Trading Copilot for Cryptocurrency Markets: A Review of Analytics, Prediction, Manipulation Detection, Automated Execution and Conversational Agents, with a Gap Analysis of Existing Systems

Abstract: Retail cryptocurrency traders operate in a market that is open around the clock, split across many venues, heavy-tailed and exposed to manipulation, yet the tools available to them are divided among charting and alert services, automated bot platforms and research prototypes that rarely work together. This paper reviews the literature and the commercial and open-source landscape relevant to a unified Trading Copilot for crypto markets with the following capabilities: trade analysis, news-based volatility analysis, continuous buy/sell prediction, strategy selection, accumulation–manipulation–distribution (AMD) phase detection, price-distribution and breakdown analysis, cross- exchange price monitoring, automated order placement, notifications and alerts, and a conversational agent. We synthesise the evidence for each capability, compare representative systems, and derive ten gaps. Our main findings are that (i) bot platforms emphasise execution but, in the sources we reviewed, document little analytical or explanatory capability; (ii) LLM trading agents are reported mainly in simulation and without live execution; (iii) manipulation detectors are not coupled to trading decisions; and (iv) conversational LLM agents are reported to retrieve data well but to predict poorly, so they should explain computed outputs rather than forecast. We then describe how a Trading Copilot can close these gaps and propose an evaluation protocol.

Keywords: cryptocurrency, trading copilot, large language models, volatility, market manipulation, Wyckoff method, trading bots, alerts, conversational agent.