I want to make sure I'm interpreting this correctly. The difference between a day's close price and its pivot for that day is very frequently very small?
Yes, the pivot is simply (C+H+L)/3. If the high and low are equally spaced around the close then pivot = close. (C + (C+x) + (C-x))/3 = C If the high is 4 times further away from the close than the low then the pivot is at: (C + (C+4x) + (C-x))/3 = C + x so it takes quite an imbalance to move the pivot. Tom
Hi, Here's what I get for EUR/USD using 30min bars. Btw, I get negative expectancy when trying to replicate your strategy (buying when close below pivot, selling when close above pivot). Did you take commissions into account? Thanks,
Nice curves - looks like your spline fit is better than mine (MATLAB?). My test was frictionless but could be reproduced with Direxion funds or similar, given the high number of trades. However, the equity curve is not great in my view. Something like this could be more promising: http://cssanalytics.wordpress.com/2...-implied-volatility-vs-historical-volatility/ using IV-HV 75%+ Tom
No, actually I use my own platform. I made a few changes to the way I compute the interpolation. The results are much better. However I still get several 10+ sigma-events. Any idea how to eliminate them? I can't get a positive expectancy strategy yet.
This only seems to be true if the underlying distribution is also gaussian. When I have fat-tails in the distribution, deviations from spline are not gaussian (altough "more" gaussian than the initial distribution).
I don't get this. By stable you mean stationary I guess? What do you mean with markets being truly gaussian? Normal distribution of daily returns? How do you position a strangle at 2 STD? Break-even points should be at current price +/- 2STD? I'm very interested in this, could anyone explain please?