July 2009 Volume 1, Edition 8
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In this edition: • Analysis by Philip Dooley - Forecasts from the PCD report
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Analysis By Philip Dooley Trader & Author of the PCD Report
DOW The month of July has seen the Dow complete the retracement from the June high and resume the up trend and in recent days rally above the June high of 8878. All this activity has been reflected in the Australian market. This has been no surprise to readers of the PCD Report who, in addition to having the benefit of my forecast from early March of a strong rally from March to June, were made aware earlier this month when the market was falling that I expected the market to be back above the June high by August 5th. That forecast covers more than just the resumption of the rally so call me if you want to open an account and I will send you my reports.
GOLD Gold reached support earlier in July at $905oz and is close to finishing the month approximately 5% higher. Again, readers of the PCD Report have known since March that my view has been for a broad rally in Gold over several months going into December. So it has to get moving now. More recently I reported that I expect the rally to encounter resistance around early October that could stop it. Now that view has absolutely nothing to do with the fundamental issues surrounding gold but is based solely on the application to gold of the timing algorithm I designed and developed. In fact if I am wrong about the direction, those dates are where a falling market will be heading.
CRUDE OIL Again the readers of the report had the benefit of the forecast of the rally in Crude oil from March to April and later from April 21st to Mid June. In the last couple of weeks Oil has rallied again but it is now critically positioned and those clients and readers of the report will know why.
AUSTRALIAN DOLLAR Having correctly forecast in early April that the Aussie dollar would rally to June, the Aussie is again heading higher after completing its retracement in July. I expect the Aussie to be on a fresh yearly high by September 14th provided it can overcome resistance expected around August 19th. Those dates are also good for the downside and will become relevant in that context if the 0.7700 level is broken.
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Maxmising Trading Profits with Money Management Introduction to Money Management If you've read Jack Schwager's book Market Wizards you'll recall that all traders each had their own way of trading; their own experiences; their own philosophies and their own outlook. One common theme among them all however seemed to be their reliance on what they called "money management". When I read the first book in 1990, I had no idea what money management actually was. Was it making sure your stops are in place? Was it just being careful with your cash? What was it? Money management in this context refers to what a gambler might call bet sizing. It is how many contracts or shares to trade on a certain strategy given a certain bankroll. Now in my experience with retail investors, this is normally an arbitrary consideration. One contract, five contracts, ten contracts - whatever you can afford really. I have known traders that spend countless hours per day studying Gann or testing indicators or drawing little lines all over their charts. But when it came to placing the bet, there appeared no consideration to the position size. I even had someone say to me "it doesn't matter how much you bet as long as it's a good trade". This article uses a really simple example to help explain why this thinking is incorrect. Suppose you are invited to play a coin toss game. Tossing heads means you win two units and a tails means you lose one unit. Suppose you were given $4 as a starting bank roll and you can bet as much or as little as you like and play the game many times over. Now it's clear the odds are stacked in your favour. $2 for a win versus $1 for a loss. That's a "good trade". But how much do you bet? Well there are a number of ways to approach this one. We will look at three: - Fixed dollar - Percent at risk - Optimal fixed fraction Fixed Dollar Amount This is an arbitrary decision of placing one bet (or trading one contract) for a fixed amount of account equity. It is probably the simplest and for that reason, most common money management technique. In the coin toss example, we would for example place one bet every $2 in the account. So in this case we would win $4 or lose $2 on the first toss. Based on the outcome, we would then adjust the amount bet, but still keep it at one bet per $2 equity. When trading futures, this is the same as trading one contract per $XXX in your account. The downside in trading multiple futures markets is this technique does not address the unique characteristics of each market with respect to your system. For example, trading one contract per $5,000 in Eurodollars is probably rather conservative. In contrast, trading one contract per $5,000 in Nasdaq futures would just be ridiculously risky. Additionally, this method does not distinguish between a volatile trading system and nice steady one. A volatile system should trade fewer contracts than a more steady system to reduce the risk of going broke. The Fixed Dollar method does not address this issue. Percent at risk method The next most common and quite a sensible approach to money management is to risk only a fixed percentage of your capital on any one bet or trade. In the coin toss it would mean picking a percentage loss you are comfortable with and betting that proportion of capital each and every bet. You might for example be willing to risk 10% of your capital each and every bet. Given six alternating outcomes, your equity would look like this:
Source: eSignal - www.esignal.com
Win/lose Amount Ending Equity
The advantage with this method is your bet size and potentially equity grows at a steadier pace. Looking at futures, let's say you have a trading system in the S&P where you run with $200 stops. Giving a small allowance for slippage and comms, let's say the worst outcome is a loss of $300 per contract on a trade. With the percent at risk method you trade a certain number of contracts that would make your maximum loss no more than a fixed percentage. If you are trading with a bankroll of say $100,000, and wish to risk not more than 5% of that value on any one trade, then you take 5% of $100,000 or $5,000. Then divide this by $300 and you get: (5% * $100,000) / $300 = 16.7 contracts So with this system and method, you would trade 16 contracts (rounded down from 16.7). In the Market Wizards books, many of the traders said they would risk anywhere from 1% to 5% of capital on any one trade. So there is some suggestion that this is the method many of them have used.
