Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions. Existing LLM-based trading systems either coordinate human-defined external experts or adopt conventional internal Mixture-of-Experts (MoE) routers that do not directly evaluate how individual experts contribute to trading decisions. Moreover, these routers receive no direct signal indicating when an inactive expert has become more suitable as market conditions change. We find that native router scores poorly reflect how much individual experts improve trading decisions, frequently leaving better alternatives unselected. We further reveal that token-specific expert usefulness exhibits a compact low-dimensional structure. Based on these findings, we propose TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts. We introduce a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys. We further propose a sparse expert selection update mechanism that samples a few inactive experts during training and estimates whether they should replace the weakest expert in the current Top-k route. This mechanism enables the router to update expert selection as market conditions change while preserving sparse computation. Experiments against 22 baselines on stock and cryptocurrency markets show that TradingMoE improves cumulative return over the best-performing baselines by 30.89% and 30.7%, respectively. Rolling paper-trading experiments further demonstrate that its advantage persists under forward-only deployment.
Crypto currency is one of most interesting financial innovation of 21st century. Crypto currency trading not only involve financial literacy while trading but also there are psychological factors affecting the decision of traders. Keeping in view the psychological factors and investorsâ decision, this research study is designed to investigate the complex interplay between psychological triggers and market dynamics in the cryptocurrency sector in Pakistan, specifically examining how these elements coalesce to drive investor behavior and market volatility. While traditional financial models often attribute asset fluctuations to technological or fundamental shifts, this study posits that cryptocurrency markets are fundamentally driven by human perception and emotional reactivity. Utilizing a quantitative methodological approach, data was collected from a sample of 175 experienced traders to analyze the impact of emotional states, market sentiment, and behavioral discipline on trading outcomes. The empirical results, derived through multiple linear regression analysis, reveal that the model possesses a high level of explanatory power, accounting for 56% of the variance in emotional trading behavior (R2=0.56R2=0.56). Market sentiment emerged as the primary determinant of impulsive trading (β=0.48β=0.48), demonstrating that external social cues often exert a stronger influence on decision-making than internal emotional states. Among specific psychological variables, Fear, Uncertainty, and Doubt (FUD) were identified as the most significant predictors of rash choices (β=0.34β=0.34), while the Fear of Missing Out (FOMO) also demonstrated a substantial, though secondary, effect (β=0.21β=0.21). Conversely, the study found that trading experience and the application of systematic strategies serve as vital moderating factors that decrease emotional reactivity and enhance behavioral stability (β=â0.19β=â0.19). The findings contribute to the fields of behavioral finance and digital economics by illustrating that the volatility inherent in digital assets is a systemic byproduct of individual psychological biases aggregated through digital narratives. The research concludes that achieving a sustainable financial ecosystem requires moving beyond purely technical regulations. Instead, it advocates for the implementation of behaviorally-informed safeguards, such as algorithmic "cooling-off" periods and sentiment-aware trading tools, to mitigate the risks associated with reactive investing. Ultimately, this work provides a blueprint for a more resilient digital financial future by prioritizing human factors in market governance.
This study investigates whether the macroscopic statistical maturity of cryptocurrencies implies dynamical equivalence with traditional equity markets. We analyze high-frequency data (2020--2025) using the Complexity--Entropy Causality Plane (CECP) and directed horizontal visibility graphs (directed HVG) to uncover complex temporal patterns and time-directed structures in the return series. While conventional stylized facts show striking convergence across all assets, structural diagnostics reveal a compelling paradox: cryptocurrencies appear more locally random than the equity benchmark during ordinary periods, yet exhibit significantly stronger directional time-irreversibility around high-visibility return events. The absolute-return results show that large cryptocurrency fluctuations tend to begin abruptly and remain elevated afterward. Separate analyses of positive returns and negative-return magnitudes show that this pattern is shared across cryptocurrencies on the upside but varies across assets on the downside. We conclude that statistical maturity is only skin-deep; the underlying dynamical processes of mature cryptocurrencies remain fundamentally distinct from traditional benchmarks.
