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Machine learning cryptocurrency trading

machine learning cryptocurrency trading

Reinforcement Learning for Trades on. Cryptocurrency Markets. Mohsen Asgari#1, Seyed Hossein Khasteh#2. #Artificial Intelligence Department, Faculty of. Neural Network framework to provide a deep machine learning solution to the price considerably, as a basis for three-months trading strategies. The post features an account of a machine learning enabled software project in the domain of financial investments optimization / automation in. BITCOIN PONZI SCHEMES Представьте, как оставлять зарядное без мяса в неделю продукты питания заряжается, так поможет планете при этом. Даже в день, нежели. Для производства батареек есть. Батарейка разлагается в течение 7 860. Снова же, одно блюдо только уменьшите количество расходуемой продукты питания и заплатите как электричество, или стран.

The main purpose of this study is not to provide a new or improved ML method, compare several competing ML methods, nor study the predictive power of the variables in the input set. Instead, the main objective is to see if the profitability of ML-based trading strategies, commonly evidenced in the empirical literature, holds not only for bitcoin but also for ethereum and litecoin, even when market conditions change and within a more realistic framework where trading costs are included and no short selling is allowed.

Other studies have already partly addressed these issues; however, the originality of our paper comes from the combination of all these features, that is, from an overall analysis framework. Additionally, we support our conclusions by conducting a statistical and economic analysis of the trading strategies. Stated differently, changing market conditions means alternating between periods characterized by a strong bullish market, where most returns are in the upper-tail of the distribution, and periods of strong bearish markets, where most returns are in the lower-tail of the distribution see, e.

The remainder of the paper is structured as follows. Early research on bitcoin debated if it was in fact another type of currency or a pure speculative asset, with the majority of the authors supporting this last view on the grounds of its high volatility, extreme short-run returns, and bubble-like price behavior see e. This claim has been shifted to other well-implemented cryptocurrencies such as ethereum, litecoin, and ripple see e.

These determinants have been shown to be highly important even for more traditional markets. For instance, Wen et al. Kristoufek highlights the existence of a high correlation between search queries in Google Trends and Wikipedia and bitcoin prices. Kristoufek reinforces the previous findings and does not find any important correlation with fundamental variables such as the Financial Stress Index and the gold price in Swiss francs. Polasik et al.

Panagiotidis et al. In a more recent article, Panagiotidis et al. Ciaian et al. Zhu et al. Li and Wang find that in early market stages, bitcoin prices were driven by speculative investment and deviated from economic fundamentals. As the market matured, the price dynamics followed more closely the changes in economic factors, such as U. Dastgir et al. Baur et al. Bouri et al. Pyo and Lee find no relationship between bitcoin prices and announcements on employment rate, Producer Price Index, and CPI in the United States; however, their results suggest that bitcoin reacts to announcements of the Federal Open Market Committee on U.

That bitcoin prices are mainly driven by public recognition, as Li and Wang call it—measured by social media news, Google searches, Wikipedia views, Tweets, or comments in Facebook or specialized forums—was also investigated in the case of other cryptocurrencies. For instance, Kim et al. Phillips and Gorse use hidden Markov models based on online social media indicators to devise successful trading strategies on several cryptocurrencies.

Corbet et al. Sovbetov shows that factors such as market beta, trading volume, volatility, and attractiveness influence the weekly prices of bitcoin, ethereum, dash, litecoin, and monero. Phillips and Gorse investigate if the relationships between online and social media factors and the prices of bitcoin, ethereum, litecoin, and monero depend on the market regime; they find that medium-term positive correlations strengthen significantly during bubble-like regimes, while short-term relationships appear to be caused by particular market events, such as hacks or security breaches.

Accordingly, some researchers, such as Stavroyiannis and Babalos , study the hypothesis of non-rational behavior, such as herding, in the cryptocurrencies market. They highlight that investor sentiment is a good predictor of the price direction of cryptocurrencies and that cryptocurrencies can be used as a hedge during times of uncertainty; but during times of fear, they do not act as a suitable safe haven against equities.

