Combining Computational Methodologies for Real-Time Learning and Signal Extraction
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Posted on by Christian Dallas Blakely
Figure 1: The main interactive iMetricaFX user interface.
Introduction
We introduce an interactive app completely written in Java/FX for doing real-time signal extraction in multivariate time series. iMetricaFX is a completely redesigned application from the previous iMetrica, focusing mostly on the multivariate direct filter approach and it’s derivatives for generating real-time signals and analyzing multivariate time series. It retains all the features of the MDFA module in iMetrica, but now with better responsive 2D graphics written in JavaFX, and focusing on real-time data analysis applications.
One might want to use iMetricaFX for any of the following reasons:
To learn visually how MDFA hyperparameters interact with each other, and how their changes affect filter coefficients, frequency domain, signal extraction results.
To understand how MDFA signal extraction parameters, transformations on time series, additional explanatory variables, or other features affect the behavior of out-of-sample signal quality on future data or some performance metrics (MSE, Sharpe ratios, seasonal/cycle adjustments, etc).
To experiment with MDFA parameter definitions for use in the MDFA-DeepLearning package.
To engineer financial trading signals and track their performance out-of-sample given specific trading requirements
To analyze correlations between a collection of (non)stationary time series and how they affect signal extractions
Overview
We now given an overview of the different components and features of the iMetricaFX system. The first, most obvious step, in getting iMetricaFX rolling is to define the data source. The easiest data source is a collection of .csv files that have DateTime stamps for the index, with the data values in the following columns. Each column should have a header to describe that column, (e.g. “DateTime”, “Timestamp”, and “Bid” or “Open”).
In the resources folder, there is a collection of .csv files which contain daily “Close” values of a few dozen NASDAQ stocks and etfs with historical data dating back the past 6 years. All files contain the same date range period.
Add files is selected from the top menu bar, and in selecting multiple files holding the ‘ctrl’ button will upload all files for streaming data from each simultaneously for multivariate signal extraction. The DateTime format of the files can be selected in the top menu as well, in the TimeFormat menu.
Menu:File Open .csv data files for time series observations, save filter parameters, load filter parameters.
Menu:Signals Create/select new signals. When a new signal is added, the filter hyperparameters will be applied to the currently selected signal. All other signal parameters will remain fixed.
Menu:Target Series In the multivariate case, select the target series among all the series loaded. The target series represents the series from which the signal will be built.
Menu:TimeFormat The DateTime stamp format of the time series. Usually for daily data this looks like “yyyy-MM-dd” or for minute data “yyyy-MM-dd HH:mm:ss”
Menu:Options A variety of options can be chosen here. For now, only Prefiltering on/off is available.
Menu:Windows Select the windows that plot the various signal extraction properties. Options are MDFA Coefficients, Frequency Response Functions, and Time/Phase delay. More window types will be added in the future.
Compute Filter Compute the filter given the latest Series size observations and current filter hyperparamter settings
New Observation This button adds a new (multivariate) time series observation from the referenced .csv files. If there are no more values left in the .csv file, then no new values will be given.
Filter length Change the length of the filter from 4 to 100
Series size Change the number of in-sample time series observations for computing MDFA coefficients
FractionalD The fractional difference exponent between 0 and 1 (first-order differencing)
Filter Customization Adjust the smoothness and timeliness parameters
Forecasting/Smoothing Adjust the forecasting (negative value) or smoothing lag (positive value)
Target Filter Adjust the frequency range for the signal using two frequency cutoffs
Filter Constraints Toggle the i1 and/or i2 constraint and the set the Phase Shift for the i2 constraint
Filter Regularization Adjust the smoothness, decay, decay strength, and cross regularization for the filter coefficients
MDFA-DeepLearning is a library for building machine learning applications on large numbers of multivariate time series data, with a heavy emphasis on noisy (non)stationary data. The goal of MDFA-DeepLearning is to learn underlying patterns, signals, and regimes in multivariate time series and to detect, predict, or forecast them in real-time with the aid of both a real-time feature extraction system based on the multivariate direct filter approach (MDFA) and deep recurrent neural networks (RNN). The feature extraction system utilizes the MDFA-Toolkit to construct K multivariate signals in real-time (the features) where each of the K features targets a certain frequency range in the underlying time series. Furthermore, each (or some) of these features can also be forecasted multiple steps ahead, or smoothed, creating many possibilities of signal or regime learning in time series.
For the deep learning components, in this package we focus on two network structures, namely a recurrent weighted average network (RWA Ostmeyer and Cowell) and a standard long-short term memory network. The RWA cell is a type of RNN cell that computes a recurrent weighted average over every past processing timestep, unlike standard RNN cells which only process information from previous timesteps.
The overall general architecture of the proposed network is given in Figure 1 in the case of an RWA network, which we will discuss in more detail below. For a given sequence of N multivariate time series values which have been transformed appropriately to a stationary sequence, which we denote Y_1, Y_2, …, Y_N, a real-time feature extraction process is applied at each observation which is then used as input to an RWA (or LSTM) network, where the univariate output is a targeted signal value (regression) or a regime value (classification)..
Figure 1: Proposed network design using RWA cells to learn form the real-time feature extractor. The Y_t values are the (multivariate) transformed time series values, and the S_t are univariate outputs describing a target signal or regime
Why Use MDFA-DeepLearning
One might want to develop predictive models in multivariate time series data using MDFA-DeepLearning if the time series exhibit any of the following properties:
High-Dimensionality (many (un)correlated nonstationary time series)
Regime changing in the underlying dynamics (or data generating process) of the time series is a common occurrence
The MDFA-DeepLearning approach differs from most machine learning methods in time series analysis in that an emphasis on real-time feature extraction is utilized where the features extractors are build using the multivariate direct filter approach. The motivation behind this coupling of MDFA with machine learning is that, while many time series decomposition methodologies exist (from empirical mode decomposition to stochastic component analysis methods), all of these rely on either in-sample decompositions on historical data (useless for future data), and/or assumptions about the boundary values, neither of which are attractive when fast, real-time out-of-sample predictions are the emphasis. Furthermore, simply applying standard recurrent neural networks for step-ahead forecasting or signal extraction directly on the original noisy data is a useless exercise – the recurrent networks typically will only learn noise, producing signals and forecasts of little to no value (in most cases, the latter).
As mentioned, the back-end used for the novel feature extraction is the multivariate direct filter approach (MDFA), and is used to extract both local (higher-frequency) and global (low-frequency) features in real-time, out-of-sample, and output these features in a multivariate time series as inputs into an RWA or LSTM recurrent neural network. Thus the package is divided into essentially four different components which all need to be defined properly in order to produce predictive models for time series data:
Labeling interface
Feature extractors
DataSetIterator interface
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Labeling interface
The package includes an interface for labeling time series. The labeling process takes segments of historical data, and labels each time series observation in some manner. There are three types of labels that can be used:
Observational labeling: every time series observation is labeled by a signal value (for example a target value computed by a symmetric target filter). This is sequence-to-sequence labeling for time series regression.
Fixed Period labeling: every period (day, week, etc) is labeled, typically by a one-hot vector. This is sequence-to-value labeling. The end of the period is labeled and the rest of the values are not (masked by nonvalues in the code).
Regime labeling: every value in a specific regime is labeled, either by a one-hot vector (for example, long (1,0) short (0,1) neutral (0,0), or trend (1,0) and mean-reverting (0,1)). This is another example of sequence-to-sequence, but using one-hot vectors and now in the form of sequence classification.
Other labeling strategies can certainly be used, but these are the three most common. We will give an outline on how to create a custom labeling strategy in a future article.
Feature Extractors
The package contains a feature extraction class called MDFAFeatureExtraction which, when instantiated, is used as the input to the a DataSetIterator. The MDFAFeatureExtraction contains a default automated feature extraction builder where a value of K is given as the number of features and a lag to indicate smoothing or forecasting steps-ahead.
One application of the MDFA feature extraction tool is to decompose a multivariate time series into K components in real-time which are close to being “orthogonal”, meaning in this sense that the frequency information from each of the components are relatively disjoint. A precise mathematical formulation of this property and examples of the MDFAFeatureExtraction to follow. Another example used for turning-point detection in trends is to decompose the multivariate series into K number of low-frequency components with different speeds and forecast/smoothing characteristics.
DataSetIterator
The DataSetIterator is an interface for ND4J that handles fast N-d array manipulation akin to numpy in Python. More specifically, the DataSetIterator handles traversing through a dataset and preparing data for a recurrent neural network. Our datasets in this package are outputs from the TimeSeries through the MDFAFeatureExtraction objects which then become the input to the RNN network. The DataSetIterator also performs the labeling and how output will be arranged. Thus it is essentially the communication from the underlying time series to the extraction process and then how it is treated as input and output to the RNN. In the package, we have designed two examples of DataSetIterators, one for regression and one for classification, that will be described in more detail in a later article.
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Finally, the learning interface is where the final network is defined, all the parameters of the network, the activation and loss functions, and number/type of layers (LSTM, FeedForward, etc). The underlying computational framework for this component uses DeepLearning4J.
Requirements and Example
MDFA-DeepLearning requires both the MDFA-Toolkit package for constructing the time series feature extractors and the Eclipse Deeplearning4j (dl4j) library for the deep recurrent neural network constructors. The dl4j library is freely available at github.com/deeplearning4j, but is included in the build of this package using Gradle as the dependency management tool.
The back-end for the dl4j package will depend on your computational hardware, but is available on a local native basis using CPUs, or can take advantage of GPUs using CUDA libraries (CUDA 8.0 was used to test current version of MDFA-DeepLearnng). In this package I have included a reference to both versions (assuming a standard linux64 architecture).
The back-end used for the novel feature extraction technique, as mentioned, is the MDFA-Toolkit (available here), which will run on the ND4J package. The feature extraction begins by defining K MDFA objects, called MDFABase from the MDFA-Toolkit, with the fixed parameters set for each MDFA object. For example, here we define K=4 MDFABase objects, that will be used to extract different types of trends at different speeds in a fractionally differenced time series. Please refer to the MDFA-Toolkit documentation for more information on the definition of each MDFA parameter.
MDFABase[] anyMDFAs = new MDFABase[4];
anyMDFAs[0] = (new MDFABase()).setLowpassCutoff(Math.PI/8.0)
.setI1(1)
.setSmooth(.2)
.setLag(-3)
.setLambda(2.0)
.setAlpha(2.0)
.setFilterLength(5);
anyMDFAs[1] = (new MDFABase()).setLowpassCutoff(Math.PI/10.0)
.setLag(-2)
.setAlpha(2.0)
.setFilterLength(5);
anyMDFAs[2] = (new MDFABase()).setLowpassCutoff(Math.PI/4.0)
.setDecayStart(.1)
.setDecayStrength(.2)
.setLag(-1)
.setFilterLength(5);
anyMDFAs[3] = (new MDFABase()).setLowpassCutoff(Math.PI/14.0)
.setSmooth(.2)
.setDecayStart(.1)
.setDecayStrength(.1)
.setFilterLength(5);
/* Define the .csv data file from where we built train/test dataIterators */
String[] dataFiles = new String[]{"AAPL.daily.csv"};
/* Information about the .csv timeseries file */
TimeSeriesFile fileInfo = new TimeSeriesFile("yyyy-MM-dd", "Index", "Open");
/* Define network parameters */
int miniBatchSize = 100;
int totalTrainExamples = 1500;
int totalTestExamples = 300;
int timeStepLength = 60;
int nHiddenLayers = 2;
int nHidden = 216;
int nEpochs = 400;
int seed = 123;
int iterations = 40;
double learningRate = .001;
double gradientNormThreshold = 10.0;
IUpdater updater = new Nesterovs(learningRate, .4);
/* Instantiate Feature Extractors as an array of MDFABase objects */
MDFAFeatureExtraction features = new MDFAFeatureExtraction(anyMDFAs);
/* Instantiate a new RecurrentMdfaRegression network using the features defined above */
RecurrentMdfaRegression myNet = new RecurrentMdfaRegression(features);
/* Set the data and the DataIterator parameters */
myNet.setTrainingTestData(dataFiles, fileInfo, miniBatchSize, totalTrainExamples, totalTestExamples, timeStepLength);
/* Usually a good idea to normalize the data */
myNet.normalizeData();
/* Build the LSTM (default network) layers */
myNet.buildNetworkLayers(nHiddenLayers, nHidden,
RecurrentMdfaRegression.setNeuralNetConfiguration(seed, iterations, learningRate, gradientNormThreshold, 0, updater));
/* An optional dl4j control panel to in the browser */
myNet.setupUserInterface();
/* Train on the number of Epochs */
myNet.train(nEpochs);
/* Print/plot results and stats */
myNet.printPredicitions();
myNet.plotBatches(10);
The main points here are that essentially three components need to be defined:
The .csv time series data file from which the DataIterator will extract the time series data for both labeling and learning. Two data sets will be created from this, a train set and a test set. Referrencing to multiple files from which to extract training and test sets is also possible. In dl4j, training and test data is built in the form of a DataSetIterator interface (org.nd4j.linalg.dataset.api.iterator). In the package, we have defined a MDFADataSetIterator and a MDFARegressionDataSetIterator. More DataSetIterators for various applications will be added on an ongoing basis.
