Obviously after the last test this daytrader would know that with him winning 80% of the time and having a higher win size than loss size he'd be rich in record time. He'd now know that he has more than a 99% chance of making a profit each and every day he trades. What do think his psychology might be like. How about ..."time to trade can't wait to see how much I make today". Notice the expectancy didn't change from this test to the last one. I did this to show that expectancy really isn't a critical component of being consistently profitable. Remember a couple of posts ago I posted the expected profit factor. In this case it worked out as: Epf = (PW * AW) / (PL * AL) Epf = (.8 * 500) / (.2 * 400) Epf = (400) / (80) Epf = 6 or the 50% level described in the test Now that we've seen the expectancy didn't improve the likelihood of achieving consistency, what effect did changing the expected profit factor. We can use the equation to keep the same win % and win size to solve for the old PF of 2.33 at the 50% level as in the previous test. 2.33 = (.8 * 500) / (.2 * AL) 2.33 = 400/.2AL 2.33*.2AL = 400 .2AL = 400/2.33 or 171.67 AL = 171.67 / .2 or 858.35 When we plug in the new test of 80% winners, $500 win for each winner and 858.35 for each loser (to keep the profit factor at 2.33) we get these results.
From this past test we can see that if we kept the profit factor the same but changed the win % and expectancy, we'd have the same confidence level as we started with 80%. From this we can tell the win % and expectancy are not critical to consistency. One of the keys that is important is the expected profit factor. The higher the profit factor, the more liklely we are to achieve consistent profitabilty. What would have happened if instead of changing the win % we just changed from 10 trades per day to 20 trades per day. Then our daytrader would have a win% of 70%, win of $500, loss of $500 and twice as many opportunities per-day. Here's the results of that test.
As you can see from this last test by increasing the number of trades from 10 to 20 and keeping the profit factor, win %, and expectancy the same we've improved the confidence level to above 95%. So if our daytrader wanted to be 95% confident that he'd make money every day he could have also increased the number of trades per-day to achieve his goal. Now we have two variables that have an impact on consistent profitability (profit factor and frequency of trades). If our daytrader wasn't using strict targets and let the profits fluctuate and used a trailing stop in addition to the initial stop we'd increase the dispersion of the trades (std. deviation). For this example I'm using our 20 trades, 70% winners, $500 win, $500 loss, and letting the std. deviation of winners grow to $500 (100% of average) and std. deviation of losers grow to $250 (50% of average). This will let you see the effect of dispersion of outcomes on consistency. As you can see, by letting the winners ride a little and dragging a stop loss behind, the trade effect of the disperison of the trades on the overall results was there but not very important in impacting the consistency we're looking for.
The main idea of this first series of posts was to show what is important in moving toward becoming a consistent winning trader. By choosing a timeframe and then working on both the profit factor and number of trades you can move toward the goal of consistent profitability. Here's some rough estimates of the relationship between trade frequency and needed profit factor to achieve a 95% level of confidence that you'll be profitable within a timeframe for a single method. # trades......profit factor needed 10............4.00 20............2.50 30............2.00 40............1.75 60............1.50 So, if you wanted to be profitable every week at the 95% confidence level (you'd still have about 3 losing weeks per-year), and all your method could produce was a 1.50 profit factor, then you'd need 60 trades out of it to achieve your goal. From this you can tell that to achieve consistent profitability on a daily basis, either you've got some miracle system or you trade like a madman. In the next series of posts I'll go over using multiple systems to improve consistency. That's all I'm going to post today. I'll be in and out at least through the first hour of trading for the SP today, so if anyone has any questions on this topic I'll try to answer them. Have a good day!
Acrary, Amazing stuff. " Model name daytrade # of trades in series 10 % of trades that are winners 70 Mean of winning trades 500 Std. Dev. of winning trades 0 Mean of losing trades 500 Std. Dev. of losing trades 0 Outcome Profit Factor Max DD 1% level 5,000.00 10.00 0 5% level 4,000.00 9.00 -500 10% level 4,000.00 9.00 -500" Could you please tell us, how did you get the probability of $5000 with 1% level.? Did you use some kind of Montecarlo analysis. Thx
It's called a Monte Carlo Var Analysis. If you do search on Google you should find lots of info. For these tests I used a normal distribution curve for the results. I have another version that I use to simulate fat tails (which is slightly more representative of actual trading). For the big picture types of tests this one works well and is easy for others to replicate. To get an idea of how accurate this is, here's a test for one of my reject models called dsc389. I ran the test for monthly numbers (only 10 trades per-month), so I can show the dsc389 numbers from tradestation for comparison.
From the test you can see it should win 70-80% of the time each month, have a monthly max drawdown of about 16k and a max profit of about 33k. Here's the graph of the monthly results for the past 5 years. The red indicates losing months and the green are winning months. As you can see there were 12 losing months out of 60 for 80% win rate, the actual max profit month was about 34k and the largest losing month was about 24k. I attribute the large losing month outside of the norm to the fat tail effect in the markets and is one of the reasons why I came up with another version to simulate the fat tails.
Just trying to follow the math and got stumped on these equations til I noticed you used AW = 500 rather than AW = 600. If you use AW = 600, Epf = 6. But if AW = 500, Epf = 5.
Yes, you're right. On the post with the daytrade4.txt the Epf should be: Epf = (PW * AW)/(PL * AL) Epf = (.8 * 600)/(.2 * 400) Epf = (480)/(80) Epf = 6 or the 50% level described in the test Maybe Magna will be nice enough to edit that post so that it won't confuse others.
The next series of posts will be about some of the methods I use to make sure I achieve my goal of consistency. In my case the goal is 99% chance of profitability per-month. If your goal is daily, weekly, or yearly profitability then just think "daily or yearly" for everything I say about the month. From the first set of posts you can see it's nearly impossible to reach a goal of 99% profitability every month based on trades or profit factor for a single method. Most of my models trade no more than 15 times per-month and the best Profit Factor I've developed that's consistent is around 2.50. With that in mind I knew I was going to have to use more than one method to achieve my goal. When I started out I had lots of questions like "How do I know if I should use more than one model?", "Since the numbers are different for each model how much should I trade of model x for every contract of model y?". I'll show you how I came up with some ideas about using more than one model to improve performance. I spent some hours going through my notebooks to pull out the couple of items to share. I think it'll be easier to understand if it's done through with examples so I've pulled the monthly performance numbers for 3 models and put them into a text file. If you want to work through the examples in a spreadsheet you'll need to save this file. To import it to Excel just use Tab delimited, and then for the first field change the format to Text. It should import correctly.