Would you trade this system?

[howardbandy]

Hi Howard,

I understand and I suspect that most of us here at ASF also understand the importance of the walk forward methodology using out of sample data in verifying the robustness of the system going forward. The concept is a very sound one and we thank you for your patience.

The one area that I personally like to see being discussed more is this concept of system decay, and how over time, the system will lose its edge and will ultimately fail. Logic would indicate (to me at least) that as more and more people trade a particular type of system, say a 30 day breakout system, that the system would have a greater chance of succeeding as an ever increasing number of traders initiate the trade upon confirmation of the breakout, to a point where it almost becomes a self-fulfilling prophecy. How then does such a system lose its edge over time ?

Does a system tend to fail as more traders use it ?? If the answer is yes then can one conclude that the system would more effective if it is used by fewer traders ?

When a system starts to lose its edge (presumably from over-exposure from too many traders using it) the number of traders using it will also tend to decrease over time as they seek out other more profitable strategies. When the number of traders have dropped off sufficiently, will the edge that the system had initially return ?? If the answer is NO, then what residual external factor that had caused the system to initially fail has remained in play to continually impact on the system even though the number of traders using it has dropped off dramatically ?

I can’t get my head around this system decay concept and hope that you may be able to shed some light on the subject.

Hi Bing --

The post I just made to points Sir Burr raised address some of your question.

To illustrate why having more traders trade a system makes it more difficult for any one of them to make a profit, think about what the order book looks like. Say a stock is trading at $100 and headed for $105. The best bid may be for 200 shares at 99.98 and the best offer for 100 shares at 100.02. There are other offers of 100 shares each at 100.05, 100.10, 100.25, and 100.50. There are more bids, but they are lower and will not play here.

Say there is one buy signal from a program that is generally correct. 100 shares are bought at the market -- 100.02.

Instead, say five buys signals arrive. Those trades take place at 100.02, 100.05, 100.10, 100.25, and 100.50.

Eventually, the system issues a sell signal. The same thing happens, but now with prices on the bid side. One order gets filled at the best bid, the other get worse fills.

Wait, I hear you say, I use limit orders to avoid that slippage -- I'll buy at 100.06 limit. If your order came in third, your limit order of 100.05 never got filled and you get no profit at all.

My point is this -- the more systems that want to buy this stock, the worse their average performance will be.

Thanks,
Howard

 

[howardbandy]

Hi Tech/a --

You are asking about trends and getting enough out-of-sample results to validate a long-term trading system.

First about trends. I agree that every trade needs a trend to be profitable. Buy low and sell high; buy high and sell higher; short high and cover low; short low and cover lower. Those are the only four profitable cases if we are dealing with prices.

The time frame makes a difference. What looks like a trading range market on a weekly chart could have nice trends on an hourly chart.

About validating long-term trading. If the system has long holding periods, or if it trades infrequently, it will be difficult validating it using a single price series. But the out-of-sample data does not have to come from the same price series. A system could be developed using one stock, then validated using other stocks.

But -- beware -- avoid "optimizing the symbol space." If I develop a system using ABC, and wish to use other stocks to validate the system, then I can test using 100 stocks. If the system is acceptable (high objective function score) for most of them, that will help validate the system. If I find it is profitable for only 5 stocks and I pick those 5 to trade, I am at great risk of losing money. Almost any trading system will show a profit for some stock for some time period when tested on a large number of stocks over a long time period -- just by being lucky.

If the long-term or infrequent system does not validate well, even using this method, do not trade it.

The validation process is one of building confidence. If we want to build confidence, we need to see lots of examples of success. Even then, there are no guarantees.

Thanks,
Howard

 

[nizar]

The one area that I personally like to see being discussed more is this concept of system decay, and how over time, the system will lose its edge and will ultimately fail. Logic would indicate (to me at least) that as more and more people trade a particular type of system, say a 30 day breakout system, that the system would have a greater chance of succeeding as an ever increasing number of traders initiate the trade upon confirmation of the breakout, to a point where it almost becomes a self-fulfilling prophecy. How then does such a system lose its edge over time ?
Does a system tend to fail as more traders use it ?? If the answer is yes then can one conclude that the system would more effective if it is used by fewer traders ?

