public void SaveState()
{
List <string[]> array_list = new List <string[]>();
array_list.Add(new string[]
{
LastUpdateDateTime.ToString(),
"",
"",
"",
""
});
foreach (TradingCalenderEvent trading_calender_event in event_list)
{
array_list.Add(new string[]
{
trading_calender_event.EventTimeUTC.ToString(),
trading_calender_event.Symbol,
trading_calender_event.Description,
trading_calender_event.IsAllDay.ToString(),
ToolsString.DictionaryToString(trading_calender_event.Tags)
});
}
ToolsIOCSV.WriteCSVFile(database_path, ToolsCollection.ConvertToArray2D(array_list));
}
public void Write(BinaryWriter writer, IBinaryWriter <FeatureType> writer_features, IBinaryWriter <LabelType> writer_labels)
{
this.DataContext.Write(writer);
writer_features.Write(ToolsCollection.ConvertToArray2D(this.feature_data));
writer.Write(ToolsCollection.ConvertToArray2D(this.missing_data));
writer_labels.Write(ToolsCollection.ConvertToArray2D(this.label_data));
}
public ITransform GenerateTransform(IDataSet <double> data_set)
{
AMatrix <MatrixType> data = algebra.Create(ToolsCollection.ConvertToArray2D(data_set.FeatureData));
SVD <MatrixType> svd = algebra.ComputeSVD(data);
AMatrix <MatrixType> projection = svd.VT.Transpose().GetColumns(0, destination_dimension_count);
return(new DimensionReductionPCA <MatrixType>(data_set.DataContext, projection));
}
public override IModelLikelihood <double, int> GenerateModelLikelihood(IDataSet <double, int> training_set)
{
svm_problem prob = new svm_problem();
prob.l = training_set.InstanceCount;
prob.x = CreateNodeArray(ToolsCollection.ConvertToArray2D(training_set.FeatureData));
prob.y = ToolsCollection.ConvertToDoubleArray(ToolsCollection.ConvertToArray2D(training_set.LabelData).Select1DIndex1(0));
//Train model---------------------------------------------------------------------
return(new ModelLibSVMCSVC(training_set.DataContext, new C_SVC(prob, KernelHelper.RadialBasisFunctionKernel(this.Gamma), this.C, this.CacheSize, true)));
}
public void DoExperiment()
{
ToolsPrice.ComposeBinary(ToolsPrice.DefaultSymbolGBPUSD);
PriceSet price_set = ToolsPrice.GetPriceSet(ToolsPrice.DefaultSymbolGBPUSD).SubSet(new DateTimeUTC(2016, 10, 17), new DateTimeUTC(2016, 10, 20));
IIndicator indicator_feature = new IndicatorSuperBollinger();
IIndicator indicator_label = new IndicatorMagicProfit(60);
MarketModelSimulation market0 = new MarketModelSimulation(10000, price_set);
MarketModelSimulation market1 = new MarketModelSimulation(10000, price_set);
double[] time = new double[price_set.Prices.Count];
for (int price_index = 0; price_index < price_set.Prices.Count; price_index++)
{
time[price_index] = price_set.Prices[price_index].Time.Ticks;
}
Tuple <double[, ], bool[]> tuple0 = indicator_feature.ComputeAll(market0, price_set.Second1.Count);
Tuple <double[, ], bool[]> tuple1 = indicator_label.ComputeAll(market1, price_set.Second1.Count);
List <string[]> list_string = new List <string[]>();
for (int index_0 = 0; index_0 < tuple0.Item2.Length; index_0++)
{
if (tuple0.Item2[index_0] && tuple1.Item2[index_0])
{
double[] array_double = ToolsCollection.Append(tuple0.Item1.Select1DIndex0(index_0), tuple1.Item1.Select1DIndex0(index_0));
string[] array_string = new string[array_double.Length];
for (int index_1 = 0; index_1 < array_double.Length; index_1++)
{
array_string[index_1] = array_double[index_1].ToString(CultureInfo.InvariantCulture);
}
list_string.Add(array_string);
}
}
ToolsIOCSV.WriteCSVFile(ToolsTradingDataSet.GetPath() + "data.csv", ToolsCollection.ConvertToArray2D(list_string));
}
//Layout: open, high, low, close, year, month, day_of_month, day of week, minute_of_day,
public static void ExportDataSet(IList <PriceCandle> prices, DateTimeUTC first_day, DateTimeUTC last_day, string file_path)
{
IList <string[]> lines = new List <string[]>();
foreach (PriceCandle price_candle in prices)
{
