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:
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Proof of change
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Modified section(s)
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Date
Version
1
29 May 2014
1.0
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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
3.10Precise Rate-of-Change (ROC) .......................................................... 21
3.11Precise Standard Moving Average (SMA) ............................................ 22
3.12Precise Standard Deviation ............................................................... 23
3.13Precise Standard Error ..................................................................... 24
3.14Precise Triple Exponential Moving Average.......................................... 25
3.15Precise Triangular Moving Average .................................................... 26
3.16Precise Triple Exponential Average .................................................... 27
3.17Precise Time Series Forecast............................................................. 28
3.18Precise 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 Quant Trade Technologies, Inc. ■ October 11, 2014 ■ 5/31

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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 Quant Trade Technologies, Inc. ■ October 11, 2014 ■ 6/31

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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 Quant Trade Technologies, Inc. ■ October 11, 2014 ■ 7/31

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any given chart in the same fashion that you would insert any built-in Bloomberg indicator. Quant Trade Technologies, Inc. ■ October 11, 2014 ■ 8/31

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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.

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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

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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.

Precision Oscillator Suite for Bloomberg Professional

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