t unwind it all even in a liquid market are also highly at risk. So before you look down on someone for their trading timeframe, vehicle, or anything else, assess the liquidity first, of their markets and their positions.

I’ll take daytrading any day, and especially today.

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

I mentioned in my last post on the topic that Ugly took a hit trying out futures. Now, it looks like Phileo caught “futures fever” and took a bigger loss than he wanted.

Phileo’s smart, and will recover stronger for it. In a way, this is the best outcome when you lose sight of proper risk management (which has happened to all of us at least once in our careers). Imagine what might have happened had the trades gone his way… He might have made a big happy post about how he made. — oh, let’s say… — $8k in a day, and kept on placing bets that were too big for his risk appetite.

So, please, everyone, be careful when jumping into a new, zero-sum, high-leverage game. To some degree, trading is trading. But, ask yourself, are you rushing to futures because you have visions of huge, easy gains in your head? Maybe wait until you’ve calmed down a bit. You need a level head in any market! I’m not saying, “don’t try futures”… I plan to add them to my trading in the next couple years, in fact. Just don’t try them because you are excited about money money money.

On the positive side of the coin, I should point out that CalTrader made an excellent post about one of the types of plays he likes to make. I hope he makes more posts like this, and in particular I would like to hear about in-trade management.

I, for one, welcome our new futures-trading overlords…

Well, you gotta admire the enthusiasm of CalTrader, who wants to show you how to build wealth by simply making 20% every day you trade. Needless to say I’m skeptical. That doesn’t mean I’m a naysayer like he’s complaining about in his post. I know nothing about CalTrader, so I’m not opining on whether he can pull it off or not. But, I want to caution people about getting too swept up in all that enthusiasm.

The key red-flag, from my perspective, is that he’s not talking about the risk of ruin. With so much leverage in play, and so little starting capital, you are essentially banking on not having a losing streak, ever. I’d like to know more about the expectancy/win rate of the system being employed. Hopefully some of those details will come to light soon.

He’s not exactly saying it’s easy to make so much money, but if you are the type that is easily excited about wealth, think about this: it is practically impossible for a beginner to make “easy” money in a futures market, because it is a zero sum game. That means, you literally cannot make money unless someone else loses the same amount of money that you made. So, what is more likely… a small set of insanely rich people lose everything every single day, to fuel moderate gains for the majority of people? Or, a small set of rich people get even richer, as hopeful newbies are sucked dry. My guess is, it’s mostly that second one…

People think the stock markets are the same way, but that’s not true. The stock markets do act like a zero sum game to a degree, but the options and futures markets are designed that way. Leverage like that does not come free!

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

I am a retired fighter pilot turned trader. A lot of traders accurately compare trading to flying. As a newbie wanna-be pilot learning to fly a small propeller aircraft, I had to heed my instructor’s every word. To ignore advice would have meant getting killed or worse- failing! I have done my best to follow the trading advice of pros. Maoxian taught me, amongst other things, to always put a stop loss in. Trader X kept me in the markets with the advice to trade thirty minute set-ups and to play only the best trades. I devoured books and watched on-line videos. Still, it wasn’t until a short while back that I applied the axiom, “Cut your losses, and let your winners run!” to my trading. My decision to change trading methodologies came about after a conversation with my wife. She works in sales to supplement her teaching income. She was disappointed that her sales were down. I was trying to cheer her up and said, “Don’t worry. It’s like trading- just a matter of probabilities.” She agreed, “Yeah, and at least I don’t lose money when I have a bad day.” That triggered something in my mind. Phantom of the Pits had written about minimizing loss, and I revisited his words of wisdom:

“Positions established must be reduced and removed until or unless the market proves the position correct.”

I realized that I could and should minimize losses by closing trades that weren’t proving themselves. My P/L has greatly increased since that time.

When I first started trading full-time, I entered the best trade I could find each day. After setting my stop at –R1, I hoped like hell the trade would go my way. If a pick looked weak, I shouted encouragement to it: “Get up there, you piece of @$%&!” I was like a gambler standing beside a roulette wheel. “Come on, red!” I had no idea that I controlled the wager and that I had the power to remove it at any time. If I had let go of my attachment to being right, I could have taken my bet off the table when the ball started bouncing against my position. I just couldn’t seem to apply insights from the best traders in the world to my trading. Paul Tudor Jones said, “I am always thinking about losing money as opposed to making money,” and Victor ‘Trader Vic’ Sperandeo claims, “The key to trading success is emotional discipline. If intelligence were the key, there would be a lot more people making money trading… I know this will sound like a cliché, but the single most important reason that people lose money in the financial markets is that they don’t cut their losses short.”

