This is a title that is hard to read or write without smiling. The “holy grail” is the mother of all Forex jokes and cynical constructions. Yet it exists, is staring us all in the face, but is widely ignored, because the psychological stresses of working with the grail are paradoxically greater than most people can cope with. Before the existence of the holy grail can be proven, it has to be defined, as many grail hunters are not really clear about what it is they are looking for.
The Forex Holy Grail Concept
The holy grail is a system or strategy with clear rules that works well enough to ensure effortless trading which is profitable overall. Very often such a system is seemingly found, only for it to fail later, at which point the grail quest must begin again. This is also a larger metaphor for the journey undergone by many retail traders as they struggle to achieve profitability by hopping between different systems and styles.
The major mistakes that less experienced traders make when they build strategies are either to base them on too limited an amount of historical data, or to over-optimize them with too many indicators that make it curve-fitted. This is important to understand, and if you are one of these traders, the sooner that you come to the realization that this is a fruitless and time-wasting path, the better it will be for you. I hope this article will shorten your path to profitability.
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The Holy Grail Revealed
The answer is simple. Instead of trying to build the perfect strategy that most profitably fits the historical data, take a step back, relax, and contemplate the big picture of how markets statistically tend to move. After all, as the holy grail is surely a robustly profitable trading strategy that never stops working, logic holds that it has to take advantage of a permanent and persistent “flaw” or phenomenon in the market. So forget about candlesticks and indicators for the time being, and think about speculative markets. What phenomena do they exhibit that might be exploited by the trader? There are two that are common and repetitive:
Mean Reversion – after the price pulls away from a longer-term average price, sooner or later it always returns to the average price, which is another way of saying “what goes up, must come down”. Fat Tails within the Returns Distribution Curve – in plain language, markets tend to overreact, rising and falling excessively due to the human sentiments of greed and fear acting upon market participants.
Can either of these phenomena be exploited? Looking at mean reversion first, it is possible but problematic, as stop losses may need to be very wide and profits are by definition limited. I cannot see this as the basis for a holy grail. The overreaction of markets and their tendency to produce excessive returns on a statistical basis is the holy grail, or rather, provides the basis for a holy grail: a methodology that will make effortless profits over time.
The best way this can be explained is to imagine taking a handful of salt grains and throwing them up in the air. Suppose you were then able to measure the distance of each grain of salt from the throwing point. You would find that most of them would be relatively close to you, with a few outliers that had travelled further away. If you make a graph showing the distribution of the results, the graph would look like a bell curve, which is a typical and “normal” distribution:
The bottom axis shows the distance travelled by each grain of salt. The percentages show how many grains travelled each given distance.
Now suppose that you were constantly buying and selling randomly in the Forex market, and you measured and recorded the maximum possible gain of each trade over thousands of trades and thousands of days. If you constructed a version of the above graph with those results and superimposed it upon the earlier graph, the result would look something like this, with the dotted lines representing the market’s returns distributions:
So, it can be established that speculative markets such as the Forex market produce more excessive returns, both positive and negative, than can be expected from a “normal” returns distributions model. A greater number of excessive price events happen than would normally be produced by simple randomness. In plain language, the market offers more big winners and losers than it really should.
Here is the holy grail: the use of tight stop losses will remove the excessive losing events, and the use of wide take profit targets will allow the “fat tail” of excessively positive returns to be captured. Yes, it can be this simple, although it is not without a few potential pitfalls.
In order to illustrate exactly how the fat tail phenomenon can be exploited, let’s examine some back test data run on Gold and the major Yen crosses from 2011 to 2013 over a period of 3 years. These were the most volatile and trending instruments in the Forex markets during most of this period. If a very simple trading strategy of entering upon the next bar break of any engulfing bar on the H4 chart in the direction of the engulf was followed, using a stop loss placed just the other side of the engulfing candle, the following results would have been achieved by instrument and reward to risk profit targets:
Notice how a very simple, straightforward strategy that takes no account whatsoever of trend, direction and support and resistance can be made into a positive expectancy of 53 cents gain for every dollar risk, simply by not taking profit until reward has reached 50 times risk!
