1.               Business Understanding

Bitcoin, the first ever decentralized cryptocurrency, was originally conceived to create a global currency and payment system that is able to reliably work without requiring a trusted third party. The main underlying technology that powers Bitcoin, and almost all other cryptocurrencies today, is known as block -chain (Lewis, 2015). The block-chain requires a peer-to-peer network. The ADEPT is one of peer-to-peer network which has been developed by IBM partnership with Samsung (Panikkar, Nair, Brody, & Pureswaran, 2015).

The emerging payment by using crypto currencies accelerating the technology pervades the positive impact to human life (Bakar & Rosbi, 2017). The individual and business has adopted  this new system in term of transact a money quickly and efficiently over the internet without to supply the credit cards or banking information and use a traditional payment system (Ahamad, Nair, & Varghese, 2013). In 2018, the cryptocurrency has a total market cap of around $800 billion USD in Jan 2018 as reported in the Global chart of total market capitalization.

The cryptocurrency has been seen as investment tool is not only focusing on investor but the private investors and brokers also interested to these digital currencies. In this regards, it is necessary to predict the future value (i.e. forecasting or estimating) of cryptocurrency prices to make decision in trading or investment. The objective of Level-I was to construct the forecasting model to predict the future values of cryptocurrencies. The case is financial time-series prediction. The case integrates knowledge from various sources – Crypto Currencies, Quantitative Finance and Machine learning. The objective of Level-II was to implement the model which involves applying “live” model to make a decision making for trading or investment. To predict the forecasting values of 20 major cryptocurrencies, the methodology shown in Figure 1 was employed.

2.      Data Understanding

For constructing the prediction model for cryptocurrencies, the dataset was provided. The provided dataset contains the historical data of 1500 different cryptocurrencies. The price of each cryptocurrency was recorded for every five minutes. In Level-I, the task was to predict the future price of 20 major cryptocurrencies. Appendix I show the code snippet for checking data quality problems.

3.      Data Preparation and Transformation

Here, the price of cryptocurrencies was transformed into three descriptive statistical values namely, mean, maximum, and minimum. To transformed the data, the sliding window concept was used. The threshold of 12 window size was used for transforming the price values into mean, minimum, and maximum. In addition, the 12 window size was used to convert 5 minutes’ timestamp to 1-hour timestamp. The resultant transformed file is shown in Table 1. Appendix II shows the code snippet for transforming the cryptocurrencies dataset into sliding window concepts and generating Mean, Maximum, and Minimum values for each sliding window.

 

Table 1: Sample of Resultant Transformed File of ‘1442’ Cryptocurrency

Max	Min	Mean	Timestamp
10807.5	10333.8	10598.46667	17/1/2018 11:25:00 AM -17/1/2018 12:25:00 PM
10807.5	10065.5	10456.425	17/1/2018 1:00:00 PM -17/1/2018 1:55:00 PM
10807.5	10065.5	10416.80556	17/1/2018 2:00:00 PM -17/1/2018 3:00:00 PM
10807.5	10065.5	10394.3	17/1/2018 3:05:00 PM -17/1/2018 4:00:00 PM
10807.5	9616.41	10288.50617	17/1/2018 4:05:00 PM -17/1/2018 5:00:00 PM
10807.5	9402.29	10170.20097	17/1/2018 5:05:00 PM -17/1/2018 6:00:00 PM
10807.5	9402.29	10138.65214	17/1/2018 6:05:00 PM -17/1/2018 7:00:00 PM
10807.5	9402.29	10148.93521	17/1/2018 7:05:00 PM -17/1/2018 8:00:00 PM
10807.5	9402.29	10133.47481	17/1/2018 8:10:00 PM -17/1/2018 9:05:00 PM
10807.5	9402.29	10141.17758	17/1/2018 9:10:00 PM -17/1/2018 10:05:00 PM
11062.4	9402.29	10205.73644	17/1/2018 10:10:00 PM -17/1/2018 11:05:00 PM
11451.8	9402.29	10281.2341	17/1/2018 11:10:00 PM -18/1/2018 12:05:00 AM

 

4.  Model Construction and Evaluation

Once, the cryptocurrencies files were transformed into useful and informative features then these transformed feature files were fed an input to prediction algorithm to learn the cryptocurrencies patterns from the historic time series data and construct the forecasting model to predict the future values of 20 major cryptocurrencies. Three different prediction algorithms were trained to learn forecasting rules and to compare which one is suitable on our corpus. These three prediction algorithms are; Support Vector Machine for Regression (SVMR), Linear Regression (LR), and Random Forest (RF).

