Taking Calculated Risks

One of the things that I learned from Pete Steidlmayer was to take calculated risks. Not that I am risk averse – that too was something I had to learn: the times when risk taking is unjustified.

What I learnt from Pete was that to succeed I had to take calculated risks and not to just take risks. There is a world of difference between the two ideas.

Just taking risk is akin to gambling, at least in my book. We take a trade not knowing if the probabilities favour our methodology, not having any idea about managing a trade, not having any idea of the risk of ruin the position size brings or whether the position size is  worth the risk. When we take a trade in this state, we enter and hope for the best.

So what are the differences when we take a calculated risk? The differences, in two words, are knowledge and consequence.

Knowledge means that we know within the boundaries of our perception and methodology that over a large sample size, our $1.00 investment will return on average ‘$X’ (and an amount that is positive). Consequence means we have worked out the worst case scenario and have accepted those  results BEFORE we take a trade.

In my own trading, I use a spreadsheet that:

  1. Asks me to consider the Risk. The $ at risk for the position size must be within my money management
  2. Then it considers the Reward:Risk. The ratio must fall into my Normal Expectancy Ratio Range. What I did here was to tabulate the results of all my winning trades and work out the mean and standard deviation of the Ratio. That gave me my ratio’s normal range: 1.89 to 2.4.

Consequently if a potential trade falls much below 1.89 I am unlikely to take it; and if it is above 2.4, I would first re-examine the trade and if the ratio holds true, I consider increasing my position size.In this way, I take calculated risks.

Being Right

After my presentation at the Share Investor Expo on December 6, I spent some time speaking about the fallacy of wanting a high win rate. I know that some ‘did not get the point’ – this blog is for you.

The key to investing/trading success lies with the Expectancy Return Formula:

(Avg$Win x Win Rate) – (Avg$Loss x Loss Rate) = Positive result.

The formula makes it clear that the first product MUST be larger than the second. So, how do we do this i.e. in periods of uncertainty?  How do we as traders increase our profit potential while limiting the risk?

Figure 1 shows the way.

Entry at the mode zone (two vertical lines labeled RISK) will mean we take a trade when the probability of loss is at its trough and the probability of profit at its zenith. In addition, we take trades when the benchmarks are clearly defined e.g. we can say that if ‘XYZ’ happens we’ll stay in; if ‘ABC’ happens we’ll exit; and we can say that as for the rest, we’ll hold for another day provided we don’t get stopped out.

Figure 1 shows that when we take the trade at the mode of probability of success, our reward:risk ratio is at its optimum.


FIGURE 1: Optimum Profitability

That’s the theoretical picture. To attain this ideal in day-to-day trading, I take a trade when I feel I have 5 items in my favour. As an example let’s have a look at Figure 2, the 12-Month swing (yearly trend) on the S&P

1) The structure of the market: In the S&P, we have a potential 313 Outside Buy Signal (See Figure 1 and The Nature of Trends). I would view such a buy signal as to the left of the Mode of Probability. The context suggests that a failure at the Failure Zone has at least an equal probability of success as the 313 Outside Buy. Unless I could take a buy trade with a relatively low dollar risk i.e. within no more than one ATR 741, I’d bypass the buy trade for the moment.

2) A Price Zone to take a trade

3) The Price/Volume relationship:

The benchmarks I’d use is the average volume per bar as the market rallies to the Failure Zone (the preferred zone is the 66.67% and 50% provided that zone is confirmed by other zone projections):

  • If the Avg Volume is at or less than 931,236, then the probability is there will be a Failure. In this case, I’d be looking to sell at the Failure Zone.
  • If the Avg Volume is 1,724, 603 or greater, the market will probably reach the Primary Sell Zone at 1553 to 1452. In this case, I’d be willing to buy on a pull back provided the Reward:Risk Ratio is satisfactory.

4) Time: I use statistical estimates to provide a time and price window for the conclusion of a move. I also use some time ratios.

5) Momentum: I’d use Ray’s Clock (see The Nature of Trends)

Once in the trade, I’d use time, structural and price stops to keep the trade within the Optimum Risk Zone (the vertical parallel lines).


