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The top of every source code file must include this line
#include "sierrachart.h"

// For reference, refer to this page:
// Advanced Custom Study Interface and Language (ACSIL)

// This line is required. Change the text within the quote
// marks to what you want to name your group of custom studies.
SCDLLName("Ocean")

//This is the basic framework of a study function. Change the name 'TemplateFunction' to what you require.
SCSFExport scsf_TemplateFunction(SCStudyInterfaceRef sc)
{
  // Section 1 - Set the configuration variables and defaults
  if (sc.SetDefaults)
  {
    #include <iostream>
#include <vector>
#include <cmath>

// Function to calculate EMA
std::vector<double> calculateEMA(const std::vector<double>& values, int length) {
std::vector<double> ema(values.size());
double alpha = 2.0 / (length + 1);
ema[0] = values[0]; // Initialize the first value of EMA with the first value in the series

for (size_t i = 1; i < values.size(); ++i) {
ema[i] = alpha * values[i] + (1 - alpha) * ema[i - 1];
}

return ema;
}

int main() {
// Example source data (e.g., closing prices)
std::vector<double> src = {1.0, 1.1, 1.2, 1.3, 1.4, 1.5}; // Replace with actual data
int length = 14; // Length period

// Step 1: Calculate ln (logarithm of source data scaled by 1000)
std::vector<double> ln(src.size());
for (size_t i = 0; i < src.size(); ++i) {
ln[i] = std::log(src[i]) * 1000;
}

// Step 2: Calculate oi
std::vector<double> oi(src.size(), 0.0); // Initialize with zeros
for (size_t i = 1; i < src.size(); ++i) {
oi[i] = (ln[i] - ln[i - 1]) * 100;
}

// Step 3: Calculate nmrSum
std::vector<double> nmrSum(src.size(), 0.0);
for (size_t i = 0; i < src.size(); ++i) {
if (i > 0) {
nmrSum[i] = nmrSum[i - 1] + oi[i] * (std::sqrt(i + 1) - std::sqrt(i));
} else {
nmrSum[i] = oi[i] * std::sqrt(1);
}
}

// Step 4: Calculate the first EMA of nmrSum
std::vector<double> ema1 = calculateEMA(nmrSum, length);

// Step 5: Calculate the second EMA of the first EMA
std::vector<double> ema2 = calculateEMA(ema1, length);

// Output the result
for (size_t i = 0; i < ema2.size(); ++i) {
std::cout << "ema2[" << i << "] = " << ema2[i] << std::endl;
}

return 0;
}
  
  // Section 2 - Do data processing here
  
  
}

/*============================================================================
  This study displays the simple, exponential, and adaptive moving
  averages.
----------------------------------------------------------------------------*/
SCSFExport scsf_MovingAverageExample1(SCStudyInterfaceRef sc)
{
  SCSubgraphRef Subgraph_SimpleMA = sc.Subgraph[0];
  SCSubgraphRef Subgraph_ExponentialMA = sc.Subgraph[1];
  SCSubgraphRef Subgraph_AdaptiveMA = sc.Subgraph[2];

  // Set configuration variables
  
  if (sc.SetDefaults)
  {
    // Set defaults
    
    sc.GraphName = "Moving Average Example 1";
    
    sc.StudyDescription = "";
    
    sc.GraphRegion = 0;
    
    Subgraph_SimpleMA.Name = "Simple Moving Average";
    Subgraph_SimpleMA.DrawStyle = DRAWSTYLE_LINE;
    Subgraph_SimpleMA.PrimaryColor = RGB(0,255,0);
    
    Subgraph_ExponentialMA.Name = "Exponential Moving Average";
    Subgraph_ExponentialMA.DrawStyle = DRAWSTYLE_LINE;
    Subgraph_ExponentialMA.PrimaryColor = RGB(255,0,255);
    
    Subgraph_AdaptiveMA.Name = "Adaptive Moving Average";
    Subgraph_AdaptiveMA.DrawStyle = DRAWSTYLE_LINE;
    Subgraph_AdaptiveMA.PrimaryColor = RGB(255,255,0);

    sc.AutoLoop = 1;

    return;
  }
  
  
  // Do data processing
  
  
  // Simple moving average in the first subgraph
  sc.SimpleMovAvg(sc.Close, Subgraph_SimpleMA, 10);
  
  // Exponential moving average in the second subgraph
  sc.ExponentialMovAvg(sc.Close, Subgraph_ExponentialMA, 10);
  
  // Adaptive moving average in the third subgraph
  sc.AdaptiveMovAvg(sc.Close, Subgraph_AdaptiveMA, 10, 2.0f, 30.0f);
}