Omicron Research Institute

QuanTek Econometrics Software

QuanTek Features

QuanTek is an Econometrics program created for the purpose of designing and testing optimal technical indicators and trading rules, as well as conducting general studies of the dynamics of financial markets.  This is accomplished by making use of state-of-the-art techniques of Adaptive Filter Theory as well as a variety of sophisticated statistical testsQuanTek makes use of a variety of different Linear Prediction filters as well as the Savitzky-Golay smoothing filter.  These digital filters make it possible to design a whole new category of technical indicators that are much more sophisticated than the traditional ones.  The sophisticated statistical tests make it possible to design custom technical indicators and then test their effectiveness over a range of past data.  In this way different indicators can be tested and optimized and their effectiveness for trading and investing compared.

QuanTek works by defining various functions of past returns (prices) using the different Linear Prediction and Savitzky-Golay smoothing filters, along with the more usual Moving Averages.  These functions are the technical indicators and are designed to display maximum correlation with future returns.  This correlation can be tested using the statistical tests in QuanTek.  This correlation is not so easy to find, because it is usually masked by high-frequency stochastic noise.  But it can be uncovered using the appropriate filtering techniques.  Then once a technical indicator is found that displays positive correlation with future returns, this indicator can serve as the basis of a set of trading rules.  Due to the verified correlation, the trading rules are then effective and their effectiveness can be verified in a variety of realistic trading scenarios by means of the Diagnostic Test.  Having verified the correlation with future returns of the technical indicator and positive gains from the Diagnostic Test over the past data set of the trading rules, this set of trading rules may then be expected to yield positive results at least a short time into the future.  In the upcoming version 3.3 there will also be incorporated an automatic optimization routine, which optimizes the trading rules automatically for each day, then tests this optimization for the next day's data.  In this way the persistence of the optimization can be tested, rather than just being assumed based on the optimization over the past data set.

QuanTek is also a portfolio management program, making use of the Markowitz Model  to construct an optimal portfolio.  You can use this optimization routine to adjust your portfolio to maximize return and minimize risk in the overall portfolio.  This risk control is very important in connection with short-term trading.  The novel feature of this portfolio optimization is that the expected returns are estimated by means of the Linear Prediction filter and Savitzky-Golay smoothing filter.

QuanTek now works with either TeleChart or MetaStock data, or directly with ASCII files in a variety of formats.

Price Projection

Price Projection:A Price Projection is computed using one of a choice of five Linear Prediction filters, and displayed on the Main Graph.  This Price Projection is also used as part of the computation of the Momentum indicators and Trading Rules.  One of the LP filters uses the Wavelet spectrum, one uses the Periodogram spectrum, and one computes the autocorrelation directly.  At present, all of these filters assume the returns data to be a stationary stochastic process over the past 1024 days, which seems a reasonable approximation to the true non-stationary stochastic process underlying the financial markets.  You can experiment with the results of using the different LP filters.

Hybrid LP Filter: The Hybrid LP Filter dialog is used to set the Linear Prediction filter for each security separately.  The type of filter may be chosen, and two parameters which are called the Order of Approximation and the Fractal Dimension can be set.  The Order of Approximation selects the degree of smoothing of the filter spectrum, by setting the length of the series of LP coefficients.  The Fractal Dimension sets the low-frequency response of the filter, corresponding to modeling the time series as a fractionally differenced (FD) or long-memory process.  (The Wavelet filter works well with the default values of these settings.)  The Hybrid LP Filter dialog also has attached to it a dialog box which displays the correlation between the filter output (returns) , and the future returns.  Using this Correlation Test - Filters dialog, the predictive power of each filter can be tested directly for any settings of the filter type and parameters

Technical Indicators

Technical Indicators: The main feature of QuanTek is that you can design and test yourself a wide variety of technical indicators based on the Linear Prediction filter and the Savitzky-Golay smoothing filter.  These consist of practically every conceivable oscillator-type indicator that can be made out of the past price data.  These indicators can then be tested for correlation with future returns using the statistical tests in QuanTek.  After a set of three of these indicators is designed, the Trading Rules indicator is formed by a weighted sum of these three indicators, with various filter rules applied.  The effectiveness of the final Trading Rules indicator can be tested in a variety of realistic trading scenarios by means of the Diagnostic Test. Harmonic Oscillator: A set of technical indicators is displayed in a splitter window, with three panes, and is called the Harmonic Oscillator display.  This set of three indicators is called the Relative Price, Velocity, and Acceleration indicators.  They are based on the Savitzky-Golay smoothing of the past prices, with zero, one, and two derivatives, respectively.  This filter is set to be acausal, so there is no time lag (zero-phase filter).  This is so the features will all line up properly.  Then the past buy/sell signals and buy/sell points are derived from the Harmonic Oscillator graphs and displayed on the Main Graph as green/red rectangles and arrows, respectively, and on the splitter windows the buy/sell points are displayed as green/red vertical lines.  (The buy/sell points are the first of a series of buy/sell signals.)  These buy/sell points serve to line up features on all the graphs, and mark the actual optimum buy/sell points for a given time horizon, for comparison to the Momentum indicators that you design and test yourself.

