廣宣學堂: R programming for_quantitative_finance_0623

R Programming
for Quantitative Finance
(Jerry Wu)

(R & RStudio)
quantmod forecast Rbitcoin igraph plyr

What is R
• R is one of the most popular platforms for data analysis
and visualization currently available.
dsjerry2017@gmail.com

Some news about R
http://spectrum.ieee.org/computing/software/the-2016-top-programming-languages

A brief history of R
Time Topic
1993 Research project in Auckland, NewZealand
Ross Ihaka and Robert Gentlemen
1995 Released as open-source software
Generally compatible with the ‘S’ language
1997 R core group formed
2000 R 1.0.0 released
2004 First international user conference in Vienna
The CRAN package repository features 5,026 available packages.
The CRAN package repository features 10,875available packages.dsjerry2017@gmail.com

Data analysts captivated by R’s
Power

Why “R”? Not “SAS”? Or others?
• 2009
• R (lingua franca)
• Google, Microsoft, Facebook, Johnson & Johnson,Pfizer, Merck, BOA,
InterContinental Hotels and Shell…, including New York Times now use it !
•
•
•
• To give researchers of all stripes an accessible data
analysis tool.
• 10,875 (2017)

R may be the disruptor !

The Tools We’re Using – Ranked by
Usage
• R is the tool used by the most data miners (70%)
Rexer Analyticsdsjerry2017@gmail.com

The Popularity of R Software is
Skyrocketing
• 70% R
• 24% R

Discussion lists and sites

KDnuggets
Four of the top five packages were open source

Hits on Google Scholar
• Google R

Why use R
• (Free/open source software)
•
•
•
•
•

Task Views
Bayesian
ChemPhys ,
ClinicalTrials
Cluster
DifferentialEquations
Distributions
Econometrics
Environmetrics
ExperimentalDesign
Finance

Task Views
Genetics
Graphics
HighPerformanceComputing
MachineLearning
MedicalImaging
MetaAnalysis
Multivariate
NaturalLanguageProcessing
NumericalMathematics
OfficialStatistics
Optimization dsjerry2017@gmail.com

Task Views
Pharmacokinetics
Phylogenetics
Psychometrics
ReproducibleResearch
Robust
SocialSciences
Spatial
SpatioTemporal
Survival
TimeSeries
WebTechnologies
gR dsjerry2017@gmail.com

Outline
• The Basics
• Vectors and Matrices, Data Frames, Factors, Lists
• Exploratory time series data analysis
• Stock Trading
• Quantitative Analysis
• Correlation analysis
• MA Model Estimation and Forecasting
• Querying the Bitcoin blockchain with R

Stock Returns
• $90 worth of Foxconn Stock
• 10% return in March
• 110% = 100% + 10%
• 1.10 = 1 + 0.10
• 99 = 90 * 1.10
+
-
Chapter1_stock_returns.R dsjerry2017@gmail.com

Stock Returns – Multiple periods
• $90 worth of Foxconn Stock
• 110% = 100% + 10%
• 1.10 = 1 + 0.10
• 99 = 90 * 1.10
• 5% return in April
• 110% = 100% + 5%
• 1.05 = 1 + 0.05
• 94.5 = 90 * 1.05
• 103.95 = 90 * 1.10 * 1.05

Fun Time
• $100 worth of Stock
• 5% return in April

The Basics
• Basic Data Types
• Vectors and Matrices
• Data Frames
• Factors
• Lists

Basic Data Types
• Numeric
• Character
• Logical

Basic Data Types
• Variables and data types
• Class()

Vector – Vectors and stock prices
• Vectors and stock prices
• Vector names()
Chapter2_vectors_and_matrices.R

Vector manipulation

Fun Time
• Try
• + – * /

A 2D vector
Row
Col

cbind() and rbind()

Subsetting

Fun Time –
What’s incorrect about subsetting
• Tsmc and foxconn columns
A. M_tsmc_foxconn[,c(“tsmc”, “foxconn”)]
B. M_tsmc_foxconn[c(“tsmc”, “foxconn”)]
C. M_tsmc_foxconn[,]

Cor()relation
• +1 perfect positive linear relationship
• -1 perfect negative linear relationship
• 0 no linear relationship

Visualize your matrix

Visualize your matrix (Time Series)