I was determined to improve my trading by appropriately reducing or removing weak positions. I studied my losing trades. What made them losers? Did I get stopped right away? Did I get slowly chopped up? I looked at my winners. What characteristics did most of them have? Were they successful right off the bat? When I enter trades now, I watch them after every candle print, after every tick. I ask myself if the trade is acting correctly. Is it breaking out similarly to past successful trades? Am I seeing a profit a designated amount of time into the trade? I ask myself if I should stay with the trade. For example, if I enter a trade long off the break of the second bar high and the next candle prints a shooting star what should I do? Should I exit? Is the trade proving correct? When my answer is no, I close the trade. Sometimes a closed trade works out. That’s OK. I prefer moving on to a pick that is proving itself and has an eighty percent chance of winning to keeping a mediocre trade with only a ten percent chance of meeting my expectations. I know that it is always more costly to let the market take out a trade than to close it first. The market is not favorable most of the time, so capital should be protected most of the time instead of hoped away.

I now have criteria for staying with trades. Some are cut and dry and depend on the charts. Some revolve around tape reading and feel. Optimal trades go my way from the get-go. Sometimes certain types of trades will go my way immediately, but they will hit less than R1 before aggressively retracing. That can be a sign of weakness. Sometimes volume drops off. Sometimes a doji signals sideways trading. Sometimes the market shoots against my position. There are hundreds of scenarios that can red-flag a mediocre trade. Recognizing each one just takes screen time.

Phantom of the Pits says:

“In a losing game such a trading, we shall start against the majority and assume we are wrong until proven correct. (We do not assume we are correct until proven wrong.)”

It took me a long while to decipher his words. Now when I see that a trade is showing weakness, I minimize losses by taking all bets off the table.

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

Ever wonder why a 1:3 Risk:Reward ratio is so prevalent as a rule of thumb? Ever since I’ve been reading about trading, I’ve heard people say that they ideally look for at least a 1:3 ratio, even if they’ll settle for 1:2. In this article, I’ll talk about how you can derive this value form the expectancy equation. Further, I’ll show how you can predict what kind of Risk:Reward ratio is right for you.

If you are math-averse, you can skip the equations; examples are given and explained.

What Should My Minimum Typical Gain Be?
Let’s say the minimum acceptable trading performance is flat. That is, at a minimum, you should not be losing money over time. How much money should you be making on your winning trades to achieve this performance? If you know your historical win rate, and assume your typical loss is a full 1 R, then this is the equation you need:

gain = (1 - winrate) / winrate

This equation answers the question “What typical R gain will make my expectancy equal 0, assuming I always lose a full 1 R when I lose?” So, obviously, typical gains greater than this value will give you a positive expectancy.

Let’s work an example: Say my win rate is 40%. Then, the equation above is (1 - .40)/.40, or 1.5. That means I should only enter trades where I average at least a 1.5 R gain when I win. If I always trade at that minimum, my account will not grow or shrink, long-term. To intuitively see how this works, imagine I take 100 trades. I will win 40 of them for 1.5 R each. I will lose 60 of them for -1 R each. That’s a gain of 60 R and a loss of 60 R. My account hasn’t budged. Taking trades with a reward smaller than 1.5 R would cause my account to lose money over time, at this win rate. In the same way, taking trades with a reward greater than 1.5 R would cause my account to grow.

Here’s a few more values:

Translate this to Risk:Reward
So, is the above saying a 40% winner should enter trades whenever they estimate a Risk:Reward of 1:1.5 or better? Probably NOT. You see, if you are like me, or any other traders I know, you don’t hit your estimated reward target all the time (or even most of the time). So, you need to understand, based on your trading history, what kind of adjustment to make in order to get the gains you need.

