We generate a large number of predictive models by applying linear kernel SVR to historical currency rates’ bid data for three currency pairs obtained from high-frequency trading. The bid tick data are converted into equally spaced (1 min) data. Differences of price between the previous successive timeframes are used as features to predict the direction of movement of the price in the next timeframe. Different values for the number of training samples, number of features, and the length of the timeframes are used when learning the models. These models are used to conduct simulated currency trading in the year following the one in which the model was learned. Proﬁts (sum of realized differences in best bid prices when order is executed), hit ratios, and number of trades executed using these models are recorded. The experiments indicate that while it is difﬁcult to construct models using only historical data that consistently perform well, there are models that show good performance under certain pre-deﬁned conditions, and that many of these models have an interesting property. Upon examining the parameters of these models, we discover that they have all negative coefﬁcients and a negligibly small intercept, while having positive proﬁts and good hit ratio. This suggests a simple yet effective trading strategy. Finally, we examine the historical data to ﬁnd corroboration for the pattern suggested by the generated models and present the results. Keywords Support vector regression (SVR) · Machine learning · Currency prediction · High-frequency limit order book 1 Introduction electronic communication networks and trading systems [1–3]. As a result of the widespread acceptance and usage Global ﬁnancial markets have undergone a technological rev- of the latest electronic systems in global ﬁnancial markets, olution in the past couple of decades. This has been made the processing time for tasks such as ordering or purchasing possible by the rapid advancements in various technical ﬁelds has gone down exponentially as compared to older traditional as well as major developments in the software and hardware markets. Since lower processing time means lower overhead, in use. Many established exchanges have widely adopted the ﬁnancial markets have a stake in pushing the process- ing time as low as possible. To achieve this, many ﬁnancial marketplaces have been using high-frequency trading sys- An earlier version of this research and paper was presented at the ACI- IDS 2017 conference at Kanazawa, Japan, in April 2017. The authors tems . These systems keep human intervention (which is are grateful to the organizers of the conference and all the participants time-consuming and thus costly) to a bare minimum, and all and reviewers who provided valuable comments and feedback. the transactions are handled by computer algorithms to keep overhead such as time and cost as low as possible. High- This paper expands the training parameters used in the experiments to a much broader range, performs the experiments for another major frequency trading systems have been playing an increasingly currency pair (GB Pound/US Dollar), and investigates the historical vital role in trading (especially online trading). One major data for the presence of the properties shown by the trained models. form of trading is currency trading or foreign exchange (forex for short). The forex market is certainly the largest, most liq- B Chanakya Serjam uid ﬁnancial market in the world, dwarﬁng all other markets email@example.com in size and volume of trading. However, it is also a very Akito Sakurai volatile market. As per a report from the Bank of Interna- firstname.lastname@example.org tional Settlements, the results from a recent survey show Graduate School of Science and Technology, Keio University, that trading in foreign exchange markets averaged $5.1 tril- Yokohama 223-8522, Japan 123 124 Vietnam Journal of Computer Science (2018) 5:123–132 lion per day in just a single month (April) of 2016. Although usually become the primary input for any prediction model this is down from an average of $5.3 trillion per day in April regardless of the technique used or the assumptions made. of 2013, it is still a very voluminous market. A variety of techniques have been used for prediction Traders investing in the currency markets are particularly tasks depending on the mathematical foundation or the value interested in predicting the direction of movement of the price of speciﬁc model parameters. There has been considerable for the currency pair which they are looking to trade. If the research [8–13] done on applying Artiﬁcial Neural Networks price of the currency is about to go up, the trader will want to (ANNs) to forex forecasting. Deng et al.  and Deng and take the buy position, so he/she can sell the currency later at Sakurai  applied complex hybrid prediction techniques a higher price to turn a proﬁt. If the price of the currency is including Multiple Kernel Learning (MKL) and Genetic about to go down, the trader will want to take the sell position. Algorithms (GA) to currency prediction and achieved good