It would be simple to improve these results by moving stop losses to break even after a certain period of time on every trade. This is because the strongest winners usually will only retest the entry, if at all, relatively quickly.
Even the Holy Grail has Pitfalls
The holy grail exists, but it has to be handled with caution. You can find the grail by trading the right instruments that move with maximum volatility, i.e. those markets that are most attractive to speculation, and using simple entry strategies to ensure you participate in the market’s excessive movements in the direction of your trade. You do not have to be right or forecast the major moves: you just have to be there, cut your losers short, and let your winners run. The natural tendency of the market to produce fat tails will do your work for you.
There are two major pitfalls that this might lead you to. The first is that you will be better served by a more intelligent exit strategy than simply aiming for a fixed reward to risk multiple. You need to be booking wins above 10 R:R, ideally towards 25 R:R or even beyond, but each trade will be different. Look to exit around those levels but use some intelligence and discretion. Also, being prepared to move stops to break even when the trade is a certain distance or time in profit should help.
The second major pitfall lies in the fact that this type of strategy will always produce very low win rates, where you will lose as much as over 90% of your trades. This will inevitably cause very large losing streaks which will severely test both your mental strength and your money management strategy. The grail gives gold, but it is hot to touch and burns the unwary! Do you have what it takes to sit through twenty or more losing trades in a row? Do you have a money management strategy that will properly protect you from ruin should you begin with a long losing streak? Will you be diversified and uncorrelated enough in order to keep losing streak risk to a minimum?
One final danger is worth a mention. It is natural to try to filter entries. However it is very problematic to distinguish entries that are likely to reach a ratio of 25:1. Furthermore, missing just one of these winners will set back your overall expectancy, unless the method used will also filter out at least 25 losing trades at the same time. These are some questions to ponder and investigate. Spend some time back testing. The holy grail has been placed in your hands!
The phenomena i that is the true “holy grail” of the Forex markets s the statistical tendency of market to produce excessive returns, which can be “gamed” profitably by letting winning trades run to large reward to risk multiples, which cutting losing trades short by using relatively tight stop losses. If the most volatile instruments are traded in this style, it is possible to be nicely profitable over time without having to really make any analysis or decisions. Despite that, this path has some serious pitfalls that must be avoided intelligently. In this week’s article I will go into more detail about what those pitfalls are, and some of the best ways to deal with them.
Back to the Data
We can begin by taking a look at the historical data showing how entries upon next bar breaks of H4 engulfing candles performed on the most volatile instruments from 2011 to 2013, a three year period, depending upon the reward to risk multiples that might have been selected as targets for trade exits:
This table contains two immediately useful pieces of information. Firstly, we would have taken a total of 2,810 trades. Secondly, the positive expectancy per trade rises dramatically until a reward to risk ratio of 25:1 is reached, after which it rises very slowly before falling off a cliff at above 50:1. Let’s say we have been following the 25:1 model. This data is not shown in the above table, but of those 2,810 trades taken, only 139 were 25:1 winners. This means that approximately 95% of the trades were losing trades. These numbers would put a severe strain on any kind of money management strategy, as the probability of suffering enormous losing streaks would be extremely high. It is more likely than not there was a streak of between 100 and 120 consecutive losing trades during that three year period.
There are three possible ways to improve the methodology:
Be more selective with entries
Be more selective with exits
Risk a consistent and very small percentage of capital per trade (Money Management)
Let’s address each one in turn.
Selective Entries
Our problem is that we are currently set to enter a very large number of trades, the vast majority of which will be losers. If we can find a way to enter significantly less trades without suffering a proportionate fall in the expectancy per trade, we can worry less about the strain of likely losing streaks. The danger here is that when profit rests upon a relatively small number of winning trades, you have to be very careful not to cut yourself out of many of those. Fortunately, using the historical data from 2011 to 2013, there seems to be a relatively simple filter which does the job.
