First this thread and result is regarding the coin toss experiment which is fundamentally a normal distribution. Second, if you add said filters to the coin toss, it doesn't matter because its an IID random variable. So no it would not shift this experiment at all.
Yes, everyone knows the market are not normally distributed. But in my experiments I was testing random entries vs trade management techniques to see if there was an inherent edge, there isn't. Which also confirms the academic literature. If you want to try it with filters on market data you can, but you can do that in a separate thread as it will no longer pertain to coin tosses. Or try to build it in a back tester to see the results.
with trading risk: reward 2:1 gives an expectation of 0. According to the video the expectancy plays out with more coin tosses. The speaker says that after a hundred coin tosses it is practically impossible to lose.
But after thousand of trades with a risk reward 2:1 the expectancy is still zero no penny is earned. Why? What is the difference?
I see that was the reason for the experiment. To show that the expectancy is zero.
But the 10 Dollar bet to get 20 Dollar can be seen as a trade with risk: reward 1:2. Here the expectancy is clearly positive (o.5*20 - 0.5*10 = 5). This bet has an "edge". So the more "trades" the more unlikely is a loss as this edge plays out over time.
What is the reason that this positive expectancy does not play out in the market with real trades instead the expectancy is zero? Why does the trade with target 20 and risk 10 does not provide an edge? Does it have to do with normal distribution which is not given in the market?
Sorry the question might sound strange but I am not a crack in statistics
If you place a stop loss at 10 ticks and your target at 10 ticks away from your entry then you have 50% chance to see your target or stop loss beeing hit. But if you place a stop loss at 10 ticks and your target at 20 ticks away from your entry then the 50% shades of grey are no more present in this scenario as you have introduced a bias in favor of your stop loss in a random experiment. In other words, your stop loss will have a 66.6% chance of being hit and your target will have a 33.3% chance of beeing hit. That's normal as your target is twice as large as your stop, 33.3% + 33.3% + 33.3% = 100%
What is missing in this scenario to make money is an edge, no edge no funny as you rely on chance. Inscreasing just one side of the reward/risk equation is not enough.
Last edited by trendisyourfriend; February 9th, 2015 at 07:19 PM.
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