Hi Bingk6.

I notice the question wasnt directed at me, but ill give my view anyway.

If an ever increasing number of traders want to BUY the open, then you will have to pay a higher price.

If an ever increasing number of traders want to SELL the open, when stops/exits are triggered, then you will have to sell at a lower price.

 

[nizar]

Anyway, back to the main purpose of this thread.

Iv decided to go with a weekly system instead of EOD.
Easier to run and monitor and gives more flexibility for trade execution.

This new system has no initial stop.
Simply because i could find an initial stop that would improve its performance.

The exit is an ATR type. Initially i went with 3*ATR, but found that 3.5*ATR works better.

This time i have factored in slippage.
TradeSim (for those not familiar) allows for "market orders" which means a random algorithm is used to determine an entry/exit price within the lows and highs of that bar. Im trading weekly, so a bar is 1 week.

This basically means i can enter at any time during the week during testing and also in real-time when the system is traded live.

Starting capital has increased to $45,000 because Iv just got a new client, a family member who wants to put $15k into the system.

Money management is set to $7,500 per trade.
Fixed risk based money management could not be used due to the absence of an initial stop.
I could make the trailing stop as an initial stop but have not yet found out how to do this.

10% of available trading capital was another alternative that was tried, and though I can understand the concept with having bigger positions as your capital base grows and less when it shrinks, its use in this system produces returns and drawdowns were more variable.

And another thing i didnt like was the equity curve, as the capital base grew towards the end of the simulation, most of its profits came from 1-2 big trades.

We didnt see this with the $7,500 model.

We will start with the 10-year simulation from 1992-2002 over the Entire ASX market (Liquidity filter $1mil per week was applied).

As usual, im looking for the same thing as initially.
Feedback!

Thanks all.

 

[nizar]

Monte Carlo Report

Trade Database Filename
C:\TradeSimData\WeeklyMaster3.5ATRwithROC.trb

Simulation Summary
Simulation Date: 18/09/2007
Simulation Time: 12:50:42 AM
Simulation Duration: 1928.19 seconds

Trade Parameters
Initial Capital: $45,000.00
Portfolio Limit: 100.00%
Maximum number of open positions: 40
Position Size Model: Equal Dollar Units
Trade Size ($ value): $7,500.00
Pyramid profits: Yes
Transaction cost (Trade Entry): $55.00
Transaction cost (Trade Exit): $55.00
Margin Requirement: 100.00%
Magnify Position Size(& Risk) according to Margin Req: No
Margin Requirement Daily Interest Rate (Long Trades): 0.0000%
Margin Requirement Yearly Interest Rate (Long Trades): 0.0000%
Margin Requirement Daily Interest Rate (Short Trades): 0.0000%
Margin Requirement Yearly Interest Rate (Short Trades): 0.0000%

Trade Preferences
Trading Instrument: Stocks
Break Even Trades: Process separately
Trade Position Type: Process all trades
Entry Order Type: Market Order
Exit Order Type: Market Order
Minimum Trade Size: $500.00
Accept Partial Trades: No
Volume Filter: Reject Trades if Position Size is greater than
10.00% of the maximum traded volume
Pyramid Trades: Yes
Favour Trade Pyramid: Yes
Start Pyramid at any level up to level: N/A
Maximum Pyramid Level Limited to: N/A
Maximum Pyramid Count Limited to: N/A

Simulation Stats
Number of trade simulations: 20000
Trades processed per simulation: 3341
Maximum Number of Trades Executed: 340
Average Number of Trades Executed: 319
Minimum Number of Trades Executed: 300
Standard Deviation: 5.17

Profit Stats
Maximum Profit: $1,191,944.64 (2648.77%)
Average Profit: $1,061,127.72 (2358.06%)
Minimum Profit: $863,892.51 (1919.76%)
Standard Deviation: $31,838.02 (70.75%)
Probability of Profit: 100.00%
Probability of Loss: 0.00%

Percent Winning Trade Stats
Maximum percentage of winning trades: 55.76%
Average percentage of winning trades: 51.73%
Minimum percentage of winning trades: 45.85%
Standard Deviation: 1.26%

