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DL4J Example of Time Series Regression with 2 Outputs:
/*
Problem Description:
Time series forecast for a single securities minor reversal points.
A "minor reversal point" is defined as either a period (day) with a high price greater than both the previous and next high prices, or a period with a low value lower than both the previous and next low prices.
This is a time series regression problem with 8 input features and 2 regression output dimensions
Input Features:
- period return r = (p2 - p1) / p1, descr: a linear return value, typically [-0.05,0.05]
- period volume, descr: standardized, z = (x - Mean) / SD
- High-Low spread, descr: standardized, z = (x - Mean) / SD
- overnight return ((open - previousClose) / previousClose), descr: a linear return value, typically [-0.05,0.05]
- open position in Range (open / (high - low)), descr: [0-1]
- close position in Range (close / (high - low)), descr: [0-1]
- return since last minor reversal point, descr: linear return value, typically [-0.10,0.10]
- periods (days) since last minor reversal point, descr: Standardized, z = (x - Mean) / SD
Regression Targets:
- linear return of next reversal point relative to current price, descr: linear return value, typically [-0.10,0.10]
- periods (days) until next reversal point, descr: Standardized, z = (x - Mean) / SD
NOTE: Regarding regression target "periods (days)" we'll have to adjust the standardized output of "periods (days)" to the problem domain scale when using the prediction value (i.e. adjustedPeriods = prediction * SD + Mean)
*/
@Test
public void recurrentTimeSeries_1Stock_2Outputs() throws Exception
{
String symbol="AAPL";
String featuresPath = String.format("/tmp/features_training_%s.csv", symbol);
File featuresFile = new File(featuresPath);
String labelsPath = String.format("/tmp/labels_training_%s.csv", symbol);
File labelsFile = new File(labelsPath);
String testFeaturesPath = String.format("/tmp/features_test_%s.csv", symbol);
File testFeaturesFile = new File(testFeaturesPath);
String testLabelsPath = String.format("/tmp/labels_test_%s.csv", symbol);
File testLabelsFile = new File(testLabelsPath);
int miniBatchSize = 1; //number of stocks we are learning on
DataSetIterator trainingIterator = getSequenceIterator(featuresFile, labelsFile, miniBatchSize);
//Set up network configuration:
int lstmLayerSize = 200;
int numOutputs = 2;
int tbpttLength = 125; //Length for truncated backpropagation through time. i.e., do parameter updates every x periods
int numEpochs = 400; //Total number of training epochs
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.iterations(1)
.learningRate(0.001)
.rmsDecay(0.95)
.seed(12345)
.regularization(true)
.l2(0.001)
.weightInit(WeightInit.XAVIER)
.updater(Updater.RMSPROP)
.list(3)
.layer(0, new GravesLSTM.Builder().nIn(trainingIterator.inputColumns()).nOut(lstmLayerSize)
.activation("tanh").build())
.layer(1, new GravesLSTM.Builder().nIn(lstmLayerSize).nOut(lstmLayerSize)
.activation("tanh").build())
.layer(2, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation("identity")
.nIn(lstmLayerSize).nOut(numOutputs).build())
.backpropType(BackpropType.TruncatedBPTT).tBPTTForwardLength(tbpttLength).tBPTTBackwardLength(tbpttLength)
.pretrain(false)
.backprop(true)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.setListeners(new ScoreIterationListener(1));
//Train the network
for( int i=0; i<numEpochs; i++ ){
System.out.println("Training epoch " + i);
net.fit(trainingIterator);
trainingIterator.reset();
}
System.out.println("Training complete.");
//Step through Test dataset making predictions and measuring Mean Square Error Deviation for each output variable
DataSetIterator testingIterator = getSequenceIterator(testFeaturesFile, testLabelsFile, miniBatchSize);
int t = 0;
while(testingIterator.hasNext())
{
DataSet dsTest = testingIterator.next();
INDArray featuresAtTimeT = dsTest.getFeatures();
INDArray prediction = net.rnnTimeStep(featuresAtTimeT);
INDArray actuals = dsTest.getLabels();
