void nextStep()
{
// compute jacobian
if (_retry == 0)
{
_jac = _jac_func(_inputs, _weights);
}
// compute hessian approximation with tykhonov damping coefficient
var jacT = new NRealMatrix(_jac.Rows, _jac.Columns);
jacT.SetArray(_jac.ToArray());
jacT.Transpose();
var dampedHessian = new NRealMatrix(_jac.Columns, _jac.Columns);
dampedHessian = jacT * _jac;
for (int idxRow = 0; idxRow < dampedHessian.Rows; idxRow++)
{
dampedHessian.SetAt(idxRow, idxRow, new NDouble(dampedHessian[idxRow, idxRow] * (1.0 + _lambda) + 1e-10));
}
var adj = new NRealMatrix(dampedHessian.Rows, 1);
var y = new NRealMatrix(dampedHessian.Rows, 1);
y = jacT * _error;
// solve dampedHessian * adj = y
LapackLib.Instance.SolveSle(dampedHessian, y, adj);
var nextWeights = new NRealMatrix(1, _weights.Columns);
for (int idxWeight = 0; idxWeight < nextWeights.Columns; idxWeight++)
{
nextWeights.SetAt(0, idxWeight, new NDouble(_weights[0, idxWeight] - adj[idxWeight, 0]));
}
// compute errors
var error = calcError(nextWeights);
var totalError = calcTotalError(error);
if (totalError > _totalError)
{
// revert step and increase damping factor
if (_retry < 100)
{
_lambda *= 11.0;
_retry++;
}
else
{
updateWeights();
throw new StallException();
}
}
else
{
// accept step and decrease damping factor
_lambda /= 9.0;
_weights.SetArray(nextWeights.ToArray());
_error = error;
_totalError = totalError;
_retry = 0;
}
}
NRealMatrix calcError(NRealMatrix weights)
{
var error = new NRealMatrix(_inputs.Rows, 1);
NRealMatrix modelOutput = _model(_inputs, weights);
for (int idxError = 0; idxError < error.Rows; idxError++)
{
error.SetAt(idxError, 0, new NDouble(_outputs[idxError, 0] - modelOutput[idxError, 0]));
}
return(error);
}
public static void Run(bool generate = false, bool generate_from_db = false)
{
Dictionary<string, string> dicSettings = new Dictionary<string, string>();
dicSettings["APP_NAME"] = "Midax";
dicSettings["PUBLISHING_START_TIME"] = "2016-01-22 08:00:00";
dicSettings["PUBLISHING_STOP_TIME"] = "2016-01-22 09:00:00";
dicSettings["REPLAY_MODE"] = "CSV";
dicSettings["REPLAY_POPUP"] = "1";
dicSettings["TRADING_START_TIME"] = "2016-01-22 08:45:00";
dicSettings["TRADING_STOP_TIME"] = "2016-01-22 08:59:00";
dicSettings["TRADING_CLOSING_TIME"] = "2016-01-22 08:57:00";
dicSettings["TRADING_MODE"] = "REPLAY";
dicSettings["TRADING_SIGNAL"] = "MacD_1_5_IX.D.DAX.DAILY.IP";
dicSettings["TRADING_LIMIT_PER_BP"] = "10";
dicSettings["TRADING_CURRENCY"] = "GBP";
Config.Settings = dicSettings;
string action = generate ? "Generating" : "Testing";
var dax = new MarketData("DAX:IX.D.DAX.DAILY.IP");
List<string> tests = new List<string>();
Console.WriteLine(action + " WMA...");
// Test weighted moving average with long intervals
tests.Add(@"..\..\expected_results\testWMA.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
if (generate)
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testWMAgen.csv");
var macDTestWMA = new ModelMacDTest(dax, 1, 2, 3);
MarketDataConnection.Instance.Connect(null);
macDTestWMA.StartSignals();
macDTestWMA.StopSignals();
// Test weighted moving average with short intervals
tests = new List<string>();
tests.Add(@"..\..\expected_results\testWMA2.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
if (generate)
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testWMA2gen.csv");
dax.Clear();
macDTestWMA = new ModelMacDTest(dax, 1, 2, 3);
MarketDataConnection.Instance.Connect(null);
macDTestWMA.StartSignals();
macDTestWMA.StopSignals();
// Test weighted moving average with linear time decay
tests = new List<string>();
tests.Add(@"..\..\expected_results\testWMA3.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
dicSettings["TIME_DECAY_FACTOR"] = "3";
if (generate)
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testWMA3gen.csv");
dax.Clear();
macDTestWMA = new ModelMacDTest(dax, 1, 2, 3);
MarketDataConnection.Instance.Connect(null);
macDTestWMA.StartSignals();
macDTestWMA.StopSignals();
// Test volume weighted moving average with linear time decay
/*
tests = new List<string>();
tests.Add(@"..\..\expected_results\testWMA4.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
if (generate)
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testWMA4gen.csv");
