How To Compute Stochastic Oscillator in MATLAB?

@thelma.stanton 

To compute the Stochastic Oscillator in MATLAB, you can follow these steps:

1. Load or generate the price data for the financial instrument you are interested in analyzing. This data could be in the form of a vector or matrix, with each row representing a time period and each column representing the price at a certain point in time.
2. Define the parameters for the Stochastic Oscillator, typically a period length (e.g. 14) and a smoothing factor (e.g. 3).
3. Calculate the %K line, which represents the current price relative to the high-low range over the specified period. This can be done using the following formula: %K = 100 * ((Close - Low_n) / (High_n - Low_n)) where Close is the closing price for the current period, Low_n is the lowest low over the specified period, and High_n is the highest high over the specified period.
4. Apply a smoothing function to the %K line to get the %D line, which is typically a simple moving average of the %K values. This can be done using the 'smoothdata' function in MATLAB, or by manually calculating the moving average.
5. Plot the %K and %D lines on a chart to visualize the Stochastic Oscillator values over time.

Here is an example MATLAB code snippet to compute the Stochastic Oscillator:

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% Load or generate price data
price_data = [100, 105, 110, 115, 120, 125, 130, 135, 140, 145];

% Define parameters
period = 5;
smooth_factor = 3;

% Calculate %K line
for i = period:length(price_data)
    low_n = min(price_data(i-period+1:i));
    high_n = max(price_data(i-period+1:i));
    k_line(i) = 100 * ((price_data(i) - low_n) / (high_n - low_n));
end

% Smooth %K line to get %D line
d_line = smoothdata(k_line, 'movmean', smooth_factor);

% Plot the Stochastic Oscillator
plot(k_line(period:end));
hold on;
plot(d_line(period:end));
legend('%K', '%D');

This code snippet calculates and plots the %K and %D lines of the Stochastic Oscillator for a simple price data sequence. You can customize the code according to your specific requirements and dataset.