Volatility control converts risk estimates into portfolio exposure, yet existing approaches often rely on a fixed volatility estimator or a pre-defined control rule that may not adapt to changing market conditions. We propose VolRouter, a modular framework that formulates volatility control as state-conditioned routing over estimator-controller pairs. VolRouter first summarizes market conditions into a control-relevant state profile and then performs routing through three stages: state inference, switch review, and pair selection. The Router can be implemented using rule-based, learnable, or LLM-based decision modules, while portfolio actions remain generated by predefined control policies. We evaluate VolRouter across S&P 500, Multi-Asset, Bitcoin, and USDT volatility-control settings. VolRouter achieves the highest Sharpe ratio in three of four settings. On S&P 500, it improves Sharpe from 0.952 for RV + Naive Scaling to 1.222 while reducing maximum drawdown from 15.10% to 12.58% and daily CVaR from 1.76% to 1.32%. On Multi-Asset, it improves Sharpe from 1.498 to 1.540 and reduces CVaR from 1.56% to 1.18%. Bitcoin shows similar improvements in risk-adjusted performance, while USDT provides a boundary case where simpler state-aware selectors remain competitive. Ablation and sensitivity analyses show that the improvement comes from relative policy evaluation and selective persistent switching rather than simply expanding the policy library. These results suggest that volatility control can be viewed as a policy-selection problem when risk management requirements vary across market states.
Velian Prapatoni, Rizky Parlika, Firza Prima Aditiawan
Cryptocurrency markets are characterized by high volatility, rapid price fluctuations, and substantial uncertainty, creating challenges for investment risk interpretation. This study develops a descriptive risk-interpretation framework, rather than a price-prediction or decision-optimization model, by integrating multi-scale volatility analysis with technical indicators. A quantitative descriptive design was applied to approximately one year of historical hourly price data for Bitcoin and Ethereum, covering open, high, low, close, volume, and percentage change attributes. The data were chronologically sorted, numerically cleaned and normalized, transformed into log returns, and analyzed through rolling standard deviation. Volatility was estimated across three explicitly defined horizons: short-term 7-period, medium-term 30-period, and long-term 90-period rolling windows. Moving Average (MA), Relative Strength Index (RSI), and Average True Range (ATR) were then incorporated to contextualize trend direction, momentum, and fluctuation intensity. The results show that volatility is strongly horizon-dependent: short-term movements responded more sharply to market shocks, whereas longer horizons produced smoother risk patterns. Across the analyzed Bitcoin and Ethereum hourly series, the reported 42.3% short-term and 21.7% medium-term increases were calculated as relative differences against long-term rolling volatility during identified high-uncertainty intervals, not as predictive accuracy measures. These findings indicate that combining rolling volatility with MA, RSI, and ATR can improve the transparency of descriptive cryptocurrency risk assessment. The framework may support preliminary interpretation for novice or risk-averse investors, although it does not empirically test investor comprehension or subsequent decision quality.
Cryptocurrencies have emerged as a prominent asset class characterized by rapid price fluctuations, growing institutional participation, and continuing debate over whether their price movements are random or predictable. This study examines the randomness and weak-form market efficiency of the top ten cryptocurrencies by market capitalizationâBitcoin, Ethereum, Tether, Binance Coin, XRP, USD Coin, Solana, TRON, Dogecoin, and Hype liquidâusing daily closing price data from April 2016 to March 2026 (subject to data availability for each coin). Daily log returns were tested using Descriptive Statistics, the JarqueâBera test of normality, the WaldâWolfowitz Run Test, and the Autocorrelation Test. The results show that daily returns for all selected cryptocurrencies are non-normally distributed, exhibiting excess kurtosis and skewness. The Run Test results indicate that seven of the ten cryptocurrenciesâBitcoin, Ethereum, Tether, Binance Coin, XRP, USD Coin, and Dogecoinâdo not follow a random walk, while Solana, TRON, and Hype liquid exhibit randomness consistent with weak-form efficiency. However, the Autocorrelation Test reveals strong positive serial correlation across all ten cryptocurrencies, indicating that the market falls short of weak-form efficiency. The study concludes that the cryptocurrency market provides mixed and largely inefficient evidence with respect to the Random Walk Hypothesis, implying that historical price information may retain some predictive value for investors.
