Intelligent Information Systems
in Technical Trading Analysis
System Overview
ABSTRACT
Traders buy and sell financial instruments in
order to make a profit. Technical analysis is a set
of tools some traders use to predict future price
movements from past price and volume trends.
By analyzing charts, traders identify technical
indicators, or specific market trend patterns,
which have associated future market
movements.
Time
An improved prediction over current technical
analysis methods is made possible by the
automated evaluation of more data points per
period and the analysis of secondary
characteristics of indicators. Additionally, the
automation of our model enables identification
of technical indicators in a fraction of the time
previously required. These improvements make it
possible for traders to perform technical analysis
more accurately and faster than they could
before.
AUTHORS:
Hicham Laalej SSE ‘11
Ranga Ramachandran SSE ‘11
Michael Zhao SSE ‘11
Volume
Quantized in
Intervals
The pre-trade analysis that traders conduct is a
lengthy procedure, during which every trader has
to go through various charts, data, and
information before executing a trade. The
amount of time required before executing a
trade limits the frequency of trades and the
ability to trade on a shorter time-frame.
By building increasingly comprehensive pattern
recognition functions based on historical data,
we have created a model which recognizes the
appearance of technical trading indicators and
evaluates a security’s past performance. Based
on long- and short-term price and volume trends,
our model identifies different conditions for
technical indicators which it applies to real-time
data.
Price
Model
Bloomberg
Excel
In order to automatically identify technical indicators our model:
• quantizes real-time market trades in 15 second intervals
• scans ~40,000 data points for relevant characteristics
• checks local behavior against custom indicator criteria
• records individual indicator attributes
• analyzes all indicator events to generate price prediction
We chose the following seven indicators based on widespread adoption,
trader preferences, and suitability for modeling:
1. Triangles: When a market makes higher lows and same level highs, a
triangle pattern is observed. This is indicative of an uptrend (ascending
triangle). When the market makes lower highs and same level lows, this
will be indicative of a downtrend (descending triangle).
Pattern
Matching
Matlab
Record
indicator
events
Identify and
Display
Indicators
Weighted
price
predictions
2.
Buy and sell
recommendations
3.
Technical Analysis
Technical analysis is one of the two important techniques used to analyze
securities and make investment decisions. It can be defined as a method that
forecasts the direction of future prices by studying past price and volume
data. Technical analysts use charts, graphs and other tools in order to
recognize patterns that can give indications of future movement.
Double Top Double Bottom: The double top chart pattern looks like the
letter M and predicts an uptrends. The double bottom pattern looks the
letter W and occurs during downtrends. These are strong indicators of a
reversal in a current trend.
Head and Shoulders: This is a chart formation in which a stock’s price
rises to a peak, then declines. After that, the price rises to a peak higher
than the previous one and again declines. Finally, the price rises to a third
peak which is lower than the second and then declines again. The second
peak forms the head, and while first and third peaks comprise the
shoulders. After the third peak, there will be an expected downtrend in
the movement of price.
Results
In order to make predictions using technical analysis, analysts use technical
indicators. These indicators are derived by applying formulae to the
price/volume data of a security.
Technical indicators can be:
•Momentum-based: Determines the rate of change of a security’s price over a
given time period. E.g. Relative Strength index
•Volatility-based: Gives an indication of the highs, lows and the range of the
price movement. E.g. Bollinger Bands
•Trend-based: Gives an indication of the trends in price over time. E.g.
Moving average convergence-divergence
•Volume-based: Gives an indication of the direction of the security based on
the volume of trades being executed. E.g. OBV
Fig 1 Trendline
• Created technical analysis platform which enables faster and more
comprehensive use of technical indicators in technical analysis
• Automated technical indicator identification allows new application of
technical analysis for shorter-term analysis
4.
5.
ADVISOR:
Prof. Linda Zhao
Special thanks to Prof. Lawrence Brown
6.
DEMO TIMES:
Thursday, April 21, 2011
11:00, 11:30 AM, 1:30, 2:00 PM
TEAM #7
Validation
A trend line is a straight line that connects two or more price points and
extends into the future, and can be used to give a visual representation of the
price/volume trends.
Moving Average Convergence-Divergence: This indicator shows the mean
price value for multiple periods. When short-term price rises significantly
above long-term price, a buy signal appears and vice versa.
Bollinger Bands: This indicator shows the volatility of stock prices via
bands placed above and below the moving average of the stock based on
local standard deviation. Relative Strength Index (RSI): This is a
momentum indicator that measures the speed and change of price
movements.
On-Balance Volume Indicator: This indicator keeps a cumulative total of
volume trends to give an indication of the momentum of the stock.
We used Matlab for our model due to its ability to process large amounts of
data efficiently. After determining average value for each 15 second interval,
our model identifies all local maxima and minima and stores their locations.
All indicators are expressed in terms of the relationships among these turning
points. For example, the Triangle indicator requires a series of peaks /valleys
which are within 0.4% of each other and a strong upward or downward trend
in the corresponding valleys/peaks.
- Minimizes lag time of algorithmic models by working with hours,
not days of data
- Reduces time requirement for technical analysis from hours to
minutes
• Information advantage over traditional rule-of-thumb technical analysis
- More detailed measurements of indicators can be taken
- Superior price prediction due to greater number of indicators
and more information collected per indicator
- Experimentally proven prediction algorithms at indicator and
overall market levels
• Provides improved trading experience and fits seamlessly into trader
decision making process
- Real-time evaluation of trading environment and new market
developments