Precision Oscillator Suite for Bloomberg Professional

Trading Algorithms Fractal indicators, predictors and trading strategies

Version 1.1

Quant Trade®

Precision Oscillator Suite

Indicators for Bloomberg Professional

Quant Trade Technologies, Inc. 2 N Riverside Plaza Suite 2325, Chicago, Illinois 60606

http://www.quant-trade.com/

Document ID:

Precision Oscillator Suite.doc

General information:

Version: 1.2 State of document: Status: released

Last modified: 07 June 2014 Created: 21 May 2014

Project: Precision Suite Author: Boris Zinchenko Project lead: Boris Zinchenko

Recipients:

Accompanying documents:

Quant Trade Technologies, Inc. ■ October 11, 2014 ■ 1/31

http://www.quant-trade.com/


Version 1.1

Proof of change

Change Modified section(s) Log message ID # Date Version

1 29 May 2014 1.0 All Document created BZ 2 07 June 2014 1.2 Price Beam Reflects new indicator EL



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Table of contents

1 Abstract 4 2 Analysis 5

2.1 Introduction ...................................................................................... 5 2.2 Background ...................................................................................... 5 2.3 Raw Data Streams ............................................................................. 6 2.4 Stable Price Aggregates ..................................................................... 7 2.5 A Solution ........................................................................................ 7 2.6 Summary ......................................................................................... 7

3 Technical indicators 9 3.1 Price Beam ....................................................................................... 9

3.1.1 Setting Chart Background ....................................................................... 9 3.1.2 Preparing Chart Template ...................................................................... 11 3.1.3 Sharing Chart with Colleagues ................................................................ 11

3.2 Precise Average .............................................................................. 13 3.3 Precise Bollinger Bands .................................................................... 14 3.4 Precise Exponential Moving Average (Precise EMA) .............................. 15 3.5 Precise Forecast Oscillator (Precise FOSC) .......................................... 16 3.6 Precise Linear Regression ................................................................. 17 3.7 Precise Moving Average Convergence/Divergence (MACD) .................... 18 3.8 Precise Percentage Price Oscillator (PPO) ............................................ 19 3.9 Precise Price Oscillator ..................................................................... 20 3.10 Precise Rate-of-Change (ROC) .......................................................... 21 3.11 Precise Standard Moving Average (SMA) ............................................ 22 3.12 Precise Standard Deviation ............................................................... 23 3.13 Precise Standard Error ..................................................................... 24 3.14 Precise Triple Exponential Moving Average .......................................... 25 3.15 Precise Triangular Moving Average .................................................... 26 3.16 Precise Triple Exponential Average .................................................... 27 3.17 Precise Time Series Forecast ............................................................. 28 3.18 Precise Weighted Moving Average (WMA) ........................................... 29

References 30



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1 Abstract We offer a simple and powerful collection of improved technical indicators

inherited from classical oscillators widely used throughout modern technical

analysis. These oscillators take advantage of full intra-bar information

provided by the Bloomberg charting package. They allow for up to four times

more precision against their classical counterparts.



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2 Analysis

2.1 Introduction A standard bar on a price chart typically has four channels: open, high, low

and close. Many technical indicators use only one of these channels for the

statistical inference (analysis). In the image below, we illustrate the benefits

of using all four channels to improve trading signal precision and illustrate it

on a simple example of a Bollinger Bands calculation. In this suite we include

all of the indicators in the figure below, as well as reconstruct many other

indicators on the same improvement principle.

The figure illustrates a simulated intra-bar probability channel associated

with price components provided on the chart. The blue price gradient depicts

the associated probability levels extrapolated from the price sample within

each bar. Over this price distribution we plot Bollinger bands calculated by

the standard method against the improved bar price resolution method

(blue, yellow, red.) One can see that the bands using full price information

are more smooth and consistent with the price channel.

2.2 Background Here we present a new method of applying trading indicators to financial

charts. By combining classical statistics with uniquely modified chart



Version 1.1 indicators, you will be able to understand classical charts from the viewpoint

of random processes.

All of you are familiar with trading charts. As a common tool to traders, we

find a myriad of variations from the very basic to the exotic. Regardless of

the chart used, the goal is the same. You want to have as much useful

information as possible on your chart so you can make good trading

decisions.

While financial charts can be vivid and attractive, they tend to contradict

modern statistics. The charts and the indicators used with them can

introduce unstable measures increasing the risk of your trades. Here we will

explain some of the pitfalls of typical charting from a simple mathematical

point of view. We will then introduce a variation on classical trading

indicators that help to overcome these shortcomings.

2.3 Raw Data Streams Pure trading data for any market instrument comes from exchanges in the

form of bid & ask prices in unison with a last traded price. This is high-

frequency data, which you can watch on a real-time trading terminal. Most

traders rarely watch, archive, and analyze this data for the simple reason

that it is too abundant. Even modern computers have difficulty in analyzing

and archiving this granular data.

