CMC Markets Trading Smart Series: Planning your trading strategy

Planning your Trading StrategyTHE CMC MARKETS TRADING SMART SERIES

CMC Markets | Planning your Trading Strategy 2

Proudly No. 1 for FX education Results from Investment Trends September 2011 Singapore FX & CFD Report, based on ratings given by 12,000 investors

Trading success is not just a matter of having more winners than losers. It is about achieving a winning combination of two different ratios: the percentage of winning trades and the average size of profits compared to losses. In this guide we discuss how thinking of trading strategy in these terms can help you to strike a balance between risk and reward. We introduce the concept of the expectancy ratio, a single figure that you can use to help measure the risk-reward ratio of a trading strategy.


Most successful traders follow long-term plans. They focus on

developing strategies that work, and then apply those strategies

consistently. It is this strategy – a clear set of rules about entering and

exiting positions – that is the basis for their success. Winning strategies

are often referred to as a trader’s edge. Even so, many people tackle

trading without a clear strategy. Their approach is ad hoc, entering new

positions for all sorts of different reasons and only thinking about where

to take profits or cut losses once the trade is already established.

Taking a different approach on each trade usually leads to inconsistent

results, making it difficult to achieve consistent, long-term profit as a

trader. Perhaps even more importantly, taking a flexible approach to

each trade makes it easy to fall into bad habits that often lead to failure.

Our natural (and very understandable) instinct is to avoid losing on each

new position. But when it comes to trading, this can cause problems

Combined with a flexible approach, our natural desire to win every time

can lead to:

• Holding onto losing positions too long hoping that things will improve – but eventually turning small losses into large ones

• Taking small profits too soon just to make sure you don’t end up losing on a position

• Erratic position sizing where traders get confident after a string of small profits, then increase their position size only to take a large loss that wipes out all their past profit and more

Traders with successful strategies know that losing on some individual

positions is unavoidable. Strategies work when there is a good balance

of risk and reward and, over time, the profits from following the strategy

exceed the losses. Strategic traders focus on a successful set of rules

that works across a large number of trades rather than sweating the

results of each individual position.

To properly assess and compare trading strategies, you need to relate

the profits a strategy makes to the amount of risk taken. It is not really

enough just to compare the value of profits earned. For example, Bob

and Jane may each have made $10,000 in trading profits over a year.

However, if Bob risked losses of only $7,000 to achieve this while Jane

risked $100,000, then Bob’s results are clearly superior.

In this guide we outline methods of evaluating trading strategies in

terms of both reward and risk.

The elements of trading success

Two key ratios impact on how profitable your trading is in a given time

period. They are:

• The success ratio, which is the percentage of profitable trades, and

• The pay-off ratio, which is the average value of each profitable position compared to the average value of each loss.

Your overall profitability depends on how these two ratios are combined.

For example, having a lot of winning trades does not guarantee overall

profitability. Table 1 shows an example of a strategy where two out

of every three positions entered is profitable, but which still yields an

overall loss due to a poor pay-off ratio.

Planning your Trading Strategy

Number

Winning trades

Losing trades

66

34

$30

–$90

Net loss

$1,980

–$3,060

–$1,080

Average Value Total

Number

Winning trades

Losing trades

30

70

$66

–$33

Net loss

$1,980

–$2,310

–$330

Average Value Total

Table 1

Table 2

Having more winning than losing trades doesn’t necessarily mean you

will make money in the long run.

You will often see suggestions that you should only take positions

where the potential profit is say two or three times the potential

loss. This can be perfectly sensible advice – but it does not tell the

whole story. You need to focus on the success ratio as well. Table 2

demonstrates how a two-to-one pay-off ratio can lead to overall losses

when combined with a low success ratio.

Making larger profits than losses won’t lead to profit in the long run

unless this can be achieved with a large enough percentage of winning

trades.

The key to overall success is a winning combination of pay-off and

success ratios. It is not even necessary for both the ratios to be positive.

In fact, it is not unusual for successful trend-following strategies to

have a lot more losing than winning trades. Success ratios as low as

35–45% are common, but when combined with a large pay-off ratio

the overall outcome can be very profitable. Some traders with this type

of strategy make most of their profit from a relatively small number of

very successful trades. All their other trades are relatively small losses

or profits taken so they can position to get set early in the life of a few

large trending price moves.


