BBZ. INVEST MALAYSIA FAIR 2012 Kuala Lumpur 11.30am, 17 June 2012 Seminar Room 3

Transcription

1 Basic Mechanics of Algorithmic Trading BBZ I) Title INVEST MALAYSIA FAIR 2012 Kuala Lumpur 11.30am, 17 June 2012 Seminar Room 3 Dr. Jacinta Chan Author of Financial Times Guide to Technical Analysis How to Trade Like a Professional

2 Overview Basic Mechanics of Algorithmic Trading BBZ Moving Average and Standard Deviations are the basic Algorithms (formulas) of Mechanical Trading. After a review of moving average and standard deviation bands, we will discuss: 1) Data Analysis of Heavy Tails Returns 2) Using Standard Deviation to Define Trends 3) The Basics on How to Set Up a Simple Mechanical Trading System,'Black Box'

3 Revie w Moving Average Page 65 SIMPLE MOVING AVERAGE Definitions Moving Average is the average of a defined set of numbers being averaged continuously while moving through time. It reflects the trend by smoothing out meaningless fluctuations in the data. In range trading market, the moving average tends to oscillate in a sideways patterns while in a trend trading trading market, the moving average tends to move in a definite upward or downward direction. Therefore, in a trend trading market, the moving average may be used as a technical indicator to give buy or sell signal. (see next slide)

4 Revie w Moving Average Page 66 Middle Band = 20 period Moving Average Preface xxi-ii Bollinger on Bollinger Bands, McGraw Hill, 2002 Chart showing a simple moving average 4

5 Moving Average and Bands Concepts Concept and Evolution of Bollinger Bands (5% Bands) Chart of Bollinger Bands on FKLI Revie w Page 89 Upper Band = Middle Band + 6% Middle Band = 20 period Moving Average Lower Band = Middle Band 6% A chart showing moving average envelope 5

6 Revie w Bollinger Bands Page 90 BOLLINGER BANDS Definitions Bollinger Bands can be viewed as an adaptation of the early trading bands of constant x% from the moving average bands used at that time to cover most observations. Two standard deviation bands from a 20-day moving Average fitted the criterion because the area between the upper band and the lower lower band covers 95.5% of observations. Bollinger Bands consists of a set of three lines drawn around the price structure, the middle band, upper band and lower band. (see next slide)

7 Revie w Page 92 Moving Average and Bollinger Bands Bollinger Bands on FKLI Upper Band = Middle Band + 2 Standard Deviations Middle Band = 20 period Moving Average Lower Band = Middle Band - 2 Standard Deviations Preface xxi-ii Bollinger on Bollinger Bands, McGraw Hill, 2002 A chart showing Bollinger Bands 7

8 1. Data Analysis of Heavy Tails Returns Page 90 Normal Distribution Curve 95.5% of data -2 σ +2 σ u where σ = ( x) n x 2 8

9 1. Data Analysis of Heavy Tails Returns...continued 2 Page Distribution of Daily Returns to to t o to to to to to to to to to to to to to to Return Mean Standard Deviation FTSE

10 1. Data Analysis of Heavy Tails Returns...contineed 4 2>Basis of BBZ using FKLI Page % of data Stdev +1 Stdev

11 2. Using Standard Deviation to Identify Trends Page 93 BBZ Upper Band = Middle Band + 1 Standard Deviations Middle Band = 20 period Moving Average Lower Band = Middle Band - 1 Standard Deviations Preface xxi-ii Bollinger on Bollinger Bands, McGraw Hill,

12 2. Using Standard Deviation to Identify Trends...continued 2 Page 129 BBZ Buy Long when P > Upper 1 standard deviation band Exit Long when P < Upper 1 standard deviation band Sell Short when P < Lower -1 standard deviation band Exit Short when P > Lower -1standard deviation band Preface xxi-ii Bollinger on Bollinger Bands, McGraw Hill, 2002 Do not trade within the bands 12

13 2. Using Standard Deviation to Identify Trends...continued 3 Page 130 BBZ SELL BUY 13

14 2. Using Standard Deviation to Identify Trends...continued 4 Page 130 BBZ 26/9 SELL at 5, /10 BUY at 4,242.5 PROFIT 846 Index Points 14

15 3. Basics of 'Black Box' 15

16 3. Basics of a Trading System. Page Profile of an Algorithm Trend Trading System 1. Well thought out design that uses appropriate technical analysis tools. 2. Parameters that have been tested not only to fit historical price data. 3. Ability to be robust in any market and any timeframe. 4. High profits to low risk ratios. 5. Number of winning trades that are about equal to, or more than, losing trades. 6. Average gain is much larger than average loss. 7. Capital preservation and low maximum drawdown with an inherent loss control mechanism. 8. Ability to avoid some of the whipsaws in a range trading market. 9. Ability to enter a new trend early. 16

17 For more information on futures and options trading You can contact me: Dr. Jacinta Chan Author of Financial Times Guide to Technical Analysis How to Trade Like a Professional