To win large trades, a trend has to be present. In an uptrend, the price pulls back within the trend making a major low, and then resumes its original direction. By only taking engulfing candles in such an uptrend that make a low lower than the previous 4 candles, or that directly follow such a candle, we are able to filter out a lot of the losing trades, without sacrificing too many of the winning trades. Here is a table of the performance over the same three year period using this entry filter:
It can be seen that overall, the total number of trades is reduced by slightly more than one third, but the winning trades tend to be reduced by a smaller percentage, resulting in rises in the expectancies from 3:1 to 50:1. The probable consecutive losing streak is reduced to somewhere between 80 and 90 trades, which is also an improvement. It is noticeable that this filter had a strongly negative effect upon the Gold trades.
Other entry filters that could improve performance would include entering only after engulfing candles with relatively small ranges, as the total positive distance required to be a winner is shorter. Time of day and trend filters can also be applied, although these can be pretty risky. For example, Gold tends to short well before the London open and long well after the London close. The Yen pairs tend to perform well following the first candle representing the initial few hours of the Tokyo session. Bounces off major support or resistance levels can also be the origins of good trades, although it is surprising how many of the best resumptions within trends begin ahead of these levels.
Selective Exits
So far, we have only looked a methodology that exits at a fixed R multiple. This could be refined by setting a target based upon an average volatility or number of pips, so that trades with larger risks can be exited at smaller R multiples. Additionally, there is the question of raising stop losses to break even and beyond. We have no hard data, but it is likely that moving the stop loss to break even somewhere between two days and one week after entry, or after the trade has moved a certain favourable distance, would enhance the results. Caution is required here as there are often retests of entry zones in long-term position trading using an H4 chart.
Of course, should the instrument being traded in an uptrend fail to make a major higher high a little way short of the desired target, it would make sense to exit at that point and take the profit.
Money Management
It is vital to use robust and intelligent money management techniques to minimise the risk of catastrophic loss. As a losing streak of 80 consecutive trades was probably during the three year period, risking a percentage of capital rather than an absolute amount based upon the starting capital is essential. For example, risking 1% of the starting capital on each trade would result in an 80% loss at the end of the losing streak followed by a 25% addition by the first winning trade, resulting in a total of 45%. Risking 1% of the total capital would result in a 55% loss, followed by a recovery of approximately 17%, resulting in a total of 72%.
Always bear in mind that the more of your account you lose, the harder it becomes to make it back. A 50% loss requires a 100% gain just to get back to break even. A more appropriate risk per trade would be something like 0.25% of the account, which would result in a draw down to about 81% of the starting total after the likely losing streak, recovering to about 93% after the close of the first winning trade. It all depends upon your individual risk tolerance and tolerance of account draw down.
One final warning: when you are trading correlated pairs, as in this example where three of the four instruments are Yen crosses, an additional defensive measure can be taken of reducing the total risk when taking multiple trades at the same time in the same direction. This will be especially important where all the trades are long Yen. In fact, a careful study might show that the best trades are the ones that set up on all three of the Yen pairs simultaneously, or at least that this situation produces an enhanced statistical edge. Trading in this systematic way requires careful study of historical data, without curve fitting. Before trying this with real money, test rigorously and be honest in answering your own questions, and be sure to study thoroughly and carefully.
Conclusion
*Update: I wrote a detailed statistical follow-up showing how a trading strategy based upon securing a few very large winners can be an extremely profitable model to follow. Today I am going to write a concluding piece to this series, and provide a refined statistical analysis of comparative entry techniques. Last week’s piece did include a statistical error, but as often happens with errors if they are discovered in time, it led me to some interesting conclusions which I am going to share with you today.
First of all, a correction of the error in this article has to be made. Statistics were shown that were described as the result of buying engulfing bars that make a 4 bar low and selling engulfing bars that make a 5 bar high, as reproduced below:
In fact, the statistics shown above were produced by different criteria for entry, which were buying engulfing bars that make a 4 bar high and selling engulfing bars that make a 4 bar low: quite an essential difference! This begs an obvious question: which entry strategy is superior? Buying the reversals at extremes, or as continuations within some kind of trend? This is what I will examine below, but to try to improve both sets of results I have added an additional filter, which is that the range of the trigger bar must not be larger than a defined maximum amount. It would have been more scientifically rigorous to use this as some measure of averaged range, but using a numerical value does about as well for our purpose. The maximum values used were as follows:
- AUD/JPY: 40 pips
- EUR/JPY: 60 pips
- GBP/JPY: 50 pips
- GOLD: $6
- Additionally, bars under 10 pips or $1 were excluded from the samples.