FIGURE 2 12-M S&P Cash

Ebb & Flow – How to Identify

I was going to start a series on the markets tonight. But, Peter Whitnall’s comment on the Ebb & Flow raised quite a few e-mails.

In this blog, I’ll answer the question: how do we know when to increase and decrease our position size?

Well first off, we can never ‘know’ but we can make solid guesses. The guesses will always be late. It’s a bit like a moving average, trend identification, system: we’ll catch the turns a little after the event. Secondly, the approach I use depends on keeping detailed records of my trades – details that allow me to calculate the Expectancy Return and Expectancy Ratio from the results.

I’ll illustrate the ideas using the Expectancy Ratio – the same process applies to the Expectancy Return.

Figure 1 below shows the statistics from my Expectancy Ratio returns. They show that 68% of the time my Reward:Risk Ratio will range from 0.80 to 2.34. This is a key statistic. I calculate the Expectancy Return after each completed trade based:

  1. from historical record and
  2. from the beginning of each month.

The historical record is my standard; the monthly results are the tracking device.

As long as the closed out trades show a Reward to Risk with 0.80 to 2.34, then I treat the market as being in its Normal State – some wins, some losses. If I see a number of trades below 0.80 to .41, then I reduce size. This area represents a withdrawal of the market from my plan and is an amber light. If I see a number of trades below .36, I stop trading or reduce size to the smallest position I am willing to take. This area is where the market has totally withdrawn from my plan and it represents a red light – the full Ebb State.

On the flip side, when I see a series of trades at or above 2.4 and below 3, I become more aggressive in my position sizing: I’ll take up to 1.5 times my normal size. If I have a series of results above 3.60, I’ll go up to 2.5 times normal size.

No magic – just keep the stats and watch the Expectancy Return and Expectancy Ratio.

  • Expectancy Return: (Avg$Win x WinRate) – (Avg$Loss x LossRate)
  • Expectancy Ratio: (Avg$Win x WinRate) /(Avg$Loss x LossRate)


FIGURE 1 Expectancy Ratio Stats

Making The Plan Your Own

I am constantly surprised by the expectations of seminar participants. Too many seem to believe that at the end of the two or three days, ‘insto-presto, I’ll be one of the world’s best traders!’ It doesn’t help the newbie that too many educators encourage this belief.

The fact is expertise will take time. The very best we can hope for at the end of a seminar is a foundation and a set of actions upon which we can build habits of success. The end of the seminar is but the first step in a never-ending journey of continuing education and self-improvement.

As a part-time educator, one of my major outcomes is to ensure the student makes the plan his own. By this I mean, he takes what is taught, learns the fundamentals and nuances of the method, and then imposes his own personality. Given that each of us has a unique combination and set of experiences, beliefs and values, it is highly unlikely that any method will fit you perfectly. So your job as a student is to ensure that you internalize the method and then go about making studied changes.

By studied changes I mean changes for which the outcomes are recorded. We then study the outcomes to see if any changes we have made have resulted in improved profitability. The students who have graduated from STC (my mentor course), have all shown this ability.

You have seen Dr. Peter Whitnall’s ‘Wham‘ contribution. It’s an interesting method using exponential moving averages as its main tool.

Pete W graduated from the STC and all graduates know how much I ‘love’ moving averages – well alright, actually I do acknowledge they have a place in identifying the zone and strength of trend. But for me, moving averages suck as trend identifiers. Bearing this ‘love’ of mine in mind and knowing that Pete W was one of the more successful graduates, you see the point I am making. Moving Averages may not suit my personality but they certainly suit Pete W’s. He took my material and blended it with tools and materials that suited him and became a successful trader.

This brings me to the second point of this blog.