In fact, there is very often a trade-off between success and pay-off

ratios. Strategies aimed at reducing the number of losing trades often

involve taking risk off the table fairly quickly – for example, using trailing

stop losses or relatively close profit targets. This approach can ensure

you have more profitable trades, but cuts down the opportunity to

capture large moves.

On the other hand, strategies aimed at letting profits run to capture

large market moves often involve greater risk. There are more instances

where positions that were initially in profit fail to make target and

eventually move back to the stop loss level. This results in a lower

success ratio.

Many different combinations of the success and pay-off ratio can

lead to success. However, the trade-off between them means that

successful strategies often fit one of two categories. They either have

a strong success ratio in combination with a good-enough pay-off ratio,

or they have a strong pay-off ratio in combination with a good-enough

success ratio.

Neither of these combinations is better than the other, although many

people will be psychologically more comfortable using strategies with

a high success ratio. Being aware that it is the combination of the

success and pay-off ratios that makes the difference provides a good

foundation for developing winning strategies.

Introducing risk multiples and the expectancy ratio

We have considered two of the elements of profitability. Now we

need to consider risk to get a proper indication of how good a trading

strategy is.

Van K. Tharp, in Trade Your Way to Financial Freedom (2nd edition,

New York, McGraw-Hill, 2006), outlines how the concept of the initial or

expected risk on a trade can be used to calculate an expectancy ratio

to compare trading strategies. We suggest his book as further reading.

The difficulty with the pay-off ratio is that it can be influenced by

changes in position size. A pay-off ratio can look good simply because

some positions which are much larger than others happen to be

successful. The pay-off ratio fails to account for the increased risk

exposure on these large winning positions.

The expectancy ratio is a single figure designed to tell you what result

you can expect (win or lose) for every dollar risked over time with a

trading strategy.

Assessing the profitability of a trading strategy compared to the risk

it takes gives a much better picture than simply looking at the value of

profit over time or the percentage return on capital. Often traders make

very high returns in the short term simply because they take large risk,

using poor risk management and dangerously high leverage, or because

they have some short-term luck with unusually large positions. This

approach does not usually stand the test of time.

Successful trading over the long term is about balancing risk and reward.

Expectancy looks at results in risk:reward terms, so it is a really useful

tool for comparing trading strategies and benchmarking your trading

results. The expectancy ratio is based on the concept of initial risk.

Good trading strategies are based on setting a stop loss level at the

time you enter a trade and never moving this stop in a direction that

makes the loss larger.

Initial or expected risk is the amount you will lose if the initial (worst)

stop loss is triggered, that is, the amount you would lose if the market

goes straight to your stop loss level and the position is closed at that

price.

Calculating expectancy

1 The first step in calculating expectancy is to record the initial risk on each trade. Van Tharp calls this initial risk ‘R’.

2 The second step is to record the profit or loss achieved on each trade. This should include financing, dividends and any other cash flows involved in the trade.

3 You are then in a position to calculate the R multiple for each trade. This is simply the profit or loss on each trade divided by the initial risk on that trade.

4 Finally, then, the expectancy ratio is the average R multiple of all the trades in a sample. All you need to do is add up the R multiples of all the trades and divide the total by the number of trades.

Initial risk (R)Trade

AUD/USD

EUR/USD

AUD/NZD

USD/JPY

EUR/GBP

AUD/CAD

USD/CHF

1,000

980

1,020

1,018

993

1,099

1,091

–1,000

2,000

–100

–1,250

5,300

–400

–650

–1.00

2.04

–0.10

–1.23

5.34

–0.36

–0.60

EUR/JPY

AUD/USD

1,078

1,094

TOTALS $

800

–1,000

3,700

Expectancy

0.74

–0.91

3.92

0.44

Profit or loss R multiple

Table 3

Table 3 shows the results of nine separate foreign exchange trades. As

we explain below, you need a much larger sample to produce a reliable

estimate of expectancy but we’ve kept this example to nine for the sake

of simplicity.