The network RecurrentMdfaRegression is initiated, and needs to contain the feature signal extractors. Any set of feature extractors can be added, here we used the ones defined above as an example.
Finally, the LSTM (or recurrent weighted average) network parameters need to be defined, this will then be used to construct the layers of the recurrent network.
With these three steps defined the network should be ready to train and test. The challenge is of course defining the feature extraction parameters. In later articles, we will give tips and tricks into what works best for what type of learning applications in large time series.
MDFA-Toolkit: A JAVA package for real-time signal extraction in large multivariate time series
Posted on by Christian Dallas Blakely
The multivariate direct filter approach (MDFA) is a generic real-time signal extraction and forecasting framework endowed with a richly parameterized interface allowing for adaptive and fully-regularized data analysis in large multivariate time series. The methodology is based primarily in the frequency domain, where all the optimization criteria is defined, from regularization, to forecasting, to filter constraints. For an in-depth tutorial on the mathematical formation, the reader is invited to check out any of the many publications or tutorials on the subject from blog.zhaw.ch.
This MDFA-Toolkit (clone here) provides a fast, modularized, and adaptive framework in JAVA for doing such real-time signal extraction for a variety of applications. Furthermore, we have developed several components to the package featuring streaming time series data analysis tools not known to be available anywhere else. Such new features include:
A fractional differencing optimization tool for transforming nonstationary time-series into stationary time series while preserving memory (inspired by Marcos Lopez de Prado’s recent book on Advances in Financial Machine Learning, Wiley 2018).
Easy to use interface to four different signal generation outputs:
Univariate series -> univariate signal
Univariate series -> multivariate signal
Multivariate series -> univariate signal
Multivariate series -> multivariate signal
Real-time adaptive parameterization control – make slight adjustments to the filter process parameterization effortlessly
Build a filtering process from simpler user-defined filters, applying customization and reducing degrees of freedom.
This package also provides an API to three other real-time data analysis frameworks that are now or soon available
iMetricaFX – An app written entirely in JavaFX for doing real-time time series data analysis with MDFA
MDFA-DeepLearning – A new recurrent neural network methodology for learning in large noisy time series
MDFA-Tradengineer – An automated algorithmic trading platform combining MDFA-Toolkit, MDFA-DeepLearning, and Esper – a library for complex event processing (CEP) and streaming analytics
To start the most basic signal extraction process using MDFA-Toolkit, three things need to be defined.
The data streaming process which determines from where and what kind of data will be streamed
A transformation of the data, which includes any logarithmic transform, normalization, and/or (fractional) differencing
A signal extraction definition which is defined by the MDFA parameterization
Data streaming
In the current version, time series data is providing by a streaming CSVReader, where the time series index is given by a String DateTime stamp is the first column, and the value(s) are given in the following columns. For multivariate data, two options are available for streaming data. 1) A multiple column .csv file, with each value of the time series in a separate column 2) or in multiple referenced single column time-stamped .csv files. In this case, the time series DateTime stamps will be checked to see if in agreement. If not, an exception will be thrown. More sophisticated multivariate time series data streamers which account for missing values will soon be available.
Transforming the data
Depending on the type of time series data and the application or objectives of the real time signal extraction process, transforming the data in real-time might be an attractive feature. The transformation of the data can include (but not limited to) several different things
A Box-Cox transform, one of the more common transformations in financial and other non-stationary time series.
(fractional)-differencing, defined by a value d in [0,1]. When d=1, standard first-order differencing is applied.
For stationary series, standard mean-variance normalization or a more exotic GARCH normalization which attempts to model the underlying volatility is also available.
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Once the data streaming and transformation procedures have been defined, the signal extraction parameters can then be set in a univariate or multivariate setting. (Multiple signals can be constructed as well, so that the output is a multivariate signal. A signal extraction process can be defined by defining and MDFABase object (or an array of MDFABase objects in the mulivariate signal case). The parameters that are defined are as follows:
Filter length: the length L in number of lags of the resulting filter
Customization: α (smoothness) and λ (timeliness) focuses on emphasizing smoothness of the filter by mollifying high-frequency noise and optimizing timeliness of filter by emphasizing error optimization in phase delay in frequency domain
Regularization parameters: controls the decay rate and strength, smoothness of the (multivariate) filter coefficients, and cross-series similarity in the multivariate case
Lag: controls the forecasting (negative values) or smoothing (positive values)
Phase-shift: the derivative of the frequency response function at the zero frequency.
All these parameters are controlled in an MDFABase object, which holds all the information associated with the filtering process. It includes it’s own interface which ensures the MDFA filter coefficients are updated automatically anytime the user changes a parameter in real-time.
Figure 1: Overview of the main module components of MDFA-Toolkit and how they are connected
Once the data stream has been defined, these are then passed into a time series transformation process, which handles automatically all the data transformations which new data is streamed. As we’ll see, the TargetSeries object defines such transformations and all streaming data is passed added directly to the TargetSeries object. A MultivariateFXSeries is then initiated with references to each TargetSeries objects. The MDFABase objects contain the MDFA parameters and are added to the MultivariateFXSeries to produce the final signal extraction output.
To demonstrate these components and how they come together, we illustrate the package with a simple example where we wish to extract three independent signals from AAPL daily open prices from the past 5 years. We also do this in a multivariate setting, to see how all the components interact, yielding a multivariate series -> multivariate signal.
//Define three data source files, the first one will be the target series
String[] dataFiles = new String[]{"AAPL.daily.csv", "QQQ.daily.csv", "GOOG.daily.csv"};
//Create a CSV market feed, where Index is the Date column and Open is the data
CsvFeed marketFeed = new CsvFeed(dataFiles, "Index", "Open");
/* Create three independent signal extraction definitions using MDFABase:
One lowpass filter with cutoff PI/20 and two bandpass filters
*/
MDFABase[] anyMDFAs = new MDFABase[3];
anyMDFAs[0] = (new MDFABase()).setLowpassCutoff(Math.PI/20.0)
.setI1(1)
.setHybridForecast(.01)
.setSmooth(.3)
.setDecayStart(.1)
.setDecayStrength(.2)
.setLag(-2.0)
.setLambda(2.0)
.setAlpha(2.0)
.setSeriesLength(400);
anyMDFAs[1] = (new MDFABase()).setLowpassCutoff(Math.PI/10.0)
.setBandPassCutoff(Math.PI/15.0)
.setSmooth(.1)
.setSeriesLength(400);
anyMDFAs[2] = (new MDFABase()).setLowpassCutoff(Math.PI/5.0)
.setBandPassCutoff(Math.PI/10.0)
.setSmooth(.1)
.setSeriesLength(400);
/*
Instantiate a multivariate series, with the MDFABase definitions,
and the Date format of the CSV market feed
*/
MultivariateFXSeries fxSeries = new MultivariateFXSeries(anyMDFAs, "yyyy-MM-dd");
/*
Now add the three series, each one a TargetSeries representing the series
we will receive from the csv market feed. The TargetSeries
defines the data transformation. Here we use differencing order with
log-transform applied
*/
fxSeries.addSeries(new TargetSeries(1.0, true, "AAPL"));
fxSeries.addSeries(new TargetSeries(1.0, true, "QQQ"));
fxSeries.addSeries(new TargetSeries(1.0, true, "GOOG"));
/*
Now start filling the fxSeries will data, we will start with
600 of the first observations from the market feed
*/
for(int i = 0; i < 600; i++) {
TimeSeriesEntry observation = marketFeed.getNextMultivariateObservation();
fxSeries.addValue(observation.getDateTime(), observation.getValue());
}
//Now compute the filter coefficients with the current data
fxSeries.computeAllFilterCoefficients();
//You can also chop off some of the data, he we chop off 70 observations
fxSeries.chopFirstObservations(70);
//Plot the data so far
fxSeries.plotSignals("Original");
Figure 2: Output of the three signals on the target series (red) AAPL
In the first line, we reference three data sources (AAPL daily open, GOOG daily open, and SPY daily open), where all signals are constructed from the target signal which is by default, the first series referenced in the data market feed. The second two series act as explanatory series. The filter coeffcients are computed using the latest 400 observations, since in this example 400 was used as the insample setSeriesLength, value for all signals. As a side note, different insample values can be used for each signal, which allows one to study the affects of insample data sizes on signal output quality. Figure 2 shows the resulting insample signals created from the latest 400 observations.
We now add 600 more observations out-of-sample, chop off the first 400, and then see how one can change a couple of parameters on the first signal (first MDFABase object).
for(int i = 0; i < 600; i++) {
TimeSeriesEntry observation = marketFeed.getNextMultivariateObservation();
fxSeries.addValue(observation.getDateTime(), observation.getValue());
}
fxSeries.chopFirstObservations(400);
fxSeries.plotSignals("New 400");
/* Now change the lowpass cutoff to PI/6
and the lag to -3.0 in the first signal (index 0) */
fxSeries.getMDFAFactory(0).setLowpassCutoff(Math.PI/6.0);
fxSeries.getMDFAFactory(0).setLag(-3.0);
/* Recompute the filter coefficients with new parameters */
fxSeries.computeFilterCoefficients(0);
fxSeries.plotSignals("Changed first signal");
In the MDFA-Toolkit, plotting is done using JFreeChart, however iMetricaFX provides an app for building signal extraction pipelines with this toolkit providing the backend where all the automated plotting, analysis, and graphics are handled in JavaFX, creating a much more interactive signal extraction environment. Many more features to the MDFA-Toolkit are being constantly added, especially in regard to features boosting applications in Machine Learning, such as we will see in the next upcoming article.
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Forecasting, Seasonal Adjustment, and Signal Extraction with uSimX13 in iMetrica
Posted on by Christian Dallas Blakely
Figire 0. The uSimX13 module in iMetrica provides interactive and dynamic forecasting and signal extraction powered by X-13-ARIMA-SEATS.
The uSimX13-SEATS (uSimX13) module featured in the iMetrica software suite is an interactive graphical user-interfaced time series modeling and simulation environment. The main attraction of uSimX13 is that it features computational modeling routines from the X- 13ARIMA-SEATS (X-13A-S) software developed and published by the Census Bureau of the US Department of Commerce. The uSimX13 environment offers a unique time series modeling software with the primary goal of analyzing economic time series data using the most commonly used features of X-13ARIMA-SEATS, while providing a large array of classical and modern goodness-of-fit tests to assess different model fits of the data, many different graphical representations of the time series data, adaptive time series decomposition capabilities, and much more all while being accessible to both beginners in the field of econometrics wanting to visualize frequently used tools, and practitioners wanting to obtain forecasts, seasonal/trend adjustments, and/or test and apply regression components to their data.
While there are other X-13A-S “engines” and interfaces in existence, including the original Fortran program and the excellent R package entitled seasonal , uSimX13 in comparison serves to provide most of the commonly used and important features of X-13-ARIMA-SEATS without the use of any programming interface – one simply loads the data into the module (I will show how in this article), and then all the aspects of the modeling, including forecasting, seasonal adjustment, auto-model, and model selection, can be done with just using the iMetrica user-interface. In addition, several interactive features are available to aid in model selection in determining the best model fit for one’s data, some of which are not available in the original Fortran program nor the R package.
To get started, once iMetrica has been launched, the easiest way to get data into the uSimX13 module is to click on the uSimX13 tab, and then access the menu for the uSimX13 tab at the top in the menu bar, as shown in Figure 1.
Figure 1. Opening the uSimX13 menu and selecting Open Data File
For a single data file, click ‘Open Data File’ and a file selection dialog box will appear to choose your data file. I have included a few dozen example real economic time series in the folder called ‘data’ that comes with the iMetrica distribution on my Github. The data files accepted for uSimX13 are very trivial, in that they are simply numerical values given for each time period in each row. If there is a date associated with each time series observation, the date and the observation must be seperated by either a comma or a space. There is also the option of loading in many time series, and this can be achieved by selecting ‘Open Metafile’, and choosing a file that lists all the files to the time series that need to be loaded. It is assumed that all the files are found in the same folder as the Metafile. An example is also given in the ‘data’ directory. Scrolling though the different series that were uploaded can be achieved by accessing the menu, then selecting ‘Simulator Panel’. This will bring up a satellite panel, where on the bottom right you will see a scroll bar with all the loaded time series.
Once the data has been loaded in uSimX13, you will see a plot of the data automatically on the main plotting canvas. The plot should be gray at this point. To turn on the automatic features of the module and begin analyzing, click on ‘Activate uSimX13 engine’ as showm io Figure 1.