This study investigates two questions relating to cryptocurrency market dynamics. First, whether a composite skew measure derived from MicroStrategy (MSTR) trading activity can predict future Bitcoin (BTC) and Ethereum (ETH) volatility. Second, whether Ethereum volatility exhibits reproducible structural properties consistent with established theories of volatility persistence and cascading shock dynamics. Using rolling out-of-sample testing, autocorrelation-adjusted significance testing, regime classification, shock-decay modelling, return-interval analysis, and earthquake-inspired cascade frameworks, the study finds no evidence that MSTR composite skew provides a useful forecasting signal. More broadly, no forecasting model tested outperforms naive benchmark models beyond horizons of approximately three to five days. However, several descriptive properties of Ethereum volatility appear robust, including volatility persistence, regime structure, extreme-event clustering, non-simple shock decay, and partially transferable aftershock dynamics. In particular, while Omori-style decay and the productivity law are supported, Bath's Law fails consistently, suggesting cryptocurrency volatility cascades may differ fundamentally from those observed in traditional financial markets. The findings contribute to the understanding of volatility organisation in digital asset markets while highlighting the difficulty of extracting persistent predictive signals from historical OHLCV
Victor Michelle, Natalie Michelle, Emilie Michelle, Elias Michelle
This paper introduces Prediction Assets â a fundamentally new class of financial instruments where the underlying asset is market consensus on probability itself. Unlike traditional prediction markets, where binary event contracts terminate abruptly upon resolution, Prediction Assets are engineered as perpetual financial instruments that evolve rather than expire. Upon event occurrence, the asset does not liquidate to zero or a fixed payout; instead, it programmatically transforms into a new functional asset form (such as a currency, index, or memory asset) via smart-contract-enforced conversion ratios, establishing an infinite lifecycle and continuous capital efficiency. Key Structural & Mathematical Contributions: Core Asset Pricing Model: Establishes the foundational pricing equation \(P_{asset} = P(E) \times M\) driven entirely by open order-book decentralized exchange (DEX/AMM) spot liquidity without reliance on subjective analytical oracles. Systemic Market Efficiency: Implements an exact arbitrage condition boundary constraint (\(\sum P_{asset,i} = M\)) to incentivize algorithmic market-making and eradicate structural price variance. Programmatic Post-Event Evolution: Introduces the deterministic conversion coefficient \(C(t, state)\) locked at genesis to handle automated migration profiles (Currency, Index, Memory, and Derivative states) with zero administrative discretion. Decentralized Governance: Outlines a 4-channel Multi-Chain Consensus Verification Layer (CVL) requiring a strict 3-of-4 quorum across official APIs, open-source replicas, academic mirrors, and market sentiment vectors. Regulatory Engineering: Delivers a comprehensive compliance analysis under the U.S. Securities Framework (Howey Test and Reves Test boundaries) and CFTC Event Contract frameworks, positioning the topology as a non-security utility asset. Prospective Implementation:The paper presents AIVA (Artificial Intelligence Valuation Asset) as the world's first prospective implementation tracking the global macro-consensus probability of achieving Artificial General Intelligence (AGI), which programmatically transforms into an operational settlement currency for autonomous multi-agent economic environments upon verification. Keywords: Prediction Assets, Probability Markets, Financial Instruments, AGI, AI Agents, Decentralized Finance, Synthetic Assets, Valuation Markets. Citation Note: This specification expands upon the sovereign fintech frameworks established in IP Stock Exchange v3.3-Evolution (DOI: 10.5281/zenodo.20687136).
