Quote from Craig66:
Ok, I'll make a random observation, it seems interesting that the formula for the expectation value of the absolute distance only depends on the number of steps, not any of the other parameters of the walk. The text at the bottom of the page is also interesting 'Tóth (2000) has proven that there are no more than three most-visited sites in a simple symmetric random walk in one dimension with unit steps'.
Quote from MAESTRO:
Good start. Now, go ahead and calculate how many steps of 1 point does the S&P make to move 100 points? Itâs easy enough to calculate using historical charts. Is that number greater, equal to or significantly lower than the mean deviation calculated using the formula above?
| T St St2 St3 | |a0| |Sp(t) |
| St St2 St3 St4 | |b0| = |S(p(t)*t) |
| St2 St3 St4 St5 | |c0| |S(p(t)*t2)|
| St3 St4 St5 St6 | |d0| |S(p(t)*t3)|
Quote from Tompson:
Maestro - very interesting thread!
So the next bar close has a normal distribution around the forecasted close using a cubic spline method?
If I am understanding correctly, this means that the fat tails seen in a regular price distribution are generally in the direction of the short term trend. (To eliminate a large upspike from the data, the forecasted center of mass would have be up large also, therefore the previous few datapoints would need to be accelerating higher).
Then the idea is to take trades at the 2 sigma levels: trend above a certain spline slope and reversion below.
If I am close, there may be a related body of work interesting (or known) to some here. The author uses a variety of polynomial fits to develop simple rules then tests out of sample with great results.Tom