Once the data has been loaded in, one exploratory feature on the module is the ‘Sliding Span Activate’ that offers a unique approach to model selection and goodness-of-fit by addressing multi-step ahead forecasting error on ‘test’ portions of the data. Such analysis can be readily achieved by using the ‘Sliding Span Activate’ component along with the ‘Sweep Time Series Control Panel’. Further details of this interactive model selection feature can be found in one of my previous articles on model selection 旋风加速器安卓下载安装.
That will get you started with the basic features in loading data into the model. To learn more, click on the .pdf file here usimx13 for a full guide on how to use all the components in the uSimX13, including a quick guide on the inference of model selection with signal extraction goodness-of-fit diagnostics that was featured in a recent paper by myself and colleague Tucker McElroy here .
Coming next week: Interactive modeling with State Space and RegComponent models using the State Space Modeling module in iMetrica.
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Posted on by Christian Dallas Blakely
The MDFA real-time signal extraction module
My first open-source release of iMetrica for Linux Ubuntu 64 can now be downloaded at my Github, with a Windows 64 version soon to follow. iMetrica is a fast, interactive, GUI-oriented software suite for predictive modeling, multivariate time series analysis, real-time signal extraction, Bayesian financial econometrics, and much more.
The principal use of iMetrica is to provide an interactive environment for the numerical and visual analysis of (multivariate) time series modeling, real-time filtering, and signal extraction. The interactive features in iMetrica boast a modeling and graphics environment for analysts, practitioners, and students of econometrics, finance, and real-time data analysis where no coding or modeling experience is necessary. All the system needs is data which can be piped into the system in many forms, including .csv, .txt, Google/Yahoo Finance, Quandle, .RData, and more. A module for connecting to MySQL databases is currently being developed. One can also simulate their own data from a one or a combination of several different popular data generating models.
With the design intending to be interactive and self-enclosed, one can change modeling data/parameter inputs and see the effects in both graphical and numerical form automatically. This feature is designed to help understand the underlying mechanics of the modeling or filtering process. One can test many attributes of the modeling or filtering process this way both visually and numerically such as sensitivity, nonlinearity, goodness-of-fit, any overfitting issues, stability, etc.
All the computational libraries were written in GNU C and/or Fortran and have been provided as Native libraries to the Java platform via JNI, where Java provides the user-interface, control, graphics, and several other components in a module format, and where each module specializes in a different data analysis paradigm. The modules available in this open-source version of iMetrica are as follows:
1) Data simulation, modeling and fitting using several popular econometric models
(S)ARIMA, (E)GARCH, (Multivariate) Factor models, Stochastic Volatility, High-frequency volatility models, Cycles/Trends, and more
Random number generators from several different types of parameterized distributions to create shocks, outliers, regression components, etc.
Visualize in real-time all components of the modeling process
2) An interactive GUI for multivariate real-time signal extraction using the multivariate direct filter approach (MDFA)
Construct mulitvariate MA filter designs, classical ARMA ZPA filtering designs, or hybrid filtering designs.
Analyze all components of the filtering and signal extraction process, from time-delay and smoothing control, to regularization.
I realize that I’ve been MIA (missing in action for non-anglophones) the past three months on this blog, but I assure you there has been good reason for my long absence. Not only have I developed a large slew of various optimization, analysis, and statistical tools in iMetrica for constructing high-performance financial trading signals geared towards intraday trading which I will (slowly) be sharing over the next several months (with some of the secret-sauce-recipes kept to myself and my current clients of course), but I have also built, engineered, tested, and finally put into commission on a daily basis the planet’s first automated financial trading platform completely based on the recently developed FT-AMDFA (adaptive multivariate direct filtering approach for financial trading). I introduce to you iMetrica’s little sister, TWS-iMetrica.
Coupled with the original software I developed for hybrid econometrics, time series analysis, signal extraction, and multivariate direct filter engineering called iMetrica, the TWS-iMetrica platform was built in a way to provide an easy to use yet powerful, adaptive, versatile, and automated trading machine for intraday financial trading with a variety of options for building your own day trading strategies using MDFA based on your own financial priorities. Being written completely in Java and gnu c, the TWS-iMetrica system currently uses the Interactive Brokers (IB) trading workstation (TWS) Java API in order to construct the automated trades, connect to the necessary historical data feeds, and provide a variety of tick data. Thus in order to run, the system will require an activated IB trading account. However, as I discuss in the conclusion of this article, the software was written in a way to be seamlessly adapted to any other brokerage/trading platform API, as long as the API is available in Java or has Java wrappers available.
The steps for setting up and building an intraday financial trading environment using iMetrica + TWS-iMetrica are easy. There are four of them. No technical analysis indicator garbage is used here, no time domain methodologies, or stochastic calculus. TWS-iMetrica is based completely on the frequency domain approach to building robust real-time multivariate filters that are designed to extract signals from tradable financial assets at any fixed observation of frequencies (the most commonly used in my trading experience with FT-AMDFA being 5, 15, 30, or 60 minute intervals). What makes this paradigm of financial trading versatile is the ability to construct trading signals based on your own trading priorities with each filter designed uniquely for a targeted asset to be traded. With that being said, the four main steps using both iMetrica and TWS-iMetrica are as follows:
The first step to building an intraday trading environment is to construct what I call an MDFA portfolio (which I’ll define in a brief moment). This is achieved in the TWS-iMetrica interface that is endowed with a user-friendly portfolio construction panel shown below in Figure 4.
With the desired MDFA portfolio, selected, one then proceeds in connecting TWS-iMetrica to IB by simply pressing the Connect button on the interface in order to download the historical data (see Figure 3).
With the historical data saved, the iMetrica software is then used to upload the saved historical data and build the filters for the given portfolio using the MDFA module in iMetrica (see Figure 2). The filters are constructed using a sequence of proprietary MDFA optimization and analysis tools. Within the iMetrica MDFA module, three different types of filters can be built 1) a trend filter that extracts a fast moving trend 2) a band-pass filter for extracting local cycles, and 3) A multi-bandpass filter that extracts both a slow moving trend and local cycles simultaneously.
Once the filters are constructed and saved in a file (a .cft file), the TWS-iMetrica system is ready to be used for intrady trading using the newly constructed and optimized filters (see Figure 6).
Figure 2: The iMetrica MDFA module for constructing the trading filters. Features dozens of design, analysis, and optimization components to fit the trading priorities of the user and is used in conjunction with the TWS-iMetrica interface.
The main TWS-iMetrica graphical user interface is composed of several components that allow for constructing a multitude of various MDFA intraday trading strategies, depending on one’s trading priorities. Figure 3 shows the layout of the GUI after first being launched. The first component is the top menu featuring TWS System, some basic TWS connection variables which, in most cases, these variables are left in their default mode, and the Portfolio menu. To access the main menu for setting up the MDFA trading environment, click Setup MDFA Portfolio under the旋风加速器官方网站menu. Once this is clicked, a panel is displayed (shown in Figure 4) featuring the required a priori parameters for building the MDFA trading environment that should all be filled before MDFA filter construction and trading is to take place. The parameters and their possible values are given below Figure 4.
Figure 4 – The Setup MDFA Portfolio panel featuring all the setting necessary to construct the automated trading MDFA environment.
Portfolio – The portfolio is the basis for the MDFA trading platform and consists of two types of assets 1) The target asset from which we construct the trading signal, engineer the trades, and use in building the MDFA filter 2) The explanatory assets that provide the explanatory data for the target asset in the multivariate filter construction. Here, one can select up to four explanatory assets.
Exchange – The exchange on which the assets are traded (according to IB).
Asset Type – If the input portfolio is a selection of Stocks or Futures (Currencies and Options soon to be available).
Expiration – If trading Futures, the 旋风加速器官方网站 date of the contract, given as a six digit number of year then month (e.g. 201306 for June 2013).
Shares/Contracts – The number of shares/contracts to trade (this number can also be changed throughout the trading day through the main panel).
Observation frequency – In the MDFA financial trading method, we consider uniformly sampled observations of market data on which to do the trading (in seconds). The options are 1, 2, 3, 5, 15, 30, and 60 minute data. The default is 5 minutes.
Data – For the intraday observations, determines the nature of data being extracted. Valid values include TRADES, MIDPOINT, BID, ASK, and BID_ASK. The default is MIDPOINT
Historical Data – Selects how many days are used to for downloading the historical data to compute the initial MDFA filters. The historical data will of course come in intervals chosen in the observation frequency.
Once all the values have been set for the MDFA portfolio, click the Set and Build button which will first begin to check if the values entered are valid and if so, create the necessary data sets for TWS-iMetrica to initialize trading. This all must be done while TWS-iMetrica is connected to IB (not set in trading mode however). If the build was successful, the historical data of the desired target financial asset up to the most recent observation in regular market trading hours will be plotted on the graphics canvas. The historical data will be saved to a file named (by default) “lastSeriesData.dat” and the data will be come in columns, where the first column is the date/time of the observation, the second column is the price of the target asset, and remaining columns are log-returns of the target and explanatory data. And that’s it, the system is now setup to be used for financial trading. These values entered in the Setup MDFA Portfolio will never have to be set again (unless changes to the MDFA portfolio are needed of course).
Continuing on to the other controls and features of TWS-iMetrica, once the portfolio has been set, one can proceed to change any of the settings in main trading control panel. All these controls can be used/modified intraday while in automated MDFA trading mode. In the left most side of the panel at the main 旋风加速器专业版下载安装 (Figure 5) of the interface includes a set of options for the following features:
Figure 5 – The main control panel for choosing and/or modifying all the options during intraday trading.
In 旋风加速器的官网在哪里 text field, one enters the amount of share (for stocks) or contracts (for futures) that one will trade throughout the day. This can be adjusted during the day while the automated trading is activated, however, one must be certain that at the end of the day, the balance between bought and shorted contracts is zero, otherwise, you risk keeping contracts or shares overnight into the next trading day.Typically, this is set at the beginning before automated trading takes place and left alone.
The data input file for loading historical data. The name of this file determines where the historical data associated with the MDFA portfolio constructed will be stored. This historical data will be needed in order to build the MDFA filters. By default this is “lastSeriesData.dat”. Usually this doesn’t need to be modified.
The stop-loss activation and stop-loss slider bar, one can turn on/off the stop-loss and the stop-loss amount. This value determines how/where a stop-loss will be triggered relative to the price being bought/sold at and is completely dependent on the asset being traded.
The interval search that determines how and when the trades will be made when the selected MDFA signal triggers a buy/sell transaction. If turned off, the transaction (a limit order determined by the bid/ask) will be made at the exact time that the buy/sell signal is triggered by the filter. If turned on, the value in the text field next to it gives how often (in seconds) the trade looks for a better price to make the transaction. This search runs until the next observation for the MDFA filter. For example, if 5 minute return data is being used to do the trading, the search looks every n seconds for 5 minutes for a better price to make the given transaction. If at the end of the 5 minute period no better price has been found, the transaction is is made at the current ask/bid price. This feature has been shown to be quite useful during sideways or highly volatile markets.
The middle of the main 旋风加速器的官网在哪里 features the main buttons for both connecting to disconnecting from Interactive Brokers, initiating the MDFA automated trading environment, as well as convenient buttons used for instantaneous buy/sell triggers that supplement the automated system. It also features an on/off toggle button for activating the trades given in the MDFA automated trading environment. When checked on, transactions according to the automated MDFA environment will proceed and go through to the IB account. If turned off, the real-time market data feeds and historical data will continue to be read into the TWS-iMetrica system and the signals according to the filters will be automatically computed, but no actual transactions/trades into the IB account will be made.
Finally, on the right hand side of the main control panel features the filter uploading and selection boxes. These are the MDFA filters that are constructed using the MDFA module in iMetrica. One convenient and useful feature of TWS-iMetrica is the ability to utilize up to three direct real-time filters in parallel and to switch at any given moment during market hours between the filters. (Such a feature enhances the adaptability of the trading using MDFA filters. I’ll discuss more about this in further detail shortly). In order to select up to three filters simultaneously, there is a filter selection panel (shown in bottom right corner of Figure 6 below) displaying three separate file choosers and a radio button corresponding to each filter. Clicking on the filter load button produces a file dialog box from which one selects a filter (a *.cft file produced by iMetrica). Once the filter is loaded properly, on success, the name of the filter is displayed in the text box to the right, and the radio button to the left is enabled. With multiple filters loaded, to select between any of them, simply click on their respective radio button and the corresponding signal will be plotted on the plot canvas (assuming market data has been loaded into the TWS-iMetrica using the market data file upload and/or has been connected to the IB TWS for live market data feeds). This is shown in Figure 6.
Figure 6 – The TWS-iMetrica main trading interface features many control options to design your own automated MDFA trading strategies.