Data Frames and Name that frame
Chapter3_data_frame.R

Data frame subsets and subset()

Present value( )
•
Cash / (1 + X /100) ^ Periods( )
Cash * (1 + X / 100) ^ -Periods( )
1000 6%
839.62 [1000 / (1 + 6%)^3]
X =

Present value( )

Present value( )
• Fun Time ( 5%) 4

Stock and Bond
Chapter4_factor.R

Cut()
Chapter4_factor.R

Plot factor
Chapter4_factor.R

Order()
Chapter4_factor.R

Subsets
Chapter4_factor.R

List
Chapter5_list.R

Split()
Chapter5_list.R

Fun time
• 1 1000 ?
Chapter5_list.R

Fun time
List
DF
Chapter5_list.R

Attributes
Chapter5_list.R

Data Input and output
Chapter6_io.R

Exploratory time series data analysis
• (Time Series)
• GDP ..

•
•

• : , ,
• (Data Granularity)
• : , ,
•

•
• {yt }
• (state space) S
• (Discrete)
• (Continuous)

• (index set)
•
• (Discrete) T={0,1,2……}
•
• (Continuous) T=[0, ∞)

Chapter7_EDA.R

0501
0501
Chapter7_EDA.R

Fun Time
Chapter7_EDA.R

• Moving Average Convergence / Divergence, MACD
DIF-MACD
MACD
DIF( )
Chapter7_EDA.R

Fun time
addADX
addATR
addBBands
addCCI
addCMF
addCMO
addDEMA
addDPO
addEMA
addEnvelope
addEVWMA
addExpiry
addMACD
addMomentum
addROC
addRSI
addSAR
addSMA
addSMI
addTRIX
addVo
addWMA
addZLEMAdsjerry2017@gmail.com

Basic Time Series Objects
Chapter7_EDA.R

Stock Trading
Chapter7_EDA.R

Stock Trading
• 20ma 60ma
• 20ma 60ma ( )
Lag
ROC log( / ) * Strategy (0,1)
Cl OHLC
cumsum
exp
Chapter7_EDA.R

Fun time
•

Correlation analysis

• Rapid Growth

• Periodic

• (linearized)

• (Periodic)

Diff( )
Log( )(linearized)
(Periodic)
Chapter8_QA.R
Diff 12 x 13 y =1+(y-x)/x

Correlation analysis - Scatterplots
Chapter8_QA.R

Correlation analysis – correlation coefficient
http://www.stat.nuk.edu.tw/prost/content2/statics_6.htm
+1
0
-1

Correlation analysis – correlation coefficient
P-value 0.05
df
Chapter8_QA.R

Fun time
•
•

MA Model Estimation and Forecasting
• ARIMA Autoregressive
Integrated Moving Average model,
)
• ARIMA p d q
• p
• d TS
• q
• ARIMA ACF
PACF p,d,q
• ACF(Sample Autocorrelation Function,
)
• PACF (Sample Partial Autocorrelation
Function, )
ACF
PACF
Chapter8_QA.R

Lag=1
Lag=3
arima(x, order = c(0,0,1))
arima(x, order = c(3,0,1))
AIC( )
(Goodness of Fit)
AICChapter8_QA.R

MA Model Estimation and
Forecasting(Non-Auto ARIMA)
Chapter8_QA.R

MA Model Estimation and
Forecasting(Auto ARIMA)
Chapter8_QA.R

Bitcoin
• (Blockchain)
•
• 0.00000001 1
• 2140 2100
• = 90,375
https://zh.wikipedia.org/wiki/

Blockchain
Blockchain is a distributed database that is used to maintain a
continuously growing list of records, called blocks. Each block contains
a timestamp and a link to a previous block.
A blockchain is typically managed by a peer-to-peer network collectively
adhering to a protocol for validating new blocks.
https://medium.com/@micheledaliessi/how-does-the-blockchain-work-98c8cd01d2ae

Querying the Bitcoin blockchain with R
Chapter9_Blockchain.R

Conclusion
• Concept of R basic
• Exploratory time series data
• Learn stock trading with R
• Quantitative analysis with forecasting
• Querying the Bitcoin Blockchain Visualization with R

jerry@mail.ntust.edu.tw

廣宣學堂: R programming for_quantitative_finance_0623

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