Post on 11-Jan-2016
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FinDELA Language-Based
Approach to Financial Analysis
Principal Investigator: A/Prof Khoo Siau ChengCo-Principal Investigator: A/Prof Chin Wei Ngan
School of Computing
External Collaborator: Professor Ng Kah HwaCentre of Financial Engineering
Contact: Khoosc@comp.nus.edu.sg
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Temporal Domain
Time-series information are expressed using patterns
Financial Forecasting
Medical Informatics
Weather Forecasting
etc.
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Financial Patterns
sell
sell
Head & ShoulderFlag
More on stock chart patterns: http://www.incrediblecharts.com/technical/chart_patterns.htm
Technical Analysis
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Medical Patterns
Hemoglobin
Week 1 Week 2 Week 3 Week 4
Anemia Anemia Anemia Anemia
Weekly Repeating Anemia
buy Vitamin
B12
Abstracting Patterns of Anemia level from series of hemoglobin tests
More on medical patterns: http://smi-web.stanford.edu/projects/resume/
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Control Chart Patterns
More on process control chart patterns: http://deming.eng.clemson.edu/pub/tutorials/qctools/ccmain1.htm
System out of control
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Problem
Extracting useful information/patterns from temporal domain can be non-trivial:
The “usefulness” can be user-dependent,intuitively simple, but hard to specify.
Extracting information for explorative purpose requires sophisticated IT skill.
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Opportunities (User Perspective)
A user-centered technology can bring the power ofexploratory information extraction to domain expertsand users
The technology should speak the same jargon as the domain experts.
The technology should emphasize on problemdescription rather than solution prescription.
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Opportunities (Technology Perspective)
Technology can be reused over a broad rangeof temporal domains:
financial forecastingmedical informativeweather forecasting
Unified base that can easily accommodate the advancement in technology
neural networkpattern recognitiondata mining
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Research Objective of FinDEL
Using programming language technology to providea unified framework for
Supporting user-centered tools for extractinginformation from financial domains
Ensuring the adaptability of existing domainsolutions to technology advances.
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Roles of a Language
Language Constructs
Domain Experts
Algorithms Compilers
Users
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Why is Language Important?
It defines a representation (temporal abstract) that is:
Abstract enough to capture user-defined patterns
Concrete enough for numerous techniques tomanipulate
It defines a set of terminologies that is:
Abstract enough to match domain jargons
Accurate enough to ensure meaningful manipulations
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What Kind of Language is Good?
Simple
Elegant
Extensible
Manipulative
Embedded Language
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Business OpportunitiesWe have not seen any commercial products that are as expressive and versatile as what we have suggested.
Some of the available products:
Chart Pattern Recognition plugin for MetaStock
Patterns (http://www.marketsonline.com/software/patterns.htm)
Bull’s-Eye Broker (http://www.archeranalysis.com/beb/index.html)
Please refer to slide 39 for more information
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Research Collaboration
We need domain experts to help us in defining the problem, and in perfecting the techniques and solutions.
Financial experts Researchers from the Centre of Financial Engineering
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A Prototype of FinDEL
The following slides (16 – 38)Demonstrates how financial chart patterns can be effectively specified and manipulated by an elegant, concise, and yet “high-level” programming language.
Technical detail is available the paper “Charting Patterns on Price History”,downloadable in http://www.comp.nus.edu.sg/~khoosc/research.html
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Outline Introduction Specifying Technical Indicators Specifying Patterns
Simple Patterns Composite Patterns
Pattern Definitions Conclusion
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Design Objectives of aChart Pattern Language Define patterns with the help of
constraints and technical indicators. The language should be “high-level ”. It should be complete enough to
specify all the well-known patterns. Pattern definitions should be reusable
and composable.
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Outline Introduction Specifying Technical Indicators Specifying Patterns
Simple Patterns Composite Patterns
Pattern Definitions Conclusion
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Technical Indicator A mathematical formula that
quantifies the market behavior. It is a time-series data. Basics indicators:
Low, high, close, open of a dayTransaction volume of a day open, low, high, close, volume
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Using Technical Indicators
4.94.94.54.64.34.34.54.33.83.63.94.14.04.44.7High
low
Day
4.54.44.14.24.24.14.03.93.63.53.73.93.84.04.5
14131211109876543210
low 3 = Just 3.9 high 10 = Just 4.3
low 15 = Nothing
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Composing Indicators
tPrice :: Indicator PricetPrice = (high + low + close) / 3.0
Typical Price:
(#) operator: (high # 10) t = high (t-10)
avgOfLast3 = (high#2 + high#1 + high)/3.0
tprice t gives value of typical price for t
Indexing into price History:
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Moving Average Denotes the trend of the market. “n days moving Average” is
calculated by averaging the prices of last n days.
