** QuanTek development:** Here is the latest news
regarding the ongoing development of the

I would like to build upon the basic **Wavelet Linear Prediction**
filter design by making this filter **adaptive**. This means that
it adjusts continuously to an incoming stream of data, or in other words
it is a type of **Kalman** filter. This should set the stage for
a version of the filter to work with **real-time data**.

Also the **volatility** will be taken into account in the **Linear
Prediction** by making use of a **GARCH** model (**Generalized
AutoRegressive Conditional Heteroskedasticity**). This should improve
the predictive power as well as setting the stage for an **Options**
version of ** QuanTek**.

Also the **Optimal Portfolio** calculation will be updated and
made more sophisticated, going beyond the basic **Markowitz **method
that is currently used. This will make the portfolio more resilient
by taking into account *extreme risks* such as market crashes.

More **Statistical Tests** are planned in the future to try to
measure the predictive power of the **Wavelet Linear Prediction**
filter used in the **Price Projection**.

Also the versions for **MetaStock** and **TeleChart** will
be resurrected.

I would like to go ahead with a new **Adaptive Wavelet Linear Prediction**
filter, which is a **Wavelet** variant of a **Kalman** filter.
This will make the **Price Projection** more accurate and hence
**increase returns** and **reduce risk **in the **Optimal Portfolio**.

More **Statistical Tests **are planned, such as **Simulated Trading**
scenarios using various **LP filters** and **Trading Rules**.

I have added some improvements to the **Main Graph**, listing
the **N-day expected return** of the **Price Projection** at the
top of the Graph window. I also improved the **Portfolio Report**,
listing the **future expected return** as well as **past average
returns** over a variety of time scales. I tweaked the **Wavelet
LP **filter for maximum stability and responsiveness, and tweaked
the **Optimal Portfolio** to take into account recent past returns
(**trend persistence**) to increase the stability of the **Optimal
Portfolio** calculation.

I decided to go back to the original form of the **Harmonic Oscillator**
indicator, using the standard **Burg Linear Prediction** filter.
This filter is best for extending cyclic or oscillator-type indicators,
whereas the **Wavelet Linear Prediction** filter is better for estimating
the **future expected returns**. I also tweaked the **Buy/Sell signals**
and rewrote parts of the **Help** file. This was a substantial improvement
to the **trading rules**.

I developed an **encrypted Authorization Key** for the program
to enable licensing for fixed subscription periods, which uses a personal
**Pass Code** for each subscriber.

Many **Help** file articles were also rewritten and improved,
in particular the **Overview of QuanTek** article.

I have added a second dialog box of **Statistical Tests** to the
**Greeting Dialog**, which I call the **Test Linear Prediction Filters
Dialog**. This second test dialog contains all the test that require
an extended computation of the **LP filters** going back 2048 days.
Then the **Greeting Dialog** contains the more generic **Statistical
Tests** of the financial data that do not require the 2048-day calculation,
along with the tests of the **Wavelet routines**. The **Test Linear
Prediction Filters Dialog** contains a **Correlation Test** of
the **Price Projection** from the **LP filter** with the **N-day
future returns**, to test the effectiveness of the **Price Projection**
and **Trading Rules** derived from it.

I have also done more work on the **Help** file, but more work
still needs to be done.

This beta version downloads **free Yahoo data** as well as parsing
**ASCII** files.

Sorry for the long delay. For this ** QuanTek 3.3 (beta)**
version, I have developed a completely new type of

A number of **Statistical Tests** have been devised and included
in a **Greeting Dialog**. These were devised to test the **Wavelet
routines** to make sure they were operating correctly.

I have also completely re-written the program and greatly expanded
the **Help** files. Now you can see a **Help** dialog almost anywhere
in the program just by right-clicking. The whole program has been streamlined
and simplified, to make it easy to use for everyday trading.

This beta version downloads **free Yahoo data** as well as parsing
**ASCII** files.

** QuanTek** enables you to design your own custom

This version of ** QuanTek** underwent much testing and development of the

Also a **Correlation Test - Filters** dialog was developed in
order to test directly the predictive properties of the various **Linear
Prediction** filters. In this version, six different **LP**
filters were included, and any one of these filters can be selected
to use for computing the **Price Projection** and **Trading Rules**.
However, it was found necessary to be able to set the filter parameters
manually, for each security individually, in particular the **Fractal
Dimension**. This has been done in the subsequent version of
** QuanTek** by bringing back the

The **Time Horizon** adjustment could now be made separately for
each security, rather than one setting for the whole program.
This is important because each security has its own optimum **time
horizon** for best performance of the **Price Projection**.
Now this may be set either in the **Correlation Test - Filters **
dialog when testing the filters or in the **Correlation Test - Indicators**
dialog when testing the indicators.

A new dialog box, called the **Trading Rules Filter & Momentum
Weights** dialog, was added, where the **Trading Rules** can be
selected and displayed. The new selections for the **Trading
Rules** include three new controls, called the **Increment**, the
**Threshold**, and the **Compression**. In addition the
three **Momentum Weights**, which are the weights of the three **
Momentum** indicators in the **Trading Rules**, are set from this
dialog. This new dialog is also called from the **Diagnostic
Test**, so a set of **Trading Rules** based on the three saved
**Momentum** indicators can be set and back-tested independently
from the data file itself.