Percent Losing Trade Stats
Maximum percentage of losing trades: 54.15%
Average percentage of losing Trades: 48.27%
Minimum percentage of losing trades: 44.24%
Standard Deviation: 1.26%

Average Relative Dollar Drawdown Stats
Maximum of the Average Relative Dollar Drawdown: $2,735.05
Average of the Average Relative Dollar Drawdown: $1,915.68
Minimum of the Average Relative Dollar Drawdown: $1,410.19
Standard Deviation: $149.11

Average Relative Percent Drawdown Stats
Maximum of the Average Relative Percent Drawdown: 0.9390%
Average of the Average Relative Percent Drawdown: 0.6321%
Minimum of the Average Relative Percent Drawdown: 0.4531%
Standard Deviation: 0.0570%

Maximum Peak-to-Valley Dollar Drawdown Stats
Maximum Absolute Dollar Drawdown: $36,667.97
Average Absolute Dollar Drawdown: $25,278.71
Minimum Absolute Dollar Drawdown: $19,736.76
Standard Deviation: $1,893.04

Maximum Peak-to-Valley Percent Drawdown Stats
Maximum Absolute Percent Drawdown: 15.7769%
Average Absolute Percent Drawdown: 12.8032%
Minimum Absolute Percent Drawdown: 9.5921%
Standard Deviation: 0.9085%

 

[nizar]

Detailed Report

Trade Database Filename
C:\TradeSimData\WeeklyMaster3.5ATRwithROCbug.trb

Simulation Summary
Simulation Date: 18/09/2007
Simulation Time: 11:27:28 PM
Simulation Duration: 1.00 seconds

Trade Summary
Earliest Entry Date in the Trade Database: 3/01/1992
Latest Entry Date in the Trade Database: 14/12/2001
Earliest Exit Date in the Trade Database: 3/04/1992
Latest Exit Date in the Trade Database: 28/12/2001

Start Trade Entry Date: 3/01/1992
Stop Trade Entry Date: 14/12/2001
First Entry Date: 3/01/1992
Last Entry Date: 30/11/2001
First Exit Date: 3/04/1992
Last Exit Date: 28/12/2001

Total Trading duration: 3647 days

Profit Summary
Profit Status: PROFITABLE
Starting Capital: $45,000.00
Finishing Capital: $1,138,647.70
Maximum Equity/(Date): $1,093,647.70 (28/12/2001)
Minimum Equity/(Date): -$2,528.90 (24/09/1992)
Gross Trade Profit: $1,258,490.90 (2796.65%)
Gross Trade Loss: -$164,843.20 (-366.32%)
Total Net Profit: $1,093,647.70 (2430.33%)
Average Profit per Trade: $3,439.14
Profit Factor: 7.6345
Profit Index: 86.90%
Total Transaction Cost: $34,980.00
Total Slippage: -$9,099.26
Total Trade Interest: $0.00
Daily Compound Interest Rate: 0.0886%
Annualized Compound Interest Rate: 38.1762%

Trade Statistics
Trades Processed: 3337
Trades Taken: 318
Partial Trades Taken: 0
Trades Rejected: 1537
Winning Trades: 163 (51.26%)
Losing Trades: 155 (48.74%)
Breakeven Trades: 0 (0.00%)

Largest Winning Trade/(Date): $55,456.23 (14/04/2000)
Largest Losing Trade/(Date): -$5,279.72 (18/07/1997)
Average Winning Trade: $7,720.80
Average Losing Trade: -$1,063.50
Average Win/Average Loss: 7.2598

Trade Breakdown Long and Short Trades Long Trades Short Trades
Normal Exit: 313 (98.43%) 313 (98.43%) 0 (0.00%)
Open Trade: 5 (1.57%) 5 (1.57%) 0 (0.00%)

Total Trades: 318 (100.00%) 318 (100.00%) 0 (0.00%)

Trade Duration Statistics Winning and Losing Trades Winning Trades Losing Trades
Maximum Trade Duration: 1400 (days) 1400 (days) 434 (days)
Minimum Trade Duration: 0 (days) 0 (days) 20 (days)
Average Trade Duration: 258.38 (days) 393.28 (days) 116.52 (days)