The first and obvious disadvantage of this method is having to round up or down on contract amounts. This has particular impact on smaller equity amounts. The real negative of this method is that most people would pick a percentage based on their risk preferences, but that is not necessarily the best percentage of the system being traded. Think about it. If we have a mechanical system where we can define things like maximum loss and probability or frequency of loss, then surely there must be some type of mathematical method of working out the optimal amount to risk. Well there is! It is called the Optimal f or Optional fixed fraction trading. Trading with Optimal f The mathematics of this system has its roots in solving the problem of interference in data transmission over telephone lines - truly. However the system was adopted for gambling and then again for trading futures. Optimal f refers to the mathematically optimal fixed fraction of total equity that is allocated to any one trade. The optimal fraction is defined at the one that offers the maximum long term growth of equity. This is really no different than the previous version except we have mathematically determined the figure that will produce the best long term profits. For trading systems, the mathematics of Optimal f simply takes into account system parameters as defined by your past results or hypothetical results and returns a figure that represent the dollars in your account that should represent one contract.
Now the formula itself is just a little detailed to list here. For those wanting to head down this path I strongly recommend Ralph Vince's first book Portfolio Management Formulas. It is heavy on the maths, but the proof is very convincing!
Playing with coins The Optimal f formula will return a decimal that represents the fraction of the total equity. To then come to the amount to trade, you divide the largest loss by this fraction. In our coin example, the optimal f is 0.25. The largest loss as we know is $1.00: $1.00 / 0.25 = 4 Therefore we would bet one unit (dollars) every $4.00. This is the figure that without question offers the best long term growth given the payoff levels ($2 profit and $1 loss) and probability (50% for each).
The same goes for more complex futures trading systems where you have a measurable string of profit and losses. You can use the Optimal f calculation to determine the fixed fraction position size that would have resulted in maximum equity growth given those parameters. The Proof Is In The Tossing So how much advantage does Optimal f give you? Is it really worth learning the maths? Would it just be OK to make a rough guess at the optimal fraction and trade that way? Well, the numbers are nothing short of astounding. Let's say we give three traders a bankroll of $100,000 each and have them trade a fixed fraction of equity. For some reason the first three names that have popped into my head are Peter, Paul and Mary - so we will use those. Peter is conservative and bets 10%. Paul has done some homework and knows to bet 25%. Mary arbitrarily chooses 40% - thinking "it's only risking less than half" of my money and that's not too bad". If we simulate using alternating returns (profit, loss, profit, loss...etc), then after 50 trades we would have the following equity curves:
Peter's (10%) and Mary's (40%) have the same outcome. Starting with $100,000, they both end up with just less than $685,000 - a return of 585%! Not too bad. Paul on the other hand has just over $1,900,000 - a return of over 1,800%! It is very interesting to note that for such seemingly small changes in bet size, the optimal fraction showed a return of more than three times the others. Another interesting point about Peter (10%) and Mary's (40%) results are that drawdowns for the 40% allocation are up to six times that of the 10% allocation. So you make the same amount of money for six times the worry! This suggests it is better to be on the lower side of the Optimal f figure.
Now ask yourself this question, if you were presented with this game (the 2:1 coin toss), and you wanted to make the most money, what would your approach be? What if you decided to be very aggressive and bet say 55% of your stake on each toss? Well you'll go broke with a probability that approaches 100% the more you play. In the 50 toss example, you would end up with a loss of 75%! That is a loss of 75% from a game that has such advantageous odds. Hopefully those figures will convince you there is something to this â€˜money managementâ€˜ stuff.