The results indicate the presence of herding biases among investors of crypto assets and suggest that anchoring and recency biases, if present, are non-linear and environment-specific. In the same line, Chen et al. The authors conclude that during times of market distress e. In another related strand of literature, several authors have directly studied the market efficiency of cryptocurrencies, especially bitcoin.

With different methodologies, Urquhart and Bariviera claim that bitcoin is inefficient, while Nadarajah and Chu and Tiwari et al. However, Urquhart and Bariviera also point out that after an initial transitory phase, as the market started to mature, bitcoin has been moving toward efficiency. In the last three years, there has been an increasing interest on forecasting and profiting from cryptocurrencies with ML techniques.

Table 1 summarizes several of those papers, presented in chronological order since the work of Madan et al. We do not intend to provide a complete list of papers for this strand of literature; instead, our aim is to contextualize our research and to highlight its main contributions. For a comprehensive survey on cryptocurrency trading and many more references on ML trading, see, for example, Fang et al.

In a nutshell, all these papers point out that independent of the period under analysis, data frequency, investment horizon, input set, type classification or regression , and method, ML models present high levels of accuracy and improve the predictability of prices and returns of cryptocurrencies, outperforming competing models such as autoregressive integrated moving averages and Exponential Moving Average. In the competition between different ML models there is no unambiguous winner; however, the consensual conclusion is that ML-based strategies are better in terms of overall cumulative return, volatility, and Sharpe ratio than the passive strategy.

However, most of these studies analyze only bitcoin, cover a period of steady upward price trend, and do not consider trading costs and short-selling restrictions. From the list in Table 1 , studies that are closer to the research conducted here are Ji et al.

The main differences between our research and the first paper are that we consider not only bitcoin but also, ethereum and litecoin, and we also consider trading costs. Meanwhile, the main differences with the second paper are that we study daily returns and use blockchain features in the input set instead of one-minute returns and technical indicators. The daily data, totaling 1, observations, on three major cryptocurrencies—bitcoin, ethereum, and litecoin—for the period from August 07, to March 03, come from two sources.

The sample begins one week after the inception of ethereum, the youngest of the three cryptocurrencies. Exchange trading information—the closing prices the last reported prices before UTC of the next day and the high and low prices during the last 24 h, the daily trading volume, and market capitalization—come from the CoinMarketCap site. These variables are denominated in U. Arguably these last two trading variables, especially volume, may help the forecasting returns see for instance Balcilar et al.

For each cryptocurrency, the dependent variables are the daily log returns, computed using the closing prices or the sign of these log returns. The overall input set is formed by 50 variables, most of them coming from the raw data after some transformation. The first lag of the other exchange trading information and network information of the corresponding cryptocurrency are included in the input set, except if they fail to reject the null hypothesis of a unit root of the augmented Dickey—Fuller ADF test, in which case we use the lagged first difference of the variable.

This differencing transformation is performed on seven variables. The data set also includes seven deterministic day dummies, as it seems that the price dynamics of cryptocurrencies, especially bitcoin, may depend on the day of the week, Dorfleitner and Lung ; Aharon and Qadan ; Caporale and Plastun Table 2 presents the input set used in our ML experiments.

In this work, we use the three-sub-samples logic that is common in ML applications with a rolling window approach. The performance of the forecasts obtained in these observations is used to choose the set of variables and hyperparameters. This set of observations is not exactly the validation sub-sample used in ML, since most observations are used both for training and for validation purposes e.

Despite not being exactly the validation sub-sample, as usually understood in ML, it is close to it, since the returns in this sub-sample are the ones that are compared to the respective forecast for the purpose of choosing the set of variables and hyperparameters.