Consecutive Trade Statistics
Maximum consecutive winning trades: 12
Maximum consecutive losing trades: 15
Average consecutive winning trades: 3.54
Average consecutive losing trades: 3.37

Relative Drawdown
Maximum Dollar Drawdown/(Date): $16,382.99 (6/10/2000)
Maximum Percentage Drawdown/(Date): 6.1090% (24/09/1992)

Absolute (Peak-to-Valley) Dollar Drawdown
Maximum Dollar Drawdown: $26,839.17 (3.3520%)
Capital Peak/(Date): $800,604.67 (28/04/2000)
Capital Valley/(Date): $773,765.50 (2/03/2001)

Absolute (Peak-to-Valley) Percent Drawdown
Maximum Percentage Drawdown: 11.3500% ($22,212.40)
Capital Peak/(Date): $195,684.10 (11/03/1994)
Capital Valley/(Date): $173,471.70 (20/10/1995)

 

[nizar]

Attached are some charts.

Just some notes about the charts created by TradeSim.
The yearly returns charts are on CLOSED equity.
So money in the bank.

What you dont want to see with these is most of the profit coming in 2 or 3 years out of the 10.
Yet thats exactly what it looks like!!

But actually its not the case. I'll explain why.

Going by the chart, id say the system actually made money in 1992 (open profit), and again in 1993 (open profit), as winning trade length is over a year, the big winners would not have been closed out until the exit was hit.

Come 1994, which was a bad year for the market, and what happens, all the exits get hit, and all the open profits from the previous years get locked in and you have a massive year.

Anyway, im in the middle of running single year simulations, and instructing tradeSim to close out all the open trades at the end of the calendar year, so that should give a better picture of the returns.

 

[nizar]

Okay attached is an Excel Spreadsheet with my own version of the Yearly Returns of the system.

Note the returns are straight returns, non-compounded.

I basically ran a M/S exploration for every year and then put it through 20,000 Monte Carlo Simulations. Average profit and max. DD values were recorded, as was probability of Profit and average number of trades executed.

For the purposes of comparison, I have included the market index returns.

Earlier on in the test, 1992-1995, $1mil weekly turnover filter that I use was pretty restrictive. Monte Carlo didnt give much variation.

Its not really fair to test a long term system over a 1-year period as most of the big winners run for much longer, but I feel this chart gives a more accurate representation of Annual Returns than the default TradeSim one, as this one reports TOTAL profit in the portfolio at the end of December as all positions are closed.

 

[theasxgorilla]

I can't help but wonder if with such a long term system taking so few trades and pyramiding into the longer term winners if such a system wouldn't be best suited to ETFs traded with margin.

If you are back testing on ASX data and you pyramid into winners then in all likelihood you are going to end up with the lion share of your capital in stocks that ended up in the ASX20...or am I missing something?

ASX.G

 

[Shane Baker]

Hi ASXG/ Nizar,

I can't help but wonder if with such a long term system taking so few trades and pyramiding into the longer term winners if such a system wouldn't be best suited to ETFs traded with margin.

If you are back testing on ASX data and you pyramid into winners then in all likelihood you are going to end up with the lion share of your capital in stocks that ended up in the ASX20...or am I missing something?

I thought I might outline the basic design principles that I might use in such a system. To my mind is that you are looking for outsize winners. This means that we would need to do research to see where the smooth long running trends may come from. Also as we may wish to use margin we might select a group of share that are marginable as you suggest eg asx 300. The selection of higher cap shares v speccies would also be helpful in exiting large parcels at reasonable slippage.

Once you have narrowed down your likely universe ie marginable shares over the asx 300 as an example, then you need to analyse where the big trends come from. Are they is the higher cap shares or the lower? Are there shares that perhaps are regularly poor trenders because they may yield plays such as property trusts or Telstra? Remember we are looking for 10 baggers here.

When you look at these you may find that the lower priced shares have a greater propensity for long trends. This may give you a ranking criteria or price filter for share selection.

Ultimately you have a filtered universe of shares with a higher probability than the overall market to exhibit some major trends eg CTX, GNS, GUD, WOR, etc etc.