First of all, let’s see the results that were obtained by taking the extreme reversals, i.e. buying after a 4 bar low and selling after a 4 bar high as described above:
Now let’s see how the “in-trend” variation performed, i.e. buying after a 4 bar high and selling after a 4 bar low:
We can see that overall, at each different profit target, the expectancy per trade given by the “in-trend” variation is superior to the “extreme reversal” variation, and markedly so. The only real exception is Gold, and also GBPJPY to a slight extent. Why should it be that the extreme reversal strategy is inferior? The simple answer is that although it gets you into the great trades early at excellent prices; it gets you into too many counter-trend trades. You get the winners, but you also get too many losers. By trading with the trend on the H4 chart as an alternative, your trades will be skewed in the direction of the prevailing trends, keeping you out of all those unnecessary losers.
What explains Gold’s contrary behaviour? Well, what has been very noticeable about Gold over the past few years is the violence and speed with which it reverses and follows through, as well as the fact that it has more or less both doubled and then halved in price in the space of just a few years, which is unlikely to repeated in the near future. One important thing remains and that is to perform an “out of sample” test along similar lines over a similar time period previous to 2011. I have not completed that yet, but when I do, there will be an update to this series.
Strategy
An “out of sample” analysis of the same strategies has now been completed. This analysis looks at the hypothetical performances of the same strategies but over a different time period. It is always important to perform such an out of sample analysis when examining trading strategies. Our original statistics were based upon performance from the beginning of 2011 to the end of 2013. The out of sample analysis was conducted from the beginning of 2008 to the end of 2010, i.e. the immediately previous period of equal length. The results were fairly disappointing. The “reversal” strategy was somewhat profitable when traded with profit targets between 3:1 and 10:1. The “trend” strategy was not profitable on any profit target ratio. The numbers were as follows:
“Reversal” Strategy
“Trend” Strategy
In spite of the disappointment, it is worth noting that the “reversal” strategy, even without a directional bias, was proven to be potentially profitable during the out of sample period. It is also worth noting that no technical criteria were used to select these currency pairs, at least for the out of sample test. However, a strategy that is prepared to hold positions for long periods of time should be able to do much better than this, for the following reasons:
Overnight Charges
Brokers charge a small fee for every night you keep a trade open (in some cases, they may pay you a small rebate). This is due at least partly to the interest differential between the currencies. Many brokers also charge an additional fee which tends to mean that you almost always pay something overnight. Something in the region of 7 to 10 pips per week is common. This means that if you hold a trade for a 3,000 pip gain, but it takes a year to get there, you could pay something like 520 pips, giving up about one-sixth of your gains. The effect of these charges has not been included in the data presented within this series.
Pair Selection
One of the most commonly overlooked decisions a trader can make is which pairs to trade. Deciding which pairs should be traded at any time is probably the most important part of any long-term trading strategy. Often traders instinctively diversify, which has some advantages. But during those periods where almost the entire market is ranging, even diversified traders can suffer serious losses.
Directional Bias
A long-term trading strategy should be able to include a directional bias on each of the pairs it is trading. This means adapting to market conditions and deciding to only take either long or short trades, or perhaps both but differentially weighted. This should improve results dramatically as a strong trend would naturally result in the survival of multiple stacked positions that together create exponential profits from any strong trend, provided the bias is correct.
Moving Ahead
Next week, I will outline how a long-term trading strategy can be constructed in a way that works with rather than against the problems we looked at previously. The strategy will:
- Begin with a methodology for selecting which pairs to trade and which directional bias to take, based upon the weekly time frame.
- Continue with an entry strategy on lower time frames that is designed to ensure maximum “survivability” of positions, based upon the 4 hour time frame.
- Conclude with a complete exit strategy.