System.out.println(String.format("t= %d, Standardized Prediction: %s, Standardized Actual: %s",
t, prediction.toString(), actuals.toString()));
//convert standardized predictions and actuals to non standardized values
double[] predictionReturns = new double[testSetLength];
double[] standardPredictionPeriods = new double[testSetLength];
for(int i=0; i < testSetLength; i++ ) {
predictionReturns[i] = prediction.getDouble(0, 0, i);
standardPredictionPeriods[i] = prediction.getDouble(0, 1, i);
}
double[] adjustedPredictionPeriods = TALib.convertZScoreToRawScore(standardPredictionPeriods, colMeans[labelIndex2], colStdDevs[labelIndex2]);
double[] actualReturns = new double[testSetLength];
double[] standardActualPeriods = new double[testSetLength];
for(int i=0; i < testSetLength; i++ ) {
actualReturns[i] = actuals.getDouble(0, 0, i);
standardActualPeriods[i] = actuals.getDouble(0, 1, i);
}
double[] adjustedActualPeriods = TALib.convertZScoreToRawScore(standardActualPeriods, colMeans[labelIndex2], colStdDevs[labelIndex2]);
System.out.println(String.format("\nt= %d, Adjusted Prediction Returns: %s, \nAdjusted Actual Returns: %s", t, join(predictionReturns, "%.3f"), join(actualReturns, "%.3f")));
System.out.println(String.format("\nt= %d, Adjusted Prediction Periods: %s, \nAdjusted Actual Periods: %s", t, join(adjustedPredictionPeriods, "%.1f"), join(adjustedActualPeriods, "%.1f")));
//compute mean root square error
double rmsdReturns = TALib.calcRootMeanSquareDeviation(predictionReturns, actualReturns);
double rmsdPeriods = TALib.calcRootMeanSquareDeviation(adjustedPredictionPeriods, adjustedActualPeriods);
System.out.println(String.format("RMSD Returns = %.4f, Periods = %.4f", rmsdReturns, rmsdPeriods));
t++;
}
System.out.println("\n\nExample complete");
}
DataSetIterator getSequenceIterator(File featuresFile, File labelsFile, int miniBatchSize)
throws IOException, InterruptedException {
int numLinesToSkip = 1;
String delimiter = ",";
boolean regression = true;
int numClasses = 1; //not used for regression
SequenceRecordReader featureReader = new CSVSequenceRecordReader(numLinesToSkip, delimiter);
SequenceRecordReader labelReader = new CSVSequenceRecordReader(numLinesToSkip, delimiter);
featureReader.initialize(new FileSplit(featuresFile));
labelReader.initialize(new FileSplit(labelsFile));
return new SequenceRecordReaderDataSetIterator(featureReader, labelReader, miniBatchSize, numClasses, regression);
}
periodReturn periodVolume highLowSpread closeToOpenReturn openPIR closePIR 1PeriodReversalReturn 1PeriodReversalPeriods
-0.006046462289169407 0.046245530213759344 -0.14247955453156053 -0.00509175771719529 0.5633802816901409 0.5211267605633804 0.01198833567339886 -0.7901988167074143
0.003094983991462113 -0.4271774916235998 -0.6724655290261705 0.005229455709711846 0.7919463087248322 0.6577181208053691 0.01512042337185441 -0.0061976377780971585
0.02819448877540164 -0.06242719125039686 -0.051388215165299164 0.01095861261836366 0.18303571428571425 0.9062499999999998 0.04374122475429312 0.77780354115122
0.01531456953642384 -0.21589554602134484 -0.20044677049190837 3.104304635761589E-4 0.2524271844660194 0.9563106796116504 0.05972567231882493 1.5618047200805372
-0.01895637994292703 -0.5077254850823205 0.4123495125174843 0.007337953526294333 0.9821428571428572 0.06071428571428572 -0.02659520679542926 -0.7901988167074143
-0.002285476833575732 -0.7061528163533337 -1.1113601641545194 -0.002701018076044047 0.20833333333333334 0.25 0.002505219206680585 -0.7901988167074143
0.008746355685131196 -0.7278032141526605 -1.094798102451563 0.002811328613077884 0.3979591836734694 0.9795918367346939 -2.063983488132095E-4 -0.7901988167074143
-0.02260528488852188 -0.8446925932178414 -0.2915381098581696 -0.00495458298926507 0.9487179487179488 0.0717948717948718 -0.02280701754385965 -0.0061976377780971585
0.0148906959552223 -0.6234912609695301 0.6276563146559199 -0.007498151863977189 0.21568627450980393 0.9084967320261437 0.02978996999571367 -0.7901988167074143