var macDVTest = new ModelMacDVTest(dax, 1, 2, 3);
MarketDataConnection.Instance.Connect(null);
macDVTest.StartSignals();
macDVTest.StopSignals();*/
dicSettings.Remove("TIME_DECAY_FACTOR");
// Test RSI and Correlation indicators
tests = new List<string>();
tests.Add(@"..\..\expected_results\testRsiCorrel.csv");
dicSettings["INDEX_ICEDOW"] = "DOW:IceConnection_DOW";
dicSettings["INDEX_DOW"] = "DOW:IX.D.DOW.DAILY.IP";
dicSettings["INDEX_DAX"] = "DAX:IX.D.DAX.DAILY.IP";
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
if (generate)
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testRsiCorrelgen.csv");
dax.Clear();
var icedow = new MarketData(dicSettings["INDEX_ICEDOW"]);
var dow = new MarketData(dicSettings["INDEX_DOW"]);
var macD = new ModelMacDTest(dax, 1, 2, 3);
//var macDV = new ModelMacDVTest(icedow, 1, 2, 3, dow);
var moleTest = new ModelMoleTest(macD);
MarketDataConnection.Instance.Connect(null);
macD.StartSignals(false);
//macDV.StartSignals(false);
moleTest.StartSignals(false);
MarketDataConnection.Instance.StartListening();
moleTest.StopSignals(false);
//macDV.StartSignals(false);
macD.StopSignals(false);
MarketDataConnection.Instance.StopListening();
Console.WriteLine(action + " calibration...");
// Test a 1mn linear regression
var mktData = new MarketData("testLRMktData");
var updateTime = Config.ParseDateTimeLocal(dicSettings["TRADING_START_TIME"]);
mktData.TimeSeries.Add(updateTime, new Price(100));
mktData.TimeSeries.Add(updateTime.AddSeconds(20), new Price(120));
mktData.TimeSeries.Add(updateTime.AddSeconds(40), new Price(140));
mktData.TimeSeries.Add(updateTime.AddSeconds(60), new Price(130));
mktData.TimeSeries.Add(updateTime.AddSeconds(80), new Price(145));
mktData.TimeSeries.Add(updateTime.AddSeconds(100), new Price(165));
mktData.TimeSeries.Add(updateTime.AddSeconds(120), new Price(145));
var linReg = new IndicatorLinearRegression(mktData, new TimeSpan(0, 2, 0));
var linRegCoeff = linReg.linearCoeff(updateTime.AddSeconds(120));
if (Math.Abs(linRegCoeff.Value - 0.821428571428573m) > 1e-8m)
throw new ApplicationException("Linear regression error");
// Test the optimization of function a * cos(b * x) + b * sin(a * x) using Levenberg Marquardt
LevenbergMarquardt.objective_func objFunc = (NRealMatrix x) => { NRealMatrix y = new NRealMatrix(x.Rows, 1);
for (int idxRow = 0; idxRow < y.Rows; idxRow++)
y.SetAt(idxRow, 0, new NDouble(2 * Math.Cos(x[idxRow, 0]) + Math.Sin(2 * x[idxRow, 0])));
return y; };
List<double> inputs = new List<double>();
Random rnd = new Random(155);
for (int idxPt = 0; idxPt < 10; idxPt++)
inputs.Add(rnd.NextDouble() * 2);
List<Value> modelParams = new List<Value>();
modelParams.Add(new Value(-0.2)); modelParams.Add(new Value(0.3));
LevenbergMarquardt.model_func modelFunc = (NRealMatrix x, NRealMatrix weights) => { NRealMatrix y = new NRealMatrix(x.Rows, 1);
double a = weights[0, 0]; double b = weights[0, 1];
for (int idxRow = 0; idxRow < y.Rows; idxRow++)
y.SetAt(idxRow, 0, new NDouble(a * Math.Cos(b * x[idxRow, 0]) + b * Math.Sin(a * x[idxRow, 0])));
return y; };
Func<double,double,double,double> derA = (double a, double b, double x) => Math.Cos(b * x) + b * x * Math.Cos(a * x);
Func<double,double,double,double> derB = (double a, double b, double x) => - a * x * Math.Sin(b * x) + Math.Sin(a * x);
LevenbergMarquardt.model_func jacFunc = (NRealMatrix x, NRealMatrix weights) =>
{
NRealMatrix jac = new NRealMatrix(x.Rows, 2);
double a = weights[0, 0]; double b = weights[0, 1];
for (int idxRow = 0; idxRow < jac.Rows; idxRow++)
{
jac.SetAt(idxRow, 0, new NDouble(-derA(a, b, x[idxRow, 0])));
jac.SetAt(idxRow, 1, new NDouble(-derB(a, b, x[idxRow, 0])));
}
return jac;
};
LevenbergMarquardt calibModel = new LevenbergMarquardt(objFunc, inputs, modelParams, modelFunc, jacFunc);
calibModel.Solve();
if (Math.Abs(modelParams[0].X - 2) > calibModel.ObjectiveError || Math.Abs(modelParams[1].X - 1) > calibModel.ObjectiveError)
throw new ApplicationException("LevenbergMarquardt calibration error");
// Parity-2 problem
NeuralNetwork ann = new NeuralNetwork(2, 1, new List<int>() { 2 });
List<List<double>> annInputs = new List<List<double>>();
annInputs.Add(new List<double>() { -1, -1 });
annInputs.Add(new List<double>() { -1, 1 });