Later, the trader can buy the currency again for a lower price results. Kuo et al.  presented a decision support system and turn a proﬁt. Finally, the trader may assume a neutral for stock trading using GA-Based Fuzzy Neural Networks position, i.e., neither buy nor sell. Therefore, the prediction (GFNN) and ANNs. Another technique utilized for currency task of a model trained for currency trading can have three rates and ﬁnancial timeseries prediction is Support Vector outputs: buy, sell, or do nothing. The advent and widespread Machines [6,7], and it has also been applied successfully for usage of high-frequency trading necessitates development high-frequency trading [16,17]. Studies [18,19]haveshown and analysis of new trading strategies that can capture the that SVM-based models achieved on-par or better perfor- short-term behavior of the markets. It is also very important mance in forecasting of exchange rates or asset prices as to make an effort to understand the structure of the market compared to NN-based models for day trading. under the inﬂuence of high-frequency trading. While the techniques mentioned above show good results In this paper, we conduct currency prediction experiments in prediction tasks, it is difﬁcult to interpret the inner work- for Euro/US Dollar, British Pound/US Dollar, and US Dol- ing of the models and how the prediction function generates lar/Japanese Yen currency pairs using support vector machine the predictions. In addition, most of the techniques discussed for regression (SVR) [6,7], and examine the results to bet- above use dynamic training sets (using sliding window tech- ter understand the structure of currency trading in the forex nique) to incorporate the latest data/information for making market. Based on the forecast of the models, we perform sim- a prediction model. We were interested to know whether a ulated trading and record the proﬁts or losses by comparing model trained on a static training set can be used for pre- the predicted price movement with the actual price move- diction tasks far beyond the time horizon for which it is ment. We also examine the coefﬁcient and intercept values supposed to be valid. Therefore, due to combination of fac- and correlate them to the proﬁt/loss and hit ratio metrics. The tors such as SVM techniques having good performance in simulated trading is performed under some assumptions and ﬁnancial forecasting tasks, the feasibility of linear models deﬁned pre-existing conditions that may not be representative for understanding the prediction making process, and very of the real world but of an ideal scenario. Finally, we examine little research available on using linear kernel SVR on static the historical data for the presence of properties exhibited by training set of historical data (only previous price differences) the models. Some interesting results are presented. in high-frequency trading environment, we were motivated This paper is divided into the following major sections. to perform this research. Section 2 describes the background (previous research) and method of research. Section 3 describes the experimental 2.2 Method of research setup and discusses the process in detail. Section 4 presents the results of the experiments and is used for analysis and The primary aim of our research was to try and establish discussion of the results. Finally, Sect. 5 presents a conclusion whether a linear model trained only on a static training set to the research and this paper. of historical data can have good predictive performance, and if so, to analyze the models to ﬁnd out about the structure of the market. In our goal of analyzing the ﬁnancial mod- 2 Background and method of research els which take historical data as input and produce relatively good performance, we planned to focus on the character- 2.1 Previous research istics and structure of the model being generated. Hence, we decided on SVR with linear kernels to be the choice of While currency rates are volatile and prone to ﬂuctuations, technique for generating models, since it would be easier to they have also been shown to be deterministically chaotic analyze a linear model as the parameters would relate to real [8,9]. While this may be due to a number of factors, it is and observable data values. For further detailed reading and generally believed that historical data capture this behavior material on Support Vector Machines (SVM) and SVM for most concretely and effectively. Concurrently, historical data Regression (SVR), please refer to [6,7,16,17,20]. 