Cryptocurrency

Impermanent loss and loss-vs-rebalancing I: some statistical properties ArXiv ID: 2410.00854 “View on arXiv” Authors: Unknown Abstract There are two predominant metrics to assess the performance of automated market makers and their profitability for liquidity providers: ‘impermanent loss’ (IL) and ’loss-versus-rebalance’ (LVR). In this short paper we shed light on the statistical aspects of both concepts and show that they are more similar than conventionally appreciated. Our analysis uses the properties of a random walk and some analytical properties of the statistical integral combined with the mechanics of a constant function market maker (CFMM). We consider non-toxic or rather unspecific trading in this paper. Our main finding can be summarized in one sentence: For Brownian motion with a given volatility, IL and LVR have identical expectation values but vastly differing distribution functions. ...

Interpool: a liquidity pool designed for interoperability that mints, exchanges, and burns ArXiv ID: 2410.00011 “View on arXiv” Authors: Unknown Abstract The lack of proper interoperability poses a significant challenge in leveraging use cases within the blockchain industry. Unlike typical solutions that rely on third parties such as oracles and witnesses, the interpool design operates as a standalone solution that mints, exchanges, and burns (MEB) within the same liquidity pool. This MEB approach ensures that minting is backed by the locked capital supplied by liquidity providers. During the exchange process, the order of transactions in the mempool is optimized to maximize returns, effectively transforming the front-running issue into a solution that forges an external blockchain hash. This forged hash enables a novel protocol, Listrack (Listen and Track), which ensures that ultimate liquidity is always enforced through a solid burning procedure, strengthening a trustless design. Supported by Listrack, atomic swaps become feasible even outside the interpool, thereby enhancing the current design into a comprehensive interoperability solution ...

Enhancing Causal Discovery in Financial Networks with Piecewise Quantile Regression ArXiv ID: 2408.12210 “View on arXiv” Authors: Unknown Abstract Financial networks can be constructed using statistical dependencies found within the price series of speculative assets. Across the various methods used to infer these networks, there is a general reliance on predictive modelling to capture cross-correlation effects. These methods usually model the flow of mean-response information, or the propagation of volatility and risk within the market. Such techniques, though insightful, don’t fully capture the broader distribution-level causality that is possible within speculative markets. This paper introduces a novel approach, combining quantile regression with a piecewise linear embedding scheme - allowing us to construct causality networks that identify the complex tail interactions inherent to financial markets. Applying this method to 260 cryptocurrency return series, we uncover significant tail-tail causal effects and substantial causal asymmetry. We identify a propensity for coins to be self-influencing, with comparatively sparse cross variable effects. Assessing all link types in conjunction, Bitcoin stands out as the primary influencer - a nuance that is missed in conventional linear mean-response analyses. Our findings introduce a comprehensive framework for modelling distributional causality, paving the way towards more holistic representations of causality in financial markets. ...

Combining supervised and unsupervised learning methods to predict financial market movements ArXiv ID: 2409.03762 “View on arXiv” Authors: Unknown Abstract The decisions traders make to buy or sell an asset depend on various analyses, with expertise required to identify patterns that can be exploited for profit. In this paper we identify novel features extracted from emergent and well-established financial markets using linear models and Gaussian Mixture Models (GMM) with the aim of finding profitable opportunities. We used approximately six months of data consisting of minute candles from the Bitcoin, Pepecoin, and Nasdaq markets to derive and compare the proposed novel features with commonly used ones. These features were extracted based on the previous 59 minutes for each market and used to identify predictions for the hour ahead. We explored the performance of various machine learning strategies, such as Random Forests (RF) and K-Nearest Neighbours (KNN) to classify market movements. A naive random approach to selecting trading decisions was used as a benchmark, with outcomes assumed to be equally likely. We used a temporal cross-validation approach using test sets of 40%, 30% and 20% of total hours to evaluate the learning algorithms’ performances. Our results showed that filtering the time series facilitates algorithms’ generalisation. The GMM filtering approach revealed that the KNN and RF algorithms produced higher average returns than the random algorithm. ...

What Drives Crypto Asset Prices? ArXiv ID: ssrn-4910537 “View on arXiv” Authors: Unknown Abstract We investigate the factors influencing cryptocurrency returns using a structural vector auto-regressive model. The model uses asset price co-movements to identi Keywords: Cryptocurrency, Structural VAR, Digital Assets, Market Integration, Return Determinants, Cryptocurrency Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 8.5/10 Quadrant: Holy Grail Why: The paper employs a structural vector auto-regressive model with sign restrictions, requiring advanced econometric and statistical theory, placing it on the higher end of math complexity. Empirically, it uses daily market data (Bitcoin, Treasury yields, S&P 500, stablecoin market cap) and applies the model to real historical periods (2020-2024) with specific event studies, demonstrating significant data processing and implementation readiness. flowchart TD A["Research Goal: Identify factors driving cryptocurrency returns"] --> B["Data: 50+ crypto assets, 2015-2023"] B --> C["Methodology: Structural VAR Model"] C --> D["Computation: Impulse Response Functions & Variance Decomposition"] D --> E["Key Findings: 1) Liquidity shocks dominate volatility; 2) Bitcoin acts as market driver; 3) Stablecoins provide safe haven"]

Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework ArXiv ID: 2408.05382 “View on arXiv” Authors: Unknown Abstract This study presents a Reinforcement Learning (RL)-based portfolio management model tailored for high-risk environments, addressing the limitations of traditional RL models and exploiting market opportunities through two-sided transactions and lending. Our approach integrates a new environmental formulation with a Profit and Loss (PnL)-based reward function, enhancing the RL agent’s ability in downside risk management and capital optimization. We implemented the model using the Soft Actor-Critic (SAC) agent with a Convolutional Neural Network with Multi-Head Attention (CNN-MHA). This setup effectively manages a diversified 12-crypto asset portfolio in the Binance perpetual futures market, leveraging USDT for both granting and receiving loans and rebalancing every 4 hours, utilizing market data from the preceding 48 hours. Tested over two 16-month periods of varying market volatility, the model significantly outperformed benchmarks, particularly in high-volatility scenarios, achieving higher return-to-risk ratios and demonstrating robust profitability. These results confirm the model’s effectiveness in leveraging market dynamics and managing risks in volatile environments like the cryptocurrency market. ...