annInputs.Add(new List<double>() { 1, -1 });
annInputs.Add(new List<double>() { 1, 1 });
List<List<double>> annOutputs = new List<List<double>>();
annOutputs.Add(new List<double>() { 1 });
annOutputs.Add(new List<double>() { -1 });
annOutputs.Add(new List<double>() { -1 });
annOutputs.Add(new List<double>() { 1 });
// test forward propagation
ann._outputs.Neurons[0].Weights[0].X = 1;
ann._outputs.Neurons[0].Weights[1].X = -1;
ann._outputs.Neurons[0].Weights[2].X = -1;
ann._innerLayers[0].Neurons[0].Weights[0].X = 1;
ann._innerLayers[0].Neurons[0].Weights[1].X = 1;
ann._innerLayers[0].Neurons[0].Weights[2].X = 1;
ann._innerLayers[0].Neurons[1].Weights[0].X = 1;
ann._innerLayers[0].Neurons[1].Weights[1].X = 1;
ann._innerLayers[0].Neurons[1].Weights[2].X = -1;
ann._inputs.Neurons[0].Value.X = -1;
ann._inputs.Neurons[1].Value.X = -1;
if (Math.Abs(ann._outputs.Neurons[0].Activation() - -0.38873457229297215) > calibModel.ObjectiveError)
throw new ApplicationException("Neural network forward propagation error");
// Test neural network training for parity-2 problem
ann = new NeuralNetwork(2, 1, new List<int>() { 2 });
ann.Train(annInputs, annOutputs);
// Test neural network training for parity-3 problem
ann = new NeuralNetwork(3, 1, new List<int>() { 2 });
annInputs = new List<List<double>>();
annInputs.Add(new List<double>() {-1,-1,-1});
annInputs.Add(new List<double>() {-1,-1, 1});
annInputs.Add(new List<double>() {-1, 1,-1});
annInputs.Add(new List<double>() {-1, 1, 1});
annInputs.Add(new List<double>() { 1,-1,-1});
annInputs.Add(new List<double>() { 1,-1, 1});
annInputs.Add(new List<double>() { 1, 1,-1});
annInputs.Add(new List<double>() { 1, 1, 1});
annOutputs = new List<List<double>>();
annOutputs.Add(new List<double>() { -1 });
annOutputs.Add(new List<double>() { 1 });
annOutputs.Add(new List<double>() { 1 });
annOutputs.Add(new List<double>() { -1 });
annOutputs.Add(new List<double>() { 1 });
annOutputs.Add(new List<double>() { -1 });
annOutputs.Add(new List<double>() { -1 });
annOutputs.Add(new List<double>() { 1 });
ann.Train(annInputs, annOutputs);
Console.WriteLine(action + " live indicators and signals...");
tests = new List<string>();
tests.Add(@"..\..\expected_results\core_22_1_2016.csv");
if (generate_from_db)
dicSettings["DB_CONTACTPOINT"] = "192.168.1.26";
dicSettings["REPLAY_MODE"] = generate_from_db ? "DB" : "CSV";
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
if (generate)
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\coregen_22_1_2016.csv");
MarketDataConnection.Instance.Connect(null);
dax.Clear();
var model = new ModelMacDTest(dax);
model.StartSignals();
Console.WriteLine(action + " daily indicators...");
model.StopSignals();
Thread.Sleep(1000);
if (!dicSettings.ContainsKey("PUBLISHING_CSV"))
{
// the program is expected to throw exceptions in this scope, just press continue if you are debugging
// all exceptions should be handled, and the program should terminate with a success message box
// test that the right numer of trades was placed. this is an extra sanity check to make sure the program is not idle
if (ReplayTester.Instance.NbProducedTrades != ReplayTester.Instance.NbExpectedTrades)
model.ProcessError(string.Format("the model did not produced the expected number of trades. It produced {0} trades instead of {1} expected",
ReplayTester.Instance.NbProducedTrades, ReplayTester.Instance.NbExpectedTrades));
// test trade booking
MarketDataConnection.Instance = new ReplayConnection();
model = new ModelMacDTest(dax);
MarketDataConnection.Instance.Connect(null);
Console.WriteLine(action + " trade booking...");
var tradeTime = Config.ParseDateTimeLocal(dicSettings["TRADING_CLOSING_TIME"]).AddSeconds(-1);
var tradeTest = new Trade(tradeTime, dax.Id, SIGNAL_CODE.SELL, 10, 10000m);
var expectedTrades = new Dictionary<KeyValuePair<string, DateTime>, Trade>();
expectedTrades[new KeyValuePair<string, DateTime>("###DUMMY_TRADE_REF1###", tradeTime)] = tradeTest;
ReplayTester.Instance.SetExpectedResults(null, null, expectedTrades, null);
model.PTF.Subscribe();
model.PTF.BookTrade(tradeTest);
Thread.Sleep(1000);
if (model.PTF.GetPosition(tradeTest.Epic).Quantity != -10)
throw new ApplicationException("SELL Trade booking error");
