SAS Learning Hub: 339.CRYPTOCURRENCY MARKET ANALYSIS AND RISK MODELLING USING PROC SQL | PROC MEANS | PROC REG | PROC UNIVARIATE | PROC SGPLOT | PROC CORR | MACROS | DATE FUNCTIONS (INTCK

CRYPTOCURRENCY MARKET ANALYSIS AND RISK MODELLING USING PROC SQL | PROC MEANS | PROC REG | PROC UNIVARIATE | PROC SGPLOT | PROC CORR | MACROS | DATE FUNCTIONS (INTCK | INTNX)

options nocenter;

1.CRYPTOCURRENCIES DATASET CREATION

data work.crypto_raw;

length Coin_Name $20;

informat Launch_Date date9.;

format Launch_Date Last_Updated date9.;

input Coin_Name $ Market_Cap Price Volatility Transactions Energy_Usage

Launch_Date :date9.;

/* Date derivations */

Last_Updated = intnx('month', Launch_Date, 6, 'same');

Days_Since_Launch = intck('day', Launch_Date, today());

datalines;

Bitcoin 850 42000 4.5 350000 707 03JAN2009

Ethereum 380 2300 5.2 900000 62 30JUL2015

BinanceCoin 90 310 3.8 1200000 12 14JUL2017

Cardano 45 0.55 6.1 250000 6 29SEP2017

Solana 65 95 7.5 1800000 5 16MAR2020

Ripple 55 0.75 5.9 1400000 2 02FEB2013

Polkadot 35 7.5 6.8 600000 8 26AUG2020

Dogecoin 20 0.15 9.2 450000 15 06DEC2013

Litecoin 30 85 4.1 300000 18 13OCT2011

Avalanche 28 38 6.4 500000 7 21SEP2020

Chainlink 25 14 5.7 420000 9 19SEP2017

Polygon 18 0.9 6.9 800000 4 10OCT2017

;

run;

proc print data=work.crypto_raw;

run;

OUTPUT:


Obs	Coin_Name	Launch_Date	Last_Updated	Market_Cap	Price	Volatility	Transactions	Energy_Usage	Days_Since_Launch
1	Bitcoin	03JAN2009	03JUL2009	850	42000.00	4.5	350000	707	6190
2	Ethereum	30JUL2015	30JAN2016	380	2300.00	5.2	900000	62	3791
3	BinanceCoin	14JUL2017	14JAN2018	90	310.00	3.8	1200000	12	3076
4	Cardano	29SEP2017	29MAR2018	45	0.55	6.1	250000	6	2999
5	Solana	16MAR2020	16SEP2020	65	95.00	7.5	1800000	5	2100
6	Ripple	02FEB2013	02AUG2013	55	0.75	5.9	1400000	2	4699
7	Polkadot	26AUG2020	26FEB2021	35	7.50	6.8	600000	8	1937
8	Dogecoin	06DEC2013	06JUN2014	20	0.15	9.2	450000	15	4392
9	Litecoin	13OCT2011	13APR2012	30	85.00	4.1	300000	18	5177
10	Avalanche	21SEP2020	21MAR2021	28	38.00	6.4	500000	7	1911
11	Chainlink	19SEP2017	19MAR2018	25	14.00	5.7	420000	9	3009
12	Polygon	10OCT2017	10APR2018	18	0.90	6.9	800000	4	2988

2.PROC SQL – DATA EXTRACTION & FILTERING

proc sql;

create table crypto_highcap as

select Coin_Name,Market_Cap,Price,Volatility,Transactions,Energy_Usage,Launch_Date,

Last_Updated

from crypto_raw

where Market_Cap > 50

order by Market_Cap desc;

quit;

proc print data=crypto_highcap;

run;

OUTPUT:


Obs	Coin_Name	Market_Cap	Price	Volatility	Transactions	Energy_Usage	Launch_Date	Last_Updated
1	Bitcoin	850	42000.00	4.5	350000	707	03JAN2009	03JUL2009
2	Ethereum	380	2300.00	5.2	900000	62	30JUL2015	30JAN2016
3	BinanceCoin	90	310.00	3.8	1200000	12	14JUL2017	14JAN2018
4	Solana	65	95.00	7.5	1800000	5	16MAR2020	16SEP2020
5	Ripple	55	0.75	5.9	1400000	2	02FEB2013	02AUG2013

