Trailing Stops

BarroMetrics Views:  Trailing Stops

Baz wrote” ….if we take a trade based on high probabilities, how do we know if its a high probability to continue, at what point does the high probability diminish. Then at what point does it register in our brains to exit”.

The point is we need to paint clear pictures about what has to happen for us to exit a trade …. what I call a qualitative exit. My quantitative exit (initial stop)is not the same as my qualitative exit.

The stop is the price at which my trend analysis is incorrect or trade analysis is incorrect.   The qualitative analysis are the conditions under which I’ll exit a trade even if the stop is not hit.

Trailing stops tend to be quant exits, but need not be.

Where my quant stops are placed depend entirely on the structure of the market – because their location identify where my view of the market structure is incorrect.

An example of a qualitative stop: in a recent GBPAUD trade, I took the view that the 290-min chart was showing the Ray Wave 3rd of a 3rd pattern. This implied we would see a strong directional move. When instead of a prolonged move down, the directional move went mean impulse and started to stall, I exited on the first bar that suggested an upward move was beginning (see Figure 1) – because the market was not behaving in a way that was consistent with the reason I took the trade.

Figure 2 illustrates my approach to a quant trailing stop.

I was long gold going into the June high. (Note for long term charts I use CSI-data’s perpetual series – useless for entry and exit prices but excellent for long-term analysis).

The first warning that we may be seeing a high was the price action at D. The spike down was the greatest retracement since the Jan 28 low.  For me it marked the ‘preliminary point of support’; if this were true the next high and sell-off would mark the buying climax.

Using C, I then  calculated some price targets:

  • The 13-week swing (black lines) suggested that the high at C was in a zone that may terminate the upmove.
  • The 18-day swing (red lines) showed a 3-wave structure that suggested a termination at the 30 % to 33% increase. The C high was within the target zone.

At E, Gold provided an upthrust change in trend pattern. That was enough for me to exit the trade. At time of exit, the stops has been raised to below the low at D.

Hope this helps Baz





Risk Management VIII

BarroMetrics Views: Risk Management VI

Turning to the last post in this series.

Paul’s second question:

2. Fund Managers advise clients to ?Buy and Hold?, with average-costing strategy.
This is investing/trading without stop-loss.
Does it work consistently?

That would depend on market conditions, and the lifetime of the investor has wouldn’t it? For example if I had bought the DJIA in Jan 1931 at 197, I would not have broken even till Oct 1945. Now at my age, that almost 20 year wait would have probably exceeded my lifetime. And this assumes, that the stock I bought had remained solvent.

On the other hand, if you had bought at  14,198 in Oct 2007, with the DJIA now at above 16,000 you would be sitting pretty.

Buy and hold is a viable strategy at the right time and in the right hands. According to me service we did see buy and hold funds post a negative return for 2012 and the first quarter of 2013.  And this brings me to the final question.

3. What should be the Money Management to trade without stop-loss?

By that I suppose you mean your position sizing.

The first point would be my admonition that ‘no stop loss’ does not mean ‘no predefined exit conditions’.  So, for me, I’d use the average loss for your predefined exit conditions, and vary that up or down depending on whether I was in Ebb or Flow stage.

Happy Easter everyone!

Risk Management VII

BarroMetrics Views: Risk Management V

Turning today to Manish’s question on leverage and Paul’s question on ‘stops’ and ‘buy and hold’.

On leverage, I view it as a matter for individual preference, assuming we are speaking about competent traders. For newbies, the advantage of ‘no leverage’ is it limits your losses to the amount invested; leverage raises the potential loss to all you have and more.

In the hands of a competent trader, leverage allows you to trade above your weight class. Moreover, in today’s world, leverage allows a competent trader to trade with a relatively small amount of capital, provided the authorities have not made this impossible. For example, in the US, trading CFDs is not permitted by law; in Singapore, margins on CFDs are expected to be raised to 25% of the notional value of a contract – thus effectively killing the non-institutional CFD business (the proposed legislation makes exceptions for institutions, accredited and experts).

When I started trading I had no choice but to trade the full futures contracts: I traded Gold and the S&P. With the knowledge I have now have, it is evident that, given the size of the account, I was overtrading by the proverbial country mile. Yet today, with US$5000.00, I can trade 5 S&P CFDs.

The reason why lies in the size of the contract. The S&P e-mini futures is US$250.00 per point (1871 to 1872 means you made US$250.00), while the CFD contract is US$1.00 per point. Using the same money management rules, that allow me to trade 5 S&P CFDs, I would need US$250,000.00 to trade 1 S&P mini futures.