Expectancy = = = 0.443.92Total R Multiple

Number of Trades 9

R Multiple =

= -1.00

-1000

1000

Let’s follow through the example of the first trade in table 3 which is in

AUD/USD. The initial risk was $1,000. This was the loss represented by

the difference between the entry price of the trade and the stop loss

level. To calculate expectancy, you need to record where you set the

stop loss then calculate the initial risk on each trade. In the first AUD/

USD trade, the stop was triggered quickly and a loss of $1,000 was

incurred. The R multiple was calculated as follows:

As you can see from table 3, losing trades have a negative R multiple

while winning trades have a positive one. If the loss is the same as the

initial risk, the R multiple will be -1. However, not all losses are the same

as the initial risk.

Many strategies involve moving the stop loss in favour of the trade over

time. In these situations, there are often losses that are smaller than the

initial risk. It is also common to have an actual loss that is worse than

the initial risk. Reasons for this may include financing and other costs

incurred over time, as well as slippage which causes stop loss orders to

be filled at a worse price than the order level.

Finally, the expectancy of a sample of trades can be calculated by simply

working out the average R multiple of all the trades in the sample. In the

example in table 3:

Interpretation warnings

While expectancy provides a very useful way of measuring and

comparing the effectiveness of individual trading strategies as well as

your overall trading, you need to be aware of its limitations.

Past performance does not guarantee future results

At the end of the day, expectancy is only a way of measuring past results.

You can never be certain that a strategy will produce the same results

in future. This is particularly the case when you are analysing theoretical

results that have not actually been traded, such as back testing based

using historical prices or paper trading. These can be very useful techniques,

but it pays to be aware that real trading can often be quite different.

A minimum sample size is essential

Expectancy is a statistical technique. It assumes that results obtained

by applying a strategy in the past can be assumed to apply in the future.

This assumption is not likely to be valid unless the sample of past trades

is large enough. For example, it would obviously be unrealistic to assume

that an expectancy ratio calculated on just two trades on a single day

could be used to confidently predict results across thousands of future

trades in years to come. The minimum sample size that allows you to

start having some confidence about the predictive power of expectancy

is considered to be 30, but ideally you should have a sample of around

100 past trades for reasonable confidence.

Expectancy is a forecast of average profits or losses over a large

sample of future trades

Expectancy does not forecast the results or each individual trade. It

is quite possible for a strategy with positive expectancy over time to

produce a large number of individual losing trades. Similarly, a strategy

with a negative or losing expectancy over time can produce a large

number of individual winning trades.

Slippage should be taken into account

Slippage refers to situations where prices gap through stop loss levels

resulting in stop loss orders being filled at worse prices. In this case,

the actual loss will be worse than 1R. This can occur when there are

major news events, and is most common in share markets which close

overnight or where stocks are suspended prior to major news events.

When calculating expectancy on instruments subject to slippage, it pays

to stress test your sample and make sure you include a representative

number of trades where slippage has occurred.

Expectancy profiles

A strategy with a positive expectancy is expected to be profitable but

many traders have a higher minimum standard than simply anything

above zero. There is no right or wrong in setting this benchmark. A

number of factors come into play. For example, it pays to allow some

tolerance for error. Expectancy is only a statistical forecast. If you decide

to use strategies that have expectancy that is only just positive based

on past results, there is not much margin for error. A small difference

between future results and the past sample could lead to overall losses.

Interpreting expectancy

Expectancy is simply a forecast of how much money you can expect to

make for every dollar you risk over a large number of trades.

For example if a strategy has an expectancy of 0.44 then you can expect

on average to make a profit of $44 if your initial risk on each trade is $100.

Strategies with expectancy above zero are forecast to be profitable

over time. Those with a negative expectancy figure (that is, below zero)

are expected to make losses over time.

The higher the expectancy the better.

Because expectancy measures the reward for every dollar of risk, it gets

around the problem of erratic position sizing where returns look better

or worse because of some large profits or losses being made when

larger risk was being taken.\

Since it captures both risk and reward in a single figure, the expectancy

ratio is a very useful tool for:

• comparing different trading strategies and getting an insight into those that are likely to give the best result for the risk taken

• benchmarking individual strategies and your overall trading results – for example, you can set a minimum acceptable expectancy for a strategy before you begin to use it