var expectedTrade = new Trade(tradeTime, dax.Id, SIGNAL_CODE.BUY, 10, 10000m);
expectedTrade.Reference = "###CLOSE_DUMMY_TRADE_REF2###";
expectedTrade.Id = "###DUMMY_TRADE_ID1###";
expectedTrades[new KeyValuePair<string, DateTime>(expectedTrade.Reference, tradeTime)] = expectedTrade;
model.PTF.ClosePosition(tradeTest, tradeTime);
Thread.Sleep(1000);
if (model.PTF.GetPosition(tradeTest.Epic).Quantity != 0)
throw new ApplicationException("Trade position closing error");
expectedTrade.Reference = "###DUMMY_TRADE_REF3###";
expectedTrade.Id = "###DUMMY_TRADE_ID2###";
expectedTrades[new KeyValuePair<string, DateTime>(expectedTrade.Reference, tradeTime)] = expectedTrade;
model.PTF.BookTrade(new Trade(tradeTest, true, tradeTime));
Thread.Sleep(1000);
if (model.PTF.GetPosition(tradeTest.Epic).Quantity != 10)
throw new ApplicationException("BUY Trade booking error");
expectedTrade = new Trade(tradeTime, dax.Id, SIGNAL_CODE.SELL, 10, 0m);
expectedTrade.Reference = "###CLOSE_DUMMY_TRADE_REF4###";
expectedTrade.Id = "###DUMMY_TRADE_ID2###";
expectedTrades[new KeyValuePair<string, DateTime>(expectedTrade.Reference, tradeTime)] = expectedTrade;
Portfolio.Instance.CloseAllPositions(tradeTest.TradingTime);
Thread.Sleep(1000);
// test synchronization issues with the broker
List<string> testsSync = new List<string>();
testsSync.Add(@"..\..\expected_results\sync.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(testsSync);
MarketDataConnection.Instance = new ReplayCrazySeller();
model = new ModelMacDTest(dax);
Console.WriteLine(action + " synchronization...");
MarketDataConnection.Instance.Connect(null);
model.StartSignals();
model.StopSignals();
testsSync = new List<string>();
testsSync.Add(@"..\..\expected_results\sync2.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(testsSync);
MarketDataConnection.Instance = new ReplayCrazyBuyer();
model = new ModelMacDTest(dax);
MarketDataConnection.Instance.Connect(null);
model.StartSignals();
model.StopSignals();
Console.WriteLine(action + " expected exceptions...");
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
MarketDataConnection.Instance = new ReplayConnection();
MarketDataConnection.Instance.Connect(null);
List<string> testError = new List<string>();
testError.Add(@"..\..\expected_results\error.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(testError);
var modelErr = new ModelMacDTest(dax);
string expected;
bool success = false;
try
{
MarketDataConnection.Instance.Connect(null);
modelErr.StartSignals();
}
catch (Exception exc)
{
expected = "Test failed: indicator EMA_1_IX.D.DAX.DAILY.IP time 08:30 expected value 9740.300000000000000000000000 != 9739.8";
success = (exc.Message.Replace(" AM", "") == expected);
if (!success)
model.ProcessError(exc.Message, expected);
}
if (!success)
model.ProcessError("An expected exception has not been thrown");
success = false;
try
{
modelErr.StopSignals();
}
catch (Exception exc)
{
model.ProcessError(exc.Message + " - Wrong daily mean exception removed");
}
success = false;
try
{
model.StopSignals();
}
catch (Exception exc)
{
model.ProcessError(exc.Message + " - Double EOD publishing exception removed");
}
success = false;
try
{
MarketDataConnection.Instance = new ReplayConnection();
MarketDataConnection.Instance.Connect(null);
model = new ModelMacDTest(new MarketData(dax.Id));
model.StartSignals();
}
catch (Exception exc)
{
expected = "Test failed: indicator EMA_1_IX.D.DAX.DAILY.IP time 08:30 expected value 9740.300000000000000000000000 != 9739.8";
success = (exc.Message.Replace(" AM", "") == expected);
if (!success)
model.ProcessError(exc.Message, expected);
}
if (!success)
model.ProcessError("An expected exception has not been thrown");
success = false;
try
{
MarketDataConnection.Instance.Resume();
}
catch (Exception exc)
{
expected = "Time series do not accept values in the past";
success = (exc.Message.Replace(" AM", "") == expected);
if (!success)
model.ProcessError(exc.Message, expected);
}
if (!success)
model.ProcessError("An expected exception has not been thrown");
model.StopSignals();
success = false;
}
}
public static void Run(bool generate = false, bool generate_from_db = false)
{
Dictionary <string, string> dicSettings = new Dictionary <string, string>();