Now you need to design your system to match the criteria you are trying to capture. You won't need the very beginning of a trend since you hope the trend will last at least a year if not longer. So you could design criteria that improves the winning percentage by entering trends slightly later. You will need a stop that is wide enough to capture the bulk of most long running trends such as a 50 week wma for example.

Once you have this you can look at the placement of your initial stop. I like to use a fixed $ allocation at this point and place an initial stop at the trailing stop. Plotting the R multiple returns against the initial stop percentage usually will show some form of cluster formation that may guide you in placing the level of your stop. If you prefer atr based stops then you may choose shares that teh atr values do not exceed a certain percentage

eg 2*atr(14)/C<0.15

would show shares that the 2 atr value was less than 15% of price.

Now we have a price point level at which we can aggressively pyramid. If our initial stop is 15% then we could pyramid every 15% as our original position moves past breakeven.

As this is a long side only system we might also want to incorporate and index filter to keep us from entering trades when the market is being unfavorable.

This is just a small part of design and structure of a system. Hopefully it helps outline some of the ideas in a more coherent fashion.

I had a long look at sector funds a while ago and my research showed that sector rotation strategies seemed to work better in these than aggressive pyramiding strategies. The reasons were related to money management issues. Of course I may revisit that and change my mind as we all do when new ideas or hybrid concepts come to fruition.

Hope this helps

Cheers

Shane

 

[nizar]

As this is a long side only system we might also want to incorporate and index filter to keep us from entering trades when the market is being unfavorable.

Shane.
Thanks for your thoughts.

Just with index filters, have you found them useful in the past?
Do they improve profit/drawdowns?

The feedback Iv been getting is that while they keep you out of the market, you are often late to get back in and miss the often aggressive rebound.

 

[Shane Baker]

Hi Nizar

Obviously it depends upon your timeframe for trading and the filter that you choose. Assuming weekly system, I have found that they are very adept at reducing drawdown and flat time but this comes at a price of reducing profits. However IMHO I feel that it is a more robust tradeoff. This is due to in part to the asymmetrical relationship between percentage drawdown and the amount need to return to the previous equity peak high

Howard Bandy alluded to the fact that it is less reliable to maximise for profit over other parameters. I agree with Howard. I quite enjoyed his book and recommend it for budding system developers.

Cheers

Shane

 

[stevo]

The feedback Iv been getting is that while they keep you out of the market, you are often late to get back in and miss the often aggressive rebound.

If getting in late is considered a problem then consider making the filter very sensitive to index moves - I wouldn't suggest using a 100 week moving average. Something simple like 1 period % change criteria or even just straight price moves - like close is greater than last weeks high to turn on the system and close is less than last week's low to turn off system.

Just some suggestions - I am not saying that these ideas work. Remember that the index filter might only turn the system on and off, not necessarily trigger exits, and also that a buy signal also has to be triggered when the index filter is "on".

I agree with Shane - they can reduce profits a little. But you can end up making substantially less trades to get not that much less money. I have been through 3 periods in the last 5 years where I didn't get a buy for 2 to 3 months - I am talking longer term systems and I would still have trades running, just no buy signals. Annual return is not the only criteria to consider - Personally drawdown is just as big an issue.

Also if you try the test with and without trade and / or profit pyramiding what happens to drawdown and profits?

stevo

 

[nizar]

Also if you try the test with and without trade and / or profit pyramiding what happens to drawdown and profits?

Thanks for your points.

Without trade pyramiding, win% decreases, average win/average loss decreases, and consequently profits are decreased.
Drawdown about the same.

Note that when i do pyramid, i do choose the option to "favour pyramid trades" as this produces better results (more profit, less maximum drawdown).

Without profit pyramiding, win% increases, profits are halved, drawdowns are halved, number of trades taken are MUCH less.

I was always planning to pyramid profits from the outset, reason being, the money i put into the system is not money i would be needing again for the forseeable future. So I just want to reinvest everything and let it compound.

 

[nizar]

If getting in late is considered a problem then consider making the filter very sensitive to index moves - I wouldn't suggest using a 100 week moving average. Something simple like 1 period % change criteria or even just straight price moves - like close is greater than last weeks high to turn on the system and close is less than last week's low to turn off system.

