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I couldn't find good resources on how to simulate a stock price data sequence including some basic effects. The basis might be a Brownian motion model; but in real stock prices, there are additional effects like support and resistances etc.

I found the book by Paul Glassermann 'Monte Carlo Methods in financial engineering'. But it couldn't find e.g. something about resistance in it.

Edit: The work by Lo and MacKinlay made me belive that there could be some work on modelling the non-random effects apparently present in stock data: Lo, Andrew W., and A. Craig MacKinlay. "Stock market prices do not follow random walks: Evidence from a simple specification test." Review of financial studies 1.1 (1988): 41-66.

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    $\begingroup$ Welcome to quant.stackexchange! I have never seen a quant finance model of support and resistance, and most people here probably don't believe support/resistance exist (they are closer to the random walk school of thought). You could just use real price data (not simulated) if you want to be sure your data is realistic. $\endgroup$ – Alex C Feb 14 '16 at 17:16
  • $\begingroup$ I'm voting to close this question as off-topic because chart analysis (support and resistance) and stochastic finance don't go together well ... $\endgroup$ – Richard Feb 15 '16 at 8:41
  • $\begingroup$ You might get this effect with some kind of a modified mean reversion model. But, as Alex C said, most of us don't believe in support/resistance, etc... $\endgroup$ – Juan Ignacio Gil Feb 17 '16 at 11:24
  • $\begingroup$ Ok, I see. I wasn't aware that quantitavtive means stochastic. And thought there could be some interesting models, following Lo and MacKinlay (citation added in the original post) $\endgroup$ – alex Feb 20 '16 at 7:09
  • $\begingroup$ Have you considered just using a (geometric) Ornstein-Uhlenbeck process? It doesn't really have support/resistance but might be a good first step anyway. $\endgroup$ – P.Windridge Feb 20 '16 at 9:13

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