0.006867845993756504 -1.0571773386978367 -0.6559034673232139 -5.202913631633715E-4 0.5298013245033113 1.0 0.03686240891555937 -0.0061976377780971585
0.001550227366680446 -0.9819751224735177 -0.713870683283562 0.00454733360892931 0.4305555555555556 0.22916666666666669 0.03846978139734248 0.77780354115122
-0.002270147559591373 -0.6867991228473054 -0.5979362513628659 -5.159426271798576E-4 0.13291139240506328 0.02531645569620253 0.03611230175739391 1.5618047200805372
0.03971455165994415 0.06939571551495992 0.86780620934879 0.009928637914986037 0.06865671641791045 0.9283582089552238 0.07726103729104158 2.345805899009854
0.002188401472197354 -0.775484577207282 -0.8712102694616493 -1.989455883815776E-4 0.696 0.8880000000000001 0.07961851693099015 3.129807077939171
0.007444168734491315 -0.5919808740489736 -0.862929238610171 -0.001687344913151365 0.10317460317460318 0.8333333333333334 0.0876553793399057 3.913808256868488
0.01487684729064039 -0.13935688548842537 0.06454621675539653 0.008571428571428572 0.42016806722689076 0.6890756302521008 -0.007132530120481928 -0.7901988167074143
-0.01106688671002815 -0.6396622371035952 -0.3577863566699957 -0.006018833122997767 0.7647058823529411 0.48663101604278075 -0.01812048192771084 -0.0061976377780971585
-0.00824580347501718 -0.8651104923770145 -0.7801189300953881 -0.01069991165210562 0.27941176470588236 0.463235294117647 -0.02621686746987952 0.77780354115122
8.908245075720083E-4 -1.070205195809823 -0.8215240843527796 0.002771454023557359 0.7938931297709924 0.648854961832061 -0.02534939759036145 1.5618047200805372
4.944620253164557E-4 -0.7564031223567094 -0.1756036779374737 0.002867879746835443 0.6028708133971292 0.48803827751196177 0.01018472291562656 -0.7901988167074143
0.01077394484530987 -1.0667353904011976 -1.2604187194811285 0.01057625778392804 0.9487179487179487 0.9743589743589743 0.02106839740389416 -0.0061976377780971585
0.002542538627029141 -1.1743142074497956 -0.9705826396793887 -0.003422648151769998 0.11504424778761063 0.6548672566371682 0.0236645032451323 0.77780354115122
0.02009364026531409 -0.4340676045034953 -0.804962022649823 0.01404603979711276 0.08270676691729323 0.5488721804511277 0.04423364952571143 1.5618047200805372
0.01329126027921209 -0.5374539462580765 -0.48200181944217 2.868617326448652E-4 0.011627906976744186 0.8023255813953488 0.0581128307538692 2.345805899009854
-0.001604227611588185 -0.7180818192560263 -0.6559034673232139 -0.00424648485420402 0.3708609271523179 0.5562913907284768 0.0564153769345981 3.129807077939171
0.00113421550094518 -0.26479455833491344 -0.8215240843527796 0.005103969754253308 0.8778625954198472 0.5572519083969465 0.05761357963055417 3.913808256868488
periodReturn periodVolume highLowSpread closeToOpenReturn openPIR closePIR 1PeriodReversalReturn 1PeriodReversalPeriods
-0.007550400871798864 -0.14564312353258288 -0.3743484183729523 0.002179497158869775 0.7297297297297297 0.05405405405405406 -0.01353965183752418 -0.7901988167074143
-0.01254901960784314 0.9498254268459729 0.776714869982529 0.005882352941176471 0.9537037037037036 0.22839506172839505 0.005912432086928731 -0.7901988167074143
0.01040508339952343 -0.5935252096944673 -0.804962022649823 0.009690230341540904 0.45112781954887216 0.5187969924812029 0.01637903483541067 -0.0061976377780971585
-0.004087728952126405 -0.7938928601098189 -0.6807465598776488 1.57220344312554E-4 0.45270270270270274 0.08783783783783784 -0.010543580131209 -0.7901988167074143
-0.02612676612203015 0.11655240043361244 0.9423354870120945 -0.001183992422448496 0.9186046511627908 0.0 -0.03639487660106217 -0.0061976377780971585
0.006970335548711298 -0.06750567962307845 -0.018264091759386373 -0.005025125628140704 0.07017543859649124 0.7192982456140351 0.0133768352365416 -0.7901988167074143
-0.007968448164842242 -0.4631476426998933 -0.42403460348182204 0.002656149388280747 0.9273743016759776 0.1899441340782123 0.005301794453507341 -0.0061976377780971585
0.02531440162271805 -0.09133398666605035 0.0811082784583531 0.006490872210953347 0.020833333333333336 0.9875 0.03075040783034258 0.77780354115122