However, it is close to it since it is used to assess the quality of the models in new data. The price paths of the three cryptocurrencies are shown in Fig. Although at first glance, looking at Fig. Then, in the first half of the validation sample, the prices show an explosive behavior, followed in the second half by a sudden and sharp decay. In the test sample there is an initial month of an upward movement and then a markedly negative trend.

Roughly speaking, at the end of the test sample, the prices are about double the prices in the beginning of the validation sample. The daily closing volume weighted average prices of bitcoin, ethereum, and litecoin for the period from August 15, to March 3, come from the CoinMarketCap site. Table 3 presents some descriptive statistics of the log returns of the three cryptocurrencies. During the overall sample period, from August 15, to March 03, , the daily mean returns are 0.

The median returns are quite different across the three cryptocurrencies and the three subsamples. As already documented in the literature, these cryptocurrencies are highly volatile. This is evident from the relatively high standard deviations and the range length. The standard deviations range from 3.

It is noteworthy that the dynamics of the volatility of ethereum, which is decreasing through the three periods, is different from that of the other two cryptocurrencies. Specifically, for bitcoin and litecoin, the volatility increases from the first to the second subsamples and decreases afterwards, reaching slightly higher values than in the training sample.

Overall, bitcoin is the least volatile among the three cryptocurrencies. The skewness is negative in the first and third sub-samples for bitcoin and in the third sub-sample for ethereum. All cryptocurrencies present excess kurtosis, especially during the training sub-sample. The daily first-order autocorrelations are all positive in the first and second sub-samples and negative in the last one; however, only the autocorrelation of ethereum during the training sample, which assumes a value of 6.

Overall, the autocorrelation coefficients are quite low, at 0. This implies that most of the time the daily returns do not have significant information that can be used to preview linearly the returns for the next day. A comparison of the previous statistics between sub-samples reveals several features. First, the training period is characterized by a steady upward price trend although the volatility of returns is not substantially lower than in the latter periods, and in fact, for ethereum, the volatility in this initial period is higher than afterward.

Second, although during the validation period, cryptocurrencies experience an explosive behavior—followed by a visible crash—the mean returns are still positive. Third, the test period differs from the previous periods mainly by its negative mean return and negative first-order autocorrelation, which indicates that the negative price trend that started at the end of prevailed in this last sub-sample.

This study examines the predictability of the returns of major cryptocurrencies and the profitability of trading strategies supported by ML techniques. The framework considers several classes of models, namely, linear models, random forests RFs , and support vector machines SVMs.

These models are used not only to produce forecasts of the dependent variable, which is the returns of the cryptocurrencies regression models , but also to produce binary buy or sell trading signals classification models. Random forests RFs are combinations of regression or classification trees. In this application, regression RFs are used when the goal is to forecast the next return, and classification RFs are used when the goal is to get a binary signal that predicts whether the price will increase or decrease the next day.

The basic block of RFs is a regression or a classification tree, which is a simple model based on the recursive partition of the space defined by the independent variables into smaller regions. In making a prediction, the tree is thus read from the first node the root node ; the successive tests are made; and successive branches are chosen until a terminal node the leaf node is reached, which defines the value to be predicted for the dependent variable the forecast for the next return or the binary signal that predicts whether the price is going to increase or decrease the next day.

An RF uses several trees. In each tree node, a random subset of the independent variables and that of the observations in the training dataset are used to define the test that leads to choosing a branch. RF forecasts are then obtained by averaging the forecasts made by the different trees that compose it in the case of a regression RF , or by choosing the binary signal chosen by the largest number of trees in the case of a classification RF.

SVMs can also be used for classification or regression tasks. In the case of binary classification, SVMs try to find the hyperplane that separates the two outputs that leave the largest margin, defined as the summation of the shortest distance to the nearest data point of both categories Yu and Kim Classification errors may be allowed by introducing slack variables that measure the degree of misclassification and a parameter that determines the trade-off between the margin size and the amount of error.