And finally, once the historical data file for the MDFA portfolio has been created, up to three filters have been created for the portfolio and entered in the filter selection boxes, and the system is connected to Interactive Brokers by pressing the Connect button, the market and signal plot panel can then be used for visualizing the different components that one will need for analyzing the market, signal, and performance of the automated trading environment. In the panel just below the plot canvas features and array of checkboxes and radiobuttons. When connected to IB and the Start MDFA Trading has been pressed, all the data and plots are updated in real-time automatically at the specific observation frequency selected in the MDFA Portfolio setup. The currently available plots are as follows:
Figure 8 – The plots for the trading interface. Features price, log-return, account cumulative returns, signal, buy/sell lines, and up to two additional auxiliary signals.
Price – Plots in real-time the price of the asset being traded, at the specific observation frequency selected for the MDFA portfolio.
Log-returns – Plots in real-time the log-returns of the price, which is the data that is being filtered to construct the trading signal.
Account – Shows the cumulative returns produced by the currently chosen MDFA filter over the current and historical data period (note that this does not necessary reflect the actual returns made by the strategy in IB, just the theoretical returns over time if this particular filter had been used).
旋风加速器安卓版安装 – Shows dashed lines where the MDFA trading signal has produced a buy/sell transaction. The green lines are the buy signals (entered a long position) and magenta lines are the sell (entered a short position).
Signal – The plot of the signal in real-time. When new data becomes available, the signal is automatically computed and replotted in real-time. This gives one the ability to closely monitory how the current filter is reacting to the incoming data.
Aux Signal 1/2 – (If available) Plots of the other available signals produced by the (up to two) other filters constructed and entered in the system. To make either of these auxillary signals the main trading signal simply select the filter associated with the signal using the radio buttons in the filter selection panel.
Along with these plots, to track specific values of any of these plots at anytime, select the desired plot in the Track Plot region of the panel bar. Once selected, specific values and their respective times/dates are displayed in the upper left corner of the plot panel by simply placing the mouse cursor over the plot panel. A small tracking ball will then be moved along the specific plot in accordance with movements by the mouse cursor.
With the graphics panel displaying the performance in real-time of each filter, one can seamlessly switch between a band-pass filter or a timely trend (low-pass) filter according to the changing intraday market conditions. To give an example, suppose at early morning trading hours there is an unusual high amount of volume pushing an uptrend or pulling a downtrend. In such conditions a trend filter is much more appropriate, being able to follow the large-variation in log-returns much better than a band-pass filter can. One can glean from the effects of the trend filter on the morning hours of the market. After automated trading using the trend filter, with the volume diffusing into the noon hour, the band-pass filter can then be applied in order to extract and trade at certain low frequency cycles in the log-return data. Towards the end of the day, with volume continuously picking up, the trend filter can then be selected again in order to track and trade any trending movement automatically.
I am in the process of currently building an automated algorithm to “intelligently” switch between the uploaded filters according to the instantaneous market conditions (with triggering of the switching being set by volume and volatility. Otherwise, for the time being, currently the user must manually switch between different filters, if such switching is at all desired (in most cases, I prefer to leave one filter running all day. Since the process is automated, I prefer to have minimal (if any) interaction with the software during the day while it’s in automated trading mode).
Conclusion
As I mentioned earlier, the main components of the TWS-iMetrica were written in a way to be adaptable to other brokerage/trading APIs. The only major condition is that the API either be available in Java, or at least have (possibly third-party?) wrappers available in Java. That being said, there are only three main types of general calls that are made automated to the connected broker 1) retrieve historical data for any asset(s), at any given time, at most commonly used observation frequencies (e.g. 1 min, 5 min, 10 min, etc.), 2) subscribe to automatic feed of bar/tick data to retrieve latest OHLC and bid/ask data, and finally 3) Place an order (buy/sell) to the broker with different any order conditions (limit, stop-loss, market order, etc.) for any given asset.
If you are interested in having TWS-iMetrica be built for your particular brokerage/trading platform (other than IB of course) and the above conditions for the API are met, I am more than happy to be hired at certain fixed compensation, simply get in contact with me. If you are interested seeing how well the automated system has performed thus far, interested in future collaboration, or becoming a client in order to use the TWS-iMetrica platform, feel free to contact me as well.
Happy extracting!
High-Frequency Financial Trading on Index Futures with MDFA and R: An Example with EURO STOXX50
Posted on by Christian Dallas Blakely
Figure 1: In-sample and Out-of-sample performance (observations 240-457) of the trading signal for the Euro Stoxx50 index futures with expiration March 18th (STXE H3) during the period of 1-9-2013 and 2-1-2013, using 15 minute log-returns. The black dotted lines indicate a buy/long signal and the blue dotted lines indicate a sell/short (top).
In this second tutorial on building high-frequency financial trading signals using the multivariate direct filter approach in R, I focus on the first example of my previous article on signal engineering in high-frequency trading of financial index futures where I consider 15-minute log-returns of the Euro STOXX50 index futures with expiration on March 18th, 2013 (STXE H3). As I mentioned in the introduction, I added a slightly new step in my approach to constructing the signals for intraday observations as I had been studying the problem of close-to-open variations in the frequency domain. With 15-minute log-return data, I look at the frequency structure related to the close-to-open variation in the price, namely when the price at close of market hours significantly differs from the price at open, an effect I’ve mentioned in my previous two articles dealing with intraday log-return data. I will show (this time in R) how MDFA can take advantage of this variation in price and profit from each one by ‘predicting’ with the extracted signal the jump or drop in the price at the open of the next trading day. Seems to good to be true, right? I demonstrate in this article how it’s possible.
The first step after looking at the log-price and the log-return data of the asset being traded is to construct the periodogram of the in-sample data being traded on. In this example, I work with the same time frame I did with my previous R tutorial by considering the in-sample portion of my data to be from 1-4-2013 to 1-23-2013, with my out-of-sample data span being from 1-23-2013 to 2-1-2013, which will be used to analyze the true performance of the trading signal. The STXE data and the accompanying explanatory series of the EURO STOXX50 are first loaded into R and then the periodogram is computed as follows.
#load the log-return and log-price SXTE data in-sample
load(paste(path.pgm,"stxe_insamp15min.RData",sep=""))
load(paste(path.pgm,"stxe_priceinsamp15min.RData",sep=""))
#load the log-return and log-price SXTE data out-of-sample
load(paste(path.pgm,"stxe_outsamp15min.RData",sep=""))
load(paste(path.pgm,"stxe_priceoutsamp15min.RData",sep=""))
len_price<-557
out_samp_len<-210
in_samp_len<-347
price_insample<-stxeprice_insamp
price_outsample<-stxeprice_outsamp
#some mdfa definitions
x<-stxe_insamp
len<-length(x[,1])
#my range for the 15-min close-to-open cycle
cutoff<-.32
ub<-.32
lb<-.23
#------------ Compute DFTs ---------------------------
spec_obj<-spec_comp(len,x,0)
weight_func<-spec_obj$weight_func
stxe_periodogram<-abs(spec_obj$weight_func[,1])^2
K<-length(weight_func[,1])-1
#----------- compute Gamma ----------------------------
Gamma<-((0:K)<(K*ub/pi))&((0:K)>(K*lb/pi))
colo<-rainbow(6)
xaxis<-0:K*(pi/(K+1))
plot(xaxis, stxe_periodogram, main="Periodogram of STXE", xlab="Frequency", ylab="Periodogram",
xlim=c(0, 3.14), ylim=c(min(stxe_periodogram), max(stxe_periodogram)),col=colo[1],type="l" )
abline(v=c(ub,lb),col=4,lty=3)
You’ll notice in the periodogram of the in-sample STXE log-returns that I’ve pinpointed a spectral peak between two blue dashed lines. This peak corresponds to an intrinsically important cycle in the 15-minute log-returns of index futures that gives access to predicting the close-to-open variation in the price. As you’ll see, the cycle flows fluidly through the 26 15-minute intervals during each trading day and will cross zero at (usually) one to two points during each trading day to signal whether to go long or go short on the index for the next day. I’ve deduced this optimal frequency range in a prior analysis of this data that I did using my target filter toolkit in iMetrica (see previous article). This frequency range will depend on the frequency of intraday observations, and can also depend on the index (but in my experiments, this range is typically consistent to be between .23 and .32 for most index futures using 15min observations). Thus in the R code above, I’ve defined a frequency cutoff at .32 and upper and lower bandpass cutoffs at .32 and .23, respectively.
Figure 2: Periodogram of the log-return STXE data. The spectral peak is extracted and highlighted between the two red dashed lines.
Figure 3: Concurrent transfer functions for the STXE (red) and explanatory series (cyan) (top). Coefficients for the STXE and explanatory series (bottom).
Notice the noise leakage past the stopband in the concurrent filter and the roughness of both sets of filter coefficients (due to overfitting). We would like to smooth both of these out, along with allowing the filter coefficients to decay as the lag increases. This ensures more consistent in-sample and out-of-sample properties of the filter. I first apply some smoothing to the stopband by applying an expweight parameter of 16, and to compensate slightly for this improved smoothness, I improve the timeliness by setting the lambda parameter to 1. After noticing the improvement in the smoothness of filter coefficients, I then proceed with the regularization and conclude with the following parameters.
Figure 4: Transfer functions and coefficients after smoothing and regularization.
A vast improvement over the mean-squared solution. Virtually no noise leakage in the stopband passed and the coefficients decay beautifully with perfect smoothness achieved. Notice the two transfer functions perfectly picking out the spectral peak that is intrinsic to the close-to-open cycle that I mentioned was between .23 and .32. To verify these filter coefficients achieve the extraction of the close-to-open cycle, I compute the trading signal from the imdfa object and then plot it against the log-returns of STXE. I then compute the trades in-sample using the signal and the log-price of STXE. The R code is below and the plots are shown in Figures 5 and 6.
Figure 5: The in-sample signal and the log-returns of SXTE in 15 minute observations from 1-9-2013 to 1-23-2013
Figure 5 shows the log-return data and the trading signal extracted from the data. The spikes in the log-return data represent the close-to-open jumps in the STOXX Europe 50 index futures contract, occurring every 27 observations. But notice how regular the signal is, and how consistent this frequency range is found in the log-return data, almost like a perfect sinusoidal wave, with one complete cycle occurring nearly every 27 observations. This signal triggers trades that are shown in Figure 6, where the black dotted lines are buys/long and the blue dotted lines are sells/shorts. The signal is extremely consistent in finding the opportune times to buy and sell at the near optimal peaks, such as at observations 140, 197, and 240. It also ‘predicts’ the jump or fall of the EuroStoxx50 index future for the next trading day by triggering the necessary buy/sell signal, such as at observations 19, 40, 51, 99, 121, 156, and, 250. The performance of this trading in-sample is shown in Figure 7.
Figure 6: The in-sample trades. Black dotted lines are buy/long and the blue dotted lines are sell/short.
Now for the real litmus test in the performance of this extracted signal, we need to apply the filter out-of-sample to check for consistency in not only performance, but also in trading characteristics. To do this in R, we bind the in-sample and out-of-sample data together and then apply the filter to the out-of-sample set (needing the final L-1 observations from the in-sample portion). The resulting signal in shown in Figure 8.
x_out<-rbind(stxe_insamp,stxe_outsamp)
xff<-matrix(nrow=out_samp_len,ncol=2)
for(i in 1:out_samp_len)
{
xff[i,]<-0
for(j in 2:3)
{
xff[i,j-1]<-xff[i,j-1]+bn[,j-1]%*%x_out[in_samp_len+i:(i-L+1),j]
}
}
trading_signal_outsamp<-xff[,1] + xff[,2]
plot(stxe_outsamp[,1],col=colo[1],type="l")
lines(trading_signal_outsamp,col=colo[4])
Figure 8: Signal produced out-of-sample on 210 observations and log-return data of STXE
The total in-sample plus out-of-sample trading performance is shown in Figure 9 and 10, with the final 210 points being out-of-sample. The out-of-sample performance is very much akin to the in-sample performance we had, with a very clear systematic trading exposed by ‘predicting’ the next day close-to-open jump or fall in a consistent manner, by triggering the necessary buy/sell signal, such as at observations 310, 363, 383, and 413, with only one loss up until the final day trading. The higher volatility during the final day of the in-sample period damages the cyclical signal and fails to trade systematically as it had been during the first 420 observations.
Figure 9: The total in-sample plus out-of-sample buys and sells.
With this kind of performance both in-sample and out-of-sample, and the beautifully consistent yet methodological trading patterns this signal provides, it would seem like attempting to improve upon it would be a pointless task. Why attempt to fix what’s not “broken”. But being the perfectionist that I am, I strive for an even “smarter” filter. If only there was a way to 1) keep the consistent cyclical trading effects as before 2) ‘predict’ the next day close-to-open jump/fall in the Euro Stoxx50 index future as before, and 3) avoid volatile periods to eliminate erroneous trading, where the signal performed worse. After hours spent in iMetrica, I figured how to do it. This is where advanced trading signal engineering comes into play.
