Quote:
Originally Posted by outspan
To put it simply: a random walk has a Gaussian distribution in which stop losses and take profits are vertical lines cutting the distrubution function, the first on the negative side, the second in the positive side (the X axis is the P/L).
The probability of incurring in a stop loss is the integral in X of the distribution function from -infinity to the stop loss value. The probability of incurring in a TP is the integral from there to infinity. Knowing the stddev of the distribution, you can calculate what are the SL and TP values that guarantees an expected value of profit that is > 0.
It's not actually that easy, but I hope I gave you the idea. Math rocks! |
I can appreciate what you are trying to do, but don't be so sure that stddev/variance will be so easy to figure out. The market is by nature prone to have bizarre spikes in prices, coinciding with spikes of emotion (fear/greed). This can be extremely difficult if not impossible to incorporate into a statistical model.
But that isn't even my main point. What I'm getting at, is if price is moving because traders/investors are reacting to news/technical support&resistance and other factors that are unknown BEFORE they happen, why would you spend your time trying to predict those events. Don't you see that you are adding an extra step between the underlying cause of the movement and your eventual reaction to that movement. Why not just learn how to model the average traders response to certain events (ie. encountering a resistance level), and profit that way? Trying to account for the market's total volatility, at all hours of the day, seems extremely futile to me.