dicSettings["APP_NAME"] = "Midax";
dicSettings["PUBLISHING_START_TIME"] = "2016-01-22 08:00:00";
dicSettings["PUBLISHING_STOP_TIME"] = "2016-01-22 09:00:00";
dicSettings["REPLAY_MODE"] = "CSV";
dicSettings["REPLAY_POPUP"] = "1";
dicSettings["TRADING_START_TIME"] = "2016-01-22 08:45:00";
dicSettings["TRADING_STOP_TIME"] = "2016-01-22 08:59:00";
dicSettings["TRADING_CLOSING_TIME"] = "2016-01-22 08:57:00";
dicSettings["TRADING_MODE"] = "REPLAY";
dicSettings["TRADING_SIGNAL"] = "MacD_1_5_IX.D.DAX.DAILY.IP";
dicSettings["TRADING_LIMIT_PER_BP"] = "10";
dicSettings["TRADING_CURRENCY"] = "GBP";
Config.Settings = dicSettings;
string action = generate ? "Generating" : "Testing";
var dax = new MarketData("DAX:IX.D.DAX.DAILY.IP");
List <string> tests = new List <string>();
Console.WriteLine(action + " WMA...");
// Test weighted moving average with long intervals
tests.Add(@"..\..\expected_results\testWMA.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
if (generate)
{
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testWMAgen.csv");
}
var macDTestWMA = new ModelMacDTest(dax, 1, 2, 3);
MarketDataConnection.Instance.Connect(null);
macDTestWMA.StartSignals();
macDTestWMA.StopSignals();
// Test weighted moving average with short intervals
tests = new List <string>();
tests.Add(@"..\..\expected_results\testWMA2.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
if (generate)
{
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testWMA2gen.csv");
}
dax.Clear();
macDTestWMA = new ModelMacDTest(dax, 1, 2, 3);
MarketDataConnection.Instance.Connect(null);
macDTestWMA.StartSignals();
macDTestWMA.StopSignals();
// Test weighted moving average with linear time decay
tests = new List <string>();
tests.Add(@"..\..\expected_results\testWMA3.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
dicSettings["TIME_DECAY_FACTOR"] = "3";
if (generate)
{
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testWMA3gen.csv");
}
dax.Clear();
macDTestWMA = new ModelMacDTest(dax, 1, 2, 3);
MarketDataConnection.Instance.Connect(null);
macDTestWMA.StartSignals();
macDTestWMA.StopSignals();
// Test volume weighted moving average with linear time decay
/*
* tests = new List<string>();
* tests.Add(@"..\..\expected_results\testWMA4.csv");
* dicSettings["REPLAY_CSV"] = Config.TestList(tests);
* if (generate)
* dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testWMA4gen.csv");
* var macDVTest = new ModelMacDVTest(dax, 1, 2, 3);
* MarketDataConnection.Instance.Connect(null);
* macDVTest.StartSignals();
* macDVTest.StopSignals();*/
dicSettings.Remove("TIME_DECAY_FACTOR");
// Test RSI and Correlation indicators
tests = new List <string>();
tests.Add(@"..\..\expected_results\testRsiCorrel.csv");
dicSettings["INDEX_ICEDOW"] = "DOW:IceConnection_DOW";
dicSettings["INDEX_DOW"] = "DOW:IX.D.DOW.DAILY.IP";
dicSettings["INDEX_DAX"] = "DAX:IX.D.DAX.DAILY.IP";
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
if (generate)
{
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\testRsiCorrelgen.csv");
}
dax.Clear();
var icedow = new MarketData(dicSettings["INDEX_ICEDOW"]);
var dow = new MarketData(dicSettings["INDEX_DOW"]);
var macD = new ModelMacDTest(dax, 1, 2, 3);
//var macDV = new ModelMacDVTest(icedow, 1, 2, 3, dow);
var moleTest = new ModelMoleTest(macD);
MarketDataConnection.Instance.Connect(null);
macD.StartSignals(false);
//macDV.StartSignals(false);
moleTest.StartSignals(false);
MarketDataConnection.Instance.StartListening();
moleTest.StopSignals(false);
//macDV.StartSignals(false);
macD.StopSignals(false);
MarketDataConnection.Instance.StopListening();
Console.WriteLine(action + " calibration...");
// Test a 1mn linear regression
var mktData = new MarketData("testLRMktData");
var updateTime = Config.ParseDateTimeLocal(dicSettings["TRADING_START_TIME"]);
mktData.TimeSeries.Add(updateTime, new Price(100));
mktData.TimeSeries.Add(updateTime.AddSeconds(20), new Price(120));
mktData.TimeSeries.Add(updateTime.AddSeconds(40), new Price(140));
mktData.TimeSeries.Add(updateTime.AddSeconds(60), new Price(130));
mktData.TimeSeries.Add(updateTime.AddSeconds(80), new Price(145));
mktData.TimeSeries.Add(updateTime.AddSeconds(100), new Price(165));