Mathematical expectation Now you may have got to this stage of the article and be thinking "Hey, remember that sorry little system I designed that never made any money? Maybe with a clever money management strategy that thing would work!". Well unfortunately, it's not that easy. Any system will still have to make money under the simplest of money management strategies to then make more money under the Optimal f method. In statistical terminology, the system will have to have a positive mathematical expectation. A mathematical expectation is what you would on average make per trade. This figure has to be positive. That is, you have to be able to make money on each trade on average. The figure is quite simple to calculate for simple examples. Back to the coin toss. If you could play the coin toss game where you win $1 for heads and lose $1 for tails, then you can easily figure out that on average you would breakeven, right? In technical terms it is calculated as such: = P(W)*W + P(L)*L Where W = profit per winning bet P(W) = probability of that win L = Loss per losing bet P(L) = probability of that loss For the coin toss the mathematical expectation is: 50% * $1.00 + 50% * -$1.00 = $0.00 That one is pretty straight forward. What about our $2.00 win and $1.00 loss game? The mathematical expectation for that one is: 50% * $2.00 + 50% * -$1.00 = +$0.50 That is, on average, each game will win 50 cents. That is why you would play it under virtually any money management method. If however, the win payout was $0.75 for a heads and the loss for a tails was still $1.00, if would not make sense to play. Your mathematical expectation is: 50% * $0.75 + 50% * -$1.00 = -$0.125 On average you would lose 12.5 cents per bet. Nothing but luck would help you make money in the long term on this one. This interestingly enough is how a casino will make their money. All casino games have a probability of winning and a probability of losing. A casino will set their payouts such that any game never has a positive mathematical expectation. When you think about it this way, you wonder why anyone would ever go to a casino! So how can you calculate your mathematical expectation on your futures system? Unless you can work out a probability distribution on your trades, then you can simply use past results. Let's say we have two systems, System A and System B. They have the following results after 20 single contract trades:
Now if we assume these 20 trades are indicative of the systems overall, then we can simply take the average to work out the mathematical expectation. System A shows a positive expectation despite have a lower proportion of winners. System B wins more often but has a negative expectation given the size of a couple of the losses. System A would be suitable for an Optimal f calculation. System B would have no Optimal f since it cannot make money in the first place! Using the spreadsheet provided, the Optimal f works out to be 0.13. Dividing the largest loss by this figure we get: 100/0.13 = $769 This means you would trade one contract per $769 to achieve maximum long term growth from this system. A lower dollar figure would mean more risk resulting in lower long term growth (on average). A higher dollar figure would mean you would not be using the system to its fullt potential and your returns will suffer. In other words a figure greater than or less than the Optimal f number will result in lower returns over the long run. Pitfalls in using Optimal f First and foremost is the assumption that future results will match that of the past. This all comes down to your testing methods. The most important figure here is the estimate of the largest loss. You probably have not been trading long enough if you have never been surprised by the extent of a loss in unusual circumstances (eg Sep 11th). There is a real problem then of picking what the exact loss should be. Should you assume your system will run into another September 11th scenario? Should you take that month out of your figures? Each side of the argument has its pros and cons. Another negative is the volatility of returns. Optimal f will show you the number of contracts to trade that maximises profits. Along the way however, this also means that drawdowns can be significant, particularly if you experience sequential losses. Anyone using Optimal f should be prepared for this. The way around this is to trade multiple un-correlated systems at any one time. In English, that means to diversify. If you on the other hand were to design a great system for one market and invest all your money in that, then you would have to be prepared for significant drawdown solely thanks to Optimal f. Varying bet size based on previous result(s) Now in all of the above, we have assumed that any one profit or loss from your system has no bearing on the next profit or loss. That is there is no sequential relationship or correlation. This is something a lot of people do not think about. Head on down to the roulette table at pretty much any casino and you will see an electronic screen showing some statistics of past spins - things like â€˜% black vs red'; â€˜% odd versus even' and the outcomes of the last few spins. The idea is the gambler will use this information to work out where to place the next bet. You would expect the number of blacks and number of reds to both be close to 50% right? So if blacks drop and reds rise, you might think "hmmm, chances are black will come up next". If you are laughing at this suggestion, don't. Some people do actually think this will work. If you are one of them ask "how?" How could it possibly work? Is there any way that the last spin has anything to do with the next spin? How could it? How then can you make a betting decision based on the past spins? It's ridiculous. Some people do make these decisions and in the markets it's even more common. After a string of losses, a trader might say "chances are my luck will turn around and the next trade will be a winner." Based on this type of thinking, a trader may increase or decrease position sizes according. In roulette this is a stupid strategy (in fact playing roulette in the first place is stupid). You see it has everything to do with dependency. One spin has no bearing on another. In the markets, most mechanical trading systems are the same. One trade will have no bearing on another. I have seen systems out there that do have some form of correlation, but these are rare. If then, we were to make one assumption, it would be that a time series of trade results are not correlated. Therefore, any betting strategy that increases or decreases the best size based on one of a string of past profits of losses, does not make sense - just like the roulette example.