Most data vendors, software developers, and traders consider raw data to be

noisy, excessive, and exorbitant. The truth of the matter is however, that

more raw data makes statistical analysis more reliable. This is particularly

true when trying to develop a rule or algorithm for predicting the future. This

is why many types of predictive tools fall short of desired performance in

market research. In many cases it is not the model, but the amount and

type of data that is used.

One simple example of how data can affect analysis is made clearly evident

with an example of typical data from a bar chart. Usually this data is painted

on the screen with one time stamp and open, high, low, close (OHLC) data.

Any experienced trader will recognize that a single time stamp for these four

data points is not accurate. In fact, most traders tolerate it as a limitation of

the equipment and tools they are using. If we were to time stamp each price

separately, we would instantly have much more useful data that can

generate more statistically accurate results. To make matters worse, most

technical indicators are designed to use one price, such as the closing price



Version 1.1 or some mathematical derivation of multiple prices using simple arithmetic

averages.

2.4 Stable Price Aggregates In regular statistics, a moving average is simple and not very useful for

aggregate data. However, when you face the danger of misshaped

aggregate data, you need to find something stable to rely on. One possible

solution is to replace the original data with a more stable average

approximation. This is better, even at the cost of some loss in data

precision.

In trading, precision is the key to success, so how can we improve the

resolution of standard price data? The best statistical practice suggests using

a median as an accurate measure of the middle point. The median is derived

from nonparametric statistics and appears more robust on data likely to

have anomalies in distribution.

2.5 A Solution One way to improve precision without acquiring additional data is to use all

available data from the provider and to smooth the data. So in the case

above, we take all four data points lumped under one time stamp and sum

up averages over several bars. Not only will you have four times the amount

of data, but the relative role of high and low prices in the average will

steadily diminish. The reason is that with many values, the high and low

prices fall within the range of a cumulative sample of several bars taken

together.

2.6 Summary We have considered a number of issues that compromise the accuracy of

price data and statistical analysis that most traders use. To alleviate some

of these issues, we have discussed how standard open, high, low, close price

data can be interpreted as a price probability channel. Using this method, it

is possible to improve the precision of standard statistical trading indicators

by modifying how they calculate the available data under one time stamp.

The Precision Oscillator Suite is a composite of seventeen classical statistical

indicators that have been re-engineered to reflect the price probability

channel. In addition to increased precision, you will find that these indicators

smooth price data much more efficiently. These indicators are designed for

the Bloomberg trading platform. You may insert and manipulate them into



Version 1.1 any given chart in the same fashion that you would insert any built-in

Bloomberg indicator.



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3 Technical indicators In the following sections we shortly describe each modified indicator as

compared to its classical counterpart. These new variations are found in the

Bloomberg package. Each indicator begins with the word, “Precise” and is

labeled as a Quant Trade indicator.

3.1 Price Beam Precise Price Beam is the fundamental basic indicator that constitutes the

core foundation of all other indicators in this suite. It visually shows the

stochastic distribution of the underlying price points in the form of a visual

heat map. Lightly colored regions reveal a higher concentration of the price

probability function, while darker regions correspond to less likely price

distribution ranges.

Price points are more concentrated in the brighter high probability channels

and gradually fade out to less probable dark outer regions where a trade is

highly unlikely to occur. Due to this feature, the Precision Price Beam

indicator should always appear on a uniform black background. Black

designates a uniform outer field of zero probability, on which the channel of

positive probabilities is depicted.

3.1.1 Setting Chart Background A user must take specific steps to ensure the proper chart background:

First, select menu “Edit” (97) and pick the option “Chart Colors/Styles”



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The chart “Property Manager” will appear

Take the following steps:

1. Select the “Color/Style” tab.

2. Select “Solid” option on “Chart Background.”

3. Select “Black” as the background color.

4. Select a color for the bars on the chart. We recommend “Green” for

the up bar color and “Red” for the down bar color.



Version 1.1 5. Hit “Update” to confirm.

Upon completion of these steps, the Precision Price Beam indicator will now

appear on the proper background.

3.1.2 Preparing Chart Template To avoid repeating the above steps for every chart, you may want to create

a predefined template with these settings and then reuse it with charts for

other symbols.

1. Go again to the “Edit” (97) menu.

2. Pick the option “Create Theme From Chart.”

3. Select a name for new theme. For instance, “Price Beam.”

4. Confirm by “Save Theme.”

You can now select this theme for another chart and also set it as a default.

3.1.3 Sharing Chart with Colleagues You may want to share the template with your colleagues. To do so, go to

chart “Actions” menu (96) and select “Share” item.