In ‘What I Learned Losing a Million Dollars’, Jim Paul makes the valid point that there are as many successful methods to winning in the markets as there are personalities – from Warren Buffett’s ultra long-term, fundamental approach to Marty (Buzz) Schwartz’s day-trading, technical method. Take a look at some of the quotes Jim gathered:

  • “I haven’t met a rich technician” – Jim Rogers.
  • “I always laugh at people who say “I’ve never met a rich technician” I love that! Its such an arrogant, nonsensical response. I used fundamentals for 9 years and got rich as a technician” – Mary Schwartz.
  • “Diversify your investments” – John Templeton.
  • “Diversification is a hedge for ignorance” – William O’Neil.
  • “Don’t bottom fish” – Peter Lynch
  • “I believe the very best money is made at the market turns. Everyone says you get killed trying to pick tops and bottoms and you make all your money by playing the trend in the middle. Well for twelve years I have been missing the meat in the middle but I have made a lot of money at tops and bottoms.” – Paul Tudor Jones.

But all successful traders have one thing in common – they accept and manage risk. Yet, this is one aspect of a newbie’s education that is usually given, at best, a cursory once-over. More often, it is emphasized only by its asbsence – when the account blows up.

Like the trading method, how you manage risk is dependent on your personality. For example: in my case, I prefer partial exits. I know that in strongly trending markets, this method adversely affects my bottom line. But for me, the partial exits smooth out my equity curve. Partial exits are but one application of the first principle of my trading philosophy : Above all, protect your capital. Despite its success for me, my risk management approach is unlikely to suit someone with a higher risk tolerance.

So, over to you: does your trading plan and money management suit your personality?

Trade Management

One of the questions that was repeatedly asked at the CMC presentations was: How do you know when to take profits?

The background to the question was the story I told about a relative whose win rate is 90% over a trading period of 30 years. Impressed? Well, what if I were to tell you that in that period, he has never made any money in any calendar year? Still impressed?

The reason for his net loss can be found in the Expectancy formula:

(Average $ Win x Win Rate) – (Average $ Loss x Loss Rate) MUST Be > 0.

In his case, he grabs small profits and incurs large losses so that the sum of his expectancy produces a negative result.

The question thus arose, to avoid this trap (I call it the Expectancy Trap), when should we take profits? In my view there are two standards:

  1. Mathematical and
  2. Technical

The mathematical relies on the size of the initial stop and the Maximum Adverse Excursion, The initial stop is the profit benchmark; you cannot continually take profits smaller than your initial stop. The lower the win rate, the truer is this statement.

My initial stops are chart based but I do rely on a few statistical studies.

The MAE tells us if the stops are too close. In addition I use another parameter that I read  in Ed Ponsi’s book, Forex Patterns and Probabilities (Forex Patterns and Probabilities). In the work, he suggests we run a simulation of losing trades with a series of higher stops. For example, how would the trade have turned out if we had increased the stop by 10 points (pips), 20, 30 etc to 50.

These technicals and mathematical studies help us determine where to place our initial stop. The first step is avoiding the Expectancy Trap.

You cannot avoid the mathematical certainty of long-term failure if our expectancy is negative. We have less control of our win rate than the Avg $ Win and Avg$Loss; so it behoves us to consciously ensure that we don’t consistently take smaller profits than our initial stop losses.

The technical benchmark tells us when we need create an exception to this general rule i.e when to accept a lower profit. After all, part of the function of a trading plan is to tell us when  the probabilities for a trade no longer favour it or at least, tell us when the probabilities favour a partial exit.

An example of this assessment recently occurred in my Soybean campaign. I had pyramided my Soybeans shorts at 1286 basis September 08. My stop was above 1305.75. The market declined to 1242 and started to stall. My view of the technicals was there was a moderate probability of a bounce returning to 1280 to 1300.

If we compare the stop to the profit target, we see the stop was 21 points. To cover the first third (see Rule of 3), I’d need 42 points i.e. an exit at 1244. But on Tuesday when the market got down to 1243, I was sloppy with the timing of my order to exit at 1244 and as a result missed the 1244s. The question was, now that 1244 was no longer available, what should I do?

Given the technical assessment, I covered 40% of the position at 1256 rather than risk the bounce. Once I exited the 40%, I was on a risk free trade for a target to the Primary Buy Zone 1120 to 1176.25.

Figure 1 shows a chart of the trade.


FIGURE 1 Soybeans September

A Foundation for Long-term Success

At the end of the day our success as traders is based on the equation below returning greater than 1:

(Average $ Win x Win Rate) – (Average $ Loss x Loss Rate) = >0.