if ((first_day.Date <= price_candle.OpenTime.Date) && (price_candle.OpenTime.Date <= last_day.Date))
{
string[] line = new string[9];
line[0] = price_candle.OpenBid.ToString(CultureInfo.InvariantCulture);
line[1] = price_candle.HighBid.ToString(CultureInfo.InvariantCulture);
line[2] = price_candle.LowBid.ToString(CultureInfo.InvariantCulture);
line[3] = price_candle.CloseBid.ToString(CultureInfo.InvariantCulture);
line[4] = price_candle.OpenTime.Year.ToString(CultureInfo.InvariantCulture);
line[5] = price_candle.OpenTime.Month.ToString(CultureInfo.InvariantCulture);
line[6] = price_candle.OpenTime.Day.ToString(CultureInfo.InvariantCulture);
line[7] = ((int)price_candle.OpenTime.DayOfWeek).ToString();
line[8] = ((price_candle.OpenTime.Hour * 60) + price_candle.OpenTime.Minute).ToString();
lines.Add(line);
}
}
ToolsIOCSV.WriteCSVFile(file_path, ToolsCollection.ConvertToArray2D(lines));
}
public override IModelDiscrete <double, int> GenerateModelDiscrete(IDataSet <double, int> training_set)
{
double[][] instance_features_array = training_set.FeatureData;
int[] outputs = ToolsCollection.ConvertToArray2D(training_set.LabelData).Select1DIndex1(0);
// Specify the input variables
List <DecisionVariable> variables = new List <DecisionVariable>();
foreach (VariableDescriptor feature_descriptor in training_set.DataContext.FeatureDescriptors)
{
variables.Add(new DecisionVariable(feature_descriptor.Name, DecisionVariableKind.Continuous));
}
// Create the discrete Decision tree
DecisionTree tree = new DecisionTree(variables, training_set.DataContext.GetLabelDescriptor(0).ValueCount);
// Create the C4.5 learning algorithm
C45Learning c45 = new C45Learning(tree); //TODO are there others?
// Learn the decision tree using C4.5
double error = c45.Run(instance_features_array, outputs);
return(new ModelDiscreteC45 <int>(training_set.DataContext, tree));
}
private static void CreateEventCSV()
{
// read CSV
string[,] source = ToolsIOCSV.ReadCSVFile(ToolsTradingDataSet.GetPath() + "events.csv", Delimiter.SemiColon);
//Allocate new array
List <string[]> result_list = new List <string[]>();
string[] exclusion = new string[] { "day", "Day", "Data" };
//For each other column
for (int index_0 = 0; index_0 < source.GetLength(0); index_0++)
{
if (!ExtensionsString.ContainsAny(source[index_0, 2], exclusion))
{
string[] result = new string[3]; // Date symbol impact
//Find dat
DateTime week_start = DateTime.Parse(source[index_0, 0]);
int year = week_start.Year;
int month = ToolsForexFactory.MonthToInt(source[index_0, 1].Split(' ')[0]);
int day = int.Parse(source[index_0, 1].Split(' ')[1]);
if ((week_start.Month == 12) && (month == 1))
{
year++;
}
int hour = int.Parse(source[index_0, 2].Split(':')[0]);
int minute = int.Parse(source[index_0, 2].Split(':')[1].Substring(0, 2));
if (source[index_0, 2].Contains("pm"))
{
hour = hour + 12;
}
if (hour == 24)
{
hour = 0;
DateTime date_time = new DateTime(year, month, day, hour, minute, 0);
date_time = date_time.AddDays(1);
result[0] = ToolsTime.DateTimeToUnixTimestampInt32(date_time).ToString();
}
else
{
DateTime date_time = new DateTime(year, month, day, hour, minute, 0);
result[0] = ToolsTime.DateTimeToUnixTimestampInt32(date_time).ToString();
}
result[1] = source[index_0, 3];
switch (source[index_0, 4])
{
case "Non-Economic":
result[2] = "0"; // Impacs
break;
case "Low Impact Expected":
result[2] = "1"; // Impacs
break;
case "Medium Impact Expected":
result[2] = "2"; // Impacs
break;
case "High Impact Expected":
result[2] = "3"; // Impacs
break;
default:
throw new Exception("Unknown impact");
}
result_list.Add(result);
}
}
ToolsIOCSV.WriteCSVFile(ToolsTradingDataSet.GetPath() + "events_small.csv", ToolsCollection.ConvertToArray2D(result_list));
}
public AMatrix <double[, ]> Create(IList <double> array, bool transpose = false)
{
return(new MatrixKozzion <double>(this, ToolsCollection.ConvertToArray2D(array), transpose));
}