Just some suggestions - I am not saying that these ideas work. Remember that the index filter might only turn the system on and off, not necessarily trigger exits, and also that a buy signal also has to be triggered when the index filter is "on".

I agree with Shane - they can reduce profits a little. But you can end up making substantially less trades to get not that much less money. I have been through 3 periods in the last 5 years where I didn't get a buy for 2 to 3 months - I am talking longer term systems and I would still have trades running, just no buy signals. Annual return is not the only criteria to consider - Personally drawdown is just as big an issue.

Stevo.

The index filter that you use in which you only buy entry triggers when the index meets certain conditions, is useful in decreasing CLOSED equity drawdowns yeh?

Because leaving open trades to hit their exit in times the system is effectively turned off I suspect will not do much help in decreasing open equity drawdown as these open trades will get hit by the overall market downturn.

In which ways does your system attempt to decrease open equity drawdowns?

There are 2 methods that come to mind:
1/ If the stock hasnt moved X% in n-period then chop it. Useful for stocks then were trending upwards then flattened.

2/ If using an ATR exit, you could apply a condition where if the placement of the ATR exit hasnt moved in n-periods, then chop it.

How to code them for backtesting is another matter!!

Nizar.

 

[theasxgorilla]

The index filter that you use in which you only buy entry triggers when the index meets certain conditions, is useful in decreasing CLOSED equity drawdowns yeh?

My thoughts...I think this is the case...you are less likely to take a new trade that moves against you and depletes closed equity when said index is in gear. That is my understanding of the concept.

In which ways does your system attempt to decrease open equity drawdowns?

There are 2 methods that come to mind:
1/ If the stock hasnt moved X% in n-period then chop it. Useful for stocks then were trending upwards then flattened.

2/ If using an ATR exit, you could apply a condition where if the placement of the ATR exit hasnt moved in n-periods, then chop it.

Would it be fair to assume that most severe open equity drawdowns occur at the end of trends? This has been my observation. And the more explosive the blow-off rally at the end the greater the likelihood that you will see a large measured open equity drawdown during the subsequent collapse. When testing '97 - '07 my worst drawdowns are always May 06 and August 07...pretty much the time when the system also performs at its best.

I think the trick is to have a stop in place that captures as much of a blow off rally as possible, if/when it occurs, whilst not over-tightening and inadvertently stopping you out of a trend too soon. Because of the way they are pulled up rapidly with price I have found that ATR based stops are most effective at this (or some other volatility derived stop anchored to an element of price).

If scenario 1 occurs ie. volatility contracts and the trend flattens first, rather than collapses, I have found that EMA based stops can be more effective. Whilst an ATR stop (particularly a non-ratcheting stop) will keep its distance an EMA based stop continues to approach price and 'tighten' as a function of how it works. So in effect as time passes you give the stock less and less downside room to prove itself.

So the answer may not be a case of one or the other.

 

[nizar]

Firstly thanks for everyones thoughts and opinions.
Its all been taken into account.

So now i have the latest version of my weekly system ready.

Just some points to made:
(1) Entry has been changed. Im no longer buying all-time highs primarily because iv only just realised that i cannot accurate backtest this strategy with the amount of data that I have.
(2) Backtesting now always starts from 1998. Starting from 1992 iv decided is too far not only in terms of the accuracy of the data but that its more likely that the market dynamics may have changed.
(3) My ATR trailing exit used to be triggered by the LOW of the price of the bar. So an intra-week spike wouldve stopped me out the following week even if the close was above the stop. This is probably why my stop needed to be so wide and consequently i was giving back alot of profit. This has been changed so that the stop is only triggered by the weekly CLOSE.
(4) Due to point (3) a tighter stop could then be applied and would still keep my in most of the trends. ie. 2.5*ATR instead of 3.5*ATR.
(5) Iv adopted the idea by Shane:

If you prefer atr based stops then you may choose shares that teh atr values do not exceed a certain percentage

eg 2*atr(14)/C<0.15

would show shares that the 2 atr value was less than 15% of price.

Now we have a price point level at which we can aggressively pyramid. If our initial stop is 15% then we could pyramid every 15% as our original position moves past breakeven.

