Fine Precision Bar Oscillator Suite

by Boris G. Zinchenko, Ph.D.

Senior Technology Officer, Quantrade LLC, 111 W. Jackson Blvd.Suite 948, Chicago, Illinois 60604

We offer 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 with Nanja Trader® charting package. They allow for up to four times better precision against their classical counterparts. Summary Standard price bar on the stock chart has typically four channels: open, high, low and close. Many technical indicators use only one of these channels for the statistical inference. In [1] we illustrate benefits of using all four channels to improve trading signal precision and illustrate it on simple example of Bollinger Bands calculation. In this suite we include Ninja script for all indicators described in [1] as well as reconstruct many other indicators on the same improvement principle.

Figure illustrates simulated intra-bar probability channel associated with price components provided on the chart. Blue price gradient depicts associated sample probability levels extrapolated from the price sample within each bar. Against this price distribution we plot Bollinger bands calculated by standard method (orange) and with improved bar price resolution (cyan). One can see that the bands using full price information are more smooth and consistent with the price channel.

Indicators In the following table we shortly describe each indicator as compared to its classical analog included into standard supply of Ninja trader. Indicator Base Comment Bar Average Typical price Average of current bar using all price

channels.

Bar Bollinger Bands Bollinger Bands Uses “Bar Standard Deviation” and “Bar Average” for better precision as described in [1].

Bar Exponential Moving Average (Bar EMA)

Exponential Moving Average (EMA)

Improved precision due to usage of all four price channel on chart as one sequence for averaging.

Bar Forecast Oscillator (Bar FOSC)

Forecast Oscillator (FOSC)

Uses “Bar Linear Regression” as the forecast base

Bar Linear Regression Linear Regression Extension of the linear regression based indicators calculated on all price components to improve precision of the regression.

Bar Moving Average Convergence/Divergence

Moving Average Convergence/Divergence

Trend following momentum based on two “Bar Averages”

Bar Percentage Price Oscillator (Bar PPO)

Percentage Price Oscillator (PPO)

Based on two “Bar Averages” expressed as a percentage.

Bar Price Oscillator Price Oscillator Shows the variation among two “Bar Averages” for the price of a security.

Bar Rate-of-Change Rate-of-Change (ROC) Difference between “Bar Average” on the current bar and the x-time periods ago.

Bar Standard Moving Average (Bar SMA)

Standard Moving Average (SMA)

Standard Moving Average calculated on all bar channels.

Bar Standard Deviation Standard Deviation Standard Deviation calculated on all price channels as described in [1].

Bar Standard Error Standard Error Standard Error shows how near prices go around a linear regression line. Linear regression is calculated on all price components.

Bar Triple Exponential Moving Average

Triple Exponential Moving Average

Using “Bar Exponential Moving Average”.

Bar Triangular Moving Average

Triangular Moving Average (TMA)

Weighted moving average over all price channels.

Bar Triple Exponential Average

Triple Exponential Average (TRIX)

Displays the percentage Rate of Change (ROC) of a triple EMA over all price channels.

Bar Time Series Forecast Time Series Forecast Calculates probable future values for the

(TSF) price by fitting a linear regression line over a given number of price bars and following that line forward into the future.

Bar Weighted Moving Average

Weighted Moving Average (WMA)

Average value of a security's price over a period of time with special emphasis on the more recent portions of the time period

Our indicators have special benefit to high frequency intraday trading, where they allow to collect far better precision on short period price oscillations. 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