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MonteCarlo Simulation

About the Indicator

This indicator is based on Monte carlo simulations on the closing prices of a financial instrument. By definition, Monte carlo is a statistical technique used to model the probability of different outcomes in  a process that involves random numbers based on previously observed outcomes.


How does it Work?

This indicator generates multiple price scenarios for a security by modelling random price changes over time based on historical data. Each simulation trial uses random variables to account for the closing price fluctuations effectively mimicking possible market movements for the future over a given timeframe.


Advantages of Monte carlo Simulation

- Monte Carlo simulations helps analyze the risk of various trading strategies by testing them against multiple possible future scenarios.

- It helps in identifying how strategies perform under different market conditions, including rare extreme events (tail risk).

- Instead of relying on a single-point forecast, Monte Carlo offers a range of potential outcomes with associated probabilities. This helps to understand the likelihood of profit or loss.


Monte Carlo is useful for traders aiming to manage risk based on the probability of different outcomes rather than absolute predictions.


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Matrix is the foundation of complex trading algorithms as it helps you perform complex calculations effortlessly and without the need for too much computation power, It's no doubt that matrix has made possible many of the calculations in modern computers as we all know that bits of information are stored in array forms in our computer memory RAM, Using some of the functions in this library I was able to create machine learning robots that could take on a large number of inputs To use this libra
This is standard library built for flexible neural Networks with performance in mind. Calling this Library is so simple and takes few lines of code:    matrix Matrix = matrix_utils.ReadCsv( "Nasdaq analysis.csv" );       matrix x_train, x_test;    vector y_train, y_test;         matrix_utils.TrainTestSplitMatrices(Matrix,x_train,y_train,x_test,y_test, 0.7 , 42 );    reg_nets = new CRegressorNets(x_train,y_train,AF_RELU_,HL, NORM_MIN_MAX_SCALER); //INitializing network       reg_nets.RegressorN
Linear regression AI powered Indicator: Linear regression is a simple yet effective AI technique that is the foundation of complex neural networks, This indicator is built based on linear regression analysis and tries to make predictions on the upcoming event in the market Inputs : train_bars: This controls the number of bars that the price information will be collected and used to train the AI inside it, The greater this value the better also the slower the indicator becomes during initializati
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Version 2.0 2024.11.15
Indicator custom theme introduced
An Informative dashboard introduced
Bugs fixed