Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Robert Ślepaczuk; Maryna Zenkova

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Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Robert Ślepaczuk

Maryna Zenkova

| 07 août 2019

Central European Economic Journal

Édition 5 (2018): Edition 52 (January 2018)

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Publié en ligne: 07 août 2019

Pages: 186 - 205

DOI: https://doi.org/10.1515/ceej-2018-0022

Mots clés
Machine learning, support vector machines, investment algorithm, algorithmic trading, strategy, optimization, cross-validation, overfitting, cryptocurrency market, technical analysis, meta parameters

© 2018 R. Ślepaczuk, M. Zenkova, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Left panel: non-linear data divided by an SVM with a polynomial kernel of degree 3. Right panel: the same non-linear data divided by an SVM with a radial kernelSource: James, G., Witten, D., Hastie, T., Tibshirani, R. An Introduction to Statistical Learning with Applications in R.

Percentage of total market capitalization by dominanceSource: https://coinmarketcap.com/charts/Note: the chart presents the dominance of the cryptocurrencies with the largest market capitalization over the period from 28/04/2013 to 05/08/2018.

Distribution of volatility-adjusted returns with 3-month history from 01/10/2014 to 12/31/2014 with cut-off lines to identify the number (training size) of the assets with the highest (green) and the lowest (red) volatility-adjusted returns to use for SVM trainingSource: own work.Note: the distribution of 91-days volatility-adjusted returns where the positive tail sets are the most positive volatility-adjusted returns, and the negative tail sets are the most negative. The vertical dotted lines represent the cut-off. The + and – regions are the ones used for SVM training.

Visualization of the data set splits and their proportionsNote: the figure represents in what proportion data is split to run the tuning of the meta parameters.

The equity line of the SVM strategy in comparison with the benchmark strategiesNote: the graph shows equity lines of the SVM strategy and four benchmark strategies over the period from 01/01/2015 to 01/08/2018. EqW with equity line drawn in black outperforms all the benchmark strategies and also SVM strategy.

Drawdowns of the SVM strategy in comparison with the benchmark strategiesNote: the graph shows the drawdown lines for SVM strategy and four benchmark strategies over the period from 01/01/2015 to 01/08/2018. SVM strategy drawn in green reaches the ‘deepest’ drawdown line if compared to the other benchmark strategies.

Equity lines of the SVM strategy with changing reallocation period RE: 1 week (base case), 1 month and 3 daysNote: the graph shows the equity lines of the SVM strategy with changing reallocation period RE over the period from 01/01/2015 to 01/08/2018. The length of the reallocation period significantly impacts the portfolio performance.

Equity lines of the SVM strategy with changing number of assets N in the portfolio: 25, 20, 15, 10, 5 and VARNote: the graph shows the equity lines of the SVM strategy with changing number of assets N in the portfolio over the period from 01/01/2015 to 01/08/2018. The worst performance is noticed when only 5 coins are kept in the portfolio during a reallocation period. The lower the number of coins in the portfolio, the higher is the portfolio turnover.

Equity lines of the SVM strategy with various transaction costs (%TC equalled 2%, 1% and 0.5%)Note: the graph shows the equity lines of the SVM strategy with changing transaction costs %TC in the portfolio over the period from 01/01/2015 to 01/08/2018. Performance of the portfolios heavily depends on the magnitude of transaction costs, which can be obviously seen from the behaviour of the equity lines.

Equity lines of the SVM strategy with changing length of the training set TS: 25%, 50%, and 100%Note: the graph shows the equity lines of the SVM strategy with changing length of training set %TS in the portfolio over the period from 01/01/2015 to 01/08/2018. As lines are evolving very close to each other, one may conclude that the change of the parameter %TS does not exercise a significant impact on the portfolio statistics.

