Price feeds of securities is a critical component for many financial services, allowing for collateral liquidation, margin trading, derivative pricing and more. With the advent of blockchain technology, value in reporting accurate prices without a third party has become apparent. There have been many attempts at trying to calculate prices without a third party, in which each of these attempts have resulted in being exploited by an exploiter artificially inflating the price. The industry has then shifted to a more centralized design, fetching price data from multiple centralized sources and then applying statistical methods to reach a consensus price. Even though this strategy is secure compared to reading from a single source, enough number of sources need to report to be able to apply statistical methods. As more sources participate in reporting the price, the feed gets more secure with the slowest feed becoming the bottleneck for query response time, introducing a tradeoff between security and speed. This paper provides the design and implementation details of a novel method to algorithmically compute security prices in a way that artificially inflating targeted pools has no effect on the reported price of the queried asset. We hypothesize that the proposed algorithm can report accurate prices given a set of possibly dishonest sources.
Christopher J. Cho, Timothy J. Norman, Manuel Nunes
In a financial exchange, market impact is a measure of the price change of an asset following a transaction. This is an important element of market microstructure, which determines the behaviour of the market following a trade. In this paper, we first provide a discussion on the market impact observed in the BTC/USD Futures market, then we present a novel multi-agent market simulation that can follow an underlying price series, whilst maintaining the ability to reproduce the market impact observed in the market in an explainable manner. This simulation of the financial exchange allows the model to interact realistically with market participants, helping its users better estimate market slippage as well as the knock-on consequences of their market actions. In turn, it allows various stakeholders such as industrial practitioners, governments and regulators to test their market hypotheses, without deploying capital or destabilising the system.
Abstract This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies. To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index, the study employs linear and nonlinear cointegration tests, a correlation coefficient measure, and fits different copula families, respectively. The strategy’s performance is then evaluated by conducting back-testing for various triggers of opening positions, assessing its returns and risks. The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.
The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.
This paper extends the existing drawdown modulation control policy to include a novel restart mechanism for trading. It is known that the drawdown modulation policy guarantees the maximum percentage drawdown no larger than a prespecified drawdown limit for all time with probability one. However, when the prespecified limit is approaching in practice, such a modulation policy becomes a stop-loss order, which may miss the profitable follow-up opportunities if any. Motivated by this, we add a data-driven restart mechanism into the drawdown modulation trading system to auto-tune the performance. We find that with the restart mechanism, our policy may achieve a superior trading performance to that without the restart, even with a nonzero transaction costs setting. To support our findings, some empirical studies using equity ETF and cryptocurrency with historical price data are provided.
Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy or hold strategies so that they may increase profitability. However, obtaining accurate and reliable predictions is challenging, noting that accuracy does not equate to reliability, especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies. To mitigate this complexity, this study provides a comprehensive method for forecasting financial time series based on tactical input output feature mapping techniques using machine learning (ML) models. During the prediction process, selecting the relevant indicators is vital to obtaining the desired results. In the financial field, limited attention has been paid to this problem with ML solutions. We investigate the use of feature selection with annealing (FSA) for the first time in this field, and we apply the least absolute shrinkage and selection operator (Lasso) method to select the features from more than 1,000 candidates obtained from 26 technical classifiers with different periods and lags. Boruta (BOR) feature selection, a wrapper method, is used as a baseline for comparison. Logistic regression (LR), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks. The dependent variables consisted of daily logarithmic returns and trends. The mean-squared error for regression, area under the receiver operating characteristic curve, and classification accuracy were used to evaluate model performance, and the statistical significance of the forecasting results was tested using paired t-tests. Experiments indicate that the FSA algorithm increased the performance of ML models, regardless of problem type.
Gamification is an effective strategy for motivating and engaging users, which is grounded in business, marketing, and management by designing games in nongame contexts. Gamifying education, which consists of the design and study of educational games, is an emerging trend. However, the existing classroom games for understanding macroeconomics have weak connections to the microfoundations of individual decision-making. We design an educational game on cryptocurrency investment for understanding macroeconomic concepts in microeconomic decisions. We contribute to the literature by designing game-based learning that engages students in understanding macroeconomics in incentivized individual investment decisions. Our game can be widely implemented in online, in-person, and hybrid classrooms. We also reflect on strategies for improving the user experience for future educational game implementations.
