** QuanTek** is a

** QuanTek** now works with either

For more information on all the features of ** QuanTek**,
please download the program and consult the

One of the best features of ** QuanTek** is
the

Here is a more detailed description of the **Main Graph**:

The **Main Graph** has five different scales, and
enough ranges on each scale to cover the whole **2048-day**
data set. Each scale increases by a factor of two, and has different
features displayed. One of the most important features of these
graphs is that they all scale the same way on the horizongal and
vertical axes, so the **aspect ratio** is preserved.
That way, you can see at a glance the **N-day return**
and **volatility** of each security relative to all
other securities and all other graph scales. This emphasizes starkly
how **risky** some securities are compared to others. A
direct display of the **risk** is the width of the
**Bollinger Bands** between different securities (on
the same scale). The difference in **risk** between,
say, an **index** and a **penny stock** is
dramatic, but the usual types of displays do not often make this
clear.

The **Price Projection** is computed from a **
Wavelet Adaptive Filter** of our own design, which is a
**Least Mean Square (LMS)** type using regressors or**
Technical Indicators **obtained by means of **Wavelet
Smoothing**. The use of the **wavelet**
decomposition results in a dramatic simplification of the filter
design, and aids in separating the weak "signal" present in the data
from the "noise". Basically, the **wavelet**
decomposition separates the signal with respect to both frequency
and time, with the frequency separation in octaves and the time
separation longer on the low octaves and shorter on the high
octaves. This is a natural separation for many real-world signals,
as the only part of the signal that is actually relevant to the
present time is that which occured most recently, on the order of
the most recent few cycles on each octave. Then the **Adaptive
filter** can utilize the most recent part of the signal on
each octave level for regression. At present we use the **
wavelet decomposition** of three different combinations of
the price data, which we call the **Relative Price**
(detrended prices), the **Velocity** (returns or 1st
difference of prices) and **Volatility** (absolute
deviation of prices). Use of the most recent values of the **
Relative Price**, **Velocity**, and **
Volatility** indicators as the regressors for the **
Wavelet Adaptive Filter** results in a vast simplification of
the filtering problem, as these three indicators are automatically
orthogonal (uncorrelated) on each wavelet level. In addition, using
the **Volatility** as one of the regressors makes the
filter an implementation of **GARCH (Generalized
Auto-Regressive Conditional Heteroskedasticity)**.

Here is a more detailed description of the **Price
Projection**:

The **Price Projection** also displays **Error
Bars** which represent the **expected N-day
future price range** for

In addition to the **Main Graph**, the current
version of * QuanTek* comes with a set of
three

These **Indicator Windows** and their associated
indicators are described in more detail here:

The most important indicators are shown in the bottom panes of
each window. These are the **Relative Price (low-pass),
Relative Price (band-pass), **and** Projected Future
Returns**. These three indicators are the source of the
**Buy/Sell Signals**, **Buy/Sell Points**,
and **Long/Short Signals**. The **Indicator
Windows** are the source of the **trading signals**
that are displayed on the **Main Graph**. They are also
important **diagnostic tools** to check the output of
the **Adaptive Filter**, as well as the **
regressors** that are used in the filter calculation. The
first two **Indicator Windows** display indicators
built from the **Relative Price, Velocity, **and**
Volatility** indicators, and these are precisely the **
regressors** used in the **Adaptive Filter**
calculation. The third window shows the actual output of the **
Adaptive Filter**, along with **N-day** and
**2048-day returns** for comparison.

One of the most important features of * QuanTek*
is the

The **Portfolio Optimization** calculation is
described in more detail here:

The securities in the **Optimal Portfolio** are
those in the **Portfolio** folder you happen to be in
(excepting **indexes**). * QuanTek*
also keeps track of the number of shares owned of each security, and
the securities that have positions are a subset of the

The **Optimal Portfolio** may be viewed in the
**Portfolio Report**, which is available from a toolbar
button on the **MainFrame** toolbar. This is an **
*.rtf** file that shows a variety of useful information,
including the **past returns** on a variety of time
scales, the **N-day expected return** from the **
Price Projection**, and the **standard deviation**.
Also shown is the **Model Portfolio** with the **
current price** along with the **basis price**.
Finally the **Optimal Portfolio** and **Model
Portfolio** are shown together, along with the actual and
optimal **positions** expressed as shares and
percentage of the portfolio equity.