The first step was to include all the lower frequencies below .23, which were not included in my previous trading signal. Due to the low amount of activity in these lower frequencies, this should only provide the effect or a ‘lift’ or a ‘push’ or the signal locally, while still retaining the cyclical component. So after changing my to a low-pass filter with cutoff set at , I then computed the filter with the new lowpass design. The transfer functions for the filter coefficients are shown below in Figure 11, with the red colored plot the transfer function for the STXE. Notice that the transfer function for the explanatory series still privileges spectral peak between .23 and .32, with only a slight lift at frequency zero (compare this with the bandpass design in Figure 4, not much has changed). The problem is that the peak exceeds 1.0 in the passband, and this will amplify the cyclical component extracted from the log-return. It might be okay, trading wise, but not what I’m looking to do. For the STXE filter, we get slightly more of a lift at frequency zero, however this has been compensated with a decreased cycle extraction between frequencies .23 and .32. Also, a slight amount of noise has entered in the stopband, another factor we must mollify.
#---- set Gamma to low-pass
cutoff<-.32
Gamma<-((0:K)<(cutoff*K/pi))
#---- compute new filter ----------
i_mdfa_obj<-IMDFA(L,i1,i2,cutoff,lambda,expweight,lambda_cross,lambda_decay,lambda_smooth,weight_func,Gamma,x)
Figure 11: The concurrent transfer functions after changing to lowpass filter.
To improve the concurrent filter properties for both, I increase the smoothing expweight to 26, which will in turn affect the lambda_smooth, so I decrease it to .70. This gives me a much better transfer function pair, shown in Figure 12. Notice the peak in the explanatory series transfer function is now much closer to 1.0, exactly what we want.
Figure 12: The concurrent transfer functions after changing to lowpass filter, increasing expweight to 26, and decreasing lambda_smooth to .70.
I’m still not satisfied with the lift at frequency zero for the STXE series. At roughly .5 at frequency zero, the filter might not provide enough push or pull that I need. The only way to ensure a guaranteed lift in the STXE log-return series is to employ constraints on the filter coefficients so that the transfer function is one at frequency zero. This can be achieved by setting i1 to true in the IMDFA function call, which effectively ensures that the sum of the filter coefficients at is one. After doing this, I get the following transfer functions and the respective filter coefficients.
Figure 13: Transfer function and filter coefficients after setting the coefficient constraint i1 to true.
Now this is exactly what I was looking for. Not only does the transfer function for the explanatory series keep the important close-to-open cycle intact, but I have also enforced the lift I need for the STXE series. The coefficients still remain smooth with a nice decaying property at the end. With the new filter coefficients, I then applied them to the data both in-sample and out-of-sample, yielding the trading signal shown in Figure 14. It posses exactly the properties that I was seeking. The close-to-open cyclical component is still being extracted (thanks in part to the explanatory series), and is still relatively consistent, although not as much as the pure bandpass design. The feature that I like is the following: When the log-return data diverges away from the cyclical component, with increasing volatility, the STXE filter reacts by pushing the signal down to avoid any erroneous trading. This can be seen in observations 100 through 120 and then at observations 390 through the end of trading. Figure 15 (same as Figure 1 at the top of the article) show the resulting trades and performance produced in-sample and out-of-sample by this signal. This is the art of meticulous signal engineering folks.
Figure 14: In-sample and out-of-sample signal produced from the low-pass with i1 coefficient constraints.
Figure 15: In-sample and out-of-sample performance of the i1 constrained filter design.
One thing I mention before concluding is that I made a slight adjustment to my filter design after employing the i1 constraint to get the results shown in Figure 13-15. I’ll leave this as an exercise for the reader to deduce what I have done. Hint: Look at the freezed degrees of freedom before and after applying the i1 constraint. If you still have trouble finding what I’ve done, email me and I’ll give you further hints.
Conclusion
The overall performance of the first filter built, in regards to total return on investment out-of-sample, was superior to the second. However, this superior performance comes only with the assumption that the cycle component defined between frequencies .23 and .32 will continue to be present in future observations of STXE up until the expiration. If volatility increases and this intrinsic cycle ceases to exist in the log-return data, the performance will deteriorate.
For a better more comfortable approach that deals with changing volatile index conditions, I would opt for ensuring that the local-bias is present in the signal, This will effectively push or pull the signal down or up when the intrinsic cycle is weak in the increasing volatility, resulting in a pullback in trading activity.
As before, you can acquire the high-freq data used in this tutorial by requesting it via email.
Out-of-sample performance (cash, blue-to-pink line) of an MDFA low-pass filter built using the approach discussed in this article on the STOXX Europe 50 index futures with expiration March 18th (STXEH3) for 200 15-minute interval log-return observations. Only one small loss out-of-sample was recorded from the period of Jan 18th through February 1st, 2013.
Continuing along the trend of my previous installment on strategies and performances of high-frequency trading using multivariate direct filtering, I take on building trading signals for high-frequency index futures, where I will focus on the STOXX Europe 50 Index, S&P 500, and the Australian Stock Exchange Index. As you will discover in this article, these filters that I build using MDFA in iMetrica have yielded some of the best performing trading signals that I have seen using any trading methodology. My strategy as I’ve been developing throughout my previous articles on MDFA has not changed much, except for one detail that I will discuss throughout and will be a major theme of this article, and that relates to an interesting structure found in index futures series for intraday returns. The structure is related to the close-to-open variation in the price, namely when the price at close of market hours significantly differs from the price at open. an effect I’ve mentioned in my previous two articles dealing with high(er)-frequency (or intraday) log-return data. I will show how MDFA can take advantage of this variation in price and profit from each one by ‘predicting’ with the extracted signal the jump or drop in the price at the open of the next trading day.
The frequency of observations on the index that are to be considered for building trading filters using MDFA is typically only a question of taste and priorities. The beauty of MDFA lies in not only the versatility and strength in building trading signals for virtually any financial trading priorities, but also in the independence on the underlying observation frequency of the data. In my previous articles, I’ve considered and built high-performing strategies for daily, hourly, and 15 minute log returns, where the focus of the strategy in building the signal began with viewing the periodogram as the main barometer in searching for optimal frequencies on which one should set the low-pass cutoff for the extracting target filter function.
The STOXX Europe 50 Index, Europe’s leading Blue-chip index, provides a representation of sector leaders in Europe. The index covers 50 stocks from 18 European countries and has the highest trading volume of any European index. One of the first things to notice with the 15-minute log-returns of STXE are the frequent large spikes. These spikes will occur every 27 observations at 13:30 (UTC time zone) due to the fact that there are 26 15-minute periods during the trading hours. These spikes represent the close-to-open jumps that the STOXX Europe 50 index has been subjected to and then reflected in the price of the futures contract. With this ‘seasonal’ pattern so obviously present in the log-return data, the frequency effects of this pattern should be clearly visible in the periodogram of the data. The beauty of MDFA (and iMetrica) is that we have the ability to explicitly engineer a trading signal to take advantage of this ‘seasonal’ pattern by building an appropriate extractor .
Figure 1: Log-returns of STXE for the 15-min observations from 1-4-2013 to 2-1-2013
Regarding the periodogram of the data, Figure 2 depicts the periodograms for the 15 minute log-returns of STXE (red) and the explanatory series (pink) together on the same discretized frequency domain. Notice that in both log-return series, there is a principal spectral peak found between .23 and .32. The trick is to localize the spectral peak that accounts for the cyclical pattern that is brought about by the close-to-open variation between 20:00 and 13:30 UTC.
Figure 2: The periodograms for the 15 minute log-returns of STXE (red) and the explanatory series (pink).
In order to see the effects of the MDFA filter when localizing this spectral peak, I use my target builder interface in iMetrica to set the necessary cutoffs for the bandpass filter directly covering both spectral peaks of the log-returns, which are found between .23 and .32. This is shown in Figure 3, where the two dashed red lines show indicate the cutoffs and spectral peak is clearly inside these two cutoffs, with the spectral peak for both series occurring in the vicinity of . Once the bandpass target was fixed on this small frequency range, I set the regularization parameters for the filter coefficients to be , , and .
Figure 3: Choosing the cutoffs for the band pass filter to localize the spectral peak.
Pinpointing this frequency range that zooms in on the largest spectral peak generates a filter that acts on the intrinsic cycles found in the 15 minute log-returns of the STXE futures index. The resulting trading signal produced by this spectral peak extraction is shown in Figure 4, with the returns (blue to pink line) generated from the trading signal (green) , and the price of the STXE futures index in gray. The cyclical effects in the signal include the close-to-open variations in the data. Notice how the signal predicts the variation of the close-to-open price in the index quite well, seen from the large jumps or falls in price every 27 observations. The performance of this simple design in extracting the spectral peak of STXE yields a 4 percent ROI on 200 observations out-of-sample with only 3 losses out of 20 total trades (85 percent trade success rate), with two of them being accounted for towards the very end of the out-of-sample observations in an uncharacteristic volatile period occurring on January 31st 2013.
Figure 4: The performance in-sample and out-of-sample of the spectral peak localizing bandpass filter.
The two concurrent frequency response (transfer) functions for the two filters acting on the STXE log-return data (purple) and the explanatory series (blue), respectively, are plotted below in Figure 5. Notice the presence of the spectral peaks for both series being accounted for in the vicinity of the frequency , with mild damping at the peak. Slow damping of the noise in the higher frequencies is aided by the addition of a smoothing expweight parameter that was set at .
Figure 5: The concurrent frequency response functions of the localizing spectral peak band-pass filter.
With the ideal characteristics of a trading signal quite present in this simple bandpass filter, namely smooth decaying filter coefficients, in-sample and out-of-sample performance properties identical, and accurate, consistent trading patterns, it would be hard to imagine on improving the trading signal for this European futures index even more. But we can. We simply keep the spectral peak frequencies intact, but also account for the local bias in log-return data by extending the lower cutoff to frequency zero. This will provide improved systematic trading characteristics by not only predicting the close-to-open variation and jumps, but also handling upswings and downswings, and highly volatile periods much better.
In this new design, I created a low-pass filter by keeping the upper cutoff from the band-pass design and setting the lower cutoff to 0. I also increased the smoothing parameter to $\alpha = 32$. In this newly designed filter, we see a vast improvement in the trading structure. As before, the filter was able to deduce the direction of every single close-to-open jump during the 200 out-of-sample observations, but notice that it was also able to become much more flexible in the trading during any upswing/downswing and volatile period. This is seen in more detail in Figure 7, where I added the letter ‘D’ to each of the 5 major buy/sell signals occurring before close.
Figure 6: Performance of filter both in-sample (left of cyan line) and on 210 observations out-of-sample (right of cyan line).
Notice that the signal predicted the jump correctly for each of these major jumps, resulting in large returns. For example, at the first “D” indicator, the signal indicated sell/short (magenta dashed line) the STXE future index 5 observations before close, namely at 18:45 UTC, before market close at 20:00 UTC. Sure enough, the price of the STXE contract went down during overnight trading hours and opened far below the previous days close, with the filter signaling a buy (green dashed line) 45 minutes into trading. At the mark of the second “D”, we see that on the final observation before market close, the signal produced a buy/long indication, and indeed, the next day the price of the future jumped significantly.
Figure 7: Zooming in on the out-of-sample performance and showing all the signal responses that predicted the major close-to-open jumps.
Only two very small losses of less than .08 percent were accounted for. One advantage of including the frequency zero along with the spectral peak frequency of STXE is that the local bias can help push-up or pull-down the signal resulting in a more ‘patient’ or ‘diligent’ filter that can better handle long upswings/downswings or volatile periods. This is seen in the improvement of the performance towards the end of the 200 observations out-of-sample, where the filter is more patient in signaling a sell/short after the previous buy. Compare this with the end of the performance from the band-pass filter, Figure 4. With this trading signal out-of-sample, I computed a旋风加速器下载:2021-4-26 · 苹果下载 安卓下载 旋风加速器安卓版 版本号：1.1.5 时间：06-16 下载 旋风加速器 安卓版 版本号：1.1.6 时间：06-16 下载 旋风加速器 苹果版 版本号：1.1.5 时间：06-16 下载 旋风加速器 苹果版 版本号：1.1.6 时间：06-16 下载 专题推荐 更多> veee+官网安 ... with only 2 small losses. The trading statistics for the entire in-sample combined with out-of-sample are shown in Figure 8.
In this experiment trading S&P 500 future contracts (E-mini) on observations of 15 minute intervals from Jan 4th to Feb 1st 2013, I apply the same regimental approach as before. In looking at the log-returns of ESH3 shown in Figure 10, the effect of close-to-open variation seem to be much less prominent here compared to that on the STXE future index. Because of this, the log-returns seem to be much closer to ‘white noise’ on this index. Let that not perturb our pursuit of a high performing trading signal however. The approach I take for extracting the trading signal, as always, begins with the periodogram.
