Applying Artificial Intelligence in Cryptocurrency Markets: A Survey
Abstract
:1. Introduction
- What factors influence the price of cryptocurrencies?
- What is the state-of-the-art in AI research in the domain of cryptocurrency price prediction?
- What are current gaps in the literature that may be addressed by conducting future research?
2. Cryptocurrency Markets
2.1. A Short Background on Cryptocurrencies
- Block-data is a set of messages or transactions;
- Chaining-hash is a copy of the hash value of the immediately preceding block; and
- Block-hash is the calculated value of the hash of the data block.
- Mining-based altcoins: they have similar characteristics to Bitcoin, and as the name implies, they use the typical mining process for generating new coins. One of the most famous leading altcoins belonging to this category is Ethereum.
- Stablecoins: One of the main issues of mining-based cryptocurrencies is high volatility and fluctuation in their prices, making their trading complicated. Hence, stablecoins were introduced to address this challenge, which is valued based on stable existing currencies such as fiat currencies. Additionally, stable assets behind stablecoins secure and support their value. For example, Diem (previously Libra), developed by Facebook, and Tether, with the highest capital among stablecoins, are two famous coins in this cryptocurrency category.
- Utility tokens: this type of cryptocurrency can give value to its investors by providing access to a future product or service. For example, Filecoin is a famous open-source cryptocurrency that aims to store data on hard drive storage spaces compared to cloud storage companies such as Amazon.
2.2. Price Determinants of Cryptocurrencies
3. Artificial Intelligence and Cryptocurrencies
3.1. Application of Machine Learning in Cryptocurrency
3.2. Reinforcement Learning
3.2.1. An Overview on Reinforcement Learning Overview
- States and observations : A state is a complete description of the circumstances of an environment, and an agent obtains all the information regarding the environment through a state. Additionally, observation is considered in case some information might be omitted due to a partial representation of a state.
- Action spaces : Each environment allows performing several actions. The set of all legitimate actions in a given environment is called an action space. Unlike a continuous action space, an agent has a finite number of available actions in a discrete action space.
- Policy : A policy is the way an agent behaves at a given time. It determines the action that has to be chosen by the agent when it is in a particular state. In a probabilistic setting, it maps the current state of the environment into a set of probabilities for taking actions from the action space.
- Rewards : A reward component is an important concept in RL. It is an immediate or instantaneous gain that an agent receives when choosing an action in the current state to move to the next state.
- Discount factor : The quantity is the discount factor and generates discounted rewards to prevent infinite cumulative rewards when running for a long period. As a general intuition, discounted rewards mean rewards today are worth more than rewards tomorrow. If it is zero, an agent considers only immediate rewards; while closer to one means that the agent evaluates its actions based on cumulative rewards in the future.
Model-Free and Model-Based Approaches
Value-Based Methods
Policy-Based Methods
3.2.2. Reinforcement Learning Applications in Cryptocurrency Markets
4. Discussion and Potential Future Research
4.1. Integration within Cryptocurrencies or with Other Financial Assets
4.2. Macroeconomic Factors as the States in RL
4.3. Minor Challenges Related to Cryptocurrencies
4.4. Sentiment Analysis
4.5. Further Attention to Altcoins
4.6. Extreme Condition Detection in Cryptocurrencies
5. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Headers | Attributes and Definitions |
---|---|
Total circulation of crypto: the total number of mined cryptocurrency coins | |
Crypto statistical info | Crypto price: the price of the coin |
Market capitalisation: the total value of cryptocurrency in circulation | |
Blockchain size: total size of the blockchain | |
Avg. block size: average block size for the past 24 h | |
Block info | Avg. trans per block: average number of transactions per block for the last 24 h |
Avg. payments per block: the average number of payments per block for last 24 h | |
Total no. of trans: the total number of transactions on blockchain | |
Median (avg.) confirmation time: the median (avg.) time for a mined block to be added to the public ledger | |
Total hash rate: the estimated number of terahashes per second | |