123 Vietnam Journal of Computer Science (2018) 5:123–132 125 In high-frequency trading, the limit order book is updated The data sets are pre-processed to remove the volume data as every time there is a change in the bid or ask price or in case of well as the ask price data. Then, the tick data are converted other events such as a transaction being executed. These data to equally spaced (1 min) data which are the last tick data are called tick data. The limit order book contains, among in the minute. Therefore, we have data sets that contain the others, the timestamp (year/month/date and h/min/s), the best date and the last price at each minute. The data sets used were (highest) bid price, the bid volume, the best (lowest) ask from 2001 to 2015 and separated by year. Since the model is price, and the ask volume. To study the timeseries properties trained on the training set of the speciﬁed size extracted from of the price data, we only worked with the price data and 2 years (3 years in the case of GB Pound/US Dollar, since eliminated the volume data. We also make use of only 1 price we need 3 years of minute data for GB Pound/US Dollar (bid price) rather than both the prices as there is not much data to construct the required training set), and then used for qualitative difference in behaviors between both prices. We prediction on the next year, the data of results for prediction also subjected the tick data to some pre-processing which analysis are from 2003 to 2015. For example, the models that included converting the tick data to equally spaced (1 min) were trained in the year 2001 and 2002 (2001 to 2003 for GB data. Since the tick data are recorded every time there is Pound/US Dollar) were used for prediction in the year 2003 a change in the order book, the data are unequally spaced (2004 for GB Pound/US Dollar); the models trained in 2002 and hence unsuitable for timeseries analysis. We wanted to and 2003 were used for prediction in 2004, and so on. check whether some patterns might emerge which can be learned by training models when the data are equally spaced. 3.1 Parameters for training the models Converting the tick data to uniformly spaced data makes it easier to analyze as a timeseries. • Number of features: The values used for the number of In our experiments, we wanted to analyze whether there features were 1, 2, 3, 4, 5, and 6. Features used in our is a correlation between performance metrics such as proﬁts model are the difference of price between successive peri- or hit ratio and the initial parameters of the model such as ods of time going back n periods from the current time size of the training set, the number of features to be used for (t). For instance, if the number of features is 1, it means prediction, and the length of timeframe (1, 2, 3 min, etc). that the model predicts the next output based on just one Therefore, we trained models for many different values of previous difference of price. Consequently, that model these parameters. The models were trained on 1 year, and will have two parameters (since we are using linear ker- then used for validation on the data from the next year by per- nel SVR), the coefﬁcient and the intercept, and we extract forming simulated trading. This is to establish the predictive those parameters to do a qualitative analysis of the model. value of the models, since validating the models on the same If the number of features is n, the model predicts the next year they were learned would not have yielded any informa- output based on n previous time frames and, therefore, tion about the predictive performance of the models on new the model will have n + 1 parameters. unseen data. Various performance metrics are observed and • Length of timeframes: The lengths of timeframes (in min- used for comparative analysis. Then, we examined the coef- utes) used were 1, 2, 3, 4, 5, 7, 10, 20, 30, 40, 50, 60, and ﬁcients and intercepts of the models generated to look for 70. These values were used to see if there is any cor- some basic learning rule or pattern in the models. Finally, we relation between the length of the timeframes and the analyze the historical data to see if the pattern suggested by performance metrics such as proﬁts or hit ratio obtained. the trained models is valid or not, and why a large number of Although this could be extended to larger timeframes, models exhibit the same property. we believe that it might not be fully reﬂective of the structure of high-frequency trading, where trading is very fast and timeframes are inherently small. We also consid- 3 Experimental setup ered that, in timeframes greater than 1 min, there may be multiple starting points from which the training set can The currency rates data used in our experiments were begin. Therefore, we generate models for all the possible acquired from ICAP. The experiments were performed on starting points (in minutes) within a timeframe and also three different sets of currency pairs, the Euro/US Dollar data average them. set, the GB Pound/US Dollar and the US Dollar/Japanese • Size of training set: The values used for the number of Yen data set. As previously mentioned, the original data sets training samples are 2000, 3000, 4000, 5000, 6000, 7000, contain the best bid and ask prices as well as the volumes. 8000, 9000, and 10,000. The initial experiments performed with both bid and ask price data Models are generated for all possible combinations of for the sake of completeness revealed that the results using either price data are very similar. these initial parameters. 