** QuanTek** now works with either

Here were some of the new features of ** QuanTek 3.0**:

**Price Projection:**A**Price Projection**is computed using one of a choice of**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 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. Linear Prediction: The**Hybrid LP Filter**dialog is used to set the**Linear Prediction**filter for each security separately. The**type**of filter may be chosen, as just described, 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)**orprocess. The*long-memory***Hybrid LP Filter**dialog also has attached to it a dialog box which displays the**correlation**between the filter output**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**.**Momentum Indicators:**The main feature ofis that you can design and test yourself a wide variety of*QuanTek***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. After a set of three of these indicators is designed, the*QuanTek***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**.**Statistical Tests:**There are two**correlation**tests which measure**correlation**with**future returns**of the**technical indicators**and the output of the**Linear Prediction**filter. These give a direct indication of the effectiveness of these two methods of estimating**future returns**. There is also a**Diagnostic Test**, which is a back-testing routine to test the**Trading Rules**in a variety of realistic trading scenarios, for a given**time horizon**. 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 stock (log) price returns. These give another indication of the possible presence of**correlation**in the returns data.**Main Graph:**The**scrolling graph**has been improved, with**four different scales**that cover the whole data set. Now there is no limit to the time span of historical data that can be displayed. This also makes it possible to do a better statistical analysis, using deep historical data. The graphs are now available in either**black**or**white**background. The black background looks great! These four graph scales display the**Price Projection**, various**buy/sell signals**,**Bollinger Bands**, and on the highest scale is a**Candlestick Graph.****Portfolio Optimization:**also incorporates a*QuanTek***portfolio optimization**routine, making use of the**Markowitz method**. The**correlation**between the**returns**of all the securities in the portfolio (data files in a folder) is computed and used, along with the**expected return**for each security derived from the**Price Projection**, to compute the**optimal portfolio**which should**maximize returns**and**minimize risk**. The parameters for**risk aversion**and**margin leverage**can be set by the user, to achieve portfolios with different degrees of**risk vs. return**.

* StockEval* was a preliminary version of our stock trading
program, designed to incorporate up-to-date methods of computation and
a

* StockEval* used only data from

These were the main features of ** StockEval**:

**Price Projection:**The main**technical indicator**in theprogram was a**StockEval****Price Projection**, in which future prices are estimated up to 100 days in the future. Two of the main approaches we tried were a**regression**of the future returns on a set of exponential moving averages of the past prices, and the**Linear Prediction**routine. The first method used a set of**exponentially weighted moving averages**on different time scales, and the differences between**EWMA**s on adjacent time scales were taken as the basis functions (essentially**MACD**indicators). The future one-day returns were then regressed on these basis functions, and the whole procedure was extrapolated into the future one day at a time. This method apparently gave reasonable results. However, a simpler method is to utilize the standard**Linear Prediction**method, which is somewhat equivalent, although it leads to a "noisier" outcome due to a greater number of parameters being fitted. Both of these methods gave good results on the**Diagnostic Test**(a back-testing routine), but the excess noise in the trading rules on short time scales was a difficult problem. However, after applying the**Savitzky-Golay**digital smoothing filter to get rid of the short-term**stochastic noise**, the results were definitely positive, and led to rather spectacular short-term trading gains according to the**Diagnostic Test**, in the strongly trending market of the late '90s.**Linear Prediction:**The**Linear Prediction**filter used inwas a standard, publicly available**StockEval****LP**filter which models the time series as an**auto-regressive (AR)**process. This filter was used with the maximum number of parameters (1024) in order to try to get the best possible long-term prediction. (The short-term**stochastic noise**was then filtered out using the**Savitzky-Golay smoothing**filter.) However, after analyzing this particular filter, it turns out that it is actually*adaptive*in a certain sense (when used with the full set of parameters), in that it gives greater weight to the most recent data. This explains why we were getting pretty good results on the short-term trading rules as well, as measured in the**Diagnostic Test**.**Harmonic Oscillator:**Theprogram featured a set of three novel**StockEval****technical indicators**based on the**Savitzky-Golay**digital smoothing filter, which we call the**Harmonic Oscillator**indicators. The**Savitzky-Golay**filter can compute derivatives (rates of change) of the smoothed prices, and these smoothed prices and their first and second derivatives were used to construct the set of**Harmonic Oscillator**indicators with*zero lag*and a more straightforward interpretation than the normal ones. From these indicators, a set of**trading rules**was derived. These trading rules gave good results on the**Diagnostic Test**, although they only applied to a trading scenario in which the position is varied smoothly by making a small adjustment every day (which would only be practical for large portfolios).**Main Graph:**The Main Graph ofwas rather novel. It was a scrolling graph of the entire data set, presented in one long "panorama" which you could scroll across. There were four different scales on the graph, separated by a factor of two, and the horizontal and vertical axes both scaled the same way, so that the slope of the graph was preserved when rescaled. The price axis of the graph was logarithmic, so a constant distance along the vertical direction represented a constant percentage change. This made it possible to judge the relative performance of different stocks directly "by eye", just by comparing directly the slopes of the graphs (a feature not seen in the usual stock price graph). Underneath the log prices, the log volumes were plotted, relative to a reference line that represented the average log volume. This made it easier to see when the volume is larger or smaller than its average value.**StockEval****Portfolio Optimization:**also incorporated a**StockEval****portfolio optimization**routine, making use of the**Markowitz method**. The**correlation**between all the stocks in the portfolio (stock data files in a folder) was computed and used, along with the**expected return**derived from the**Price Projection**, to compute the**optimal portfolio**which would**maximize returns**and**minimize risk**. The parameters for**risk aversion**and**margin leverage**could be set by the user, to achieve portfolios with different degrees of**risk vs. return**.