Hash rate distribution: an estimation of hash rate distribution amongst the largest mining pools | |
Mining info | Network difficulty: the difficulty of mining a new block |
Miners revenue: total value of cryptocurrency block rewards and transaction fees paid to miners | |
Total transaction fee | |
Fees per transaction: average transaction fees per transaction |
Money | Electronic | Virtual | |
---|---|---|---|
Attributes | |||
Money format | Digital | Digital | |
Acceptance | By undertakings other than the issuer | Usually within a specific virtual community | |
Legal status | Regulated | Unregulated | |
Issuer | Legally established electronic money institution | Non-financial private company | |
Supply of money | Fixed | Not fixed (depends on issuer’s decisions) | |
Supervision | Yes | No | |
Type of risk | Mainly operational | Legal, credit, liquidity, and operational |
Exchange | No. Supported Coins | Transaction Fee (%) | Headquarter Location | Founded |
---|---|---|---|---|
Binance | 320+ | 0.100 | Malta | 2017 |
Coinbase | 40+ | 0.500 | San Francisco, US | 2012 |
BitMex | 160+ | 0.075 | Eden Island, Seychelles | 2014 |
Okex | 230 | 0.150 | Malta | 2017 |
Huobi | 310+ | 0.200 | Seychelles | 2013 |
Bitfinex | 30+ | 0.200 | Hong Hong | 2012 |
Kraken | 60 | 0.260 | San Fransisco, US | 2011 |
Bitterx | 320+ | 0.350 | Seattle, US | 2014 |
BitStamp | 10+ | 0.500 | Luxembourg | 2011 |
KuCoin | 270+ | 0.100 | Mahe, Seychelles | 2017 |
No | Reference | No | Reference | No | Reference |
---|---|---|---|---|---|
1 | Mittal et al. [4] | 6 | Chowdhury et al. [51] | 11 | Chen et al. [52] |
2 | Poongodi et al. [53] | 7 | McNally [54] | 12 | Kim et al. [55] |
3 | Patel et al. [28] | 8 | McNally [54] | 13 | Derbentsev et al. [34] |
4 | Alessandretti et al. [56] | 9 | Peng et al. [57] | 14 | Lamon et al. [58] |
5 | Sun et al. [59] | 10 | Jang and Lee [36] | 15 | Lahmiri and Bekiros [60] |
No | Method | Baseline(s) | Prediction Feature | Frequency Prediction | Performance Metric(s) | Data Period | Crypto(s) | Data Source(s) |
---|---|---|---|---|---|---|---|---|
1 | Multivariate LR | - | Highest price | 1D | F-score | - | 10 coins | kaggle.com |
2 | LR and SVM | - | Price | 1H | Cost function accuracy score | - | Ethereum | etherchain.org |
3 | LSTM and GRU | LSTM | Price | 1D, 3D, 7D | MAE, MSE, MAPE, and RMSE | 2016–2020 | Litecoin Monero | investing.com |
4 | LSTM, regression | Simple moving average strategy | Cumulative Return | 3D, 5D, 7D, and 10D | Geometric mean return Sharpe ratio | 2015–2018 | 10 coins | coinmarketcap.com |
5 | LightGBM | SVM and RF | Price | 2D, 2W, and 2M | AUC indicator | 2018 | 42 coins | investing.com |
6 | Gboosted trees, NNs, and K-NN | LSTM, RNN, and ARIMA | Closing price | 1D | RMSE and squared correlation | 2016–2019 | 9 coins and cci30 | coinmarketcap.com cci30.com |
7 | LDA, LR, RF, XGB, QDA, SVM, and LSTM | - | Price | 1D and 5 min | Precision, accuracy, recall, and F1-score | 2017–2019 | Bitcoin | coinmarketcap.com Bitcoinity.org blockchain.com |
8 | RF, LSTM, and RNN | ARIMA | Price | 1D | Sensitivity, specificity, precision, accuracy, and RMSE | 2013–2016 | Bitcoin | Coindesk Blockchain.info |
9 | SVR-GARCH, and SVR | Price | 1H and 1D | RMSE and MAE | 2016–2017 | Bitcoin, Ethereum, and DashCoin | alt19.com fxhistoricaldata.com | |
10 | BNN | SVR, and LR | Price | 1D | RMSE and MAPE | 2011–2017 | Bitcoin | Bitcoincharts.com |
11 | VADER | - | Price | 1D | Pearson R and p-value | 2018 | Bitcoin and Ethereum | Twitter’s API, Google Trends |
12 | AODE | - | Price | - | Accuracy rate, F-measure, and Matthews correlation coefficient | 2013–2015 | Bitcoin, Ethereum, and Ripple | Coindesk CoinMarketCap Etherscan RippleCharts |
13 | BART | ARIMA and ARFIMA | Price | 5D, 10D, 14D, 21D, and 30D | RMSE | 2017–2019 | Bitcoin, Ethereum, and Ripple | Yahoo Finance |
14 | Logistic Regression, SVM, and Naive Bayes | - | Price | 1D | Confusion matrix accuracy | 2017 | Bitcoin, Ethereum, and Litecoin | Kaggle.com, Twitter’s API |
15 | SVR, GRP, RT, kNN, FFNN, BRNN, and RBFNN | - | Price | 5 min | RMSE | 2016–2018 | Bitcoin | - |
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Amirzadeh, R.; Nazari, A.; Thiruvady, D. Applying Artificial Intelligence in Cryptocurrency Markets: A Survey. Algorithms 2022, 15, 428. https://doi.org/10.3390/a15110428
Amirzadeh R, Nazari A, Thiruvady D. Applying Artificial Intelligence in Cryptocurrency Markets: A Survey. Algorithms. 2022; 15(11):428. https://doi.org/10.3390/a15110428
Chicago/Turabian StyleAmirzadeh, Rasoul, Asef Nazari, and Dhananjay Thiruvady. 2022. "Applying Artificial Intelligence in Cryptocurrency Markets: A Survey" Algorithms 15, no. 11: 428. https://doi.org/10.3390/a15110428