To produce the desired result we need to consider three factors:

  1. The Win/Loss Rate. Elsewhere I have postulated why the Win or Loss Rate is largely outside our control. (see Ebb & Flow in Position Sizing 2).
  2. Our Position Sizing (see Position Sizing and Position Sizing 2)
  3. Our entry and exit. These are two factors totally within our control.

In this blog I want to examine entry and exit strategies that will maximize the probability our losses will be less than our profits i.e. our Avg$Win will be greater than our Avg$Loss. In the process, I’ll use my most recent AUDUSD to illustrate the ideas.

We’ve often heard that the key to our trading success lies in the maxims, ‘cut your losses and let your profits run’ and ‘never let a profit run into a loss’. As stated, the advices are next to meaningless. (!)

Let me ask you this, what would constitute ‘profits’? If I entered, say Gold long, at 970 and the market declines to 969, should I cut the position? Or if Gold runs to 995, should I not take profits? Or if Gold first goes to 971 and then declines to 969, have I let a profit turn into a loss?

The key to all these questions lies in our initial stop loss. This is the benchmark for our risk management positions. We cannot consistently take smaller profits than losses and hope to succeed. Here I am not talking about the times when the market tells us our analysis is incorrect and therefore it’s time to exit. So we exit with a small loss or profit. No, here I am talking about a perspective of a large sample size. If on the whole, we find that we take larger losses than profits, we’ll find we fail as traders.

Yet my experience with traders shows that this tendency to consistently grab profits smaller than our stop loss is the strategy employed by most newbies. Oh, there will always be a good excuse: a change of timeframes; a change in the condition of the market etc. But the fact remains that if we examine their data, we’ll see consistently larger losses than profits.

Let’s have a look at the AUDUSD. I sold the AUDUSD at an average price of .9600. My stops was .9768, a risk of 168 points. Figure 1 shows the target to be around .9100 to .8955.



Figure 2 shows the support zones on an 18d basis.



Notice too that there is intra-day support at 9493 to 9515. (See Figure 3)



Armed with support zones, let’s see an application of the risk management approach I favour.

My stop is 168 points. As long as I see no evidence that my original analysis is incorrect, I’ll risk the bounce from the intra-day Primary Buy Zone. Of course I’d love for the market to break that support zone at its first attempt but there is nothing in my analysis that says it must; indeed I rate a bounce as a 40% probability.

If the market bounces back up to the .9600 area, I’ll be feeling some heat; so should I exit at the .9500 zone? If I KNEW that the market would bounce, the answer is of course. BUT I DON’T KNOW if it will; it may and then again it may not. If I take an .80 profit and the market proceeds to my target, I’d have risked 168 points for an 80 point when there was much more to gain.

If I do this over a large sample size, I’ll need a Win Rate of around 70% to be profitable and for my trading style that would neigh be impossible. So, I would have to forgo taking profits at this level and run the possibility of being stopped out. I’d have to treat the move to the .9500 as noise.

Note that if I were trading a different timeframe with a different stop structure, I may come to a different conclusion.

The next support level is the Primary Buy Zone at .9365 to .9325. If I took profits at .9370, I’d make 230 for a risk of 168. At this point, I can start thinking of protecting my position.

The stop in this case was a little wider than normal, so I’d amend the normal Rule of Three process (see Risk Management 5). I’d take profits for 1/3 of my position and bring my stop down to 67% retracement area of the zone .9667 to .9327 (Top of Value). I’d bring the stop on the remaining position down to breakeven.

If all goes well, I’d liquidate the rest of the position at my target zone around the .9100 area.

By employing our initial stop as a benchmark we’ll go a long way against mathematically taking premature profits, a step that guarantees long-term failure.

Tomorrow I’ll look at the S&P. The market generated a buy signal today.

A new breed – Index Speculators

Index speculators, a new breed

from http://awanginvest.com/?p=415

It is educational for readers/traders to understand why commodity prices keep rising in spite of ample supply. Please read the testimony and you will be enlightened!