mktData.TimeSeries.Add(updateTime.AddSeconds(120), new Price(145));
var linReg = new IndicatorLinearRegression(mktData, new TimeSpan(0, 2, 0));
var linRegCoeff = linReg.linearCoeff(updateTime.AddSeconds(120));
if (Math.Abs(linRegCoeff.Value - 0.821428571428573m) > 1e-8m)
{
throw new ApplicationException("Linear regression error");
}
// Test the optimization of function a * cos(b * x) + b * sin(a * x) using Levenberg Marquardt
LevenbergMarquardt.objective_func objFunc = (NRealMatrix x) => { NRealMatrix y = new NRealMatrix(x.Rows, 1);
for (int idxRow = 0; idxRow < y.Rows; idxRow++)
{
y.SetAt(idxRow, 0, new NDouble(2 * Math.Cos(x[idxRow, 0]) + Math.Sin(2 * x[idxRow, 0])));
}
return(y); };
List <double> inputs = new List <double>();
Random rnd = new Random(155);
for (int idxPt = 0; idxPt < 10; idxPt++)
{
inputs.Add(rnd.NextDouble() * 2);
}
List <Value> modelParams = new List <Value>();
modelParams.Add(new Value(-0.2)); modelParams.Add(new Value(0.3));
LevenbergMarquardt.model_func modelFunc = (NRealMatrix x, NRealMatrix weights) => { NRealMatrix y = new NRealMatrix(x.Rows, 1);
double a = weights[0, 0]; double b = weights[0, 1];
for (int idxRow = 0; idxRow < y.Rows; idxRow++)
{
y.SetAt(idxRow, 0, new NDouble(a * Math.Cos(b * x[idxRow, 0]) + b * Math.Sin(a * x[idxRow, 0])));
}
return(y); };
Func <double, double, double, double> derA = (double a, double b, double x) => Math.Cos(b * x) + b * x * Math.Cos(a * x);
Func <double, double, double, double> derB = (double a, double b, double x) => - a * x * Math.Sin(b * x) + Math.Sin(a * x);
LevenbergMarquardt.model_func jacFunc = (NRealMatrix x, NRealMatrix weights) =>
{
NRealMatrix jac = new NRealMatrix(x.Rows, 2);
double a = weights[0, 0]; double b = weights[0, 1];
for (int idxRow = 0; idxRow < jac.Rows; idxRow++)
{
jac.SetAt(idxRow, 0, new NDouble(-derA(a, b, x[idxRow, 0])));
jac.SetAt(idxRow, 1, new NDouble(-derB(a, b, x[idxRow, 0])));
}
return(jac);
};
LevenbergMarquardt calibModel = new LevenbergMarquardt(objFunc, inputs, modelParams, modelFunc, jacFunc);
calibModel.Solve();
if (Math.Abs(modelParams[0].X - 2) > calibModel.ObjectiveError || Math.Abs(modelParams[1].X - 1) > calibModel.ObjectiveError)
{
throw new ApplicationException("LevenbergMarquardt calibration error");
}
// Parity-2 problem
NeuralNetwork ann = new NeuralNetwork(2, 1, new List <int>()
{
2
});
List <List <double> > annInputs = new List <List <double> >();
annInputs.Add(new List <double>()
{
-1, -1
});
annInputs.Add(new List <double>()
{
-1, 1
});
annInputs.Add(new List <double>()
{
1, -1
});
annInputs.Add(new List <double>()
{
1, 1
});
List <List <double> > annOutputs = new List <List <double> >();
annOutputs.Add(new List <double>()
{
1
});
annOutputs.Add(new List <double>()
{
-1
});
annOutputs.Add(new List <double>()
{
-1
});
annOutputs.Add(new List <double>()
{
1
});
// test forward propagation
ann._outputs.Neurons[0].Weights[0].X = 1;
ann._outputs.Neurons[0].Weights[1].X = -1;
ann._outputs.Neurons[0].Weights[2].X = -1;
ann._innerLayers[0].Neurons[0].Weights[0].X = 1;
ann._innerLayers[0].Neurons[0].Weights[1].X = 1;
ann._innerLayers[0].Neurons[0].Weights[2].X = 1;
ann._innerLayers[0].Neurons[1].Weights[0].X = 1;
ann._innerLayers[0].Neurons[1].Weights[1].X = 1;
ann._innerLayers[0].Neurons[1].Weights[2].X = -1;
ann._inputs.Neurons[0].Value.X = -1;
ann._inputs.Neurons[1].Value.X = -1;
if (Math.Abs(ann._outputs.Neurons[0].Activation() - -0.38873457229297215) > calibModel.ObjectiveError)
{
throw new ApplicationException("Neural network forward propagation error");
}
// Test neural network training for parity-2 problem
ann = new NeuralNetwork(2, 1, new List <int>()
{
2
});
ann.Train(annInputs, annOutputs);
// Test neural network training for parity-3 problem
ann = new NeuralNetwork(3, 1, new List <int>()
{
2
});
annInputs = new List <List <double> >();
annInputs.Add(new List <double>()
{
-1, -1, -1
});
annInputs.Add(new List <double>()
{
-1, -1, 1
});
annInputs.Add(new List <double>()
{
-1, 1, -1
});
annInputs.Add(new List <double>()
{
-1, 1, 1
});
annInputs.Add(new List <double>()
{
1, -1, -1
});
annInputs.Add(new List <double>()
{
1, -1, 1
});
annInputs.Add(new List <double>()
{
1, 1, -1
});
annInputs.Add(new List <double>()
{
1, 1, 1
});