123 126 Vietnam Journal of Computer Science (2018) 5:123–132 Table 1 Summary statistics for Currency pairs Average no. of price Average no. of Average bid-ask spreads the three currency pairs used in quote updates per transactions (deals) per for minute data our experiments from the year minute minute 2001–2015 EURUSD 15.87 10.68 0.00021 GBPUSD 10.17 0.49 0.00087 USDJPY 13.90 6.73 0.02024 Table 2 Average ratios of Currencypairs 1min 2min 3min 4min 5min 7min 10 min positive changes vs negative changes for all 13 timeframes EURUSD 1.009 1.006 1.007 1.007 1.007 1.009 1.010 used in the experiments (table GBPUSD 1.020 1.013 1.011 1.009 1.008 1.007 1.006 continued below) USDJPY 1.016 1.014 1.013 1.013 1.014 1.016 1.017 Currency pairs 20 min 30 min 40 min 50 min 60 min 70 min EURUSD 1.011 1.013 1.011 1.012 1.011 1.011 GBPUSD 1.008 1.010 1.011 1.012 1.014 1.014 USDJPY 1.021 1.023 1.025 1.024 1.025 1.026 3.2 Performance metrics traded. This was done under the assumption that a small transaction of 1 unit will not change or alter the market prices • Hit ratio: The hit ratio, also known as directional symme- condition substantially and thus the following data set will try, is a measure of how many times the model predicted not be disrupted. No fee is charged for transactions. In the the change correctly. In other words, if the model predicts real world, there is a small fee charged for every transac- upward movement and the actual data used for validation tion, but we have chosen to ignore that to focus solely on the conﬁrm it, then it counts as a hit. timeseries properties of currency trading. • Proﬁts: Proﬁts are obtained as a result of simulated trad- In the simulated trading, a trade is counted when we have ing based on the predictions of our models and are simply a change in the predicted direction of movement of the cur- the sum of the realized differences in best bid prices when rency. Since we are only trading 1 unit, if, for instance, the the orders are executed. If the price at the closing of a prediction of direction is downward movement more than timeframe t is price(t) and the prediction at the closing one times in a row, we do not execute or count those trades. of the timeframe t is pred(t), then proﬁt is given as fol- The summary statistics of the data for our experiments lows: are displayed in Tables 1 and 2. Table 1 provides us metadata about average no. of price quote updates per minute, average no. of transactions (deals) per minute, and average bid-ask Proﬁt = [price(t + 1) − price(t )]× pred(t ). (1) spreads observed for 1-min data. Table 2 shows the ratio of positive changes in best bid price vs. negative changes in For the Euro/US Dollar and the GB Pound/US Dollar the best bid price. Since all the ratios in Table 2 are slightly currency pair, the proﬁts were in US Dollars, and for the larger than 1, it implies that the number of positive changes US Dollar/Japanese Yen currency pair, the proﬁts were in in best bid prices has been slightly higher than the number Japanese Yen. It should be emphasized that the proﬁts cal- of negative changes for all timeframes aggregated from 2001 culated in Eq. (1) are not representative of actual proﬁts. to 2015. The next section discusses the results of the experi- In real-world trading, the concept of spread-crossing is an ments. important and integral part of the proﬁt calculation. Since we are working with only best bid prices, the spread does not factor into the equation. It is also important to point out that the bid-ask spread per trade is larger than the proﬁts 4 Results and analysis obtained per trade in most cases, and hence, proﬁts calcu- lated by Eq. (1) would not be positive if we did take the The results of the experiments consisted of the proﬁts per bid-ask spread into account. year, the hit ratios, and the no. of trades executed over the For simulated trading, we put certain conditions in place. period of a year using those models, as well as the inter- We assume that only 1 unit of the currency pair is being cept and coefﬁcients of the models. Since the models were 123 Vietnam Journal of Computer Science (2018) 5:123–132 127 grouped based on the number of features (1–6) used for the (1–6) for the Euro/US Dollar pair, the GB Pound/US Dollar models, we calculated the average proﬁts and hit ratios with pair, and the US Dollar/Japanese Yen pair respectively. respect to the length of timeframes and the size of training It is interesting to note that, as the length of timeframe set (for each value of no. of features). This gave us four plots increases, the avg. hit ratio increases too irrespective of the for each currency pair and gave insight into the performance no. of features, meaning an increase in the accuracy of trend of the models for different input parameters. prediction. However, at the same time, the proﬁts from sim- ulated trading go down as the length of timeframe increases. This is an interesting result, because normally proﬁt would 4.1 Performance metrics vs. length of timeframes be expected to rise when hit ratio rises and vice versa. One reason for this might be that as the length of the timeframe Figures 1, 2, and 3 below show the performance metrics (avg. increases, the no. of trades executed in our simulated trad- hit