So pyramiding only triggered when there is at least 15% profit on the previous pyramid level (including level 0).
Before there was only a trigger function.
Trigger + Percent Profit together does not seem to improve any of the stats compared to Percernt Profit alone.
(6) Initial Stop has been set at the trailing exit level for money management purposes.
I have set this to 1.5% for the following tests.

 

[nizar]

Monte Carlo Report

Trade Database Filename
C:\TradeSimData\NewWeeklyMASTER 1998-2003 15pc 2ATR Turtle Entry Actual 10.trb

Simulation Summary
Simulation Date: 23/09/2007
Simulation Time: 8:37:23 AM
Simulation Duration: 286.52 seconds

Trade Parameters
Initial Capital: $45,000.00
Portfolio Limit: 100.00%
Maximum number of open positions: 100
Position Size Model: Fixed Percent Risk
Percentage of capital risked per trade: 1.50%
Position size limit: 100.00%
Portfolio Heat: 100.00%
Pyramid profits: Yes
Transaction cost (Trade Entry): $22.00
Transaction cost (Trade Exit): $22.00
Margin Requirement: 100.00%
Magnify Position Size(& Risk) according to Margin Req: No
Margin Requirement Daily Interest Rate (Long Trades): 0.0000%
Margin Requirement Yearly Interest Rate (Long Trades): 0.0000%
Margin Requirement Daily Interest Rate (Short Trades): 0.0000%
Margin Requirement Yearly Interest Rate (Short Trades): 0.0000%

Trade Preferences
Trading Instrument: Stocks
Break Even Trades: Process separately
Trade Position Type: Process all trades
Entry Order Type: Market Order
Exit Order Type: Market Order
Minimum Trade Size: $500.00
Accept Partial Trades: No
Volume Filter: Reject Trades if Position Size is greater than
10.00% of the maximum traded volume
Pyramid Trades: Yes
Favour Trade Pyramid: Yes
Start Pyramid at any level up to level: N/A
Maximum Pyramid Level Limited to: N/A
Maximum Pyramid Count Limited to: N/A

Simulation Stats
Number of trade simulations: 20000
Trades processed per simulation: 1265
Maximum Number of Trades Executed: 179
Average Number of Trades Executed: 158
Minimum Number of Trades Executed: 138
Standard Deviation: 5.21

Profit Stats
Maximum Profit: $421,785.64 (937.30%)
Average Profit: $206,605.81 (459.12%)
Minimum Profit: $105,442.93 (234.32%)
Standard Deviation: $34,823.52 (77.39%)
Probability of Profit: 100.00%
Probability of Loss: 0.00%

Percent Winning Trade Stats
Maximum percentage of winning trades: 62.58%
Average percentage of winning trades: 52.10%
Minimum percentage of winning trades: 41.62%
Standard Deviation: 2.64%

Percent Losing Trade Stats
Maximum percentage of losing trades: 58.38%
Average percentage of losing Trades: 47.90%
Minimum percentage of losing trades: 37.42%
Standard Deviation: 2.64%

Average Relative Dollar Drawdown Stats
Maximum of the Average Relative Dollar Drawdown: $2,503.85
Average of the Average Relative Dollar Drawdown: $1,119.33
Minimum of the Average Relative Dollar Drawdown: $584.95
Standard Deviation: $213.27

Average Relative Percent Drawdown Stats
Maximum of the Average Relative Percent Drawdown: 1.7230%
Average of the Average Relative Percent Drawdown: 0.9085%
Minimum of the Average Relative Percent Drawdown: 0.5263%
Standard Deviation: 0.1387%

Maximum Peak-to-Valley Dollar Drawdown Stats
Maximum Absolute Dollar Drawdown: $60,067.90
Average Absolute Dollar Drawdown: $16,956.65
Minimum Absolute Dollar Drawdown: $4,375.91
Standard Deviation: $6,799.79

Maximum Peak-to-Valley Percent Drawdown Stats
Maximum Absolute Percent Drawdown: 27.8473%
Average Absolute Percent Drawdown: 9.8650%
Minimum Absolute Percent Drawdown: 5.0345%
Standard Deviation: 2.2823%

 

[nizar]

I forgot to add.
Testing period is on the whole ASX ($1million turnover per week liquidity filter) from 01-01-1998 until 31-12-2003 (6 years).