3.PROC MEANS – DESCRIPTIVE STATISTICS

proc means data=crypto_raw mean min max std;

var Market_Cap Price Volatility Transactions Energy_Usage Days_Since_Launch;

run;

OUTPUT:

The MEANS Procedure


Variable	Mean	Minimum	Maximum	Std Dev
Market_Cap Price Volatility Transactions Energy_Usage Days_Since_Launch	136.7500000 3737.65 6.0083333 747500.00 71.2500000 3522.42	18.0000000 0.1500000 3.8000000 250000.00 2.0000000 1911.00	850.0000000 42000.00 9.2000000 1800000.00 707.0000000 6190.00	245.7134787 12067.10 1.5240248 490716.08 200.8487106 1355.87

4.PROC UNIVARIATE – DISTRIBUTION ANALYSIS

proc univariate data=crypto_raw;

var Volatility Energy_Usage;

histogram Volatility Energy_Usage;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Volatility


Moments
N	12	Sum Weights	12
Mean	6.00833333	Sum Observations	72.1
Std Deviation	1.52402478	Variance	2.32265152
Skewness	0.47424428	Kurtosis	0.41892236
Uncorrected SS	458.75	Corrected SS	25.5491667
Coeff Variation	25.3651835	Std Error Mean	0.43994806


Basic Statistical Measures
Location		Variability
Mean	6.008333	Std Deviation	1.52402
Median	6.000000	Variance	2.32265
Mode	.	Range	5.40000
		Interquartile Range	2.00000


Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	13.65692	Pr > \|t\|	<.0001
Sign	M	6	Pr >= \|M\|	0.0005
Signed Rank	S	39	Pr >= \|S\|	0.0005


Quantiles (Definition 5)
Level	Quantile
100% Max	9.20
99%	9.20
95%	9.20
90%	7.50
75% Q3	6.85
50% Median	6.00
25% Q1	4.85
10%	4.10
5%	3.80
1%	3.80
0% Min	3.80


Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
3.8	3	6.4	10
4.1	9	6.8	7
4.5	1	6.9	12
5.2	2	7.5	5
5.7	11	9.2	8

The UNIVARIATE Procedure

The UNIVARIATE Procedure

Variable: Energy_Usage


Moments
N	12	Sum Weights	12
Mean	71.25	Sum Observations	855
Std Deviation	200.848711	Variance	40340.2045
Skewness	3.42602819	Kurtosis	11.8003497
Uncorrected SS	504661	Corrected SS	443742.25
Coeff Variation	281.892927	Std Error Mean	57.9800286


Basic Statistical Measures
Location		Variability
Mean	71.25000	Std Deviation	200.84871
Median	8.50000	Variance	40340
Mode	.	Range	705.00000
		Interquartile Range	11.00000


Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	1.228871	Pr > \|t\|	0.2448
Sign	M	6	Pr >= \|M\|	0.0005
Signed Rank	S	39	Pr >= \|S\|	0.0005


Quantiles (Definition 5)
Level	Quantile
100% Max	707.0
99%	707.0
95%	707.0
90%	62.0
75% Q3	16.5
50% Median	8.5
25% Q1	5.5
10%	4.0
5%	2.0
1%	2.0
0% Min	2.0


Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
2	6	12	3
4	12	15	8
5	5	18	9
6	4	62	2
7	10	707	1

The UNIVARIATE Procedure

5.PROC REG – REGRESSION MODELING

proc reg data=crypto_raw;

model Price = Market_Cap Volatility Energy_Usage;

run;

quit;

OUTPUT:

The REG Procedure

Model: MODEL1

Dependent Variable: Price


Number of Observations Read	12
Number of Observations Used	12


Analysis of Variance
Source	DF	Sum of Squares	Mean Square	F Value	Pr > F
Model	3	1600742908	533580969	4183.16	<.0001
Error	8	1020435	127554
Corrected Total	11	1601763344