This information may also be viewed in the **Short-Term
Trades** dialog, also available from a toolbar button. In
addition, the **Short-Term Trades** dialog can be
viewed anywhere in the program by pressing the **Alt**
key. This dialog box is a handy quick reference for **buy/sell**
decisions for the **portfolio**. In addition, there is
a handy list box which estimates the **expected average price**
and **price range** for the next day, which can be used
for **day trading**. This **next-day average
price** is estimated using the **Standard LP**
filter.

* QuanTek* has several

The most important of these is the **
Correlation -- LP Filter** test, which actually consists of
two dialogs, the **Indicators -- Linear Prediction Filters**
dialog and the** Correlation Test -- LP Filters & Indicators**
dialog. The **Indicators** dialog displays the **
Adaptive Filter** output (which needs to be computed first)
or five other indicators, in the present or past, with various
degrees of **Wavelet** smoothing (either **
low-pass** or **band-pass**). After this is
selected, the **Correlation Test** dialog creates a
display of the **correlation** between the **
indicator** and **future returns**, as a
function of lead time, time horizon, correlation scale, and date in
the historical past. The purpose of this dialog is to ferret out
whatever real correlation exists between the indicators and future
returns, as a function of time, and try to distinguish the real
correlations from spurious ones. As stated, this test is mainly used
to determine if the **Adaptive Filter** is actually
effective at predicting **future returns**.

Next, there is the **Scatter Graph -- Returns**
dialog. This dialog measures the **correlation**
directly between two securities, or the same security, in which you
can vary the time lag between the two. The result is displayed in
the form of a **scatter graph**, with a point on the
graph for each point of the data. If there is **correlation**,
it can be seen as a non-uniform distribution of the points on the
graph. The actual degree of **correlation** is
displayed, computed three different ways, along with the confidence
level that the correlation is not spurious. There is also another
dialog connected with this one, that displays the **
auto-correlation **or** cross-correlation**
**sequence** between the same two securities, as a
function of time lag. This **correlation** uses the
**Fourier** **transform** method. The
**Scatter Graph -- Returns** dialog could be useful for
choosing securities in the **portfolio** to achieve
maximum **diversification**, since to achieve this it
is important to find pairs of securities that are **
anti-correlated**.

Two other statistical tests are the **Wavelet Analysis**
and **Wavelet Variance** dialogs. These are mainly used
to test whether the **Wavelet** routines are
functioning correctly, and to display their output graphically. The**
Wavelet Analysis** dialog** **displays the
output of the **Wavelet** filters in terms of the
**wavelet coefficients** and the **
multi-resolution analysis (MRA)**. The **Wavelet
Variance** dialog uses the actual price data to compute its
**wavelet variance** and then display a **
covariance matrix** based upon it.

Finallythere are two statistical tests that show a standard
spectral decomposition of the price returns. The **Periodogram
Spectrum** displays a standard **Fourier**
spectrum of the returns. This is a standard computation in **
time-series analysis**. The **Wavelet Spectrum**
displays the corresponding **Wavelet** spectrum of the
returns, averaged over time, so there is one average value per
frequency octave. Theoretically, if the returns data are correlated
(assumed stationary, so the correlation is time-independent), then
the correlation would show up in a non-constant spectrum of the
returns. On the other hand, if the spectrum is constant, then the
returns are "white noise", in accordance with the **Random
Walk** model. Actually, though, the theoretical variance of
the spectrum is about 100%, so it is hard to tell from the spectral
graph alone whether there are really any meaningful correlations in
the data. (Also, the correlation is not expected to be stationary.)
This is why the **Correlation** test described above is
more relevant for non-stationary correlations.

Here is a summary of the
**history** of the * QuanTek*
program, going back to the late-90s:

The * QuanTek* program started
in the mid-90s as a simple

The other aspect of * QuanTek* is its

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