public StockInfo[] flatMapOfData(Map<String, StockInfo[]> dataToProcess) {
SimpleDateFormat dateFormat = new SimpleDateFormat();
dateFormat.applyPattern("dd/MM/YYYY");
ArrayList<StockInfo> listOfInfo = new ArrayList<StockInfo>();
int numOfIndexesOutOfBound = 0;
Set<String> keySetOfDataToProcess = dataToProcess.keySet();
int[] indexOnStockInfo = new int[keySetOfDataToProcess.size()];
for (int index = 0; index < indexOnStockInfo.length; index++) {
indexOnStockInfo[index] = 0;
}
// Get the key in an array
String[] keys = keySetOfDataToProcess.toArray(new String[keySetOfDataToProcess.size()]);
// Get the min start index to process, this index contains the min startTime
long minStartIndexTime = Long.MAX_VALUE;
for (int index = 0; index < keys.length; index++) {
long tempDate = dataToProcess.get(keys[index])[0].date.getTime();
minStartIndexTime = minStartIndexTime < tempDate ? minStartIndexTime : tempDate;
}
// While any index is still inside of its array boundaries, continue to flat array
while (numOfIndexesOutOfBound != indexOnStockInfo.length) {
float openSum = 0;
float highSum = 0;
float lowSum = 0;
float closeSum = 0;
long volumeSum = 0;
int count = 0;
long newMinStartTime = 0;
// For every key in the set of keys
for (int index = 0; index < keys.length; index++) {
// logger.log("if indexOnStockInfo[index] " + indexOnStockInfo[index] + " <
// dataToProcess.get(keys[index]).length " + dataToProcess.get(keys[index]).length);
if (indexOnStockInfo[index] < dataToProcess.get(keys[index]).length) {
StockInfo infoArray = dataToProcess.get(keys[index])[indexOnStockInfo[index]];
long tempDate = infoArray.date.getTime();
// logger.log("tempDate = " + dateFormat.format(tempDate));
if (tempDate == minStartIndexTime) {
openSum += infoArray.open;
highSum += infoArray.high;
lowSum += infoArray.low;
closeSum += infoArray.close;
volumeSum += infoArray.volume;
count++;
indexOnStockInfo[index]++;
newMinStartTime = tempDate;
}
} else {
numOfIndexesOutOfBound++;
}
}
minStartIndexTime = Long.MAX_VALUE;
for (int index = 0; index < keys.length; index++) {
// logger.log("if indexOnStockInfo[index] " + indexOnStockInfo[index] + " <
// dataToProcess.get(keys[index]).length " + dataToProcess.get(keys[index]).length);
if (indexOnStockInfo[index] < dataToProcess.get(keys[index]).length) {
long tempDate = dataToProcess.get(keys[index])[indexOnStockInfo[index]].date.getTime();
minStartIndexTime = minStartIndexTime < tempDate ? minStartIndexTime : tempDate;
logger.log("minStartIndexTime = " + minStartIndexTime);
}
}
// logger.log("count = " + count);
if (count != 0) {
StockInfo newStockInfo = new StockInfo();
newStockInfo.open = openSum / count;
newStockInfo.high = highSum / count;
newStockInfo.low = lowSum / count;
newStockInfo.close = closeSum / count;
newStockInfo.volume = volumeSum / count;
newStockInfo.date = new Date(newMinStartTime);
logger.log("flat date date is " + dateFormat.format(newStockInfo.date.getTime()));
listOfInfo.add(newStockInfo);
} else {
logger.log("count is 0");
}
}
return listOfInfo.toArray(new StockInfo[listOfInfo.size()]);
}
// Run prediction method on data based on file name
public StockInfo[] runPrediction(Map<String, StockInfo[]> dataToProcess) {
double[] resultArrayOpen = null;
double[] resultArrayHigh = null;
double[] resultArrayLow = null;
double[] resultArrayClose = null;
double[] resultArrayVolume = null;
StockInfo[] predictedDataArray = null;
try {
logger.log("prediction will start");
// Flat the data to perform prediction on it
StockInfo[] flatStockInfo = this.flatMapOfData(dataToProcess);
Collections.reverse(Arrays.asList(flatStockInfo));
REXP x;
RVector v;
// Check for installed packages
x = re.eval("installed.packages()");
v = x.asVector();
String[] packages = x.asStringArray();
boolean isForecastInstalled = false;
logger.log("<R> getting installed packages");