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Now the chart sharing dialog will appear. Fill out the fields and hit the

“Send” button when you are ready. The chart will now be shared.

You may pick “Firm” in the “Share with” field to share company wide.



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3.2 Precise Average Precise Average is the average of current bar using all price channels.

Settings:

Classical analog: Typical price



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3.3 Precise Bollinger Bands Precise Bollinger Bands uses “Precise Standard Deviation” and “Precise

Average” for more precision of the standard Bollinger Band calculation.

Settings:

Classical analog: Bollinger Bands



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3.4 Precise Exponential Moving Average (Precise EMA) Precise Exponential Moving Average has improved precision due to use of all

four price channels on the chart as one sequence for averaging.

Settings:

Classical analog: Exponential Moving Average



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3.5 Precise Forecast Oscillator (Precise FOSC) Precise Forecast Oscillator compares the actual price to the time series

forecast and calculates a percentage between negative 100% and positive

100%. This version uses “Precise Linear Regression” as the forecast base.

Settings:

Classical analog: Forecast Oscillator (FOSC)



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3.6 Precise Linear Regression Precise Linear Regression is the extension of the linear regression based

indicators calculated across all price channels to improve precision of the

regression.

Settings:

Classical analog: Linear Regression



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3.7 Precise Moving Average Convergence/Divergence (MACD) Precise Moving Average Convergence/Divergence (MACD) is the trend

following momentum based on two “Precise Average” indicators.

Settings:

Classical analog: Moving Average Convergence/Divergence (MACD)



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3.8 Precise Percentage Price Oscillator (PPO) Precise Percentage Price Oscillator (PPO) is based on two “Precise Average”

indicators expressed as a percentage.

Settings:

Classical analog: Percentage Price Oscillator (PPO)



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3.9 Precise Price Oscillator Precise Price Oscillator shows the variation between two “Precise - Averages”

for the price of a security.

Settings:

Classical analog: Price Oscillator



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3.10 Precise Rate-of-Change (ROC) Precise Rate-of-Change (ROC) is the difference between “Precise Average”

indicators on the current bar and a previous bar a number of periods ago.

Settings:

Classical analog: Rate-of-Change (ROC)



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3.11 Precise Standard Moving Average (SMA) Precise Standard Moving Average (SMA) is the Standard Moving Average

calculated on all bar channels.

Settings:

Classical analog: Standard Moving Average (SMA)



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3.12 Precise Standard Deviation Precise Standard Deviation is the Standard Deviation calculated on all price

channels.

Settings:

Classical analog: Standard Deviation



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3.13 Precise Standard Error Precise Standard Error is Standard Error shows how far prices oscillate

around a linear regression line. Linear regression is calculated on all price

channels.

Settings:

Classical analog: Standard Error



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3.14 Precise Triple Exponential Moving Average Precise Triple Exponential Moving Average is the combination of a single,

double and triple “Precise Exponential Moving Average” indicator.

Settings:

Classical analog: Triple Exponential Moving Average



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3.15 Precise Triangular Moving Average Precise Triangular Moving Average (TMA) is a double smoothed (i.e.

averaged twice) weighted moving average using all price channels.

Settings:

Classical analog: Triangular Moving Average (TMA)



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3.16 Precise Triple Exponential Average Precise Triple Exponential Average (TRIX) displays the percentage Rate of

Change (ROC) of a triple EMA over all price channels.

Settings:

Classical analog: Triple Exponential Average (TRIX)



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3.17 Precise Time Series Forecast Precise Time Series Forecast (TSF) calculates probable future values for the

price by fitting a linear regression line over a given number of price bars and

following that line forward into the future. It is calculated across all price

channels.

Settings:

Classical analog: Time Series Forecast (TSF)



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3.18 Precise Weighted Moving Average (WMA) Precise Weighted Moving Average (WMA) is average value of a security's

price over a period of time with special emphasis on the more recent

portions of the time period. It is calculated across all price channels.

Settings:

Classical analog: Weighted Moving Average (WMA)



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References

[1] Boris G. Zinchenko, Unraveling the Mystery of Stock Prices, Stocks & Commodities, March 2010 [2] Mosteller, Frederick and Rourke, Robert E.K. Sturdy Statistics: Nonparametrics and Order Statistics Reading, MA: Addison-Wesley, 1973. [3] L. D. Landau, L. M. Lifshitz, Quantum Mechanics: Non-Relativistic Theory, Volume 3, Third Edition, Elsevier, 2003



Version 1.1 Legal notice The product names used in this document are for identification purposes only. All trademarks and registered trademarks are the property of their respective owners. The following trademarks may or may not be marked in this document: Quant Trader is a trademark or registered trademark of Quant Trade Technologies, Inc. in United States and/or other countries. Other company, product, and service names may be trademarks, registered trademarks, or service marks of other owners.


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