Momentum Indicators: Another set of technical indicators is displayed in a second splitter window, with three panes.  These are the three Momentum indicators that you design yourself, using the Technical Indicators dialog box.  You can design a technical indicator of one of the three types listed under the Harmonic Oscillator indicator, and you can also set the time scale for smoothing of the indicator for any value from 0 to 512 days.  You can also vary the lead time, or phase of the technical indicators, for maximum correlation with future returns, and you can reverse their sign.  These technical indicators are causal in the sense that they depend only on past data, with no future data included.  To compute these, you need to compute 1024 Price Projections, one for each day in the past going back 1024 days, which is why they are much more computation-intensive than the Harmonic Oscillator indicators, which are acausal.  You can then vary the proportion of these three Momentum indicators to form the Trading Rules indicator, along with specifying a set of filter rules, which gives the set of rules for short-term trading.  The effectiveness of the three Momentum indicators can be measured by means of the Correlation Test - Indicators, and the effectiveness of the Trading Rules in a variety of realistic trading scenarios can be tested directly by means of a back-testing routine called the Diagnostic Test. Forward MA Indicators: The third set of technical indicators is displayed in a third splitter window, also with three panes.  In the bottom pane is the Weighted Sum of Momentum Indicators just described, with weights that you can set (for each stock).  This indicator forms the basis for the N-day trading rules, after applying the filter rules, which are listed on the Short-Term Trades dialog box and in the Portfolio Report.  The other two indicators shown in this splitter window are the N-day future returns, where N is the time horizon, which you set for each security individually and can range from 1 to 40 days.  This is the N-day future moving average of the returns, which is placed next to the Weighted Sum of Momentum Indicators, because the two are supposed to be correlated.  Also shown is an N-day future volatility, which may be used in a future version of QuanTek which incorporates GARCH.

Statistical Tests

Correlation Tests: The most important statistical tests in QuanTek are the tests for correlation with future returns.  There are two of these tests, called the Correlation Test - Filters and the Correlation Test - Indicators.  The Correlation Test - Filters tests the output of the Linear Prediction filters, the Price Projection (returns), directly for correlation with future returns, which is what they are supposed to predict.  This test verifies that the LP filters are working and compares the performance of the different filters.  It turns out that several of the LP filters, in particular the Wavelet filter, show nice correlation with future returns, but the time horizon must be set to something like 10 days or greater, otherwise the correlation gets masked by high-frequency stochastic noise.  The Correlation Test - Indicators tests for correlation between the Momentum indicators and future returns.  This enables the optimum indicators to be chosen for the most effective Trading Rules for a given time horizon.  The time horizon can also be set from either of these dialogs. Spectrum Tests: Two of the statistical tests are the standard Periodogram spectrum and the Wavelet spectrum, which are two different methods for computing the power spectrum of the (log) price returns.  According to the theory of stationary time series, if the (smoothed) spectrum of the returns series is flat, then the returns are a white noise series, and the log price returns form a stationary stochastic process which when integrated is known as a Random Walk.  However, in many cases there is a significant deviation from a white noise spectrum, and these cases imply a non-zero autocorrelation sequence (the Fourier transform of the power spectrum), implying (partial) predictability of the future returns.  The LP filter coefficients may also be computed directly from the power spectrum.   Scatter Graph: One more statistical test is a scatter graph which measures correlation between the returns of two different securities or the same security.  This scatter graph has attached to it a display of the measured autocorrelation sequence or cross-correlation sequence, between the returns of one security or of two different securities, respectively.  This test is useful for choosing securities in the optimal portfolio, as well as general statistical studies of the market.

Portfolio Optimization

Markowitz Method: The Markowitz Method of portfolio optimization is familiar from Modern Portfolio Theory.  This method makes use of an estimated expected return for each security in the portfolio, and also computes the variance matrix of the returns between all the securities in the portfolio.  Then the goal is to find the proportion of each security that maximizes returns for the overall portfolio, and which also minimizes risk.  The risk is defined as the standard deviation (square root of variance) for the portfolio return.  This portfolio variance is in turn computed from the variance matrix, obtained by measuring the volatility of each security and the correlation of the returns between all the securities in the portfolio.  The computation is a complex one in linear programming in general, but in QuanTek a slight modification to this procedure is made to simplify the calculation.  Instead of letting the sum of the amounts invested in each security remain constant, the square root of the sum of the squares (root mean square value) remains constant.  This has the effect that the total amount invested will be decreased somewhat unless the amount of equity allocated to each security happens to be the same.  But this is actually an advantage, because it helps to control risk.  You can set the risk tolerance, the opposite of risk aversion, in QuanTek to adjust the optimization of the portfolio for different desired ratios of overall portfolio return to risk.

Other Features

Main Graph: The scrolling graph has four different scales that cover the whole data set.  There is no limit to the time span of historical data that can be displayed.  The graphs are now available in either black or white background.  The black background looks great!  These four graph scales display prices together with various smoothings, the Price Projection, various buy/sell signals, Bollinger Bands, and on the highest scale is a Candlestick Graph.   S-T Trades Dialog: The Short-Term Trades Dialog is a modeless dialog box that may be viewed from anywhere in the program simply by right-clicking the mouse.  It shows a summary of the securities in the portfolio including the current values of the Trading Rules, and also other information such as the recommended and actual positions in the optimal portfolio, as well as the expected return of each security.  Of course the current price information is also displayed.  There is also a Portfolio Report which displays the same as well as additional information, which is in the form of a text file that can be printed or saved.  These displays are convenient for short-term trading as well as maintaining the optimal portfolio.