Detailed Report

Trade Database Filename
C:\TradeSimData\NewWeeklyMASTER 1998-2003 15pc 2ATR Turtle Entry Actual 10.trb

Simulation Summary
Simulation Date: 23/09/2007
Simulation Time: 9:00:16 AM
Simulation Duration: 0.45 seconds

Trade Summary
Earliest Entry Date in the Trade Database: 2/01/1998
Latest Entry Date in the Trade Database: 12/12/2003
Earliest Exit Date in the Trade Database: 20/02/1998
Latest Exit Date in the Trade Database: 9/01/2004

Start Trade Entry Date: 2/01/1998
Stop Trade Entry Date: 12/12/2003
First Entry Date: 2/01/1998
Last Entry Date: 28/11/2003
First Exit Date: 9/04/1998
Last Exit Date: 24/12/2003

Total Trading duration: 2182 days

Profit Summary
Profit Status: PROFITABLE
Starting Capital: $45,000.00
Finishing Capital: $281,900.17
Maximum Equity/(Date): $236,900.17 (24/12/2003)
Minimum Equity/(Date): $687.26 (9/04/1998)
Gross Trade Profit: $328,373.16 (729.72%)
Gross Trade Loss: -$91,472.99 (-203.27%)
Total Net Profit: $236,900.17 (526.44%)
Average Profit per Trade: $1,548.37
Profit Factor: 3.5898
Profit Index: 72.14%
Total Transaction Cost: $6,732.00
Total Slippage: $77,130.18
Total Trade Interest: $0.00
Daily Compound Interest Rate: 0.0841%
Annualized Compound Interest Rate: 35.9254%

Trade Statistics
Trades Processed: 1271
Trades Taken: 153
Partial Trades Taken: 0
Trades Rejected: 739
Winning Trades: 82 (53.59%)
Losing Trades: 71 (46.41%)
Breakeven Trades: 0 (0.00%)

Largest Winning Trade/(Date): $27,505.82 (7/12/2001)
Largest Losing Trade/(Date): -$4,233.32 (28/06/2002)
Average Winning Trade: $4,004.55
Average Losing Trade: -$1,288.35
Average Win/Average Loss: 3.1083

Trade Breakdown Long and Short Trades Long Trades Short Trades
Normal Exit: 121 (79.08%) 121 (79.08%) 0 (0.00%)
Protective Stop: 18 (11.76%) 18 (11.76%) 0 (0.00%)
Open Trade: 14 (9.15%) 14 (9.15%) 0 (0.00%)

Total Trades: 153 (100.00%) 153 (100.00%) 0 (0.00%)

Trade Duration Statistics Winning and Losing Trades Winning Trades Losing Trades
Maximum Trade Duration: 1239 (days) 1239 (days) 245 (days)
Minimum Trade Duration: 0 (days) 20 (days) 0 (days)
Average Trade Duration: 138.98 (days) 203.99 (days) 63.90 (days)

Consecutive Trade Statistics
Maximum consecutive winning trades: 8
Maximum consecutive losing trades: 6
Average consecutive winning trades: 2.22
Average consecutive losing trades: 1.97

Trade Expectation Statistics
Normalized Expectation per dollar risked: $1.33
Maximum Reward/Risk ratio: 35.43
Minimum Reward/Risk ratio: -1.23
Average Positive Reward/Risk ratio: $3.00
Average Negative Reward/Risk ratio: -$0.59

Relative Drawdown
Maximum Dollar Drawdown/(Date): $13,533.74 (5/07/2002)
Maximum Percentage Drawdown/(Date): 5.9480% (5/07/2002)

Absolute (Peak-to-Valley) Dollar Drawdown
Maximum Dollar Drawdown: $24,202.46 (10.5600%)
Capital Peak/(Date): $229,178.79 (5/04/2002)
Capital Valley/(Date): $204,976.33 (14/03/2003)

Absolute (Peak-to-Valley) Percent Drawdown
Maximum Percentage Drawdown: 10.5600% ($24,202.46)
Capital Peak/(Date): $229,178.79 (5/04/2002)
Capital Valley/(Date): $204,976.33 (14/03/2003)

 

[nizar]

And the charts.