-0.01535174487615732 -0.341511411442313 -0.14247955453156053 -0.002215715755321674 0.8122065727699531 0.03286384976525822 -0.01628587240098031 -0.7901988167074143
-0.001446596479948565 -0.4154217315016517 -0.23357089389782154 0.003134292373221892 0.8514851485148515 0.5693069306930693 0.00934199837530463 -0.7901988167074143
0.008611670020120725 -0.8232104884055236 -0.7718378992439098 0.006277665995975855 0.6131386861313868 0.8248175182481751 0.01803411860276198 -0.0061976377780971585
0.01619853175869773 -0.5875656580368566 0.7270286848736593 -0.006782636450686243 0.04402515723270441 0.949685534591195 0.03452477660438668 0.77780354115122
-0.0105221829603455 -0.6895957563078952 -0.13419852368008206 0.002277188849627012 0.7757009345794391 0.014018691588785045 -0.01646893537308773 -0.7901988167074143
-0.003253710023014047 -0.5721371509631251 -0.7221517141350403 -0.001269740496785969 0.6153846153846154 0.4405594405594406 -0.01966906025600999 -0.0061976377780971585
0.007643312101910828 -0.8240024554032127 -0.3163812024126044 0.001990445859872611 0.6197916666666666 0.9895833333333334 0.01524145676239371 -0.7901988167074143
0.004266750948166877 -0.4308502385753832 -0.2915381098581696 -0.004819848293299621 0.3538461538461538 0.9435897435897437 0.01957323921065298 -0.0061976377780971585
-0.001966955153422502 -0.6131214430935379 -0.2832570790066913 0.00999213217938631 0.8979591836734694 0.12244897959183673 -0.01337792642140468 -0.7901988167074143
-0.0043358297201419 -1.1497038329966056 -0.7635568683924318 0.001182499014584155 0.7898550724637682 0.28260869565217395 0.003097450559923755 -0.7901988167074143
0.003800475059382423 -0.9851578398454809 -0.9788636705308669 8.709422011084719E-4 0.35714285714285715 0.6875 0.006909697402906838 -0.0061976377780971585
-0.004811484461271494 -1.0323590729077534 -1.0865170716000847 -0.003943839722353684 0.17171717171717174 0.06060606060606061 0.00206496703994917 0.77780354115122
-0.01125465641594674 0.12730830222097803 -0.5151259428480832 -0.004914004914004914 0.6488095238095238 0.17261904761904762 0.002330065884621565 -0.7901988167074143
0.02284569138276553 -0.08402809111236806 0.5365649752896589 0.00657314629258517 0.13559322033898305 0.823728813559322 0.02522898923348867 -0.0061976377780971585
-0.005407523510971787 -0.8156026554339724 -0.6393414056202573 0.003918495297805643 0.9346405228758169 0.1568627450980392 0.01968503937007874 0.77780354115122
0.01347411551493184 -0.5621979651421265 0.20532374123052727 6.303679773067528E-4 0.2627450980392157 0.9019607843137255 0.0182077264091197 -0.7901988167074143
0.008163582646555746 -0.16531855363142237 -0.018264091759386373 -0.002487948997045561 0.07017543859649124 0.6710526315789475 0.02651994933502217 -0.0061976377780971585
0.004704249248091309 -0.2300915544549226 -0.7469948066894752 0.006323744890876841 0.9 0.7500000000000001 0.03134895503483217 0.77780354115122
0.01819158735032238 1.7834696877885003 -0.2666950173037347 0.01558182376420018 0.5858585858585859 0.7575757575757576 0.05011082963901203 1.5618047200805372
-0.01575574820957407 3.456381175357148 2.2093332072882714 0.01364493026762156 0.9839034205231388 0.19919517102615694 -0.02958228036271741 -0.7901988167074143
-0.01470588235294118 0.6760869838884584 0.8181200242399204 -0.003063725490196078 0.5653495440729484 0.1033434650455927 -0.04385312918091274 -0.0061976377780971585
-0.02712997512437811 1.669268046721727 1.4557593998037477 0.0 1.0 0.14039408866995073 0.00457537325413389 -0.7901988167074143
0.03036356372353176 0.41791564222927113 2.093398775367576 0.00759089093088294 0.16563146997929606 0.7556935817805382 0.03507786161502649 -0.0061976377780971585
-0.001938735944164405 0.10452935144994423 0.0065790007950486794 0.004265219077161691 0.5367965367965368 0.19047619047619047 -0.01432181971356361 -0.7901988167074143
-0.02253302253302253 0.008676595760880928 0.30469611144826675 -0.004273504273504274 0.8876404494382023 0.00749063670411985 -0.03653212836026652 -0.0061976377780971585