Francesco Cesarone, Gianna FigĂ âTalamanca, Francesca Luciani
Abstract This study develops a large-scale framework to evaluate whether, and under what conditions, adding cryptocurrencies to equity investment universes improves portfolio performance.We apply four long-only portfolio strategies, Global Minimum Variance, Risk Parity, Most Diversified Portfolio, and Equally Weighted, to 10,000 randomly generated investment universes. These universes consist of baskets containing either only equities or varying combinations of equities and cryptocurrencies. We conduct an out-of-sample analysis on real-world data from 2018 to 2023 to assess the influence of cryptocurrencies on portfolio outcomes. The empirical findings reveal that portfolios constructed from mixed equity and cryptocurrency universes provide a better risk-return profile compared to purely equity-based portfolios, particularly for Risk Parity, Most Diversified, and Equally Weighted.
Proof-of-stake protocols lock tokens into staking positions, shrinking the tradable float. We develop a continuous-time model in which price-impact volatility is a convex decreasing function of the float. Two results emerge. Conditional return variance amplifies as the float contracts, with amplification accelerating in high-staking regimes. Protocol changes that shift the long-run staking target produce persistent volatility regime transitions, with convergence speed governed by protocol adjustment capacity. Liquid-staking tokens attenuate both effects, with attenuation increasing in their liquidity parameter.
Liu Hong Yuan Tom, Ruilin Wang, Hairui Wang, Ziqi Cao ¡ 5 authors
This study examines the impact of social media sentiment on Bit-coin market volatility. While existing literature often relies on single-source data or isolated factors, this research introduces a novel three-source pricing framework that integrates Twitter-derived social media sentiment, investor leverage ratios, and historical market data. Using a Weighted Least Squares (WLS) regression model to address heteroscedasticity in financial time series, we analyze daily Bitcoin returns from 2021 to the first half of 2022. Our results indicate that both social media sentiment has a statistically significant positive effect on Bitcoin returns. The model successfully identified high-risk market conditions, as validated by the May-June 2021 crash. These findings demonstrate that social media sentiment has a huge impact on cryptocurrency markets.
Tokenization promises to convert lumpy, illiquid real-world assets into divisible, transferable claims, yet secondary markets for these instruments remain thin and trading is infrequent. Standard asset pricing models, including the capital asset pricing model and its liquidity-adjusted extensions, were not designed for assets whose holders derive consumption, access, or governance value directly from ownership. This paper develops a conceptual asset pricing framework for utility-backed non-fungible tokens (NFTs) and tokenized real-world assets by augmenting the liquidity-adjusted capital asset pricing model with a utility (convenience) yield. The framework decomposes the required pecuniary return into a risk-free rate, a systematic liquidity-risk premium, an amortized illiquidity level premium that scales with transaction costs and turnover, and a utility-yield offset that lowers the return investors require in cash. Two analytical implications follow. First, utility backing compresses observed pecuniary returns without eliminating the underlying illiquidity premium. Second, where utility flows covary positively with illiquidity, estimates that regress pecuniary returns on liquidity proxies understate the gross illiquidity premium. An illustrative calibration, with parameter ranges drawn from the empirical tokenization literature, quantifies the mechanism rather than estimating it. The framework yields testable predictions and implications for valuation and disclosure.
Daniel Pereira Alves de Abreu, OctĂĄvio Valente Campos, Aureliano Angel Bressan
Objective: This study aims to evaluate the performance of different ARMA-GARCH model specifications in the risk management of major cryptocurrencies, investigating whether the inclusion of exogenous variables improves the calibration of risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). Methodology: To achieve this objective, 4,032 specifications of the ARMA-GARCH model applied to the ten main cryptocurrencies in trading were tested. The study incorporated the Fear and Greed Index and Bitcoin Trading Volume as exogenous variables in an ARMA-GARCH-X framework, comparing the performance of the different specifications against an ARMA(1,1)-GARCH(1,1) benchmark. Originality: Despite growing interest in crypto asset risk management, there are still gaps in the literature regarding the effectiveness of incorporating exogenous variables into forecasting models, as well as the increase in the quality of forecasts when using more complex models. Main results: The results indicate that the inclusion of external variables improves risk calibration in some assets, although the gains are marginal and heterogeneous. There is also no single optimal parameterization, requiring ARMA orders, GARCH specifications, and error distributions to be adjusted for each cryptocurrency. Theoretical/methodological contributions: From a methodological point of view, the study contributes by demonstrating the importance of specific calibration of ARMA-GARCH models for different cryptocurrencies in risk estimation. Furthermore, the results suggest that, although more complex models can improve tail risk estimation, the gains in predictive power over simpler models are limited. Keywords: Cryptocurrencies; Risk Management; ARMA-GARCH; Value-at-Risk; Expected Shortfall.