Root MSE	357.14764	R-Square	0.9994
Dependent Mean	3737.65417	Adj R-Sq	0.9991
Coeff Var	9.55540


Parameter Estimates
Variable	DF	Parameter Estimate	Standard Error	t Value	Pr > \|t\|
Intercept	1	-358.66926	518.45460	-0.69	0.5086
Market_Cap	1	-2.74932	1.34947	-2.04	0.0760
Volatility	1	-5.15661	77.82243	-0.07	0.9488
Energy_Usage	1	63.20387	1.60454	39.39	<.0001

The REG Procedure

Model: MODEL1

Dependent Variable: Price

Panel of scatterplots of residuals by regressors for Price.

6.PROC SGPLOT – VISUALIZATION

proc sgplot data=crypto_raw;

scatter x=Market_Cap y=Price;

reg x=Market_Cap y=Price;

title "Market Capitalization vs Price";

run;

OUTPUT:

The SGPlot Procedure

7.ADDITIONAL PROC – PROC CORR

proc corr data=crypto_raw;

var Market_Cap Price Volatility Transactions Energy_Usage;

run;

OUTPUT:

The CORR Procedure


5 Variables:	Market_Cap Price Volatility Transactions Energy_Usage


Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Market_Cap	12	136.75000	245.71348	1641	18.00000	850.00000
Price	12	3738	12067	44852	0.15000	42000
Volatility	12	6.00833	1.52402	72.10000	3.80000	9.20000
Transactions	12	747500	490716	8970000	250000	1800000
Energy_Usage	12	71.25000	200.84871	855.00000	2.00000	707.00000


Pearson Correlation Coefficients, N = 12 Prob > \|r\| under H0: Rho=0
	Market_Cap	Price	Volatility	Transactions	Energy_Usage
Market_Cap	1.00000	0.93449 <.0001	-0.39796 0.2001	-0.14777 0.6467	0.94128 <.0001
Price	0.93449 <.0001	1.00000	-0.32574 0.3015	-0.24805 0.4370	0.99951 <.0001
Volatility	-0.39796 0.2001	-0.32574 0.3015	1.00000	0.09351 0.7725	-0.33020 0.2945
Transactions	-0.14777 0.6467	-0.24805 0.4370	0.09351 0.7725	1.00000	-0.25894 0.4164
Energy_Usage	0.94128 <.0001	0.99951 <.0001	-0.33020 0.2945	-0.25894 0.4164	1.00000

8.MACRO FOR RISK GROUPING

%macro risk_group(var=, out=);

if &var < 4 then &out = "Low_Risk";

else if &var < 6 then &out = "Medium_Risk";

else &out = "High_Risk";

%mend risk_group;

data crypto_with_risk;

set crypto_raw;

length Risk_Level $12;

%risk_group(var=Volatility, out=Risk_Level);

run;

proc print data=crypto_with_risk;

run;

OUTPUT:


Obs	Coin_Name	Launch_Date	Last_Updated	Market_Cap	Price	Volatility	Transactions	Energy_Usage	Days_Since_Launch	Risk_Level
1	Bitcoin	03JAN2009	03JUL2009	850	42000.00	4.5	350000	707	6190	Medium_Risk
2	Ethereum	30JUL2015	30JAN2016	380	2300.00	5.2	900000	62	3791	Medium_Risk
3	BinanceCoin	14JUL2017	14JAN2018	90	310.00	3.8	1200000	12	3076	Low_Risk
4	Cardano	29SEP2017	29MAR2018	45	0.55	6.1	250000	6	2999	High_Risk
5	Solana	16MAR2020	16SEP2020	65	95.00	7.5	1800000	5	2100	High_Risk
6	Ripple	02FEB2013	02AUG2013	55	0.75	5.9	1400000	2	4699	Medium_Risk
7	Polkadot	26AUG2020	26FEB2021	35	7.50	6.8	600000	8	1937	High_Risk
8	Dogecoin	06DEC2013	06JUN2014	20	0.15	9.2	450000	15	4392	High_Risk
9	Litecoin	13OCT2011	13APR2012	30	85.00	4.1	300000	18	5177	Medium_Risk
10	Avalanche	21SEP2020	21MAR2021	28	38.00	6.4	500000	7	1911	High_Risk
11	Chainlink	19SEP2017	19MAR2018	25	14.00	5.7	420000	9	3009	Medium_Risk
12	Polygon	10OCT2017	10APR2018	18	0.90	6.9	800000	4	2988	High_Risk