So, what do I look at to manage my leverage? I look at:

  • The probability of consecutive losses given my trading track record. I want to keep the potential loss at 50% of my long-term average profitability. And,
  • The maximum total portfolio risk at any given time: no more than 40% of my long-term average profitability.

Turning to Paul’s questions (see attachment):

1. Just like your mentor Peter traded without stop-loss, my basic question is
“Can we make consistent profits without using stop-loss?”

This depends on whether you predefine your exit conditions, and on your  ability to execute the exit plan in face of losses, especially larger than expected losses.

Peter took the view that exiting a trade depended on whether or not a market accepted beyond key reference level.

Let’s say, for example,  I am short the S&P at 1843. My key resistance is 1867; acceptance above 1867 would mean I am wrong about the trend being down. Now. there is a world of difference between prices going above 1867 and then selling off (i.e. rejection the probe above 1867), and prices going above1867 and moving higher (accepting above 1867). In the case of rejection,  we may look to exit at better prices, or may look to stay in trade; in the case of acceptance, we look to exit, and usually exit immediately.

When we place a hard stop above 1867. we have no way of telling whether the fill will denote rejection or acceptance.  So, why do I advocate a hard stop for newbies?

There is a trade-off if we use an acceptance exit strategy. One the positive side, for example, rejection means we avoid being stopped out, only to see the market move in our favour; on the negative side, exiting on acceptance above 1867 means we will see a worse exit price than our hard stop level.

The key to this exit strategy is to be able to ruthlessly execute; and this comment explains why I advocate newbies use a hard stop: in my experience, few newbies have the necessary discipline to exit once there is acceptance beyond a key level

More tomorrow.


Risk Management VI

BarroMetrics  Views: Risk Management IV

Both Baz and Paul raised some questions.

From Baz’s comment, I feel I may have given my readers a wrong impression. I assess Ebb, Flow or Normal, from the Expectancy Return, as well as % of capital lost  and made. I use Steidlmayer’s frequency distribution method to assess if I am normal or at one of the other extremes (positive or negative).

I calculate 3 sets of stats:

  1. A set that encompasses both profits and losses.
  2. A set that encompasses only % of capital lost.
  3. A set that encompasses only % of capital made.

Figure 1 is an example of a set that includes profits and losses. In this set I am using the expectancy ratio i.e. % of profit when compared to % of capital risked. My normal range is between ‘2.0’ and ‘0.8’.

So, any drop into the range ‘0.4’ to ‘<0.8’ for 3 consecutive trades, sends a warning signal that I am below Normal. If the other two sets  confirm, then I would deem me in Ebb Stage. Sometimes it only takes two consecutive trades to make this assessment. At the end of the day, it is a judgement call based on the info.

I do the same for Flow – I enter flow once I have 3 consecutive trades in the ‘2’ to ‘<2.4’ zone.

Paul asks :

“Should we:
1. Determine
Average Profit/Loss per trade
Average Profit/Loss per standard contract/lot
b. Also “normalized” the Expectancy by unit risk taken”

Paul, these are your stats, use what assists you. I use both ‘avg per trade’ and ‘avg per contract’. In an ideal world, the two would be equal. In the real world, there will be a difference. If the difference is substantial, then you have important info.

For example, if currently my avg per contract is greater than my avg per trade, that tells me I am increasing and/or decreasing position at times that are adversely affecting my bottom line. On the other hand, if currently my avg per contract is less than my avg per trade, that tells me I am increasing and/or decreasing position at times that are beneficially affecting my bottom line i.e. I am increasing position size at the right time. 

I don’t use normalised Expectancy because I prefer to assess the results on a per instrument basis. Figure 2 shows the stats produced. In addition to all the trades, the stats are given for each instrument and rule.

I’ll turn to Manish’s question on leverage tomorrow.


Expectancy Return



Risk Management V

BarroMetrics Views: Risk Management V

I  was going to write a post on the elements of a robust trading plan. But given the questions from Baz and Paul, it;s clear that the questions have now coalesced around the Expectancy Return Formula.

As I see it, Baz is most keen to have some idea of how to identify an Ebb and Flow Phase.

The answer lies in our trading results. I calculate what, based on past trades, will be my losses within a 70% occurrence. I do this using Steidlmayer’s idea on how to calculate a Value Area (see attachment). The reason I use Pete’s approach is because the normal bell curve assumes a distribution where the mean and mode are roughly in line. This is not the case for trading results.