-0.006279809220985692 1.1418229760544976 0.9009303327547032 0.006041335453100159 0.943952802359882 0.48672566371681414 0.01337548638132296 -0.7901988167074143
0.001999840012798976 -0.23813496927520292 -0.20044677049190837 -0.001919846412287017 0.3640776699029126 0.6019417475728155 0.01540207522697795 -0.0061976377780971585
0.0188408111128852 0.336511334454299 -0.6559034673232139 0.01133642024588855 0.37748344370860926 1.0 0.0 -0.7901988167074143
-0.01018649114558847 -0.33243348973130127 -0.3081001715611261 -0.00180222535652719 0.9119170984455959 0.35751295336787564 -0.01018649114558847 -0.0061976377780971585
-0.003562381253958201 -0.0813106543515472 -0.20044677049190837 -0.005699810006333122 0.3786407766990291 0.5097087378640777 0.008412113443358436 -0.7901988167074143
0.001112258679590053 -0.7142506789047051 -0.8132430535013012 0.002224517359180106 0.21212121212121213 0.10606060606060606 0.009533728569139562 -0.0061976377780971585
0.02333148162844219 -0.21107939671664777 -0.42403460348182204 0.01111022934687723 0.13966480446927373 1.0 0.03308764621054318 0.77780354115122
-0.001395889879798371 -0.5360977027745338 -0.8463671769072144 9.305932531989143E-4 0.671875 0.43750000000000006 0.03164556962025316 1.5618047200805372
0.01102741321736429 0.07051931869293138 0.04798415505243994 -0.003028655742797235 0.00847457627118644 0.7754237288135594 0.04302195161031886 2.345805899009854
-9.217297795529611E-4 -0.21782596557821213 -0.8794913003131276 0.003840540748137338 0.8467741935483871 0.34677419354838707 0.04206056721679218 3.129807077939171
-7.688167909587145E-5 -0.6329255678295019 -0.5482500662539964 -5.381717536711002E-4 0.4024390243902439 0.43902439024390244 0.005566723364775012 -0.7901988167074143
0.01022604951560818 -0.46747871221850573 -0.41575357263034374 7.688759034291864E-5 0.13333333333333333 0.8666666666666667 0.01584969846915107 -0.0061976377780971585
0.008752568688636882 -0.17958385917729794 -0.6062172822143442 0.001598295151838039 0.12738853503184713 0.7261146496815286 -0.003233812138076258 -0.7901988167074143
-0.02203108495548514 1.0613244805331323 1.2321715668138344 4.526935264825713E-4 0.9182058047493403 0.13192612137203166 0.003872366790582404 -0.7901988167074143
0.01866995833976238 -0.15290447094264514 -0.07623130771973421 0.005554698349020213 0.13122171945701358 0.9004524886877828 0.02261462205700124 -0.0061976377780971585
-0.001969100272644653 -0.896140749305222 -1.2024515035207806 -0.001363223265677068 0.8941176470588236 0.8 0.02060099132589839 0.77780354115122
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0.005561172901921132 1.2829705022506461 0.4123495125174843 0.004954499494438827 0.6607142857142858 0.6821428571428572 -0.01515151515151515 -0.0061976377780971585
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0.03451592220559949 0.758147139333392 0.5531270369926155 0.007480230818550972 0.14814814814814814 1.0 0.0 -0.7901988167074143
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0.008035062089116142 -0.11286558941572374 -0.13419852368008206 3.130543671084212E-4 0.3130841121495327 0.6588785046728971 -0.007500256858111579 -0.7901988167074143
-0.02670807453416149 -0.11551867885798235 0.7684338391310506 -8.281573498964803E-4 0.8761609907120743 0.1021671826625387 -0.03400801397308127 -0.0061976377780971585
0.01052967453733248 0.2608289882376309 0.2964150805967886 -0.00946607104871304 0.03383458646616541 0.7406015037593985 0.02117368873602752 -0.7901988167074143
-2.105041574571098E-4 -0.22219663344670904 -0.2418519247493 -0.007578149668455952 0.17910447761194032 0.527363184079602 0.02095872742906277 -0.0061976377780971585
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nextReversalReturn nextReversalPeriods
0.05538954108858058 1.5618047200805354
0.05213320566017661 0.7778035411512185
0.02328228476821192 -0.006197637778098375
0.007847533632286996 -0.7901988167074152
-0.004778724288385622 -0.7901988167074152
0.00895460224906289 -0.7901988167074152
-0.03674649050371594 -0.006197637778098375
-0.01446826486429401 -0.7901988167074152