5. Experimental Setup and Implementation Tools

To test the outcome of Level-I prediction model, 60 analyses (20 Cryptocurrencies files x 3 prediction algorithms) were run. Moreover, to test the performance of constructed model five different performance metrics were used namely, Mean Absolute Percentage Error (MAPE), Directional Symmetry (DS), Coefficient of Variation Mean Absolute Percentage Error (CVMAPE), Computational Efficiency (CE), and Combined Model Score (CS). Matlab was used for data pre-processing step and data transformation step. Java Weka API was used for construction and evaluation of prediction models.

6. Result

This section presents the results of all 60 analyses (20 Cryptocurrencies files x 3 prediction algorithms). For each analyses, four different performance metrics (namely, MAPE, DS, CVMAPE, CS and CE) are shown. In addition, the combined model score for all 20 predicted models is also shown in Table 2 and Table 3.

Table 2- Experimental results for MAPE and DS  predicted the 20 major cryptocurrencies

Table 2- Experimental Results

CID	Linear Regression			SVM Regression			Random Forest
	MAPE	DS	CE	MAPE	DS	CE	MAPE	DS	CE
1442	0.0204	93.0645	10822.83	0.0151	97.6613	99.83333	0.0401	87.5806	10.16667
1443	0.1637	87.1774	3447.333	0.0214	97.5806	104.2222	0.0261	95.4839	12
1443	0.0167	94.0323	21903.5	0.0361	96.9355	186.8333	0.0792	74.4355	11.66667
1444	0.2287	88.871	20230.33	0.0261	98.7903	149	0.0652	94.7581	10
1445	0.0302	95.3226	9960.5	0.0532	97.9032	136.8333	0.0717	95.4032	10.66667
1446	0.036	97.0968	3047	0.0541	98.7903	131.6111	0.1039	96.9355	10.66667
1447	0.5237	82.6613	5311.127	0.0164	98.0645	188.6667	0.0248	96.4516	11
1448	0.5155	80.5645	20240.33	0.0204	97.0968	169.6667	0.0385	94.7581	11
1449	0.4043	89.7581	20519.17	0.0334	97.6613	187.1667	0.0378	91.6129	11.66667
1450	0.0828	94.1935	18514	0.0365	98.7903	144.1667	0.062	95.7258	10.66667
1451	0.3644	92.3387	9103	0.0348	99.3548	213.1667	0.0353	97.7419	11.66667
1452	0.0446	92.4194	18152.33	0.0184	97.2581	141.8333	0.0483	91.2903	5.5
1453	0.0575	89.6774	18282.33	0.0205	96.5323	124.8333	0.0489	92.8226	7
1454	0.4911	94.5161	5918.667	0.0459	99.1129	168.6667	0.1302	73.3065	7.333333
1455	0.2037	94.9681	3514.333	0.0577	99.1214	189.1667	0.0792	99.1129	9.833333
1456	0.8155	87.6613	22892.22	0.0197	98.7097	130.3333	0.027	95.9677	6.833333
1457	0.1853	93.2258	6648	0.0272	96.7742	176.1667	0.029	92.7419	9.333333
1458	0.0677	92.8226	6724.667	0.0226	97.9032	110.6667	0.0373	94.5968	9.666667
1459	0.0386	78.5484	34454	0.0019	85.8871	54.66667	0.005	46.129	6.833333
1460	0.0193	92.0968	5519.083	0.0311	96.8548	53.89815	0.4796	28.5484	8.5

 

Table 3- Experimental result of average prediction on Combine Score  20 major cryptocurrencies

 

Linear Regression					SVM Regression					Random Forest
R	M	D	U	Z	R	M	D	U	Z	R	M	D	U	Z
1.0594308449457146	0.215485	90.55083	13260.24	-13171	0.49118014318682995	0.029625	97.364621	143.07	-46.2262	1.3628232655093842	0.073455	86.77016	9.60	75.73388

7.  Deployment

 

The modelling approach of the predict the forecast price in trading market size. The model has been illustrated in the previous section. After training and testing as shown in Figure 1 previously (see Section 1). The proposed model will be ready to use. In this section, the proposed approach is described by applied the best model that has been evaluated which had the highest performance and accuracy in previous section. This phase contain four main layer:

 

Layer 1 Crawling Agent (CA): CA is responsible for crawling cryptocurrencies data market from Internet. The suitable language for create crawling agent is python by using Scrapy package. It is an open source and collaborative framework for extracting the data from website. The advantages of Scrapy it can done job in a fast, simple and extensible way.

Layer 2 Transform Agent (TA): TA is agent used for running the transformation of data for cleaning process by using sliding window technique. At the same time, the agent also checking the missing values in the data extraction data in CA.

 

Layer 3 (Analyzer Agent): This layer will analyze data provide by TA agent. This agent using forecasting weka feature that can integrate with others system through weka python plugin to forecast the time series prediction. Based on the previous experiment Random Forest the best machine learning performed with the dataset.