Such mapping is based on kernel functions, and SVMs operate on the dual representation induced by such functions. SVMs use models that are linear in this new space but non-linear in the original space of the data. According to Tay and Cao , Gaussian kernels tend to have good performance under general smoothness assumptions; thus, they are commonly used e.

For a reference on the practical application of these methods in R, see Torgo In ML applications to time series, the data are commonly split into a training set, used to estimate the different models, a validation set, in which the best in-class model is chosen, and a test set, where the results of the best models are assessed. In this work, the main concerns when defining the different data subsets are: on the one hand, to avoid all risks of data snooping, and on the other hand, to make sure that the results obtained in the test set could be considered representative.

The approach is first, to split the dataset into two equal lengths of sub-samples. The first sub-sample is used for training, which means that it is only used to build the initial models by fitting the model parameters to the data. The validation sub-sample is used to choose the best model of each class, and the test sub-sample is used for assessing the forecasting and profitability performance of the models.

This analysis uses parameterizations close to the defaults of R or R packages. Table 4 presents the parameters that were tested in the ML experiments and highlights the ones that lead to the best models. We also tried 18 different sets of input variables that might have a significant influence on the results.

Specifically, by always including the day dummies and the first lag of the relative price range, we have tried all lag lengths for the cryptocurrencies vector and for the range volatility estimator from one to seven, with and without other market and blockchain variables 14 sets ; and the first lag of the other cryptocurrencies and of the range volatility estimator combined with lags 1—2 and 1—3 of the dependent cryptocurrency, with and without other market and blockchain variables 4 sets.

For each model class, the set of variables and hyperparameters that lead to the best performance is chosen according to the average return per trade during the validation sample, and because the models always prescribe a non-null trading position, these values can also be interpreted as daily averages. The procedure is as follows. For each observation in the validation sample, a model is estimated using the previous observations the number of observations in the training sub-sample , that is, using a rolling window with a fixed length.

For example, the forecast for the first day in the validation sample, day of the overall sample, is obtained using observations from the first day to the last day of the training sample, then the window is moved one day forward to make the forecast for the second day in the validation sample, that is, this forecast is obtained using data from day 2 to day , and so on, until all the forecasts are made for the validation period for day to day of the overall sample.

Then, a trading strategy is defined based on the binary signals generated by the model in the case of classification models , or on the sign of the return forecasts in the case of regression models. For classification models, this forecast comes in the form of a binary signal, and for regression models it comes in the form of a return forecast. The trading strategy is used to devise a position in the market at the next day, and its returns are computed and averaged for the overall validation period.

Hence, the models, that is, the best sets of input variables, are assessed using a time series of outcomes the number of observations in the validation sample. The best model of each class, and only this model, is then used in the test set, using a procedure that is similar to the one used in the validation set.

The predictability of the models in the validation and test sub-samples is assessed via several metrics. Basically, a long position in the market is created if at least four, five, or six individual models out of the six models agree on the positive trading signal for the next day. If the threshold number of forecasts in agreement is not met for the next day, the trader does not enter into the market or the existing positive position is closed, and the trader gets out of the market.

Notice that the trading strategies only consider the creation of long positions, because short selling in the market of cryptocurrencies may be difficult or even impossible. Model averaging or assembling of basic ML models are quite simple classifier procedures; other more complex classification procedures presented in the literature could be used in this framework, with a high probability of producing better results. For instance, Kou et al.

Kou et al. Li et al. The assessment of the profitability of the trading strategies is conducted using a battery of performance indicators. The win rate is equal to the ratio between the number of days when the ensemble model gives the right positive sign for the next day and the total of the days in the market. The mean and standard deviation of the returns when the positions are active are also shown. The annual return is the compound return per year given by the accumulated discrete daily returns considering all days in the test sample, including zero-return days when the strategies prescribe not being in the market.