The adjustment is to divide the R gain you need by the percentage of your reward target that you tend to win. The example will make this clearer…

So, to continue the above example, recall that my win rate is 40%. I know from the table above that I need to average a minimum of 1.5 R gain on my winning trades. But what Risk:Reward estimate gets me a 1.5 R gain when I win? I’ll need to look through my trading journal. My records show that I tend to take home about 50% of the reward I estimate for winning trades. So, I’ll divide that 1.5 R by 0.5, to get 3 R. That means I should enter trades when I estimate a 1:3 Risk:Reward. That way, I’ll tend to win 1.5 R or greater, and my account will be healthy.

… And there’s our magic 1:3 number! My example case was not an accident. You see, I think the number is so prevalent because of what you can assume about the typical profitable trader. It’s pretty safe to assume that they generally win 40% or more of the time, and that they get around half the gains they thought they would. Given that, 1:3 pops right out as the kind of Risk:Reward they should be looking for.

And, it follows that if your stats aren’t anywhere close to that hypothetical trader’s, then 1:3 is a meaningless guideline for you. For instance, if you win 60% of the time, and tend to take home a third of the projected reward, then 1:2 is a more appropriate minimum ratio. Of course, we are calculating the minimum for profitability here… traders should always strive to find the best risk:reward trades available within their style of trading.

Summary
After reading this article, you should have an idea of why conventional wisdom says 1:3 Risk:Reward ratios are good. You should also have the tools you need to find out what your minimum Risk:Reward targets should be. If you need help with this calculation (which is not to be construed as investment advice), just drop me a line.

Most everyone intuitively knows that it’s harder to bankrupt bigger accounts than it is to bankrupt smaller ones (assuming all else approximately equal). But, for any two account sizes, just how much more or less danger is there? Twice as much? Ten times? Can you put a number on it? In this article, I’ll show how you can numerically judge the relative danger of bankrupting accounts, and then show how you can adjust risk to trade a small account with the same loss profile as bigger accounts.

Definitions

First, lets all get on the same page about what I’m calling “danger.” In a previous article, I talked about the probability that someone using a trading system will sustain enough consecutive losses to empty the trading account. I considered an account to be empty when it drops below the $25,000 freeze point for pattern day traders in the USA. I called the chances that an account would end up empty the “Probability of Financial Ruin.” If that’s not a scary enough name, maybe pick “Probability of Having to Go Back to Your Day Job.”

In this article, I use the same concept, but I drop the probability and trading system, and think purely in terms of the number of losses an account can sustain before reaching the freeze point. This is one way of characterizing the relative danger of trading two account sizes, if you assume most other factors are about equal. For instance, if account A can sustain 40 losses, and account B can sustain 80, then I’ll say account B is twice as “safe” as account A. I’ll also say account A is twice as “scary” as account B. I use “scary” because terms like “risky” have too many meanings already.

We’ll use three example accounts throughout: a $50k account, a $200k account, and a $1Million account.

Risking a Fixed Value per Trade

One approach to trading is to risk a fixed dollar amount per trade. The dollar amount chosen is typically a percentage of the initial trading stake. I don’t recommend this approach for any account size, and the numbers below will show you why.

The number of losses an account can sustain, under this approach is:

Where “n” is the number of consecutive losses an account can sustain, “stake” is the amount of money a trader starts with, and “pct” is the percent of the initial stake that a trader chooses to use as the amount to risk per trade.

Here’s how our accounts fare under this approach, assuming they risk 2% of their initial stake on each trade:
$50k account can lose 25 consecutive times
$200k account can lose 43 consecutive times.
$1Mil account can lose 48 consecutive times.

So, from this perspective, a $50k account is 1.72 times and 1.95 times more scary than the respective larger accounts. Notice that the larger accounts look a lot closer, with the $200k only 1.11 times as scary as the $1Mil account. This makes sense, because if it weren’t for the $25,000 freeze point, all accounts would have a worst losing streak of 50 trades, at 2% each. Even a $2Billion account could only lose 50 times in a row this way. Surely there is something better?

Risking Fixed Pct of Current Equity per Trade

This is the approach I recommend, and the approach most traders I know use. Instead of risking a fixed value, you risk a fixed percentage of whatever your current account value is. The advantages of this approach, such as risking smaller amounts during losing streaks, have been discussed in detail elsewhere. Here, I’m only concerned with the practical effects related to financial ruin.