ratio and avg. proﬁts per year) as a function of the length ing decreases drastically. Thus, even if the hit ratio is higher, of timeframe for all different values of number of features Fig. 1 Plots for avg. hit ratio (left) and avg. proﬁt per year (right) vs. length of timeframe for the Euro/US Dollar currency pair Fig. 2 Plots for avg. hit ratio (left) and avg. proﬁt per year (right) vs. length of timeframe for the GB Pound/US Dollar currency pair 123 128 Vietnam Journal of Computer Science (2018) 5:123–132 Fig. 3 Plots for avg. hit ratio (left) and avg. proﬁt per year (right) vs. length of timeframe for the US Dollar/Japanese Yen currency pair Fig. 4 Plots for avg. hit ratio (left) and avg. proﬁt per year (right) vs. training set size for the Euro/US Dollar currency pair the number of trades executed might simply not be enough The plots show that there is an increase in both the hit ratio to generate proﬁts comparable to shorter timeframes, which and the proﬁts as the size of the training set increases. This have lower hit ratio but a large number of executed trades, might be because smaller training sets lead to over-ﬁtting, and thus more average proﬁt per year. whereas larger training sets can ﬁne tune the parameters a bit In addition, we can see that fewer number of features better. On average, fewer number of features results in higher results in higher hit ratio but lower proﬁts on average. hit ratio and higher proﬁts; although, in US Dollar/Japanese Yen (Fig. 6), lower proﬁts for fewer number of features are observed. 4.2 Performance metrics vs. training set size Figures 4, 5, and 6 show the performance metrics as a function 4.3 Analyzing trained model parameters of the size of the training set for all different values of number of features for the Euro/US Dollar pair, the GB Pound/US While taking a cursory glance at our results, we noticed that Dollar pair, and the US Dollar/JP Yen pair, respectively. a large number of models generated had similarities in the 123 Vietnam Journal of Computer Science (2018) 5:123–132 129 Fig. 5 Plots for avg. hit ratio (left) and avg. proﬁt per year (right) vs. training set size for the GB Pound/US Dollar currency pair Fig. 6 Plots for avg. hit ratio (left) and avg. proﬁt per year (right) vs. training set size for the US Dollar/Japanese Yen currency pair correlation between the values of the intercept and the coef- • Case 1 (C1): Absolute value of intercept < 0.1, all coefﬁ- ﬁcients. These models had negligibly small intercept (which cients < −10 (< 0 for US Dollar/Japanese Yen), proﬁts would not inﬂuence the predictions) as well as negative coef- > 0, and hit ratio ≥ 60%. ﬁcients (although the number of models like this decreased as • Case 2 (C2): Absolute value of intercept < 0.1, all coef- the no. of features, and thus the no. of coefﬁcients, increased) ﬁcients < −10 (< 0 for US Dollar/Japanese Yen), proﬁts while still giving good hit ratios and proﬁts. We checked for > 0, and hit ratio ≥ 50% and < 60%. the number of models that satisﬁed the condition of very small intercept, negative coefﬁcients, and positive proﬁt and hit ratio. The results for the Euro/US Dollar pair, the GB The difference between the values of coefﬁcients being checked for Pound/Japanese Yen pair, and the US Dollar/Japanese Yen the US Dollar/Japanese Yen pair as compared to the other currency pair are shown in Figs. 7, 8, and 9 respectively. The cases C1, pairs is due to the difference in tick rate. US Dollar/Japanese Yen count C2, C3, and C4 are described as follows: the smallest tick at the second decimal place. The other two currencies count the smallest tick at the fourth decimal place. 123 130 Vietnam Journal of Computer Science (2018) 5:123–132 Fig. 7 Stacked bar plots representing the percentage of models for cases Fig. 9 Stacked bar plots representing the percentage of models for C1–C4 for different values of no. of features on x-axis (Euro/US Dollar cases C1–C4 for different values of no. of features on x-axis (US Dol- currency pair) lar/Japanese Yen currency pair) Thus, for models trained using linear SVR with a single feature, we can give a simple rule which states that the next prediction will be the opposite of the most recent (previ- ous) movement direction. Concretely, if the previous trend is down, the model will predict up for the next change, and if the previous trend is up, the model will predict down for the next change. Using this simple trading rule, we get proﬁt and good hit ratio in our simulated trading when using a single previous movement in direction of the price. This property is called return reversal. From the bar plots below, we can see that out of all the models with just one feature, a large percentage of models fall into case 1 of having positive prof- its and good hit ratio with negligible intercept and negative coefﬁcients. This includes models from all the different time- frames used when training the models. The positive proﬁts and high hit ratio suggest that the strategy may be viable under certain pre-deﬁned circumstances irrespective of the timeframe used. Fig. 8 Stacked bar plots representing