Copyright 2011 - 2019 Jon Danielsson. This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. The GNU General Public License is available at: https://www.gnu.org/licenses/.

Last updated June 2018

```
x = 10 # assign x the value 10
print(x) # print x
```

Last updated June 2018

```
x = 10; % assign x the value 10, silencing output print with ;
disp(x) % display x
```

Last updated June 2018

```
y = c(1,3,5,7,9) # create vector using c()
print(y)
print(y[3]) # calling 3rd element (R indices start at 1)
print(dim(y)) # gives NULL since y is a vector, not a matrix
print(length(y)) # as expected, y has length 5
v = matrix(nrow=2,ncol=3) # fill a 2 x 3 matrix with NaN values (default)
print(dim(v)) # as expected, v is size (2,3)
w = matrix(c(1,2,3),nrow=6,ncol=3) # repeats matrix twice by rows, thrice by columns
print(w)
s = 1:10 # s is a list of integers from 1 to 10 inclusive
print(s)
```

Last updated June 2018

```
y = [1,3,5,7,9] % lists are denoted by square brackets
y(3) % calling 3rd element (MATLAB indices start at 1)
size(y) % shows that y is 1 x 5 (a row vector, by default)
length(y) % as expected, y has length 5
v = nan(2,3) % fill a 2 x 3 matrix with NaN values
size(v) % as expected, v is size (2,3)
w = repmat([1,2,3]', 2, 3) % repeats matrix twice by rows, thrice by columns
s = 1:10 % s is a list of integers from 1 to 10 inclusive
```

Last updated June 2018

```
## There are many data sources for financial data, for instance
## Yahoo Finance, AlphaVantage and Quandl. However, some of the
## free data sources have numerous issues with accuracy and
## handling of missing data, so only CSV importing is shown here.
##
## For csv data, one can use read.csv to read it
##
## Example:
## data = read.csv('Ch1aprices.csv', header=TRUE, sep=',')
## one can use the zoo() function from the package zoo
## to turn the data into a timeseries (see Listing 1.1/1.2)
```

Last updated June 2018

```
%% There are many data sources for financial data, for instance
%% Yahoo Finance, AlphaVantage and Quandl. However, some of the
%% free data sources have numerous issues with accuracy and
%% handling of missing data, so only CSV importing is shown here.
%%
%% For csv data, one can use csvread to read it
%%
%% Example:
%% data = csvread('data.csv', 1, 0);
%% the two numbers behind are the row offset and column offset
%% so here we ignore the first row (ie. the header)
```

Last updated June 2018

```
y=matrix(c(3.1,4.15,9))
sum(y) # sum of all elements of y
prod(y) # product of all elements of y
max(y) # maximum value of y
min(y) # minimum value of y
range(y) # min, max value of y
mean(y) # arithmetic mean
median(y) # median
var(y) # variance
cov(y) # covar matrix = variance for single vector
cor(y) # corr matrix = [1] for single vector
sort(y) # sorting in ascending order
log(y) # natural log
```

Last updated June 2018

```
y = [3.14,15,9.26,5];
sum(y) % sum of all elements of y
prod(y) % product of all elements of y
max(y) % maximum value of y
min(y) % minimum value of y
range(y) % min, max value of y
mean(y) % arithmetic mean
median(y) % median
var(y) % variance
cov(y) % covar matrix = variance for single vector
corrcoef(y) % corr matrix = [1] for single vector
sort(y) % sorting in ascending order
log(y) % natural log
```

Last updated June 2018

```
library(moments)
mean(y) # mean
var(y) # variance
sd(y) # unbiased standard deviation, by default
skewness(y) # skewness
kurtosis(y) # kurtosis
```

Last updated June 2018

```
mean(y) % mean
var(y) % variance
std(y) % unbiased standard deviation, by default
skewness(y) % skewness
kurtosis(y) % kurtosis
```

Last updated June 2018

```
z = matrix(c(1,2,3,4),2,2) # z is a 2 x 2 matrix
x = matrix(c(1,2),1,2) # x is a 1 x 2 matrix
## Note: z * x is undefined since the two matrices are not conformable
z %*% t(x) # this evaluates to a 2 x 1 matrix
rbind(z,x) # "stacking" z and x vertically
cbind(z,t(x)) # "stacking z and x' horizontally
## Note: dimensions must match along the combining axis
```

Last updated June 2018

```
z = [1, 2; 3, 4] % z is a 2 x 2 matrix (Note the use of ; as row separator)
x = [1, 2] % x is a 1 x 2 matrix
%% Note: z * x is undefined since the two matrices are not conformable
z * x' % this evaluates to a 2 x 1 matrix
vertcat(z,x) % "stacking" z and x vertically
horzcat(z,x') % "stacking z and x' horizontally
%% Note: dimensions must match along the combining axis)
```