With this approach, the number of losses an account can sustain is:

Again, “n” is the number of sustainable consecutive losses, “stake” is the amount of money the trader starts with, and this time “pct” is the percent of current account value risked per trade.

Here’s how our accounts fare under this approach, assuming 2% risk per trade:
$50k account can lose 34 consecutive times
$200k account can lose 103 consecutive times.
$1Mil account can lose 182 consecutive times.

First, note that this is a less scary picture all around than the previous approach. Also, this approach scales better with account size. As such, the $50k is 3.2 times and 5.6 times more scary than the respective bigger accounts. The $1Mil account owner should be happy to spot that the $200k account is 1.77 times more scary with this approach. At least he or she gets something for all that wealth this time!

As it seems better in every way, this is the trading approach assumed for the rest of this article.

Reducing Small Account Danger

So, now that we have a numerical way to judge the relative danger level of different account sizes, we can also numerically even out the playing field by adjusting the percent risked. In plainer english, we know that a small account will always be scarier than a large account when they both risk the same percent of current equity. We want to know how much less a small account should risk than a larger account, if it wants to be just as safe as the larger account.

This rather ugly equation will tell you just that:

Here, “pct” and “stake” have the same meanings as in the previous section. They are subscripted “little” and “big” to differentiate the values for the two account sizes in question.

This tells us that if a $200k account wants to be as safe as a $1Mil 2% account, it should only risk 1.13%. To double check, we can use the formula from the previous section to calculate that at 1.13%, it can lose 182 consecutive times. That is exactly as safe as the $1Mil account at 2%, so this equation works.

As you might expect, our smaller account must cut risk much more sharply to be as safe as the bigger players. For the $50k account to be as safe as the $200k 2% account, it should only risk 0.67%. To be as safe as the $1Mil 2% account, it should only risk 0.38%. So, initially, that’d be a risk of $335 and $190 per trade, respectively.

A Slightly Different Spin

Instead of comparing account sizes, we can also just ask, “what percent can I risk if I want to be able to sustain n consecutive losses?” This equation will tell us that:

Summary

We’ve seen a way to judge the relative safety of different accounts and risk amounts, in terms of the number of losses it would take to freeze the account. We’ve also seen a couple of ways to tailor the percent risked so that an account will have the desired level of safety.

As always with these articles, I hope there was some food for thought for you in here. There’s a lot of general discussion about percent risked, but I don’t see anyone spelling the consequences out numerically. And, for some reason, it seems like nobody takes the $25,000 pattern day trader threshold into account, even though it’s a very important number for stock traders.

I’ve always thought about diversification as the buy-and-holder’s game.  As in, not applicable to me. It always struck me as a strategy for people holding only long positions… they want to be in several industries so that if one declines, it doesn’t pummel them too badly.

The way to build wealth fast for a trader is concentration. You are in the right place at the right time, with buckets of money. And if you’re wrong, you get out before taking a bath. No need to have some other stock balancing your loss for two hours. Right?

But, can diversification help across several trades? In other words, can trading several different stocks help lessen the impact of your system’s drawdowns in one stock? Here’s why I use my doomsday entry title again: I claim that the answer is at best “maybe.” Not very satisfying, is it?

Here’s why… Say I have a system with a positive expectancy, but occasional protracted drawdowns of, say, 35%. Now I start trading the system on two stocks. Does this help? Think of it like superposition of waves… depending on the phasing, the gains and losses could be flatter, or the gains and losses could be steeper. In other words, trading against two stocks could double the drawdown. And, I claim there’s no way for me to predict when or if this scenario would play out… if I could do that, I could use that information to avoid the drawdown altogether.

Ok, you say, but if you trade 10 stocks, surely some of them will be making money at any given time… I agree that it increases the probability that at least one stock is making money, but there’s always a chance that enough losing streaks coincide that you could do real damage to your account. (and of course in the worse case you have 10 times the original drawdown). But, won’t making sure your stocks are in different sectors, or otherwise not correlated help? I’d say it’s highly dependent on the system you’re using, but that generally speaking there’s no reason to expect that to help for fast-trading technical systems.