CLVR Ordering of Transactions on AMMs ArXiv ID: 2408.02634 “View on arXiv” Authors: Unknown Abstract This paper introduces a trade ordering rule that aims to reduce intra-block price volatility in Automated Market Maker (AMM) powered decentralized exchanges. The ordering rule introduced here, Clever Look-ahead Volatility Reduction (CLVR), operates under the (common) framework in decentralized finance that allows some entities to observe trade requests before they are settled, assemble them into “blocks”, and order them as they like. On AMM exchanges, asset prices are continuously and transparently updated as a result of each trade and therefore, transaction order has high financial value. CLVR aims to order transactions for traders’ benefit. Our primary focus is intra-block price stability (minimizing volatility), which has two main benefits for traders: it reduces transaction failure rate and allows traders to receive closer prices to the reference price at which they submit their transactions accordingly. We show that CLVR constructs an ordering which approximately minimizes price volatility with a small computation cost and can be trivially verified externally. ...

Automated Market Making and Decentralized Finance ArXiv ID: 2407.16885 “View on arXiv” Authors: Unknown Abstract Automated market makers (AMMs) are a new type of trading venues which are revolutionising the way market participants interact. At present, the majority of AMMs are constant function market makers (CFMMs) where a deterministic trading function determines how markets are cleared. Within CFMMs, we focus on constant product market makers (CPMMs) which implements the concentrated liquidity (CL) feature. In this thesis we formalise and study the trading mechanism of CPMMs with CL, and we develop liquidity provision and liquidity taking strategies. Our models are motivated and tested with market data. We derive optimal strategies for liquidity takers (LTs) who trade orders of large size and execute statistical arbitrages. First, we consider an LT who trades in a CPMM with CL and uses the dynamics of prices in competing venues as market signals. We use Uniswap v3 data to study price, liquidity, and trading cost dynamics, and to motivate the model. Next, we consider an LT who trades a basket of crypto-currencies whose constituents co-move. We use market data to study lead-lag effects, spillover effects, and causality between trading venues. We derive optimal strategies for strategic liquidity providers (LPs) who provide liquidity in CPMM with CL. First, we use stochastic control tools to derive a self-financing and closed-form optimal liquidity provision strategy where the width of the LP’s liquidity range is determined by the profitability of the pool, the dynamics of the LP’s position, and concentration risk. Next, we use a model-free approach to solve the problem of an LP who provides liquidity in multiple CPMMs with CL. We do not specify a model for the stochastic processes observed by LPs, and use a long short-term memory (LSTM) neural network to approximate the optimal liquidity provision strategy. ...

Reinforcement Learning Pair Trading: A Dynamic Scaling approach ArXiv ID: 2407.16103 “View on arXiv” Authors: Unknown Abstract Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent volatility of the crypto market. This study investigates whether Reinforcement Learning (RL) can enhance decision-making in cryptocurrency algorithmic trading compared to traditional methods. In order to address this question, we combined reinforcement learning with a statistical arbitrage trading technique, pair trading, which exploits the price difference between statistically correlated assets. We constructed RL environments and trained RL agents to determine when and how to trade pairs of cryptocurrencies. We developed new reward shaping and observation/action spaces for reinforcement learning. We performed experiments with the developed reinforcement learner on pairs of BTC-GBP and BTC-EUR data separated by 1 min intervals (n=263,520). The traditional non-RL pair trading technique achieved an annualized profit of 8.33%, while the proposed RL-based pair trading technique achieved annualized profits from 9.94% to 31.53%, depending upon the RL learner. Our results show that RL can significantly outperform manual and traditional pair trading techniques when applied to volatile markets such as~cryptocurrencies. ...

Adaptive Money Market Interest Rate Strategy Utilizing Control Theory ArXiv ID: 2407.10426 “View on arXiv” Authors: Unknown Abstract Decentralized Finance (DeFi) money markets have seen explosive growth in recent years, with billions of dollars borrowed in various cryptocurrency assets. Key to the safety of money markets is the implementation of interest rates that determine the cost of borrowing, and govern counterparty exposure and return. In traditional markets, interest rates are set by risk managers, portfolio managers, the Federal Reserve, and a myriad of other sources depending on the market function. DeFi enables an algorithmic approach that typically relies on interest rates being directly dependent on market utilization. The benefit of algorithmic interest rate management is the system’s continual response to market behaviors in real time, and thus an inherent ability to mitigate risks on behalf of protocols and users. These interest rate strategies target an optimal utilization based on the protocol’s risk threshold, but historically lack the ability to compensate for excessive or diminished utilization over time. This research investigates contemporary DeFi interest rate management strategies and their limitations. Furthermore, this paper introduces a time-weighted approach to interest rate management that implements a Proportional-Integral-Derivative (PID) control system to constantly adapt to market utilization patterns, addressing observed limitations. ...