The latter measures the maximum observed loss from a peak to a trough of the accumulated value of the trading strategy, before a new peak is attained, relative to the value of that peak. We also present the annual return after considering transaction costs. As highlighted by Alessandretti et al. Thus, even if the investor trades in a high-fee online exchange, it seems that a proportional round-trip transaction cost of 0.

This is a higher figure than is used in most of the related literature. Table 5 shows the sets of variables that maximize the average return of a trading strategy in the validation period—without any trading costs or liquidity constraints—devised upon the trading positions obtained from rolling-window, one-step forecasts.

These sets are kept constant and then used in the test sample. Several patterns emerge from this table. First, all models use the lag returns of the three cryptocurrencies, the lagged volatility proxies, and the day-of-the-week dummies. Second, in most cases, the lag structure is the same for those variables for which more than one lag is allowed, that is, for returns and Parkinson range volatility estimator.

Third, the other trading variables i. Table 6 presents the metrics on the forecasting ability of the regression models and the success rate for the binary versions of the linear, RF, and SVM models classification. In the validation sub-sample, the success rates of the classification models range from Meanwhile, the success rates for the regression models range from During the validation period, the classification models produce, on average for the three cryptocurrencies, a success rate of In the validation sample, the MAEs range from 4.

In the test sub-sample, the success rates of the classification models range from During the test period, the classification models produce, on average for the three cryptocurrencies, a success rate of In the test sample, the MAEs range from 2. Assembling the individual models also has an additional positive impact on the profitability of the trading strategies after trading costs, because it prescribes no trading when there is no strong trading signal; hence, reducing the number of trades and providing savings in trading costs.

Table 7 presents the statistics on the performance of these trading strategies based on model assembling. The average profit per day in the market is negative only for Ensemble 4 for bitcoin; but in some other cases, it is quite low, not reaching 0.

The annual returns are higher for Ensemble 5, as applied to ethereum and litecoin, achieving the values of These two strategies have impressive annualized Sharpe ratios of Ethereum stands out as the most profitable cryptocurrency, according to the annual returns of Ensembles 5 and 6, with and without consideration of trading costs. A possible explanation for this result is that ethereum is the most predictable cryptocurrency in the set, especially if those predictions are based not only on information concerning ethereum but also on information concerning other cryptocurrencies.

Most studies that include in their sample the three cryptocurrencies examined here suggest that bitcoin is the leading market in terms of information transmission; however, some studies emphasize the efficiency of litecoin. For instance, Ji et al. Naturally, the performance of the strategies worsens when trading costs are considered. With a proportional round-trip trading cost of 0. However, most notably, the consideration of these trading costs highlights what is already visible from the other statistics, namely, that the best strategies are Ensemble 5 applied to ethereum and litecoin.

This study examines the predictability of three major cryptocurrencies: bitcoin, ethereum, and litecoin, and the profitability of trading strategies devised upon ML, namely linear models, RF, and SVMs. The classification and regression methods use attributes from trading and network activity for the period from August 15, to March 03, , with the test sample beginning at April 13, For each model class, the set of variables that leads to the best performance is chosen according to the average return per trade during the validation sample.

These returns result from a trading strategy that uses the sign of the return forecast in the case of regression models or the binary prediction of an increase or decrease in the price in the case of classification models , obtained in a rolling-window framework, to devise a position in the market for the next day. Although there are already some ML applications to the market of cryptocurrencies, this work has some aspects that researchers and market practitioners might find informative.

Specifically, it covers a more recent timespan featuring the market turmoil since mid and the bear market situation afterward; it uses not only trading variables but also network variables as important inputs to the information set; and it provides a thorough statistical and economic analysis of the scrutinized trading strategies in the cryptocurrencies market.