Technical indicators used for the creation of feature set to train SVMs

Feature	Full name	Parameters
MOM, n days	Momentum for close prices, n days	n = 10 days
ΔV, n days	Volume change n days	n = 10 days
RSI	Relative Strength Index	n = 10 days
FI	Force Index	N/A
Williams %R	Williams Percent Range	n = 10 days
PSAR	Parabolic stop and reversal system	Acceleration factor by default set to 2% increasing by 2% with a maximum of 20%

Descriptive statistics for 10 largest and 10 smallest cryptocurrencies by MarketCap in TOP100 as of date 01-08-2018

		The largest 10 cryptocurrencies in TOP100 as of 01-08-2018

Name	%ARC	%ASD	%MDD	IR1	IR2	Date of start	Volume, mUSD	MarketCap, USD
bitcoin	118	75.8	69.7	1.6	2.6	01-10-2014	1888	43839225862
ethereum	437.7	145.2	84.3	3	15.7	20-08-2015	323	17124399552
ripple	226.7	164.6	87.1	1.4	3.6	01-10-2014	499	13468236361
bitcoin-cash	83.5	198.5	84.4	0.4	0.4	05-08-2017	699	6672807179
eos	516	253	87.9	2	12	14-07-2017	78	5220519698
stellar	228.6	178.8	82.6	1.3	3.5	01-10-2014	301	4634665748
litecoin	111.1	119.2	79.1	0.9	1.3	01-10-2014	80	3709789644
cardano	698.6	263.8	89.3	2.6	20.7	14-10-2017	32	2624893338
iota	48.4	188.4	82.9	0.3	0.1	26-06-2017	3059	2460207729
tether	-5.4	45.7	49.9	-0.1	0	15-03-2015	140	2233258238

		The smallest 10 cryptocurrencies in TOP100 as of 01-08-2018

Name	%ARC	%ASD	%MDD	IR1	IR2	Date of start	Volume, mUSD	MarketCap, USD

loom-network	649.7	217.4	80	3	24.3	21-04-2018	2.8	98040413
gas	365.8	265.4	89.6	1.4	5.6	09-08-2017	2.6	91875052
tenx	-97.6	247	99.2	-0.4	-0.4	10-07-2017	8.1	91154578
nxt	34.2	158.1	95.6	0.2	0.1	01-10-2014	2.8	90165499
cybermiles	-44.8	202.2	87.4	-0.2	-0.1	04-05-2018	7.1	88375828
nuls	5083.5	382.2	79.1	13.3	854.8	22-03-2018	4.1	88067102
byteball	160	236.8	91.2	0.7	1.2	09-01-2017	0.56	86950232
bibox-token	53.2	265.7	87.9	0.2	0.1	08-06-2018	67.5	83456610
odem	89471	229.6	40.7	389.7	856638	01-08-2018	0.135	82906522
electroneum	-92.5	243	95.3	-0.4	-0.4	15-11-2017	0.552	81946739

Descriptive statistics for SVM strategy (sensitivity analysis). Descriptive statistics for the benchmark strategies have been placed above for convenient comparison.

					Benchmark Strategies

		Name			%ARC	%ASD	%MDD	IR1	IR2	%MT
		S&P B&H			13.6	15.5	14.2	0.9	0.8
		BTC B&H			147.4	76.8	69.7	1.9	4.1	6.3
		EqW			425.8	96.2	81.7	4.4	23.1	10.8
		McW			141.9	74.9	73.1	1.9	3.7	6.3
		SVM			173.6	103.1	83.1	1.7	3.5	143.7

		Parameters					SVM Strategy

N	Position	%TS	RE	%TC	%ARC	%ASD	%MDD	IR1	IR2	%MT

25	long only	50	3d	1	19.4	108.7	90.6	0.2	0.0	115.3
25	long only	50	1w	1	173.6	103.1	83.1	1.7	3.5	143.7
25	long only	50	1m	1	224.2	101.5	86.0	2.2	5.8	148.8