Anna G. Hughes, Jack S. Baker, Santosh Kumar Radha
Advancements in quantum computing are fuelling emerging applications across disciplines, including finance, where quantum and quantum-inspired algorithms can now make market predictions, detect fraud, and optimize portfolios. Expanding this toolbox, we propose the selector algorithm: a method for selecting the most representative subset of data from a larger dataset. The selected subset includes data points that simultaneously meet the two requirements of being maximally close to neighboring data points and maximally far from more distant data points where the precise notion of distance is given by any kernel or generalized similarity function. The cost function encoding the above requirements naturally presents itself as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is well-suited for quantum optimization algorithms - including quantum annealing. While the selector algorithm has applications in multiple areas, it is particularly useful in finance, where it can be used to build a diversified portfolio from a more extensive selection of assets. After experimenting with synthetic datasets, we show two use cases for the selector algorithm with real data: (1) approximately reconstructing the NASDAQ 100 index using a subset of stocks, and (2) diversifying a portfolio of cryptocurrencies. In our analysis of use case (2), we compare the performance of two quantum annealers provided by D-Wave Systems.
Abstract This study examines blockchain technologies and their pivotal role in the evolving Metaverse, shedding light on topics such as how to invest in cryptocurrency, the mechanics behind crypto mining, and strategies to effectively buy and trade cryptocurrencies. While it contextualises the common queries of "why is crypto crashing?" and "why is crypto down?", the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency. Contrasting existing literature, this comprehensive investigation encompasses both the economic and cybersecurity risks inherent in the blockchain and fintech spheres. Through an interdisciplinary approach, the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse. Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies, the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies. Moreover, it probes into both enduring and dubious crypto projects, drawing a distinct line between genuine blockchain applications and Ponzi-like schemes. The conclusion resolutely affirms the staying power of blockchain technologies, underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confronting individual investors.
Blockchain has empowered computer systems to be more secure using a distributed network. However, the current blockchain design suffers from fairness issues in transaction ordering. Miners are able to reorder transactions to generate profits, the so-called miner extractable value (MEV). Existing research recognizes MEV as a severe security issue and proposes potential solutions, including prominent Flashbots. However, previous studies have mostly analyzed blockchain data, which might not capture the impacts of MEV in a much broader AI society. Thus, in this research, we applied natural language processing (NLP) methods to comprehensively analyze topics in tweets on MEV. We collected more than 20000 tweets with #MEV and #Flashbots hashtags and analyzed their topics. Our results show that the tweets discussed profound topics of ethical concern, including security, equity, emotional sentiments, and the desire for solutions to MEV. We also identify the co-movements of MEV activities on blockchain and social media platforms. Our study contributes to the literature at the interface of blockchain security, MEV solutions, and AI ethics.
Alexis Asseman, Tomasz Kornuta, Patel, Anirudh, Matt Deible · 5 authors
The Graph Protocol indexes historical blockchain transaction data and makes it available for querying. As the protocol is decentralized, there are many independent Indexers that index and compete with each other for serving queries to the Consumers. One dimension along which Indexers compete is pricing. In this paper, we propose a bandit-based algorithm for maximization of Indexers' revenue via Consumer budget discovery. We present the design and the considerations we had to make for a dynamic pricing algorithm being used by multiple agents simultaneously. We discuss the results achieved by our dynamic pricing bandits both in simulation and deployed into production on one of the Indexers operating on Ethereum. We have open-sourced both the simulation framework and tools we created, which other Indexers have since started to adapt into their own workflows.
With the increasing enrichment and development of the financial derivatives market, the frequency of transactions is also faster and faster. Due to human limitations, algorithms and automatic trading have recently become the focus of discussion. In this paper, we propose a bidirectional LSTM neural network based on an attention mechanism, which is based on two popular assets, gold and bitcoin. In terms of Feature Engineering, on the one hand, we add traditional technical factors, and at the same time, we combine time series models to develop factors. In the selection of model parameters, we finally chose a two-layer deep learning network. According to AUC measurement, the accuracy of bitcoin and gold is 71.94% and 73.03% respectively. Using the forecast results, we achieved a return of 1089.34% in two years. At the same time, we also compare the attention Bi-LSTM model proposed in this paper with the traditional model, and the results show that our model has the best performance in this data set. Finally, we discuss the significance of the model and the experimental results, as well as the possible improvement direction in the future.
This paper investigates how changes in investor base is related to idiosyncratic volatility in cryptocurrency markets. For each cryptocurrency, we set change in its subreddit followers as a proxy for the change in its investor base, and find out that the latter can significantly increase cryptocurrencies idiosyncratic volatility. This finding is not subsumed by effects of size, momentum, liquidity and volume and is robust to various measures of idiosyncratic volatility.
This paper discusses a decentralized finance (DeFi) application called MakerDAO. The Maker Protocol, built on the Ethereum blockchain, enables users to create and hold currency. Current elements of the Maker Protocol are the Dai stable coin, Maker Vaults, and Voting. MakerDAO governs the Maker Protocol by deciding on key parameters (e.g., stability fees, collateral types and rates, etc.) through the voting power of Maker (MKR) holders. The Maker Protocol is one of the largest decentralized applications (DApps) on the Ethereum blockchain and is the first decentralized finance (DeFi) application to earn significant adoption. The objective of this paper is to analyze and discuss the significance, uses, and functions of this DeFi application.
