CMC Markets Trading Smart Series: Planning your trading strategy
1. THE CMC MARKETS TRADING SMART SERIES
Planning your Trading Strategy
2. 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.
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CMC Markets | Planning your Trading Strategy
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3. Planning your 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:
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.
Number
Average Value
Total
Winning trades
66
$30
$1,980
Losing trades
34
–$90
–$3,060
Net loss
–$1,080
Table 1
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.
Number
Average Value
Total
Winning trades
30
$66
$1,980
Losing trades
70
–$33
–$2,310
Net loss
–$330
Table 2
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.
•
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.
CMC Markets | Planning your Trading Strategy
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4. 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.
Trade
Initial risk (R)
Profit or loss
R multiple
AUD/USD
1,000
–1,000
–1.00
EUR/USD
980
2,000
2.04
AUD/NZD
1,020
–100
–0.10
USD/JPY
1,018
–1,250
–1.23
EUR/GBP
993
5,300
5.34
AUD/CAD
1,099
–400
–0.36
USD/CHF
1,091
–650
–0.60
EUR/JPY
1,078
800
0.74
AUD/USD
1,094
–1,000
–0.91
3,700
3.92
Expectancy
0.44
TOTALS $
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.
CMC Markets | Planning your Trading Strategy
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5. 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:
R Multiple =
-1000
1000
= -1.00
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:
Expectancy =
Total R Multiple
Number of Trades
=
3.92
9
= 0.44
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
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.
•
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
CMC Markets | Planning your Trading Strategy
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6. 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.
Trade
Initial risk (R)
Profit or loss
R multiple
AUD/USD
1,000
–1,000
–1.00
EUR/USD
980
600
0.61
AUD/NZD
992
–400
–0.40
USD/JPY
992
1,500
1.52
EUR/GBP
1,014
1,500
1.48
AUD/CAD
1,044
1,100
1.05
USD/CHF
1,066
200
0.19
EUR/JPY
1,070
–1,050
–0.98
AUD/USD
1,049
1,550
1.48
4,000
3.95
Expectancy
0.44
TOTALS $
Table 4
Success ratio 67%. Pay-off ratio 1.32 and positive expectancy 0.44.
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.
Trade
Initial risk (R)
Profit or loss
R multiple
AUD/USD
1,000
–1,000
–1.00
EUR/USD
980
2,000
2.04
AUD/NZD
1,020
–100
–0.10
USD/JPY
1,018
–1,250
–1.23
EUR/GBP
993
5,300
5.34
AUD/CAD
1,099
–400
–0.36
USD/CHF
1,091
–650
–0.60
EUR/JPY
1,078
800
0.74
AUD/USD
1,094
–1,000
–0.91
3,700
3.92
Expectancy
0.44
TOTALS $
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.
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.
CMC Markets | Planning your Trading Strategy
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7. Trade
Initial risk (R)
Profit or loss
R multiple
AUD/USD
1,000
–1,000
–1.00
EUR/USD
1,000
1,300
1.30
AUD/NZD
1,000
–1,700
–1.70
USD/JPY
1,000
–3,000
–3.00
EUR/GBP
500
300
0.60
AUD/CAD
500
400
0.80
USD/CHF
1,000
650
0.65
EUR/JPY
1,500
800
0.53
AUD/USD
2,000
-2,500
–1.25
4,750
–3.07
Expectancy
–0.34
TOTALS $
Table 6
Success ratio 56%. Pay-off ratio 0.34. Expectancy –0.34.
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.
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:
• strategy with expected opportunity to complete three trades
a
per month would have expected annual profit of $14,400
• strategy with expected opportunity to complete three trades
a
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 Warning
Increasing 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.
CMC Markets | Planning your Trading Strategy
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8. Summary
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.
Despite 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.
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.
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.
Expectancy is a useful tool for assessing and comparing trading
strategies and for setting benchmarks for your own trading.
Our Dealing with Risk guide covers this important aspect of trading.
Hint - estimating expectancy if you haven’t recorded initial risk
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
Average net profit per trade =
Total Profits - Total Losses
Number of Trades
2 estimate of Expectancy can then be calculated by
An
assuming that the initial risk over the long term is the
same as the average loss:
Average Loss =
Total Losses
Number of Losing Trades
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.
Estimated Expectancy =
Average net profit per trade
(Average Loss)
CMC Markets | Planning your Trading Strategy
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