Conclusion The Optimal f method of position sizing shows incredible results over and above any other method of sizing. Ultimately however the benefits rest entirely on the accuracy of the parameters used in the calculation. This of course comes down to your testing and your homework. Unfortunately, nothing comes for free.
References Balsara, Nauzer. Money Management Strategies for Futures Traders Schwager, Jack. Market Wizards and The New Market Wizards Vince, Ralph. Portfolio Management Formulas Vince, Ralph. The Mathematics of Money Management Vince, Ralph. The New Money Management
Contributor information: Guy Bower is professional futures, options and spread trader. Guy is the author of two books: Options: A Complete Guide and Hedging: Simple Strategies
Technical Indicator of the Month By: Jason Achjian Average Directional Index (ADX) Indicator Name Average Directional Index – ADX What The ADX is an indicator used in technical analysis as an objective value for the strength of a trend. ADX is nondirectional so it will quantify a trend's strength regardless of whether it is up or down. The ADX is plotted as an oscillator that fluctuates between 0 and 100. Even though the scale is from 0 to 100, readings above 60 are relatively rare. Low readings, below 20, indicate a weak trend and high readings, above 40, indicate a strong trend. ADX is usually plotted in a chart window along with two lines known as the DMI (Directional Movement Indicators). ADX is derived from the relationship of the DMI lines. How The ADX is a combination of two other indicators developed by Welles Wilder, the positive directional indicator (abbreviated +DI) and negative directional indicator (-DI). The ADX combines them and smoothes the result with an exponential moving average. To calculate +DI and -DI, one needs price data consisting of high, low, and closing prices of each period (typically each day). One first calculates the Directional Movement (+DM and -DM): Up Move = Today's High − Yesterday's High Down Move = Yesterday's Low − Today's Low if Up Move > Down Move and Up Move > 0, then +DM = Up Move, else +DM = 0 if Down Move > Up Move and Down Move > 0, then -DM = Down Move, else -DM = 0 After selecting the number of periods (Wilder used 14 days originally), +DI and -DI are: +DI = exponential moving average of +DM divided by Average True Range -DI = exponential moving average of -DM divided by Average True Range The exponential moving average is calculated over the number of periods selected and the average true range is an exponential average of the true ranges. Then: ADX = 100 times the exponential moving average of the Absolute value of (+DI − -DI) divided by (+DI + -DI) Variations of this calculation typically involve using different types of moving averages, such as a weighted moving average or an adaptive moving average. When The ADX indicator does not grade the trend as bullish or bearish, but merely assesses the strength of the current trend. A reading above 40 can indicate a strong downtrend as well as a strong uptrend. ADX can also be used to identify potential changes in a market from trending to non-trending. When ADX begins to strengthen from below 20 and moves above 20, it is a sign that the trading range is ending and a trend may be developing. When ADX begins to weaken from above 40 and moves below 40, it is a sign that the current trend is losing strength and a trading range could develop.
How to add this to your Charts in eBridge Trader Whilst in your workspace viewing a chart, click this then >>> Moving Averages >>> Moving Average:
symbol at the top of the chart,
The default fields that appear can be adjusted at this point for this indicator; you can also adjust them later if you wish. You can also adjust colour and the weight of the line:
Click OK once you’re satisfied with the settings. You will now see the ADX added to your chart. You can later adjust settings by clicking the ‘ADX#’ at the top left of the lower pane and then clicking on ‘Properties’, you can also delete the indicator from this menu If there is any feature of eBridge Trader that you’d like covered in next months newsletter please email your request to: firstname.lastname@example.org
To open a eBridge Trader account or get further information on adding technical indicators to charts using eBridge Trader please contact Jason Achjian by Email: Jason.email@example.com Phone: 1300-73-66-11.
William Chien http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42 http://www.toptraderthinking.com/toptrader/shop.asp?i d=42
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In edition #9 of Top Trader Thinking Your Markets Monthlyâ€Ś
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Psychology versus Emotion (From Bullseye - Top Trader Thinking)
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General magazine cover trading in financial markets, recommendations, technical analysis & trader psychology
Published on Feb 12, 2010
General magazine cover trading in financial markets, recommendations, technical analysis & trader psychology