annOutputs = new List <List <double> >();
annOutputs.Add(new List <double>()
{
-1
});
annOutputs.Add(new List <double>()
{
1
});
annOutputs.Add(new List <double>()
{
1
});
annOutputs.Add(new List <double>()
{
-1
});
annOutputs.Add(new List <double>()
{
1
});
annOutputs.Add(new List <double>()
{
-1
});
annOutputs.Add(new List <double>()
{
-1
});
annOutputs.Add(new List <double>()
{
1
});
ann.Train(annInputs, annOutputs);
Console.WriteLine(action + " live indicators and signals...");
tests = new List <string>();
tests.Add(@"..\..\expected_results\core_22_1_2016.csv");
if (generate_from_db)
{
dicSettings["DB_CONTACTPOINT"] = "192.168.1.26";
}
dicSettings["REPLAY_MODE"] = generate_from_db ? "DB" : "CSV";
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
if (generate)
{
dicSettings["PUBLISHING_CSV"] = string.Format("..\\..\\expected_results\\coregen_22_1_2016.csv");
}
MarketDataConnection.Instance.Connect(null);
dax.Clear();
var model = new ModelMacDTest(dax);
model.StartSignals();
Console.WriteLine(action + " daily indicators...");
model.StopSignals();
Thread.Sleep(1000);
if (!dicSettings.ContainsKey("PUBLISHING_CSV"))
{
// the program is expected to throw exceptions in this scope, just press continue if you are debugging
// all exceptions should be handled, and the program should terminate with a success message box
// test that the right numer of trades was placed. this is an extra sanity check to make sure the program is not idle
if (ReplayTester.Instance.NbProducedTrades != ReplayTester.Instance.NbExpectedTrades)
{
model.ProcessError(string.Format("the model did not produced the expected number of trades. It produced {0} trades instead of {1} expected",
ReplayTester.Instance.NbProducedTrades, ReplayTester.Instance.NbExpectedTrades));
}
// test trade booking
MarketDataConnection.Instance = new ReplayConnection(true);
model = new ModelMacDTest(dax);
MarketDataConnection.Instance.Connect(null);
Console.WriteLine(action + " trade booking...");
var tradeTime = Config.ParseDateTimeLocal(dicSettings["TRADING_CLOSING_TIME"]).AddSeconds(-1);
var tradeTest = new Trade(tradeTime, dax.Id, SIGNAL_CODE.SELL, 10, 10000m);
var expectedTrades = new Dictionary <KeyValuePair <string, DateTime>, Trade>();
expectedTrades[new KeyValuePair <string, DateTime>("###DUMMY_TRADE_REF1###", tradeTime)] = tradeTest;
ReplayTester.Instance.SetExpectedResults(null, null, expectedTrades, null);
var task = model.PTF.Subscribe();
task.Wait();
model.PTF.BookTrade(tradeTest, dax.Name);
Thread.Sleep(5000);
if (model.PTF.GetPosition(tradeTest.Epic).Quantity != -10)
{
throw new ApplicationException("SELL Trade booking error");
}
var expectedTrade = new Trade(tradeTime, dax.Id, SIGNAL_CODE.BUY, 10, 10000m);
expectedTrade.Reference = "###CLOSE_DUMMY_TRADE_REF2###";
expectedTrade.Id = "###DUMMY_TRADE_ID1###";
expectedTrades[new KeyValuePair <string, DateTime>(expectedTrade.Reference, tradeTime)] = expectedTrade;
model.PTF.ClosePosition(tradeTest, tradeTime);
Thread.Sleep(5000);
if (model.PTF.GetPosition(tradeTest.Epic).Quantity != 0)
{
throw new ApplicationException("Trade position closing error");
}
expectedTrade.Reference = "###DUMMY_TRADE_REF3###";
expectedTrade.Id = "###DUMMY_TRADE_ID2###";
expectedTrades[new KeyValuePair <string, DateTime>(expectedTrade.Reference, tradeTime)] = expectedTrade;
model.PTF.BookTrade(new Trade(tradeTest, true, tradeTime), dax.Name);
Thread.Sleep(5000);
if (model.PTF.GetPosition(tradeTest.Epic).Quantity != 10)
{
throw new ApplicationException("BUY Trade booking error");
}
expectedTrade = new Trade(tradeTime, dax.Id, SIGNAL_CODE.SELL, 10, 0m);
expectedTrade.Reference = "###CLOSE_DUMMY_TRADE_REF4###";
expectedTrade.Id = "###DUMMY_TRADE_ID2###";
expectedTrades[new KeyValuePair <string, DateTime>(expectedTrade.Reference, tradeTime)] = expectedTrade;
Portfolio.Instance.CloseAllPositions(tradeTest.TradingTime);
Thread.Sleep(5000);
// test synchronization issues with the broker
List <string> testsSync = new List <string>();
testsSync.Add(@"..\..\expected_results\sync.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(testsSync);
MarketDataConnection.Instance = new ReplayCrazySeller();
model = new ModelMacDTest(dax);
Console.WriteLine(action + " synchronization...");