5	long only	50	1w	1	-21.8	142.2	95.1	-0.2	0.0	189.3
10	long only	50	1w	1	89.3	131.7	85.0	0.7	0.7	176.8
15	long only	50	1w	1	207.2	115.7	82.0	1.8	4.5	166.2
20	long only	50	1w	1	215.9	110.0	82.3	2.0	5.1	154.3
25	long only	50	1w	1	173.6	103.1	83.1	1.7	3.5	143.7
VAR	long only	50	1w	1	326.4	92.6	57.6	3.5	20.0	105.6

25	long only	100	1w	1	177.9	103.3	85.1	1.7	3.6	144.3
25	long only	50	1w	1	173.6	103.1	83.1	1.7	3.5	143.7
25	long only	25	1w	1	210.6	103.6	85.5	2.0	5.0	160.5

25	long only	50	1w	0,5	368.8	110.2	76.5	3.3	16.1	155.4
25	long only	50	1w	1	173.6	103.1	83.1	1.7	3,5	143.7
25	long only	50	1w	2	29.6	110.9	88,1	0.3	0,1	154.9



			Best performance of SVM strategy with a selected set of parameters

N	Position	%TS	RE	%TC	%ARC	%ASD	%MDD	IR1	IR2	%MT
VAR	long only	50	1m	1	392.43	88.97	53.45	4.41	32.38	105.9

Descriptive statistics of the SVM strategy compared with the benchmark strategies

	N	RE	%TC	V	%ARC	%ASD	%MDD	IR1	IR2	%MT
S&P B&H	-	-	-	-	13.6	15.5	14.2	0.9	0.8	-
BTC B&H	-	-	-	-	147.4	76.8	69.7	1.9	4.1	-
EqW	100	1 w	1	100	425.8	96.2	81.7	4.4	23.1	10.8
McW	100	1 w	1	100	141.9	74.9	73.1	1.9	3.7	6.3
SVM	25	1 w	1	100	173.6	103.1	83.1	1.7	3.5	143.7

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Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Publié en ligne: 07 août 2019

Pages: 186 - 205

DOI: https://doi.org/10.1515/ceej-2018-0022

Mots clés
Machine learning, support vector machines, investment algorithm, algorithmic trading, strategy, optimization, cross-validation, overfitting, cryptocurrency market, technical analysis, meta parameters

© 2018 R. Ślepaczuk, M. Zenkova, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Fig. 2.1

Fig. 3.1

Fig. 3.2

Fig. 3.3

Fig. 4.1

Fig 4.2

Fig. 4.3

Fig. 4.4

Fig. 4.5

Fig. 4.6

Technical indicators used for the creation of feature set to train SVMs

Descriptive statistics for 10 largest and 10 smallest cryptocurrencies by MarketCap in TOP100 as of date 01-08-2018

Descriptive statistics for SVM strategy (sensitivity analysis). Descriptive statistics for the benchmark strategies have been placed above for convenient comparison.

Descriptive statistics of the SVM strategy compared with the benchmark strategies

Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Publié en ligne: 07 août 2019

Pages: 186 - 205

DOI: https://doi.org/10.1515/ceej-2018-0022

Mots clésMachine learning, support vector machines, investment algorithm, algorithmic trading, strategy, optimization, cross-validation, overfitting, cryptocurrency market, technical analysis, meta parameters

© 2018 R. Ślepaczuk, M. Zenkova, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Fig. 2.1

Fig. 3.1

Fig. 3.2

Fig. 3.3

Fig. 4.1

Fig 4.2

Fig. 4.3

Fig. 4.4

Fig. 4.5

Fig. 4.6

Technical indicators used for the creation of feature set to train SVMs

Descriptive statistics for 10 largest and 10 smallest cryptocurrencies by MarketCap in TOP100 as of date 01-08-2018

Descriptive statistics for SVM strategy (sensitivity analysis). Descriptive statistics for the benchmark strategies have been placed above for convenient comparison.

Descriptive statistics of the SVM strategy compared with the benchmark strategies

Mots clés
Machine learning, support vector machines, investment algorithm, algorithmic trading, strategy, optimization, cross-validation, overfitting, cryptocurrency market, technical analysis, meta parameters