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Edge: (from backtesting your system) #trades - (#system parameters) >= 30; #wins*(avg win) - #losses*|avg loss| > Buy&Hold.

I've heard that IV predicts future HV. IOW if the IV is higher(lower) than the current HV, then the HV will move up(down) towards the IV. But you're saying it's actually the opposite: HV leads IV. :confused: I thought maybe there was some trend analysis applied to HV to indicate the 'true'...

I haven't used the CFB or any of Jurik's other products but I have gotten invaluable insights from his extensive writings on how to design my own. I even 'back-engineered' the CFB to some extent. What I got worked in a theoretical sense but was too noisy to be practical for real trading. I...

I haven't read the book but, for what it's worth, I'm always suspicious of "magic number" systems. Anytime somebody says "Always use the 10-day SMA" or anything similar, my BS detector starts creeping towards the redline. Securities trading isn't a cookie-cutter operation. Different markets...

Legend says that if you catch a leprecaun, he must give you his treasure. Common sense says that if you catch a trend, you'll make money. But you can't even get people here to agree on what a trend is, much less how to go about catching one. Long-term trends, however you chose define them...

Does anybody actually use historical volatility in option trading? It seems to me that all the emphasis is on implied volatility, and I've looked but see nowhere anybody is actually using HV, although there is a lot of stuff about how to calculate it. :confused:

I don't believe in the "never lose" system. But I do believe systems can be designed as net-positive in expectation. So the HG would be a net-positive system that tells me what my most likely expectation on the next trade would be so I could do optimal position sizing with that info.

How much are you willing to pay? :)

First, you have to realize that you're mixing apples and oranges. LR is a model. You assume the data has a linear form and you "force" the best fit by finding the model parameters that give the least overall estimation error. There is no lag here; once you've found the best model...

Composite Fractal Behavior : http://www.jurikres.com/catalog/ms_cfb.htm#top Mandelbrot, B. B. [1999] "A Fractal Walk Down Wall Street," Scientific American, February 1999, pp50-53.

I understand the math just fine. More importantly, I know better than to force the data to fit my "pet" theory, dumbass.

I hear you. What's so special about the 'best' quadratic? Why not the 'best' cubic, or the 'best' quintic? :confused: Curve-fitting can be good or bad. I suspect this is [Xander Harris]not of the good[/Xander Harris]. :p

"Do not buy any moving average based system!" -- abogdan :D :D :D

No, you can use cubic (3rd degree) splines. I assume MAESTRO meant cubic spline smoother, because cubic spline interpolation fits every point, and so it models noise right along with signal. :eek: But there's a problem even with the smoother: the endpoints are fitted, so you don't get...

Daily is the norm. Yeah, this one is all over the place, although the end result usually winds up being annualized. This brings up the key subject of optimal sample size determination for a time series. If you have any ideas, please share. :) I would choose the sample definition, unless you...

historical volatility (HV) aka statistical volatility (SV): annualized HV = sqrt[ 252 * variance of natural log differences in daily prices for the calendar month ] source : http://www.cbot.com/cbot/pub/page/0,3181,774,00.html implied volatility (IV): The market's expectation of...

You have to include the previous close, or it isn't the so-called "true" range, it's just plain real range. :p True range is defined as the greatest of the high minus the low, the high minus the previous close, or the previous close minus the low. It would be nice if the OP stated what he had...

bump

J. Welles Wilder's RSI and George Lane's Stochastics are two famous overbought/oversold indicators. There are literally hundreds of books and websites where you can find out complete details about them, including hybrids like StochRSI.

You want to use the log definition, because a key assumption of Black-Scholes is that price is lognormal, that is, price returns as measured by ln[CLOSEtoday/CLOSEyesterday] have a normalized Gaussian probability distribution.