mvAvgt = (hight+hight-1+hight-2+…+hight-n+1)/n
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Example
4.94.94.54.64.34.34.54.33.83.63.94.14.04.44.7High
low
Day
4.54.44.14.24.24.14.03.93.63.53.73.93.84.04.5
14131211109876543210
movingAvg 10 11 = sum(…)/10 = Just 4.15
movingAvg 10 8 = Nothing
movingAvg n = (high + high#1 + high#2 + …. + high#n)/n
“10 days moving average” is, movingAvg 10“10 days moving average” for 11th day is, movingAvg 10
11
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Microsoft stock
Moving average
price cross below mvAvg
SELL
price cross above mvAvg
BUY
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Buy and Sell signals buy = close `riseAbove` (movingAvg 14)sell = close `fallBelow` (movingAvg 14)
f1 `riseAbove` f2 = (f1 > f2) && (f1 # 1) < (f2 # 1)f1 `fallBelow` f2 = (f1 < f2) && (f1 # 1) > (f2 # 1)
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Outline Introduction Specifying Technical Indicators Specifying Patterns
Simple Patterns Composite Patterns
Pattern Definitions Conclusion
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Patterns Simple Patterns
Bar, Up, Down, Horizontal, Support Line, Resistance Line
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Landmarks & sub-Components
Landmarks are critical points of a pattern.
lms :: Patt -> [Bar]
sub :: Patt -> [Patt]
returns the landmarks for e.g. a, b, c, d, e, f, g
returns the sub-components for e.g. ab,
bc, cd, de, ef, fg
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Operations on Patterns Imposing constraints Composing horizontally Overlaying patterns
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∞ : Imposing Constraints
∞ :: Pattern -> Constraints -> Pattern
in [ low t > low (t-1), high t < high (t-1)
]
insideDay = bar ∞ u. let [t] = lms u
Inside day
t
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Inside Days
Yesterday was an
inside-day
Buy 1Million stocks
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Fuzzy ConstraintsEg : ‘bigDown’ is a primitive down pattern, with
the decrease in price being usually greater than 10 units.
a
b
bigDown = down ∞ u. let [a,b] = lms uin [ high a – low b >F 10 ]
<F, >F , =F are the fuzzy comparison operators
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Followed-By composition
p1 p2
hill = (up » down) ∞ h . let [a,b,c] = lms h
in [low a =F low c]
a c
b
» :: Pattern -> Pattern -> Pattern
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Head & Shoulderhead_shoulder = hill » hill » hill ∞ hs. let [a,b,c,d,e,f,g] = lms hs in [
high d > high b, high d > high f, high b =F high f
]
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Overlay Composition
s
p
◇ :: Pattern -> Pattern ->Pattern
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Overlay Composition
rect = res ◇ sprt ∞ u. let [r,s] = sub u [m,n] = lms r in [slope r =F slope s, breakout r n]
r
s
breakout
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Diamonddiamond = ((res » res) ◇ (sprt » sprt)) ∞ u. let [rl,sl] = sub u
[m,n] = sub rl [p,q] = sub sl [c,d] = lms q in [diverge m p, converge n q, breakout q d]
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Outline Introduction Specifying Technical Indicators Specifying Patterns
Simple Patterns Composite Patterns Making Patterns Reusable
Conclusion
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Related Products Most popular one is,
John Murphy’s Chart Pattern Recognition plugin for MetaStock (http://www.murphymorris.com/products/cpr.html)
Others, Patterns
(http://www.marketsonline.com/software/patterns.htm) Bull’s-Eye Broker
(http://www.archeranalysis.com/beb/index.html) Fxtrek.com
(http://www.fxtrek.com/universityEN/ai/ai_stock_trading01.asp) http://www.pitstock.com/support/index.html http://www.tarnsoft.com/headandshoulder.html
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Business Opportunities
We believe our solution is
unique in the marketplace
technically advantageous
more expressive than existing tools
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Research Opportunities Efficient implementation From graphical patterns to
language Pattern Mining Similarity Pattern matching Other domain applications
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Related Mini-ProjectsUser-Centered
Tech: GUI Design Predictive Power
of Technical Patterns
Trading Strategy
Algorithmic Tech: Analyzing use of
patterns Optimizing use of
pattern Discovery of
Patterns Refinement of
Patterns
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Predictive Power of Patterns To determine the ability of a
pattern in determining the trend of the market performance
Tools required: Statistical testing Adjusting patterns and data for such
test
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Trading Strategy To enable user to make trading
decision Two phases:
On historical data Real-time data
Tools required: Extended CPL with trading operators
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Analyzing use of Patterns A type system to ensure the
correct use of patterns, and to identify potential optimization
Tools required: Type-based/Constrain-based analysis Possibly Cameleon system
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Optimizing use of Patterns To improve the efficiency of
pattern matching Tools required:
Constraint solving Meta-Haskell for the purpose of meta-
programming
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Discovery of Patterns To discover profitable patterns
automatically Tools required:
Genetic programming Neural network Machine learning Statistical testing
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Refinement of Patterns To refine a pattern so as to
increase its predictive power Tools required:
Machine learning Statistical testing
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Projects in the future: Inclusion of Financial Derivatives
Futures and Options Running FinDEL in different platform
.NET platform Compiled to FPGA Mobile Computing
Application to other temporal domains Medical Clinic tests
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Thank you
Contact: Khoosc@comp.nus.edu.sg