Layer 4 (Decision Maker): This layer is making decision to alert investor to buy and sell of forecast price according time series. The first step is finding the features input based on statistical naive method by shifting the previous price as predict price in advance so that we can be prepared to make trading and do the prediction based on random forest as shown in the pseudocode of forecast model (see Figure 3). Then, the similarity is employed to form a similarity relationship between one rules to another rules from the fuzzy case base. The fuzzy rules generate the rules (see Figure 4),  which indicates the highest and lowest prices status for the trading which can make the decision for investor to trade the current and future currencies. This is applied to make the trading strategies at various level which making the decision, suggestion and recommendation to prevent the predicted currencies failure.

Figure 2- Proposed Approach

 

Pseudocode Forecast model

1.  Start open price T1 2.  Used Naïve forecasting method for predict next T2 by shifting the T1 as predict value. 3.  Loop: Naïve Forecasting method T1 -> T24  Log the data 4.  Transform log data form T1 – T24 (2-hour data collected) to mean, min and max 5. Create sliding window based on T12 threshold value (12 window size was used to convert 5 minutes’ timestamp to 1-hour timestamp) 6.  Set transform data as TransData 7.  Training and Testing TransData in Random forest to forecast predicted value in Mean, Min and Max.

Figure 3- Forecast Model Pseudocode

 

Fuzzy Rules : Trading Strategies

Rules 1: Start trading with small amounts (1% of total assets) Check the (predict_min, predict_max) compare (previous actual value) If (predict_min <  previous actual value) { Skip and wait for the next price } If (predict_max) > previous actual value){ Sell more than 1% } Rules 2: Buy Low, sell high referring Figure 1. If (predict_min < previous actual value ){ Invest } If  (predict_max > previous actual value){ Trade }

 

Figure 4- Fuzzy rules for trading strategies

8. Discussion

 

Table 2 present the result of accuracy of forecasting measure by mean absolute percentage error (MAPE)  and the percentage of occurrences by using directional symmetry (DS) in which the measure the performance of a model in predicting the direction of value changes. The three models which are Linear Regression, SVM Regression and Random Forest were tested. The result obtained was as expected as Random Forest has an edge over other two machine learning algorithms. Among the three algorithms, Random Forest was the fastest to be executed but Linear Regression suffering badly to predict mean value of Bitcoin price. On the other hand, SVM Regression was able to produce accurate predictions while Random Forest came second in that criteria. However, Random Forest was the most stable algorithm when handling different types of datasets. Lastly, again SVM Regression proved that it can predict the direction of the Bitcoin price accurately. Nevertheless, Random Forest emerged as the most optimum and most suitable algorithm to predict prices of the Bitcoin currency (Z= 75.73388).

(Please refer the results of this experiment at Section 6 Results)

 

9. Conclusion

Based on the prediction scoring formulas, we have calculated Combined Score Model (Z) for all three machine learning algorithms (SVM Regression, Random Forest, Linear Regression). However, in the end, we have found out that Random Forest has the lowest Z value which is 75.73388 and was the fastest as it has the lowest U value. Thus concluding this article by mentioning that Random Forest is the best machine algorithm to predict Bitcoin price value for every 5 minutes and hour.

 

References

 

1. Ahamad, S., Nair, M., & Varghese, B. (2013). A survey on crypto currencies. Proceedings of the Fourth International Conference on Advances in Computer Science, 42–48. https://doi.org/02.AETACS.2013.4.131.
2. Bakar, N. A., & Rosbi, S. (2017). Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction. International Journal of Advanced Engineering Research and Science, 4(11), 130–137. https://doi.org/10.22161/ijaers.4.11.20.
3. Lewis, A. (2015). Blockchain Technology Explained. Blockchain Technologies, 1–27. https://doi.org/10.15358/0935-0381-2015-4-5-222.
4. Panikkar, S., Nair, S., Brody, P., & Pureswaran, V. (2015). ADEPT : An IoT Practitioner Perspective, 1–20. Retrieved from http://ibm.biz/devicedemocracy.
5. Global Charts; Total Market Capitalization, Retrieved from https://coinmarketcap.com/charts/, 27 April 2018.

 

Appendix

 

Appendix I: Code Snippet: Identify the data quality problems

Appendix II: Code Snippet: Transformation cryptocurrencies dataset into sliding window concepts and generate mean, maximum and minimum value.

Appendix III: Answer to question

 

Answer to Level-I Questions:

1-      What necessary data preparation did you need?

Answer: Please see Section 2.

2-      Which is the most suitable method for forecasting cryptocurrencies?

Answer: Please see Section 2.

3-      Which cryptocurrencies were hardest to predict?

Answer: Please see Section 2.

4-      What anomalies in behavior of the cryptocurrencies did you detect?

Answer: Please see Section 2.

SourceCode

check_missing&null_data UMalayasourcecode