AUD/USD

EUR/USD

AUD/NZD

USD/JPY

EUR/GBP

AUD/CAD

USD/CHF

1,000

980

1,020

1,018

993

1,099

1,091

–1,000

2,000

–100

–1,250

5,300

–400

–650

–1.00

2.04

–0.10

–1.23

5.34

–0.36

–0.60

EUR/JPY

AUD/USD

1,078

1,094

TOTALS $

800

–1,000

3,700

Expectancy

0.74

–0.91

3.92

0.44


Table 5

Success ratio 33%. Pay-off ratio 3.7. Expectancy 0.44

Table 5 also achieves a positive expectancy of 0.44, but goes about it a

different way. This is more likely to be a trend-following strategy based

on the concept of letting your profits run. In this case only three of the

nine trades made money. The overall success was heavily reliant on the

single EUR/GBP trade that achieved a profit of 5.34R. In a larger sample,

perhaps only 10–15% of trades would achieve large positive R multiples.

In this case the pay-off ratio averaged 3.7 and the success ratio was

good enough at 33%. Note that although this profile relies on making a

smaller number of relatively large profits it has not increased the risk to

do so. As with the first table the trader has used a consistent approach

to risk taking.

Table 5 also achieves a positive expectancy of 0.44, but goes about it a

different way. This is more likely to be a trend-following strategy based

on the concept of letting your profits run. In this case only three of the

nine trades made money. The overall success was heavily reliant on the

single EUR/GBP trade that achieved a profit of 5.34R. In a larger sample,

perhaps only 10–15% of trades would achieve large positive R multiples.

In this case the pay-off ratio averaged 3.7 and the success ratio was

good enough at 33%. Note that although this profile relies on making a

smaller number of relatively large profits it has not increased the risk to

do so. As with the first table the trader has used a consistent approach

to risk taking.


AUD/USD

EUR/USD

AUD/NZD

USD/JPY

EUR/GBP

AUD/CAD

USD/CHF

1,000

980

992

992

1,014

1,044

1,066

–1,000

600

–400

1,500

1,500

1,100

200

–1.00

0.61

–0.40

1.52

1.48

1.05

0.19

EUR/JPY

AUD/USD

1,070

1,049

TOTALS $

–1,050

1,550

4,000

Expectancy

–0.98

1.48

3.95

0.44


Table 4

Success ratio 67%. Pay-off ratio 1.32 and positive expectancy 0.44.

Where you have a large sample of trades and a lot of experience with

how a strategy works, you may be comfortable with a relatively low

expectancy. On the other hand, when looking at a new strategy or

a smaller sample of past results you may set a higher expectancy

benchmark. Depending on your situation, you may need to take account

of the time you devote to trading and other costs when setting a

minimum benchmark. A minimum standard somewhere in the range of

0.1 to 0.4 may be appropriate.

Although a vast number of combinations is possible, the tables 4, 5 and

6 show three different examples of how the R multiples on individual

trades can fit together to form an expectancy ratio. Again we have used

an unrealistically small sample size of nine just to provide a simple view

of how the relationship between risk and reward can interact.

The first two samples consist of nine separate trades with a positive

expectancy ratio of 0.44. This suggests that for every $1000 risked, a

profit of $440 can be expected over time.

In the first sample, six out of nine trades win, that is, a success ratio of

67%. However, the largest individual profits are around 1.5R. Note the

average losing trade is less than 1R. If you calculate the pay-off ratio

(average profit divided by average loss) you will see it is 1.32. This is an

example of a strategy with a good success ratio and a good-enough

pay-off ratio. In addition, the expectancy ratio reflects the fact that the

risk:reward outcome is good. The trader has used a consistent approach

to the amount of risk taken. The good results have not come by getting

lucky taking larger risk on one or two trades that happen to win.

You can read about how to use Fixed Percentage Position Sizing and

stop losses to avoid the problems of erratic position sizing in our

Trading Smart Series guide, Dealing with Risk.


Expectancy and opportunity

The amount of profit that can be made from a strategy with positive

expectancy will depend on how much opportunity there is to enter

trades using the strategy.

For example, if two strategies both have a positive expectancy of 0.4 and

average initial risk is $1,000 then:

• a strategy with expected opportunity to complete three trades per month would have expected annual profit of $14,400

• a strategy with expected opportunity to complete three trades per week would have an expected annual profit of $62,400

Various things can impact opportunity. These can include:

• How common the entry set-ups under a strategy are. The best strategies are based on situations that are frequently repeated. You can’t base a strategy around a one off situation.