SAMUEL OBOH, Boniface Dondo, S. Yakura Bassa, Gambo I. Bature
Ethereum, a leading digital asset by market value, has gained increasing attention from investors and researchers because of its high price volatility and market unpredictability. This study forecasts Ethereum cryptocurrency daily closing prices using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology, drawing on data from January 1, 2019, to December 31, 2025. Stationarity analysis via the Augmented Dickey-Fuller ADF and KwiatkowskiâPhillipsâSchmidtâShin (KPSS) tests confirmed that first differencing was required to render the series suitable for the modeling. Through systematic model identification, estimation, and comparison of ten candidate ARIMA specifications, the ARIMA(1,1,0) model emerged as the optimal fit, yielding the lowest information criterion values of Akaike information criterion, Bayesian information criterion (AIC = 24,943.883; AICc = 24,943.84; BIC = 24,955.12). Residual diagnostic tests, including the Ljung-Box test for serial correlation, the Autoregressive Conditional Heteroskedasticity (ARCH-LM) test for heteroscedasticity, and the Shapiro-Wilk test for normality, confirmed that the model residuals are free of serial dependence, although they exhibit time-varying volatility and non-normal distribution, features commonly associated with financial time series. The fitted model was subsequently applied to generate 30-day ahead forecasts with 95% confidence intervals, revealing relatively stable price expectations in the near term alongside progressively widening prediction bands that reflect growing uncertainty over longer horizons. These findings underscore the practical utility of the parsimonious ARIMA(1,1,0) model as a transparent and accessible tool for short-term Ethereum-price forecasting and investment risk assessment.
This paper presents an open-economy macroeconomic equilibrium model for Proof-of-Stake (PoS) networks with fee-burn mechanics (EIP-1559) that formalizes the strategic interplay between a Kelly-optimizing rational institutional investor and a utility-driven retail consumer. We analyze network dynamics across two behavioral regimes. In The Unbounded Accumulation Model, the consumer purely accumulates tokens, creating an exclusive buy-side pressure that interacts with institutional portfolio rebalancing to fuel an ever-expanding speculative bubble and generate compounding excess returns for investors. Conversely, in The Utility-Consumption Model, the consumer dynamically buys and sells tokens to balance crypto wealth against real-world fiat consumption. Within this framework, we derive an explicit steady-state equilibrium price for ETH, demonstrating how token valuation anchors to a stable fundamental baseline that scales directly with network adoption while completely dissolving the institutional yield premium. Our numerical simulations show that while exogenous traditional finance (TradFi) shocks propagate through portfolio rebalancing to drive high token price volatility, network inflation remains highly stable. Furthermore, we prove that network security is insulated from institutional monopoly by counter-cyclical consumer behavior. Our findings reveal that institutional excess wealth creation in PoS ecosystems is not native to the staking protocol itself, but is strictly driven by the leveraged extraction of the retail consumer's continuous demand for transactional utility.
This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of N=10 ETFs over T=514 trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment à BrandScore interaction is significant (β^=2.930, p=0.038), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects (p=0.012) and winsorization (p=0.004). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets.