the percentage of models for cases For models with two or more features, while case 1 is still C1–C4 for different values of no. of features on x-axis (GB Pound/US Dollar currency pair) a signiﬁcant percentage of the total models, it decreases as the number of features increases. Since two or more previ- • Case 3 (C3): Absolute value of intercept < 0.1, all coef- ous difference in prices is being considered, it is possible that ﬁcients < −10 (< 0 for US Dollar/Japanese Yen), proﬁts some of the features are negative, while others are positive. > 0, and hit ratio < 50%. In this case, it is difﬁcult to make a deﬁnitive statement about • Case 4 (C4): Rest of the models (where not all coefﬁ- the presence of return reversal, as the condition of all neg- cients are negative or absolute value of intercept > 0.1, ative coefﬁcients is nulliﬁed. However, for n features, if all or proﬁts < 0). n features are the same sign, then we can see the next price The stacked bar plots conﬁrmed our initial observation that a large number of models had negative coefﬁcients and negli- gible intercept values while giving proﬁt and good hit ratio. 123 Vietnam Journal of Computer Science (2018) 5:123–132 131 Table 3 Checking the return 1min 5min 20 min 60min reversal property (in percentages rounded to two decimal places) − 1 + 1 − 1 + 1 − 1 + 1 − 1 + 1 for t = 1, 5, 20, and60min for Euro/US Dollar historical bid − 1 46.97 53.03 47.38 52.62 47.00 53.00 47.03 52.97 price data + 1 52.58 47.42 52.20 47.80 52.36 47.64 52.58 47.42 − 1, − 1 46.89 53.11 45.92 54.08 45.38 54.62 45.56 54.44 + 1, + 1 53.34 46.66 54.03 45.97 53.78 46.22 53.86 46.14 − 1, − 1, − 1 46.18 53.82 45.00 55.00 44.07 55.93 43.95 56.05 + 1, + 1, + 1 54.50 45.50 55.48 44.52 54.89 45.11 55.09 44.91 Table 4 Checking the return 1min 5min 20 min 60min reversal property (in percentages rounded to two decimal places) − 1 + 1 − 1 + 1 − 1 + 1 − 1 + 1 for t = 1, 5, 20, and60min for GB Pound/US Dollar historical − 1 45.90 54.10 46.69 53.31 46.67 53.33 46.51 53.49 bid price data + 1 53.03 46.97 52.88 47.12 52.89 47.11 52.61 47.39 − 1, − 1 46.12 53.88 45.77 54.23 45.90 54.10 45.75 54.25 + 1, + 1 53.34 46.66 54.18 45.82 54.01 45.99 53.74 46.26 − 1, − 1, − 1 45.67 54.33 44.96 55.04 45.16 54.84 45.20 54.80 + 1, + 1, + 1 53.87 46.13 55.05 44.95 54.60 45.40 53.70 46.30 Table 5 Checking the return 1min 5min 20 min 60min reversal property (in percentages rounded to two decimal places) − 1 + 1 − 1 + 1 − 1 + 1 − 1 + 1 for t = 1, 5, 20, and60min for US Dollar/Japanese Yen − 1 46.13 53.87 47.10 52.90 46.91 53.09 46.76 53.24 historical bid price data + 1 53.06 46.94 52.22 47.78 52.23 47.77 51.95 48.05 − 1, −1 45.97 54.03 45.69 54.31 45.69 54.31 45.60 54.40 + 1, + 1 53.86 46.14 53.85 46.15 53.73 46.27 52.71 47.29 − 1, − 1, − 1 45.12 54.88 44.37 55.63 44.60 55.40 44.39 55.61 + 1, + 1, + 1 54.80 45.20 55.44 44.56 54.90 45.10 54.10 45.90 movement will be the opposite sign with a much higher prob- models. In case the change in price at the next step is 0, we ability irrespective of timeframe, as this would satisfy the look for the nearest non-zero value in the future. Only bid models in case 1. data are used, since we also used bid data in training the In the next sub-section, we take a look at the percentages models. of return reversal when all n features are the same sign for Tables 3, 4, and 5 show the number of times (in per- models with two or more features. We do this for different centages) the sign of the next value changes based on the timeframes to see if the condition is still satisﬁed. previous consecutive opposite signs. The rows show the pre- vious direction of movement of the price up to time t. − 1s 4.4 Checking historical data for occurrence of return represent a negative change in price (the price goes down), reversal whereas + 1 s represent a positive change in price (the price goes up). Concurrently, two or more consecutive − 1sor We check for return reversal using 1, 2, and 3 features over + 1 s represent two or more such consecutive moves in the sample timeframes of t = 1, 5, 20, and 60 min for all three same direction. The columns show the probability of the fol- currency pairs. The reason which we chose to check for return lowing direction of movement of the price for time (t + 1). reversal at those timeframes is because it provides a good The results are very consistent for all three currency pairs and spread from all the timeframes that we used to generate the for all the timeframes checked. One or more than one con- secutive − 1 s is consistently followed by + 1 with a higher This does not mean that the actual movement will be of opposite sign. percentage or probability in all timeframes. 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Burges, C.J.C.: A tutorial on support vector machines for pattern Commons license, and indicate if changes were made. recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998) Publisher’s Note Springer Nature remains neutral with regard to juris- dictional claims in published maps and institutional afﬁliations.
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Published: May 26, 2018