Figure 10: The log-return data of ES H3 at 15 minute intervals from 1-4-2013 to 2-1-2013.
As the large variations in the close-to-open price are not nearly as prominent, it would make sense that the same spectral peak found before at near is not nearly as prominent either. We can clearly see this in the periodogram plotted below in Figure 11. In fact, the spectral peak at is slightly larger in the explanatory series (pink), thus we should still be able to take advantage of any sort of close-to-open variation that exists in the E-min future index.
Figure 11: Periodograms of ES H3 log-returns (red) and the explanatory series (pink). The red dashed vertical lines are framing the spectral peak between .23 and .32.
With this spectral peak extracted from the series, the resulting trading signal is shown in Figure 12 with the performance of the bandpass signal shown in Figure 13.
Figure 12: The signal built from the extracted spectral peak and the log-return ESH3 data.
One can clearly see that the trading signal performs very well during the consistent cyclical behavior in the ESH3 price, However, when breakdown occurs in this stochastic structure and follows more prominently another frequency, the trading signal dies and no longer trades systematically taking advantage of the intrinsic cycle found near . This can be seen in the middle 90 or so observations. The price can be seen to follow more closely a random walk and the trading becomes inconsistent. After this period of 90 or so observations however, just after the beginning of the out-of-sample period, the trajectory of the ESH3 follows back on its consistent course with a cyclical component it had before.
Figure 13: The performance in-sample and out-of-sample of the simple bandpass filter extracting the spectral peak.
Now to improve on these results, we include the frequency zero by moving the lower cutoff of the previous band-pass filter to $\latex \omega_0 = 0$. As I mentioned before, this lifts or pushes down the signal from the local bias and will trade much more systematically. I then lessened the amount of smoothing in the expweight function to , down from as I had on the band-pass filter. This allows for slightly higher frequencies than to be traded on. I then proceeded to adjust the regularization parameters to obtain a healthy dosage of smoothness and decay in the coefficients. The result of this new low-pass filter design is shown in Figure 14.
Figure 14: Performance out-of-sample (right of cyan line) of the ES H3 filter on 200 15 minute observations.
The improvement in the overall systematic trading and performance is clear. Most of the major improvements came from the middle 90 points where the trading became less cyclical. With 6 losses in the band-pass design during this period alone, I was able to turn those losses into two large gains and no losses. Only one major loss was accounted for during the 200 observation out-of-sample testing of filter from January 18th to February 1st, with an ROI of nearly 4 percent during the 9 trading days. As with the STXE filter in the previous example, I was able to successfully build a filter that correctly predicts close-to-open variations, despite the added difficulty that such variations were much smaller. Both in-sample and out-of-sample, the filter performs consistently, which is exactly what one strives for thanks to regularization.
ASX Futures (YAPH3, Expiration March 18, 2013)
In the final experiment, I build a trading signal for the Australian Stock Exchange futures, during the same period of the previous two experiments. The log-returns show moderately large jumps/drops in price during the entire sample from Jan 4th to Feb 1st, but not quite as large as in the STXE index. We still should be able to take advantage of these close-to-open variations.
Figure 15: The log-returns of the YAPH3 in 15-minute interval observations.
In looking at the periodograms for both the YAPH3 15 minute log-returns (red) and the explanatory series (pink), it is clear that the spectral peaks don’t align like they did in the previous two exampls. In fact, there hardly exists a dominant spectral peak in the explanatory series, whereas the spectral peak in YAPH3 is very prominent. This ultimately might effect the performance of the filter, and consequently the trades. After building the low-pass filter and setting a high smoothing expweight parameter , I then set the regularization parameters to be , , and (same as first example).
Figure 16: The periodograms for YAPH3 and explanatory series with spectral peak in YAPH3 framed by the red dashed lines.
The performance of the filter in-sample and out-of-sample is shown in Figure 18. This was one of the more challenging index futures series to work with as I struggled finding an appropriate explanatory series (likely because I was lazy since it was late at night and I was getting tired). Nonetheless, the filter still seems to predict the close-to-open variation on the Australian stock exchange index fairly well. All the major jumps in price are accounted for if you look closely at the trades (green dashed lines are buys/long and magenta lines are sells/shorts) and the corresponding action on the price of the futures contract. Five losses out-of-sample for a旋风加速器专业版安卓 percent and an ROI out-of-sample on 200 observations of 4.2 percent. As with all other experiments in building trading signals with MDFA, we check the consistency of the in-sample and out-of-sample performance, and these seem to match up nicely.
Figure 18 : The out-of-sample performance of the low-pass filter on YAPH3.
The filter coefficients for the YAPH3 log-difference series is shown in Figure 19. Notice the perfectly smooth undulating yet decaying structure of the coefficients as the lag increases. What a beauty.
Figure 19: Filter coefficients for the YAPH3 series.
旋风加速器APP
Studying the trading performance of spectral peaks by first constructing band-pass filters to extract the signal corresponding to the peak in these index futures enabled me to understand how I can better construct the lowpass filter to yield even better performance. In these examples, I demonstrated that the close-to-open variation in the index futures price can be seen in the periodogram and thus be controlled for in the MDFA trading signal construction. This trading frequency corresponds to roughly in the 15 minute observation data that I had from Jan 4th to Feb 1st. As I witnessed in my empirical studies using iMetrica, this peak is more prominent when the close-to-open variations are larger and more often, promoting a very cyclical structure in the log-return data. As I look deeper and deeper into studying the effects of extracting spectral peaks in the periodogram of financial data log-returns and the trading performance, I seem to improve on results even more and building the trading signals becomes even easier.
Stay tuned very soon for a tutorial using R (and MDFA) for one of these examples on high-frequency trading on index futures. If you have any questions or would like to request a certain index future (out of one of the above examples or another) to be dissected in my second and upcoming R tutorial, feel free to shoot me an email.
Happy extracting!
High-Frequency Financial Trading on FOREX with MDFA and R: An Example with the Japanese Yen
Posted on by Christian Dallas Blakely
Figure 1: In-sample (observations 1-250) and out-of-sample performance of the trading signal built in this tutorial using MDFA. (Top) The log price of the Yen (FXY) in 15 minute intervals and the trades generated by the trading signal. Here black line is a buy (long), blue is sell (short position). (Bottom) The returns accumulated (cash) generated by the trading, in percentage gained or lost.
In my previous article on high-frequency trading in iMetrica on the FOREX/GLOBEX, I introduced some robust signal extraction strategies in iMetrica using the multidimensional direct filter approach (MDFA) to generate high-performance signals for trading on the foreign exchange and Futures market. In this article I take a brief leave-of-absence from my world of developing financial trading signals in iMetrica and migrate into an uber-popular language used in finance due to its exuberant array of packages, quick data management and graphics handling, and of course the fact that it’s free (as in speech and beer) on nearly any computing platform in the world.
This article gives an intro tutorial on using R for high-frequency trading on the FOREX market using the R package for MDFA (offered by Herr Doktor Marc Wildi von Bern) and some strategies that I’ve developed for generating financially robust trading signals. For this tutorial, I consider the second example given in my previous article where I engineered a trading signal for 15-minute log-returns of the Japanese Yen (from opening bell to market close EST). This presented slightly new challenges than before as the close-to-open jump variations are much larger than those generated by hourly or daily returns. But as I demonstrated, these larger variations on close-to-open price posed no problems for the MDFA. In fact, it exploited these jumps and made large profits by predicting the direction of the jump. Figure 1 at the top of this article shows the in-sample (observations 1-250) and out-of-sample (observations 251 onward) performance of the filter I will be building in the first part of this tutorial.
Throughout this tutorial, I attempt to replicate these results that I built in iMetrica and expand on them a bit using the R language and the implementation of the MDFA available in here. The data that we consider are 15-minute log-returns of the Yen from January 4th to January 17th and I have them saved as an .RData file given by 旋风加速器APP. I have an additional explanatory series embedded in the .RData file that I’m using to predict the price of the Yen. Additionally, I also will be using price_fxy_insamp which is the log price of Yen, used to compute the performance (buy/sells) of the trading signal. The ld_fxy_insamp will be used as the in-sample data to construct the filter and trading signal for FXY. To obtain this data so you can perform these examples at home, email me and I’ll send you all the necessary .RData files (the in-sample and out-of-sample data) in a .zip file. Taking a quick glance at the ld_fxy_insamp data, we see log-returns of the Yen at every 15 minutes starting at market open (time zone UTC). The target data (Yen) is in the first column along with the two explanatory series (Yen and another asset co-integrated with movement of Yen).
> head(ld_fxy_insamp)
[,1] [,2] [,3]
2013-01-04 13:30:00 0.000000e+00 0.000000e+00 0.0000000000
2013-01-04 13:45:00 4.763412e-03 4.763412e-03 0.0033465833
2013-01-04 14:00:00 -8.966599e-05 -8.966599e-05 0.0040635638
2013-01-04 14:15:00 2.597055e-03 2.597055e-03 -0.0008322064
2013-01-04 14:30:00 -7.157556e-04 -7.157556e-04 0.0020792190
2013-01-04 14:45:00 -4.476075e-04 -4.476075e-04 -0.0014685198
Moving on, to begin constructing the first trading signal for the Yen, we begin by uploading the data into our R environment, define some initial parameters for the MDFA function call, and then compute the DFTs and periodogram for the Yen.
load(paste(path.pgm,"ld_fxy_in15min.RData",sep="")) #load in-sample log-returns of Yen
load(paste(path.pgm,"price_fxy_in15min.RData",sep="")) #load in-sample log-price of Yen
in_samp_lenprice_insample<-price_fxy_insamp
#setup some MDFA variables
x<-ld_fxy_insamp
len<-length(x[,1])
shift_constraint<-rep(0,length(x[1,])-1)
weight_constraint<-rep(0,length(x[1,])-1)
d<-0
plots<-T
lin_expweight<-F
# Compute DFTs and periodogram for initial analysis
spec_obj<-spec_comp(len,x,d)
weight_func<-spec_obj$weight_func
K<-length(weight_func[,1])-1
fxy_periodogram<-abs(spec_obj$weight_func[,1])^2
As I’ve mentioned in my previous articles, my step-by-step strategy for building trading signals always begin by a quick analysis of the periodogram of the asset being traded on. Holding the key to providing insight into the characteristics of how the asset trades, the periodogram is an essential tool for navigating how the extractor is chosen. Here, I look for principal spectral peaks that correspond in the time domain to how and where my signal will trigger buy/sell trades. Figure 2 shows the periodogram of the 15-minute log-returns of the Japanese Yen during the in-sample period from January 4 to January 17 2013. The arrows point to the main spectral peaks that I look for and provides a guide to how I will define my function. The black dotted lines indicate the two frequency cutoffs that I will consider in this example, the first being and the second at . Notice that both cutoffs are set directly after a spectral peak, something that I highly recommend. In high-frequency trading on the FOREX using MDFA, as we’ll see, the trick is to seek out the spectral peak which accounts for the close-to-open variation in the price of the foreign currency. We want to take advantage of this spectral peak as this is where the big gains in foreign currency trading using MDFA will occur.
Figure 2: Periodogram of FXY (Japanese Yen) along with spectral peaks and two different frequency cutoffs.
In our first example we consider the larger frequency as the cutoff for by setting it to (the right most line in the figure of the periodogram). I then initially set the timeliness and smoothness parameters, and expweight to 0 along with setting all the regularization parameters to 0 as well. This will give me a barometer for where and how much to adjust the filter parameters. In selecting the filter length , my empirical studies over numerous experiments in building trading signals using iMetrica have demonstrated that a ‘good’ choice is anywhere between 1/4 and 1/5 of the total in-sample length of the time series data. Of course, the length depends on the frequency of the data observations (i.e. 15 minute, hourly, daily, etc.), but in general you will most likely never need more than being greater than 1/4 the in-sample size. Otherwise, regularization can become too cumbersome to handle effectively. In this example, the total in-sample length is 335 and thus I set which I’ll stick to for the remainder of this tutorial. In any case, the length of the filter is not the most crucial parameter to consider in building good trading signals. For a good robust selection of the filter parameters couple with appropriate explanatory series, the results of the trading signal with compared with, say, should hardly differ. If they do, then the parameterization is not robust enough.