Abstract : This article investigates the efficacy of implementing an AI-powered automated trading system on the blockchain using advanced machine learning algorithms and smart contract technology. The work addresses the challenges of cryptocurrency market volatility, the need for real-time decision making and the limitations of traditional trading approaches that often result in suboptimal returns and exposure to increased risk. This work develops a comprehensive trading platform that combines Long Short-Term Memory (LSTM) neural networks, Q-Learning reinforcement learning algorithms and blockchain-based smart contracts to create an autonomous, intelligent trading system. The methodology follows a multi-layered approach that integrates real-time market data collection from CoinGecko and Snowtrace APIs, advanced AI model training using TensorFlow.js, and smart contract deployment on the Avalanche C-Chain using Hardhat and OpenZeppelin libraries. LSTM model is used for price prediction and Q-Learning agent is used for trading strategy optimization, while comprehensive risk management is implemented using Value at Risk (VaR) calculations, portfolio rebalancing algorithms and automated stop-loss mechanisms. The trading execution is facilitated through direct integration with Pangolin DEX smart contracts to ensure decentralized and trustless trade execution. The performance of the system is evaluated using a sophisticated backtesting engine with Monte Carlo simulations, comparing the AI-driven strategy against traditional buy-and-hold approaches. The performance metrics used were Sharpe ratio, maximum drawdown, win rate, and total return. The AI-powered token prediction system demonstrates a superior performance due to its ability to process complex, non-linear market patterns and adapt to changing market conditions through reinforcement learning, and execute trades with minimal latency through blockchain integration. The findings are expected to provide cryptocurrency traders and institutional investors with a robust and automated trading solution that leverages the benefits of both artificial intelligence and blockchain technology for improved investment outcomes and risk management.
ABSTRACT This paper examines the relationship between skewness and kurtosis in Bitcoin spot and futures markets using highâfrequency data. We document a strong convex skewnessâkurtosis relationship consistent with theoretical moment restrictions. Trading activity is positively associated with realized kurtosis, particularly in futures markets, though sensitive to specification and driven by extremeâreturn episodes. Allowing the relationship to evolve over time reveals substantial curvature variation, indicating stateâdependent higherâmoment dynamics. The close coâmovement of results across markets suggests patterns reflect broad marketâwide conditions. The empirical framework is reducedâform and results should be interpreted as conditional associations rather than causal effects.
Sovereign AI-Native Multi-Asset Trading, Execution, and Financial nfrastructure Charter Sovereign AIâNative Financial Execution Infrastructure PARRALAXâAIHFTFUND is a multiâasset, AIânative financial organism engineered to operate across traditional and blockchainâbased markets. It provides a unified execution layer where autonomous agents can observe markets, interpret structure, execute trades, manage risk, govern portfolios, issue digital assets, coordinate token economies, and maintain verifiable proofâofâcomputation. This repository contains the core infrastructure, protocol stack, and governance architecture for building sovereign, agentâdriven financial systems. Mission To build a sovereign AIânative financial infrastructure capable of coordinating autonomous trading agents, multiâasset execution, fund governance, risk control, digitalâasset creation, and market intelligence across both traditional and blockchainânative markets. The system exists to move beyond bots, dashboards, and scripts. Its purpose is to become a real execution organism for financial markets. Vision PARRALAXâAIHFTFUND aims to create a longâhorizon financial intelligence layer where AI agents can: Observe and interpret global market structure Execute trades across heterogeneous venues Manage risk and exposure Govern portfolios and internal policy Issue and manage digital assets Coordinate internal token economies Maintain proofâofâcomputation and decision lineage Operate across crypto, fiat, equities, FX, derivatives, AI tokens, NFTs, and future asset classes Build market memory over time The system is designed to evolve as markets evolve. Foundational Premise Modern markets are: Machineâdriven Fragmented Multiâasset Tokenized Agentâmediated A serious financial infrastructure must therefore operate across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, onâchain liquidity) Tokenized and synthetic assets AIânative markets Autonomous agent economies Highâspeed execution environments Governanceâcontrolled fund structures Programmable financial instruments PARRALAXâAIHFTFUND is built to bridge