• The duration of positions. In some cases having your trading capital tied up in existing positions can limit your capacity to take on new positions

• The time taken to research new set-ups

• Your availability. It is best to concentrate on opportunities that occur when you can capitalise on them.

Profit opportunity is also influenced by position size and the amount you

can afford to trade. However, it is not as simple as taking the biggest

positions you can. Risk management is an essential component of trading

success. In our Trading Smart Series guide Dealing with Risk[link to PDF],

we explain how short-term losing streaks can lead to trading failure

without good risk management, even with strategies that are successful

over the long run.

A Word of WarningIncreasing the number of trades leads to bigger losses with losing

strategies. This includes cases where you have calculated a

positive expectancy based on historical results but the strategy

does not perform as expected and actually loses in future.

Table 6 outlines a losing sample of trades with a negative expectancy.

It also shows the consequences of some of the bad trading habits we

discussed earlier. In this sample the trader actually has more winning

than losing trades, and a success ratio of 56%. But chasing winning

trades with inconsistent strategy has led to a pay-off ratio that is not

good enough in combination with this success rate.

Two large losses of –1.7R and –3.0R are suffered on the AUD/NZD and

USD/JPY, suggesting that the trader is not disciplined with using firm

stop losses. The trader then loses confidence and reduces the initial

risk taken to $500. The next trades are profitable partly because the

trader has changed tack and is now looking to take quick profits to help

ensure success. The first two of these profits don’t go very far towards

recovering recent losses because of the small initial risk and position

size. Even so, the trader becomes more confident and starts taking

more risk, but loses all the profit on the previous four positions with a

single 1.25R loss on the AUD/USD trade that has a larger initial risk of

$2,000, which is four times the size of risk taken when after they lost

confidence.

The expectancy of –0.34 forecasts that the trader can expect to lose

$340 on average every time they risk $1,000 even though they will have

more winning than losing trades. In this case, the amount of money lost

has been made worse by erratic position sizing.


AUD/USD

EUR/USD

AUD/NZD

USD/JPY

EUR/GBP

AUD/CAD

USD/CHF

1,000

1,000

1,000

1,000

500

500

1,000

–1,000

1,300

–1,700

–3,000

300

400

650

–1.00

1.30

–1.70

–3.00

0.60

0.80

0.65

EUR/JPY

AUD/USD

1,500

2,000

TOTALS $

800

-2,500

4,750

Expectancy

0.53

–1.25

–3.07

–0.34


Table 6

Success ratio 56%. Pay-off ratio 0.34. Expectancy –0.34.


Successful traders tend to use consistent strategies and not a separate

approach to each trade.

The profitability of a trading strategy over time depends on the combination

of its success and pay off ratios. It is not necessarily enough just to have

more winning than losing trades, or to have larger profits than losses

Successful trading depends on balancing risk and reward.

The expectancy ratio is a single figure designed to tell you what result you can

expect (win or lose) for every dollar risked over time with a trading strategy.

Expectancy is a useful tool for assessing and comparing trading

strategies and for setting benchmarks for your own trading.

SummaryDespite its usefulness, you need to be aware that a positive expectancy

in past results does not guarantee future success. You also need

to ensure that you use a large enough sample of past trades to be

reasonably confident of expected future results.

The profitability of a strategy with positive expectancy depends on how

much opportunity there is to trade it.

Risk management is an essential component to trading success. Even if

strategies are successful over the long run, large short-term losses can

lead to failure and expose you to more risk than you can afford.

Our Dealing with Risk guide covers this important aspect of trading.

Hint - estimating expectancy if you haven’t recorded initial risk

Average net profit per trade = Total Profits - Total Losses

Number of Trades

Estimated Expectancy = Average net profit per trade

(Average Loss)

Average Loss = Total Losses

Number of Losing Trades

You need to know the initial risk on all the trades in a sample to calculate

expectancy. There may be times when you don’t know this. For example

you if you are new to this technique you may have a record of past trade

results but not of the initial risk. In these circumstances the following

technique can be used to estimate expectancy:

1 Calculate the average net profit per trade

2 An estimate of Expectancy can then be calculated by assuming that the initial risk over the long term is the same as the average loss:

3 You can then estimate expectancy by comparing the how much net profit you make on an average trade to the average size of a losing trade.

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CMC Markets Trading Smart Series: Planning your trading strategy

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