June 10, 2008 – 10:58 am



This excerpt of a Testimony by Michael W Masters before the US Senate has been submitted by NicT of HK which I reproduce hereunder:

What we are experiencing is a demand shock coming from a new category of participant in the commodities futures markets: Institutional Investors. Specifically, these are Corporate and Government Pension Funds, Sovereign Wealth Funds, University Endowments and other Institutional Investors. Collectively, these investors now account on average for a larger share of outstanding commodities futures contracts than any other market participant.

These parties, who I call Index Speculators, allocate a portion of their portfolios to “investments” in the commodities futures market, and behave very differently from the traditional speculators that have always existed in this marketplace. I refer to them as “Index” Speculators because of their investing strategy: they distribute their allocation of dollars across the 25 key commodities futures according to the popular indices – the Standard & Poors – Goldman Sachs Commodity Index and the Dow Jones – AIG Commodity Index.

Please read more at link at the top of page.

It is educational for readers/traders to understand why commodity prices keep rising in spite of ample supply.

Here is a bonus to go with the abovementioned article – NUGGET OF TRADING WISDOM: 2% Rule

The 2 % rule is a basic tenet of “risk management” or “capital preservation” as they are more descriptive than “money management”.

Larry Hite, in Jack Schwager’s Market Wizards (1989), mentions two lessons :

  1. Never bet your lifestyle ie never risk a large chunk of your capital on a single trade.
  2. Always know what the worst possible outcome is.

Hite goes on to describe his 1 % rule which he applies to a wide range of markets. This has since been adapted by short-term equity traders as the 2% rule:

The 2 Percent Rule: Never risk more than 2 percent of your capital on any one stock.

This means that a drawdown of 10 consecutive losses would only consume 20% of your capital.

Ana aka IDkit


Ag. Moderator

Risk Management 5

So far we have completed the way I assess position sizing. The next step is to manage the trade.

The first step is to identify the entry, initial stop and core profit exit- this is function of your plan.

The next step is to assess the trade’s risk-reward. Because I use the Rule of Three, I assess the risk-reward based on the core profit contract (usually the opposite Primary Zone) and average profit. The risk-reward must be at least 1:2 (based on the structural exit) and 1:1 (based risk compared to historical average profit).

Let’s look at an example.

Figure 1 shows the 5-d and 18-d swings in the ES. Let’s assume we are trading the 5-d and that the average profit per trade in the ES is 80 points. In Figure 1, the risk to average profit would be 1:2.2. Figure 1 shows that the structural risk-reward is 1:3.2

This trade has a qualified risk-reward.


FIGURE 1 ES Risk:Reward

So, before I even enter a trade, I first assess whether or not the risk is worth trading.

Once we are in a trade, we need to manage our risk.

The Rule of Three states that we cover the first 1/3 when the profits cover our risk on the remaining contracts. Unlike the core profit exit, this is a guideline rather than a fixed exit.

Figure 1 shows that the risk on two contracts is 66 points. Our entry was 1277; so, we’d exit the 1st third at (1277 + 66) 1343. But if we look at Figure 1, we see that the Point of Control is coming in at 1339.98.

We know that the 18-d is in a downtrend, as is the 13-w. So, we are trading a contra-trend move in the higher timeframes. In this situation, I’d look to exit before 1339.98, say 1335 to 1337. I then move my initial stop on the 2nd third to allow for the lower exit. So, if my exit is 1335, there is a difference of 5 points. I’d move my 2nd third’s stop to 1247 (originally at 1242; plus 5 = 1247). This way if stopped out, I’d preserve my capital.

Once the market has a bar’s range above the top of the Value Area, 1386 (i.e. a Whole Point Count of 1 above 1386), I move the stop on the 2nd contract to under the low of the Value Area. In the case of Figure 1, this area is between 1306 and 1275.

Once I exit the 2nd contract at the Primary Sell Zone, I’d bring the stop on the 3rd contract to breakeven.

On a breakout, to manage the 3rd contract, I use a trailing stop using:

  1. Swing lows or deemed swing lows and
  2. Bring the stops closer as the impulse move’s magnitudes moves to mean +3 standard deviations. At this target, I tend to bring the stops no farther away than breach of the 3-d low.