oldâworld and newâworld markets. What PARRALAXâAIHFTFUND Is A sovereign trading infrastructure framework An AIânative market execution system A multiâasset financial operating layer A protocol stack for autonomous trading agents A fund governance and charter framework A digitalâasset issuance and management environment A blockchainâcompatible coordination layer A riskâaware execution engine A computeâreceipt and proofâtrace system A foundation for future AIâmanaged financial organisms It is built for real execution, not passive analysis. What PARRALAXâAIHFTFUND Is Not Not a research repo Not a toy trading bot Not a simulation Not a dashboard Not a signal script collection Not a crypto hype project Not a singleâasset system Not a predictionâonly model Research supports the system. Research does not define the system. Status Active development. Core modules stabilizing. Execution layer expanding. Governance and digitalâasset subsystems in progress. PARRALAXâAIHFTFUND is an AIânative financial execution framework designed to coordinate autonomous agents across traditional and blockchainâbased markets. The system provides a unified operating layer for multiâasset execution, risk management, fund governance, digitalâasset issuance, and verifiable computeâtraceability. System Mission To construct a sovereign financial intelligence layer capable of continuous operation across heterogeneous markets, enabling agents to observe market conditions, interpret structure, execute trades, manage exposure, and maintain internal governance. Operational Scope The system is engineered to function across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, onâchain liquidity) Tokenized and synthetic assets AIânative markets and agent economies Governanceâcontrolled fund structures Highâspeed execution environments Programmable financial instruments System Definition PARRALAXâAIHFTFUND comprises: A sovereign trading and execution infrastructure A multiâasset financial operating layer A protocol stack for autonomous trading agents A governance and charter framework A digitalâasset issuance and management environment A blockchainâcompatible coordination layer A riskâaware execution engine with compute receipts NonâScope The system is not a researchâonly repository, simulation toy, dashboard, signal script collection, or predictionâonly model. It is infrastructureâfirst and executionâoriented.
This research examines 42 countries and investigates the relationship between geopolitical risk and global non-fungible token (NFT) investor attention. We use Google search volumes related to NFTs across different regions as a proxy for such attention. Our findings indicate that geopolitical risk positively impacts global NFT investor attention, suggesting that investors in countries with higher geopolitical risk may pay more attention to the NFT market. We further explore the effects across different NFT segments and find that geopolitical risk particularly influences investor attention in the metaverse segment. This positive nexus is further amplified during the Russia-Ukraine war and the COVID-19 pandemic.
This paper demonstrates practical arbitrage trading on the cryptocurrency market. It provides guidance on how to build a high-frequency trading system that benefits from exhibiting arbitrage opportunities. It reveals the algorithm of the trading bot that incorporates the order placement and execution strategy between decentral and central cryptocurrency exchanges. The arbitrage algorithm is implemented on two different blockchains that interact with Uniswap and Balancer folks. Current research explores arbitrage opportunities with back-testing models, the paper focuses on trades with realized arbitrage trades. Practical arbitrage includes all operational costs, liquidity constraints, direct effects on markets, and competition with peer arbitrage traders.
I replicate and extend the cross-sectional trend-factor methodology of Liebi, Stulz, and Tsyvinski (2024) on a contemporaneous out-of-sample period and a liquidity-restricted coin universe. Using 141 USDT spot pairs over 128 weekly observations from November 2023 to April 2026, an Elastic-Net cross-sectional regression aggregating 29 technical indicators generates a long-short portfolio with a mean weekly return of 3.82% (Newey-West t = 5.03) and an annualized Sharpe ratio of 4.54. The strategy delivers market-neutral alpha of 3.82% per week (t = 7.09) with a CAPM beta of 0.020. Three findings warrant emphasis. First, value-weighting destroys the alpha entirely, confirming concentration in smaller, dispersed names. Second, twelve of the top fifteen Elastic-Net coefficients are negative, indicating that the underlying pattern is short-term reversal rather than trend continuation. Third, returns are heavily regime-dependent, with Sharpe ratios near 1.0 in trending markets and above 7.0 in dispersive regimes. Cross-sectional dispersion-harvesting alpha persists in cryptocurrency markets, but its empirical realization is sharply sensitive to universe liquidity, weighting scheme, rebalance horizon, and market regime.