Non-fungible tokens (NFTs) are digital assets stored on a blockchain representing real-world objects such as art or collectibles. An NFT collection comprises numerous tokens; each token can be transacted multiple times. It is a multibillion-dollar market where the number of collections has more than doubled in 2022. In this paper, we want to obtain a generative model that, given the early transactions history (first quarter Q1) of a newly minted collection, generates subsequent transactions (quarters Q2, Q3, Q4), where the generative model is trained using the transaction history of a few mature collections. The goal is to use the generated transactions to project the potential market value of this newly minted collection over the next few quarters. A technical challenge exists in that different collections have diverse characteristics, and the generative model should generate based on the appropriate "contexts" of the collection. Our method takes a two-step approach. First, it employs unsupervised learning on the early transactions to extract characteristics (which we call contexts) of NFT collections. Next, it generates future transactions of each token based on these contexts and the early transactions, projecting the target collection's potential market value. Comprehensive experiments demonstrate our contextual generative approach's NFT projection capabilities.
The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.
A trading system is said to be {robust} if it generates a robust return regardless of market direction. To this end, a consistently positive expected trading gain is often used as a robustness metric for a trading system. In this paper, we propose a new class of trading policies called the {double linear policy} in an asset trading scenario when the transaction costs are involved. Unlike many existing papers, we first show that the desired robust positive expected gain may disappear when transaction costs are involved. Then we quantify under what conditions the desired positivity can still be preserved. In addition, we conduct heavy Monte-Carlo simulations for an underlying asset whose prices are governed by a geometric Brownian motion with jumps to validate our theory. A more realistic backtesting example involving historical data for cryptocurrency Bitcoin-USD is also studied.
The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM). Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we applied this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options at the popular trading platform Deribit. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion.
Maximal Extractable Value (MEV) refers to excess value captured by miners (or validators) from users in a cryptocurrency network. This excess value often comes from reordering users' transactions to maximize fees or from inserting new transactions that front-run users' transactions. One of the most common types of MEV involves a `sandwich attack' against a user trading on a constant function market maker (CFMM), which is a popular class of automated market maker. We analyze game theoretic properties of MEV in CFMMs that we call \textit{routing} and \textit{reordering} MEV. In the case of routing, we present examples where the existence of MEV both degrades and, counterintuitively, \emph{improves} the quality of routing. We construct an analogue of the price of anarchy for this setting and demonstrate that if the impact of a sandwich attack is localized in a suitable sense, then the price of anarchy is constant. In the case of reordering, we show conditions when the maximum price impact caused by the reordering of sandwich attacks in a sequence of trades, relative to the average price, impact is $O(\log n)$ in the number of user trades. Combined, our results suggest methods that both MEV searchers and CFMM designers can utilize for estimating costs and profits of MEV.
Recent advances in Artificial Intelligence (AI) have made algorithmic trading play a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating the algorithmic trading of stock and crypto assets. Moreover, we demonstrate how our data science pipeline works with respect to four conventional algorithms: the moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage algorithms. Our study offers a systematic way to program, evaluate, and compare different trading strategies. Furthermore, we implement our algorithms through object-oriented programming in Python3, which serves as open-source software for future academic research and applications.
Lending Protocols (LPs), as blockchain-based lending systems, allow any agents to borrow and lend cryptocurrencies. However, liquidity risks could occur, especially when salient loans are initiated by a particular group of borrowers. This paper proposes measurements of liquidity risks, focusing on both available liquidity and market concentration in LPs. By using Aave as a case study, we find that liquidity risks are highly volatile and show complex effects on Aave, and liquidity in Aave may affect across on-chain lending market. Compared to new users, regular users that repeatedly borrow cryptocurrencies may negatively affect Aave protocol, implying that user loyalty is a double-edged sword for LPs.
We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve from high (15 s) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its pairwise realizations and intra-sector ones.
Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword `Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a `hold' or moving average strategy.
The current design space of derivatives in Decentralized Finance (DeFi) relies heavily on oracle systems. Replicating market makers (RMMs) provide a mechanism for converting specific payoff functions to an associated Constant Function Market Makers (CFMMs). We leverage RMMs to replicate the approximate payoff of a Black-Scholes covered call option. RMM-01 is the first implementation of an on-chain expiring option mechanism that relies on arbitrage rather than an external oracle for price. We provide frameworks for derivative instruments and structured products achievable on-chain without relying on oracles. We construct long and binary options and briefly discuss perpetual covered call strategies commonly referred to as "theta vaults." Moreover, we introduce a procedure to eliminate liquidation risk in lending markets. The results suggest that CFMMs are essential for structured product design with minimized trust dependencies.