After uploading both the in-sample log-return data along with the corresponding log price of the Yen for computing the trading performance, we the proceed in R to setting initial filter settings for the MDFA routine and then compute the filter using the IMDFA_comp function. This returns both the i_mdfa& object holding coefficients, frequency response functions, and statistics of filter, along with the signal produced for each explanatory series. We combine these signals to get the final trading signal in-sample. All this is all done in R as follows:
cutoff<-pi/6 #set frequency cutoff
Gamma<-((0:K)<(cutoff*K/pi)) #define Gamma
grand_mean<-F
Lag<-0
L<-82
lambda_smooth<-0
lambda_cross<-0
lambda_decay<-c(0.,0.) #regularization - decay
lambda<-0
expweight<-0
i1<-F
i2<-F
# compute the filter for the given parameter definitions
i_mdfa_obj<-IMDFA_comp(Lag,K,L,lambda,weight_func,Gamma,expweight,cutoff,i1,i2,weight_constraint,
lambda_cross,lambda_decay,lambda_smooth,x,plots,lin_expweight,shift_constraint,grand_mean)
# after computing filter, we save coefficients
bn<-i_mdfa_obj$i_mdfa$b
# now we build trading signal
trading_signal<-i_mdfa_obj$xff[,1] + i_mdfa_obj$xff[,2]
The resulting frequency response functions of the filter and the coefficients are plotted in the figure below.
Figure 3: The Frequency response functions of the filter (top) and the filter coefficients (below)
Notice the abundance of noise still present passed the cutoff frequency. This is mollified by increasing the expweight smoothness parameter. The coefficients for each explanatory series show some correlation in their movement as the lags increase. However, the smoothness and decay of the coefficients leaves much to be desired. We will remedy this by introducing regularization parameters. Plots of the in-sample trading signal and the performance in-sample of the signal are shown in the two figures below. Notice that the trading signal behaves quite nicely in-sample. However, looks can be deceiving. This stellar performance is due in large part to a filtering phenomenon called overfitting. One can deduce that overfitting is the culprit here by simply looking at the nonsmoothness of the coefficients along with the number of freezed degrees of freedom, which in this example is roughly 174 (out of 174), way too high. We would like to get this number at around half the total amount of degrees of freedom (number of explanatory series x L).
Figure 4: The trading signal and the log-return data of the Yen.
The in-sample performance of this filter demonstrates the type of results we would like to see after regularization is applied. But now comes for the sobering effects of overfitting. We apply these filter coeffcients to 200 15-minute observations of the Yen and the explanatory series from January 18 to February 1 2013 and compare with the characteristics in-sample. To do this in R, we first load the out-of-sample data into the R environment, and then apply the filter to the out-of-sample data that I defined as x_out.
load(paste(path.pgm,"ld_fxy_out15min.RData",sep=""))
load(paste(path.pgm,"price_fxy_out15min.RData",sep=""))
x_out<-rbind(ld_fxy_insamp,ld_fxy_outsamp) #bind the in-sample with out-of-sample data
xff<-matrix(nrow=out_samp_len,ncol=2)
#apply filter built in-sample
for(i in 1:out_samp_len)
{
xff[i,]<-0
for(j in 2:3)
{
xff[i,j-1]<-xff[i,j-1]+bn[,j-1]%*%x_out[335+i:(i-L+1),j]
}
}
trading_signal_outsamp<-xff[,1] + xff[,2] #assemble the trading signal out-of-sample
trade_outsamp<-trading_logdiff(trading_signal_outsamp,price_outsample,.0005) #compute the performance
The plot in Figure 5 shows the out-of-sample trading signal. Notice that the signal is not nearly as smooth as it was in-sample. Overshooting of the data in some areas is also obviously present. Although the out-of-sample overfitting characteristics of the signal are not horribly suspicious, I would not trust this filter to produce stellar returns in the long run.
Figure 5 : Filter applied to 200 15 minute observations of Yen out-of-sample to produce trading signal (shown in blue)
Following the previous analysis of the mean-squared solution (no customization or regularization), we now proceed to clean up the problem of overfitting that was apparent in the coefficients along with mollifying the noise in the stopband (frequencies after ). In order to choose the parameters for smoothing and regularization, one approach is to first apply the smoothness parameter first, as this will generally smooth the coefficients while acting as a ‘pre’-regularizer, and then advance to selecting appropriate regularization controls. In looking at the coefficients (Figure 3), we can see that a fair amount of smoothing is necessary, with only a slight touch of decay. To select these two parameters in R, one option is to use the Troikaner optimizer (found here) to find a suitable combination (I have a secret sauce algorithmic approach I developed for iMetrica for choosing optimal combinations of parameters given an extractor and a performance indicator, although it’s lengthy (even in GNU C) and cumbersome to use, so I typically prefer the strategy discussed in this tutorial). In this example, I began by setting the lambda_smooth to .5 and the decay to (.1,.1) along with an expweight smoothness parameter set to 8.5. After viewing the coefficients, it still wasn’t enough smoothness, so I proceeded to add more finally reaching .63, which did the trick. I then chose lambda to balance the effects of the smoothing expweight (lambda is always the last resort tweaking parameter).
lambda_smooth<-0.63
lambda_cross<-0.
lambda_decay<-c(0.119,0.099)
lambda<-9
expweight<-8.5
i_mdfa_obj<-IMDFA_comp(Lag,K,L,lambda,weight_func,Gamma,expweight,cutoff,i1,i2,weight_constraint,
lambda_cross,lambda_decay,lambda_smooth,x,plots,lin_expweight,shift_constraint,grand_mean)
bn<-i_mdfa_obj$i_mdfa$b #save the filter coefficients
trading_signal<-i_mdfa_obj$xff[,1] + i_mdfa_obj$xff[,2] #compute the trading signal
trade<-trading_logdiff(trading_signal[L:len],price_insample[L:len],0) #compute the in-sample performance
Figure 6 shows the resulting frequency response function for both explanatory series (Yen in red). Notice that the largest spectral peak found directly before the frequency cutoff at is being emphasized and slightly mollified (value near .8 instead of 1.0). The other spectral peaks below are also present. For the coefficients, just enough smoothing and decay was applied to keep the lag, cyclical, and correlated structure of the coefficients intact, but now they look much nicer in their smoothed form. The number of freezed degrees of freedom has been reduced to approximately 102.
Figure 6: The frequency response functions and the coefficients after regularization and smoothing have been applied (top). The smoothed coefficients with slight decay at the end (bottom). Number of freezed degrees of freedom is approximately 102 (out of 172).
Along with an improved freezed degrees of freedom and no apparent havoc of overfitting, we apply this filter out-of-sample to the 200 out-of-sample observations in order to verify the improvement in the structure of the filter coefficients (shown below in Figure 7). Notice the tremendous improvement in the properties of the trading signal (compared with Figure 5). The overshooting of the data has be eliminated and the overall smoothness of the signal has significantly improved. This is due to the fact that we’ve eradicated the presence of overfitting.
Figure 7: Out-of-sample trading signal with regularization.
With all indications of a filter endowed with exactly the characteristics we need for robustness, we now apply the trading signal both in-sample and out of sample to activate the buy/sell trades and see the performance of the trading account in cash value. When the signal crosses below zero, we sell (enter short position) and when the signal rises above zero, we buy (enter long position).
The top plot of Figure 8 is the log price of the Yen for the 15 minute intervals and the dotted lines represent exactly where the trading signal generated trades (crossing zero). The black dotted lines represent a buy (long position) and the blue lines indicate a sell (and short position). Notice that the signal predicted all the close-to-open jumps for the Yen (in part thanks to the explanatory series). This is exactly what we will be striving for when we add regularization and customization to the filter. The cash account of the trades over the in-sample period is shown below, where transaction costs were set at .05 percent. In-sample, the signal earned roughly 6 percent in 9 trading days and a 76 percent trading success ratio.
Figure 8: In-sample performance of the new filter and the trades that are generated.
Now for the ultimate test to see how well the filter performs in producing a winning trading signal, we applied the filter to the 200 15-minute out-of-sample observation of the Yen and the explanatory series from Jan 18th to February 1st and make trades based on the zero crossing. The results are shown below in Figure 9. The black lines represent the buys and blue lines the sells (shorts). Notice the filter is still able to predict the close-to-open jumps even out-of-sample thanks to the regularization. The filter succumbs to only three tiny losses at less than .08 percent each between observations 160 and 180 and one small loss at the beginning, with an out-of-sample trade success ratio hitting 82 percent and an ROI of just over 4 percent over the 9 day interval.
Figure 9: Out-of-sample performance of the regularized filter on 200 out-of-sample 15 minute returns of the Yen. The filter achieved 4 percent ROI over the 200 observations and an 82 percent trade success ratio.
Now we take a stab at producing another trading filter for the Yen, only this time we wish to identify only the lowest frequencies to generate a trading signal that trades less often, only seeking the largest cycles. As with the performance of the previous filter, we still wish to target the frequencies that might be responsible to the large close-to-open variations in the price of Yen. To do this, we select our cutoff to be which will effectively keep the largest three spectral peaks intact in the low-pass band of .
For this new filter, we keep things simple by continuing to use the same regularization parameters chosen in the previous filter as they seemed to produce good results out-of-sample. The and expweight customization parameters however need to be adjusted to account for the new noise suppression requirements in the stopband and the phase properties in the smaller passband. Thus I increase the smoothing parameter and decreased the timeliness parameter (which only affects the passband) to account for this change. The new frequency response functions and filter coefficients for this smaller lowpass design are shown below in Figure 11. Notice that the second spectral peak is accounted for and only slightly mollified under the new changes. The coefficients still have the noticeable smoothness and decay at the largest lags.
To test the effectiveness of this new lower trading frequency design, we apply the filter coefficients to the 200 out-of-sample observations of the 15-minute Yen log-returns. The performance is shown below in Figure 12. In this filter, we clearly see that the filter still succeeds in predicting correctly the large close-to-open jumps in the price of the Yen. Only three total losses are observed during the 9 day period. The overall performance is not as appealing as the previous filter design as less amount of trades are made, with a near 2 percent ROI and 76 percent trade success ratio. However, this design could fit the priorities for a trader much more sensitive to transaction costs.
Figure 12: Out-of-sample performance of filter with lower cutoff.
Conclusion
Verification and cross-validation is important, just as the most interesting man in the world will tell you.
Summary of strategies for building trading signal using MDFA in R:
As I mentioned before, the periodogram is your best friend. Apply the cutoff directly after any range of spectral peaks that you want to consider. These peaks are what generate the trades.
Utilize a choice of filter length no greater than 1/4. Anything larger is unnecessary.
Begin by computing the filter in the mean-square sense, namely without using any customization or regularization and see exactly what needs to be approved upon by viewing the frequency response functions and coefficients for each explanatory series. Good performance of the trading signal in-sample (and even out-of-sample in most cases) is meaningless unless the coefficients have solid robust characteristics in both the frequency domain and the lag domain.
I recommend beginning with tweaking the smoothness customization parameter expweight and the lambda_smooth regularization parameters first. Then proceed with only slight adjustments to the lambda_decay parameters. Finally, as a last resort, the lambda customization. I really never bother to look at lambda_cross. It has seldom helped in any significant manner. Since the data we are using to target and build trading signals are log-returns, no need to ever bother with i1 and i2. Those are for the truly advanced and patient signal extractors, and should only be left for those endowed with iMetrica 😉
If you have any questions, or would like the high-frequency Yen data I used in these examples, feel free to contact me and I’ll send them to you. Until next time, happy extracting!
High-Frequency Financial Trading with Multivariate Direct Filtering I: FOREX and Futures
I recently acquired over 300 GBs of financial data that includes tick data for over 7000 financial assets traded on multiple markets for the past 5 years up until February 1st 2013. This USB drive packed with nearly every detail of world financial markets coupled with iMetrica gave me an opportunity to explore at any fashion to my desire the ability of multivariate direct filtering to produce high performance financial trading signals on nearly any high-frequency. Let me begin this article with saying that I am more than ecstatic with the results, as I hope you will too after reading this article. In this first article in a series of high-frequency trading with MDFA and iMetrica that I plan to write, I provide some initial experiments with building and extracting financial trading signals for high-frequency intraday observations on foreign exchange (FOREX) data, and by high-frequency in the context of this article, I mean higher frequencies than the daily log-returns I’ve been working with in my previous articles. In the first part of this high-frequency series, I begin by exploring hourly, 30 minute, and 15 minute log-returns, and test different strategies, mostly using low-pass and the recently introduced multi-bandpass (MBP) filter to deduce the best approach to tackle the problem of building successful trading signals in higher frequency data.
In my previous articles, I was working uniquely with daily log-return data from different time spans from a year to a year and a half. This enabled the in-sample period of computing the filter coefficients for the signal extraction to include all the most recent annual phases and seasons of markets, from holiday effects, to the transitioning period of August to September that is regularly highly influential on stock market prices and commodities as trading volume increases a significant amount. One immediate question that is raised in migrating to higher-frequency intraday data is what kind of in-sample/out-of-sample time spans should be used to compute the filter in-sample and then for how long do we apply the filter out-of-sample to produce the trades? Another question that is raised with intraday data is how do we account for the close-to-open variation in price? Certainly, after close, the after-hour bids and asks will force a jump into the next trading day. How do we deal with this jump in an optimal manner? As the observation frequency gets higher, say from one hour to 30 minutes, this close-to-open jump/fall should most likely be larger. I will start by saying that, as you will see in the results of this article, with a clever choice of the extractor and explanatory series, MDFA can handle these jumps beautifully (both aesthetically and financially). In fact, I would go so far as to say that the MDFA does a superb job in predicting the overnight variation.