9.DATE FUNCTIONS DEMONSTRATION

data crypto_dates;

set crypto_raw;

Months_Since_Launch = intck('month', Launch_Date, today());

Next_Review_Date = intnx('month', today(), 3, 'same');

Launch_Year = year(Launch_Date);

run;

proc print data=crypto_dates;

run;

OUTPUT:


Obs	Coin_Name	Launch_Date	Last_Updated	Market_Cap	Price	Volatility	Transactions	Energy_Usage	Days_Since_Launch	Months_Since_Launch	Next_Review_Date	Launch_Year
1	Bitcoin	03JAN2009	03JUL2009	850	42000.00	4.5	350000	707	6190	203	24180	2009
2	Ethereum	30JUL2015	30JAN2016	380	2300.00	5.2	900000	62	3791	125	24180	2015
3	BinanceCoin	14JUL2017	14JAN2018	90	310.00	3.8	1200000	12	3076	101	24180	2017
4	Cardano	29SEP2017	29MAR2018	45	0.55	6.1	250000	6	2999	99	24180	2017
5	Solana	16MAR2020	16SEP2020	65	95.00	7.5	1800000	5	2100	69	24180	2020
6	Ripple	02FEB2013	02AUG2013	55	0.75	5.9	1400000	2	4699	154	24180	2013
7	Polkadot	26AUG2020	26FEB2021	35	7.50	6.8	600000	8	1937	64	24180	2020
8	Dogecoin	06DEC2013	06JUN2014	20	0.15	9.2	450000	15	4392	144	24180	2013
9	Litecoin	13OCT2011	13APR2012	30	85.00	4.1	300000	18	5177	170	24180	2011
10	Avalanche	21SEP2020	21MAR2021	28	38.00	6.4	500000	7	1911	63	24180	2020
11	Chainlink	19SEP2017	19MAR2018	25	14.00	5.7	420000	9	3009	99	24180	2017
12	Polygon	10OCT2017	10APR2018	18	0.90	6.9	800000	4	2988	98	24180	2017

YESTERDAY INVALID CODE ANSWER

5.CREATING NEW VARIABLE

data logic_speed;

set transport_enhanced;

Speed_Growth = Years_Operational / Speed; (WRONG)

Speed_Growth = Speed / Years_Operational ;

run;

proc print data=logic_speed;

run;

/* Note: In practice above there is an Invalid in this code Find it,Correct it and Use it /*

To Visit My Previous Software Company Analysis Dataset:Click Here

To Visit My Previous Vote Program Dataset:Click Here

To Visit My Previous Audi Cars Performance Analysis Dataset:Click Here

To Visit My Previous Global Clothing Trends Dataset:Click Here

Follow Us On :

--- FOLLOW OUR BLOG FOR MORE INFORMATION.

--->PLEASE DO COMMENTS AND SHARE OUR BLOG.

SAS Learning Hub

Sunday, 14 December 2025

339.CRYPTOCURRENCY MARKET ANALYSIS AND RISK MODELLING USING PROC SQL | PROC MEANS | PROC REG | PROC UNIVARIATE | PROC SGPLOT | PROC CORR | MACROS | DATE FUNCTIONS (INTCK | INTNX)

CRYPTOCURRENCY MARKET ANALYSIS AND RISK MODELLING USING PROC SQL | PROC MEANS | PROC REG | PROC UNIVARIATE | PROC SGPLOT | PROC CORR | MACROS | DATE FUNCTIONS (INTCK | INTNX)

Follow Us On :

No comments:

Post a Comment

Popular Posts