for (int index = 0; index < packages.length && isForecastInstalled == false; index++) {
logger.log("<R> has installed " + packages[index]);
if (packages[index] != null && packages[index].compareTo("forecast") == 0) {
isForecastInstalled = true;
}
}
// If forecast needs to be installed
if (isForecastInstalled == false) {
logger.log("<R> will set repos");
// Set CRAN
re.eval("r <- getOption(\"repos\")");
re.eval("r[\"CRAN\"] <- \"http://cran.us.r-project.org\"");
re.eval("options(repos = r)");
re.eval("rm(r)");
// Install forecast
re.eval("install.packages(\"forecast\")");
logger.log("<R> will install forecast package");
}
// Load forecast library
re.eval("library(\"forecast\")");
logger.log("<R> loaded forecast");
// Make prediction for Open value -----------------------
// Load data into R
logger.log("<R> loading data into R");
StringBuilder builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].open);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
String stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
logger.log("<R> forecasting open values BestFit");
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1); // instead of 3
resultArrayOpen = x.asDoubleArray();
// Make prediction for High value ------------------------
builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].high);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
logger.log("<R> forecasting high values BestFit");
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1);
resultArrayHigh = x.asDoubleArray();
// Make prediction for Low value ------------------------
builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].low);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
logger.log("<R> forecasting low values BestFit");
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1);
resultArrayLow = x.asDoubleArray();
// Make prediction for Close value ------------------------
builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].close);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
logger.log("<R> forecasting close values BestFit");
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1);
resultArrayClose = x.asDoubleArray();
// Make prediction for Close value ------------------------
builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].volume);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1);
resultArrayVolume = x.asDoubleArray();
// Create a single StockInfo[] for all data
StockInfo predictedData;
predictedDataArray = new StockInfo[30];
Date lastDate = flatStockInfo[flatStockInfo.length - 1].date;
Calendar c = Calendar.getInstance();
c.setTime(lastDate);
c.add(Calendar.DATE, 1);
logger.log("<R> values for forecasted data");
SimpleDateFormat dateFormat = new SimpleDateFormat();
dateFormat.applyPattern("dd/MM/YYYY");
// For all days that were predicted
for (int index = 0; index < 30; index++) {
predictedData = new StockInfo();
predictedData.open = (float) resultArrayOpen[index];
float maxHigh =
(float)
StrictMath.max(
resultArrayClose[index],
StrictMath.max(resultArrayHigh[index], resultArrayOpen[index]));
predictedData.high = maxHigh;
float minLow =
(float)
StrictMath.min(
resultArrayClose[index],
StrictMath.min(resultArrayLow[index], resultArrayOpen[index]));
predictedData.low = minLow;
predictedData.close = (float) resultArrayClose[index];
predictedData.volume = (int) resultArrayVolume[index];
while (c.get(Calendar.DAY_OF_WEEK) == Calendar.SUNDAY
|| c.get(Calendar.DAY_OF_WEEK) == Calendar.SATURDAY) {
c.add(Calendar.DATE, 1);
}
predictedData.date = c.getTime();
predictedDataArray[index] = predictedData;
logger.log(
"<R> stock prediction "
+ dateFormat.format(predictedData.date.getTime())
+ " open: "
+ predictedData.open
+ " high: "
+ predictedData.high
+ " low: "
+ predictedData.low
+ " close: "
+ predictedData.close);
c.add(Calendar.DATE, 1);
}
} catch (Exception e) {
logger.logException(e);
}
return predictedDataArray;
}