Once I have the data, (generally) if I find that I have three consecutive trades with losses outside my ‘normal’ range, I start to reduce my position size. In short, three losses moving into the beginning of the second standard deviation of greater than normal losses triggers a reduction in position size.

As for profits, I first calculate the numbers for profitable trades. With those in hand, for Flow stage. I prefer to see three consecutive trades where I have profits are in the upper region (or higher) of the  second standard deviation.

I’ll deal with Paul and Manish’s questions on Tuesday.


Through the courtesy of 

Risk Management III

BarroMetrics Views: Risk Management III

I asked Baz the question in yesterday’s blog because most newbies focus on a high win rate. But, to produce a consistent profit, successful traders know that this is only one part of the success equation,  the other is the Average Dollar Win/Loss. And this leads me to the final context needed to answer Baz’s question:

Our profitability is governed by:

  • Our expectancy return and
  • Where we are in the Ebb-Flow Continuum

Our success is measured by expectancy return. In turn our expectancy per trader is derived from the formula:

(Average$Win x WinRate) – (Average$Loss x Loss Rate) = + Result

An implication of the formula is that a high win rate can lead to negative result: because the Avg$Win is too low and/or the Avg$Loss is too high for the Win Rate.

For example, plugging in the numbers below to the formula:

(1.00 x .90) – (100 x . 1) = – $0.10

High Win Rate with a negative expectancy result

Now, the Win Rate is less within our control than the Avg$Win. I see the Win Rate as governed by how well current market conditions suit our trading plan. And how well our plan fits is reflected by the Ebb-Flow Continuum. 

There are times when our plan suits market conditions to a ‘T” – under these conditions, we’ll have a high win rate. Everything we do turns as as we expect.  We are ‘God’s gift to the trading world’ the flow stage. The Ebb Stage is the reverse: everything we do disappoints our expectations. Our Win Rate plummets.

The successful traders I know have tools to recognise the two stages, plan and position sizing  strategies appropriate to the cycle stage.

The reason why so many traders fail is because they fail to recognise or are unaware of Ebb & Flow – they continually blow up in Ebb Phase. This assumes, of course, that they have a plan that has a positive expectancy in the Normal Phase. This is the phase that we usually find ourselves. Win some, lose sum and return a positive expectancy.

We return a positive expectancy because, given our Win Rate, our Avg$Win is greater than our Avg$Loss. This is a function of the robustness of our plan and how well we execute it.  The latter is totally within our control.

And this leads me to the elements of a robust trading plan – the answer to Baz’s question.

Risk Management

BarroMetrics Views: Risk Management

Whenever I am asked to answer a question, I first seek to understand the outcome being sought by the inquirer. In this case, I have assumed that Baz, Manish and Paul seek to improve their trading profits.

So, let me begin by first identifying what is required for trading success:

  1. Mind: (winning psychology) which I define as the processes that lead to the consistent execution of our trading plan’s rules and our money management’s rules.
  2. Money: (money management rules) which I define as the process of balancing risk of ruin with optimization of profitability. It’s important here to understand that to make money we have to embrace risk. We risk enough to provide a probability of securing an adequate return. What is ‘adequate’ will be dependent upon our psychological make-up, knowledge and skill. Moreover, ‘adequate’ will change over time. For example: if you have never made money from the markets, expecting a return of 25% for 2014 is probably unreasonable. On the other hand, if you have been  making consistently 20%, then a target of 25% for 2014 would be doable and adequate. 
  3. Method: (trading method and trading rules) which I define as the processes by which we determine when the probabilities favour a trade and when they don’t.  Different methods will suit different personalities e.g. a discretionary versus a mechanical trader, a visual (chartist) versus a digital (numbers orientated like Robert Hanna) trader etc. 

I’ll look to answering the questions posed Baz, Manish and Paul from the perspective of Risk Management. This is made up of trade management and money management. I’ll start with Paul’s questions tomorrow, and then proceed to those posed by Baz and Manish.

Attached are snapshots of the questions.







Entry Zones

BarroMetrics Views: Entry Zones

This week I’ll reply to the questions I have received – either to my email or comments here.

ZH raised the questions in the attached doc file. For those who have not read Nature of Trends, I use statistical price and time info as the first filter to position sizing.


You have misunderstood what I said in NOT – easy to do, I admit.

Let’s turn the chart you sent me. I have added the black line, labelled 0-1.

