Published on February 12, 2014
THE CMC MARKETS TRADING SMART SERIES Planning your Trading Strategy
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. Proudly No. 1 for FX education Results from Investment Trends September 2011 Singapore FX & CFD Report, based on ratings given by 12,000 investors CMC Markets | Planning your Trading Strategy 2
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 3
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 4
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 5
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 6
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 7
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 8
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