Last updated June 2018

```
q = seq(from = -3, to = 3, length = 7) # specify a set of values
p = seq(from = 0.1, to = 0.9, length = 9) # specify a set of probabilities
qnorm(p, mean = 0, sd = 1) # element-wise inverse Normal quantile
pt(q, df = 4) # element-wise cdf under Student-t(4)
dchisq(q, df = 2) # element-wise pdf under Chisq(2)
## Similar syntax for other distributions
## q for quantile, p for cdf, d for pdf
## followed by the abbreviation of the distribution
## One can also obtain pseudorandom samples from distributions
x = rt(100, df = 5) # Sampling 100 times from TDist with 5 df
y = rnorm(50, mean = 0, sd = 1) # Sampling 50 times from a standard normal
## Given data, we obtain MLE estimates of distribution parameters with package MASS:
library(MASS)
res = fitdistr(x, densfun = "normal") # Fitting x to normal dist
print(res)
```

Last updated June 2018

```
q = -3:1:3 % specify a set of values
p = 0.1:0.1:0.9 % specify a set of probabilities
norminv(p, 0, 1) % element-wise inverse Normal quantile
tcdf(q, 4) % element-wise cdf under Student-t(4)
chi2pdf(q, 2) % element-wise pdf under Chisq(2)
%% One can also obtain pseudorandom samples from distributions
x = trnd(5, 100, 1); % Sampling 100 times from t dist with 5 df
y = normrnd(0, 1, 100, 1); % Sampling 50 times from a standard normal
%% Given sample data, we can also obtain MLE estimates of distribution parameters:
res = fitdist(x, "Normal") % Fitting x to normal dist
```

Last updated June 2018

```
library(tseries)
x = rt(500, df = 5) # Create hypothetical dataset x
jarque.bera.test(x) # Jarque-Bera test for normality
Box.test(x, lag = 20, type = c("Ljung-Box")) # Ljung-Box test for serial correlation
```

Last updated June 2018

```
x = trnd(5, 500, 1); % Create hypothetical dataset x
[h1, p1, jbstat] = jbtest(x) % Jarque-Bera test for normality
[h2, p2, lbstat] = lbqtest(x,'lags',20) % Ljung-Box test for serial correlation
```

Last updated June 2018

```
x = rt(60, df = 5) # Create hypothetical dataset x
par(mfrow=c(1,2), pty='s')
acf(x,20) # autocorrelation for lags 1:20
pacf(x,20) # partial autocorrelation for lags 1:20
```

Last updated June 2018

```
x = trnd(5, 60, 1); % Create hypothetical dataset x
subplot(1,2,1)
autocorr(x, 20) % autocorrelation for lags 1:20
subplot(1,2,2)
parcorr(x,20) % partial autocorrelation for lags 1:20
```

Last updated June 2018

```
## For loops
for (i in 3:7) # iterates through [3,4,5,6,7]
print(i^2)
## If-else loops
X = 10
if (X %% 3 == 0) {
print("X is a multiple of 3")
} else {
print("X is not a multiple of 3")
}
## Functions (example: a simple excess kurtosis function)
excess_kurtosis = function(x, excess = 3){ # note: excess optional, default=3
m4 = mean((x-mean(x))^4)
excess_kurt = m4/(sd(x)^4) - excess
excess_kurt
}
x = rt(60, df = 5) # Create hypothetical dataset x
excess_kurtosis(x)
```

Last updated June 2018

```
%% For loops
for i = 3:7 % iterates through [3,4,5,6,7]
i^2
end
%% If-else loops
X = 10;
if (rem(X,3) == 0)
disp("X is a multiple of 3")
else
disp("X is not a multiple of 3")
end
%% Functions (example: a simple excess kurtosis function)
%% NOTE: in MATLAB, functions can be defined in 2 locations:
%% 1) in a separate file (e.g. excess_kurtosis.m in this case) in the workspace
%% 2) in the same file as the rest of the code, BUT at the end of the file
%% function k = excess_kurtosis(x, excess)
%% if nargin == 1 % if there is only 1 argument
%% excess = 3; % set excess = 3
%% end % this is how optional param excess is set
%% m4 = mean((x-mean(x)).^4);
%% k = m4/(std(x)^4) - excess;
%% end
```

Last updated June 2018

```
y = rnorm(50, mean = 0, sd = 1)
par(mfrow=c(2,2)) # sets up space for subplots
barplot(y) # bar plot
plot(y,type='l') # line plot
hist(y) # histogram
plot(y) # scatter plot
```

Last updated June 2018

```
y = normrnd(0, 1, 50, 1);
z = trnd(4, 50, 1);
subplot(2,2,1)
bar(y) % bar plot
subplot(2,2,2)
plot(y) % line plot
subplot(2,2,3)
histogram(y) % histogram
subplot(2,2,4)
scatter(y,z) % scatter plot
```

Last updated June 2018

```
## Convert objects from one type to another with as.integer() etc
## To check type, use typeof(object)
x = 8.0
print(typeof(x))
x = as.integer(x)
print(typeof(x))
```

Last updated June 2018

```
%% Convert objects from one type to another with int8() etc
%% To check type, use isfloat(object), isinteger(object) and so on
x = 8.0;
isfloat(x)
x = int8(x);
isinteger(x)
```