The stats I refer to in NOT I use as a first filter to determine position size. The stats you refer to are based on the magnitude of the swing line of the first higher timeframe. If we assume that 60-minutes is your trader’s timeframe, then the black line will represent the 290-minute (EURUSD trading hours/5). If your charting software does not chart 290-minutes, then use the 240-minute.

So, if:

  1. you assess that the trend is down, and
  2. you assess the trend is likely to continue, and
  3. you see sell signal at a zone when
  4. the black line has gone mean -1 one stdev,

then you look add a normal size position.

Where you have misunderstood my comments in NOT is the timeframe to which the passage refers.

If you are applying the time and price windows to the corrective moves in the trader’s timeframe, then normally I’d be looking to trade a normal position size at “mean +1 to mean -0.5” (i.e. normal correction) of the corrective mode. This comment assumes we are seeing, on the first higher timeframe, a swing magnitude that is normal, or in some cases, less than normal.

It is very important to appreciate that I do not use the swing magnitude of the first higher timeframe to determine if I should take a  trade. I first determine if current conditions qualify as a trade, and only if so, do I determine position size: ‘below normal’, ‘normal’ or ‘above normal’.

As for the second question: you need to calculate impulse and corrective stats separately. Combining them in one population will not produce the results you seek.



Resource for Position Sizing

BarroMterics Views:  Resource for Position Sizing

Forex Decoder is offering an interesting free resource for FX position sizing (DDSMM). The free download will work with Futures, CFDs and Shares; but you need to normalise the pip results to conform to FX sizing.

The claims for their spreadsheet are interesting. They claim that their position sizing software will dramatically improve performance and show a number of examples. We tested the software and found:

  1. If you keep to the risk management rules, the software will perform as expected.
  2. Breach of the risk management rules can produce adverse consequences. In some of our tests, where we broke the risk management rules, our position sizing approach produced a -4% result;  DDSMM produced a -10%. So, not only was there no improvement, there was a significant diminution.
  3. DDSMM will not turn a negative edge to a positive one.

So, what results can we expect from DDSMM – if we follow all the rules. Amazingly quite a lot. My trading this year has produced a return of +4%. Using the same results and adopting the DDSMM method, we’d see a 44.7% return. That is quite a difference! Admittedly, based on our testing, we introduced some minor tweeks; nevertheless, the improvement is significant. Best of all the manual software is free.

Expectancy Return

BarroMetrics Views: Expectancy Return

In recent years, the Expectancy Return Formula has become the flavour of the month. The formula:

(Average Dollar Win x Win Rate) – (Average Dollar Loss x Loss Rate) = Expectancy Return


  • Expectancy Return: is the return we believe we’ll see per trade over a large sample size
  • Average Dollar Win: is the total dollars made/the number of winning trades
  • Win Rate is: the number of winning trades/total number of trades
  • Average Dollar Loss: is the total dollars lost/the number of losing trades
  • Loss Rate is: the number of losing trades/total number of trades

I have written about the formula in other contexts; but today I want to focus on what the Win Rate/Loss Rate tells us and what the Avg$W/Avg$L tells us.

The Win Rate/Loss Rate tells us how well we are reading the market. It’s a function of our market understanding and/or the relationship of  our understanding and the state of market i.e. is our understanding in Ebb State or Flow State (see There is no doubt that my understanding of market action is much better than it was when I started trading 30 years ago. I am able to have some chance of being correct when I assess the future path a market may take. When I started 30 years ago, I could no more do this than pigs could fly. So what does this better understanding mean?

A low Win Rate or high Loss Rate tells us either that:

  • We need to improve our understanding i.e. get an education or
  • We are in Ebb State and we need to take defensive action until the state passes.

What is HIGH/LOW in this context? This depends on your trading timeframe and your past results. The shorter the timeframe, the higher the Win Rate and the smaller the AVG$Win. Understanding this relationship is key to improving our Win Rate.

What about the AVG$Win/AVG$Loss? What does that tell us?

The AVG$Win/AVG$Loss tells us how well we are executing the trades. When I am trading well, I seldom get stopped out: the reason is I recognise early that my perceived low risk opportunity exists in my mind rather than in reality; and as a result, I exit my position before the stop is elected. Needless to say, when trading well, the early exit is correct…by that I mean, my stop would have been hit but for the early exit.

So are the two elements as distinct as I have made them here? If you think about it, the answer must be ‘no’; the two, WinRate and AVG$Win do merge together. That said,  I do find it useful, for my journal reviews, to separate  them. By approaching them in this way, I have often been able to take the action I need to improve my bottom line.