This is an experimental study designed to forecast the range of price movement from a specified starting point using a Monte Carlo simulation. Monte Carlo experiments are a broad class of computational algorithms that utilize random sampling to derive real world numerical results. These types of algorithms have a number of applications in numerous fields of study...

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Hello All, Monte Carlo Simulation is a model used to predict the probability of different outcomes when the intervention of random variables is present. it is used by professionals in such widely disparate fields as finance, project management etc. You can find many articles about Monte Carlo Simulation on the net. In this script I tried to make Monte Carlo...

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Example function of a markov chain monte carlo simulation.

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Experimental: Example execution of Monte Carlo Simulation applied to the markets(this is my interpretation of the algo so inconsistencys may appear). note: the algorithm is very demanding so performance is limited.

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Inspired by the Brownian Motion ("BM") model that could be applied to conducting Monte Carlo Simulations, this indicator plots out the Drift factor contributing to BM. Interpretation : If the Drift value is positive, then prices are possibly moving in an uptrend. Vice versa for negative drifts.

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Library "FunctionBlackScholes" Some methods for the Black Scholes Options Model, which demonstrates several approaches to the valuation of a European call. // reference: // people.math.sc.edu // people.math.sc.edu asset_path(s0, mu, sigma, t1, n) Simulates the behavior of an asset price over time. Parameters: s0 : float, asset price at...

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Library "FunctionSMCMC" Methods to implement Markov Chain Monte Carlo Simulation (MCMC) markov_chain(weights, actions, target_path, position, last_value) a basic implementation of the markov chain algorithm Parameters: weights : float array, weights of the Markov Chain. actions : float array, actions of the Markov Chain. target_path : float...

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