One advantage of building trading signals for higher intraday frequencies is that the signals produce trading strategies that are immediately actionable. Namely one can act upon a signal to enter a long or short position immediately when they happen. In building trading signals for the daily log-return, this is not the case since the observations are not actionable points, namely the log difference of today’s ending price with yesterday’s ending price are produced after hours and thus not actionable during open market hours and only actionable the next trading day. Thus trading on intraday observations can lead to better efficiency in trading.
In this first installment in my series on high-frequency financial trading using multivariate direct filtering in iMetrica, I consider building trading signals on hourly returns of foreign exchange currencies. I’ve received a few requests after my recent articles on the Frequency Effect in seeing iMetrica and MDFA in action on the FOREX sector. So to satisfy those curiosities, I give a series of (financially) satisfying and exciting results in combining MDFA and the FOREX. I won’t give all my secretes away into building these signals (as that would of course wipe out my competitive advantage), but I will give some of the parameters and strategies used so any courageously curious reader may try them at home (or the office). In the conclusion, I give a series of even more tricks and hacks. The results below speak for themselves So without further ado, let the games begin.
Japanese Yen
Frequency: One hour returns 30 day out-of-sample ROI: 12 percent 旋风加速器的官网在哪里92 percent
In the first experiment, I consider hourly log-returns of a ETF index that mimics the Japanese Yen called FXY. As for one of the explanatory series, I consider the hourly log-returns of the price of GOLD which is traded on NASDAQ. The out-of-sample results of the trading signal built using a low-pass filter and the parameters above are shown in Figure 1. The in-sample trading signal (left of cyan line) was built using 400 hourly observations of the Yen during US market hours dating back to 1 October 2012. The filter was then applied to the out-of-sample data for 180 hours, roughly 30 trading days up until Friday, 1 February 2013.
Figure 1: Out-of-sample results for the Japanese Yen. The in-sample trading signal was built using 400 hourly observations of the Yen during US market hours dating back to October 1st, 2012. The out-of-sample portion passed the cyan line is on 180 hourly observations, about 30 trading days.
This beauty of this filter is that it yields a trading signal exhibiting all the characteristics that one should strive for in building a robust and successful trading filter.
Consistency: The in-sample portion of the filter performs exactly as it does out-of-sample (after cyan line) in both trade success ratio and systematic trading performance.
Dropdowns: One small dropdown out-of-sample for a loss of only .8 percent (nearly the cost of the transaction).
Detects the cycles as it should: Although the filter is not able to pinpoint with perfect accuracy every local small upturn during the descent of the Yen against the dollar, it does detect them nonetheless and knows when to sell at their peaks (the magenta lines).
Self-correction: What I love about a robust filter is that it will tend to self-correct itself very quickly to minimize a loss in an erroneous trade. Notice how it did this in the second series of buy-sell transactions during the only loss out-of-sample. The filter detects momentum but quickly sold right before the ensuing downfall. My intuition is that only frequency-based methods such as the MDFA are able to achieve this consistently. This is the sign of a skillfully smart filter.
The coefficients for this Yen filter are shown below. Notice the smoothness of the coefficients from applying the heavy smooth regularization and the strong decay at the very end. This is exactly the type of smooth/decay combo that one should desire. There is some obvious correlation between the first and second explanatory series in the first 30 lags or so as well. The third explanatory series seems to not provide much support until the middle lags .
Figure 2: Coefficients of the Yen filter. Here we use three different explanatory series to extract the trading signal.
One of the first things that I always recommend doing when first attempting to build a trading signal is to take a glance at the periodogram. Figure 2 shows the periodogram of the log-return data of the Japanese Yen over 580 hours. Compare this with the periodogram of the same asset using log-returns of daily data over 580 days, shown in Figure 3. Notice the much larger prominent spectral peaks at the lower frequencies in the daily log-return data. These prominent spectral peaks renders multibandpass filters much more advantageous and to use as we can take advantage of them by placing a band-pass filter directly over them to extract that particular frequency (see my article on multibandpass filters). However, in the hourly data, we don’t see any obvious spectral peaks to consider, thus I chose a low-pass filter and set the cutoff frequency at $\pi/5$, a standard choice, and good place to begin.
Figure 3: Periodogram of hourly log-returns of the Japanese Yen over 580 hours.
Figure 4: Periodogram of Japanese Yen using 580 daily log-return observations. Many more spectral peaks are present in the lower frequencies.
In the next trading experiment, I consider the Japanese Yen again, only this time I look at trading on even high-frequency log-return data than before, namely on 15 minute log-returns of the Yen from the opening bell to market close. This presents slightly new challenges than before as the close-to-open jumps are much larger than before, but these larger jumps do not necessarily pose problems for the MDFA. In fact, I look to exploit these and take advantage to gain profit by predicting the direction of the jump. For this higher frequency experiment, I considered 350 15-minute in-sample observations to build and optimize the trading signal, and then applied it over the span of 200 15-minute out-of-sample observations. This produced the results shown in the Figure 5 below. Out of 17 total trades out-of-sample, there were only 3 small losses each less than .5 percent drops and thus 14 gains during the 200 15-minute out-of-sample time period. The beauty of this filter is its impeccable ability to predict the close-to-open jump in the price of the Yen. Over the nearly 7 day trading span, it was able to correctly deduce whether to buy or short-sell before market close on every single trading day change. In the figure below, the four largest close-to-open variation in Yen price is marked with a “D” and you can clearly see how well the signal was able to correctly deduce a short-sell before market close. This is also consistent with the in-sample performance as well, where you can notice the buys and/or short-sells at the largest close-to-open jumps (notice the large gain in the in-sample period right before the out-of-sample period begins, when the Yen jumped over 1 percent over night. This performance is most likely aided by the explanatory time series I used for helping predict the close-to-open variation in the price of the Yen. In this example, I only used two explanatory series (the price of Yen, and another closely related to the Yen).
Figure 5: Out-of-sample performance of the Japanese Yen filter on 15 minute log-return data.
We look at the filter transfer functions to see what frequencies they are being privileged in the construction of the filter. Notice that some noise leaks out passed the frequency cutoff at , but this is typically normal and a non-issue. I had to balance for both timeliness and smoothness in this filter using both the customization parameters and . Not much at frequency 0 is emphasized, with more emphasis stemming from the large spectral peak found right at .
In this example we consider the frequency of the data to 30 minute returns and attempt to build a robust trading signal for a derivative of the British Pound (BP) on this higher frequency. Instead of using the cash value of the BP, I use 30 minute returns of the BP Futures contract expiring in March (BPH3). Although I don’t have access to tick data from the FOREX, I do have tick data from GLOBEX for the past 5 years. Thus the futures series won’t be an exact replication of the cash price series of the BP, but it should be quite close due to very low interest rates.
The results of the out-of-sample performance of the BP futures filter are shown in Figure 7. I constructed the filter using an initial in-sample size of 390 30 minute returns dating back to 1 December 2012. After pinpointing a frequency cutoff in the frequency domain for the that yielded decent trading results in-sample, I then proceeded to optimize the filter in-sample on smoothness and regularization to achieve similar out-of-sample performance. Applying the resulting filter out-of-sample on 168 30-minute log-return observations of the BP futures series along with 3 explanatory series, I get the results shown below. There were 13 trades made and 10 of them were successful. Notice that the filter does an exquisite job at triggering trades near local optimums associated with the frequencies inside the cutoff of the filter.
Figure 7: The out-of-sample results of the British Pound using 30-minute return data.
In looking at the coefficients of the filter for each series in the extraction, we can clearly see the effects of the regularization: the smoothness of the coefficients the fast decay at the very end. Notice that I never really apply any cross regularization to stress the latitudinal likeliness between the 3 explanatory series as I feel this would detract from the predicting advantages brought by the explanatory series that I used.
Figure 8: The coefficients for the 3 explanatory series of the BP futures,
Continuing with the 30 minute frequency of log-returns, in this example I build a trading signal for the Euro futures contract with expiration on 18 March 2013 (UROH3 on the GLOBEX). My in-sample period, being the same as my previous experiment, is from 1 December 2012 to 4 January 2013 on 30 minute returns using three explanatory time series. In this example, after inspecting the periodogram, I decided upon a low-pass filter with a frequency cutoff of . After optimizing the customization and applying the filter to one month of 30 minute frequency return data out-of-sample (month of January 2013, after cyan line) we see the performance is akin to the performance in-sample, exactly what one strives for. This is due primarily to the heavy regularization of the filter coefficients involved. Only four very small losses of less than .02 percent are suffered during the out-of-sample span that includes 10 successful trades, with the losses only due to the transaction costs. Without transaction costs, there is only one loss suffered at the very beginning of the out-of-sample period.
Figure 9 : Out-of-sample performance on the 30-min log-returns of Euro futures contract UROH3.
As in the first example using hourly returns, this filter again exhibits the desired characteristics of a robust and high-performing financial trading filter. Notice the out-of-sample performance behaves akin to the in-sample performance, where large upswings and downswings are pinpointed to high-accuracy. In fact, this is where the filter performs best during these periods. No need for taking advantage of a multibandpass filter here, all the profitable trading frequencies are found at less than . Just as with the previous two experiments with the Yen and the British Pound, notice that the filter cleanly predicts the close-to-open variation (jump or drop) in the futures value and buys or sells as needed. This can be seen from many of the large jumps in the out-of-sample period (after cyan line).
One reason why these trading signals perform so well is due to their approximation power of the symmetric filter. In comparing the trading signal (green) with a high-order approximation of the symmetric filter (gray line) transfer function shown in Figure 10, we see that trading signal does an outstanding job at approximating the symmetric filter uniformly. Even at the latest observation (the right most point), the asymmetric filter hones in on the symmetric signal (gray line) with near perfection. Most importantly, the signal crosses zero almost exactly where required. This is exactly what you want when building a high-performing trading signal.
Figure 10: Plot of approximation of the real-time trading signal for UROH3 with a high order approximation of the symmetric filter transfer function.
In looking at the periodogram of the log-return data and the output trading signal differences (colored in blue), we see that the majority of the frequencies were accounted for as expected in comparing the signal with the symmetric signal. Only an inconsequential amount of noise leakage passed the frequency cutoff of is found. Notice the larger trading frequencies, the more prominent spectral peaks, are located just after . These could be taken into account with a smart multibandpass filter in order to manifest even more trades, but I wanted to keep things simple for my first trials with high-frequency foreign exchange data. I’m quite content with the results that I’ve achieved so far.
Figure 11: Comparing the periodogram of the signal with the log-return data.
As my confidence has now been bolstered and amplified even more after my experience with building financial trading signals with MDFA and iMetrica for high-frequency data on foreign exchange log-returns at nearly any frequency, I’d be willing to engage in a friendly competition with anyone out there who is certain that they can build better trading strategies using time domain based methods such as technical analysis or any other statistical arbitrage technique. I strongly believe these frequency based methods are the way to go, and the new wave in financial trading. But it takes experience and a good eye for the frequency domain and periodograms to get used to. I haven’t seen many trading benchmarks that utilize other types of strategies, but i’m willing to bet that they are not as consistent as these results using this large of an out-of-sample to in-sample ratio (the ratios in these experiments were between .50 and .80). If anyone would like to take me up on my offer for a friendly competition (or know anyone that would), feel free to contact me.
After working with a multitude of different financial time series and building many different types of filters, I have come to the point where I can almost eyeball many of the filter parameter choices including the most important ones being the extractor along with the regularization parameters, without resorting to time consuming, and many times inconsistent, optimization routines. Thanks to iMetrica, transitioning from visualizing the periodogram to the transfer functions and to the filter coefficients and back to the time domain to compare with the approximate symmetric filter in order to gauge parameter choices is an easy task, and an imperative one if one wants to build successful trading signals using MDFA.
Pay close attention to the periodogram. This is your best friend in choosing the extractor . The best performing signals are not the ones that trade often, but trade on the most important frequencies found in the data. Not all frequencies are created equal. This is true when building either low-pass or multibandpass frequencies.
When tweaking customization, always begin with , the parameter for smoothness. for timeliness should be the last resort. In fact, this parameter will most likely be next to useless due to the fact that the log-return of financial data is stationary. You probably won’t ever need it.
In my next article, I will continue with even more high-frequency trading strategies with the MDFA and iMetrica where I will engage in the sector of Funds and ETFs. If any curious reader would like even more advice/hints/comments on how to build these trading signals on high-frequency data for the FOREX (or the coefficients built in these examples), feel free to get in contact with me via email. I’ll be happy to help.
Happy extracting!
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