PINE LIBRARY
已更新 FunctionSMCMC
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 array, target path array.
position: int, index of the path.
last_value: float, base value to increment.
Returns: void, updates target array
mcmc(weights, actions, start_value, n_iterations) uses a monte carlo algorithm to simulate a markov chain at each step.
Parameters:
weights: float array, weights of the Markov Chain.
actions: float array, actions of the Markov Chain.
start_value: float, base value to start simulation.
n_iterations: integer, number of iterations to run.
Returns: float array with path.
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 array, target path array.
position: int, index of the path.
last_value: float, base value to increment.
Returns: void, updates target array
mcmc(weights, actions, start_value, n_iterations) uses a monte carlo algorithm to simulate a markov chain at each step.
Parameters:
weights: float array, weights of the Markov Chain.
actions: float array, actions of the Markov Chain.
start_value: float, base value to start simulation.
n_iterations: integer, number of iterations to run.
Returns: float array with path.
版本注释
v2outsourced the probability distribution sample selection to a external library:
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Pine脚本库
本着真正的TradingView精神，作者将此Pine代码发布为开源库，以便我们社区的其他Pine程序员可以重复使用它。向作者致敬！您可以私密或在其他开源出版物中使用此库，但在出版物中重复使用此代码受网站规则约束。
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。