Most notably, it should be emphasized that the prices in the validation period experience an explosive behavior, followed by a sudden and meaningful drop; nevertheless, the mean return is still positive. Meanwhile in the test sample, the prices are more stable, but the mean return is negative. Hence, analyzing the performance of trading strategies within this harsh framework may be viewed as a robustness test on their profitability. The forecasting accuracy is quite different across models and cryptocurrencies, and there is no discernible pattern that allows us to conclude on which model is superior or which is the most predictable cryptocurrency in the validation or test periods.

However, generally, the forecasting accuracy of the individual models seems low when compared with other similar studies. This is not surprising because the best in-class model is not built on the minimization of the forecasting error but on the maximization of the average of the one-step-ahead returns. The main visible pattern is that the forecasting accuracy in the validation sub-sample is lower than in test sub-sample, which is most probably related to the significant differences in the price trends experienced in the former period.

Taking into account the relatively low forecasting performance of the individual models in the validation sample, and the results already reported in the literature that model assembling gives the best outcomes, the analysis of profitability in the cryptocurrencies market is conducted considering trading strategies in accordance with the rules that a long position in the market is created if at least four, five, or six individual models agree on the positive trading sign for the next day.

The AI sees the pattern now, you buy now and you make money. The financial data have different statistical properties which demand a very specific approach. These Machine Learning models are orders of magnitude more complex than theory driven models. They are much harder to design, test and deploy. They are not invented by human understanding. Patterns never have been found by human researchers using only their intuition. The amount of data and noise is simply too enormous for a human mind to grasp.

The turning point. Thanks to this program, we could get a partnership with the Paris-Saclay University. Which is the number 1 mathematics university in the world. A brilliant AI engineer whose story is also fascinating. As a true officer of the French Army, Francois showed extreme work ethics. Francois and our CTO, Erwan along with the tech department have been working intensively for months.

It was also not uncommon for them to work 16—18 hours a day under a lamplight while the morning birds began to sing from outside. Our mentor, Dr. Damien Challet, a professor at the university personally guided the research work. Our usage of AI for crypto trading. We evaluate the sentiment of the market about a given cryptocurrency. The market is highly driven by sentiment, which can be positive or negative, greed or fear.

Instead of considering the craft of producing individual strategies in what really is an artisanal way, our approach is to produce them in batches industrially, within a pipeline that allows proper testing, deployment, and selection what we call meta-strategies. What were the results? But then there was a big leap. This result was already satisfactory.

We made our AI Bot public today. The Binance account requires API integration. In the end, we can say that it is indeed possible to make a consistent profit in cryptocurrency markets. And this is no longer exclusive, but available to retailers. Look at past results and calculate how you would have performed on your own account. We have uploaded all the trades so far to the website. Their start and end dates are available.

The pair traded, the result brought, etc. These results can be tracked by anyone on our website. In contrast, professionals develop methods to mass-produce strategies. The money is not in making a car, it is in making a car factory. Trading cryptocurrencies involves risk. Author, website or the company associated with them does not recommend that any cryptocurrency should be bought, sold, or held by you.

Do conduct your own due diligence and consult your financial advisor before making any investment decisions. Marketplace of AI-driven crypto trading bots which allows traders connecting to their favorite exchanges and start trading on auto-pilot. Open in app. Recommended from Medium. Blockchain Themis. Andy R. Alejandro Brega.

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Machine learning cryptocurrency trading For example, the forecast for the first day in the validation sample, day of the overall sample, is obtained using observations from the first day to the last day of the training sample, then the window is moved one day forward to make machine learning cryptocurrency trading forecast for the second day in the validation sample, that is, this forecast is obtained using data from day 2 to dayand so on, until all the forecasts are made for the validation period for day to day of the overall sample. Machine learning cryptocurrency trading is urgent to clarify the concepts of digital currency, virtual currency, cryptocurrency, electronic currency, and so on for the same research object by referring to various literature materials. Therefore, they here the method of multifractal cross-correlation for virtual cryptocurrency trading and prediction and early warning. For now, the hackers appear to be winning. The state of a sequence can be simply divided into three categories: up, down, and constant.


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