So, this is sounding a lot like the last post… you can make the catastrophic event less likely, but it can still happen, and it’s impossible to predict. Scary! Worse, I have actually seen it happen in my backtester. On one system, trading either IBM or AMGN alone produced okay results. Trading IBM and AMGN together crippled my account at one point. One of their drawdowns happened to coincide.

One way to combat this problem would be to segregate account funds so that, for instance, 5 stocks each get 20% of your account. But, for the type of systems I am trying to use and develop, this is not desireable. For one thing, lots of systems only trade a few times a year, so you could spend a lot of time 80% in cash this way. But, it does seem to give you the diversification advantages, with the worst case scenario being a drawdown about as deep as the one-stock case, but far less likely.

A second way to diversify would be trade multiple systems at once. Again, I claim that you must have no way of knowing that the two systems won’t decide to draw down at the same time. If you did, you could use that knowledge to avoid trading the drawdowns in the first place. But, just as one would imagine that trading several stocks makes a steep drawdown less likely, it seems obvious that multiple systems on multiple stocks are less likely to all underperform in concert.

So, other than making the drawndown less likely, is there anything we can do? How can we survive if the big crippling diversified drawdown does arrive? I don’t know. Here are two thoughts:

A) Take a break from trading when your balance declines too much? I’ve seen this advice, and in general, I respond NO WAY! If you are trading a system that you believe in, then you could be walking away from the winning streak that recovers your account. Only stop trading if you need to re-evaluate your systems, of if you feel that the human side of the trades is “off” (like if you think the stress of the drawdown is affecting your judgement or execution).

B) Another idea would be to siphon off some funds during winning streaks (in addition to what you already take for living expenses), for reserves. Then, if your account draws down under $25k, you add $5k to it and keep going. Assuming you are risking 2% of current equity, you will be trading somewhat small by this timeframe, which is good. Keep nursing it to $30k every time it hits $25k again, until the drawdown is over and your account recovers naturally via trading gains. That approach actually sounds pretty good to me, but then the natural question is “how big should that reserve be?” I imagine you could do some backtesting-type statistics and derive the likely worst-case scenario, but I haven’t tried to do this.

Any others?

We are all, theoretically, at risk of total financial ruin. Imagine you find a system with a 99.9% success rate, an average per-trade gain of 50% equity, and an average loss of only 0.01% of equity. You would use it, right? So would I! After all, the expectancy would be: .999 * 50 - (0.001 * 0.01) = 49.95% average equity gain on every trade!

BUT, it is not impossible for you to pick this system, and empty your account without making a penny. Think about it… you don’t expect to flip a coin and get tails 100 times in a row, but you know that it’s theoretically possible, right? Consider, too, that the average loss of 0.01% of equity is the AVERAGE loss. In theory, you could use the above system and blow your whole account on one trade. How likely this is depends on the variance associated with that average.

What can we learn from this line of thinking? Well, let’s start by getting it straight in our heads that, almost all the common statistical measures of trading success are only accurate under two conditions:

• You make an Infinite number of trades
• You can trade with a negative account balance

Obviously, neither of these conditions match reality. So, they can easily mislead you. The above example’s amazing expectancy is only guaranteed to materialize as the number of trades I make approaches infinity, and if I could somehow still trade with a $10 account.

Does this mean expectancy, and other such measures, are useless? No, but it does mean we need to take extra steps help ensure that our trading account doesn’t vanish. A small account doesn’t have the padding to weather long strings of losses. So early on, it can even make sense to choose a worse-performing system that has better-bounded worst case behavior. Right now, I’m thinking of three specific steps:

• Always risk a % of current equity
• Prefer systems with higher win rates and lower avg losses
• Prefer systems with less variance in the average loss data

1. Always risk a % of current equity

Clearly, if you want to stay in this game, you have to make money in the long run. But, you also need to never go broke in the short run. I’m going to define broke as $25k, since at that point your pattern day-trading account will be frozen. The conclusions are the same if you define broke to be $0.

