public static MarketMLDataSet GrabData(string newfileLoad)
{
IMarketLoader loader = new CSVFinal();
loader.GetFile(newfileLoad);
var result = new MarketMLDataSet(loader,
CONFIG.INPUT_WINDOW, CONFIG.PREDICT_WINDOW);
// var desc = new MarketDataDescription(Config.TICKER,
// MarketDataType.Close, true, true);
var desc = new MarketDataDescription(CONFIG.TICKER,
MarketDataType.Close, true, true);
result.AddDescription(desc);
var end = DateTime.Now; // end today
var begin = new DateTime(end.Ticks); // begin 30 days ago
begin = begin.AddDays(-950);
result.Load(begin, end);
result.Generate();
return(result);
}
public static MarketMLDataSet GrabData(string newfileLoad)
{
IMarketLoader loader = new CSVFileLoader();//CSVLoader();
loader.GetFile(newfileLoad);
var result = new MarketMLDataSet(loader,
Config.INPUT_WINDOW, Config.PREDICT_WINDOW);
// var desc = new MarketDataDescription(Config.TICKER,
// MarketDataType.Close, true, true);
var desc = new MarketDataDescription(Config.TICKER,
MarketDataType.Close, TemporalDataDescription.Type.PercentChange, true, true);
result.AddDescription(desc);
var begin = DateTime.ParseExact("29.05.2005", "dd.MM.yyyy", CultureInfo.CurrentCulture); // begin 30 days ago
var end = DateTime.ParseExact("22.07.2005", "dd.MM.yyyy", CultureInfo.CurrentCulture); // begin 30 days ago
begin = begin.AddDays(Config.DAYS_OFFSET).AddDays(Config.TEST_OFFSET);
end = end.AddDays(Config.DAYS_OFFSET).AddDays(Config.TEST_OFFSET).AddDays(Config.TEST_STRATCH);
result.Load(begin, end);
result.Generate();
return(result);
}
public static void Generate(FileInfo dataDir)
{
IMarketLoader loader = new YahooFinanceLoader();
var market = new MarketMLDataSet(loader,
Config.INPUT_WINDOW, Config.PREDICT_WINDOW);
var desc = new MarketDataDescription(
Config.TICKER, MarketDataType.AdjustedClose, true, true);
market.AddDescription(desc);
var end = DateTime.Now; // end today
var begin = new DateTime(end.Ticks); // begin 30 days ago
// Gather training data for the last 2 years, stopping 60 days short of today.
// The 60 days will be used to evaluate prediction.
begin = begin.AddDays(-60);
end = end.AddDays(-60);
begin = begin.AddYears(-2);
market.Load(begin, end);
market.Generate();
EncogUtility.SaveEGB(FileUtil.CombinePath(dataDir, Config.TRAINING_FILE), market);
// create a network
BasicNetwork network = EncogUtility.SimpleFeedForward(
market.InputSize,
Config.HIDDEN1_COUNT,
Config.HIDDEN2_COUNT,
market.IdealSize,
true);
// save the network and the training
EncogDirectoryPersistence.SaveObject(FileUtil.CombinePath(dataDir, Config.NETWORK_FILE), network);
}
public static void Generate(string fileName)
{
FileInfo dataDir = new FileInfo(@Environment.CurrentDirectory);
IMarketLoader loader = new CSVFinal();
var market = new MarketMLDataSet(loader, CONFIG.INPUT_WINDOW, CONFIG.PREDICT_WINDOW);
// var desc = new MarketDataDescription(Config.TICKER, MarketDataType.Close, true, true);
var desc = new MarketDataDescription(CONFIG.TICKER, MarketDataType.Close, true, true);
market.AddDescription(desc);
string currentDirectory = @"c:\";
loader.GetFile(fileName);
var end = DateTime.Now; // end today
var begin = new DateTime(end.Ticks); // begin 30 days ago
// Gather training data for the last 2 years, stopping 60 days short of today.
// The 60 days will be used to evaluate prediction.
begin = begin.AddDays(-600);
end = begin.AddDays(200);
Console.WriteLine("You are loading date from:" + begin.ToShortDateString() + " To :" + end.ToShortDateString());
market.Load(begin, end);
market.Generate();
EncogUtility.SaveEGB(FileUtil.CombinePath(dataDir, CONFIG.SVMTRAINING_FILE), market);
// create a network
//BasicNetwork network = EncogUtility.SimpleFeedForward(
// market.InputSize,
// CONFIG.HIDDEN1_COUNT,
// CONFIG.HIDDEN2_COUNT,
// market.IdealSize,
// true);
SupportVectorMachine network = new SupportVectorMachine(CONFIG.INPUT_WINDOW, true);
TrainNetworks(network, market);
// save the network and the training
EncogDirectoryPersistence.SaveObject(FileUtil.CombinePath(dataDir, CONFIG.SVMTRAINING_FILE), network);
}
public static void Generate(string fileName)
{
FileInfo dataDir = new FileInfo(@Environment.CurrentDirectory);
//Lets use the CSVFinal..(and not the CSV Form loader).
IMarketLoader loader = new CSVFinal();
loader.GetFile(fileName);
var market = new MarketMLDataSet(loader, Config.INPUT_WINDOW, Config.PREDICT_WINDOW);
// var desc = new MarketDataDescription(Config.TICKER, MarketDataType.Close, true, true);
var desc = new MarketDataDescription(Config.TICKER, MarketDataType.Close, TemporalDataDescription.Type.PercentChange, true, true);
market.AddDescription(desc);
loader.GetFile(fileName);
var begin = DateTime.ParseExact("01.01.2000", "dd.MM.yyyy", CultureInfo.CurrentCulture); // begin 30 days ago
var end = DateTime.ParseExact("22.06.2005", "dd.MM.yyyy", CultureInfo.CurrentCulture); // begin 30 days ago
begin = begin.AddDays(Config.DAYS_OFFSET);
end = end.AddDays(Config.DAYS_OFFSET);
// Gather training data for the last 2 years, stopping 60 days short of today.
// The 60 days will be used to evaluate prediction.
//begin = begin.AddDays(-200);
//end = begin.AddDays(1);
Console.WriteLine("You are loading date from:" + begin.ToShortDateString() + " To :" + end.ToShortDateString());
market.Load(begin, end);
market.Generate();
EncogUtility.SaveEGB(FileUtil.CombinePath(dataDir, Config.TRAINING_FILE), market);
// create a network
BasicNetwork network = EncogUtility.SimpleFeedForward(
market.InputSize,
Config.HIDDEN1_COUNT,
Config.HIDDEN2_COUNT,
market.IdealSize,
true);
// save the network and the training
EncogDirectoryPersistence.SaveObject(FileUtil.CombinePath(dataDir, Config.NETWORK_FILE), network);
}
public static MarketMLDataSet GrabData()
{
IMarketLoader loader = new YahooFinanceLoader();
var result = new MarketMLDataSet(loader,
Config.INPUT_WINDOW, Config.PREDICT_WINDOW);
var desc = new MarketDataDescription(Config.TICKER,
MarketDataType.AdjustedClose, true, true);
result.AddDescription(desc);
var end = DateTime.Now; // end today
var begin = new DateTime(end.Ticks); // begin 30 days ago
begin = begin.AddDays(-60);
result.Load(begin, end);
result.Generate();
return(result);
}