Of course if I have a Change in Trend Pattern, I’d exit on that even if the trailing stop is not hit.
There you have it.

We have covered the ground from Money Management to Trade Management: the two components of Risk Management. Happy trading guys and gals.

Risk Management 4

In the last blog, we completed the formula for a normal position size and now we come to “Ebb and Flow”.

I discussed the idea briefly in the Art of Position Sizing. As Pete Steidlmayer said: “Risk is managed by knowledge – knowledge of self and of the markets”. ‘Ebb and Flow’ depends on having solid data base.

The analogy I use is to say that our plan is a beach front; the market takes the form of rolling waves that at times totally cover the beach (Flow State); at times totally withdraw (Ebb State); and at times partially cover the beach (Normal State).

The times when the waves totally cover the beach are the times when we can do no wrong. Everything we touch turns to gold – these are the results some system/seminar vendors use to sell their wares. You have read them: “Look! A 90% return in capital in only 3 months!”. The implication is this ‘golden touch’ will continue.

But of course it doesn’t. The waves start to withdraw. Our ‘golden touch’ turns to copper – with some wins and some losses. This is the normal state.

Finally the wave totally withdraws – nothing we do is right (the Ebb State). We sell and the market goes up; we buy and it goes down. Sometimes it seems the drawdown will never cease. But of course, this state also passes; and the whole cycles starts anew.

Our job as traders is to be:

  • Super aggressive during the Flow State – I use Double Normal Size.
  • Use Normal Size during the Normal State
  • Be ultra-cautious during the Ebb State – I use Half Normal Size or totally with from trading.

How do you tell in which State you are in? You need to keep a detailed equity journal and learn to look out for tell-tale signs. One point is worth mentioning – we will always be late in identifying the Ebb or Flow. We identify an Ebb State only after a series of losses and identify the Flow State after a series of profits.

Tomorrow we’ll look at Trade Management and see how this ties in with Position Sizing.

Risk Management 3

In this blog, I’ll be looking at the way I would calculate my position size using a dollar criteria.

The inputs are:

  1. The average dollar win per trade.
  2. The average dollar win on a one-contract basis.
  3. The maximum dollar loss
  4. The volatility of the market as measured by the stop location. My stops are based on swing extremes. For this reason, the volatility are an automatic part of the stop calculation.

The process takes place over 4 steps:

Work out the units to determine the maximum loss. I do this by:

  • Avg$Win per trade/Avg$Win on a single contract basis. For example my Avg$Win per trade is $12K, and Avg$Win on a single contract basis is $1k. My units are 12k/1k = 12.
  • Multiply the units by 3 (as a buffer against a Black Swan). In this case, 12 x 3 = 36.
  • The Avg$Win per trade is the total profits/number of winning trades. The Avg$Win per trade includes the total number of contracts of every trade. The Avg$Win on a single contract is the average $ profit of all winning trades on the basis that each trade had only 1 contract.

2. Work out the maximum loss: calculate the mean and standard deviation of losses. Our loss should not exceed this figure. This is standard statistics. So, let’s assume that our mean is $9k, and the standard deviation is $3k. The maximum loss will be $9 + 3×3 = $27k. I normally round up to the next ‘5’ or ‘0’. In this case, the maximum loss would be $30k.

3. I then divide step (2) by step (1). In this case: $(30,000/36) = $833. Rounded up, this gives a maximum dollar loss of $850.00.

4. The $850 represents the Normal Position Risk and controls the normal position size. Let’s say our entry and stop show a risk of $400. This means we can take 2 contracts. If the risk is $1000, we would have to skip the trade.

The advantage of this approach is it utilizes our trading statistics. But it does have one drawback, it fails to normalize. By that I mean that it assumes, for example, that an $800 movement in the ES is the same as an $800 in oats. This is clearly untrue. I get around the problem by including in my stats normalization of results using the initiating trade price.

When I first did this, I struck two problems but I eventually solved them. Since the material forms part of the seminar material in August 2008, it would be unfair for me to disclose the problems and solution here. However, the process above is still better, in my view, than many of the position sizing algorithms currently available.