Let’s say you start with $100k, and you risk a fixed 2%, or $2k, per trade. Your account will be frozen if you lose 38R. In the worst case (assuming away slippage), this means losing 1R 38 times in a row. (100k - 38*2k) = 24k = busted

Now assume you start with $100k, and you risk 2% of current equity per trade. This way, you would have to lose 69 times in a row before your account is frozen. (100K * (0.98)^69) = 24.8k = busted

Obviously, it is more likely you will go bust with a fixed risk. In the coin toss analogy, 38 consecutive tails is much more likely than 69 consecutive tails. (Again, never forget that 100 million consecutive tails is still possible, no matter how unlikely).

2. Prefer systems with higher win rates and lower avg losses

When talking about expectancy, it’s common for people to point out that you can win less than half the time, and still make money. They should really add the qualifiers “in the long run, if you don’t go broke first.” We’ll start with an absurd example, and then get more realistic:

• winRate = 3%
• avgGain = 200%
• avgLoss = 4%

… expectancy of 2.12 % equity gain per trade. Better than many systems! But, not usable in practice. At 4% avg loss, it would take 34 consecutive losing trades to bust a $100k stake. With a 3% win rate, there is (1 - 0.03)^34 = 35% chance that this will occur. WAY too risky. In general, the probability of busting an account without any wins is: (1 - winRate)^( (ln (25000/initialStake)) / (ln (1 - avgLoss)) ). Incidentally, that exponent will be the number of consecutive losing trades needed to bring down the account.

So, when comparing any two systems (or versions of the same system, if you are building one), it can be helpful to compare their probability of immediate ruin as well as their expectancy. In general (due to the equation above) higher win rates and lower average losses will perform better on the immediate ruin test.

Certainly, if two systems have the same expectancy, then you should prefer the one with the smaller probability of immediate ruin. That goes without saying. But, what about these two systems:

• winRate = 40%
• avgGain = 2%
• avgLoss = 1%

… expectancy of 0.20% equity gain per trade.

• winRate = 60%
• avgGain = 0.6%
• avgLoss = 0.5%

… expectancy of 0.16% equity gain per trade.

After 500 trades with a $100k stake, the first system is expected to make about $50k more than the second. But, it is also 2.8 x 10^79 times more likely to burn an account straight down to the $25k freeze point without winning once.

Now, at this point, you may do the math and point out that the probability of immediate ruin on the first system is only 2.5 x 10^-29 % in the first place. Isn’t that good enough that you wouldn’t want to give up $50k expected gains for more protection? I actually don’t know yet. It seems to me like the only way to answer would be to work out all 2^500 strings of trades and get a probability distribution of the outcomes. I can only say that minimizing that straight drop path’s probability should also reduce the probability of the other account busting paths, since all losing paths will have at least as many losses as the straight drop.

3. Prefer systems with less variance in the avg loss data

With the analysis we just did, on the probability of the account going straight down, we assumed we always experienced the average loss. This is another one of those statistical generalities that can mess you up. If the variance in the loss samples is large, then the average loss across a small number of trades could be way out of line with the overall average loss. Not only would that somewhat invalidate our supposed “worst case” analysis, but it could decimate our trading account!

For example, an average loss of 3% equity could have come about from either of these two strings of losses, for instance:

• string1 = 10% 5% 1% 1% 0.7% 0.25%
• string2 = 3% 4% 2% 3% 4% 2%

Across the first 2 losses, though, string1 averaged 7.5%, while string2 was still pretty much in line with 3.5%. And that’s just the difference between using 2 samples and 6! If 100’s of losses went into the expectancy calculation, then any string of 5 losses could could hurt your account far worse than the average would indicate.

As it happens, one way to remove some of the variation from the losses is to prefer systems that incorporate stops (lots of systems don’t!). This puts a cap on the loss amount, which has a side effect of making losses much more predictable. Further, in any worst-case analysis, you can assume all losing trades hit the stop, rather than assuming the average loss. That’s a very safe and conservative path to follow.

Why don’t all systems have stops, if they have so many desirable properties? Well, it would appear that lots of winning systems require you to ride out substantial paper losses on some of their trades, prior to exiting at a profit. Note that this is also why you can’t just add stops to a system that doesn’t have them… they wouldn’t be nearly as successful if they were stopped out in those cases.

But, for the small ($100k or less) account, I’m starting to think that systems without stops should be deemed too risky to use, no matter what the expectancy is. Small accounts just can’t take enough oversized losses to make it. My backtesting seems to bear this assumption out.