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Coin Toss Experiment (Strategy)
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Coin Toss Experiment (Strategy)

  #81 (permalink)
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No there is no edge in trade management, given a coin toss/ normally distributed time series. No indicator I know of is going to help you with this problem.

but the market is not normally distributed.So if we add a simple formula to the equation:

Open>Close for longs

Open<Close for shorts

that would shift the edge in our favorite immensly,don`t you?

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  #82 (permalink)
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Cachevary View Post
but the market is not normally distributed.So if we add a simple formula to the equation:

Open>Close for longs

Open<Close for shorts

that would shift the edge in our favorite immensly,don`t you?

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.

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  #83 (permalink)
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treydog999 View Post
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.

Yes,i`m aware the thread is about.Was just trying to mix things up a bit.Why not?

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How Do You Model This Coin Flip Bet?

How Do You Model This Coin Flip Bet? | WIRED




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The bet is this: we flip a coin (actually, Derek is the one making the bet). If you win the flip, you get twenty dollars. If you lose the flip, you lose only ten dollars. Got it? Would you take the bet? If you watch the video above you will find that most people would NOT take this bet. No one wanted to lose ten dollars. But Derek Muller (from Veritasium) claims that its worth considering especially if you run the bet multiple times. So, what are the chances that you would lose money if you flipped the coin ten times? What about 100 times? Lets find out.


dont believe anything you hear and only half of what you see

\_(ツ)_/

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I was seriously saying what is wrong with these people at my screen. Even telling my wife these guys are idiots, wtf. Take the +EV bet, especially when you are offered 2:1 and 10 flips in a row.

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treydog999 View Post
I was seriously saying what is wrong with these people at my screen. Even telling my wife these guys are idiots, wtf. Take the +EV bet, especially when you are offered 2:1 and 10 flips in a row.

it really is amazing, but if the fear is that strong you can see how it would be so much stronger in trading.

dont believe anything you hear and only half of what you see

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I was reading this thread my brain hurts ;-)

I have a question:

The whole thread proves that a strategy with a coin toss and a risk reward of 2:1 has an expectancy of zero. Correct?

But why does the coin toss experiment in the Youtube Video give a positive expectation but
the experiment in post

https://futures.io/psychology-money-management/19803-coin-toss-experiment-strategy-6.html#post333201

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?

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Heiku View Post
...
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?

Because of the trade expectancy is = 0. You can only make money if there is a market bias and you manage to decipher it. The risk reward has nothing to do with that aspect.

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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

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Heiku View Post
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 08:19 PM.
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