0.07960457856399585 3.9138082568684864
0.0722405952873088 3.129807077939169
0.07058095139820451 2.3458058990098523
0.07301685799979316 1.5618047200805354
0.032030239729434 0.7778035411512185
0.02977667493796526 -0.006197637778098375
0.02216748768472906 -0.7901988167074152
-0.02776429472866712 1.5618047200805354
-0.01688426425836851 0.7778035411512185
-0.008710284074037416 -0.006197637778098375
-0.009592563291139241 -0.7901988167074152
0.06405060788771375 3.9138082568684864
0.05270878153725797 3.129807077939169
0.0500390167772142 2.3458058990098523
0.02935551730732454 1.5618047200805354
0.01585354345569501 0.7778035411512185
0.01748582230623819 -0.006197637778098375
0.01633308157099698 -0.7901988167074152
nextReversalReturn nextReversalPeriods
-0.01835294117647059 -0.7901988167074152
0.01699761715647339 -0.006197637778098375
0.006524644288970993 -0.7901988167074152
-0.03228352671876233 -0.006197637778098375
-0.006321932241854433 -0.7901988167074152
0.01811010946555055 0.7778035411512185
0.02628803245436105 -0.006197637778098375
9.495924665664319E-4 -0.7901988167074152
-0.01068874065739773 -0.7901988167074152
0.03114688128772636 0.7778035411512185
0.02234280242578998 -0.006197637778098375
0.006046329014526894 -0.7901988167074152
-0.01071343544163162 -0.006197637778098375
-0.007484076433121019 -0.7901988167074152
0.01588179519595449 -0.006197637778098375
0.01156569630212431 -0.7901988167074152
-0.007410327158060702 -0.7901988167074152
-0.01456848772763262 0.7778035411512185
-0.01829941631172109 -0.006197637778098375
-0.01355314258540065 -0.7901988167074152
0.01258517034068136 0.7778035411512185
-0.01003134796238245 -0.006197637778098375
-0.004648963832637302 -0.7901988167074152
0.04602705644534287 1.5618047200805354
0.03755687514459783 0.7778035411512185
0.03269880257906049 -0.006197637778098375
0.01424802110817942 -0.7901988167074152
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0.04330803036356372 -0.006197637778098375
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0.02756812584412489 3.129807077939169
0.02642647408935799 2.3458058990098523
0.003024428072896472 1.5618047200805354
0.004426496854857498 0.7778035411512185
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0.02237428878978933 -0.006197637778098375
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0.002160160469063416 0.7778035411512185
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0.007540781779008926 -0.7901988167074152
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0.02166064981949458 -0.006197637778098375
0.0100869025450031 -0.7901988167074152
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0.01095178064922786 0.7778035411512185
0.005564263322884013 -0.006197637778098375
0.00793401413982718 -0.7901988167074152
-0.01157335001563966 -0.7901988167074152
0.01153238546603476 -0.7901988167074152
-0.005720554815453334 -0.7901988167074152
0.02180587262851295 -0.7901988167074152
-0.02833502458824448 0.7778035411512185
-0.02368627450980392 -0.006197637778098375
-0.01790927021696252 -0.7901988167074152
0.01935276640167028 -0.006197637778098375
0.0120385872598262 -0.7901988167074152
-0.05829383886255924 2.3458058990098523
-0.05710218285352736 1.5618047200805354
-0.05380952380952381 0.7778035411512185
-0.05147585328984008 -0.006197637778098375
-0.02733132087786571 -0.7901988167074152
0.107437328225202 3.9138082568684864
0.0786015574302401 3.129807077939169
0.05817284736590801 2.3458058990098523
0.05859406098240586 1.5618047200805354
0.04849392840246018 0.7778035411512185
0.03470547039140923 -0.006197637778098375
0.02584477704058016 -0.7901988167074152
-0.0763231619595669 -0.006197637778098375
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0.01493371665868072 -0.7901988167074152
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@jvence
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jvence commented Jan 10, 2018

Did you managed to get this working? Is minibatch size really equal the number of stocks you're training?

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