Version

Deep Signal™ Library Version

Version of the Deep Signal Library

DSTLibraryPath

Deep Signal™ Library File Path

File location of Deep Signal™ Library

BaseFolder

Base Folder Name

Deep Signal™ Data folder to be created to save data

LogMessages

Log Messages

Messages can be sent to the Ninjascript Output Window, to the log file or both

BarsToTarget

Bars To Target

The number of bars needed to reach a long or short sell profit target

TicksUpToTarget

Ticks Up To Target

The number of ticks up in x bars that the price must move to qualify our long profit target

TicksDownToTarget

Ticks Down To Target

The number of ticks down in x bars that the price must move to qualify our short profit target

UseStop

Use Stop

If Use Stop is True then our model will not use a buy/sell signal that gets stopped out. If Use Stop is False then our model will include signals that hit the profit target even if the instrument got stopped out.

StopTicks

Stop (Ticks)

The stop price in ticks from the trade entry. If the price moves against the trade by x ticks then the algorithm assumes the strategy has stopped out.

WindowSizeBars

Pre Signal Window Size (1-50)

The number of bars to include for data analysis up the point where we see a profit target hit. For example if Window Size is 10 then 10 bars of data before we hit the profit target will be included in our analysis.

UseCloseOrOpenForPrediction

Use Close or Open

When we do our prediction either we try to predict either the close prices or open prices

DontAllowPredictedPricesBetweenDays

Don't Predict Next Day

If we are trying to look 10 bars into the future to see if we go up 10 ticks then don't use data if the 10 bars goes into the opening of the next day.

BinaryClassificationMetricStr

Binary Classification Metric

The following are classification metrics provided by Microsoft that are passed to the Microsoft ML.Net Library when training a model. When training, the algorithm will try to optimize for the selected metric. The default is the F1 Score.

• Accuracy - Gets the accuracy of a classifier which is the proportion of correct predictions in the test set.
• Area Under Precision Recall Curve - Gets the area under the precision/recall curve of the classifier. 

Remarks: The area under the precision/recall curve is a single number summary of the information in the precision/recall curve. It is increasingly used in the machine learning community, particularly for imbalanced data sets where one class is observed more frequently than the other. On these datasets, Area Under Precision Recall Curve can highlight performance differences that are lost with AreaUnderRocCurve.

• Under Roc Curve - Gets the area under the ROC curve.

Remarks: The area under the ROC curve is equal to the probability that the classifier ranks a randomly chosen positive instance higher than a randomly chosen negative one (assuming 'positive' ranks higher than 'negative'). Area under the ROC curve ranges between 0 and 1, with a value closer to 1 indicating a better model.

• Confusion Matrix - The confusion matrix giving the counts of the true positives, true negatives, false positives and false negatives for the two classes of data.

• F1 Score - Gets the F1 score of the classifier, which is a measure of the classifier's quality considering both precision and recall. 

Remarks: F1 score is the harmonic mean of precision and recall: 2 * precision * recall / (precision + recall). F1 ranges between 0 and 1, with a value of 1 indicating perfect precision and recall.

• Negative Precision - Gets the negative precision of a classifier which is the proportion of correctly predicted negative instances among all the negative predictions (i.e., the number of negative instances predicted as negative, divided by the total number of instances predicted as negative).

• Negative Recall - Gets the negative recall of a classifier which is the proportion of correctly predicted negative instances among all the negative instances (i.e., the number of negative instances predicted as negative, divided by the total number of negative instances).

• Positive Precision - Gets the positive precision of a classifier which is the proportion of correctly predicted positive instances among all the positive predictions (i.e., the number of positive instances predicted as positive, divided by the total number of instances predicted as positive).

• Positive Recall - Gets the positive recall of a classifier which is the proportion of correctly predicted positive instances among all the positive instances (i.e., the number of positive instances predicted as positive, divided by the total number of positive instances).

PercentageOfDataForTraining

Percentage of Data for Training (0-100%)

The percentage (0-100%) of the data to be used for training the model, the remaining percentage will be used for testing.

BarByBarModelCreation

Allow Bar by Bar Model Creation

If true, when training a new model and model finds a profit target, the Deep Signal Library will only advance one bar instead of moving ahead the total of bars to target plus window size in bars.

CreateCustomDataWindows

Create Custom Training Data Set

If true, allow the user to add their own data windows for training a machine learning model. This will override the existing functionality and only data windows added via DSTAddLongDataWindow, DSTAddShortDataWindow, DSTAddFailedLongDataWindow and DSTAddFailedShortDataWindow will be used in training.

MaxAutoCreateModelTimeMins

Create Model Time (mins)

If true, training model will use a temporary folder to save experiment results. If false, training model will use memory to store experiment results. Note, for large data sets and longer training times this may consume large amounts of memory on your computer.

DeleteOldDataFiles

Delete Old Files

If true, after a model is trained, the unnecessary data files will be deleted. This will save megabytes of hard drive space.

DataFolder

Strategy Data Folder

When selecting a strategy that is derived from DSTPredictModelBase, the Deep Signal Library will display a list of machine learning models that have the same parameters added using AddDSTIndicator and AddDSTInstrument in the create model strategy as the selected predict strategy.

MaxLongDataSets

Max Long Data Sets

When creating a new machine learning model, set this value to something other than 0 to set the maximum number of long profit target data sets to be used in training a model. If set to 0, the Deep Signal Library will use the total number of data sets found.

MaxShortDataSets

Max Short Data Sets

When creating a new machine learning model, set this value to something other than 0 to set the maximum number of short profit target data sets to be used in training a model. If set to 0, the Deep Signal Library will use the total number of data sets found.

MaxFailedToReachLongDataSets

Max Failed to Reach Long Data Sets

When creating a new machine learning model, set this value to something other than 0 to set the maximum number of failed to reach profit target data sets for long trades in training a model. If set to 0, the Deep Signal Library will use the total number of data sets found.

MaxFailedToReachShortDataSets

Max Failed to Reach Short Data Sets

When creating a new machine learning model, set this value to something other than 0 to set the maximum number of failed to reach profit target data sets for short trades in training a model. If set to 0, the Deep Signal Library will use the total number of data sets found.

UsePositiveNegativeDatasetRatio

Use Positive Negative Data Set Ratio

If checked, will ignore any max short or long data sets above and automatically set the max short or long data sets based on ratio. The ratio is based on Profit Target Reached/Profit Target Not Reached.

PositiveNegativeDatasetRatio

Positive Negative Data Set Ratio

The ratio of Profit Target Reached/Profit Target Not Reached data sets that get set when creating a model. For example, if the ratio is set to 2, then the total Profit Target Reached data sets will be twice that of Profit Target Not Reached.

DSTDataFolderSelection

Data Folder Options

The Strategy Data Folder can display all models in the Base Folder Name or models which match the same parameters as the selected strategy.

DSTMinLongConfidenceScore

Min Long Confidence Score (0.0-1.0)

The minimum confidence score (0.0-1.0) for predicting a long trade. The confidence score for a trade prediction can be returned in the DSTLongTrade method.

DSTMinShortConfidenceScore

Min Short Confidence Score (0.0-1.0)

The minimum confidence score (0.0-1.0) for predicting a short trade. The confidence score for a trade prediction can be returned in the DSTShortTrade method.