MarketDataConnection.Instance.Connect(null);
model.StartSignals();
model.StopSignals();
testsSync = new List <string>();
testsSync.Add(@"..\..\expected_results\sync2.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(testsSync);
MarketDataConnection.Instance = new ReplayCrazyBuyer();
model = new ModelMacDTest(dax);
MarketDataConnection.Instance.Connect(null);
model.StartSignals();
model.StopSignals();
Console.WriteLine(action + " expected exceptions...");
dicSettings["REPLAY_CSV"] = Config.TestList(tests);
MarketDataConnection.Instance = new ReplayConnection(true);
MarketDataConnection.Instance.Connect(null);
List <string> testError = new List <string>();
testError.Add(@"..\..\expected_results\error.csv");
dicSettings["REPLAY_CSV"] = Config.TestList(testError);
var modelErr = new ModelMacDTest(dax);
string expected;
bool success = false;
try
{
MarketDataConnection.Instance.Connect(null);
modelErr.StartSignals();
}
catch (Exception exc)
{
expected = "Test failed: indicator EMA_1_IX.D.DAX.DAILY.IP time 08:30 expected value 9740.791666666666666666666667 != 9740.3";
success = (exc.Message.Replace(" AM", "") == expected);
if (!success)
{
model.ProcessError(exc.Message, expected);
}
}
if (!success)
{
model.ProcessError("An expected exception has not been thrown");
}
success = false;
try
{
modelErr.StopSignals();
}
catch (Exception exc)
{
model.ProcessError(exc.Message + " - Wrong daily mean exception removed");
}
success = false;
try
{
model.StopSignals();
}
catch (Exception exc)
{
model.ProcessError(exc.Message + " - Double EOD publishing exception removed");
}
success = false;
try
{
MarketDataConnection.Instance = new ReplayConnection(true);
MarketDataConnection.Instance.Connect(null);
model = new ModelMacDTest(new MarketData(dax.Id));
model.StartSignals();
}
catch (Exception exc)
{
expected = "Test failed: indicator EMA_1_IX.D.DAX.DAILY.IP time 08:30 expected value 9740.791666666666666666666667 != 9740.3";
success = (exc.Message.Replace(" AM", "") == expected);
if (!success)
{
model.ProcessError(exc.Message, expected);
}
}
if (!success)
{
model.ProcessError("An expected exception has not been thrown");
}
success = false;
try
{
MarketDataConnection.Instance.Resume();
}
catch (Exception exc)
{
expected = "Time series do not accept values in the past";
success = (exc.Message.Replace(" AM", "") == expected);
if (!success)
{
model.ProcessError(exc.Message, expected);
}
}
if (!success)
{
model.ProcessError("An expected exception has not been thrown");
}
model.StopSignals();
success = false;
}
}
void nextStep()
{
// compute jacobian
if (_retry == 0)
_jac = _jac_func(_inputs, _weights);
// compute hessian approximation with tykhonov damping coefficient
var jacT = new NRealMatrix(_jac.Rows, _jac.Columns);
jacT.SetArray(_jac.ToArray());
jacT.Transpose();
var dampedHessian = new NRealMatrix(_jac.Columns, _jac.Columns);
dampedHessian = jacT * _jac;
for (int idxRow = 0; idxRow < dampedHessian.Rows; idxRow++)
dampedHessian.SetAt(idxRow, idxRow, new NDouble(dampedHessian[idxRow, idxRow] * (1.0 + _lambda) + 1e-10));
var adj = new NRealMatrix(dampedHessian.Rows, 1);
var y = new NRealMatrix(dampedHessian.Rows, 1);
y = jacT * _error;
// solve dampedHessian * adj = y
LapackLib.Instance.SolveSle(dampedHessian, y, adj);
var nextWeights = new NRealMatrix(1, _weights.Columns);
for (int idxWeight = 0; idxWeight < nextWeights.Columns; idxWeight++)
nextWeights.SetAt(0, idxWeight, new NDouble(_weights[0, idxWeight] - adj[idxWeight, 0]));
// compute errors
var error = calcError(nextWeights);
var totalError = calcTotalError(error);
if (totalError > _totalError)
{
// revert step and increase damping factor
if (_retry < 100)
{
_lambda *= 11.0;
_retry++;
}
else
{
updateWeights();
throw new StallException();
}
}
else
{
// accept step and decrease damping factor
_lambda /= 9.0;
_weights.SetArray(nextWeights.ToArray());
_error = error;
_totalError = totalError;
_retry = 0;
}
}
NRealMatrix calcError(NRealMatrix weights)
{
var error = new NRealMatrix(_inputs.Rows, 1);
NRealMatrix modelOutput = _model(_inputs, weights);
for (int idxError = 0; idxError < error.Rows; idxError++)
error.SetAt(idxError, 0, new NDouble(_outputs[idxError, 0] - modelOutput[idxError, 0]));
return error;
}